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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A Survey on Technology Choice\n",
+ "======\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Hypothesis\n",
+ "\n",
+ "I think the priority of helpful discussion on StackExchange is going to be affected by development experience and English proficiency."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# For nicer printing\n",
+ "options(digits=2);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Read in the data\n",
+ "data <- read.csv(\"TechSurvey - Survey.csv\",header=T);\n",
+ "\n",
+ "#convert date to unix second\n",
+ "for (i in c(\"Start\", \"End\")) \n",
+ " data[,i] = as.numeric(as.POSIXct(strptime(data[,i], \"%Y-%m-%d %H:%M:%S\")))\n",
+ "for (i in 0:12){\n",
+ " vnam = paste(c(\"PG\",i,\"Submit\"), collapse=\"\")\n",
+ " data[,vnam] = as.numeric(as.POSIXct(strptime(data[,vnam], \"%Y-%m-%d %H:%M:%S\")))\n",
+ "}\n",
+ "#calculate differences in time \n",
+ "for (i in 12:0){\n",
+ " pv = paste(c(\"PG\",i-1,\"Submit\"), collapse=\"\");\n",
+ " if (i==0) \n",
+ " pv=\"Start\";\n",
+ " vnam = paste(c(\"PG\",i,\"Submit\"), collapse=\"\");\n",
+ " data[,vnam] = data[,vnam] -data[,pv];\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " Device Completed Start End PG0Dis \n",
+ " : 2 0 : 2 Min. :1.54e+09 Min. :1.54e+09 Min. : 0 \n",
+ " Bot : 1 FALSE:546 1st Qu.:1.54e+09 1st Qu.:1.54e+09 1st Qu.: 0 \n",
+ " PC :955 TRUE :805 Median :1.54e+09 Median :1.54e+09 Median : 1 \n",
+ " Phone :376 Mean :1.54e+09 Mean :1.54e+09 Mean : 44 \n",
+ " Tablet : 16 3rd Qu.:1.54e+09 3rd Qu.:1.54e+09 3rd Qu.: 24 \n",
+ " Unknown: 3 Max. :1.54e+09 Max. :1.54e+09 Max. :168 \n",
+ " NA's :2 NA's :548 NA's :73 \n",
+ " PG0Shown PG0Submit \n",
+ " Min. : 0 Min. : 2 \n",
+ " 1st Qu.: 0 1st Qu.: 6 \n",
+ " Median : 102 Median : 9 \n",
+ " Mean : 249 Mean : 299 \n",
+ " 3rd Qu.: 428 3rd Qu.: 15 \n",
+ " Max. :1190 Max. :76226 \n",
+ " NA's :73 NA's :199 \n",
+ " PG1PsnUse \n",
+ " For personal work and/or research use :727 \n",
+ " :613 \n",
+ " Chapter book : 1 \n",
+ " For training attendees of my sessions : 1 \n",
+ " It's a coures on tidyverse that I developed: 1 \n",
+ " Learning how to create a package : 1 \n",
+ " (Other) : 9 \n",
+ " PG1WdAuth \n",
+ " :1145 \n",
+ " Because Microsoft was paying me to do it : 1 \n",
+ " For a wider audience, such as developers of other packages or other software: 205 \n",
+ " Hackathon : 1 \n",
+ " Tool for other researchers to analyse their data : 1 \n",
+ " \n",
+ " \n",
+ " PG1Trn \n",
+ " :1168 \n",
+ " For a training / class that I took: 184 \n",
+ " teaching economics : 1 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " PG1Other \n",
+ " :1272 \n",
+ " Other : 23 \n",
+ " Teaching : 3 \n",
+ " teaching : 3 \n",
+ " For training that I gave : 2 \n",
+ " How does software technology spreads in the open source community?: 2 \n",
+ " (Other) : 48 \n",
+ " PG1Submit PG2Resp PG2Submit \n",
+ " Min. : 1 :431 Min. : 1 \n",
+ " 1st Qu.: 11 No :374 1st Qu.: 9 \n",
+ " Median : 16 Not sure:303 Median : 13 \n",
+ " Mean : 39 Yes :245 Mean : 29 \n",
+ " 3rd Qu.: 30 3rd Qu.: 29 \n",
+ " Max. :6892 Max. :1470 \n",
+ " NA's :282 NA's :377 \n",
+ " PG2Resp.1 \n",
+ " :480 \n",
+ " The core \"data.frame\" object lacked functionality that I needed :354 \n",
+ " I saw a recommendation for the package :149 \n",
+ " Chose the package to be compatible with other packages in my project :137 \n",
+ " I didn't choose to use the package, it was included implicitly / unintentionally: 39 \n",
+ " Other : 6 \n",
+ " (Other) :188 \n",
+ " PG3Submit PG4Dtr0_6 PG4Psv7_8 PG4Prm9_10 PG4AllResp \n",
+ " Min. : 1 Min. :0 Min. :7 Min. : 9 Min. : 0 \n",
+ " 1st Qu.: 16 1st Qu.:3 1st Qu.:7 1st Qu.:10 1st Qu.: 8 \n",
+ " Median : 23 Median :5 Median :8 Median :10 Median : 9 \n",
+ " Mean : 44 Mean :4 Mean :8 Mean :10 Mean : 8 \n",
+ " 3rd Qu.: 40 3rd Qu.:6 3rd Qu.:8 3rd Qu.:10 3rd Qu.:10 \n",
+ " Max. :4648 Max. :6 Max. :8 Max. :10 Max. :10 \n",
+ " NA's :451 NA's :1232 NA's :1115 NA's :869 NA's :510 \n",
+ " PG4Submit PG5_1RRPQ PG5_1Order PG5_1Time \n",
+ " Min. : 1 :877 Min. : 1 :877 \n",
+ " 1st Qu.: 6 Essential : 60 1st Qu.: 4 2018-10-11 13:32:57: 3 \n",
+ " Median : 7 High Priority :102 Median : 7 2018-10-11 13:29:20: 2 \n",
+ " Mean : 9 Low Priority : 85 Mean : 7 2018-10-11 13:34:56: 2 \n",
+ " 3rd Qu.: 9 Medium Priority:134 3rd Qu.:11 2018-10-11 13:14:25: 1 \n",
+ " Max. :332 Not a Priority : 95 Max. :20 2018-10-11 13:14:45: 1 \n",
+ " NA's :473 NA's :877 (Other) :467 \n",
+ " PG5_2BNUI PG5_2Order PG5_2Time \n",
+ " :923 Min. : 1 :923 \n",
+ " Essential : 3 1st Qu.: 5 2018-10-11 13:21:46: 2 \n",
+ " High Priority : 26 Median : 8 2018-10-11 13:38:07: 2 \n",
+ " Low Priority :121 Mean : 8 2018-10-11 13:14:27: 1 \n",
+ " Medium Priority: 92 3rd Qu.:11 2018-10-11 13:14:40: 1 \n",
+ " Not a Priority :188 Max. :21 2018-10-11 13:15:18: 1 \n",
+ " NA's :923 (Other) :423 \n",
+ " PG5_3HDS PG5_3Order PG5_3Time \n",
+ " :768 Min. : 1 :768 \n",
+ " Essential :103 1st Qu.: 2 2018-10-11 13:54:00: 2 \n",
+ " High Priority :200 Median : 4 2018-10-11 14:21:45: 2 \n",
+ " Low Priority : 69 Mean : 6 2018-10-11 14:25:51: 2 \n",
+ " Medium Priority:162 3rd Qu.: 9 2018-10-11 17:21:39: 2 \n",
+ " Not a Priority : 51 Max. :19 2018-10-11 13:14:18: 1 \n",
+ " NA's :768 (Other) :576 \n",
+ " PG5_4VGP PG5_4Order PG5_4Time \n",
+ " :852 Min. : 1 :852 \n",
+ " Essential : 22 1st Qu.: 4 2018-10-11 13:16:50: 2 \n",
+ " High Priority :111 Median : 6 2018-10-11 13:29:51: 2 \n",
+ " Low Priority : 88 Mean : 7 2018-10-11 13:37:39: 2 \n",
+ " Medium Priority:164 3rd Qu.:10 2018-10-11 13:38:11: 2 \n",
+ " Not a Priority :116 Max. :18 2018-10-11 15:42:22: 2 \n",
+ " NA's :852 (Other) :491 \n",
+ " PG5_5PHR PG5_5Order PG5_5Time \n",
+ " :753 Min. : 1 :753 \n",
+ " Essential : 79 1st Qu.: 2 2018-10-11 13:18:47: 2 \n",
+ " High Priority :252 Median : 4 2018-10-11 13:18:48: 2 \n",
+ " Low Priority : 63 Mean : 6 2018-10-11 13:32:40: 2 \n",
+ " Medium Priority:162 3rd Qu.: 8 2018-10-11 13:38:12: 2 \n",
+ " Not a Priority : 44 Max. :18 2018-10-11 13:45:48: 2 \n",
+ " NA's :753 (Other) :590 \n",
+ " PG5_6SSYOP PG5_6Order PG5_6Time \n",
+ " :852 Min. : 1 :852 \n",
+ " Essential : 63 1st Qu.: 3 2018-10-11 13:20:00: 2 \n",
+ " High Priority :137 Median : 6 2018-10-11 13:40:53: 2 \n",
+ " Low Priority : 84 Mean : 7 2018-10-11 13:44:00: 2 \n",
+ " Medium Priority:110 3rd Qu.:10 2018-10-11 13:45:41: 2 \n",
+ " Not a Priority :107 Max. :17 2018-10-11 16:22:38: 2 \n",
+ " NA's :852 (Other) :491 \n",
+ " PG5_7NDYP PG5_7Order PG5_7Time \n",
+ " :934 Min. : 1 :934 \n",
+ " Essential : 8 1st Qu.: 4 2018-10-11 13:18:50: 2 \n",
+ " High Priority : 31 Median : 7 2018-10-11 14:23:19: 2 \n",
+ " Low Priority : 93 Mean : 7 2018-10-11 13:14:22: 1 \n",
+ " Medium Priority: 52 3rd Qu.:11 2018-10-11 13:14:50: 1 \n",
+ " Not a Priority :235 Max. :17 2018-10-11 13:15:08: 1 \n",
+ " NA's :934 (Other) :412 \n",
+ " PG5_8CP PG5_8Order PG5_8Time \n",
+ " :715 Min. : 1 :715 \n",
+ " Essential :232 1st Qu.: 1 2018-10-11 13:29:46: 2 \n",
+ " High Priority :197 Median : 4 2018-10-11 13:37:00: 2 \n",
+ " Low Priority : 52 Mean : 5 2018-10-11 13:38:36: 2 \n",
+ " Medium Priority:121 3rd Qu.: 8 2018-10-11 13:39:22: 2 \n",
+ " Not a Priority : 36 Max. :20 2018-10-11 14:02:46: 2 \n",
+ " NA's :715 (Other) :628 \n",
+ " PG5_9FRP PG5_9Order PG5_9Time \n",
+ " :738 Min. : 1 :738 \n",
+ " Essential :165 1st Qu.: 2 2018-10-11 13:35:13: 2 \n",
+ " High Priority :243 Median : 4 2018-10-11 13:37:34: 2 \n",
+ " Low Priority : 42 Mean : 5 2018-10-11 14:02:44: 2 \n",
+ " Medium Priority:125 3rd Qu.: 9 2018-10-11 13:14:17: 1 \n",
+ " Not a Priority : 40 Max. :19 2018-10-11 13:14:52: 1 \n",
+ " NA's :738 (Other) :607 \n",
+ " PG5_10RPA PG5_10Order PG5_10Time \n",
+ " :779 Min. : 1 :779 \n",
+ " Essential : 55 1st Qu.: 2 2018-10-11 13:17:47: 2 \n",
+ " High Priority :204 Median : 5 2018-10-11 13:27:48: 2 \n",
+ " Low Priority : 79 Mean : 6 2018-10-11 13:45:33: 2 \n",
+ " Medium Priority:151 3rd Qu.: 9 2018-10-11 15:30:40: 2 \n",
+ " Not a Priority : 85 Max. :22 2018-10-11 15:48:40: 2 \n",
+ " NA's :779 (Other) :564 \n",
+ " PG5_11NSG PG5_11Order PG5_11Time \n",
+ " :890 Min. : 1 :890 \n",
+ " Essential : 6 1st Qu.: 4 2018-10-11 13:19:44: 2 \n",
+ " High Priority : 29 Median : 6 2018-10-11 13:21:53: 2 \n",
+ " Low Priority : 89 Mean : 7 2018-10-11 13:31:08: 2 \n",
+ " Medium Priority: 68 3rd Qu.:10 2018-10-11 13:40:48: 2 \n",
+ " Not a Priority :271 Max. :18 2018-10-11 14:55:47: 2 \n",
+ " NA's :890 (Other) :453 \n",
+ " PG5_12NWG PG5_12Order PG5_12Time \n",
+ " :916 Min. : 1 :916 \n",
+ " High Priority : 10 1st Qu.: 5 2018-10-11 13:30:08: 2 \n",
+ " Low Priority : 77 Median : 7 2018-10-11 13:31:20: 2 \n",
+ " Medium Priority: 25 Mean : 7 2018-10-11 14:55:40: 2 \n",
+ " Not a Priority :325 3rd Qu.:11 2018-10-11 13:14:46: 1 \n",
+ " Max. :18 2018-10-11 13:14:50: 1 \n",
+ " NA's :916 (Other) :429 \n",
+ " PG5_13NFG PG5_13Order PG5_13Time PG5Submit \n",
+ " :920 Min. : 1 :920 Min. : 3 \n",
+ " High Priority : 10 1st Qu.: 4 2018-10-11 13:17:39: 2 1st Qu.: 45 \n",
+ " Low Priority : 76 Median : 7 2018-10-11 13:35:20: 2 Median : 62 \n",
+ " Medium Priority: 37 Mean : 7 2018-10-11 13:35:56: 2 Mean : 87 \n",
+ " Not a Priority :310 3rd Qu.:10 2018-10-11 15:42:36: 2 3rd Qu.: 84 \n",
+ " Max. :17 2018-10-11 13:14:41: 1 Max. :4130 \n",
+ " NA's :920 (Other) :424 NA's :544 \n",
+ " PG6Resp PG6Submit PG7R PG7C.C.. PG7Java \n",
+ " :549 Min. : 1 R :684 :1268 :1306 \n",
+ " 13 - 19 years : 50 1st Qu.: 7 :614 C/C++: 84 Java : 46 \n",
+ " 2 - 5 years :332 Median : 9 Perl : 6 Cobol: 1 scala: 1 \n",
+ " 20 years or more : 45 Mean : 24 PHP : 5 \n",
+ " 6 - 8 years :112 3rd Qu.: 12 SQL : 5 \n",
+ " 9 - 12 years : 70 Max. :5759 Ruby : 4 \n",
+ " Less than 2 years:195 NA's :543 (Other): 35 \n",
+ " PG7Python PG7Javascript PG7Go PG7C. PG7Other \n",
+ " :1129 :1304 :1351 :1330 :1283 \n",
+ " Python: 223 Javascript: 48 Go: 2 C#: 23 Other : 62 \n",
+ " perl : 1 sql : 1 PHP : 2 \n",
+ " Matlab : 1 \n",
+ " Ruby : 1 \n",
+ " SAS : 1 \n",
+ " (Other): 3 \n",
+ " PG7Submit PG8Resp PG8Submit PG9Resp \n",
+ " Min. : 1 :570 Min. : 1 :562 \n",
+ " 1st Qu.: 6 Data Scientist :379 1st Qu.: 5 2 - 3 :229 \n",
+ " Median : 8 Software Engineer: 55 Median : 8 4 - 6 :166 \n",
+ " Mean : 11 Student : 24 Mean : 12 7 - 10 :122 \n",
+ " 3rd Qu.: 11 Researcher : 15 3rd Qu.: 14 1 : 94 \n",
+ " Max. :777 PhD student : 10 Max. :207 More than 25: 92 \n",
+ " NA's :542 (Other) :300 NA's :546 (Other) : 88 \n",
+ " PG9Submit PG10Resp \n",
+ " Min. : 0 :565 \n",
+ " 1st Qu.: 7 Native :424 \n",
+ " Median : 10 Not native - full working proficiency :263 \n",
+ " Mean : 40 Not native - limited working proficiency : 17 \n",
+ " 3rd Qu.: 14 Not native - passable : 5 \n",
+ " Max. :20126 Not native - sufficient working proficiency: 77 \n",
+ " NA's :547 Not native - very limited : 2 \n",
+ " PG10Submit PG11Resp PG11Submit PG12Resp \n",
+ " Min. : 1 :576 Min. : 1 :590 \n",
+ " 1st Qu.: 5 Female : 96 1st Qu.: 4 18 - 24 : 34 \n",
+ " Median : 7 Male :652 Median : 4 25 - 34 :338 \n",
+ " Mean : 17 Prefer not to answer: 29 Mean : 6 35 - 44 :258 \n",
+ " 3rd Qu.: 11 3rd Qu.: 5 45 - 54 : 89 \n",
+ " Max. :5966 Max. :605 55 - 64 : 36 \n",
+ " NA's :546 NA's :548 65 and over: 8 \n",
+ " PG12Submit \n",
+ " Min. : 1 \n",
+ " 1st Qu.: 4 \n",
+ " Median : 5 \n",
+ " Mean : 8 \n",
+ " 3rd Qu.: 6 \n",
+ " Max. :1566 \n",
+ " NA's :548 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#now explore variables\n",
+ "summary(data);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Descriptive Analysis of the Proposed Measures\n",
+ "\n",
+ "Intuitively, if someone does not have very much development experience, then he or she will be more likely to benefit from discussion on the help board. However, if someone is more experienced, then the discussion may not be a priority because he or she would have a lot more insight. In addition, if someone has more experience, he or she may be less likely to utilize StackExchange as a resource, leaving those are less experienced more likely to use it and therefore benefit from it. Also, if one does not have extensive knowledge of the English language, then he or she may be predisposed to not benefitting as much from helpful discussion because the ideas may not be conveyed as clearly to the reader. From the above code cell, we observe that the English language proficiency (PG10Resp) responses are primarily \"Native\", and there are fewer responses of lower proficiencies. This means that respondents are more likely to be at least fully working proficient than otherwise. We also observe that the development experience (PG6Resp) responses indicate that the respondents mostly have 2-8 years of experience. Therefore, there are relatively few responses by those with nine or more years of experience. Furthermore, there are fewer and fewer reponses belonging to the higher and higher experience categories."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Interpret basic summaries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "Start End PG0Dis PG0Shown PG0Submit PG1Submit PG2Submit PG3Submit PG4Dtr0_6 PG4Psv7_8 ... PG5_12Order PG5_13Order PG5Submit PG6Submit PG7Submit PG8Submit PG9Submit PG10Submit PG11Submit PG12Submit \n",
+ "\n",
+ "\tStart 1.0000 0.9952 -0.0417 -0.11507 0.1350 0.1156 0.0791 0.0384 0.01210 0.00371 ... -0.0369 0.0598 0.08512 0.0054 0.0776 0.0441 0.04101 0.047 7.9e-02 0.0746 \n",
+ "\tEnd 0.9952 1.0000 -0.0415 -0.09879 0.1142 0.1550 0.0791 0.0511 -0.05185 -0.04576 ... -0.0359 0.0661 0.09088 0.0051 0.0759 0.0435 0.04071 0.052 7.9e-02 0.0772 \n",
+ "\tPG0Dis -0.0417 -0.0415 1.0000 0.87220 0.0153 0.0065 0.0041 0.0567 0.16368 0.02668 ... 0.0151 0.0384 0.00601 0.0277 0.0097 0.0354 0.00995 -0.029 -4.5e-02 0.0546 \n",
+ "\tPG0Shown -0.1151 -0.0988 0.8722 1.00000 0.0360 0.0205 0.0023 0.0497 0.08226 0.00036 ... 0.0074 0.0407 -0.00888 0.0401 0.0121 0.0264 0.00056 -0.045 -7.1e-02 0.0436 \n",
+ "\tPG0Submit 0.1350 0.1142 0.0153 0.03596 1.0000 0.1088 0.1037 0.1273 -0.00802 -0.03763 ... -0.0161 -0.0280 0.17671 0.1518 0.1365 0.1258 0.17579 0.225 1.1e-01 0.1096 \n",
+ "\tPG1Submit 0.1156 0.1550 0.0065 0.02047 0.1088 1.0000 0.1452 0.2688 -0.06852 0.05661 ... 0.0512 -0.0651 0.24670 0.2414 0.1133 0.1069 0.10895 0.170 7.4e-02 0.1137 \n",
+ "\tPG2Submit 0.0791 0.0791 0.0041 0.00235 0.1037 0.1452 1.0000 0.2045 0.00146 0.00897 ... 0.0210 -0.0047 0.21851 0.2696 0.1245 0.1567 0.20127 0.099 1.1e-01 0.1073 \n",
+ "\tPG3Submit 0.0384 0.0511 0.0567 0.04968 0.1273 0.2688 0.2045 1.0000 0.00865 0.04424 ... 0.0464 -0.0222 0.26048 0.2706 0.1316 0.1822 0.27450 0.161 1.4e-01 0.1642 \n",
+ "\tPG4Dtr0_6 0.0121 -0.0518 0.1637 0.08226 -0.0080 -0.0685 0.0015 0.0087 1.00000 NA ... 0.1774 -0.1289 -0.05214 -0.1618 0.1560 0.0695 -0.07292 0.044 8.4e-04 -0.0272 \n",
+ "\tPG4Psv7_8 0.0037 -0.0458 0.0267 0.00036 -0.0376 0.0566 0.0090 0.0442 NA 1.00000 ... -0.0008 -0.0218 0.08974 -0.0146 -0.0363 0.0526 0.05977 0.069 -4.9e-02 -0.0217 \n",
+ "\tPG4Prm9_10 -0.0267 -0.0267 -0.0092 0.03279 -0.0939 0.0120 -0.0587 -0.0062 NA NA ... 0.0233 0.0916 0.00077 -0.0418 -0.0633 -0.0550 -0.02989 -0.061 -8.6e-05 0.0169 \n",
+ "\tPG4AllResp 0.0063 -0.0158 0.0018 -0.02094 -0.0236 0.0297 0.0293 -0.0193 1.00000 1.00000 ... -0.0306 -0.0166 0.01248 -0.0040 -0.0753 -0.1294 -0.03812 -0.106 -8.2e-02 -0.0668 \n",
+ "\tPG4Submit 0.0187 0.0172 -0.0539 -0.05978 0.2191 0.1651 0.1515 0.1956 -0.14272 -0.08350 ... -0.0119 -0.0376 0.27328 0.3326 0.2775 0.1821 0.33236 0.391 2.8e-01 0.2360 \n",
+ "\tPG5_1Order 0.0218 0.0196 0.0140 0.01254 -0.0240 0.0750 -0.0069 0.0578 -0.09488 -0.01399 ... -0.0785 -0.0950 0.08054 0.0334 0.0173 -0.0388 0.01033 -0.096 -2.5e-02 0.0238 \n",
+ "\tPG5_2Order 0.0014 0.0002 -0.0386 -0.03617 0.0402 -0.0226 -0.0297 0.0048 -0.00954 0.08081 ... 0.0527 -0.0652 0.03898 -0.0571 -0.0551 -0.0567 -0.01925 0.015 7.3e-03 0.0469 \n",
+ "\tPG5_3Order -0.0089 -0.0177 0.0441 0.04228 0.0155 0.0391 -0.0131 0.0172 0.13631 0.04396 ... -0.0698 -0.0250 0.23450 0.0419 0.0058 -0.0086 0.02604 0.063 3.4e-02 0.0538 \n",
+ "\tPG5_4Order 0.0931 0.0949 -0.0262 -0.02214 -0.0172 0.0293 0.0560 -0.0462 0.01735 -0.12489 ... -0.0447 -0.0329 0.14487 0.0414 0.0460 0.0182 -0.03734 -0.075 -8.6e-02 -0.0211 \n",
+ "\tPG5_5Order -0.0523 -0.0466 -0.0087 -0.01058 0.0860 0.0345 0.0570 -0.0165 0.04533 -0.06369 ... -0.0499 -0.0981 0.28698 0.0498 0.0372 0.0427 -0.02976 0.076 2.4e-02 0.0085 \n",
+ "\tPG5_6Order 0.0237 0.0217 -0.0480 -0.04902 0.0762 0.0327 0.1077 0.0370 -0.13255 0.01662 ... -0.0430 -0.0031 0.22759 0.0165 0.0222 0.0639 -0.02974 0.015 2.8e-02 0.0539 \n",
+ "\tPG5_7Order -0.0200 -0.0236 0.0220 -0.00444 -0.1174 -0.0815 -0.0285 0.0242 -0.16196 -0.06938 ... 0.0410 0.0944 0.06439 -0.0222 -0.1082 -0.0804 -0.01762 -0.017 -4.4e-02 -0.0617 \n",
+ "\tPG5_8Order -0.0804 -0.0852 -0.0147 -0.00433 -0.0421 -0.0205 -0.0387 -0.0129 -0.16181 0.01019 ... -0.0896 -0.1168 0.13787 -0.1227 -0.0126 -0.0526 -0.05699 -0.065 -3.7e-02 -0.0182 \n",
+ "\tPG5_9Order 0.0171 0.0167 -0.0712 -0.10235 0.0476 0.0260 -0.0410 -0.0641 -0.07948 -0.09747 ... -0.0431 -0.0837 0.18534 -0.0188 -0.0314 -0.0898 -0.01354 -0.034 6.2e-03 -0.0280 \n",
+ "\tPG5_10Order -0.0214 -0.0112 0.0200 0.02736 0.0086 -0.0017 -0.0418 0.0581 -0.05310 0.15949 ... -0.0905 -0.1214 0.22151 0.0427 0.0311 0.0345 0.02011 0.102 1.8e-02 -0.0092 \n",
+ "\tPG5_11Order -0.0241 -0.0196 0.0190 0.02551 0.0927 -0.0047 -0.1043 0.0279 0.02791 -0.03487 ... 0.1233 0.1434 0.02942 -0.0055 0.0210 0.0232 0.05579 -0.038 -2.5e-03 0.0275 \n",
+ "\tPG5_12Order -0.0369 -0.0359 0.0151 0.00743 -0.0161 0.0512 0.0210 0.0464 0.17741 -0.00080 ... 1.0000 0.1231 0.10997 0.0777 0.0529 0.0149 0.00295 0.016 2.4e-02 0.0167 \n",
+ "\tPG5_13Order 0.0598 0.0661 0.0384 0.04072 -0.0280 -0.0651 -0.0047 -0.0222 -0.12890 -0.02179 ... 0.1231 1.0000 0.00976 -0.0209 0.0355 0.0556 0.07229 -0.023 1.3e-02 -0.0614 \n",
+ "\tPG5Submit 0.0851 0.0909 0.0060 -0.00888 0.1767 0.2467 0.2185 0.2605 -0.05214 0.08974 ... 0.1100 0.0098 1.00000 0.3224 0.2312 0.2035 0.30291 0.269 2.4e-01 0.2588 \n",
+ "\tPG6Submit 0.0054 0.0051 0.0277 0.04005 0.1518 0.2414 0.2696 0.2706 -0.16179 -0.01463 ... 0.0777 -0.0209 0.32240 1.0000 0.3086 0.2065 0.44528 0.343 2.8e-01 0.2904 \n",
+ "\tPG7Submit 0.0776 0.0759 0.0097 0.01212 0.1365 0.1133 0.1245 0.1316 0.15596 -0.03631 ... 0.0529 0.0355 0.23120 0.3086 1.0000 0.1606 0.27819 0.312 2.8e-01 0.2523 \n",
+ "\tPG8Submit 0.0441 0.0435 0.0354 0.02635 0.1258 0.1069 0.1567 0.1822 0.06953 0.05260 ... 0.0149 0.0556 0.20351 0.2065 0.1606 1.0000 0.25569 0.200 2.1e-01 0.1932 \n",
+ "\tPG9Submit 0.0410 0.0407 0.0099 0.00056 0.1758 0.1090 0.2013 0.2745 -0.07292 0.05977 ... 0.0029 0.0723 0.30291 0.4453 0.2782 0.2557 1.00000 0.290 2.8e-01 0.2755 \n",
+ "\tPG10Submit 0.0474 0.0517 -0.0293 -0.04481 0.2248 0.1701 0.0989 0.1614 0.04433 0.06942 ... 0.0159 -0.0227 0.26881 0.3428 0.3121 0.2000 0.29018 1.000 3.5e-01 0.3131 \n",
+ "\tPG11Submit 0.0790 0.0792 -0.0454 -0.07102 0.1093 0.0738 0.1147 0.1383 0.00084 -0.04870 ... 0.0240 0.0130 0.23552 0.2777 0.2768 0.2065 0.27906 0.346 1.0e+00 0.2513 \n",
+ "\tPG12Submit 0.0746 0.0772 0.0546 0.04364 0.1096 0.1137 0.1073 0.1642 -0.02721 -0.02169 ... 0.0167 -0.0614 0.25876 0.2904 0.2523 0.1932 0.27550 0.313 2.5e-01 1.0000 \n",
+ " \n",
+ "
\n"
+ ],
+ "text/latex": [
+ "\\begin{tabular}{r|llllllllllllllllllllllllllllllllll}\n",
+ " & Start & End & PG0Dis & PG0Shown & PG0Submit & PG1Submit & PG2Submit & PG3Submit & PG4Dtr0\\_6 & PG4Psv7\\_8 & ... & PG5\\_12Order & PG5\\_13Order & PG5Submit & PG6Submit & PG7Submit & PG8Submit & PG9Submit & PG10Submit & PG11Submit & PG12Submit\\\\\n",
+ "\\hline\n",
+ "\tStart & 1.0000 & 0.9952 & -0.0417 & -0.11507 & 0.1350 & 0.1156 & 0.0791 & 0.0384 & 0.01210 & 0.00371 & ... & -0.0369 & 0.0598 & 0.08512 & 0.0054 & 0.0776 & 0.0441 & 0.04101 & 0.047 & 7.9e-02 & 0.0746 \\\\\n",
+ "\tEnd & 0.9952 & 1.0000 & -0.0415 & -0.09879 & 0.1142 & 0.1550 & 0.0791 & 0.0511 & -0.05185 & -0.04576 & ... & -0.0359 & 0.0661 & 0.09088 & 0.0051 & 0.0759 & 0.0435 & 0.04071 & 0.052 & 7.9e-02 & 0.0772 \\\\\n",
+ "\tPG0Dis & -0.0417 & -0.0415 & 1.0000 & 0.87220 & 0.0153 & 0.0065 & 0.0041 & 0.0567 & 0.16368 & 0.02668 & ... & 0.0151 & 0.0384 & 0.00601 & 0.0277 & 0.0097 & 0.0354 & 0.00995 & -0.029 & -4.5e-02 & 0.0546 \\\\\n",
+ "\tPG0Shown & -0.1151 & -0.0988 & 0.8722 & 1.00000 & 0.0360 & 0.0205 & 0.0023 & 0.0497 & 0.08226 & 0.00036 & ... & 0.0074 & 0.0407 & -0.00888 & 0.0401 & 0.0121 & 0.0264 & 0.00056 & -0.045 & -7.1e-02 & 0.0436 \\\\\n",
+ "\tPG0Submit & 0.1350 & 0.1142 & 0.0153 & 0.03596 & 1.0000 & 0.1088 & 0.1037 & 0.1273 & -0.00802 & -0.03763 & ... & -0.0161 & -0.0280 & 0.17671 & 0.1518 & 0.1365 & 0.1258 & 0.17579 & 0.225 & 1.1e-01 & 0.1096 \\\\\n",
+ "\tPG1Submit & 0.1156 & 0.1550 & 0.0065 & 0.02047 & 0.1088 & 1.0000 & 0.1452 & 0.2688 & -0.06852 & 0.05661 & ... & 0.0512 & -0.0651 & 0.24670 & 0.2414 & 0.1133 & 0.1069 & 0.10895 & 0.170 & 7.4e-02 & 0.1137 \\\\\n",
+ "\tPG2Submit & 0.0791 & 0.0791 & 0.0041 & 0.00235 & 0.1037 & 0.1452 & 1.0000 & 0.2045 & 0.00146 & 0.00897 & ... & 0.0210 & -0.0047 & 0.21851 & 0.2696 & 0.1245 & 0.1567 & 0.20127 & 0.099 & 1.1e-01 & 0.1073 \\\\\n",
+ "\tPG3Submit & 0.0384 & 0.0511 & 0.0567 & 0.04968 & 0.1273 & 0.2688 & 0.2045 & 1.0000 & 0.00865 & 0.04424 & ... & 0.0464 & -0.0222 & 0.26048 & 0.2706 & 0.1316 & 0.1822 & 0.27450 & 0.161 & 1.4e-01 & 0.1642 \\\\\n",
+ "\tPG4Dtr0\\_6 & 0.0121 & -0.0518 & 0.1637 & 0.08226 & -0.0080 & -0.0685 & 0.0015 & 0.0087 & 1.00000 & NA & ... & 0.1774 & -0.1289 & -0.05214 & -0.1618 & 0.1560 & 0.0695 & -0.07292 & 0.044 & 8.4e-04 & -0.0272 \\\\\n",
+ "\tPG4Psv7\\_8 & 0.0037 & -0.0458 & 0.0267 & 0.00036 & -0.0376 & 0.0566 & 0.0090 & 0.0442 & NA & 1.00000 & ... & -0.0008 & -0.0218 & 0.08974 & -0.0146 & -0.0363 & 0.0526 & 0.05977 & 0.069 & -4.9e-02 & -0.0217 \\\\\n",
+ "\tPG4Prm9\\_10 & -0.0267 & -0.0267 & -0.0092 & 0.03279 & -0.0939 & 0.0120 & -0.0587 & -0.0062 & NA & NA & ... & 0.0233 & 0.0916 & 0.00077 & -0.0418 & -0.0633 & -0.0550 & -0.02989 & -0.061 & -8.6e-05 & 0.0169 \\\\\n",
+ "\tPG4AllResp & 0.0063 & -0.0158 & 0.0018 & -0.02094 & -0.0236 & 0.0297 & 0.0293 & -0.0193 & 1.00000 & 1.00000 & ... & -0.0306 & -0.0166 & 0.01248 & -0.0040 & -0.0753 & -0.1294 & -0.03812 & -0.106 & -8.2e-02 & -0.0668 \\\\\n",
+ "\tPG4Submit & 0.0187 & 0.0172 & -0.0539 & -0.05978 & 0.2191 & 0.1651 & 0.1515 & 0.1956 & -0.14272 & -0.08350 & ... & -0.0119 & -0.0376 & 0.27328 & 0.3326 & 0.2775 & 0.1821 & 0.33236 & 0.391 & 2.8e-01 & 0.2360 \\\\\n",
+ "\tPG5\\_1Order & 0.0218 & 0.0196 & 0.0140 & 0.01254 & -0.0240 & 0.0750 & -0.0069 & 0.0578 & -0.09488 & -0.01399 & ... & -0.0785 & -0.0950 & 0.08054 & 0.0334 & 0.0173 & -0.0388 & 0.01033 & -0.096 & -2.5e-02 & 0.0238 \\\\\n",
+ "\tPG5\\_2Order & 0.0014 & 0.0002 & -0.0386 & -0.03617 & 0.0402 & -0.0226 & -0.0297 & 0.0048 & -0.00954 & 0.08081 & ... & 0.0527 & -0.0652 & 0.03898 & -0.0571 & -0.0551 & -0.0567 & -0.01925 & 0.015 & 7.3e-03 & 0.0469 \\\\\n",
+ "\tPG5\\_3Order & -0.0089 & -0.0177 & 0.0441 & 0.04228 & 0.0155 & 0.0391 & -0.0131 & 0.0172 & 0.13631 & 0.04396 & ... & -0.0698 & -0.0250 & 0.23450 & 0.0419 & 0.0058 & -0.0086 & 0.02604 & 0.063 & 3.4e-02 & 0.0538 \\\\\n",
+ "\tPG5\\_4Order & 0.0931 & 0.0949 & -0.0262 & -0.02214 & -0.0172 & 0.0293 & 0.0560 & -0.0462 & 0.01735 & -0.12489 & ... & -0.0447 & -0.0329 & 0.14487 & 0.0414 & 0.0460 & 0.0182 & -0.03734 & -0.075 & -8.6e-02 & -0.0211 \\\\\n",
+ "\tPG5\\_5Order & -0.0523 & -0.0466 & -0.0087 & -0.01058 & 0.0860 & 0.0345 & 0.0570 & -0.0165 & 0.04533 & -0.06369 & ... & -0.0499 & -0.0981 & 0.28698 & 0.0498 & 0.0372 & 0.0427 & -0.02976 & 0.076 & 2.4e-02 & 0.0085 \\\\\n",
+ "\tPG5\\_6Order & 0.0237 & 0.0217 & -0.0480 & -0.04902 & 0.0762 & 0.0327 & 0.1077 & 0.0370 & -0.13255 & 0.01662 & ... & -0.0430 & -0.0031 & 0.22759 & 0.0165 & 0.0222 & 0.0639 & -0.02974 & 0.015 & 2.8e-02 & 0.0539 \\\\\n",
+ "\tPG5\\_7Order & -0.0200 & -0.0236 & 0.0220 & -0.00444 & -0.1174 & -0.0815 & -0.0285 & 0.0242 & -0.16196 & -0.06938 & ... & 0.0410 & 0.0944 & 0.06439 & -0.0222 & -0.1082 & -0.0804 & -0.01762 & -0.017 & -4.4e-02 & -0.0617 \\\\\n",
+ "\tPG5\\_8Order & -0.0804 & -0.0852 & -0.0147 & -0.00433 & -0.0421 & -0.0205 & -0.0387 & -0.0129 & -0.16181 & 0.01019 & ... & -0.0896 & -0.1168 & 0.13787 & -0.1227 & -0.0126 & -0.0526 & -0.05699 & -0.065 & -3.7e-02 & -0.0182 \\\\\n",
+ "\tPG5\\_9Order & 0.0171 & 0.0167 & -0.0712 & -0.10235 & 0.0476 & 0.0260 & -0.0410 & -0.0641 & -0.07948 & -0.09747 & ... & -0.0431 & -0.0837 & 0.18534 & -0.0188 & -0.0314 & -0.0898 & -0.01354 & -0.034 & 6.2e-03 & -0.0280 \\\\\n",
+ "\tPG5\\_10Order & -0.0214 & -0.0112 & 0.0200 & 0.02736 & 0.0086 & -0.0017 & -0.0418 & 0.0581 & -0.05310 & 0.15949 & ... & -0.0905 & -0.1214 & 0.22151 & 0.0427 & 0.0311 & 0.0345 & 0.02011 & 0.102 & 1.8e-02 & -0.0092 \\\\\n",
+ "\tPG5\\_11Order & -0.0241 & -0.0196 & 0.0190 & 0.02551 & 0.0927 & -0.0047 & -0.1043 & 0.0279 & 0.02791 & -0.03487 & ... & 0.1233 & 0.1434 & 0.02942 & -0.0055 & 0.0210 & 0.0232 & 0.05579 & -0.038 & -2.5e-03 & 0.0275 \\\\\n",
+ "\tPG5\\_12Order & -0.0369 & -0.0359 & 0.0151 & 0.00743 & -0.0161 & 0.0512 & 0.0210 & 0.0464 & 0.17741 & -0.00080 & ... & 1.0000 & 0.1231 & 0.10997 & 0.0777 & 0.0529 & 0.0149 & 0.00295 & 0.016 & 2.4e-02 & 0.0167 \\\\\n",
+ "\tPG5\\_13Order & 0.0598 & 0.0661 & 0.0384 & 0.04072 & -0.0280 & -0.0651 & -0.0047 & -0.0222 & -0.12890 & -0.02179 & ... & 0.1231 & 1.0000 & 0.00976 & -0.0209 & 0.0355 & 0.0556 & 0.07229 & -0.023 & 1.3e-02 & -0.0614 \\\\\n",
+ "\tPG5Submit & 0.0851 & 0.0909 & 0.0060 & -0.00888 & 0.1767 & 0.2467 & 0.2185 & 0.2605 & -0.05214 & 0.08974 & ... & 0.1100 & 0.0098 & 1.00000 & 0.3224 & 0.2312 & 0.2035 & 0.30291 & 0.269 & 2.4e-01 & 0.2588 \\\\\n",
+ "\tPG6Submit & 0.0054 & 0.0051 & 0.0277 & 0.04005 & 0.1518 & 0.2414 & 0.2696 & 0.2706 & -0.16179 & -0.01463 & ... & 0.0777 & -0.0209 & 0.32240 & 1.0000 & 0.3086 & 0.2065 & 0.44528 & 0.343 & 2.8e-01 & 0.2904 \\\\\n",
+ "\tPG7Submit & 0.0776 & 0.0759 & 0.0097 & 0.01212 & 0.1365 & 0.1133 & 0.1245 & 0.1316 & 0.15596 & -0.03631 & ... & 0.0529 & 0.0355 & 0.23120 & 0.3086 & 1.0000 & 0.1606 & 0.27819 & 0.312 & 2.8e-01 & 0.2523 \\\\\n",
+ "\tPG8Submit & 0.0441 & 0.0435 & 0.0354 & 0.02635 & 0.1258 & 0.1069 & 0.1567 & 0.1822 & 0.06953 & 0.05260 & ... & 0.0149 & 0.0556 & 0.20351 & 0.2065 & 0.1606 & 1.0000 & 0.25569 & 0.200 & 2.1e-01 & 0.1932 \\\\\n",
+ "\tPG9Submit & 0.0410 & 0.0407 & 0.0099 & 0.00056 & 0.1758 & 0.1090 & 0.2013 & 0.2745 & -0.07292 & 0.05977 & ... & 0.0029 & 0.0723 & 0.30291 & 0.4453 & 0.2782 & 0.2557 & 1.00000 & 0.290 & 2.8e-01 & 0.2755 \\\\\n",
+ "\tPG10Submit & 0.0474 & 0.0517 & -0.0293 & -0.04481 & 0.2248 & 0.1701 & 0.0989 & 0.1614 & 0.04433 & 0.06942 & ... & 0.0159 & -0.0227 & 0.26881 & 0.3428 & 0.3121 & 0.2000 & 0.29018 & 1.000 & 3.5e-01 & 0.3131 \\\\\n",
+ "\tPG11Submit & 0.0790 & 0.0792 & -0.0454 & -0.07102 & 0.1093 & 0.0738 & 0.1147 & 0.1383 & 0.00084 & -0.04870 & ... & 0.0240 & 0.0130 & 0.23552 & 0.2777 & 0.2768 & 0.2065 & 0.27906 & 0.346 & 1.0e+00 & 0.2513 \\\\\n",
+ "\tPG12Submit & 0.0746 & 0.0772 & 0.0546 & 0.04364 & 0.1096 & 0.1137 & 0.1073 & 0.1642 & -0.02721 & -0.02169 & ... & 0.0167 & -0.0614 & 0.25876 & 0.2904 & 0.2523 & 0.1932 & 0.27550 & 0.313 & 2.5e-01 & 1.0000 \\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "| | Start | End | PG0Dis | PG0Shown | PG0Submit | PG1Submit | PG2Submit | PG3Submit | PG4Dtr0_6 | PG4Psv7_8 | ... | PG5_12Order | PG5_13Order | PG5Submit | PG6Submit | PG7Submit | PG8Submit | PG9Submit | PG10Submit | PG11Submit | PG12Submit | \n",
+ "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+ "| Start | 1.0000 | 0.9952 | -0.0417 | -0.11507 | 0.1350 | 0.1156 | 0.0791 | 0.0384 | 0.01210 | 0.00371 | ... | -0.0369 | 0.0598 | 0.08512 | 0.0054 | 0.0776 | 0.0441 | 0.04101 | 0.047 | 7.9e-02 | 0.0746 | \n",
+ "| End | 0.9952 | 1.0000 | -0.0415 | -0.09879 | 0.1142 | 0.1550 | 0.0791 | 0.0511 | -0.05185 | -0.04576 | ... | -0.0359 | 0.0661 | 0.09088 | 0.0051 | 0.0759 | 0.0435 | 0.04071 | 0.052 | 7.9e-02 | 0.0772 | \n",
+ "| PG0Dis | -0.0417 | -0.0415 | 1.0000 | 0.87220 | 0.0153 | 0.0065 | 0.0041 | 0.0567 | 0.16368 | 0.02668 | ... | 0.0151 | 0.0384 | 0.00601 | 0.0277 | 0.0097 | 0.0354 | 0.00995 | -0.029 | -4.5e-02 | 0.0546 | \n",
+ "| PG0Shown | -0.1151 | -0.0988 | 0.8722 | 1.00000 | 0.0360 | 0.0205 | 0.0023 | 0.0497 | 0.08226 | 0.00036 | ... | 0.0074 | 0.0407 | -0.00888 | 0.0401 | 0.0121 | 0.0264 | 0.00056 | -0.045 | -7.1e-02 | 0.0436 | \n",
+ "| PG0Submit | 0.1350 | 0.1142 | 0.0153 | 0.03596 | 1.0000 | 0.1088 | 0.1037 | 0.1273 | -0.00802 | -0.03763 | ... | -0.0161 | -0.0280 | 0.17671 | 0.1518 | 0.1365 | 0.1258 | 0.17579 | 0.225 | 1.1e-01 | 0.1096 | \n",
+ "| PG1Submit | 0.1156 | 0.1550 | 0.0065 | 0.02047 | 0.1088 | 1.0000 | 0.1452 | 0.2688 | -0.06852 | 0.05661 | ... | 0.0512 | -0.0651 | 0.24670 | 0.2414 | 0.1133 | 0.1069 | 0.10895 | 0.170 | 7.4e-02 | 0.1137 | \n",
+ "| PG2Submit | 0.0791 | 0.0791 | 0.0041 | 0.00235 | 0.1037 | 0.1452 | 1.0000 | 0.2045 | 0.00146 | 0.00897 | ... | 0.0210 | -0.0047 | 0.21851 | 0.2696 | 0.1245 | 0.1567 | 0.20127 | 0.099 | 1.1e-01 | 0.1073 | \n",
+ "| PG3Submit | 0.0384 | 0.0511 | 0.0567 | 0.04968 | 0.1273 | 0.2688 | 0.2045 | 1.0000 | 0.00865 | 0.04424 | ... | 0.0464 | -0.0222 | 0.26048 | 0.2706 | 0.1316 | 0.1822 | 0.27450 | 0.161 | 1.4e-01 | 0.1642 | \n",
+ "| PG4Dtr0_6 | 0.0121 | -0.0518 | 0.1637 | 0.08226 | -0.0080 | -0.0685 | 0.0015 | 0.0087 | 1.00000 | NA | ... | 0.1774 | -0.1289 | -0.05214 | -0.1618 | 0.1560 | 0.0695 | -0.07292 | 0.044 | 8.4e-04 | -0.0272 | \n",
+ "| PG4Psv7_8 | 0.0037 | -0.0458 | 0.0267 | 0.00036 | -0.0376 | 0.0566 | 0.0090 | 0.0442 | NA | 1.00000 | ... | -0.0008 | -0.0218 | 0.08974 | -0.0146 | -0.0363 | 0.0526 | 0.05977 | 0.069 | -4.9e-02 | -0.0217 | \n",
+ "| PG4Prm9_10 | -0.0267 | -0.0267 | -0.0092 | 0.03279 | -0.0939 | 0.0120 | -0.0587 | -0.0062 | NA | NA | ... | 0.0233 | 0.0916 | 0.00077 | -0.0418 | -0.0633 | -0.0550 | -0.02989 | -0.061 | -8.6e-05 | 0.0169 | \n",
+ "| PG4AllResp | 0.0063 | -0.0158 | 0.0018 | -0.02094 | -0.0236 | 0.0297 | 0.0293 | -0.0193 | 1.00000 | 1.00000 | ... | -0.0306 | -0.0166 | 0.01248 | -0.0040 | -0.0753 | -0.1294 | -0.03812 | -0.106 | -8.2e-02 | -0.0668 | \n",
+ "| PG4Submit | 0.0187 | 0.0172 | -0.0539 | -0.05978 | 0.2191 | 0.1651 | 0.1515 | 0.1956 | -0.14272 | -0.08350 | ... | -0.0119 | -0.0376 | 0.27328 | 0.3326 | 0.2775 | 0.1821 | 0.33236 | 0.391 | 2.8e-01 | 0.2360 | \n",
+ "| PG5_1Order | 0.0218 | 0.0196 | 0.0140 | 0.01254 | -0.0240 | 0.0750 | -0.0069 | 0.0578 | -0.09488 | -0.01399 | ... | -0.0785 | -0.0950 | 0.08054 | 0.0334 | 0.0173 | -0.0388 | 0.01033 | -0.096 | -2.5e-02 | 0.0238 | \n",
+ "| PG5_2Order | 0.0014 | 0.0002 | -0.0386 | -0.03617 | 0.0402 | -0.0226 | -0.0297 | 0.0048 | -0.00954 | 0.08081 | ... | 0.0527 | -0.0652 | 0.03898 | -0.0571 | -0.0551 | -0.0567 | -0.01925 | 0.015 | 7.3e-03 | 0.0469 | \n",
+ "| PG5_3Order | -0.0089 | -0.0177 | 0.0441 | 0.04228 | 0.0155 | 0.0391 | -0.0131 | 0.0172 | 0.13631 | 0.04396 | ... | -0.0698 | -0.0250 | 0.23450 | 0.0419 | 0.0058 | -0.0086 | 0.02604 | 0.063 | 3.4e-02 | 0.0538 | \n",
+ "| PG5_4Order | 0.0931 | 0.0949 | -0.0262 | -0.02214 | -0.0172 | 0.0293 | 0.0560 | -0.0462 | 0.01735 | -0.12489 | ... | -0.0447 | -0.0329 | 0.14487 | 0.0414 | 0.0460 | 0.0182 | -0.03734 | -0.075 | -8.6e-02 | -0.0211 | \n",
+ "| PG5_5Order | -0.0523 | -0.0466 | -0.0087 | -0.01058 | 0.0860 | 0.0345 | 0.0570 | -0.0165 | 0.04533 | -0.06369 | ... | -0.0499 | -0.0981 | 0.28698 | 0.0498 | 0.0372 | 0.0427 | -0.02976 | 0.076 | 2.4e-02 | 0.0085 | \n",
+ "| PG5_6Order | 0.0237 | 0.0217 | -0.0480 | -0.04902 | 0.0762 | 0.0327 | 0.1077 | 0.0370 | -0.13255 | 0.01662 | ... | -0.0430 | -0.0031 | 0.22759 | 0.0165 | 0.0222 | 0.0639 | -0.02974 | 0.015 | 2.8e-02 | 0.0539 | \n",
+ "| PG5_7Order | -0.0200 | -0.0236 | 0.0220 | -0.00444 | -0.1174 | -0.0815 | -0.0285 | 0.0242 | -0.16196 | -0.06938 | ... | 0.0410 | 0.0944 | 0.06439 | -0.0222 | -0.1082 | -0.0804 | -0.01762 | -0.017 | -4.4e-02 | -0.0617 | \n",
+ "| PG5_8Order | -0.0804 | -0.0852 | -0.0147 | -0.00433 | -0.0421 | -0.0205 | -0.0387 | -0.0129 | -0.16181 | 0.01019 | ... | -0.0896 | -0.1168 | 0.13787 | -0.1227 | -0.0126 | -0.0526 | -0.05699 | -0.065 | -3.7e-02 | -0.0182 | \n",
+ "| PG5_9Order | 0.0171 | 0.0167 | -0.0712 | -0.10235 | 0.0476 | 0.0260 | -0.0410 | -0.0641 | -0.07948 | -0.09747 | ... | -0.0431 | -0.0837 | 0.18534 | -0.0188 | -0.0314 | -0.0898 | -0.01354 | -0.034 | 6.2e-03 | -0.0280 | \n",
+ "| PG5_10Order | -0.0214 | -0.0112 | 0.0200 | 0.02736 | 0.0086 | -0.0017 | -0.0418 | 0.0581 | -0.05310 | 0.15949 | ... | -0.0905 | -0.1214 | 0.22151 | 0.0427 | 0.0311 | 0.0345 | 0.02011 | 0.102 | 1.8e-02 | -0.0092 | \n",
+ "| PG5_11Order | -0.0241 | -0.0196 | 0.0190 | 0.02551 | 0.0927 | -0.0047 | -0.1043 | 0.0279 | 0.02791 | -0.03487 | ... | 0.1233 | 0.1434 | 0.02942 | -0.0055 | 0.0210 | 0.0232 | 0.05579 | -0.038 | -2.5e-03 | 0.0275 | \n",
+ "| PG5_12Order | -0.0369 | -0.0359 | 0.0151 | 0.00743 | -0.0161 | 0.0512 | 0.0210 | 0.0464 | 0.17741 | -0.00080 | ... | 1.0000 | 0.1231 | 0.10997 | 0.0777 | 0.0529 | 0.0149 | 0.00295 | 0.016 | 2.4e-02 | 0.0167 | \n",
+ "| PG5_13Order | 0.0598 | 0.0661 | 0.0384 | 0.04072 | -0.0280 | -0.0651 | -0.0047 | -0.0222 | -0.12890 | -0.02179 | ... | 0.1231 | 1.0000 | 0.00976 | -0.0209 | 0.0355 | 0.0556 | 0.07229 | -0.023 | 1.3e-02 | -0.0614 | \n",
+ "| PG5Submit | 0.0851 | 0.0909 | 0.0060 | -0.00888 | 0.1767 | 0.2467 | 0.2185 | 0.2605 | -0.05214 | 0.08974 | ... | 0.1100 | 0.0098 | 1.00000 | 0.3224 | 0.2312 | 0.2035 | 0.30291 | 0.269 | 2.4e-01 | 0.2588 | \n",
+ "| PG6Submit | 0.0054 | 0.0051 | 0.0277 | 0.04005 | 0.1518 | 0.2414 | 0.2696 | 0.2706 | -0.16179 | -0.01463 | ... | 0.0777 | -0.0209 | 0.32240 | 1.0000 | 0.3086 | 0.2065 | 0.44528 | 0.343 | 2.8e-01 | 0.2904 | \n",
+ "| PG7Submit | 0.0776 | 0.0759 | 0.0097 | 0.01212 | 0.1365 | 0.1133 | 0.1245 | 0.1316 | 0.15596 | -0.03631 | ... | 0.0529 | 0.0355 | 0.23120 | 0.3086 | 1.0000 | 0.1606 | 0.27819 | 0.312 | 2.8e-01 | 0.2523 | \n",
+ "| PG8Submit | 0.0441 | 0.0435 | 0.0354 | 0.02635 | 0.1258 | 0.1069 | 0.1567 | 0.1822 | 0.06953 | 0.05260 | ... | 0.0149 | 0.0556 | 0.20351 | 0.2065 | 0.1606 | 1.0000 | 0.25569 | 0.200 | 2.1e-01 | 0.1932 | \n",
+ "| PG9Submit | 0.0410 | 0.0407 | 0.0099 | 0.00056 | 0.1758 | 0.1090 | 0.2013 | 0.2745 | -0.07292 | 0.05977 | ... | 0.0029 | 0.0723 | 0.30291 | 0.4453 | 0.2782 | 0.2557 | 1.00000 | 0.290 | 2.8e-01 | 0.2755 | \n",
+ "| PG10Submit | 0.0474 | 0.0517 | -0.0293 | -0.04481 | 0.2248 | 0.1701 | 0.0989 | 0.1614 | 0.04433 | 0.06942 | ... | 0.0159 | -0.0227 | 0.26881 | 0.3428 | 0.3121 | 0.2000 | 0.29018 | 1.000 | 3.5e-01 | 0.3131 | \n",
+ "| PG11Submit | 0.0790 | 0.0792 | -0.0454 | -0.07102 | 0.1093 | 0.0738 | 0.1147 | 0.1383 | 0.00084 | -0.04870 | ... | 0.0240 | 0.0130 | 0.23552 | 0.2777 | 0.2768 | 0.2065 | 0.27906 | 0.346 | 1.0e+00 | 0.2513 | \n",
+ "| PG12Submit | 0.0746 | 0.0772 | 0.0546 | 0.04364 | 0.1096 | 0.1137 | 0.1073 | 0.1642 | -0.02721 | -0.02169 | ... | 0.0167 | -0.0614 | 0.25876 | 0.2904 | 0.2523 | 0.1932 | 0.27550 | 0.313 | 2.5e-01 | 1.0000 | \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ " Start End PG0Dis PG0Shown PG0Submit PG1Submit PG2Submit\n",
+ "Start 1.0000 0.9952 -0.0417 -0.11507 0.1350 0.1156 0.0791 \n",
+ "End 0.9952 1.0000 -0.0415 -0.09879 0.1142 0.1550 0.0791 \n",
+ "PG0Dis -0.0417 -0.0415 1.0000 0.87220 0.0153 0.0065 0.0041 \n",
+ "PG0Shown -0.1151 -0.0988 0.8722 1.00000 0.0360 0.0205 0.0023 \n",
+ "PG0Submit 0.1350 0.1142 0.0153 0.03596 1.0000 0.1088 0.1037 \n",
+ "PG1Submit 0.1156 0.1550 0.0065 0.02047 0.1088 1.0000 0.1452 \n",
+ "PG2Submit 0.0791 0.0791 0.0041 0.00235 0.1037 0.1452 1.0000 \n",
+ "PG3Submit 0.0384 0.0511 0.0567 0.04968 0.1273 0.2688 0.2045 \n",
+ "PG4Dtr0_6 0.0121 -0.0518 0.1637 0.08226 -0.0080 -0.0685 0.0015 \n",
+ "PG4Psv7_8 0.0037 -0.0458 0.0267 0.00036 -0.0376 0.0566 0.0090 \n",
+ "PG4Prm9_10 -0.0267 -0.0267 -0.0092 0.03279 -0.0939 0.0120 -0.0587 \n",
+ "PG4AllResp 0.0063 -0.0158 0.0018 -0.02094 -0.0236 0.0297 0.0293 \n",
+ "PG4Submit 0.0187 0.0172 -0.0539 -0.05978 0.2191 0.1651 0.1515 \n",
+ "PG5_1Order 0.0218 0.0196 0.0140 0.01254 -0.0240 0.0750 -0.0069 \n",
+ "PG5_2Order 0.0014 0.0002 -0.0386 -0.03617 0.0402 -0.0226 -0.0297 \n",
+ "PG5_3Order -0.0089 -0.0177 0.0441 0.04228 0.0155 0.0391 -0.0131 \n",
+ "PG5_4Order 0.0931 0.0949 -0.0262 -0.02214 -0.0172 0.0293 0.0560 \n",
+ "PG5_5Order -0.0523 -0.0466 -0.0087 -0.01058 0.0860 0.0345 0.0570 \n",
+ "PG5_6Order 0.0237 0.0217 -0.0480 -0.04902 0.0762 0.0327 0.1077 \n",
+ "PG5_7Order -0.0200 -0.0236 0.0220 -0.00444 -0.1174 -0.0815 -0.0285 \n",
+ "PG5_8Order -0.0804 -0.0852 -0.0147 -0.00433 -0.0421 -0.0205 -0.0387 \n",
+ "PG5_9Order 0.0171 0.0167 -0.0712 -0.10235 0.0476 0.0260 -0.0410 \n",
+ "PG5_10Order -0.0214 -0.0112 0.0200 0.02736 0.0086 -0.0017 -0.0418 \n",
+ "PG5_11Order -0.0241 -0.0196 0.0190 0.02551 0.0927 -0.0047 -0.1043 \n",
+ "PG5_12Order -0.0369 -0.0359 0.0151 0.00743 -0.0161 0.0512 0.0210 \n",
+ "PG5_13Order 0.0598 0.0661 0.0384 0.04072 -0.0280 -0.0651 -0.0047 \n",
+ "PG5Submit 0.0851 0.0909 0.0060 -0.00888 0.1767 0.2467 0.2185 \n",
+ "PG6Submit 0.0054 0.0051 0.0277 0.04005 0.1518 0.2414 0.2696 \n",
+ "PG7Submit 0.0776 0.0759 0.0097 0.01212 0.1365 0.1133 0.1245 \n",
+ "PG8Submit 0.0441 0.0435 0.0354 0.02635 0.1258 0.1069 0.1567 \n",
+ "PG9Submit 0.0410 0.0407 0.0099 0.00056 0.1758 0.1090 0.2013 \n",
+ "PG10Submit 0.0474 0.0517 -0.0293 -0.04481 0.2248 0.1701 0.0989 \n",
+ "PG11Submit 0.0790 0.0792 -0.0454 -0.07102 0.1093 0.0738 0.1147 \n",
+ "PG12Submit 0.0746 0.0772 0.0546 0.04364 0.1096 0.1137 0.1073 \n",
+ " PG3Submit PG4Dtr0_6 PG4Psv7_8 ... PG5_12Order PG5_13Order PG5Submit\n",
+ "Start 0.0384 0.01210 0.00371 ... -0.0369 0.0598 0.08512 \n",
+ "End 0.0511 -0.05185 -0.04576 ... -0.0359 0.0661 0.09088 \n",
+ "PG0Dis 0.0567 0.16368 0.02668 ... 0.0151 0.0384 0.00601 \n",
+ "PG0Shown 0.0497 0.08226 0.00036 ... 0.0074 0.0407 -0.00888 \n",
+ "PG0Submit 0.1273 -0.00802 -0.03763 ... -0.0161 -0.0280 0.17671 \n",
+ "PG1Submit 0.2688 -0.06852 0.05661 ... 0.0512 -0.0651 0.24670 \n",
+ "PG2Submit 0.2045 0.00146 0.00897 ... 0.0210 -0.0047 0.21851 \n",
+ "PG3Submit 1.0000 0.00865 0.04424 ... 0.0464 -0.0222 0.26048 \n",
+ "PG4Dtr0_6 0.0087 1.00000 NA ... 0.1774 -0.1289 -0.05214 \n",
+ "PG4Psv7_8 0.0442 NA 1.00000 ... -0.0008 -0.0218 0.08974 \n",
+ "PG4Prm9_10 -0.0062 NA NA ... 0.0233 0.0916 0.00077 \n",
+ "PG4AllResp -0.0193 1.00000 1.00000 ... -0.0306 -0.0166 0.01248 \n",
+ "PG4Submit 0.1956 -0.14272 -0.08350 ... -0.0119 -0.0376 0.27328 \n",
+ "PG5_1Order 0.0578 -0.09488 -0.01399 ... -0.0785 -0.0950 0.08054 \n",
+ "PG5_2Order 0.0048 -0.00954 0.08081 ... 0.0527 -0.0652 0.03898 \n",
+ "PG5_3Order 0.0172 0.13631 0.04396 ... -0.0698 -0.0250 0.23450 \n",
+ "PG5_4Order -0.0462 0.01735 -0.12489 ... -0.0447 -0.0329 0.14487 \n",
+ "PG5_5Order -0.0165 0.04533 -0.06369 ... -0.0499 -0.0981 0.28698 \n",
+ "PG5_6Order 0.0370 -0.13255 0.01662 ... -0.0430 -0.0031 0.22759 \n",
+ "PG5_7Order 0.0242 -0.16196 -0.06938 ... 0.0410 0.0944 0.06439 \n",
+ "PG5_8Order -0.0129 -0.16181 0.01019 ... -0.0896 -0.1168 0.13787 \n",
+ "PG5_9Order -0.0641 -0.07948 -0.09747 ... -0.0431 -0.0837 0.18534 \n",
+ "PG5_10Order 0.0581 -0.05310 0.15949 ... -0.0905 -0.1214 0.22151 \n",
+ "PG5_11Order 0.0279 0.02791 -0.03487 ... 0.1233 0.1434 0.02942 \n",
+ "PG5_12Order 0.0464 0.17741 -0.00080 ... 1.0000 0.1231 0.10997 \n",
+ "PG5_13Order -0.0222 -0.12890 -0.02179 ... 0.1231 1.0000 0.00976 \n",
+ "PG5Submit 0.2605 -0.05214 0.08974 ... 0.1100 0.0098 1.00000 \n",
+ "PG6Submit 0.2706 -0.16179 -0.01463 ... 0.0777 -0.0209 0.32240 \n",
+ "PG7Submit 0.1316 0.15596 -0.03631 ... 0.0529 0.0355 0.23120 \n",
+ "PG8Submit 0.1822 0.06953 0.05260 ... 0.0149 0.0556 0.20351 \n",
+ "PG9Submit 0.2745 -0.07292 0.05977 ... 0.0029 0.0723 0.30291 \n",
+ "PG10Submit 0.1614 0.04433 0.06942 ... 0.0159 -0.0227 0.26881 \n",
+ "PG11Submit 0.1383 0.00084 -0.04870 ... 0.0240 0.0130 0.23552 \n",
+ "PG12Submit 0.1642 -0.02721 -0.02169 ... 0.0167 -0.0614 0.25876 \n",
+ " PG6Submit PG7Submit PG8Submit PG9Submit PG10Submit PG11Submit\n",
+ "Start 0.0054 0.0776 0.0441 0.04101 0.047 7.9e-02 \n",
+ "End 0.0051 0.0759 0.0435 0.04071 0.052 7.9e-02 \n",
+ "PG0Dis 0.0277 0.0097 0.0354 0.00995 -0.029 -4.5e-02 \n",
+ "PG0Shown 0.0401 0.0121 0.0264 0.00056 -0.045 -7.1e-02 \n",
+ "PG0Submit 0.1518 0.1365 0.1258 0.17579 0.225 1.1e-01 \n",
+ "PG1Submit 0.2414 0.1133 0.1069 0.10895 0.170 7.4e-02 \n",
+ "PG2Submit 0.2696 0.1245 0.1567 0.20127 0.099 1.1e-01 \n",
+ "PG3Submit 0.2706 0.1316 0.1822 0.27450 0.161 1.4e-01 \n",
+ "PG4Dtr0_6 -0.1618 0.1560 0.0695 -0.07292 0.044 8.4e-04 \n",
+ "PG4Psv7_8 -0.0146 -0.0363 0.0526 0.05977 0.069 -4.9e-02 \n",
+ "PG4Prm9_10 -0.0418 -0.0633 -0.0550 -0.02989 -0.061 -8.6e-05 \n",
+ "PG4AllResp -0.0040 -0.0753 -0.1294 -0.03812 -0.106 -8.2e-02 \n",
+ "PG4Submit 0.3326 0.2775 0.1821 0.33236 0.391 2.8e-01 \n",
+ "PG5_1Order 0.0334 0.0173 -0.0388 0.01033 -0.096 -2.5e-02 \n",
+ "PG5_2Order -0.0571 -0.0551 -0.0567 -0.01925 0.015 7.3e-03 \n",
+ "PG5_3Order 0.0419 0.0058 -0.0086 0.02604 0.063 3.4e-02 \n",
+ "PG5_4Order 0.0414 0.0460 0.0182 -0.03734 -0.075 -8.6e-02 \n",
+ "PG5_5Order 0.0498 0.0372 0.0427 -0.02976 0.076 2.4e-02 \n",
+ "PG5_6Order 0.0165 0.0222 0.0639 -0.02974 0.015 2.8e-02 \n",
+ "PG5_7Order -0.0222 -0.1082 -0.0804 -0.01762 -0.017 -4.4e-02 \n",
+ "PG5_8Order -0.1227 -0.0126 -0.0526 -0.05699 -0.065 -3.7e-02 \n",
+ "PG5_9Order -0.0188 -0.0314 -0.0898 -0.01354 -0.034 6.2e-03 \n",
+ "PG5_10Order 0.0427 0.0311 0.0345 0.02011 0.102 1.8e-02 \n",
+ "PG5_11Order -0.0055 0.0210 0.0232 0.05579 -0.038 -2.5e-03 \n",
+ "PG5_12Order 0.0777 0.0529 0.0149 0.00295 0.016 2.4e-02 \n",
+ "PG5_13Order -0.0209 0.0355 0.0556 0.07229 -0.023 1.3e-02 \n",
+ "PG5Submit 0.3224 0.2312 0.2035 0.30291 0.269 2.4e-01 \n",
+ "PG6Submit 1.0000 0.3086 0.2065 0.44528 0.343 2.8e-01 \n",
+ "PG7Submit 0.3086 1.0000 0.1606 0.27819 0.312 2.8e-01 \n",
+ "PG8Submit 0.2065 0.1606 1.0000 0.25569 0.200 2.1e-01 \n",
+ "PG9Submit 0.4453 0.2782 0.2557 1.00000 0.290 2.8e-01 \n",
+ "PG10Submit 0.3428 0.3121 0.2000 0.29018 1.000 3.5e-01 \n",
+ "PG11Submit 0.2777 0.2768 0.2065 0.27906 0.346 1.0e+00 \n",
+ "PG12Submit 0.2904 0.2523 0.1932 0.27550 0.313 2.5e-01 \n",
+ " PG12Submit\n",
+ "Start 0.0746 \n",
+ "End 0.0772 \n",
+ "PG0Dis 0.0546 \n",
+ "PG0Shown 0.0436 \n",
+ "PG0Submit 0.1096 \n",
+ "PG1Submit 0.1137 \n",
+ "PG2Submit 0.1073 \n",
+ "PG3Submit 0.1642 \n",
+ "PG4Dtr0_6 -0.0272 \n",
+ "PG4Psv7_8 -0.0217 \n",
+ "PG4Prm9_10 0.0169 \n",
+ "PG4AllResp -0.0668 \n",
+ "PG4Submit 0.2360 \n",
+ "PG5_1Order 0.0238 \n",
+ "PG5_2Order 0.0469 \n",
+ "PG5_3Order 0.0538 \n",
+ "PG5_4Order -0.0211 \n",
+ "PG5_5Order 0.0085 \n",
+ "PG5_6Order 0.0539 \n",
+ "PG5_7Order -0.0617 \n",
+ "PG5_8Order -0.0182 \n",
+ "PG5_9Order -0.0280 \n",
+ "PG5_10Order -0.0092 \n",
+ "PG5_11Order 0.0275 \n",
+ "PG5_12Order 0.0167 \n",
+ "PG5_13Order -0.0614 \n",
+ "PG5Submit 0.2588 \n",
+ "PG6Submit 0.2904 \n",
+ "PG7Submit 0.2523 \n",
+ "PG8Submit 0.1932 \n",
+ "PG9Submit 0.2755 \n",
+ "PG10Submit 0.3131 \n",
+ "PG11Submit 0.2513 \n",
+ "PG12Submit 1.0000 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#get numeric fields only for correlation\n",
+ "sel = c()\n",
+ "for (i in 1:dim(data)[2]) if (is.numeric(data[,i])) sel = c(sel, i);\n",
+ "\n",
+ "\n",
+ "cor(data[,sel],method=\"spearman\",use=\"pairwise.complete.obs\"); #OK for any: uses ranks"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Interpret correlations: onlys start vs End, calculate differene instead\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "### Simple questions\n",
+ "\n",
+ "- Time to take entire survey?\n",
+ "- Question that took the longest to complete?\n",
+ "- Question that took the least time?\n",
+ "- Top-ranked criteria?\n",
+ "- Demographic distribution by age?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exploration of the Data\n",
+ "\n",
+ "I plot the response frequencies to get a better understanding of the distribution of the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 120,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Only examine the non-empty responses.\n",
+ "#print(dim(data))\n",
+ "mydim <- data[,28]\n",
+ "#print(data[,29])\n",
+ "sum <- 0\n",
+ "not <- 0\n",
+ "low <- 0\n",
+ "med <- 0\n",
+ "hgh <- 0\n",
+ "ess <- 0\n",
+ "for (i in 1:dim(data)[1]) {\n",
+ " if (mydim[i] != \"\") {\n",
+ " if(mydim[i] == \"Not a Priority\")\n",
+ " {\n",
+ " not <- not + 1\n",
+ " }\n",
+ " else if(mydim[i] == \"Low Priority\")\n",
+ " {\n",
+ " low <- low + 1\n",
+ " }\n",
+ " else if(mydim[i] == \"Medium Priority\")\n",
+ " {\n",
+ " med <- med + 1\n",
+ " }\n",
+ " else if(mydim[i] == \"High Priority\")\n",
+ " {\n",
+ " hgh <- hgh + 1\n",
+ " }\n",
+ " else #if(mydim[i] == \"Essential\")\n",
+ " {\n",
+ " ess <- ess + 1\n",
+ " }\n",
+ " \n",
+ " sum <- sum + 1\n",
+ " #print(paste(\"[\",i,\"]=\",mydim[i]));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "valids = vector(,length=sum)\n",
+ "ctr <- 1\n",
+ "for (i in 1:dim(data)[1]) {\n",
+ " if (mydim[i] != \"\") {\n",
+ " valids[ctr] <- i;\n",
+ " ctr <- ctr +1;\n",
+ " }\n",
+ "}\n",
+ "#print(sum)\n",
+ "#print(not)\n",
+ "#print(low)\n",
+ "#print(med)\n",
+ "#print(hgh)\n",
+ "#print(ess)\n",
+ "#print(histo)\n",
+ "#hist(data$PG5_3HDS)\n",
+ "#hist(histo)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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MUOAABAEhQ7AAAASVDsAAAAJEGxAwAA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h0TE+PocVkkayHgpAh26NIURamoqJg1a1ZgYODWrVsd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",
+ "text/plain": [
+ "Plot with title \"Frequency of Choice\""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Build the histogram.\n",
+ "histo <- c(not, low, med, hgh, ess)\n",
+ "histo <- histo/sum\n",
+ "\n",
+ "# Standardize the data.\n",
+ "avg <- 0\n",
+ "expec_sq <- 0\n",
+ "for (i in 1:5) {\n",
+ " avg <- avg + i*histo[i];\n",
+ " expec_sq <- expec_sq + i*i*histo[i];\n",
+ "}\n",
+ "std_dev <- expec_sq - (avg*avg)\n",
+ "\n",
+ "#print(avg)\n",
+ "#print(std_dev)\n",
+ "\n",
+ "x_1 <- (1-avg)/std_dev\n",
+ "x_2 <- (2-avg)/std_dev\n",
+ "x_3 <- (3-avg)/std_dev\n",
+ "x_4 <- (4-avg)/std_dev\n",
+ "x_5 <- (5-avg)/std_dev\n",
+ "\n",
+ "# Plot the histogram.\n",
+ "xvals = c(x_1, x_2, x_3, x_4, x_5)\n",
+ "names(histo) <- c(\"1 - Not a Priority\", \"2 - Low Priority\", \"3 - Med. Priority\", \"4 - High Priority\", \"5 - Essential\")\n",
+ "#namelab <- c(\"1 - Not Priority\", \"2 - Low Priority\", \"3 - Med. Priority\", \"4 - High Priority\", \"5 - Essential\")\n",
+ "#names(histo) <- c(\"1\", \"2\", \"3\", \"4\", \"5\")\n",
+ "plt <- barplot(histo, ylab=\"Frequency\", main=\"Frequency of Choice\", col=\"blue3\", cex.names=0.75)\n",
+ "#text(plt, namelab, srt=60)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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VHx559/spdrvp4oCm7cSWFhYa3V5969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\nGE4UIyajAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "Plot with title \"Frequency of Choice - Standardized\""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot against standardized data.\n",
+ "plot(xvals, histo, xlab=\"Standardized Input\", ylab=\"Frequency\", main=\"Frequency of Choice - Standardized\", col=\"red\")\n",
+ "lines(xvals, histo)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Observations\n",
+ "\n",
+ "The responses are skewed to the right. This means that there are more survey respondents who found helpful discussion on StackExchange to be a higher priority than there are who found it to be a lower priority. Standardizing these results does not change the skewness of the distribution."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Explanation of How Measures are Calculated from the Data, Transformation, and Cleaning\n",
+ "\n",
+ "Calculations and cleaning are both done is this segment of code. First, I only parse the data samples for which a response was given. Then, I encode the answer options from the survey into numerical values. These values are in the interval [0,8], where '0' represents not answered (for optional questions) or the first choice (for required questions). In this scenario, the cleaning of the data is the replacement of missing values by '0'. '8' represents \"other.\" The other values are the relative placement of the answer choice in the given options scaled in the range [0,8]. For example, if there are three answer choices for a required question, then if the respondent selects the first answer, then the encoded value is '0'; if he or she selects the second, then the value is '4' (50% of 8); and if he or she selects the third, then the value is '8'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 269,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Pair down data before running this next line.\n",
+ "data[is.na(data)] <- \"\"\n",
+ "\n",
+ "# Convert non-numeric values to numeric values.\n",
+ "for (i in 1:dim(data)[1]) {\n",
+ " for (j in 1:dim(data)[2]) {\n",
+ " #print(i+j)\n",
+ " #print(is.na(data[i,][j]))\n",
+ " #if( is.na(data[i,][j]) == FALSE )\n",
+ " if( data[i,][j] != \"\")\n",
+ " {\n",
+ " # Generic cases:\n",
+ " if ( data[i,][j] == \"Not a Priority\" || data[i,][j] == \"Less than 2 years\" || data[i,][j] == \"R\" || data[i,][j] == \"C/C++\" || data[i,][j] == \"Java\" || data[i,][j] == \"Python\" || data[i,][j] == \"Javascript\" || data[i,][j] == \"Go\" || data[i,][j] == \"C#\" || data[i,][j] == \"1\" || data[i,][j] == \"Native\" || data[i,][j] == \"18 - 24\" || data[i,][j] == \"For personal work and/or research use\" || data[i,][j] == \"For a wider audience, such as developers of other packages or other software\" || data[i,][j] == \"For a training / class that I took\") {\n",
+ " newdata[i,][j] <- 1;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"Low Priority\" || data[i,][j] == \"2 - 5 years\" || data[i,][j] == \"2 - 3\" || data[i,][j] == \"Not native - full working proficiency\" || data[i,][j] == \"25 - 34\") {\n",
+ " newdata[i,][j] <- 2;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"Medium Priority\" || data[i,][j] == \"6 - 8 years\" || data[i,][j] == \"4 - 6\" || data[i,][j] == \"Not native - sufficient working proficiency\" || data[i,][j] == \"35 - 44\") {\n",
+ " newdata[i,][j] <- 3;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"High Priority\" || data[i,][j] == \"9 - 12 years\" || data[i,][j] == \"7 - 10\" || data[i,][j] == \"Not native - limited working proficiency\" || data[i,][j] == \"45 - 54\") {\n",
+ " newdata[i,][j] <- 4;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"Essential\" || data[i,][j] == \"13 - 19 years\" || data[i,][j] == \"11 - 15\" || data[i,][j] == \"Not native - passable\" || data[i,][j] == \"55 - 64\") {\n",
+ " newdata[i,][j] <- 5;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"20 years or more\" || data[i,][j] == \"16 - 25\" || data[i,][j] == \"Not native - very limited\" || data[i,][j] == \"65 and over\") {\n",
+ " newdata[i,][j] <- 6;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"More than 25\") {\n",
+ " newdata[i,][j] <- 7;\n",
+ " }\n",
+ " else # Not sure\n",
+ " {\n",
+ " newdata[i,][j] <- 8;\n",
+ " }\n",
+ " \n",
+ " # Special cases:\n",
+ " if( data[i,][j] == \"Yes\" || data[i,][j] == \"Software Engineer\" || data[i,][j] == \"The core \\\"data.frame\\\" object lacked functionality that I needed\") {\n",
+ " newdata[i,][j] <- 0.0;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"Female\" || data[i,][j] == \"Chose the package to be compatible with other packages in my project\") {\n",
+ " newdata[i,][j] <- 2.0; \n",
+ " }\n",
+ " else if ( data[i,][j] == \"Data Scientist\" || data[i,][j] == \"No\" || data[i,][j] == \"Male\" || data[i,][j] == \"I saw a recommendation for the package\" ) {\n",
+ " newdata[i,][j] <- 4.0;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"Prefer not to answer\" || data[i,][j] == \"I didn't choose to use the package, it was included implicitly / unintentionally\" ) {\n",
+ " newdata[i,][j] <- 6.0;\n",
+ " }\n",
+ " }\n",
+ " else\n",
+ " {\n",
+ " newdata[i,][j] <- 0; # Denotes missing value.\n",
+ " }\n",
+ " }\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 382,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create the data frame for the regression.\n",
+ "cleandata = newdata[valids,]\n",
+ "indexointerest = c(8,9,10,11,13,15,62,64,65,66,67,68,69,70,73,75,77,79,81)\n",
+ "indexoaug = c(indexointerest, 28)\n",
+ "\n",
+ "y <- cleandata[,28]\n",
+ "x <- cleandata[,indexointerest]\n",
+ "aug <- cleandata[,indexoaug]\n",
+ "augfrm <- as.data.frame(aug)\n",
+ "aug <- augfrm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Correlation Analysis Using Sanitized Data\n",
+ "\n",
+ "We examine the correlations to estimate the most influential predictors. There are no highly correlated measures and therefore none of them need to be dropped."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 383,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/latex": [],
+ "text/markdown": [],
+ "text/plain": [
+ "<0 x 0 matrix>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Find all correlations.\n",
+ "#cor(aug,method=\"spearman\",use=\"pairwise.complete.obs\"); #OK for any: uses ranks\n",
+ "\n",
+ "# Find top correlations.\n",
+ "hiCor <- function(x, level){\n",
+ " res <- cor(x,method=\"spearman\");\n",
+ " res1 <- res;\n",
+ " res1[res<0] <- -res[res < 0];\n",
+ " for (i in 1:dim(x)[2]){\n",
+ " res1[i,i] <- 0;\n",
+ " }\n",
+ " sel <- apply(res1,1,max) > level;\n",
+ " res[sel,sel];\n",
+ "}\n",
+ "\n",
+ "hiCor(aug,.7)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Perform Principal Component Analysis\n",
+ "\n",
+ "We use Principal Component Analysis to reduce the dimensionality of the data. By inspection of the plot of the Fraction of Variance explained, we see that the first eleven principal components explain 70% of the variance. The relatively gradual progression of variance explained indicates that the variables are relatively independent."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 384,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " V7 V16 V19\n",
+ "PC1 0.51 0.49 0.31\n",
+ " V1 V3 V4 V11\n",
+ "PC2 0.36 -0.35 0.37 -0.39\n",
+ " V1 V2 V8 V14\n",
+ "PC3 -0.34 0.37 0.33 0.42\n",
+ " V1 V4 V6 V12 V17\n",
+ "PC4 -0.39 -0.37 -0.31 -0.32 -0.32\n",
+ " V17 V18\n",
+ "PC5 0.51 0.62\n",
+ " V3 V5 V11 V15\n",
+ "PC6 0.48 0.36 0.31 0.32\n",
+ " V6 V10 V20\n",
+ "PC7 -0.54 -0.31 0.45\n",
+ " V2 V3 V8 V19\n",
+ "PC8 -0.49 0.39 0.37 0.39\n",
+ " V5 V13\n",
+ "PC9 0.4 0.8\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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2+V5kniHiHBwqS6/7fF2PZogz/qHiTu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\nc70AAAAASUVORK5CYII=",
+ "text/plain": [
+ "plot without title"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#augfrm <- as.data.frame(aug)\n",
+ "#logs <- cbind(augfrm$V20, log(augfrm$V1+1), log(augfrm$V2+1), log(augfrm$V3+1), log(augfrm$V4+1),\n",
+ "# log(augfrm$V5+1), log(augfrm$V6+1), log(augfrm$V7+1), log(augfrm$V8+1), log(augfrm$V9+1),\n",
+ "# log(augfrm$V10+1), log(augfrm$V11+1), log(augfrm$V12+1), log(augfrm$V13+1), log(augfrm$V14+1),\n",
+ "# log(augfrm$V15+1), log(augfrm$V16+1), log(augfrm$V17+1), log(augfrm$V18+1), log(augfrm$V19+1));\n",
+ "#aug <- data.frame(logs)\n",
+ "#augfrm <- aug\n",
+ "plot(1:20,cumsum(prcomp(aug, retx=F,scale=T)$sdev^2)/sum(prcomp(aug, retx=F,scale=T)$sdev^2),ylim=c(0,1),xlab=\"Number of Components\",ylab=\"Fraction of Variance\");\n",
+ "\n",
+ "#cumsum(prcomp(x, retx=F,scale=T)$sdev^2)/sum(prcomp(x, retx=F,scale=T)$sdev^2)\n",
+ "res<-prcomp(aug, retx=F,scale=T)$rotation[,1:9];\n",
+ "resAbs <- res;\n",
+ "resAbs[res<0] <- -res[res<0];\n",
+ "for (i in 1:9)\n",
+ " print(t(res[resAbs[,i]>.3,i,drop=FALSE]));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Regress Predictors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 385,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\tV7 7 0.38 \n",
+ "\tV16 16 0.34 \n",
+ "\tV19 19 0.15 \n",
+ "\tV11 11 0.13 \n",
+ "\tV14 14 0.13 \n",
+ "\tV15 15 0.13 \n",
+ "\tV12 12 0.12 \n",
+ "\tV9 9 0.11 \n",
+ "\tV3 3 0.08 \n",
+ "\tV18 18 0.08 \n",
+ "\tV10 10 0.07 \n",
+ "\tV6 6 0.06 \n",
+ "\tV2 2 0.05 \n",
+ "\tV4 4 0.05 \n",
+ "\tV8 8 0.05 \n",
+ "\tV17 17 0.05 \n",
+ "\tV20 20 0.04 \n",
+ "\tV5 5 0.03 \n",
+ "\tV13 13 0.02 \n",
+ " \n",
+ "
\n"
+ ],
+ "text/latex": [
+ "\\begin{tabular}{r|ll}\n",
+ "\tV7 & 7 & 0.38\\\\\n",
+ "\tV16 & 16 & 0.34\\\\\n",
+ "\tV19 & 19 & 0.15\\\\\n",
+ "\tV11 & 11 & 0.13\\\\\n",
+ "\tV14 & 14 & 0.13\\\\\n",
+ "\tV15 & 15 & 0.13\\\\\n",
+ "\tV12 & 12 & 0.12\\\\\n",
+ "\tV9 & 9 & 0.11\\\\\n",
+ "\tV3 & 3 & 0.08\\\\\n",
+ "\tV18 & 18 & 0.08\\\\\n",
+ "\tV10 & 10 & 0.07\\\\\n",
+ "\tV6 & 6 & 0.06\\\\\n",
+ "\tV2 & 2 & 0.05\\\\\n",
+ "\tV4 & 4 & 0.05\\\\\n",
+ "\tV8 & 8 & 0.05\\\\\n",
+ "\tV17 & 17 & 0.05\\\\\n",
+ "\tV20 & 20 & 0.04\\\\\n",
+ "\tV5 & 5 & 0.03\\\\\n",
+ "\tV13 & 13 & 0.02\\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "| V7 | 7 | 0.38 | \n",
+ "| V16 | 16 | 0.34 | \n",
+ "| V19 | 19 | 0.15 | \n",
+ "| V11 | 11 | 0.13 | \n",
+ "| V14 | 14 | 0.13 | \n",
+ "| V15 | 15 | 0.13 | \n",
+ "| V12 | 12 | 0.12 | \n",
+ "| V9 | 9 | 0.11 | \n",
+ "| V3 | 3 | 0.08 | \n",
+ "| V18 | 18 | 0.08 | \n",
+ "| V10 | 10 | 0.07 | \n",
+ "| V6 | 6 | 0.06 | \n",
+ "| V2 | 2 | 0.05 | \n",
+ "| V4 | 4 | 0.05 | \n",
+ "| V8 | 8 | 0.05 | \n",
+ "| V17 | 17 | 0.05 | \n",
+ "| V20 | 20 | 0.04 | \n",
+ "| V5 | 5 | 0.03 | \n",
+ "| V13 | 13 | 0.02 | \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ " [,1] [,2]\n",
+ "V7 7 0.38\n",
+ "V16 16 0.34\n",
+ "V19 19 0.15\n",
+ "V11 11 0.13\n",
+ "V14 14 0.13\n",
+ "V15 15 0.13\n",
+ "V12 12 0.12\n",
+ "V9 9 0.11\n",
+ "V3 3 0.08\n",
+ "V18 18 0.08\n",
+ "V10 10 0.07\n",
+ "V6 6 0.06\n",
+ "V2 2 0.05\n",
+ "V4 4 0.05\n",
+ "V8 8 0.05\n",
+ "V17 17 0.05\n",
+ "V20 20 0.04\n",
+ "V5 5 0.03\n",
+ "V13 13 0.02"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Analyze aug.\n",
+ "#augfrm <- as.data.frame(aug)\n",
+ "# Swap columns so that response variable is the first.\n",
+ "#augfrm <- augfrm[,c(\"V20\",\"V1\",\"V2\",\"V3\",\"V4\",\"V5\",\"V6\",\"V7\",\"V8\",\"V9\",\"V10\",\"V11\",\"V12\",\"V13\",\"V14\",\"V15\",\"V16\",\"V17\",\"V18\",\"V19\")]\n",
+ "\n",
+ "res <- c();\n",
+ "vnam <- names(augfrm);\n",
+ "#print(vnam)\n",
+ "for (i in 2:dim(augfrm)[2]){\n",
+ " fmla <- as.formula(paste(vnam[i],paste(vnam[-c(1,i)],collapse=\"+\"),sep=\"~\"));\n",
+ " res <- rbind(res,c(i,round(summary(lm(fmla,data=augfrm))$r.squared,2)));\n",
+ "}\n",
+ "row.names(res) <- vnam[res[,1]];\n",
+ "res[order(-res[,2]),];\n",
+ "#print(augfrm)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Fitting of the Statistical Model\n",
+ "\n",
+ "We do the regression to select a model. Interestingly, the best model was found to be the quasipoisson distribution of the single variable, which is the respondents' answer to question three: \"Which of the following is the closest to why you chose that package?\" The p-value is within the threshold of statistical significance, and the error is minimized using this model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 388,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "fmla ~ V20 ~ V6"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#fmla ~ V20 ~ V1+V2+V3+V4+V5+V6+V7+V8+V9+V10+V11+V12+V13+V14+V15+V16+V17+V18+V19\n",
+ "#fmla ~ V20 ~ V6\n",
+ "fmla ~ V20 ~ V6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 394,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\n",
+ "Call:\n",
+ "glm(formula = V20 ~ V6, family = quasipoisson, data = augfrm)\n",
+ "\n",
+ "Deviance Residuals: \n",
+ " Min 1Q Median 3Q Max \n",
+ "-1.576 -0.270 0.265 0.375 0.909 \n",
+ "\n",
+ "Coefficients:\n",
+ " Estimate Std. Error t value Pr(>|t|) \n",
+ "(Intercept) 1.25058 0.01872 66.81 <2e-16 ***\n",
+ "V6 -0.00962 0.00457 -2.11 0.036 * \n",
+ "---\n",
+ "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+ "\n",
+ "(Dispersion parameter for quasipoisson family taken to be 0.4)\n",
+ "\n",
+ " Null deviance: 262.33 on 584 degrees of freedom\n",
+ "Residual deviance: 260.55 on 583 degrees of freedom\n",
+ "AIC: NA\n",
+ "\n",
+ "Number of Fisher Scoring iterations: 4\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "library(mgcv)\n",
+ "library(MASS)\n",
+ "#mod <- glm(V20 ~ V1+V2+V3+V4+V5+V6+V7+V8+V9+V10+V11+V12+V13+V14+V15+V16+V17+V18+V19,family=Gamma,data=augfrm);\n",
+ "#mod <- lm(V20 ~ V1+V2+V3+V4+V5+V6+V7+V8+V9+V10+V11+V12+V13+V14+V15+V16+V17+V18+V19,data=augfrm);\n",
+ "#summary(mod);\n",
+ "#mod <- glm(V20 ~ V16+V17,family=quasibinomial,data=augfrm);\n",
+ "#mod <- glm(V20 ~ V6+V16+V17,family=quasibinomial,data=augfrm);\n",
+ "#mod <- glm(V20 ~ V6,family=quasibinomial,data=augfrm); #-- good\n",
+ "#summary(mod);\n",
+ "#mod <- glm(V20 ~ V6,family=inverse.gaussian,data=augfrm); #-- good\n",
+ "#summary(mod);\n",
+ "#mod <- glm(V20 ~ V6,family=gaussian,data=augfrm); #-- good\n",
+ "#summary(mod);\n",
+ "#---------------\n",
+ "mod <- glm(V20 ~ V6,family=quasipoisson,data=augfrm); #-- good\n",
+ "summary(mod);\n",
+ "#mod <- glm(V20 ~ V6,family=Gamma,data=augfrm); #-- good\n",
+ "#summary(mod);\n",
+ "#mod <- glm.nb(V20 ~ V6,data=augfrm); #-- good\n",
+ "#summary(mod);\n",
+ "###start\n",
+ "#augfrm$V20 <- augfrm$V20/5.0\n",
+ "#mod <- glm(V20 ~ V6,family=quasibinomial,data=augfrm); #-- good\n",
+ "#summary(mod);\n",
+ "###end\n",
+ "#mod <- glm(V20 ~ V6,family=quasi,data=augfrm); #-- good\n",
+ "#summary(mod);\n",
+ "##\n",
+ "mod <- lm(V20 ~ V6,data=augfrm); #-- good\n",
+ "##summary(mod);\n",
+ "##mod <- lm(V20 ~ V7+V16+V19,data=augfrm); #-- good\n",
+ "##summary(mod);\n",
+ "#mod <- glm(V20 ~ V7+V16+V19,family=quasipoisson,data=augfrm); #-- good\n",
+ "#summary(mod);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Interpretation of the Coefficients\n",
+ "\n",
+ "For this model, there are only two coefficients, including the intercept, due to the model being represented most accurately using a single variable. The intercept is 1.25125, and the slope of the variable corresponding to the answers for question three is -0.00966. This means that as the sought-out need of the package decreased, the helpfulness of the discussion on StackExchange increased. This is interesting because it seems to be more intuitive that if a package is more sought-out, then one might be more likely benefit from helpful discussion. This was not the case, however."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 390,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "Df Deviance Resid. Df Resid. Dev Pr(>Chi) \n",
+ "\n",
+ "\tNULL NA NA 584 262 NA \n",
+ "\tV6 1 1.8 583 261 0.035 \n",
+ " \n",
+ "
\n"
+ ],
+ "text/latex": [
+ "\\begin{tabular}{r|lllll}\n",
+ " & Df & Deviance & Resid. Df & Resid. Dev & Pr(>Chi)\\\\\n",
+ "\\hline\n",
+ "\tNULL & NA & NA & 584 & 262 & NA\\\\\n",
+ "\tV6 & 1 & 1.8 & 583 & 261 & 0.035\\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "| | Df | Deviance | Resid. Df | Resid. Dev | Pr(>Chi) | \n",
+ "|---|---|\n",
+ "| NULL | NA | NA | 584 | 262 | NA | \n",
+ "| V6 | 1 | 1.8 | 583 | 261 | 0.035 | \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ " Df Deviance Resid. Df Resid. Dev Pr(>Chi)\n",
+ "NULL NA NA 584 262 NA \n",
+ "V6 1 1.8 583 261 0.035 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "Df Deviance Resid. Df Resid. Dev Pr(>Chi) \n",
+ "\n",
+ "\tNULL NA NA 584 262 NA \n",
+ "\tV6 1 1.8 583 261 0.035 \n",
+ " \n",
+ "
\n"
+ ],
+ "text/latex": [
+ "\\begin{tabular}{r|lllll}\n",
+ " & Df & Deviance & Resid. Df & Resid. Dev & Pr(>Chi)\\\\\n",
+ "\\hline\n",
+ "\tNULL & NA & NA & 584 & 262 & NA\\\\\n",
+ "\tV6 & 1 & 1.8 & 583 & 261 & 0.035\\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "| | Df | Deviance | Resid. Df | Resid. Dev | Pr(>Chi) | \n",
+ "|---|---|\n",
+ "| NULL | NA | NA | 584 | 262 | NA | \n",
+ "| V6 | 1 | 1.8 | 583 | 261 | 0.035 | \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ " Df Deviance Resid. Df Resid. Dev Pr(>Chi)\n",
+ "NULL NA NA 584 262 NA \n",
+ "V6 1 1.8 583 261 0.035 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "Df Deviance scaled dev. Pr(>Chi) \n",
+ "\n",
+ "\t<none> NA 261 NA NA \n",
+ "\tV6 1 262 4.5 0.035 \n",
+ " \n",
+ "
\n"
+ ],
+ "text/latex": [
+ "\\begin{tabular}{r|llll}\n",
+ " & Df & Deviance & scaled dev. & Pr(>Chi)\\\\\n",
+ "\\hline\n",
+ "\t & NA & 261 & NA & NA\\\\\n",
+ "\tV6 & 1 & 262 & 4.5 & 0.035\\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "| | Df | Deviance | scaled dev. | Pr(>Chi) | \n",
+ "|---|---|\n",
+ "| | NA | 261 | NA | NA | \n",
+ "| V6 | 1 | 262 | 4.5 | 0.035 | \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ " Df Deviance scaled dev. Pr(>Chi)\n",
+ " NA 261 NA NA \n",
+ "V6 1 262 4.5 0.035 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "anova(mod, test=\"Chi\");\n",
+ "anova(mod, test=\"Chisq\");\n",
+ "drop1(mod, test=\"Chi\");\n",
+ "library(car)\n",
+ "#vif(mod);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 391,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#ys <- augfrm$V20\n",
+ "#xs <- augfrm$V6\n",
+ "#print(ys)\n",
+ "#print(xs)\n",
+ "#plot(xs,ys,ylim=c(0,20),xlab=\"Measure\",ylab=\"Response\");\n",
+ "#plot(V20 ~ V6, augfrm)\n",
+ "#plot(V20 ~ V6, augfrm)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Recommendations for how to improve the survey:\n",
+ "\n",
+ "It may be useful to ask those who are taking the survey if they had trouble answering any of the questions. Some of them may have taken longer to complete certain questions by virtue of the questions having different forms (e.g. click-and-drag vs multiple choice). There could also be an option for \"Other\" or \"I don't know\" for more of the questions. This could potentially help remove noise in the data if a person did not know to which category an idea should belong (the form of question PG5_3HDS) - as in the case of the third question. There could also be a final question regarding how thoroughly they believe their thoughts to this survey are captured by their responses. If a survey respondent has 'very limited' English proficiency, then there may be noise in the survey data by virtue of them not being able to fully understand the questions and answers. "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "R",
+ "language": "R",
+ "name": "ir"
+ },
+ "language_info": {
+ "codemirror_mode": "r",
+ "file_extension": ".r",
+ "mimetype": "text/x-r-source",
+ "name": "R",
+ "pygments_lexer": "r",
+ "version": "3.4.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/dbarry.ipynb b/dbarry.ipynb
new file mode 100644
index 0000000..1f8b07e
--- /dev/null
+++ b/dbarry.ipynb
@@ -0,0 +1,1389 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A Survey on Technology Choice\n",
+ "======\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Hypothesis\n",
+ "\n",
+ "I think the priority of helpful discussion on StackExchange is going to be affected by development experience and English proficiency."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# For nicer printing\n",
+ "options(digits=2);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Read in the data\n",
+ "data <- read.csv(\"TechSurvey - Survey.csv\",header=T);\n",
+ "\n",
+ "#convert date to unix second\n",
+ "for (i in c(\"Start\", \"End\")) \n",
+ " data[,i] = as.numeric(as.POSIXct(strptime(data[,i], \"%Y-%m-%d %H:%M:%S\")))\n",
+ "for (i in 0:12){\n",
+ " vnam = paste(c(\"PG\",i,\"Submit\"), collapse=\"\")\n",
+ " data[,vnam] = as.numeric(as.POSIXct(strptime(data[,vnam], \"%Y-%m-%d %H:%M:%S\")))\n",
+ "}\n",
+ "#calculate differences in time \n",
+ "for (i in 12:0){\n",
+ " pv = paste(c(\"PG\",i-1,\"Submit\"), collapse=\"\");\n",
+ " if (i==0) \n",
+ " pv=\"Start\";\n",
+ " vnam = paste(c(\"PG\",i,\"Submit\"), collapse=\"\");\n",
+ " data[,vnam] = data[,vnam] -data[,pv];\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " Device Completed Start End PG0Dis \n",
+ " : 2 0 : 2 Min. :1.54e+09 Min. :1.54e+09 Min. : 0 \n",
+ " Bot : 1 FALSE:546 1st Qu.:1.54e+09 1st Qu.:1.54e+09 1st Qu.: 0 \n",
+ " PC :955 TRUE :805 Median :1.54e+09 Median :1.54e+09 Median : 1 \n",
+ " Phone :376 Mean :1.54e+09 Mean :1.54e+09 Mean : 44 \n",
+ " Tablet : 16 3rd Qu.:1.54e+09 3rd Qu.:1.54e+09 3rd Qu.: 24 \n",
+ " Unknown: 3 Max. :1.54e+09 Max. :1.54e+09 Max. :168 \n",
+ " NA's :2 NA's :548 NA's :73 \n",
+ " PG0Shown PG0Submit \n",
+ " Min. : 0 Min. : 2 \n",
+ " 1st Qu.: 0 1st Qu.: 6 \n",
+ " Median : 102 Median : 9 \n",
+ " Mean : 249 Mean : 299 \n",
+ " 3rd Qu.: 428 3rd Qu.: 15 \n",
+ " Max. :1190 Max. :76226 \n",
+ " NA's :73 NA's :199 \n",
+ " PG1PsnUse \n",
+ " For personal work and/or research use :727 \n",
+ " :613 \n",
+ " Chapter book : 1 \n",
+ " For training attendees of my sessions : 1 \n",
+ " It's a coures on tidyverse that I developed: 1 \n",
+ " Learning how to create a package : 1 \n",
+ " (Other) : 9 \n",
+ " PG1WdAuth \n",
+ " :1145 \n",
+ " Because Microsoft was paying me to do it : 1 \n",
+ " For a wider audience, such as developers of other packages or other software: 205 \n",
+ " Hackathon : 1 \n",
+ " Tool for other researchers to analyse their data : 1 \n",
+ " \n",
+ " \n",
+ " PG1Trn \n",
+ " :1168 \n",
+ " For a training / class that I took: 184 \n",
+ " teaching economics : 1 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " PG1Other \n",
+ " :1272 \n",
+ " Other : 23 \n",
+ " Teaching : 3 \n",
+ " teaching : 3 \n",
+ " For training that I gave : 2 \n",
+ " How does software technology spreads in the open source community?: 2 \n",
+ " (Other) : 48 \n",
+ " PG1Submit PG2Resp PG2Submit \n",
+ " Min. : 1 :431 Min. : 1 \n",
+ " 1st Qu.: 11 No :374 1st Qu.: 9 \n",
+ " Median : 16 Not sure:303 Median : 13 \n",
+ " Mean : 39 Yes :245 Mean : 29 \n",
+ " 3rd Qu.: 30 3rd Qu.: 29 \n",
+ " Max. :6892 Max. :1470 \n",
+ " NA's :282 NA's :377 \n",
+ " PG2Resp.1 \n",
+ " :480 \n",
+ " The core \"data.frame\" object lacked functionality that I needed :354 \n",
+ " I saw a recommendation for the package :149 \n",
+ " Chose the package to be compatible with other packages in my project :137 \n",
+ " I didn't choose to use the package, it was included implicitly / unintentionally: 39 \n",
+ " Other : 6 \n",
+ " (Other) :188 \n",
+ " PG3Submit PG4Dtr0_6 PG4Psv7_8 PG4Prm9_10 PG4AllResp \n",
+ " Min. : 1 Min. :0 Min. :7 Min. : 9 Min. : 0 \n",
+ " 1st Qu.: 16 1st Qu.:3 1st Qu.:7 1st Qu.:10 1st Qu.: 8 \n",
+ " Median : 23 Median :5 Median :8 Median :10 Median : 9 \n",
+ " Mean : 44 Mean :4 Mean :8 Mean :10 Mean : 8 \n",
+ " 3rd Qu.: 40 3rd Qu.:6 3rd Qu.:8 3rd Qu.:10 3rd Qu.:10 \n",
+ " Max. :4648 Max. :6 Max. :8 Max. :10 Max. :10 \n",
+ " NA's :451 NA's :1232 NA's :1115 NA's :869 NA's :510 \n",
+ " PG4Submit PG5_1RRPQ PG5_1Order PG5_1Time \n",
+ " Min. : 1 :877 Min. : 1 :877 \n",
+ " 1st Qu.: 6 Essential : 60 1st Qu.: 4 2018-10-11 13:32:57: 3 \n",
+ " Median : 7 High Priority :102 Median : 7 2018-10-11 13:29:20: 2 \n",
+ " Mean : 9 Low Priority : 85 Mean : 7 2018-10-11 13:34:56: 2 \n",
+ " 3rd Qu.: 9 Medium Priority:134 3rd Qu.:11 2018-10-11 13:14:25: 1 \n",
+ " Max. :332 Not a Priority : 95 Max. :20 2018-10-11 13:14:45: 1 \n",
+ " NA's :473 NA's :877 (Other) :467 \n",
+ " PG5_2BNUI PG5_2Order PG5_2Time \n",
+ " :923 Min. : 1 :923 \n",
+ " Essential : 3 1st Qu.: 5 2018-10-11 13:21:46: 2 \n",
+ " High Priority : 26 Median : 8 2018-10-11 13:38:07: 2 \n",
+ " Low Priority :121 Mean : 8 2018-10-11 13:14:27: 1 \n",
+ " Medium Priority: 92 3rd Qu.:11 2018-10-11 13:14:40: 1 \n",
+ " Not a Priority :188 Max. :21 2018-10-11 13:15:18: 1 \n",
+ " NA's :923 (Other) :423 \n",
+ " PG5_3HDS PG5_3Order PG5_3Time \n",
+ " :768 Min. : 1 :768 \n",
+ " Essential :103 1st Qu.: 2 2018-10-11 13:54:00: 2 \n",
+ " High Priority :200 Median : 4 2018-10-11 14:21:45: 2 \n",
+ " Low Priority : 69 Mean : 6 2018-10-11 14:25:51: 2 \n",
+ " Medium Priority:162 3rd Qu.: 9 2018-10-11 17:21:39: 2 \n",
+ " Not a Priority : 51 Max. :19 2018-10-11 13:14:18: 1 \n",
+ " NA's :768 (Other) :576 \n",
+ " PG5_4VGP PG5_4Order PG5_4Time \n",
+ " :852 Min. : 1 :852 \n",
+ " Essential : 22 1st Qu.: 4 2018-10-11 13:16:50: 2 \n",
+ " High Priority :111 Median : 6 2018-10-11 13:29:51: 2 \n",
+ " Low Priority : 88 Mean : 7 2018-10-11 13:37:39: 2 \n",
+ " Medium Priority:164 3rd Qu.:10 2018-10-11 13:38:11: 2 \n",
+ " Not a Priority :116 Max. :18 2018-10-11 15:42:22: 2 \n",
+ " NA's :852 (Other) :491 \n",
+ " PG5_5PHR PG5_5Order PG5_5Time \n",
+ " :753 Min. : 1 :753 \n",
+ " Essential : 79 1st Qu.: 2 2018-10-11 13:18:47: 2 \n",
+ " High Priority :252 Median : 4 2018-10-11 13:18:48: 2 \n",
+ " Low Priority : 63 Mean : 6 2018-10-11 13:32:40: 2 \n",
+ " Medium Priority:162 3rd Qu.: 8 2018-10-11 13:38:12: 2 \n",
+ " Not a Priority : 44 Max. :18 2018-10-11 13:45:48: 2 \n",
+ " NA's :753 (Other) :590 \n",
+ " PG5_6SSYOP PG5_6Order PG5_6Time \n",
+ " :852 Min. : 1 :852 \n",
+ " Essential : 63 1st Qu.: 3 2018-10-11 13:20:00: 2 \n",
+ " High Priority :137 Median : 6 2018-10-11 13:40:53: 2 \n",
+ " Low Priority : 84 Mean : 7 2018-10-11 13:44:00: 2 \n",
+ " Medium Priority:110 3rd Qu.:10 2018-10-11 13:45:41: 2 \n",
+ " Not a Priority :107 Max. :17 2018-10-11 16:22:38: 2 \n",
+ " NA's :852 (Other) :491 \n",
+ " PG5_7NDYP PG5_7Order PG5_7Time \n",
+ " :934 Min. : 1 :934 \n",
+ " Essential : 8 1st Qu.: 4 2018-10-11 13:18:50: 2 \n",
+ " High Priority : 31 Median : 7 2018-10-11 14:23:19: 2 \n",
+ " Low Priority : 93 Mean : 7 2018-10-11 13:14:22: 1 \n",
+ " Medium Priority: 52 3rd Qu.:11 2018-10-11 13:14:50: 1 \n",
+ " Not a Priority :235 Max. :17 2018-10-11 13:15:08: 1 \n",
+ " NA's :934 (Other) :412 \n",
+ " PG5_8CP PG5_8Order PG5_8Time \n",
+ " :715 Min. : 1 :715 \n",
+ " Essential :232 1st Qu.: 1 2018-10-11 13:29:46: 2 \n",
+ " High Priority :197 Median : 4 2018-10-11 13:37:00: 2 \n",
+ " Low Priority : 52 Mean : 5 2018-10-11 13:38:36: 2 \n",
+ " Medium Priority:121 3rd Qu.: 8 2018-10-11 13:39:22: 2 \n",
+ " Not a Priority : 36 Max. :20 2018-10-11 14:02:46: 2 \n",
+ " NA's :715 (Other) :628 \n",
+ " PG5_9FRP PG5_9Order PG5_9Time \n",
+ " :738 Min. : 1 :738 \n",
+ " Essential :165 1st Qu.: 2 2018-10-11 13:35:13: 2 \n",
+ " High Priority :243 Median : 4 2018-10-11 13:37:34: 2 \n",
+ " Low Priority : 42 Mean : 5 2018-10-11 14:02:44: 2 \n",
+ " Medium Priority:125 3rd Qu.: 9 2018-10-11 13:14:17: 1 \n",
+ " Not a Priority : 40 Max. :19 2018-10-11 13:14:52: 1 \n",
+ " NA's :738 (Other) :607 \n",
+ " PG5_10RPA PG5_10Order PG5_10Time \n",
+ " :779 Min. : 1 :779 \n",
+ " Essential : 55 1st Qu.: 2 2018-10-11 13:17:47: 2 \n",
+ " High Priority :204 Median : 5 2018-10-11 13:27:48: 2 \n",
+ " Low Priority : 79 Mean : 6 2018-10-11 13:45:33: 2 \n",
+ " Medium Priority:151 3rd Qu.: 9 2018-10-11 15:30:40: 2 \n",
+ " Not a Priority : 85 Max. :22 2018-10-11 15:48:40: 2 \n",
+ " NA's :779 (Other) :564 \n",
+ " PG5_11NSG PG5_11Order PG5_11Time \n",
+ " :890 Min. : 1 :890 \n",
+ " Essential : 6 1st Qu.: 4 2018-10-11 13:19:44: 2 \n",
+ " High Priority : 29 Median : 6 2018-10-11 13:21:53: 2 \n",
+ " Low Priority : 89 Mean : 7 2018-10-11 13:31:08: 2 \n",
+ " Medium Priority: 68 3rd Qu.:10 2018-10-11 13:40:48: 2 \n",
+ " Not a Priority :271 Max. :18 2018-10-11 14:55:47: 2 \n",
+ " NA's :890 (Other) :453 \n",
+ " PG5_12NWG PG5_12Order PG5_12Time \n",
+ " :916 Min. : 1 :916 \n",
+ " High Priority : 10 1st Qu.: 5 2018-10-11 13:30:08: 2 \n",
+ " Low Priority : 77 Median : 7 2018-10-11 13:31:20: 2 \n",
+ " Medium Priority: 25 Mean : 7 2018-10-11 14:55:40: 2 \n",
+ " Not a Priority :325 3rd Qu.:11 2018-10-11 13:14:46: 1 \n",
+ " Max. :18 2018-10-11 13:14:50: 1 \n",
+ " NA's :916 (Other) :429 \n",
+ " PG5_13NFG PG5_13Order PG5_13Time PG5Submit \n",
+ " :920 Min. : 1 :920 Min. : 3 \n",
+ " High Priority : 10 1st Qu.: 4 2018-10-11 13:17:39: 2 1st Qu.: 45 \n",
+ " Low Priority : 76 Median : 7 2018-10-11 13:35:20: 2 Median : 62 \n",
+ " Medium Priority: 37 Mean : 7 2018-10-11 13:35:56: 2 Mean : 87 \n",
+ " Not a Priority :310 3rd Qu.:10 2018-10-11 15:42:36: 2 3rd Qu.: 84 \n",
+ " Max. :17 2018-10-11 13:14:41: 1 Max. :4130 \n",
+ " NA's :920 (Other) :424 NA's :544 \n",
+ " PG6Resp PG6Submit PG7R PG7C.C.. PG7Java \n",
+ " :549 Min. : 1 R :684 :1268 :1306 \n",
+ " 13 - 19 years : 50 1st Qu.: 7 :614 C/C++: 84 Java : 46 \n",
+ " 2 - 5 years :332 Median : 9 Perl : 6 Cobol: 1 scala: 1 \n",
+ " 20 years or more : 45 Mean : 24 PHP : 5 \n",
+ " 6 - 8 years :112 3rd Qu.: 12 SQL : 5 \n",
+ " 9 - 12 years : 70 Max. :5759 Ruby : 4 \n",
+ " Less than 2 years:195 NA's :543 (Other): 35 \n",
+ " PG7Python PG7Javascript PG7Go PG7C. PG7Other \n",
+ " :1129 :1304 :1351 :1330 :1283 \n",
+ " Python: 223 Javascript: 48 Go: 2 C#: 23 Other : 62 \n",
+ " perl : 1 sql : 1 PHP : 2 \n",
+ " Matlab : 1 \n",
+ " Ruby : 1 \n",
+ " SAS : 1 \n",
+ " (Other): 3 \n",
+ " PG7Submit PG8Resp PG8Submit PG9Resp \n",
+ " Min. : 1 :570 Min. : 1 :562 \n",
+ " 1st Qu.: 6 Data Scientist :379 1st Qu.: 5 2 - 3 :229 \n",
+ " Median : 8 Software Engineer: 55 Median : 8 4 - 6 :166 \n",
+ " Mean : 11 Student : 24 Mean : 12 7 - 10 :122 \n",
+ " 3rd Qu.: 11 Researcher : 15 3rd Qu.: 14 1 : 94 \n",
+ " Max. :777 PhD student : 10 Max. :207 More than 25: 92 \n",
+ " NA's :542 (Other) :300 NA's :546 (Other) : 88 \n",
+ " PG9Submit PG10Resp \n",
+ " Min. : 0 :565 \n",
+ " 1st Qu.: 7 Native :424 \n",
+ " Median : 10 Not native - full working proficiency :263 \n",
+ " Mean : 40 Not native - limited working proficiency : 17 \n",
+ " 3rd Qu.: 14 Not native - passable : 5 \n",
+ " Max. :20126 Not native - sufficient working proficiency: 77 \n",
+ " NA's :547 Not native - very limited : 2 \n",
+ " PG10Submit PG11Resp PG11Submit PG12Resp \n",
+ " Min. : 1 :576 Min. : 1 :590 \n",
+ " 1st Qu.: 5 Female : 96 1st Qu.: 4 18 - 24 : 34 \n",
+ " Median : 7 Male :652 Median : 4 25 - 34 :338 \n",
+ " Mean : 17 Prefer not to answer: 29 Mean : 6 35 - 44 :258 \n",
+ " 3rd Qu.: 11 3rd Qu.: 5 45 - 54 : 89 \n",
+ " Max. :5966 Max. :605 55 - 64 : 36 \n",
+ " NA's :546 NA's :548 65 and over: 8 \n",
+ " PG12Submit \n",
+ " Min. : 1 \n",
+ " 1st Qu.: 4 \n",
+ " Median : 5 \n",
+ " Mean : 8 \n",
+ " 3rd Qu.: 6 \n",
+ " Max. :1566 \n",
+ " NA's :548 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#now explore variables\n",
+ "summary(data);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Descriptive Analysis of the Proposed Measures\n",
+ "\n",
+ "Intuitively, if someone does not have very much development experience, then he or she will be more likely to benefit from discussion on the help board. However, if someone is more experienced, then the discussion may not be a priority because he or she would have a lot more insight. In addition, if someone has more experience, he or she may be less likely to utilize StackExchange as a resource, leaving those are less experienced more likely to use it and therefore benefit from it. Also, if one does not have extensive knowledge of the English language, then he or she may be predisposed to not benefitting as much from helpful discussion because the ideas may not be conveyed as clearly to the reader. From the above code cell, we observe that the English language proficiency (PG10Resp) responses are primarily \"Native\", and there are fewer responses of lower proficiencies. This means that respondents are more likely to be at least fully working proficient than otherwise. We also observe that the development experience (PG6Resp) responses indicate that the respondents mostly have 2-8 years of experience. Therefore, there are relatively few responses by those with nine or more years of experience. Furthermore, there are fewer and fewer reponses belonging to the higher and higher experience categories."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Interpret basic summaries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "Start End PG0Dis PG0Shown PG0Submit PG1Submit PG2Submit PG3Submit PG4Dtr0_6 PG4Psv7_8 ... PG5_12Order PG5_13Order PG5Submit PG6Submit PG7Submit PG8Submit PG9Submit PG10Submit PG11Submit PG12Submit \n",
+ "\n",
+ "\tStart 1.0000 0.9952 -0.0417 -0.11507 0.1350 0.1156 0.0791 0.0384 0.01210 0.00371 ... -0.0369 0.0598 0.08512 0.0054 0.0776 0.0441 0.04101 0.047 7.9e-02 0.0746 \n",
+ "\tEnd 0.9952 1.0000 -0.0415 -0.09879 0.1142 0.1550 0.0791 0.0511 -0.05185 -0.04576 ... -0.0359 0.0661 0.09088 0.0051 0.0759 0.0435 0.04071 0.052 7.9e-02 0.0772 \n",
+ "\tPG0Dis -0.0417 -0.0415 1.0000 0.87220 0.0153 0.0065 0.0041 0.0567 0.16368 0.02668 ... 0.0151 0.0384 0.00601 0.0277 0.0097 0.0354 0.00995 -0.029 -4.5e-02 0.0546 \n",
+ "\tPG0Shown -0.1151 -0.0988 0.8722 1.00000 0.0360 0.0205 0.0023 0.0497 0.08226 0.00036 ... 0.0074 0.0407 -0.00888 0.0401 0.0121 0.0264 0.00056 -0.045 -7.1e-02 0.0436 \n",
+ "\tPG0Submit 0.1350 0.1142 0.0153 0.03596 1.0000 0.1088 0.1037 0.1273 -0.00802 -0.03763 ... -0.0161 -0.0280 0.17671 0.1518 0.1365 0.1258 0.17579 0.225 1.1e-01 0.1096 \n",
+ "\tPG1Submit 0.1156 0.1550 0.0065 0.02047 0.1088 1.0000 0.1452 0.2688 -0.06852 0.05661 ... 0.0512 -0.0651 0.24670 0.2414 0.1133 0.1069 0.10895 0.170 7.4e-02 0.1137 \n",
+ "\tPG2Submit 0.0791 0.0791 0.0041 0.00235 0.1037 0.1452 1.0000 0.2045 0.00146 0.00897 ... 0.0210 -0.0047 0.21851 0.2696 0.1245 0.1567 0.20127 0.099 1.1e-01 0.1073 \n",
+ "\tPG3Submit 0.0384 0.0511 0.0567 0.04968 0.1273 0.2688 0.2045 1.0000 0.00865 0.04424 ... 0.0464 -0.0222 0.26048 0.2706 0.1316 0.1822 0.27450 0.161 1.4e-01 0.1642 \n",
+ "\tPG4Dtr0_6 0.0121 -0.0518 0.1637 0.08226 -0.0080 -0.0685 0.0015 0.0087 1.00000 NA ... 0.1774 -0.1289 -0.05214 -0.1618 0.1560 0.0695 -0.07292 0.044 8.4e-04 -0.0272 \n",
+ "\tPG4Psv7_8 0.0037 -0.0458 0.0267 0.00036 -0.0376 0.0566 0.0090 0.0442 NA 1.00000 ... -0.0008 -0.0218 0.08974 -0.0146 -0.0363 0.0526 0.05977 0.069 -4.9e-02 -0.0217 \n",
+ "\tPG4Prm9_10 -0.0267 -0.0267 -0.0092 0.03279 -0.0939 0.0120 -0.0587 -0.0062 NA NA ... 0.0233 0.0916 0.00077 -0.0418 -0.0633 -0.0550 -0.02989 -0.061 -8.6e-05 0.0169 \n",
+ "\tPG4AllResp 0.0063 -0.0158 0.0018 -0.02094 -0.0236 0.0297 0.0293 -0.0193 1.00000 1.00000 ... -0.0306 -0.0166 0.01248 -0.0040 -0.0753 -0.1294 -0.03812 -0.106 -8.2e-02 -0.0668 \n",
+ "\tPG4Submit 0.0187 0.0172 -0.0539 -0.05978 0.2191 0.1651 0.1515 0.1956 -0.14272 -0.08350 ... -0.0119 -0.0376 0.27328 0.3326 0.2775 0.1821 0.33236 0.391 2.8e-01 0.2360 \n",
+ "\tPG5_1Order 0.0218 0.0196 0.0140 0.01254 -0.0240 0.0750 -0.0069 0.0578 -0.09488 -0.01399 ... -0.0785 -0.0950 0.08054 0.0334 0.0173 -0.0388 0.01033 -0.096 -2.5e-02 0.0238 \n",
+ "\tPG5_2Order 0.0014 0.0002 -0.0386 -0.03617 0.0402 -0.0226 -0.0297 0.0048 -0.00954 0.08081 ... 0.0527 -0.0652 0.03898 -0.0571 -0.0551 -0.0567 -0.01925 0.015 7.3e-03 0.0469 \n",
+ "\tPG5_3Order -0.0089 -0.0177 0.0441 0.04228 0.0155 0.0391 -0.0131 0.0172 0.13631 0.04396 ... -0.0698 -0.0250 0.23450 0.0419 0.0058 -0.0086 0.02604 0.063 3.4e-02 0.0538 \n",
+ "\tPG5_4Order 0.0931 0.0949 -0.0262 -0.02214 -0.0172 0.0293 0.0560 -0.0462 0.01735 -0.12489 ... -0.0447 -0.0329 0.14487 0.0414 0.0460 0.0182 -0.03734 -0.075 -8.6e-02 -0.0211 \n",
+ "\tPG5_5Order -0.0523 -0.0466 -0.0087 -0.01058 0.0860 0.0345 0.0570 -0.0165 0.04533 -0.06369 ... -0.0499 -0.0981 0.28698 0.0498 0.0372 0.0427 -0.02976 0.076 2.4e-02 0.0085 \n",
+ "\tPG5_6Order 0.0237 0.0217 -0.0480 -0.04902 0.0762 0.0327 0.1077 0.0370 -0.13255 0.01662 ... -0.0430 -0.0031 0.22759 0.0165 0.0222 0.0639 -0.02974 0.015 2.8e-02 0.0539 \n",
+ "\tPG5_7Order -0.0200 -0.0236 0.0220 -0.00444 -0.1174 -0.0815 -0.0285 0.0242 -0.16196 -0.06938 ... 0.0410 0.0944 0.06439 -0.0222 -0.1082 -0.0804 -0.01762 -0.017 -4.4e-02 -0.0617 \n",
+ "\tPG5_8Order -0.0804 -0.0852 -0.0147 -0.00433 -0.0421 -0.0205 -0.0387 -0.0129 -0.16181 0.01019 ... -0.0896 -0.1168 0.13787 -0.1227 -0.0126 -0.0526 -0.05699 -0.065 -3.7e-02 -0.0182 \n",
+ "\tPG5_9Order 0.0171 0.0167 -0.0712 -0.10235 0.0476 0.0260 -0.0410 -0.0641 -0.07948 -0.09747 ... -0.0431 -0.0837 0.18534 -0.0188 -0.0314 -0.0898 -0.01354 -0.034 6.2e-03 -0.0280 \n",
+ "\tPG5_10Order -0.0214 -0.0112 0.0200 0.02736 0.0086 -0.0017 -0.0418 0.0581 -0.05310 0.15949 ... -0.0905 -0.1214 0.22151 0.0427 0.0311 0.0345 0.02011 0.102 1.8e-02 -0.0092 \n",
+ "\tPG5_11Order -0.0241 -0.0196 0.0190 0.02551 0.0927 -0.0047 -0.1043 0.0279 0.02791 -0.03487 ... 0.1233 0.1434 0.02942 -0.0055 0.0210 0.0232 0.05579 -0.038 -2.5e-03 0.0275 \n",
+ "\tPG5_12Order -0.0369 -0.0359 0.0151 0.00743 -0.0161 0.0512 0.0210 0.0464 0.17741 -0.00080 ... 1.0000 0.1231 0.10997 0.0777 0.0529 0.0149 0.00295 0.016 2.4e-02 0.0167 \n",
+ "\tPG5_13Order 0.0598 0.0661 0.0384 0.04072 -0.0280 -0.0651 -0.0047 -0.0222 -0.12890 -0.02179 ... 0.1231 1.0000 0.00976 -0.0209 0.0355 0.0556 0.07229 -0.023 1.3e-02 -0.0614 \n",
+ "\tPG5Submit 0.0851 0.0909 0.0060 -0.00888 0.1767 0.2467 0.2185 0.2605 -0.05214 0.08974 ... 0.1100 0.0098 1.00000 0.3224 0.2312 0.2035 0.30291 0.269 2.4e-01 0.2588 \n",
+ "\tPG6Submit 0.0054 0.0051 0.0277 0.04005 0.1518 0.2414 0.2696 0.2706 -0.16179 -0.01463 ... 0.0777 -0.0209 0.32240 1.0000 0.3086 0.2065 0.44528 0.343 2.8e-01 0.2904 \n",
+ "\tPG7Submit 0.0776 0.0759 0.0097 0.01212 0.1365 0.1133 0.1245 0.1316 0.15596 -0.03631 ... 0.0529 0.0355 0.23120 0.3086 1.0000 0.1606 0.27819 0.312 2.8e-01 0.2523 \n",
+ "\tPG8Submit 0.0441 0.0435 0.0354 0.02635 0.1258 0.1069 0.1567 0.1822 0.06953 0.05260 ... 0.0149 0.0556 0.20351 0.2065 0.1606 1.0000 0.25569 0.200 2.1e-01 0.1932 \n",
+ "\tPG9Submit 0.0410 0.0407 0.0099 0.00056 0.1758 0.1090 0.2013 0.2745 -0.07292 0.05977 ... 0.0029 0.0723 0.30291 0.4453 0.2782 0.2557 1.00000 0.290 2.8e-01 0.2755 \n",
+ "\tPG10Submit 0.0474 0.0517 -0.0293 -0.04481 0.2248 0.1701 0.0989 0.1614 0.04433 0.06942 ... 0.0159 -0.0227 0.26881 0.3428 0.3121 0.2000 0.29018 1.000 3.5e-01 0.3131 \n",
+ "\tPG11Submit 0.0790 0.0792 -0.0454 -0.07102 0.1093 0.0738 0.1147 0.1383 0.00084 -0.04870 ... 0.0240 0.0130 0.23552 0.2777 0.2768 0.2065 0.27906 0.346 1.0e+00 0.2513 \n",
+ "\tPG12Submit 0.0746 0.0772 0.0546 0.04364 0.1096 0.1137 0.1073 0.1642 -0.02721 -0.02169 ... 0.0167 -0.0614 0.25876 0.2904 0.2523 0.1932 0.27550 0.313 2.5e-01 1.0000 \n",
+ " \n",
+ "
\n"
+ ],
+ "text/latex": [
+ "\\begin{tabular}{r|llllllllllllllllllllllllllllllllll}\n",
+ " & Start & End & PG0Dis & PG0Shown & PG0Submit & PG1Submit & PG2Submit & PG3Submit & PG4Dtr0\\_6 & PG4Psv7\\_8 & ... & PG5\\_12Order & PG5\\_13Order & PG5Submit & PG6Submit & PG7Submit & PG8Submit & PG9Submit & PG10Submit & PG11Submit & PG12Submit\\\\\n",
+ "\\hline\n",
+ "\tStart & 1.0000 & 0.9952 & -0.0417 & -0.11507 & 0.1350 & 0.1156 & 0.0791 & 0.0384 & 0.01210 & 0.00371 & ... & -0.0369 & 0.0598 & 0.08512 & 0.0054 & 0.0776 & 0.0441 & 0.04101 & 0.047 & 7.9e-02 & 0.0746 \\\\\n",
+ "\tEnd & 0.9952 & 1.0000 & -0.0415 & -0.09879 & 0.1142 & 0.1550 & 0.0791 & 0.0511 & -0.05185 & -0.04576 & ... & -0.0359 & 0.0661 & 0.09088 & 0.0051 & 0.0759 & 0.0435 & 0.04071 & 0.052 & 7.9e-02 & 0.0772 \\\\\n",
+ "\tPG0Dis & -0.0417 & -0.0415 & 1.0000 & 0.87220 & 0.0153 & 0.0065 & 0.0041 & 0.0567 & 0.16368 & 0.02668 & ... & 0.0151 & 0.0384 & 0.00601 & 0.0277 & 0.0097 & 0.0354 & 0.00995 & -0.029 & -4.5e-02 & 0.0546 \\\\\n",
+ "\tPG0Shown & -0.1151 & -0.0988 & 0.8722 & 1.00000 & 0.0360 & 0.0205 & 0.0023 & 0.0497 & 0.08226 & 0.00036 & ... & 0.0074 & 0.0407 & -0.00888 & 0.0401 & 0.0121 & 0.0264 & 0.00056 & -0.045 & -7.1e-02 & 0.0436 \\\\\n",
+ "\tPG0Submit & 0.1350 & 0.1142 & 0.0153 & 0.03596 & 1.0000 & 0.1088 & 0.1037 & 0.1273 & -0.00802 & -0.03763 & ... & -0.0161 & -0.0280 & 0.17671 & 0.1518 & 0.1365 & 0.1258 & 0.17579 & 0.225 & 1.1e-01 & 0.1096 \\\\\n",
+ "\tPG1Submit & 0.1156 & 0.1550 & 0.0065 & 0.02047 & 0.1088 & 1.0000 & 0.1452 & 0.2688 & -0.06852 & 0.05661 & ... & 0.0512 & -0.0651 & 0.24670 & 0.2414 & 0.1133 & 0.1069 & 0.10895 & 0.170 & 7.4e-02 & 0.1137 \\\\\n",
+ "\tPG2Submit & 0.0791 & 0.0791 & 0.0041 & 0.00235 & 0.1037 & 0.1452 & 1.0000 & 0.2045 & 0.00146 & 0.00897 & ... & 0.0210 & -0.0047 & 0.21851 & 0.2696 & 0.1245 & 0.1567 & 0.20127 & 0.099 & 1.1e-01 & 0.1073 \\\\\n",
+ "\tPG3Submit & 0.0384 & 0.0511 & 0.0567 & 0.04968 & 0.1273 & 0.2688 & 0.2045 & 1.0000 & 0.00865 & 0.04424 & ... & 0.0464 & -0.0222 & 0.26048 & 0.2706 & 0.1316 & 0.1822 & 0.27450 & 0.161 & 1.4e-01 & 0.1642 \\\\\n",
+ "\tPG4Dtr0\\_6 & 0.0121 & -0.0518 & 0.1637 & 0.08226 & -0.0080 & -0.0685 & 0.0015 & 0.0087 & 1.00000 & NA & ... & 0.1774 & -0.1289 & -0.05214 & -0.1618 & 0.1560 & 0.0695 & -0.07292 & 0.044 & 8.4e-04 & -0.0272 \\\\\n",
+ "\tPG4Psv7\\_8 & 0.0037 & -0.0458 & 0.0267 & 0.00036 & -0.0376 & 0.0566 & 0.0090 & 0.0442 & NA & 1.00000 & ... & -0.0008 & -0.0218 & 0.08974 & -0.0146 & -0.0363 & 0.0526 & 0.05977 & 0.069 & -4.9e-02 & -0.0217 \\\\\n",
+ "\tPG4Prm9\\_10 & -0.0267 & -0.0267 & -0.0092 & 0.03279 & -0.0939 & 0.0120 & -0.0587 & -0.0062 & NA & NA & ... & 0.0233 & 0.0916 & 0.00077 & -0.0418 & -0.0633 & -0.0550 & -0.02989 & -0.061 & -8.6e-05 & 0.0169 \\\\\n",
+ "\tPG4AllResp & 0.0063 & -0.0158 & 0.0018 & -0.02094 & -0.0236 & 0.0297 & 0.0293 & -0.0193 & 1.00000 & 1.00000 & ... & -0.0306 & -0.0166 & 0.01248 & -0.0040 & -0.0753 & -0.1294 & -0.03812 & -0.106 & -8.2e-02 & -0.0668 \\\\\n",
+ "\tPG4Submit & 0.0187 & 0.0172 & -0.0539 & -0.05978 & 0.2191 & 0.1651 & 0.1515 & 0.1956 & -0.14272 & -0.08350 & ... & -0.0119 & -0.0376 & 0.27328 & 0.3326 & 0.2775 & 0.1821 & 0.33236 & 0.391 & 2.8e-01 & 0.2360 \\\\\n",
+ "\tPG5\\_1Order & 0.0218 & 0.0196 & 0.0140 & 0.01254 & -0.0240 & 0.0750 & -0.0069 & 0.0578 & -0.09488 & -0.01399 & ... & -0.0785 & -0.0950 & 0.08054 & 0.0334 & 0.0173 & -0.0388 & 0.01033 & -0.096 & -2.5e-02 & 0.0238 \\\\\n",
+ "\tPG5\\_2Order & 0.0014 & 0.0002 & -0.0386 & -0.03617 & 0.0402 & -0.0226 & -0.0297 & 0.0048 & -0.00954 & 0.08081 & ... & 0.0527 & -0.0652 & 0.03898 & -0.0571 & -0.0551 & -0.0567 & -0.01925 & 0.015 & 7.3e-03 & 0.0469 \\\\\n",
+ "\tPG5\\_3Order & -0.0089 & -0.0177 & 0.0441 & 0.04228 & 0.0155 & 0.0391 & -0.0131 & 0.0172 & 0.13631 & 0.04396 & ... & -0.0698 & -0.0250 & 0.23450 & 0.0419 & 0.0058 & -0.0086 & 0.02604 & 0.063 & 3.4e-02 & 0.0538 \\\\\n",
+ "\tPG5\\_4Order & 0.0931 & 0.0949 & -0.0262 & -0.02214 & -0.0172 & 0.0293 & 0.0560 & -0.0462 & 0.01735 & -0.12489 & ... & -0.0447 & -0.0329 & 0.14487 & 0.0414 & 0.0460 & 0.0182 & -0.03734 & -0.075 & -8.6e-02 & -0.0211 \\\\\n",
+ "\tPG5\\_5Order & -0.0523 & -0.0466 & -0.0087 & -0.01058 & 0.0860 & 0.0345 & 0.0570 & -0.0165 & 0.04533 & -0.06369 & ... & -0.0499 & -0.0981 & 0.28698 & 0.0498 & 0.0372 & 0.0427 & -0.02976 & 0.076 & 2.4e-02 & 0.0085 \\\\\n",
+ "\tPG5\\_6Order & 0.0237 & 0.0217 & -0.0480 & -0.04902 & 0.0762 & 0.0327 & 0.1077 & 0.0370 & -0.13255 & 0.01662 & ... & -0.0430 & -0.0031 & 0.22759 & 0.0165 & 0.0222 & 0.0639 & -0.02974 & 0.015 & 2.8e-02 & 0.0539 \\\\\n",
+ "\tPG5\\_7Order & -0.0200 & -0.0236 & 0.0220 & -0.00444 & -0.1174 & -0.0815 & -0.0285 & 0.0242 & -0.16196 & -0.06938 & ... & 0.0410 & 0.0944 & 0.06439 & -0.0222 & -0.1082 & -0.0804 & -0.01762 & -0.017 & -4.4e-02 & -0.0617 \\\\\n",
+ "\tPG5\\_8Order & -0.0804 & -0.0852 & -0.0147 & -0.00433 & -0.0421 & -0.0205 & -0.0387 & -0.0129 & -0.16181 & 0.01019 & ... & -0.0896 & -0.1168 & 0.13787 & -0.1227 & -0.0126 & -0.0526 & -0.05699 & -0.065 & -3.7e-02 & -0.0182 \\\\\n",
+ "\tPG5\\_9Order & 0.0171 & 0.0167 & -0.0712 & -0.10235 & 0.0476 & 0.0260 & -0.0410 & -0.0641 & -0.07948 & -0.09747 & ... & -0.0431 & -0.0837 & 0.18534 & -0.0188 & -0.0314 & -0.0898 & -0.01354 & -0.034 & 6.2e-03 & -0.0280 \\\\\n",
+ "\tPG5\\_10Order & -0.0214 & -0.0112 & 0.0200 & 0.02736 & 0.0086 & -0.0017 & -0.0418 & 0.0581 & -0.05310 & 0.15949 & ... & -0.0905 & -0.1214 & 0.22151 & 0.0427 & 0.0311 & 0.0345 & 0.02011 & 0.102 & 1.8e-02 & -0.0092 \\\\\n",
+ "\tPG5\\_11Order & -0.0241 & -0.0196 & 0.0190 & 0.02551 & 0.0927 & -0.0047 & -0.1043 & 0.0279 & 0.02791 & -0.03487 & ... & 0.1233 & 0.1434 & 0.02942 & -0.0055 & 0.0210 & 0.0232 & 0.05579 & -0.038 & -2.5e-03 & 0.0275 \\\\\n",
+ "\tPG5\\_12Order & -0.0369 & -0.0359 & 0.0151 & 0.00743 & -0.0161 & 0.0512 & 0.0210 & 0.0464 & 0.17741 & -0.00080 & ... & 1.0000 & 0.1231 & 0.10997 & 0.0777 & 0.0529 & 0.0149 & 0.00295 & 0.016 & 2.4e-02 & 0.0167 \\\\\n",
+ "\tPG5\\_13Order & 0.0598 & 0.0661 & 0.0384 & 0.04072 & -0.0280 & -0.0651 & -0.0047 & -0.0222 & -0.12890 & -0.02179 & ... & 0.1231 & 1.0000 & 0.00976 & -0.0209 & 0.0355 & 0.0556 & 0.07229 & -0.023 & 1.3e-02 & -0.0614 \\\\\n",
+ "\tPG5Submit & 0.0851 & 0.0909 & 0.0060 & -0.00888 & 0.1767 & 0.2467 & 0.2185 & 0.2605 & -0.05214 & 0.08974 & ... & 0.1100 & 0.0098 & 1.00000 & 0.3224 & 0.2312 & 0.2035 & 0.30291 & 0.269 & 2.4e-01 & 0.2588 \\\\\n",
+ "\tPG6Submit & 0.0054 & 0.0051 & 0.0277 & 0.04005 & 0.1518 & 0.2414 & 0.2696 & 0.2706 & -0.16179 & -0.01463 & ... & 0.0777 & -0.0209 & 0.32240 & 1.0000 & 0.3086 & 0.2065 & 0.44528 & 0.343 & 2.8e-01 & 0.2904 \\\\\n",
+ "\tPG7Submit & 0.0776 & 0.0759 & 0.0097 & 0.01212 & 0.1365 & 0.1133 & 0.1245 & 0.1316 & 0.15596 & -0.03631 & ... & 0.0529 & 0.0355 & 0.23120 & 0.3086 & 1.0000 & 0.1606 & 0.27819 & 0.312 & 2.8e-01 & 0.2523 \\\\\n",
+ "\tPG8Submit & 0.0441 & 0.0435 & 0.0354 & 0.02635 & 0.1258 & 0.1069 & 0.1567 & 0.1822 & 0.06953 & 0.05260 & ... & 0.0149 & 0.0556 & 0.20351 & 0.2065 & 0.1606 & 1.0000 & 0.25569 & 0.200 & 2.1e-01 & 0.1932 \\\\\n",
+ "\tPG9Submit & 0.0410 & 0.0407 & 0.0099 & 0.00056 & 0.1758 & 0.1090 & 0.2013 & 0.2745 & -0.07292 & 0.05977 & ... & 0.0029 & 0.0723 & 0.30291 & 0.4453 & 0.2782 & 0.2557 & 1.00000 & 0.290 & 2.8e-01 & 0.2755 \\\\\n",
+ "\tPG10Submit & 0.0474 & 0.0517 & -0.0293 & -0.04481 & 0.2248 & 0.1701 & 0.0989 & 0.1614 & 0.04433 & 0.06942 & ... & 0.0159 & -0.0227 & 0.26881 & 0.3428 & 0.3121 & 0.2000 & 0.29018 & 1.000 & 3.5e-01 & 0.3131 \\\\\n",
+ "\tPG11Submit & 0.0790 & 0.0792 & -0.0454 & -0.07102 & 0.1093 & 0.0738 & 0.1147 & 0.1383 & 0.00084 & -0.04870 & ... & 0.0240 & 0.0130 & 0.23552 & 0.2777 & 0.2768 & 0.2065 & 0.27906 & 0.346 & 1.0e+00 & 0.2513 \\\\\n",
+ "\tPG12Submit & 0.0746 & 0.0772 & 0.0546 & 0.04364 & 0.1096 & 0.1137 & 0.1073 & 0.1642 & -0.02721 & -0.02169 & ... & 0.0167 & -0.0614 & 0.25876 & 0.2904 & 0.2523 & 0.1932 & 0.27550 & 0.313 & 2.5e-01 & 1.0000 \\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "| | Start | End | PG0Dis | PG0Shown | PG0Submit | PG1Submit | PG2Submit | PG3Submit | PG4Dtr0_6 | PG4Psv7_8 | ... | PG5_12Order | PG5_13Order | PG5Submit | PG6Submit | PG7Submit | PG8Submit | PG9Submit | PG10Submit | PG11Submit | PG12Submit | \n",
+ "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+ "| Start | 1.0000 | 0.9952 | -0.0417 | -0.11507 | 0.1350 | 0.1156 | 0.0791 | 0.0384 | 0.01210 | 0.00371 | ... | -0.0369 | 0.0598 | 0.08512 | 0.0054 | 0.0776 | 0.0441 | 0.04101 | 0.047 | 7.9e-02 | 0.0746 | \n",
+ "| End | 0.9952 | 1.0000 | -0.0415 | -0.09879 | 0.1142 | 0.1550 | 0.0791 | 0.0511 | -0.05185 | -0.04576 | ... | -0.0359 | 0.0661 | 0.09088 | 0.0051 | 0.0759 | 0.0435 | 0.04071 | 0.052 | 7.9e-02 | 0.0772 | \n",
+ "| PG0Dis | -0.0417 | -0.0415 | 1.0000 | 0.87220 | 0.0153 | 0.0065 | 0.0041 | 0.0567 | 0.16368 | 0.02668 | ... | 0.0151 | 0.0384 | 0.00601 | 0.0277 | 0.0097 | 0.0354 | 0.00995 | -0.029 | -4.5e-02 | 0.0546 | \n",
+ "| PG0Shown | -0.1151 | -0.0988 | 0.8722 | 1.00000 | 0.0360 | 0.0205 | 0.0023 | 0.0497 | 0.08226 | 0.00036 | ... | 0.0074 | 0.0407 | -0.00888 | 0.0401 | 0.0121 | 0.0264 | 0.00056 | -0.045 | -7.1e-02 | 0.0436 | \n",
+ "| PG0Submit | 0.1350 | 0.1142 | 0.0153 | 0.03596 | 1.0000 | 0.1088 | 0.1037 | 0.1273 | -0.00802 | -0.03763 | ... | -0.0161 | -0.0280 | 0.17671 | 0.1518 | 0.1365 | 0.1258 | 0.17579 | 0.225 | 1.1e-01 | 0.1096 | \n",
+ "| PG1Submit | 0.1156 | 0.1550 | 0.0065 | 0.02047 | 0.1088 | 1.0000 | 0.1452 | 0.2688 | -0.06852 | 0.05661 | ... | 0.0512 | -0.0651 | 0.24670 | 0.2414 | 0.1133 | 0.1069 | 0.10895 | 0.170 | 7.4e-02 | 0.1137 | \n",
+ "| PG2Submit | 0.0791 | 0.0791 | 0.0041 | 0.00235 | 0.1037 | 0.1452 | 1.0000 | 0.2045 | 0.00146 | 0.00897 | ... | 0.0210 | -0.0047 | 0.21851 | 0.2696 | 0.1245 | 0.1567 | 0.20127 | 0.099 | 1.1e-01 | 0.1073 | \n",
+ "| PG3Submit | 0.0384 | 0.0511 | 0.0567 | 0.04968 | 0.1273 | 0.2688 | 0.2045 | 1.0000 | 0.00865 | 0.04424 | ... | 0.0464 | -0.0222 | 0.26048 | 0.2706 | 0.1316 | 0.1822 | 0.27450 | 0.161 | 1.4e-01 | 0.1642 | \n",
+ "| PG4Dtr0_6 | 0.0121 | -0.0518 | 0.1637 | 0.08226 | -0.0080 | -0.0685 | 0.0015 | 0.0087 | 1.00000 | NA | ... | 0.1774 | -0.1289 | -0.05214 | -0.1618 | 0.1560 | 0.0695 | -0.07292 | 0.044 | 8.4e-04 | -0.0272 | \n",
+ "| PG4Psv7_8 | 0.0037 | -0.0458 | 0.0267 | 0.00036 | -0.0376 | 0.0566 | 0.0090 | 0.0442 | NA | 1.00000 | ... | -0.0008 | -0.0218 | 0.08974 | -0.0146 | -0.0363 | 0.0526 | 0.05977 | 0.069 | -4.9e-02 | -0.0217 | \n",
+ "| PG4Prm9_10 | -0.0267 | -0.0267 | -0.0092 | 0.03279 | -0.0939 | 0.0120 | -0.0587 | -0.0062 | NA | NA | ... | 0.0233 | 0.0916 | 0.00077 | -0.0418 | -0.0633 | -0.0550 | -0.02989 | -0.061 | -8.6e-05 | 0.0169 | \n",
+ "| PG4AllResp | 0.0063 | -0.0158 | 0.0018 | -0.02094 | -0.0236 | 0.0297 | 0.0293 | -0.0193 | 1.00000 | 1.00000 | ... | -0.0306 | -0.0166 | 0.01248 | -0.0040 | -0.0753 | -0.1294 | -0.03812 | -0.106 | -8.2e-02 | -0.0668 | \n",
+ "| PG4Submit | 0.0187 | 0.0172 | -0.0539 | -0.05978 | 0.2191 | 0.1651 | 0.1515 | 0.1956 | -0.14272 | -0.08350 | ... | -0.0119 | -0.0376 | 0.27328 | 0.3326 | 0.2775 | 0.1821 | 0.33236 | 0.391 | 2.8e-01 | 0.2360 | \n",
+ "| PG5_1Order | 0.0218 | 0.0196 | 0.0140 | 0.01254 | -0.0240 | 0.0750 | -0.0069 | 0.0578 | -0.09488 | -0.01399 | ... | -0.0785 | -0.0950 | 0.08054 | 0.0334 | 0.0173 | -0.0388 | 0.01033 | -0.096 | -2.5e-02 | 0.0238 | \n",
+ "| PG5_2Order | 0.0014 | 0.0002 | -0.0386 | -0.03617 | 0.0402 | -0.0226 | -0.0297 | 0.0048 | -0.00954 | 0.08081 | ... | 0.0527 | -0.0652 | 0.03898 | -0.0571 | -0.0551 | -0.0567 | -0.01925 | 0.015 | 7.3e-03 | 0.0469 | \n",
+ "| PG5_3Order | -0.0089 | -0.0177 | 0.0441 | 0.04228 | 0.0155 | 0.0391 | -0.0131 | 0.0172 | 0.13631 | 0.04396 | ... | -0.0698 | -0.0250 | 0.23450 | 0.0419 | 0.0058 | -0.0086 | 0.02604 | 0.063 | 3.4e-02 | 0.0538 | \n",
+ "| PG5_4Order | 0.0931 | 0.0949 | -0.0262 | -0.02214 | -0.0172 | 0.0293 | 0.0560 | -0.0462 | 0.01735 | -0.12489 | ... | -0.0447 | -0.0329 | 0.14487 | 0.0414 | 0.0460 | 0.0182 | -0.03734 | -0.075 | -8.6e-02 | -0.0211 | \n",
+ "| PG5_5Order | -0.0523 | -0.0466 | -0.0087 | -0.01058 | 0.0860 | 0.0345 | 0.0570 | -0.0165 | 0.04533 | -0.06369 | ... | -0.0499 | -0.0981 | 0.28698 | 0.0498 | 0.0372 | 0.0427 | -0.02976 | 0.076 | 2.4e-02 | 0.0085 | \n",
+ "| PG5_6Order | 0.0237 | 0.0217 | -0.0480 | -0.04902 | 0.0762 | 0.0327 | 0.1077 | 0.0370 | -0.13255 | 0.01662 | ... | -0.0430 | -0.0031 | 0.22759 | 0.0165 | 0.0222 | 0.0639 | -0.02974 | 0.015 | 2.8e-02 | 0.0539 | \n",
+ "| PG5_7Order | -0.0200 | -0.0236 | 0.0220 | -0.00444 | -0.1174 | -0.0815 | -0.0285 | 0.0242 | -0.16196 | -0.06938 | ... | 0.0410 | 0.0944 | 0.06439 | -0.0222 | -0.1082 | -0.0804 | -0.01762 | -0.017 | -4.4e-02 | -0.0617 | \n",
+ "| PG5_8Order | -0.0804 | -0.0852 | -0.0147 | -0.00433 | -0.0421 | -0.0205 | -0.0387 | -0.0129 | -0.16181 | 0.01019 | ... | -0.0896 | -0.1168 | 0.13787 | -0.1227 | -0.0126 | -0.0526 | -0.05699 | -0.065 | -3.7e-02 | -0.0182 | \n",
+ "| PG5_9Order | 0.0171 | 0.0167 | -0.0712 | -0.10235 | 0.0476 | 0.0260 | -0.0410 | -0.0641 | -0.07948 | -0.09747 | ... | -0.0431 | -0.0837 | 0.18534 | -0.0188 | -0.0314 | -0.0898 | -0.01354 | -0.034 | 6.2e-03 | -0.0280 | \n",
+ "| PG5_10Order | -0.0214 | -0.0112 | 0.0200 | 0.02736 | 0.0086 | -0.0017 | -0.0418 | 0.0581 | -0.05310 | 0.15949 | ... | -0.0905 | -0.1214 | 0.22151 | 0.0427 | 0.0311 | 0.0345 | 0.02011 | 0.102 | 1.8e-02 | -0.0092 | \n",
+ "| PG5_11Order | -0.0241 | -0.0196 | 0.0190 | 0.02551 | 0.0927 | -0.0047 | -0.1043 | 0.0279 | 0.02791 | -0.03487 | ... | 0.1233 | 0.1434 | 0.02942 | -0.0055 | 0.0210 | 0.0232 | 0.05579 | -0.038 | -2.5e-03 | 0.0275 | \n",
+ "| PG5_12Order | -0.0369 | -0.0359 | 0.0151 | 0.00743 | -0.0161 | 0.0512 | 0.0210 | 0.0464 | 0.17741 | -0.00080 | ... | 1.0000 | 0.1231 | 0.10997 | 0.0777 | 0.0529 | 0.0149 | 0.00295 | 0.016 | 2.4e-02 | 0.0167 | \n",
+ "| PG5_13Order | 0.0598 | 0.0661 | 0.0384 | 0.04072 | -0.0280 | -0.0651 | -0.0047 | -0.0222 | -0.12890 | -0.02179 | ... | 0.1231 | 1.0000 | 0.00976 | -0.0209 | 0.0355 | 0.0556 | 0.07229 | -0.023 | 1.3e-02 | -0.0614 | \n",
+ "| PG5Submit | 0.0851 | 0.0909 | 0.0060 | -0.00888 | 0.1767 | 0.2467 | 0.2185 | 0.2605 | -0.05214 | 0.08974 | ... | 0.1100 | 0.0098 | 1.00000 | 0.3224 | 0.2312 | 0.2035 | 0.30291 | 0.269 | 2.4e-01 | 0.2588 | \n",
+ "| PG6Submit | 0.0054 | 0.0051 | 0.0277 | 0.04005 | 0.1518 | 0.2414 | 0.2696 | 0.2706 | -0.16179 | -0.01463 | ... | 0.0777 | -0.0209 | 0.32240 | 1.0000 | 0.3086 | 0.2065 | 0.44528 | 0.343 | 2.8e-01 | 0.2904 | \n",
+ "| PG7Submit | 0.0776 | 0.0759 | 0.0097 | 0.01212 | 0.1365 | 0.1133 | 0.1245 | 0.1316 | 0.15596 | -0.03631 | ... | 0.0529 | 0.0355 | 0.23120 | 0.3086 | 1.0000 | 0.1606 | 0.27819 | 0.312 | 2.8e-01 | 0.2523 | \n",
+ "| PG8Submit | 0.0441 | 0.0435 | 0.0354 | 0.02635 | 0.1258 | 0.1069 | 0.1567 | 0.1822 | 0.06953 | 0.05260 | ... | 0.0149 | 0.0556 | 0.20351 | 0.2065 | 0.1606 | 1.0000 | 0.25569 | 0.200 | 2.1e-01 | 0.1932 | \n",
+ "| PG9Submit | 0.0410 | 0.0407 | 0.0099 | 0.00056 | 0.1758 | 0.1090 | 0.2013 | 0.2745 | -0.07292 | 0.05977 | ... | 0.0029 | 0.0723 | 0.30291 | 0.4453 | 0.2782 | 0.2557 | 1.00000 | 0.290 | 2.8e-01 | 0.2755 | \n",
+ "| PG10Submit | 0.0474 | 0.0517 | -0.0293 | -0.04481 | 0.2248 | 0.1701 | 0.0989 | 0.1614 | 0.04433 | 0.06942 | ... | 0.0159 | -0.0227 | 0.26881 | 0.3428 | 0.3121 | 0.2000 | 0.29018 | 1.000 | 3.5e-01 | 0.3131 | \n",
+ "| PG11Submit | 0.0790 | 0.0792 | -0.0454 | -0.07102 | 0.1093 | 0.0738 | 0.1147 | 0.1383 | 0.00084 | -0.04870 | ... | 0.0240 | 0.0130 | 0.23552 | 0.2777 | 0.2768 | 0.2065 | 0.27906 | 0.346 | 1.0e+00 | 0.2513 | \n",
+ "| PG12Submit | 0.0746 | 0.0772 | 0.0546 | 0.04364 | 0.1096 | 0.1137 | 0.1073 | 0.1642 | -0.02721 | -0.02169 | ... | 0.0167 | -0.0614 | 0.25876 | 0.2904 | 0.2523 | 0.1932 | 0.27550 | 0.313 | 2.5e-01 | 1.0000 | \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ " Start End PG0Dis PG0Shown PG0Submit PG1Submit PG2Submit\n",
+ "Start 1.0000 0.9952 -0.0417 -0.11507 0.1350 0.1156 0.0791 \n",
+ "End 0.9952 1.0000 -0.0415 -0.09879 0.1142 0.1550 0.0791 \n",
+ "PG0Dis -0.0417 -0.0415 1.0000 0.87220 0.0153 0.0065 0.0041 \n",
+ "PG0Shown -0.1151 -0.0988 0.8722 1.00000 0.0360 0.0205 0.0023 \n",
+ "PG0Submit 0.1350 0.1142 0.0153 0.03596 1.0000 0.1088 0.1037 \n",
+ "PG1Submit 0.1156 0.1550 0.0065 0.02047 0.1088 1.0000 0.1452 \n",
+ "PG2Submit 0.0791 0.0791 0.0041 0.00235 0.1037 0.1452 1.0000 \n",
+ "PG3Submit 0.0384 0.0511 0.0567 0.04968 0.1273 0.2688 0.2045 \n",
+ "PG4Dtr0_6 0.0121 -0.0518 0.1637 0.08226 -0.0080 -0.0685 0.0015 \n",
+ "PG4Psv7_8 0.0037 -0.0458 0.0267 0.00036 -0.0376 0.0566 0.0090 \n",
+ "PG4Prm9_10 -0.0267 -0.0267 -0.0092 0.03279 -0.0939 0.0120 -0.0587 \n",
+ "PG4AllResp 0.0063 -0.0158 0.0018 -0.02094 -0.0236 0.0297 0.0293 \n",
+ "PG4Submit 0.0187 0.0172 -0.0539 -0.05978 0.2191 0.1651 0.1515 \n",
+ "PG5_1Order 0.0218 0.0196 0.0140 0.01254 -0.0240 0.0750 -0.0069 \n",
+ "PG5_2Order 0.0014 0.0002 -0.0386 -0.03617 0.0402 -0.0226 -0.0297 \n",
+ "PG5_3Order -0.0089 -0.0177 0.0441 0.04228 0.0155 0.0391 -0.0131 \n",
+ "PG5_4Order 0.0931 0.0949 -0.0262 -0.02214 -0.0172 0.0293 0.0560 \n",
+ "PG5_5Order -0.0523 -0.0466 -0.0087 -0.01058 0.0860 0.0345 0.0570 \n",
+ "PG5_6Order 0.0237 0.0217 -0.0480 -0.04902 0.0762 0.0327 0.1077 \n",
+ "PG5_7Order -0.0200 -0.0236 0.0220 -0.00444 -0.1174 -0.0815 -0.0285 \n",
+ "PG5_8Order -0.0804 -0.0852 -0.0147 -0.00433 -0.0421 -0.0205 -0.0387 \n",
+ "PG5_9Order 0.0171 0.0167 -0.0712 -0.10235 0.0476 0.0260 -0.0410 \n",
+ "PG5_10Order -0.0214 -0.0112 0.0200 0.02736 0.0086 -0.0017 -0.0418 \n",
+ "PG5_11Order -0.0241 -0.0196 0.0190 0.02551 0.0927 -0.0047 -0.1043 \n",
+ "PG5_12Order -0.0369 -0.0359 0.0151 0.00743 -0.0161 0.0512 0.0210 \n",
+ "PG5_13Order 0.0598 0.0661 0.0384 0.04072 -0.0280 -0.0651 -0.0047 \n",
+ "PG5Submit 0.0851 0.0909 0.0060 -0.00888 0.1767 0.2467 0.2185 \n",
+ "PG6Submit 0.0054 0.0051 0.0277 0.04005 0.1518 0.2414 0.2696 \n",
+ "PG7Submit 0.0776 0.0759 0.0097 0.01212 0.1365 0.1133 0.1245 \n",
+ "PG8Submit 0.0441 0.0435 0.0354 0.02635 0.1258 0.1069 0.1567 \n",
+ "PG9Submit 0.0410 0.0407 0.0099 0.00056 0.1758 0.1090 0.2013 \n",
+ "PG10Submit 0.0474 0.0517 -0.0293 -0.04481 0.2248 0.1701 0.0989 \n",
+ "PG11Submit 0.0790 0.0792 -0.0454 -0.07102 0.1093 0.0738 0.1147 \n",
+ "PG12Submit 0.0746 0.0772 0.0546 0.04364 0.1096 0.1137 0.1073 \n",
+ " PG3Submit PG4Dtr0_6 PG4Psv7_8 ... PG5_12Order PG5_13Order PG5Submit\n",
+ "Start 0.0384 0.01210 0.00371 ... -0.0369 0.0598 0.08512 \n",
+ "End 0.0511 -0.05185 -0.04576 ... -0.0359 0.0661 0.09088 \n",
+ "PG0Dis 0.0567 0.16368 0.02668 ... 0.0151 0.0384 0.00601 \n",
+ "PG0Shown 0.0497 0.08226 0.00036 ... 0.0074 0.0407 -0.00888 \n",
+ "PG0Submit 0.1273 -0.00802 -0.03763 ... -0.0161 -0.0280 0.17671 \n",
+ "PG1Submit 0.2688 -0.06852 0.05661 ... 0.0512 -0.0651 0.24670 \n",
+ "PG2Submit 0.2045 0.00146 0.00897 ... 0.0210 -0.0047 0.21851 \n",
+ "PG3Submit 1.0000 0.00865 0.04424 ... 0.0464 -0.0222 0.26048 \n",
+ "PG4Dtr0_6 0.0087 1.00000 NA ... 0.1774 -0.1289 -0.05214 \n",
+ "PG4Psv7_8 0.0442 NA 1.00000 ... -0.0008 -0.0218 0.08974 \n",
+ "PG4Prm9_10 -0.0062 NA NA ... 0.0233 0.0916 0.00077 \n",
+ "PG4AllResp -0.0193 1.00000 1.00000 ... -0.0306 -0.0166 0.01248 \n",
+ "PG4Submit 0.1956 -0.14272 -0.08350 ... -0.0119 -0.0376 0.27328 \n",
+ "PG5_1Order 0.0578 -0.09488 -0.01399 ... -0.0785 -0.0950 0.08054 \n",
+ "PG5_2Order 0.0048 -0.00954 0.08081 ... 0.0527 -0.0652 0.03898 \n",
+ "PG5_3Order 0.0172 0.13631 0.04396 ... -0.0698 -0.0250 0.23450 \n",
+ "PG5_4Order -0.0462 0.01735 -0.12489 ... -0.0447 -0.0329 0.14487 \n",
+ "PG5_5Order -0.0165 0.04533 -0.06369 ... -0.0499 -0.0981 0.28698 \n",
+ "PG5_6Order 0.0370 -0.13255 0.01662 ... -0.0430 -0.0031 0.22759 \n",
+ "PG5_7Order 0.0242 -0.16196 -0.06938 ... 0.0410 0.0944 0.06439 \n",
+ "PG5_8Order -0.0129 -0.16181 0.01019 ... -0.0896 -0.1168 0.13787 \n",
+ "PG5_9Order -0.0641 -0.07948 -0.09747 ... -0.0431 -0.0837 0.18534 \n",
+ "PG5_10Order 0.0581 -0.05310 0.15949 ... -0.0905 -0.1214 0.22151 \n",
+ "PG5_11Order 0.0279 0.02791 -0.03487 ... 0.1233 0.1434 0.02942 \n",
+ "PG5_12Order 0.0464 0.17741 -0.00080 ... 1.0000 0.1231 0.10997 \n",
+ "PG5_13Order -0.0222 -0.12890 -0.02179 ... 0.1231 1.0000 0.00976 \n",
+ "PG5Submit 0.2605 -0.05214 0.08974 ... 0.1100 0.0098 1.00000 \n",
+ "PG6Submit 0.2706 -0.16179 -0.01463 ... 0.0777 -0.0209 0.32240 \n",
+ "PG7Submit 0.1316 0.15596 -0.03631 ... 0.0529 0.0355 0.23120 \n",
+ "PG8Submit 0.1822 0.06953 0.05260 ... 0.0149 0.0556 0.20351 \n",
+ "PG9Submit 0.2745 -0.07292 0.05977 ... 0.0029 0.0723 0.30291 \n",
+ "PG10Submit 0.1614 0.04433 0.06942 ... 0.0159 -0.0227 0.26881 \n",
+ "PG11Submit 0.1383 0.00084 -0.04870 ... 0.0240 0.0130 0.23552 \n",
+ "PG12Submit 0.1642 -0.02721 -0.02169 ... 0.0167 -0.0614 0.25876 \n",
+ " PG6Submit PG7Submit PG8Submit PG9Submit PG10Submit PG11Submit\n",
+ "Start 0.0054 0.0776 0.0441 0.04101 0.047 7.9e-02 \n",
+ "End 0.0051 0.0759 0.0435 0.04071 0.052 7.9e-02 \n",
+ "PG0Dis 0.0277 0.0097 0.0354 0.00995 -0.029 -4.5e-02 \n",
+ "PG0Shown 0.0401 0.0121 0.0264 0.00056 -0.045 -7.1e-02 \n",
+ "PG0Submit 0.1518 0.1365 0.1258 0.17579 0.225 1.1e-01 \n",
+ "PG1Submit 0.2414 0.1133 0.1069 0.10895 0.170 7.4e-02 \n",
+ "PG2Submit 0.2696 0.1245 0.1567 0.20127 0.099 1.1e-01 \n",
+ "PG3Submit 0.2706 0.1316 0.1822 0.27450 0.161 1.4e-01 \n",
+ "PG4Dtr0_6 -0.1618 0.1560 0.0695 -0.07292 0.044 8.4e-04 \n",
+ "PG4Psv7_8 -0.0146 -0.0363 0.0526 0.05977 0.069 -4.9e-02 \n",
+ "PG4Prm9_10 -0.0418 -0.0633 -0.0550 -0.02989 -0.061 -8.6e-05 \n",
+ "PG4AllResp -0.0040 -0.0753 -0.1294 -0.03812 -0.106 -8.2e-02 \n",
+ "PG4Submit 0.3326 0.2775 0.1821 0.33236 0.391 2.8e-01 \n",
+ "PG5_1Order 0.0334 0.0173 -0.0388 0.01033 -0.096 -2.5e-02 \n",
+ "PG5_2Order -0.0571 -0.0551 -0.0567 -0.01925 0.015 7.3e-03 \n",
+ "PG5_3Order 0.0419 0.0058 -0.0086 0.02604 0.063 3.4e-02 \n",
+ "PG5_4Order 0.0414 0.0460 0.0182 -0.03734 -0.075 -8.6e-02 \n",
+ "PG5_5Order 0.0498 0.0372 0.0427 -0.02976 0.076 2.4e-02 \n",
+ "PG5_6Order 0.0165 0.0222 0.0639 -0.02974 0.015 2.8e-02 \n",
+ "PG5_7Order -0.0222 -0.1082 -0.0804 -0.01762 -0.017 -4.4e-02 \n",
+ "PG5_8Order -0.1227 -0.0126 -0.0526 -0.05699 -0.065 -3.7e-02 \n",
+ "PG5_9Order -0.0188 -0.0314 -0.0898 -0.01354 -0.034 6.2e-03 \n",
+ "PG5_10Order 0.0427 0.0311 0.0345 0.02011 0.102 1.8e-02 \n",
+ "PG5_11Order -0.0055 0.0210 0.0232 0.05579 -0.038 -2.5e-03 \n",
+ "PG5_12Order 0.0777 0.0529 0.0149 0.00295 0.016 2.4e-02 \n",
+ "PG5_13Order -0.0209 0.0355 0.0556 0.07229 -0.023 1.3e-02 \n",
+ "PG5Submit 0.3224 0.2312 0.2035 0.30291 0.269 2.4e-01 \n",
+ "PG6Submit 1.0000 0.3086 0.2065 0.44528 0.343 2.8e-01 \n",
+ "PG7Submit 0.3086 1.0000 0.1606 0.27819 0.312 2.8e-01 \n",
+ "PG8Submit 0.2065 0.1606 1.0000 0.25569 0.200 2.1e-01 \n",
+ "PG9Submit 0.4453 0.2782 0.2557 1.00000 0.290 2.8e-01 \n",
+ "PG10Submit 0.3428 0.3121 0.2000 0.29018 1.000 3.5e-01 \n",
+ "PG11Submit 0.2777 0.2768 0.2065 0.27906 0.346 1.0e+00 \n",
+ "PG12Submit 0.2904 0.2523 0.1932 0.27550 0.313 2.5e-01 \n",
+ " PG12Submit\n",
+ "Start 0.0746 \n",
+ "End 0.0772 \n",
+ "PG0Dis 0.0546 \n",
+ "PG0Shown 0.0436 \n",
+ "PG0Submit 0.1096 \n",
+ "PG1Submit 0.1137 \n",
+ "PG2Submit 0.1073 \n",
+ "PG3Submit 0.1642 \n",
+ "PG4Dtr0_6 -0.0272 \n",
+ "PG4Psv7_8 -0.0217 \n",
+ "PG4Prm9_10 0.0169 \n",
+ "PG4AllResp -0.0668 \n",
+ "PG4Submit 0.2360 \n",
+ "PG5_1Order 0.0238 \n",
+ "PG5_2Order 0.0469 \n",
+ "PG5_3Order 0.0538 \n",
+ "PG5_4Order -0.0211 \n",
+ "PG5_5Order 0.0085 \n",
+ "PG5_6Order 0.0539 \n",
+ "PG5_7Order -0.0617 \n",
+ "PG5_8Order -0.0182 \n",
+ "PG5_9Order -0.0280 \n",
+ "PG5_10Order -0.0092 \n",
+ "PG5_11Order 0.0275 \n",
+ "PG5_12Order 0.0167 \n",
+ "PG5_13Order -0.0614 \n",
+ "PG5Submit 0.2588 \n",
+ "PG6Submit 0.2904 \n",
+ "PG7Submit 0.2523 \n",
+ "PG8Submit 0.1932 \n",
+ "PG9Submit 0.2755 \n",
+ "PG10Submit 0.3131 \n",
+ "PG11Submit 0.2513 \n",
+ "PG12Submit 1.0000 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#get numeric fields only for correlation\n",
+ "sel = c()\n",
+ "for (i in 1:dim(data)[2]) if (is.numeric(data[,i])) sel = c(sel, i);\n",
+ "\n",
+ "\n",
+ "cor(data[,sel],method=\"spearman\",use=\"pairwise.complete.obs\"); #OK for any: uses ranks"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Interpret correlations: onlys start vs End, calculate differene instead\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "### Simple questions\n",
+ "\n",
+ "- Time to take entire survey?\n",
+ "- Question that took the longest to complete?\n",
+ "- Question that took the least time?\n",
+ "- Top-ranked criteria?\n",
+ "- Demographic distribution by age?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exploration of the Data\n",
+ "\n",
+ "I plot the response frequencies to get a better understanding of the distribution of the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 120,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Only examine the non-empty responses.\n",
+ "#print(dim(data))\n",
+ "mydim <- data[,28]\n",
+ "#print(data[,29])\n",
+ "sum <- 0\n",
+ "not <- 0\n",
+ "low <- 0\n",
+ "med <- 0\n",
+ "hgh <- 0\n",
+ "ess <- 0\n",
+ "for (i in 1:dim(data)[1]) {\n",
+ " if (mydim[i] != \"\") {\n",
+ " if(mydim[i] == \"Not a Priority\")\n",
+ " {\n",
+ " not <- not + 1\n",
+ " }\n",
+ " else if(mydim[i] == \"Low Priority\")\n",
+ " {\n",
+ " low <- low + 1\n",
+ " }\n",
+ " else if(mydim[i] == \"Medium Priority\")\n",
+ " {\n",
+ " med <- med + 1\n",
+ " }\n",
+ " else if(mydim[i] == \"High Priority\")\n",
+ " {\n",
+ " hgh <- hgh + 1\n",
+ " }\n",
+ " else #if(mydim[i] == \"Essential\")\n",
+ " {\n",
+ " ess <- ess + 1\n",
+ " }\n",
+ " \n",
+ " sum <- sum + 1\n",
+ " #print(paste(\"[\",i,\"]=\",mydim[i]));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "valids = vector(,length=sum)\n",
+ "ctr <- 1\n",
+ "for (i in 1:dim(data)[1]) {\n",
+ " if (mydim[i] != \"\") {\n",
+ " valids[ctr] <- i;\n",
+ " ctr <- ctr +1;\n",
+ " }\n",
+ "}\n",
+ "#print(sum)\n",
+ "#print(not)\n",
+ "#print(low)\n",
+ "#print(med)\n",
+ "#print(hgh)\n",
+ "#print(ess)\n",
+ "#print(histo)\n",
+ "#hist(data$PG5_3HDS)\n",
+ "#hist(histo)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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MUOAABAEhQ7AAAASVDsAAAAJEGxAwAA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h0TE+PocVkkayHgpAh26NIURamoqJg1a1ZgYODWrVsd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",
+ "text/plain": [
+ "Plot with title \"Frequency of Choice\""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Build the histogram.\n",
+ "histo <- c(not, low, med, hgh, ess)\n",
+ "histo <- histo/sum\n",
+ "\n",
+ "# Standardize the data.\n",
+ "avg <- 0\n",
+ "expec_sq <- 0\n",
+ "for (i in 1:5) {\n",
+ " avg <- avg + i*histo[i];\n",
+ " expec_sq <- expec_sq + i*i*histo[i];\n",
+ "}\n",
+ "std_dev <- expec_sq - (avg*avg)\n",
+ "\n",
+ "#print(avg)\n",
+ "#print(std_dev)\n",
+ "\n",
+ "x_1 <- (1-avg)/std_dev\n",
+ "x_2 <- (2-avg)/std_dev\n",
+ "x_3 <- (3-avg)/std_dev\n",
+ "x_4 <- (4-avg)/std_dev\n",
+ "x_5 <- (5-avg)/std_dev\n",
+ "\n",
+ "# Plot the histogram.\n",
+ "xvals = c(x_1, x_2, x_3, x_4, x_5)\n",
+ "names(histo) <- c(\"1 - Not a Priority\", \"2 - Low Priority\", \"3 - Med. Priority\", \"4 - High Priority\", \"5 - Essential\")\n",
+ "#namelab <- c(\"1 - Not Priority\", \"2 - Low Priority\", \"3 - Med. Priority\", \"4 - High Priority\", \"5 - Essential\")\n",
+ "#names(histo) <- c(\"1\", \"2\", \"3\", \"4\", \"5\")\n",
+ "plt <- barplot(histo, ylab=\"Frequency\", main=\"Frequency of Choice\", col=\"blue3\", cex.names=0.75)\n",
+ "#text(plt, namelab, srt=60)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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VHx559/spdrvp4oCm7cSWFhYa3V5969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\nGE4UIyajAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "Plot with title \"Frequency of Choice - Standardized\""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot against standardized data.\n",
+ "plot(xvals, histo, xlab=\"Standardized Input\", ylab=\"Frequency\", main=\"Frequency of Choice - Standardized\", col=\"red\")\n",
+ "lines(xvals, histo)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Observations\n",
+ "\n",
+ "The responses are skewed to the right. This means that there are more survey respondents who found helpful discussion on StackExchange to be a higher priority than there are who found it to be a lower priority. Standardizing these results does not change the skewness of the distribution."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Explanation of How Measures are Calculated from the Data, Transformation, and Cleaning\n",
+ "\n",
+ "Calculations and cleaning are both done is this segment of code. First, I only parse the data samples for which a response was given. Then, I encode the answer options from the survey into numerical values. These values are in the interval [0,8], where '0' represents not answered (for optional questions) or the first choice (for required questions). In this scenario, the cleaning of the data is the replacement of missing values by '0'. '8' represents \"other.\" The other values are the relative placement of the answer choice in the given options scaled in the range [0,8]. For example, if there are three answer choices for a required question, then if the respondent selects the first answer, then the encoded value is '0'; if he or she selects the second, then the value is '4' (50% of 8); and if he or she selects the third, then the value is '8'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 269,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Pair down data before running this next line.\n",
+ "data[is.na(data)] <- \"\"\n",
+ "\n",
+ "# Convert non-numeric values to numeric values.\n",
+ "for (i in 1:dim(data)[1]) {\n",
+ " for (j in 1:dim(data)[2]) {\n",
+ " #print(i+j)\n",
+ " #print(is.na(data[i,][j]))\n",
+ " #if( is.na(data[i,][j]) == FALSE )\n",
+ " if( data[i,][j] != \"\")\n",
+ " {\n",
+ " # Generic cases:\n",
+ " if ( data[i,][j] == \"Not a Priority\" || data[i,][j] == \"Less than 2 years\" || data[i,][j] == \"R\" || data[i,][j] == \"C/C++\" || data[i,][j] == \"Java\" || data[i,][j] == \"Python\" || data[i,][j] == \"Javascript\" || data[i,][j] == \"Go\" || data[i,][j] == \"C#\" || data[i,][j] == \"1\" || data[i,][j] == \"Native\" || data[i,][j] == \"18 - 24\" || data[i,][j] == \"For personal work and/or research use\" || data[i,][j] == \"For a wider audience, such as developers of other packages or other software\" || data[i,][j] == \"For a training / class that I took\") {\n",
+ " newdata[i,][j] <- 1;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"Low Priority\" || data[i,][j] == \"2 - 5 years\" || data[i,][j] == \"2 - 3\" || data[i,][j] == \"Not native - full working proficiency\" || data[i,][j] == \"25 - 34\") {\n",
+ " newdata[i,][j] <- 2;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"Medium Priority\" || data[i,][j] == \"6 - 8 years\" || data[i,][j] == \"4 - 6\" || data[i,][j] == \"Not native - sufficient working proficiency\" || data[i,][j] == \"35 - 44\") {\n",
+ " newdata[i,][j] <- 3;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"High Priority\" || data[i,][j] == \"9 - 12 years\" || data[i,][j] == \"7 - 10\" || data[i,][j] == \"Not native - limited working proficiency\" || data[i,][j] == \"45 - 54\") {\n",
+ " newdata[i,][j] <- 4;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"Essential\" || data[i,][j] == \"13 - 19 years\" || data[i,][j] == \"11 - 15\" || data[i,][j] == \"Not native - passable\" || data[i,][j] == \"55 - 64\") {\n",
+ " newdata[i,][j] <- 5;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"20 years or more\" || data[i,][j] == \"16 - 25\" || data[i,][j] == \"Not native - very limited\" || data[i,][j] == \"65 and over\") {\n",
+ " newdata[i,][j] <- 6;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"More than 25\") {\n",
+ " newdata[i,][j] <- 7;\n",
+ " }\n",
+ " else # Not sure\n",
+ " {\n",
+ " newdata[i,][j] <- 8;\n",
+ " }\n",
+ " \n",
+ " # Special cases:\n",
+ " if( data[i,][j] == \"Yes\" || data[i,][j] == \"Software Engineer\" || data[i,][j] == \"The core \\\"data.frame\\\" object lacked functionality that I needed\") {\n",
+ " newdata[i,][j] <- 0.0;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"Female\" || data[i,][j] == \"Chose the package to be compatible with other packages in my project\") {\n",
+ " newdata[i,][j] <- 2.0; \n",
+ " }\n",
+ " else if ( data[i,][j] == \"Data Scientist\" || data[i,][j] == \"No\" || data[i,][j] == \"Male\" || data[i,][j] == \"I saw a recommendation for the package\" ) {\n",
+ " newdata[i,][j] <- 4.0;\n",
+ " }\n",
+ " else if ( data[i,][j] == \"Prefer not to answer\" || data[i,][j] == \"I didn't choose to use the package, it was included implicitly / unintentionally\" ) {\n",
+ " newdata[i,][j] <- 6.0;\n",
+ " }\n",
+ " }\n",
+ " else\n",
+ " {\n",
+ " newdata[i,][j] <- 0; # Denotes missing value.\n",
+ " }\n",
+ " }\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 382,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create the data frame for the regression.\n",
+ "cleandata = newdata[valids,]\n",
+ "indexointerest = c(8,9,10,11,13,15,62,64,65,66,67,68,69,70,73,75,77,79,81)\n",
+ "indexoaug = c(indexointerest, 28)\n",
+ "\n",
+ "y <- cleandata[,28]\n",
+ "x <- cleandata[,indexointerest]\n",
+ "aug <- cleandata[,indexoaug]\n",
+ "augfrm <- as.data.frame(aug)\n",
+ "aug <- augfrm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Correlation Analysis Using Sanitized Data\n",
+ "\n",
+ "We examine the correlations to estimate the most influential predictors. There are no highly correlated measures and therefore none of them need to be dropped."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 383,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/latex": [],
+ "text/markdown": [],
+ "text/plain": [
+ "<0 x 0 matrix>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Find all correlations.\n",
+ "#cor(aug,method=\"spearman\",use=\"pairwise.complete.obs\"); #OK for any: uses ranks\n",
+ "\n",
+ "# Find top correlations.\n",
+ "hiCor <- function(x, level){\n",
+ " res <- cor(x,method=\"spearman\");\n",
+ " res1 <- res;\n",
+ " res1[res<0] <- -res[res < 0];\n",
+ " for (i in 1:dim(x)[2]){\n",
+ " res1[i,i] <- 0;\n",
+ " }\n",
+ " sel <- apply(res1,1,max) > level;\n",
+ " res[sel,sel];\n",
+ "}\n",
+ "\n",
+ "hiCor(aug,.7)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Perform Principal Component Analysis\n",
+ "\n",
+ "We use Principal Component Analysis to reduce the dimensionality of the data. By inspection of the plot of the Fraction of Variance explained, we see that the first eleven principal components explain 70% of the variance. The relatively gradual progression of variance explained indicates that the variables are relatively independent."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 384,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " V7 V16 V19\n",
+ "PC1 0.51 0.49 0.31\n",
+ " V1 V3 V4 V11\n",
+ "PC2 0.36 -0.35 0.37 -0.39\n",
+ " V1 V2 V8 V14\n",
+ "PC3 -0.34 0.37 0.33 0.42\n",
+ " V1 V4 V6 V12 V17\n",
+ "PC4 -0.39 -0.37 -0.31 -0.32 -0.32\n",
+ " V17 V18\n",
+ "PC5 0.51 0.62\n",
+ " V3 V5 V11 V15\n",
+ "PC6 0.48 0.36 0.31 0.32\n",
+ " V6 V10 V20\n",
+ "PC7 -0.54 -0.31 0.45\n",
+ " V2 V3 V8 V19\n",
+ "PC8 -0.49 0.39 0.37 0.39\n",
+ " V5 V13\n",
+ "PC9 0.4 0.8\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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2+V5kniHiHBwqS6/7fF2PZogz/qHiTu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\nc70AAAAASUVORK5CYII=",
+ "text/plain": [
+ "plot without title"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#augfrm <- as.data.frame(aug)\n",
+ "#logs <- cbind(augfrm$V20, log(augfrm$V1+1), log(augfrm$V2+1), log(augfrm$V3+1), log(augfrm$V4+1),\n",
+ "# log(augfrm$V5+1), log(augfrm$V6+1), log(augfrm$V7+1), log(augfrm$V8+1), log(augfrm$V9+1),\n",
+ "# log(augfrm$V10+1), log(augfrm$V11+1), log(augfrm$V12+1), log(augfrm$V13+1), log(augfrm$V14+1),\n",
+ "# log(augfrm$V15+1), log(augfrm$V16+1), log(augfrm$V17+1), log(augfrm$V18+1), log(augfrm$V19+1));\n",
+ "#aug <- data.frame(logs)\n",
+ "#augfrm <- aug\n",
+ "plot(1:20,cumsum(prcomp(aug, retx=F,scale=T)$sdev^2)/sum(prcomp(aug, retx=F,scale=T)$sdev^2),ylim=c(0,1),xlab=\"Number of Components\",ylab=\"Fraction of Variance\");\n",
+ "\n",
+ "#cumsum(prcomp(x, retx=F,scale=T)$sdev^2)/sum(prcomp(x, retx=F,scale=T)$sdev^2)\n",
+ "res<-prcomp(aug, retx=F,scale=T)$rotation[,1:9];\n",
+ "resAbs <- res;\n",
+ "resAbs[res<0] <- -res[res<0];\n",
+ "for (i in 1:9)\n",
+ " print(t(res[resAbs[,i]>.3,i,drop=FALSE]));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Regress Predictors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 385,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\tV7 7 0.38 \n",
+ "\tV16 16 0.34 \n",
+ "\tV19 19 0.15 \n",
+ "\tV11 11 0.13 \n",
+ "\tV14 14 0.13 \n",
+ "\tV15 15 0.13 \n",
+ "\tV12 12 0.12 \n",
+ "\tV9 9 0.11 \n",
+ "\tV3 3 0.08 \n",
+ "\tV18 18 0.08 \n",
+ "\tV10 10 0.07 \n",
+ "\tV6 6 0.06 \n",
+ "\tV2 2 0.05 \n",
+ "\tV4 4 0.05 \n",
+ "\tV8 8 0.05 \n",
+ "\tV17 17 0.05 \n",
+ "\tV20 20 0.04 \n",
+ "\tV5 5 0.03 \n",
+ "\tV13 13 0.02 \n",
+ " \n",
+ "
\n"
+ ],
+ "text/latex": [
+ "\\begin{tabular}{r|ll}\n",
+ "\tV7 & 7 & 0.38\\\\\n",
+ "\tV16 & 16 & 0.34\\\\\n",
+ "\tV19 & 19 & 0.15\\\\\n",
+ "\tV11 & 11 & 0.13\\\\\n",
+ "\tV14 & 14 & 0.13\\\\\n",
+ "\tV15 & 15 & 0.13\\\\\n",
+ "\tV12 & 12 & 0.12\\\\\n",
+ "\tV9 & 9 & 0.11\\\\\n",
+ "\tV3 & 3 & 0.08\\\\\n",
+ "\tV18 & 18 & 0.08\\\\\n",
+ "\tV10 & 10 & 0.07\\\\\n",
+ "\tV6 & 6 & 0.06\\\\\n",
+ "\tV2 & 2 & 0.05\\\\\n",
+ "\tV4 & 4 & 0.05\\\\\n",
+ "\tV8 & 8 & 0.05\\\\\n",
+ "\tV17 & 17 & 0.05\\\\\n",
+ "\tV20 & 20 & 0.04\\\\\n",
+ "\tV5 & 5 & 0.03\\\\\n",
+ "\tV13 & 13 & 0.02\\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "| V7 | 7 | 0.38 | \n",
+ "| V16 | 16 | 0.34 | \n",
+ "| V19 | 19 | 0.15 | \n",
+ "| V11 | 11 | 0.13 | \n",
+ "| V14 | 14 | 0.13 | \n",
+ "| V15 | 15 | 0.13 | \n",
+ "| V12 | 12 | 0.12 | \n",
+ "| V9 | 9 | 0.11 | \n",
+ "| V3 | 3 | 0.08 | \n",
+ "| V18 | 18 | 0.08 | \n",
+ "| V10 | 10 | 0.07 | \n",
+ "| V6 | 6 | 0.06 | \n",
+ "| V2 | 2 | 0.05 | \n",
+ "| V4 | 4 | 0.05 | \n",
+ "| V8 | 8 | 0.05 | \n",
+ "| V17 | 17 | 0.05 | \n",
+ "| V20 | 20 | 0.04 | \n",
+ "| V5 | 5 | 0.03 | \n",
+ "| V13 | 13 | 0.02 | \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ " [,1] [,2]\n",
+ "V7 7 0.38\n",
+ "V16 16 0.34\n",
+ "V19 19 0.15\n",
+ "V11 11 0.13\n",
+ "V14 14 0.13\n",
+ "V15 15 0.13\n",
+ "V12 12 0.12\n",
+ "V9 9 0.11\n",
+ "V3 3 0.08\n",
+ "V18 18 0.08\n",
+ "V10 10 0.07\n",
+ "V6 6 0.06\n",
+ "V2 2 0.05\n",
+ "V4 4 0.05\n",
+ "V8 8 0.05\n",
+ "V17 17 0.05\n",
+ "V20 20 0.04\n",
+ "V5 5 0.03\n",
+ "V13 13 0.02"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Analyze aug.\n",
+ "#augfrm <- as.data.frame(aug)\n",
+ "# Swap columns so that response variable is the first.\n",
+ "#augfrm <- augfrm[,c(\"V20\",\"V1\",\"V2\",\"V3\",\"V4\",\"V5\",\"V6\",\"V7\",\"V8\",\"V9\",\"V10\",\"V11\",\"V12\",\"V13\",\"V14\",\"V15\",\"V16\",\"V17\",\"V18\",\"V19\")]\n",
+ "\n",
+ "res <- c();\n",
+ "vnam <- names(augfrm);\n",
+ "#print(vnam)\n",
+ "for (i in 2:dim(augfrm)[2]){\n",
+ " fmla <- as.formula(paste(vnam[i],paste(vnam[-c(1,i)],collapse=\"+\"),sep=\"~\"));\n",
+ " res <- rbind(res,c(i,round(summary(lm(fmla,data=augfrm))$r.squared,2)));\n",
+ "}\n",
+ "row.names(res) <- vnam[res[,1]];\n",
+ "res[order(-res[,2]),];\n",
+ "#print(augfrm)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Fitting of the Statistical Model\n",
+ "\n",
+ "We do the regression to select a model. Interestingly, the best model was found to be the quasipoisson distribution of the single variable, which is the respondents' answer to question three: \"Which of the following is the closest to why you chose that package?\" The p-value is within the threshold of statistical significance, and the error is minimized using this model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 388,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "fmla ~ V20 ~ V6"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#fmla ~ V20 ~ V1+V2+V3+V4+V5+V6+V7+V8+V9+V10+V11+V12+V13+V14+V15+V16+V17+V18+V19\n",
+ "#fmla ~ V20 ~ V6\n",
+ "fmla ~ V20 ~ V6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 394,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\n",
+ "Call:\n",
+ "glm(formula = V20 ~ V6, family = quasipoisson, data = augfrm)\n",
+ "\n",
+ "Deviance Residuals: \n",
+ " Min 1Q Median 3Q Max \n",
+ "-1.576 -0.270 0.265 0.375 0.909 \n",
+ "\n",
+ "Coefficients:\n",
+ " Estimate Std. Error t value Pr(>|t|) \n",
+ "(Intercept) 1.25058 0.01872 66.81 <2e-16 ***\n",
+ "V6 -0.00962 0.00457 -2.11 0.036 * \n",
+ "---\n",
+ "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+ "\n",
+ "(Dispersion parameter for quasipoisson family taken to be 0.4)\n",
+ "\n",
+ " Null deviance: 262.33 on 584 degrees of freedom\n",
+ "Residual deviance: 260.55 on 583 degrees of freedom\n",
+ "AIC: NA\n",
+ "\n",
+ "Number of Fisher Scoring iterations: 4\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "library(mgcv)\n",
+ "library(MASS)\n",
+ "#mod <- glm(V20 ~ V1+V2+V3+V4+V5+V6+V7+V8+V9+V10+V11+V12+V13+V14+V15+V16+V17+V18+V19,family=Gamma,data=augfrm);\n",
+ "#mod <- lm(V20 ~ V1+V2+V3+V4+V5+V6+V7+V8+V9+V10+V11+V12+V13+V14+V15+V16+V17+V18+V19,data=augfrm);\n",
+ "#summary(mod);\n",
+ "#mod <- glm(V20 ~ V16+V17,family=quasibinomial,data=augfrm);\n",
+ "#mod <- glm(V20 ~ V6+V16+V17,family=quasibinomial,data=augfrm);\n",
+ "#mod <- glm(V20 ~ V6,family=quasibinomial,data=augfrm); #-- good\n",
+ "#summary(mod);\n",
+ "#mod <- glm(V20 ~ V6,family=inverse.gaussian,data=augfrm); #-- good\n",
+ "#summary(mod);\n",
+ "#mod <- glm(V20 ~ V6,family=gaussian,data=augfrm); #-- good\n",
+ "#summary(mod);\n",
+ "#---------------\n",
+ "mod <- glm(V20 ~ V6,family=quasipoisson,data=augfrm); #-- good\n",
+ "summary(mod);\n",
+ "#mod <- glm(V20 ~ V6,family=Gamma,data=augfrm); #-- good\n",
+ "#summary(mod);\n",
+ "#mod <- glm.nb(V20 ~ V6,data=augfrm); #-- good\n",
+ "#summary(mod);\n",
+ "###start\n",
+ "#augfrm$V20 <- augfrm$V20/5.0\n",
+ "#mod <- glm(V20 ~ V6,family=quasibinomial,data=augfrm); #-- good\n",
+ "#summary(mod);\n",
+ "###end\n",
+ "#mod <- glm(V20 ~ V6,family=quasi,data=augfrm); #-- good\n",
+ "#summary(mod);\n",
+ "##\n",
+ "mod <- lm(V20 ~ V6,data=augfrm); #-- good\n",
+ "##summary(mod);\n",
+ "##mod <- lm(V20 ~ V7+V16+V19,data=augfrm); #-- good\n",
+ "##summary(mod);\n",
+ "#mod <- glm(V20 ~ V7+V16+V19,family=quasipoisson,data=augfrm); #-- good\n",
+ "#summary(mod);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Interpretation of the Coefficients\n",
+ "\n",
+ "For this model, there are only two coefficients, including the intercept, due to the model being represented most accurately using a single variable. The intercept is 1.25125, and the slope of the variable corresponding to the answers for question three is -0.00966. This means that as the sought-out need of the package decreased, the helpfulness of the discussion on StackExchange increased. This is interesting because it seems to be more intuitive that if a package is more sought-out, then one might be more likely benefit from helpful discussion. This was not the case, however."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 390,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "Df Deviance Resid. Df Resid. Dev Pr(>Chi) \n",
+ "\n",
+ "\tNULL NA NA 584 262 NA \n",
+ "\tV6 1 1.8 583 261 0.035 \n",
+ " \n",
+ "
\n"
+ ],
+ "text/latex": [
+ "\\begin{tabular}{r|lllll}\n",
+ " & Df & Deviance & Resid. Df & Resid. Dev & Pr(>Chi)\\\\\n",
+ "\\hline\n",
+ "\tNULL & NA & NA & 584 & 262 & NA\\\\\n",
+ "\tV6 & 1 & 1.8 & 583 & 261 & 0.035\\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "| | Df | Deviance | Resid. Df | Resid. Dev | Pr(>Chi) | \n",
+ "|---|---|\n",
+ "| NULL | NA | NA | 584 | 262 | NA | \n",
+ "| V6 | 1 | 1.8 | 583 | 261 | 0.035 | \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ " Df Deviance Resid. Df Resid. Dev Pr(>Chi)\n",
+ "NULL NA NA 584 262 NA \n",
+ "V6 1 1.8 583 261 0.035 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "Df Deviance Resid. Df Resid. Dev Pr(>Chi) \n",
+ "\n",
+ "\tNULL NA NA 584 262 NA \n",
+ "\tV6 1 1.8 583 261 0.035 \n",
+ " \n",
+ "
\n"
+ ],
+ "text/latex": [
+ "\\begin{tabular}{r|lllll}\n",
+ " & Df & Deviance & Resid. Df & Resid. Dev & Pr(>Chi)\\\\\n",
+ "\\hline\n",
+ "\tNULL & NA & NA & 584 & 262 & NA\\\\\n",
+ "\tV6 & 1 & 1.8 & 583 & 261 & 0.035\\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "| | Df | Deviance | Resid. Df | Resid. Dev | Pr(>Chi) | \n",
+ "|---|---|\n",
+ "| NULL | NA | NA | 584 | 262 | NA | \n",
+ "| V6 | 1 | 1.8 | 583 | 261 | 0.035 | \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ " Df Deviance Resid. Df Resid. Dev Pr(>Chi)\n",
+ "NULL NA NA 584 262 NA \n",
+ "V6 1 1.8 583 261 0.035 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "Df Deviance scaled dev. Pr(>Chi) \n",
+ "\n",
+ "\t<none> NA 261 NA NA \n",
+ "\tV6 1 262 4.5 0.035 \n",
+ " \n",
+ "
\n"
+ ],
+ "text/latex": [
+ "\\begin{tabular}{r|llll}\n",
+ " & Df & Deviance & scaled dev. & Pr(>Chi)\\\\\n",
+ "\\hline\n",
+ "\t & NA & 261 & NA & NA\\\\\n",
+ "\tV6 & 1 & 262 & 4.5 & 0.035\\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "| | Df | Deviance | scaled dev. | Pr(>Chi) | \n",
+ "|---|---|\n",
+ "| | NA | 261 | NA | NA | \n",
+ "| V6 | 1 | 262 | 4.5 | 0.035 | \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ " Df Deviance scaled dev. Pr(>Chi)\n",
+ " NA 261 NA NA \n",
+ "V6 1 262 4.5 0.035 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "anova(mod, test=\"Chi\");\n",
+ "anova(mod, test=\"Chisq\");\n",
+ "drop1(mod, test=\"Chi\");\n",
+ "library(car)\n",
+ "#vif(mod);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 391,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#ys <- augfrm$V20\n",
+ "#xs <- augfrm$V6\n",
+ "#print(ys)\n",
+ "#print(xs)\n",
+ "#plot(xs,ys,ylim=c(0,20),xlab=\"Measure\",ylab=\"Response\");\n",
+ "#plot(V20 ~ V6, augfrm)\n",
+ "#plot(V20 ~ V6, augfrm)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Recommendations for how to improve the survey:\n",
+ "\n",
+ "It may be useful to ask those who are taking the survey if they had trouble answering any of the questions. Some of them may have taken longer to complete certain questions by virtue of the questions having different forms (e.g. click-and-drag vs multiple choice). There could also be an option for \"Other\" or \"I don't know\" for more of the questions. This could potentially help remove noise in the data if a person did not know to which category an idea should belong (the form of question PG5_3HDS) - as in the case of the third question. There could also be a final question regarding how thoroughly they believe their thoughts to this survey are captured by their responses. If a survey respondent has 'very limited' English proficiency, then there may be noise in the survey data by virtue of them not being able to fully understand the questions and answers. "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "R",
+ "language": "R",
+ "name": "ir"
+ },
+ "language_info": {
+ "codemirror_mode": "r",
+ "file_extension": ".r",
+ "mimetype": "text/x-r-source",
+ "name": "R",
+ "pygments_lexer": "r",
+ "version": "3.4.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/example.ipynb b/example.ipynb
index 310a50d..7f60587 100644
--- a/example.ipynb
+++ b/example.ipynb
@@ -1,624 +1,970 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "A Survey on Technology Choice\n",
- "======\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# For nicer printing\n",
- "options(digits=2);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Read in the data\n",
- "data <- read.csv(\"TechSurvey - Survey.csv\",header=T);\n",
- "\n",
- "#convert date to unix second\n",
- "for (i in c(\"Start\", \"End\")) \n",
- " data[,i] = as.numeric(as.POSIXct(strptime(data[,i], \"%Y-%m-%d %H:%M:%S\")))\n",
- "for (i in 0:12){\n",
- " vnam = paste(c(\"PG\",i,\"Submit\"), collapse=\"\")\n",
- " data[,vnam] = as.numeric(as.POSIXct(strptime(data[,vnam], \"%Y-%m-%d %H:%M:%S\")))\n",
- "}\n",
- "#calculate differences in time \n",
- "for (i in 12:0){\n",
- " pv = paste(c(\"PG\",i-1,\"Submit\"), collapse=\"\");\n",
- " if (i==0) \n",
- " pv=\"Start\";\n",
- " vnam = paste(c(\"PG\",i,\"Submit\"), collapse=\"\");\n",
- " data[,vnam] = data[,vnam] -data[,pv];\n",
- "}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- " Device Completed Start End PG0Dis \n",
- " : 2 0 : 2 Min. :1.54e+09 Min. :1.54e+09 Min. : 0 \n",
- " Bot : 1 FALSE:546 1st Qu.:1.54e+09 1st Qu.:1.54e+09 1st Qu.: 0 \n",
- " PC :955 TRUE :805 Median :1.54e+09 Median :1.54e+09 Median : 1 \n",
- " Phone :376 Mean :1.54e+09 Mean :1.54e+09 Mean : 44 \n",
- " Tablet : 16 3rd Qu.:1.54e+09 3rd Qu.:1.54e+09 3rd Qu.: 24 \n",
- " Unknown: 3 Max. :1.54e+09 Max. :1.54e+09 Max. :168 \n",
- " NA's :2 NA's :548 NA's :73 \n",
- " PG0Shown PG0Submit \n",
- " Min. : 0 Min. : 2 \n",
- " 1st Qu.: 0 1st Qu.: 6 \n",
- " Median : 102 Median : 9 \n",
- " Mean : 249 Mean : 299 \n",
- " 3rd Qu.: 428 3rd Qu.: 15 \n",
- " Max. :1190 Max. :76226 \n",
- " NA's :73 NA's :199 \n",
- " PG1PsnUse \n",
- " For personal work and/or research use :727 \n",
- " :613 \n",
- " Chapter book : 1 \n",
- " For training attendees of my sessions : 1 \n",
- " It's a coures on tidyverse that I developed: 1 \n",
- " Learning how to create a package : 1 \n",
- " (Other) : 9 \n",
- " PG1WdAuth \n",
- " :1145 \n",
- " Because Microsoft was paying me to do it : 1 \n",
- " For a wider audience, such as developers of other packages or other software: 205 \n",
- " Hackathon : 1 \n",
- " Tool for other researchers to analyse their data : 1 \n",
- " \n",
- " \n",
- " PG1Trn \n",
- " :1168 \n",
- " For a training / class that I took: 184 \n",
- " teaching economics : 1 \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " PG1Other \n",
- " :1272 \n",
- " Other : 23 \n",
- " Teaching : 3 \n",
- " teaching : 3 \n",
- " For training that I gave : 2 \n",
- " How does software technology spreads in the open source community?: 2 \n",
- " (Other) : 48 \n",
- " PG1Submit PG2Resp PG2Submit \n",
- " Min. : 1 :431 Min. : 1 \n",
- " 1st Qu.: 11 No :374 1st Qu.: 9 \n",
- " Median : 16 Not sure:303 Median : 13 \n",
- " Mean : 39 Yes :245 Mean : 29 \n",
- " 3rd Qu.: 30 3rd Qu.: 29 \n",
- " Max. :6892 Max. :1470 \n",
- " NA's :282 NA's :377 \n",
- " PG2Resp.1 \n",
- " :480 \n",
- " The core \"data.frame\" object lacked functionality that I needed :354 \n",
- " I saw a recommendation for the package :149 \n",
- " Chose the package to be compatible with other packages in my project :137 \n",
- " I didn't choose to use the package, it was included implicitly / unintentionally: 39 \n",
- " Other : 6 \n",
- " (Other) :188 \n",
- " PG3Submit PG4Dtr0_6 PG4Psv7_8 PG4Prm9_10 PG4AllResp \n",
- " Min. : 1 Min. :0 Min. :7 Min. : 9 Min. : 0 \n",
- " 1st Qu.: 16 1st Qu.:3 1st Qu.:7 1st Qu.:10 1st Qu.: 8 \n",
- " Median : 23 Median :5 Median :8 Median :10 Median : 9 \n",
- " Mean : 44 Mean :4 Mean :8 Mean :10 Mean : 8 \n",
- " 3rd Qu.: 40 3rd Qu.:6 3rd Qu.:8 3rd Qu.:10 3rd Qu.:10 \n",
- " Max. :4648 Max. :6 Max. :8 Max. :10 Max. :10 \n",
- " NA's :451 NA's :1232 NA's :1115 NA's :869 NA's :510 \n",
- " PG4Submit PG5_1RRPQ PG5_1Order PG5_1Time \n",
- " Min. : 1 :877 Min. : 1 :877 \n",
- " 1st Qu.: 6 Essential : 60 1st Qu.: 4 2018-10-11 13:32:57: 3 \n",
- " Median : 7 High Priority :102 Median : 7 2018-10-11 13:29:20: 2 \n",
- " Mean : 9 Low Priority : 85 Mean : 7 2018-10-11 13:34:56: 2 \n",
- " 3rd Qu.: 9 Medium Priority:134 3rd Qu.:11 2018-10-11 13:14:25: 1 \n",
- " Max. :332 Not a Priority : 95 Max. :20 2018-10-11 13:14:45: 1 \n",
- " NA's :473 NA's :877 (Other) :467 \n",
- " PG5_2BNUI PG5_2Order PG5_2Time \n",
- " :923 Min. : 1 :923 \n",
- " Essential : 3 1st Qu.: 5 2018-10-11 13:21:46: 2 \n",
- " High Priority : 26 Median : 8 2018-10-11 13:38:07: 2 \n",
- " Low Priority :121 Mean : 8 2018-10-11 13:14:27: 1 \n",
- " Medium Priority: 92 3rd Qu.:11 2018-10-11 13:14:40: 1 \n",
- " Not a Priority :188 Max. :21 2018-10-11 13:15:18: 1 \n",
- " NA's :923 (Other) :423 \n",
- " PG5_3HDS PG5_3Order PG5_3Time \n",
- " :768 Min. : 1 :768 \n",
- " Essential :103 1st Qu.: 2 2018-10-11 13:54:00: 2 \n",
- " High Priority :200 Median : 4 2018-10-11 14:21:45: 2 \n",
- " Low Priority : 69 Mean : 6 2018-10-11 14:25:51: 2 \n",
- " Medium Priority:162 3rd Qu.: 9 2018-10-11 17:21:39: 2 \n",
- " Not a Priority : 51 Max. :19 2018-10-11 13:14:18: 1 \n",
- " NA's :768 (Other) :576 \n",
- " PG5_4VGP PG5_4Order PG5_4Time \n",
- " :852 Min. : 1 :852 \n",
- " Essential : 22 1st Qu.: 4 2018-10-11 13:16:50: 2 \n",
- " High Priority :111 Median : 6 2018-10-11 13:29:51: 2 \n",
- " Low Priority : 88 Mean : 7 2018-10-11 13:37:39: 2 \n",
- " Medium Priority:164 3rd Qu.:10 2018-10-11 13:38:11: 2 \n",
- " Not a Priority :116 Max. :18 2018-10-11 15:42:22: 2 \n",
- " NA's :852 (Other) :491 \n",
- " PG5_5PHR PG5_5Order PG5_5Time \n",
- " :753 Min. : 1 :753 \n",
- " Essential : 79 1st Qu.: 2 2018-10-11 13:18:47: 2 \n",
- " High Priority :252 Median : 4 2018-10-11 13:18:48: 2 \n",
- " Low Priority : 63 Mean : 6 2018-10-11 13:32:40: 2 \n",
- " Medium Priority:162 3rd Qu.: 8 2018-10-11 13:38:12: 2 \n",
- " Not a Priority : 44 Max. :18 2018-10-11 13:45:48: 2 \n",
- " NA's :753 (Other) :590 \n",
- " PG5_6SSYOP PG5_6Order PG5_6Time \n",
- " :852 Min. : 1 :852 \n",
- " Essential : 63 1st Qu.: 3 2018-10-11 13:20:00: 2 \n",
- " High Priority :137 Median : 6 2018-10-11 13:40:53: 2 \n",
- " Low Priority : 84 Mean : 7 2018-10-11 13:44:00: 2 \n",
- " Medium Priority:110 3rd Qu.:10 2018-10-11 13:45:41: 2 \n",
- " Not a Priority :107 Max. :17 2018-10-11 16:22:38: 2 \n",
- " NA's :852 (Other) :491 \n",
- " PG5_7NDYP PG5_7Order PG5_7Time \n",
- " :934 Min. : 1 :934 \n",
- " Essential : 8 1st Qu.: 4 2018-10-11 13:18:50: 2 \n",
- " High Priority : 31 Median : 7 2018-10-11 14:23:19: 2 \n",
- " Low Priority : 93 Mean : 7 2018-10-11 13:14:22: 1 \n",
- " Medium Priority: 52 3rd Qu.:11 2018-10-11 13:14:50: 1 \n",
- " Not a Priority :235 Max. :17 2018-10-11 13:15:08: 1 \n",
- " NA's :934 (Other) :412 \n",
- " PG5_8CP PG5_8Order PG5_8Time \n",
- " :715 Min. : 1 :715 \n",
- " Essential :232 1st Qu.: 1 2018-10-11 13:29:46: 2 \n",
- " High Priority :197 Median : 4 2018-10-11 13:37:00: 2 \n",
- " Low Priority : 52 Mean : 5 2018-10-11 13:38:36: 2 \n",
- " Medium Priority:121 3rd Qu.: 8 2018-10-11 13:39:22: 2 \n",
- " Not a Priority : 36 Max. :20 2018-10-11 14:02:46: 2 \n",
- " NA's :715 (Other) :628 \n",
- " PG5_9FRP PG5_9Order PG5_9Time \n",
- " :738 Min. : 1 :738 \n",
- " Essential :165 1st Qu.: 2 2018-10-11 13:35:13: 2 \n",
- " High Priority :243 Median : 4 2018-10-11 13:37:34: 2 \n",
- " Low Priority : 42 Mean : 5 2018-10-11 14:02:44: 2 \n",
- " Medium Priority:125 3rd Qu.: 9 2018-10-11 13:14:17: 1 \n",
- " Not a Priority : 40 Max. :19 2018-10-11 13:14:52: 1 \n",
- " NA's :738 (Other) :607 \n",
- " PG5_10RPA PG5_10Order PG5_10Time \n",
- " :779 Min. : 1 :779 \n",
- " Essential : 55 1st Qu.: 2 2018-10-11 13:17:47: 2 \n",
- " High Priority :204 Median : 5 2018-10-11 13:27:48: 2 \n",
- " Low Priority : 79 Mean : 6 2018-10-11 13:45:33: 2 \n",
- " Medium Priority:151 3rd Qu.: 9 2018-10-11 15:30:40: 2 \n",
- " Not a Priority : 85 Max. :22 2018-10-11 15:48:40: 2 \n",
- " NA's :779 (Other) :564 \n",
- " PG5_11NSG PG5_11Order PG5_11Time \n",
- " :890 Min. : 1 :890 \n",
- " Essential : 6 1st Qu.: 4 2018-10-11 13:19:44: 2 \n",
- " High Priority : 29 Median : 6 2018-10-11 13:21:53: 2 \n",
- " Low Priority : 89 Mean : 7 2018-10-11 13:31:08: 2 \n",
- " Medium Priority: 68 3rd Qu.:10 2018-10-11 13:40:48: 2 \n",
- " Not a Priority :271 Max. :18 2018-10-11 14:55:47: 2 \n",
- " NA's :890 (Other) :453 \n",
- " PG5_12NWG PG5_12Order PG5_12Time \n",
- " :916 Min. : 1 :916 \n",
- " High Priority : 10 1st Qu.: 5 2018-10-11 13:30:08: 2 \n",
- " Low Priority : 77 Median : 7 2018-10-11 13:31:20: 2 \n",
- " Medium Priority: 25 Mean : 7 2018-10-11 14:55:40: 2 \n",
- " Not a Priority :325 3rd Qu.:11 2018-10-11 13:14:46: 1 \n",
- " Max. :18 2018-10-11 13:14:50: 1 \n",
- " NA's :916 (Other) :429 \n",
- " PG5_13NFG PG5_13Order PG5_13Time PG5Submit \n",
- " :920 Min. : 1 :920 Min. : 3 \n",
- " High Priority : 10 1st Qu.: 4 2018-10-11 13:17:39: 2 1st Qu.: 45 \n",
- " Low Priority : 76 Median : 7 2018-10-11 13:35:20: 2 Median : 62 \n",
- " Medium Priority: 37 Mean : 7 2018-10-11 13:35:56: 2 Mean : 87 \n",
- " Not a Priority :310 3rd Qu.:10 2018-10-11 15:42:36: 2 3rd Qu.: 84 \n",
- " Max. :17 2018-10-11 13:14:41: 1 Max. :4130 \n",
- " NA's :920 (Other) :424 NA's :544 \n",
- " PG6Resp PG6Submit PG7R PG7C.C.. PG7Java \n",
- " :549 Min. : 1 R :684 :1268 :1306 \n",
- " 13 - 19 years : 50 1st Qu.: 7 :614 C/C++: 84 Java : 46 \n",
- " 2 - 5 years :332 Median : 9 Perl : 6 Cobol: 1 scala: 1 \n",
- " 20 years or more : 45 Mean : 24 PHP : 5 \n",
- " 6 - 8 years :112 3rd Qu.: 12 SQL : 5 \n",
- " 9 - 12 years : 70 Max. :5759 Ruby : 4 \n",
- " Less than 2 years:195 NA's :543 (Other): 35 \n",
- " PG7Python PG7Javascript PG7Go PG7C. PG7Other \n",
- " :1129 :1304 :1351 :1330 :1283 \n",
- " Python: 223 Javascript: 48 Go: 2 C#: 23 Other : 62 \n",
- " perl : 1 sql : 1 PHP : 2 \n",
- " Matlab : 1 \n",
- " Ruby : 1 \n",
- " SAS : 1 \n",
- " (Other): 3 \n",
- " PG7Submit PG8Resp PG8Submit PG9Resp \n",
- " Min. : 1 :570 Min. : 1 :562 \n",
- " 1st Qu.: 6 Data Scientist :379 1st Qu.: 5 2 - 3 :229 \n",
- " Median : 8 Software Engineer: 55 Median : 8 4 - 6 :166 \n",
- " Mean : 11 Student : 24 Mean : 12 7 - 10 :122 \n",
- " 3rd Qu.: 11 Researcher : 15 3rd Qu.: 14 1 : 94 \n",
- " Max. :777 PhD student : 10 Max. :207 More than 25: 92 \n",
- " NA's :542 (Other) :300 NA's :546 (Other) : 88 \n",
- " PG9Submit PG10Resp \n",
- " Min. : 0 :565 \n",
- " 1st Qu.: 7 Native :424 \n",
- " Median : 10 Not native - full working proficiency :263 \n",
- " Mean : 40 Not native - limited working proficiency : 17 \n",
- " 3rd Qu.: 14 Not native - passable : 5 \n",
- " Max. :20126 Not native - sufficient working proficiency: 77 \n",
- " NA's :547 Not native - very limited : 2 \n",
- " PG10Submit PG11Resp PG11Submit PG12Resp \n",
- " Min. : 1 :576 Min. : 1 :590 \n",
- " 1st Qu.: 5 Female : 96 1st Qu.: 4 18 - 24 : 34 \n",
- " Median : 7 Male :652 Median : 4 25 - 34 :338 \n",
- " Mean : 17 Prefer not to answer: 29 Mean : 6 35 - 44 :258 \n",
- " 3rd Qu.: 11 3rd Qu.: 5 45 - 54 : 89 \n",
- " Max. :5966 Max. :605 55 - 64 : 36 \n",
- " NA's :546 NA's :548 65 and over: 8 \n",
- " PG12Submit \n",
- " Min. : 1 \n",
- " 1st Qu.: 4 \n",
- " Median : 5 \n",
- " Mean : 8 \n",
- " 3rd Qu.: 6 \n",
- " Max. :1566 \n",
- " NA's :548 "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#now explore variables\n",
- "summary(data);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Interpret basic summaries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "Start End PG0Dis PG0Shown PG0Submit PG1Submit PG2Submit PG3Submit PG4Dtr0_6 PG4Psv7_8 ... PG5_12Order PG5_13Order PG5Submit PG6Submit PG7Submit PG8Submit PG9Submit PG10Submit PG11Submit PG12Submit \n",
- "\n",
- "\tStart 1.0000 0.9952 -0.0417 -0.11507 0.1350 0.1156 0.0791 0.0384 0.01210 0.00371 ... -0.0369 0.0598 0.08512 0.0054 0.0776 0.0441 0.04101 0.047 7.9e-02 0.0746 \n",
- "\tEnd 0.9952 1.0000 -0.0415 -0.09879 0.1142 0.1550 0.0791 0.0511 -0.05185 -0.04576 ... -0.0359 0.0661 0.09088 0.0051 0.0759 0.0435 0.04071 0.052 7.9e-02 0.0772 \n",
- "\tPG0Dis -0.0417 -0.0415 1.0000 0.87220 0.0153 0.0065 0.0041 0.0567 0.16368 0.02668 ... 0.0151 0.0384 0.00601 0.0277 0.0097 0.0354 0.00995 -0.029 -4.5e-02 0.0546 \n",
- "\tPG0Shown -0.1151 -0.0988 0.8722 1.00000 0.0360 0.0205 0.0023 0.0497 0.08226 0.00036 ... 0.0074 0.0407 -0.00888 0.0401 0.0121 0.0264 0.00056 -0.045 -7.1e-02 0.0436 \n",
- "\tPG0Submit 0.1350 0.1142 0.0153 0.03596 1.0000 0.1088 0.1037 0.1273 -0.00802 -0.03763 ... -0.0161 -0.0280 0.17671 0.1518 0.1365 0.1258 0.17579 0.225 1.1e-01 0.1096 \n",
- "\tPG1Submit 0.1156 0.1550 0.0065 0.02047 0.1088 1.0000 0.1452 0.2688 -0.06852 0.05661 ... 0.0512 -0.0651 0.24670 0.2414 0.1133 0.1069 0.10895 0.170 7.4e-02 0.1137 \n",
- "\tPG2Submit 0.0791 0.0791 0.0041 0.00235 0.1037 0.1452 1.0000 0.2045 0.00146 0.00897 ... 0.0210 -0.0047 0.21851 0.2696 0.1245 0.1567 0.20127 0.099 1.1e-01 0.1073 \n",
- "\tPG3Submit 0.0384 0.0511 0.0567 0.04968 0.1273 0.2688 0.2045 1.0000 0.00865 0.04424 ... 0.0464 -0.0222 0.26048 0.2706 0.1316 0.1822 0.27450 0.161 1.4e-01 0.1642 \n",
- "\tPG4Dtr0_6 0.0121 -0.0518 0.1637 0.08226 -0.0080 -0.0685 0.0015 0.0087 1.00000 NA ... 0.1774 -0.1289 -0.05214 -0.1618 0.1560 0.0695 -0.07292 0.044 8.4e-04 -0.0272 \n",
- "\tPG4Psv7_8 0.0037 -0.0458 0.0267 0.00036 -0.0376 0.0566 0.0090 0.0442 NA 1.00000 ... -0.0008 -0.0218 0.08974 -0.0146 -0.0363 0.0526 0.05977 0.069 -4.9e-02 -0.0217 \n",
- "\tPG4Prm9_10 -0.0267 -0.0267 -0.0092 0.03279 -0.0939 0.0120 -0.0587 -0.0062 NA NA ... 0.0233 0.0916 0.00077 -0.0418 -0.0633 -0.0550 -0.02989 -0.061 -8.6e-05 0.0169 \n",
- "\tPG4AllResp 0.0063 -0.0158 0.0018 -0.02094 -0.0236 0.0297 0.0293 -0.0193 1.00000 1.00000 ... -0.0306 -0.0166 0.01248 -0.0040 -0.0753 -0.1294 -0.03812 -0.106 -8.2e-02 -0.0668 \n",
- "\tPG4Submit 0.0187 0.0172 -0.0539 -0.05978 0.2191 0.1651 0.1515 0.1956 -0.14272 -0.08350 ... -0.0119 -0.0376 0.27328 0.3326 0.2775 0.1821 0.33236 0.391 2.8e-01 0.2360 \n",
- "\tPG5_1Order 0.0218 0.0196 0.0140 0.01254 -0.0240 0.0750 -0.0069 0.0578 -0.09488 -0.01399 ... -0.0785 -0.0950 0.08054 0.0334 0.0173 -0.0388 0.01033 -0.096 -2.5e-02 0.0238 \n",
- "\tPG5_2Order 0.0014 0.0002 -0.0386 -0.03617 0.0402 -0.0226 -0.0297 0.0048 -0.00954 0.08081 ... 0.0527 -0.0652 0.03898 -0.0571 -0.0551 -0.0567 -0.01925 0.015 7.3e-03 0.0469 \n",
- "\tPG5_3Order -0.0089 -0.0177 0.0441 0.04228 0.0155 0.0391 -0.0131 0.0172 0.13631 0.04396 ... -0.0698 -0.0250 0.23450 0.0419 0.0058 -0.0086 0.02604 0.063 3.4e-02 0.0538 \n",
- "\tPG5_4Order 0.0931 0.0949 -0.0262 -0.02214 -0.0172 0.0293 0.0560 -0.0462 0.01735 -0.12489 ... -0.0447 -0.0329 0.14487 0.0414 0.0460 0.0182 -0.03734 -0.075 -8.6e-02 -0.0211 \n",
- "\tPG5_5Order -0.0523 -0.0466 -0.0087 -0.01058 0.0860 0.0345 0.0570 -0.0165 0.04533 -0.06369 ... -0.0499 -0.0981 0.28698 0.0498 0.0372 0.0427 -0.02976 0.076 2.4e-02 0.0085 \n",
- "\tPG5_6Order 0.0237 0.0217 -0.0480 -0.04902 0.0762 0.0327 0.1077 0.0370 -0.13255 0.01662 ... -0.0430 -0.0031 0.22759 0.0165 0.0222 0.0639 -0.02974 0.015 2.8e-02 0.0539 \n",
- "\tPG5_7Order -0.0200 -0.0236 0.0220 -0.00444 -0.1174 -0.0815 -0.0285 0.0242 -0.16196 -0.06938 ... 0.0410 0.0944 0.06439 -0.0222 -0.1082 -0.0804 -0.01762 -0.017 -4.4e-02 -0.0617 \n",
- "\tPG5_8Order -0.0804 -0.0852 -0.0147 -0.00433 -0.0421 -0.0205 -0.0387 -0.0129 -0.16181 0.01019 ... -0.0896 -0.1168 0.13787 -0.1227 -0.0126 -0.0526 -0.05699 -0.065 -3.7e-02 -0.0182 \n",
- "\tPG5_9Order 0.0171 0.0167 -0.0712 -0.10235 0.0476 0.0260 -0.0410 -0.0641 -0.07948 -0.09747 ... -0.0431 -0.0837 0.18534 -0.0188 -0.0314 -0.0898 -0.01354 -0.034 6.2e-03 -0.0280 \n",
- "\tPG5_10Order -0.0214 -0.0112 0.0200 0.02736 0.0086 -0.0017 -0.0418 0.0581 -0.05310 0.15949 ... -0.0905 -0.1214 0.22151 0.0427 0.0311 0.0345 0.02011 0.102 1.8e-02 -0.0092 \n",
- "\tPG5_11Order -0.0241 -0.0196 0.0190 0.02551 0.0927 -0.0047 -0.1043 0.0279 0.02791 -0.03487 ... 0.1233 0.1434 0.02942 -0.0055 0.0210 0.0232 0.05579 -0.038 -2.5e-03 0.0275 \n",
- "\tPG5_12Order -0.0369 -0.0359 0.0151 0.00743 -0.0161 0.0512 0.0210 0.0464 0.17741 -0.00080 ... 1.0000 0.1231 0.10997 0.0777 0.0529 0.0149 0.00295 0.016 2.4e-02 0.0167 \n",
- "\tPG5_13Order 0.0598 0.0661 0.0384 0.04072 -0.0280 -0.0651 -0.0047 -0.0222 -0.12890 -0.02179 ... 0.1231 1.0000 0.00976 -0.0209 0.0355 0.0556 0.07229 -0.023 1.3e-02 -0.0614 \n",
- "\tPG5Submit 0.0851 0.0909 0.0060 -0.00888 0.1767 0.2467 0.2185 0.2605 -0.05214 0.08974 ... 0.1100 0.0098 1.00000 0.3224 0.2312 0.2035 0.30291 0.269 2.4e-01 0.2588 \n",
- "\tPG6Submit 0.0054 0.0051 0.0277 0.04005 0.1518 0.2414 0.2696 0.2706 -0.16179 -0.01463 ... 0.0777 -0.0209 0.32240 1.0000 0.3086 0.2065 0.44528 0.343 2.8e-01 0.2904 \n",
- "\tPG7Submit 0.0776 0.0759 0.0097 0.01212 0.1365 0.1133 0.1245 0.1316 0.15596 -0.03631 ... 0.0529 0.0355 0.23120 0.3086 1.0000 0.1606 0.27819 0.312 2.8e-01 0.2523 \n",
- "\tPG8Submit 0.0441 0.0435 0.0354 0.02635 0.1258 0.1069 0.1567 0.1822 0.06953 0.05260 ... 0.0149 0.0556 0.20351 0.2065 0.1606 1.0000 0.25569 0.200 2.1e-01 0.1932 \n",
- "\tPG9Submit 0.0410 0.0407 0.0099 0.00056 0.1758 0.1090 0.2013 0.2745 -0.07292 0.05977 ... 0.0029 0.0723 0.30291 0.4453 0.2782 0.2557 1.00000 0.290 2.8e-01 0.2755 \n",
- "\tPG10Submit 0.0474 0.0517 -0.0293 -0.04481 0.2248 0.1701 0.0989 0.1614 0.04433 0.06942 ... 0.0159 -0.0227 0.26881 0.3428 0.3121 0.2000 0.29018 1.000 3.5e-01 0.3131 \n",
- "\tPG11Submit 0.0790 0.0792 -0.0454 -0.07102 0.1093 0.0738 0.1147 0.1383 0.00084 -0.04870 ... 0.0240 0.0130 0.23552 0.2777 0.2768 0.2065 0.27906 0.346 1.0e+00 0.2513 \n",
- "\tPG12Submit 0.0746 0.0772 0.0546 0.04364 0.1096 0.1137 0.1073 0.1642 -0.02721 -0.02169 ... 0.0167 -0.0614 0.25876 0.2904 0.2523 0.1932 0.27550 0.313 2.5e-01 1.0000 \n",
- " \n",
- "
\n"
- ],
- "text/latex": [
- "\\begin{tabular}{r|llllllllllllllllllllllllllllllllll}\n",
- " & Start & End & PG0Dis & PG0Shown & PG0Submit & PG1Submit & PG2Submit & PG3Submit & PG4Dtr0\\_6 & PG4Psv7\\_8 & ... & PG5\\_12Order & PG5\\_13Order & PG5Submit & PG6Submit & PG7Submit & PG8Submit & PG9Submit & PG10Submit & PG11Submit & PG12Submit\\\\\n",
- "\\hline\n",
- "\tStart & 1.0000 & 0.9952 & -0.0417 & -0.11507 & 0.1350 & 0.1156 & 0.0791 & 0.0384 & 0.01210 & 0.00371 & ... & -0.0369 & 0.0598 & 0.08512 & 0.0054 & 0.0776 & 0.0441 & 0.04101 & 0.047 & 7.9e-02 & 0.0746 \\\\\n",
- "\tEnd & 0.9952 & 1.0000 & -0.0415 & -0.09879 & 0.1142 & 0.1550 & 0.0791 & 0.0511 & -0.05185 & -0.04576 & ... & -0.0359 & 0.0661 & 0.09088 & 0.0051 & 0.0759 & 0.0435 & 0.04071 & 0.052 & 7.9e-02 & 0.0772 \\\\\n",
- "\tPG0Dis & -0.0417 & -0.0415 & 1.0000 & 0.87220 & 0.0153 & 0.0065 & 0.0041 & 0.0567 & 0.16368 & 0.02668 & ... & 0.0151 & 0.0384 & 0.00601 & 0.0277 & 0.0097 & 0.0354 & 0.00995 & -0.029 & -4.5e-02 & 0.0546 \\\\\n",
- "\tPG0Shown & -0.1151 & -0.0988 & 0.8722 & 1.00000 & 0.0360 & 0.0205 & 0.0023 & 0.0497 & 0.08226 & 0.00036 & ... & 0.0074 & 0.0407 & -0.00888 & 0.0401 & 0.0121 & 0.0264 & 0.00056 & -0.045 & -7.1e-02 & 0.0436 \\\\\n",
- "\tPG0Submit & 0.1350 & 0.1142 & 0.0153 & 0.03596 & 1.0000 & 0.1088 & 0.1037 & 0.1273 & -0.00802 & -0.03763 & ... & -0.0161 & -0.0280 & 0.17671 & 0.1518 & 0.1365 & 0.1258 & 0.17579 & 0.225 & 1.1e-01 & 0.1096 \\\\\n",
- "\tPG1Submit & 0.1156 & 0.1550 & 0.0065 & 0.02047 & 0.1088 & 1.0000 & 0.1452 & 0.2688 & -0.06852 & 0.05661 & ... & 0.0512 & -0.0651 & 0.24670 & 0.2414 & 0.1133 & 0.1069 & 0.10895 & 0.170 & 7.4e-02 & 0.1137 \\\\\n",
- "\tPG2Submit & 0.0791 & 0.0791 & 0.0041 & 0.00235 & 0.1037 & 0.1452 & 1.0000 & 0.2045 & 0.00146 & 0.00897 & ... & 0.0210 & -0.0047 & 0.21851 & 0.2696 & 0.1245 & 0.1567 & 0.20127 & 0.099 & 1.1e-01 & 0.1073 \\\\\n",
- "\tPG3Submit & 0.0384 & 0.0511 & 0.0567 & 0.04968 & 0.1273 & 0.2688 & 0.2045 & 1.0000 & 0.00865 & 0.04424 & ... & 0.0464 & -0.0222 & 0.26048 & 0.2706 & 0.1316 & 0.1822 & 0.27450 & 0.161 & 1.4e-01 & 0.1642 \\\\\n",
- "\tPG4Dtr0\\_6 & 0.0121 & -0.0518 & 0.1637 & 0.08226 & -0.0080 & -0.0685 & 0.0015 & 0.0087 & 1.00000 & NA & ... & 0.1774 & -0.1289 & -0.05214 & -0.1618 & 0.1560 & 0.0695 & -0.07292 & 0.044 & 8.4e-04 & -0.0272 \\\\\n",
- "\tPG4Psv7\\_8 & 0.0037 & -0.0458 & 0.0267 & 0.00036 & -0.0376 & 0.0566 & 0.0090 & 0.0442 & NA & 1.00000 & ... & -0.0008 & -0.0218 & 0.08974 & -0.0146 & -0.0363 & 0.0526 & 0.05977 & 0.069 & -4.9e-02 & -0.0217 \\\\\n",
- "\tPG4Prm9\\_10 & -0.0267 & -0.0267 & -0.0092 & 0.03279 & -0.0939 & 0.0120 & -0.0587 & -0.0062 & NA & NA & ... & 0.0233 & 0.0916 & 0.00077 & -0.0418 & -0.0633 & -0.0550 & -0.02989 & -0.061 & -8.6e-05 & 0.0169 \\\\\n",
- "\tPG4AllResp & 0.0063 & -0.0158 & 0.0018 & -0.02094 & -0.0236 & 0.0297 & 0.0293 & -0.0193 & 1.00000 & 1.00000 & ... & -0.0306 & -0.0166 & 0.01248 & -0.0040 & -0.0753 & -0.1294 & -0.03812 & -0.106 & -8.2e-02 & -0.0668 \\\\\n",
- "\tPG4Submit & 0.0187 & 0.0172 & -0.0539 & -0.05978 & 0.2191 & 0.1651 & 0.1515 & 0.1956 & -0.14272 & -0.08350 & ... & -0.0119 & -0.0376 & 0.27328 & 0.3326 & 0.2775 & 0.1821 & 0.33236 & 0.391 & 2.8e-01 & 0.2360 \\\\\n",
- "\tPG5\\_1Order & 0.0218 & 0.0196 & 0.0140 & 0.01254 & -0.0240 & 0.0750 & -0.0069 & 0.0578 & -0.09488 & -0.01399 & ... & -0.0785 & -0.0950 & 0.08054 & 0.0334 & 0.0173 & -0.0388 & 0.01033 & -0.096 & -2.5e-02 & 0.0238 \\\\\n",
- "\tPG5\\_2Order & 0.0014 & 0.0002 & -0.0386 & -0.03617 & 0.0402 & -0.0226 & -0.0297 & 0.0048 & -0.00954 & 0.08081 & ... & 0.0527 & -0.0652 & 0.03898 & -0.0571 & -0.0551 & -0.0567 & -0.01925 & 0.015 & 7.3e-03 & 0.0469 \\\\\n",
- "\tPG5\\_3Order & -0.0089 & -0.0177 & 0.0441 & 0.04228 & 0.0155 & 0.0391 & -0.0131 & 0.0172 & 0.13631 & 0.04396 & ... & -0.0698 & -0.0250 & 0.23450 & 0.0419 & 0.0058 & -0.0086 & 0.02604 & 0.063 & 3.4e-02 & 0.0538 \\\\\n",
- "\tPG5\\_4Order & 0.0931 & 0.0949 & -0.0262 & -0.02214 & -0.0172 & 0.0293 & 0.0560 & -0.0462 & 0.01735 & -0.12489 & ... & -0.0447 & -0.0329 & 0.14487 & 0.0414 & 0.0460 & 0.0182 & -0.03734 & -0.075 & -8.6e-02 & -0.0211 \\\\\n",
- "\tPG5\\_5Order & -0.0523 & -0.0466 & -0.0087 & -0.01058 & 0.0860 & 0.0345 & 0.0570 & -0.0165 & 0.04533 & -0.06369 & ... & -0.0499 & -0.0981 & 0.28698 & 0.0498 & 0.0372 & 0.0427 & -0.02976 & 0.076 & 2.4e-02 & 0.0085 \\\\\n",
- "\tPG5\\_6Order & 0.0237 & 0.0217 & -0.0480 & -0.04902 & 0.0762 & 0.0327 & 0.1077 & 0.0370 & -0.13255 & 0.01662 & ... & -0.0430 & -0.0031 & 0.22759 & 0.0165 & 0.0222 & 0.0639 & -0.02974 & 0.015 & 2.8e-02 & 0.0539 \\\\\n",
- "\tPG5\\_7Order & -0.0200 & -0.0236 & 0.0220 & -0.00444 & -0.1174 & -0.0815 & -0.0285 & 0.0242 & -0.16196 & -0.06938 & ... & 0.0410 & 0.0944 & 0.06439 & -0.0222 & -0.1082 & -0.0804 & -0.01762 & -0.017 & -4.4e-02 & -0.0617 \\\\\n",
- "\tPG5\\_8Order & -0.0804 & -0.0852 & -0.0147 & -0.00433 & -0.0421 & -0.0205 & -0.0387 & -0.0129 & -0.16181 & 0.01019 & ... & -0.0896 & -0.1168 & 0.13787 & -0.1227 & -0.0126 & -0.0526 & -0.05699 & -0.065 & -3.7e-02 & -0.0182 \\\\\n",
- "\tPG5\\_9Order & 0.0171 & 0.0167 & -0.0712 & -0.10235 & 0.0476 & 0.0260 & -0.0410 & -0.0641 & -0.07948 & -0.09747 & ... & -0.0431 & -0.0837 & 0.18534 & -0.0188 & -0.0314 & -0.0898 & -0.01354 & -0.034 & 6.2e-03 & -0.0280 \\\\\n",
- "\tPG5\\_10Order & -0.0214 & -0.0112 & 0.0200 & 0.02736 & 0.0086 & -0.0017 & -0.0418 & 0.0581 & -0.05310 & 0.15949 & ... & -0.0905 & -0.1214 & 0.22151 & 0.0427 & 0.0311 & 0.0345 & 0.02011 & 0.102 & 1.8e-02 & -0.0092 \\\\\n",
- "\tPG5\\_11Order & -0.0241 & -0.0196 & 0.0190 & 0.02551 & 0.0927 & -0.0047 & -0.1043 & 0.0279 & 0.02791 & -0.03487 & ... & 0.1233 & 0.1434 & 0.02942 & -0.0055 & 0.0210 & 0.0232 & 0.05579 & -0.038 & -2.5e-03 & 0.0275 \\\\\n",
- "\tPG5\\_12Order & -0.0369 & -0.0359 & 0.0151 & 0.00743 & -0.0161 & 0.0512 & 0.0210 & 0.0464 & 0.17741 & -0.00080 & ... & 1.0000 & 0.1231 & 0.10997 & 0.0777 & 0.0529 & 0.0149 & 0.00295 & 0.016 & 2.4e-02 & 0.0167 \\\\\n",
- "\tPG5\\_13Order & 0.0598 & 0.0661 & 0.0384 & 0.04072 & -0.0280 & -0.0651 & -0.0047 & -0.0222 & -0.12890 & -0.02179 & ... & 0.1231 & 1.0000 & 0.00976 & -0.0209 & 0.0355 & 0.0556 & 0.07229 & -0.023 & 1.3e-02 & -0.0614 \\\\\n",
- "\tPG5Submit & 0.0851 & 0.0909 & 0.0060 & -0.00888 & 0.1767 & 0.2467 & 0.2185 & 0.2605 & -0.05214 & 0.08974 & ... & 0.1100 & 0.0098 & 1.00000 & 0.3224 & 0.2312 & 0.2035 & 0.30291 & 0.269 & 2.4e-01 & 0.2588 \\\\\n",
- "\tPG6Submit & 0.0054 & 0.0051 & 0.0277 & 0.04005 & 0.1518 & 0.2414 & 0.2696 & 0.2706 & -0.16179 & -0.01463 & ... & 0.0777 & -0.0209 & 0.32240 & 1.0000 & 0.3086 & 0.2065 & 0.44528 & 0.343 & 2.8e-01 & 0.2904 \\\\\n",
- "\tPG7Submit & 0.0776 & 0.0759 & 0.0097 & 0.01212 & 0.1365 & 0.1133 & 0.1245 & 0.1316 & 0.15596 & -0.03631 & ... & 0.0529 & 0.0355 & 0.23120 & 0.3086 & 1.0000 & 0.1606 & 0.27819 & 0.312 & 2.8e-01 & 0.2523 \\\\\n",
- "\tPG8Submit & 0.0441 & 0.0435 & 0.0354 & 0.02635 & 0.1258 & 0.1069 & 0.1567 & 0.1822 & 0.06953 & 0.05260 & ... & 0.0149 & 0.0556 & 0.20351 & 0.2065 & 0.1606 & 1.0000 & 0.25569 & 0.200 & 2.1e-01 & 0.1932 \\\\\n",
- "\tPG9Submit & 0.0410 & 0.0407 & 0.0099 & 0.00056 & 0.1758 & 0.1090 & 0.2013 & 0.2745 & -0.07292 & 0.05977 & ... & 0.0029 & 0.0723 & 0.30291 & 0.4453 & 0.2782 & 0.2557 & 1.00000 & 0.290 & 2.8e-01 & 0.2755 \\\\\n",
- "\tPG10Submit & 0.0474 & 0.0517 & -0.0293 & -0.04481 & 0.2248 & 0.1701 & 0.0989 & 0.1614 & 0.04433 & 0.06942 & ... & 0.0159 & -0.0227 & 0.26881 & 0.3428 & 0.3121 & 0.2000 & 0.29018 & 1.000 & 3.5e-01 & 0.3131 \\\\\n",
- "\tPG11Submit & 0.0790 & 0.0792 & -0.0454 & -0.07102 & 0.1093 & 0.0738 & 0.1147 & 0.1383 & 0.00084 & -0.04870 & ... & 0.0240 & 0.0130 & 0.23552 & 0.2777 & 0.2768 & 0.2065 & 0.27906 & 0.346 & 1.0e+00 & 0.2513 \\\\\n",
- "\tPG12Submit & 0.0746 & 0.0772 & 0.0546 & 0.04364 & 0.1096 & 0.1137 & 0.1073 & 0.1642 & -0.02721 & -0.02169 & ... & 0.0167 & -0.0614 & 0.25876 & 0.2904 & 0.2523 & 0.1932 & 0.27550 & 0.313 & 2.5e-01 & 1.0000 \\\\\n",
- "\\end{tabular}\n"
- ],
- "text/markdown": [
- "\n",
- "| | Start | End | PG0Dis | PG0Shown | PG0Submit | PG1Submit | PG2Submit | PG3Submit | PG4Dtr0_6 | PG4Psv7_8 | ... | PG5_12Order | PG5_13Order | PG5Submit | PG6Submit | PG7Submit | PG8Submit | PG9Submit | PG10Submit | PG11Submit | PG12Submit | \n",
- "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
- "| Start | 1.0000 | 0.9952 | -0.0417 | -0.11507 | 0.1350 | 0.1156 | 0.0791 | 0.0384 | 0.01210 | 0.00371 | ... | -0.0369 | 0.0598 | 0.08512 | 0.0054 | 0.0776 | 0.0441 | 0.04101 | 0.047 | 7.9e-02 | 0.0746 | \n",
- "| End | 0.9952 | 1.0000 | -0.0415 | -0.09879 | 0.1142 | 0.1550 | 0.0791 | 0.0511 | -0.05185 | -0.04576 | ... | -0.0359 | 0.0661 | 0.09088 | 0.0051 | 0.0759 | 0.0435 | 0.04071 | 0.052 | 7.9e-02 | 0.0772 | \n",
- "| PG0Dis | -0.0417 | -0.0415 | 1.0000 | 0.87220 | 0.0153 | 0.0065 | 0.0041 | 0.0567 | 0.16368 | 0.02668 | ... | 0.0151 | 0.0384 | 0.00601 | 0.0277 | 0.0097 | 0.0354 | 0.00995 | -0.029 | -4.5e-02 | 0.0546 | \n",
- "| PG0Shown | -0.1151 | -0.0988 | 0.8722 | 1.00000 | 0.0360 | 0.0205 | 0.0023 | 0.0497 | 0.08226 | 0.00036 | ... | 0.0074 | 0.0407 | -0.00888 | 0.0401 | 0.0121 | 0.0264 | 0.00056 | -0.045 | -7.1e-02 | 0.0436 | \n",
- "| PG0Submit | 0.1350 | 0.1142 | 0.0153 | 0.03596 | 1.0000 | 0.1088 | 0.1037 | 0.1273 | -0.00802 | -0.03763 | ... | -0.0161 | -0.0280 | 0.17671 | 0.1518 | 0.1365 | 0.1258 | 0.17579 | 0.225 | 1.1e-01 | 0.1096 | \n",
- "| PG1Submit | 0.1156 | 0.1550 | 0.0065 | 0.02047 | 0.1088 | 1.0000 | 0.1452 | 0.2688 | -0.06852 | 0.05661 | ... | 0.0512 | -0.0651 | 0.24670 | 0.2414 | 0.1133 | 0.1069 | 0.10895 | 0.170 | 7.4e-02 | 0.1137 | \n",
- "| PG2Submit | 0.0791 | 0.0791 | 0.0041 | 0.00235 | 0.1037 | 0.1452 | 1.0000 | 0.2045 | 0.00146 | 0.00897 | ... | 0.0210 | -0.0047 | 0.21851 | 0.2696 | 0.1245 | 0.1567 | 0.20127 | 0.099 | 1.1e-01 | 0.1073 | \n",
- "| PG3Submit | 0.0384 | 0.0511 | 0.0567 | 0.04968 | 0.1273 | 0.2688 | 0.2045 | 1.0000 | 0.00865 | 0.04424 | ... | 0.0464 | -0.0222 | 0.26048 | 0.2706 | 0.1316 | 0.1822 | 0.27450 | 0.161 | 1.4e-01 | 0.1642 | \n",
- "| PG4Dtr0_6 | 0.0121 | -0.0518 | 0.1637 | 0.08226 | -0.0080 | -0.0685 | 0.0015 | 0.0087 | 1.00000 | NA | ... | 0.1774 | -0.1289 | -0.05214 | -0.1618 | 0.1560 | 0.0695 | -0.07292 | 0.044 | 8.4e-04 | -0.0272 | \n",
- "| PG4Psv7_8 | 0.0037 | -0.0458 | 0.0267 | 0.00036 | -0.0376 | 0.0566 | 0.0090 | 0.0442 | NA | 1.00000 | ... | -0.0008 | -0.0218 | 0.08974 | -0.0146 | -0.0363 | 0.0526 | 0.05977 | 0.069 | -4.9e-02 | -0.0217 | \n",
- "| PG4Prm9_10 | -0.0267 | -0.0267 | -0.0092 | 0.03279 | -0.0939 | 0.0120 | -0.0587 | -0.0062 | NA | NA | ... | 0.0233 | 0.0916 | 0.00077 | -0.0418 | -0.0633 | -0.0550 | -0.02989 | -0.061 | -8.6e-05 | 0.0169 | \n",
- "| PG4AllResp | 0.0063 | -0.0158 | 0.0018 | -0.02094 | -0.0236 | 0.0297 | 0.0293 | -0.0193 | 1.00000 | 1.00000 | ... | -0.0306 | -0.0166 | 0.01248 | -0.0040 | -0.0753 | -0.1294 | -0.03812 | -0.106 | -8.2e-02 | -0.0668 | \n",
- "| PG4Submit | 0.0187 | 0.0172 | -0.0539 | -0.05978 | 0.2191 | 0.1651 | 0.1515 | 0.1956 | -0.14272 | -0.08350 | ... | -0.0119 | -0.0376 | 0.27328 | 0.3326 | 0.2775 | 0.1821 | 0.33236 | 0.391 | 2.8e-01 | 0.2360 | \n",
- "| PG5_1Order | 0.0218 | 0.0196 | 0.0140 | 0.01254 | -0.0240 | 0.0750 | -0.0069 | 0.0578 | -0.09488 | -0.01399 | ... | -0.0785 | -0.0950 | 0.08054 | 0.0334 | 0.0173 | -0.0388 | 0.01033 | -0.096 | -2.5e-02 | 0.0238 | \n",
- "| PG5_2Order | 0.0014 | 0.0002 | -0.0386 | -0.03617 | 0.0402 | -0.0226 | -0.0297 | 0.0048 | -0.00954 | 0.08081 | ... | 0.0527 | -0.0652 | 0.03898 | -0.0571 | -0.0551 | -0.0567 | -0.01925 | 0.015 | 7.3e-03 | 0.0469 | \n",
- "| PG5_3Order | -0.0089 | -0.0177 | 0.0441 | 0.04228 | 0.0155 | 0.0391 | -0.0131 | 0.0172 | 0.13631 | 0.04396 | ... | -0.0698 | -0.0250 | 0.23450 | 0.0419 | 0.0058 | -0.0086 | 0.02604 | 0.063 | 3.4e-02 | 0.0538 | \n",
- "| PG5_4Order | 0.0931 | 0.0949 | -0.0262 | -0.02214 | -0.0172 | 0.0293 | 0.0560 | -0.0462 | 0.01735 | -0.12489 | ... | -0.0447 | -0.0329 | 0.14487 | 0.0414 | 0.0460 | 0.0182 | -0.03734 | -0.075 | -8.6e-02 | -0.0211 | \n",
- "| PG5_5Order | -0.0523 | -0.0466 | -0.0087 | -0.01058 | 0.0860 | 0.0345 | 0.0570 | -0.0165 | 0.04533 | -0.06369 | ... | -0.0499 | -0.0981 | 0.28698 | 0.0498 | 0.0372 | 0.0427 | -0.02976 | 0.076 | 2.4e-02 | 0.0085 | \n",
- "| PG5_6Order | 0.0237 | 0.0217 | -0.0480 | -0.04902 | 0.0762 | 0.0327 | 0.1077 | 0.0370 | -0.13255 | 0.01662 | ... | -0.0430 | -0.0031 | 0.22759 | 0.0165 | 0.0222 | 0.0639 | -0.02974 | 0.015 | 2.8e-02 | 0.0539 | \n",
- "| PG5_7Order | -0.0200 | -0.0236 | 0.0220 | -0.00444 | -0.1174 | -0.0815 | -0.0285 | 0.0242 | -0.16196 | -0.06938 | ... | 0.0410 | 0.0944 | 0.06439 | -0.0222 | -0.1082 | -0.0804 | -0.01762 | -0.017 | -4.4e-02 | -0.0617 | \n",
- "| PG5_8Order | -0.0804 | -0.0852 | -0.0147 | -0.00433 | -0.0421 | -0.0205 | -0.0387 | -0.0129 | -0.16181 | 0.01019 | ... | -0.0896 | -0.1168 | 0.13787 | -0.1227 | -0.0126 | -0.0526 | -0.05699 | -0.065 | -3.7e-02 | -0.0182 | \n",
- "| PG5_9Order | 0.0171 | 0.0167 | -0.0712 | -0.10235 | 0.0476 | 0.0260 | -0.0410 | -0.0641 | -0.07948 | -0.09747 | ... | -0.0431 | -0.0837 | 0.18534 | -0.0188 | -0.0314 | -0.0898 | -0.01354 | -0.034 | 6.2e-03 | -0.0280 | \n",
- "| PG5_10Order | -0.0214 | -0.0112 | 0.0200 | 0.02736 | 0.0086 | -0.0017 | -0.0418 | 0.0581 | -0.05310 | 0.15949 | ... | -0.0905 | -0.1214 | 0.22151 | 0.0427 | 0.0311 | 0.0345 | 0.02011 | 0.102 | 1.8e-02 | -0.0092 | \n",
- "| PG5_11Order | -0.0241 | -0.0196 | 0.0190 | 0.02551 | 0.0927 | -0.0047 | -0.1043 | 0.0279 | 0.02791 | -0.03487 | ... | 0.1233 | 0.1434 | 0.02942 | -0.0055 | 0.0210 | 0.0232 | 0.05579 | -0.038 | -2.5e-03 | 0.0275 | \n",
- "| PG5_12Order | -0.0369 | -0.0359 | 0.0151 | 0.00743 | -0.0161 | 0.0512 | 0.0210 | 0.0464 | 0.17741 | -0.00080 | ... | 1.0000 | 0.1231 | 0.10997 | 0.0777 | 0.0529 | 0.0149 | 0.00295 | 0.016 | 2.4e-02 | 0.0167 | \n",
- "| PG5_13Order | 0.0598 | 0.0661 | 0.0384 | 0.04072 | -0.0280 | -0.0651 | -0.0047 | -0.0222 | -0.12890 | -0.02179 | ... | 0.1231 | 1.0000 | 0.00976 | -0.0209 | 0.0355 | 0.0556 | 0.07229 | -0.023 | 1.3e-02 | -0.0614 | \n",
- "| PG5Submit | 0.0851 | 0.0909 | 0.0060 | -0.00888 | 0.1767 | 0.2467 | 0.2185 | 0.2605 | -0.05214 | 0.08974 | ... | 0.1100 | 0.0098 | 1.00000 | 0.3224 | 0.2312 | 0.2035 | 0.30291 | 0.269 | 2.4e-01 | 0.2588 | \n",
- "| PG6Submit | 0.0054 | 0.0051 | 0.0277 | 0.04005 | 0.1518 | 0.2414 | 0.2696 | 0.2706 | -0.16179 | -0.01463 | ... | 0.0777 | -0.0209 | 0.32240 | 1.0000 | 0.3086 | 0.2065 | 0.44528 | 0.343 | 2.8e-01 | 0.2904 | \n",
- "| PG7Submit | 0.0776 | 0.0759 | 0.0097 | 0.01212 | 0.1365 | 0.1133 | 0.1245 | 0.1316 | 0.15596 | -0.03631 | ... | 0.0529 | 0.0355 | 0.23120 | 0.3086 | 1.0000 | 0.1606 | 0.27819 | 0.312 | 2.8e-01 | 0.2523 | \n",
- "| PG8Submit | 0.0441 | 0.0435 | 0.0354 | 0.02635 | 0.1258 | 0.1069 | 0.1567 | 0.1822 | 0.06953 | 0.05260 | ... | 0.0149 | 0.0556 | 0.20351 | 0.2065 | 0.1606 | 1.0000 | 0.25569 | 0.200 | 2.1e-01 | 0.1932 | \n",
- "| PG9Submit | 0.0410 | 0.0407 | 0.0099 | 0.00056 | 0.1758 | 0.1090 | 0.2013 | 0.2745 | -0.07292 | 0.05977 | ... | 0.0029 | 0.0723 | 0.30291 | 0.4453 | 0.2782 | 0.2557 | 1.00000 | 0.290 | 2.8e-01 | 0.2755 | \n",
- "| PG10Submit | 0.0474 | 0.0517 | -0.0293 | -0.04481 | 0.2248 | 0.1701 | 0.0989 | 0.1614 | 0.04433 | 0.06942 | ... | 0.0159 | -0.0227 | 0.26881 | 0.3428 | 0.3121 | 0.2000 | 0.29018 | 1.000 | 3.5e-01 | 0.3131 | \n",
- "| PG11Submit | 0.0790 | 0.0792 | -0.0454 | -0.07102 | 0.1093 | 0.0738 | 0.1147 | 0.1383 | 0.00084 | -0.04870 | ... | 0.0240 | 0.0130 | 0.23552 | 0.2777 | 0.2768 | 0.2065 | 0.27906 | 0.346 | 1.0e+00 | 0.2513 | \n",
- "| PG12Submit | 0.0746 | 0.0772 | 0.0546 | 0.04364 | 0.1096 | 0.1137 | 0.1073 | 0.1642 | -0.02721 | -0.02169 | ... | 0.0167 | -0.0614 | 0.25876 | 0.2904 | 0.2523 | 0.1932 | 0.27550 | 0.313 | 2.5e-01 | 1.0000 | \n",
- "\n",
- "\n"
- ],
- "text/plain": [
- " Start End PG0Dis PG0Shown PG0Submit PG1Submit PG2Submit\n",
- "Start 1.0000 0.9952 -0.0417 -0.11507 0.1350 0.1156 0.0791 \n",
- "End 0.9952 1.0000 -0.0415 -0.09879 0.1142 0.1550 0.0791 \n",
- "PG0Dis -0.0417 -0.0415 1.0000 0.87220 0.0153 0.0065 0.0041 \n",
- "PG0Shown -0.1151 -0.0988 0.8722 1.00000 0.0360 0.0205 0.0023 \n",
- "PG0Submit 0.1350 0.1142 0.0153 0.03596 1.0000 0.1088 0.1037 \n",
- "PG1Submit 0.1156 0.1550 0.0065 0.02047 0.1088 1.0000 0.1452 \n",
- "PG2Submit 0.0791 0.0791 0.0041 0.00235 0.1037 0.1452 1.0000 \n",
- "PG3Submit 0.0384 0.0511 0.0567 0.04968 0.1273 0.2688 0.2045 \n",
- "PG4Dtr0_6 0.0121 -0.0518 0.1637 0.08226 -0.0080 -0.0685 0.0015 \n",
- "PG4Psv7_8 0.0037 -0.0458 0.0267 0.00036 -0.0376 0.0566 0.0090 \n",
- "PG4Prm9_10 -0.0267 -0.0267 -0.0092 0.03279 -0.0939 0.0120 -0.0587 \n",
- "PG4AllResp 0.0063 -0.0158 0.0018 -0.02094 -0.0236 0.0297 0.0293 \n",
- "PG4Submit 0.0187 0.0172 -0.0539 -0.05978 0.2191 0.1651 0.1515 \n",
- "PG5_1Order 0.0218 0.0196 0.0140 0.01254 -0.0240 0.0750 -0.0069 \n",
- "PG5_2Order 0.0014 0.0002 -0.0386 -0.03617 0.0402 -0.0226 -0.0297 \n",
- "PG5_3Order -0.0089 -0.0177 0.0441 0.04228 0.0155 0.0391 -0.0131 \n",
- "PG5_4Order 0.0931 0.0949 -0.0262 -0.02214 -0.0172 0.0293 0.0560 \n",
- "PG5_5Order -0.0523 -0.0466 -0.0087 -0.01058 0.0860 0.0345 0.0570 \n",
- "PG5_6Order 0.0237 0.0217 -0.0480 -0.04902 0.0762 0.0327 0.1077 \n",
- "PG5_7Order -0.0200 -0.0236 0.0220 -0.00444 -0.1174 -0.0815 -0.0285 \n",
- "PG5_8Order -0.0804 -0.0852 -0.0147 -0.00433 -0.0421 -0.0205 -0.0387 \n",
- "PG5_9Order 0.0171 0.0167 -0.0712 -0.10235 0.0476 0.0260 -0.0410 \n",
- "PG5_10Order -0.0214 -0.0112 0.0200 0.02736 0.0086 -0.0017 -0.0418 \n",
- "PG5_11Order -0.0241 -0.0196 0.0190 0.02551 0.0927 -0.0047 -0.1043 \n",
- "PG5_12Order -0.0369 -0.0359 0.0151 0.00743 -0.0161 0.0512 0.0210 \n",
- "PG5_13Order 0.0598 0.0661 0.0384 0.04072 -0.0280 -0.0651 -0.0047 \n",
- "PG5Submit 0.0851 0.0909 0.0060 -0.00888 0.1767 0.2467 0.2185 \n",
- "PG6Submit 0.0054 0.0051 0.0277 0.04005 0.1518 0.2414 0.2696 \n",
- "PG7Submit 0.0776 0.0759 0.0097 0.01212 0.1365 0.1133 0.1245 \n",
- "PG8Submit 0.0441 0.0435 0.0354 0.02635 0.1258 0.1069 0.1567 \n",
- "PG9Submit 0.0410 0.0407 0.0099 0.00056 0.1758 0.1090 0.2013 \n",
- "PG10Submit 0.0474 0.0517 -0.0293 -0.04481 0.2248 0.1701 0.0989 \n",
- "PG11Submit 0.0790 0.0792 -0.0454 -0.07102 0.1093 0.0738 0.1147 \n",
- "PG12Submit 0.0746 0.0772 0.0546 0.04364 0.1096 0.1137 0.1073 \n",
- " PG3Submit PG4Dtr0_6 PG4Psv7_8 ... PG5_12Order PG5_13Order PG5Submit\n",
- "Start 0.0384 0.01210 0.00371 ... -0.0369 0.0598 0.08512 \n",
- "End 0.0511 -0.05185 -0.04576 ... -0.0359 0.0661 0.09088 \n",
- "PG0Dis 0.0567 0.16368 0.02668 ... 0.0151 0.0384 0.00601 \n",
- "PG0Shown 0.0497 0.08226 0.00036 ... 0.0074 0.0407 -0.00888 \n",
- "PG0Submit 0.1273 -0.00802 -0.03763 ... -0.0161 -0.0280 0.17671 \n",
- "PG1Submit 0.2688 -0.06852 0.05661 ... 0.0512 -0.0651 0.24670 \n",
- "PG2Submit 0.2045 0.00146 0.00897 ... 0.0210 -0.0047 0.21851 \n",
- "PG3Submit 1.0000 0.00865 0.04424 ... 0.0464 -0.0222 0.26048 \n",
- "PG4Dtr0_6 0.0087 1.00000 NA ... 0.1774 -0.1289 -0.05214 \n",
- "PG4Psv7_8 0.0442 NA 1.00000 ... -0.0008 -0.0218 0.08974 \n",
- "PG4Prm9_10 -0.0062 NA NA ... 0.0233 0.0916 0.00077 \n",
- "PG4AllResp -0.0193 1.00000 1.00000 ... -0.0306 -0.0166 0.01248 \n",
- "PG4Submit 0.1956 -0.14272 -0.08350 ... -0.0119 -0.0376 0.27328 \n",
- "PG5_1Order 0.0578 -0.09488 -0.01399 ... -0.0785 -0.0950 0.08054 \n",
- "PG5_2Order 0.0048 -0.00954 0.08081 ... 0.0527 -0.0652 0.03898 \n",
- "PG5_3Order 0.0172 0.13631 0.04396 ... -0.0698 -0.0250 0.23450 \n",
- "PG5_4Order -0.0462 0.01735 -0.12489 ... -0.0447 -0.0329 0.14487 \n",
- "PG5_5Order -0.0165 0.04533 -0.06369 ... -0.0499 -0.0981 0.28698 \n",
- "PG5_6Order 0.0370 -0.13255 0.01662 ... -0.0430 -0.0031 0.22759 \n",
- "PG5_7Order 0.0242 -0.16196 -0.06938 ... 0.0410 0.0944 0.06439 \n",
- "PG5_8Order -0.0129 -0.16181 0.01019 ... -0.0896 -0.1168 0.13787 \n",
- "PG5_9Order -0.0641 -0.07948 -0.09747 ... -0.0431 -0.0837 0.18534 \n",
- "PG5_10Order 0.0581 -0.05310 0.15949 ... -0.0905 -0.1214 0.22151 \n",
- "PG5_11Order 0.0279 0.02791 -0.03487 ... 0.1233 0.1434 0.02942 \n",
- "PG5_12Order 0.0464 0.17741 -0.00080 ... 1.0000 0.1231 0.10997 \n",
- "PG5_13Order -0.0222 -0.12890 -0.02179 ... 0.1231 1.0000 0.00976 \n",
- "PG5Submit 0.2605 -0.05214 0.08974 ... 0.1100 0.0098 1.00000 \n",
- "PG6Submit 0.2706 -0.16179 -0.01463 ... 0.0777 -0.0209 0.32240 \n",
- "PG7Submit 0.1316 0.15596 -0.03631 ... 0.0529 0.0355 0.23120 \n",
- "PG8Submit 0.1822 0.06953 0.05260 ... 0.0149 0.0556 0.20351 \n",
- "PG9Submit 0.2745 -0.07292 0.05977 ... 0.0029 0.0723 0.30291 \n",
- "PG10Submit 0.1614 0.04433 0.06942 ... 0.0159 -0.0227 0.26881 \n",
- "PG11Submit 0.1383 0.00084 -0.04870 ... 0.0240 0.0130 0.23552 \n",
- "PG12Submit 0.1642 -0.02721 -0.02169 ... 0.0167 -0.0614 0.25876 \n",
- " PG6Submit PG7Submit PG8Submit PG9Submit PG10Submit PG11Submit\n",
- "Start 0.0054 0.0776 0.0441 0.04101 0.047 7.9e-02 \n",
- "End 0.0051 0.0759 0.0435 0.04071 0.052 7.9e-02 \n",
- "PG0Dis 0.0277 0.0097 0.0354 0.00995 -0.029 -4.5e-02 \n",
- "PG0Shown 0.0401 0.0121 0.0264 0.00056 -0.045 -7.1e-02 \n",
- "PG0Submit 0.1518 0.1365 0.1258 0.17579 0.225 1.1e-01 \n",
- "PG1Submit 0.2414 0.1133 0.1069 0.10895 0.170 7.4e-02 \n",
- "PG2Submit 0.2696 0.1245 0.1567 0.20127 0.099 1.1e-01 \n",
- "PG3Submit 0.2706 0.1316 0.1822 0.27450 0.161 1.4e-01 \n",
- "PG4Dtr0_6 -0.1618 0.1560 0.0695 -0.07292 0.044 8.4e-04 \n",
- "PG4Psv7_8 -0.0146 -0.0363 0.0526 0.05977 0.069 -4.9e-02 \n",
- "PG4Prm9_10 -0.0418 -0.0633 -0.0550 -0.02989 -0.061 -8.6e-05 \n",
- "PG4AllResp -0.0040 -0.0753 -0.1294 -0.03812 -0.106 -8.2e-02 \n",
- "PG4Submit 0.3326 0.2775 0.1821 0.33236 0.391 2.8e-01 \n",
- "PG5_1Order 0.0334 0.0173 -0.0388 0.01033 -0.096 -2.5e-02 \n",
- "PG5_2Order -0.0571 -0.0551 -0.0567 -0.01925 0.015 7.3e-03 \n",
- "PG5_3Order 0.0419 0.0058 -0.0086 0.02604 0.063 3.4e-02 \n",
- "PG5_4Order 0.0414 0.0460 0.0182 -0.03734 -0.075 -8.6e-02 \n",
- "PG5_5Order 0.0498 0.0372 0.0427 -0.02976 0.076 2.4e-02 \n",
- "PG5_6Order 0.0165 0.0222 0.0639 -0.02974 0.015 2.8e-02 \n",
- "PG5_7Order -0.0222 -0.1082 -0.0804 -0.01762 -0.017 -4.4e-02 \n",
- "PG5_8Order -0.1227 -0.0126 -0.0526 -0.05699 -0.065 -3.7e-02 \n",
- "PG5_9Order -0.0188 -0.0314 -0.0898 -0.01354 -0.034 6.2e-03 \n",
- "PG5_10Order 0.0427 0.0311 0.0345 0.02011 0.102 1.8e-02 \n",
- "PG5_11Order -0.0055 0.0210 0.0232 0.05579 -0.038 -2.5e-03 \n",
- "PG5_12Order 0.0777 0.0529 0.0149 0.00295 0.016 2.4e-02 \n",
- "PG5_13Order -0.0209 0.0355 0.0556 0.07229 -0.023 1.3e-02 \n",
- "PG5Submit 0.3224 0.2312 0.2035 0.30291 0.269 2.4e-01 \n",
- "PG6Submit 1.0000 0.3086 0.2065 0.44528 0.343 2.8e-01 \n",
- "PG7Submit 0.3086 1.0000 0.1606 0.27819 0.312 2.8e-01 \n",
- "PG8Submit 0.2065 0.1606 1.0000 0.25569 0.200 2.1e-01 \n",
- "PG9Submit 0.4453 0.2782 0.2557 1.00000 0.290 2.8e-01 \n",
- "PG10Submit 0.3428 0.3121 0.2000 0.29018 1.000 3.5e-01 \n",
- "PG11Submit 0.2777 0.2768 0.2065 0.27906 0.346 1.0e+00 \n",
- "PG12Submit 0.2904 0.2523 0.1932 0.27550 0.313 2.5e-01 \n",
- " PG12Submit\n",
- "Start 0.0746 \n",
- "End 0.0772 \n",
- "PG0Dis 0.0546 \n",
- "PG0Shown 0.0436 \n",
- "PG0Submit 0.1096 \n",
- "PG1Submit 0.1137 \n",
- "PG2Submit 0.1073 \n",
- "PG3Submit 0.1642 \n",
- "PG4Dtr0_6 -0.0272 \n",
- "PG4Psv7_8 -0.0217 \n",
- "PG4Prm9_10 0.0169 \n",
- "PG4AllResp -0.0668 \n",
- "PG4Submit 0.2360 \n",
- "PG5_1Order 0.0238 \n",
- "PG5_2Order 0.0469 \n",
- "PG5_3Order 0.0538 \n",
- "PG5_4Order -0.0211 \n",
- "PG5_5Order 0.0085 \n",
- "PG5_6Order 0.0539 \n",
- "PG5_7Order -0.0617 \n",
- "PG5_8Order -0.0182 \n",
- "PG5_9Order -0.0280 \n",
- "PG5_10Order -0.0092 \n",
- "PG5_11Order 0.0275 \n",
- "PG5_12Order 0.0167 \n",
- "PG5_13Order -0.0614 \n",
- "PG5Submit 0.2588 \n",
- "PG6Submit 0.2904 \n",
- "PG7Submit 0.2523 \n",
- "PG8Submit 0.1932 \n",
- "PG9Submit 0.2755 \n",
- "PG10Submit 0.3131 \n",
- "PG11Submit 0.2513 \n",
- "PG12Submit 1.0000 "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#get numeric fields only for correlation\n",
- "sel = c()\n",
- "for (i in 1:dim(data)[2]) if (is.numeric(data[,i])) sel = c(sel, i);\n",
- "\n",
- "\n",
- "cor(data[,sel],method=\"spearman\",use=\"pairwise.complete.obs\"); #OK for any: uses ranks"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Interpret correlations: onlys start vs End, calculate differene instead\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": true
- },
- "source": [
- "### Simple questions\n",
- "\n",
- "- Time to take entire survey?\n",
- "- Question that took the longest to complete?\n",
- "- Question that took the least time?\n",
- "- Top-ranked criteria?\n",
- "- Demographic distribution by age?"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "R",
- "language": "R",
- "name": "ir"
- },
- "language_info": {
- "codemirror_mode": "r",
- "file_extension": ".r",
- "mimetype": "text/x-r-source",
- "name": "R",
- "pygments_lexer": "r",
- "version": "3.4.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
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+ Miniproject3/example.ipynb at master · fdac18/Miniproject3
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+ Oct 29, 2018
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+ 2 contributors
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