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+lizard diff --git a/DataVizProj3/travel_b_processed.PNG b/DataVizProj3/travel_b_processed.PNG new file mode 100644 index 0000000..8d7723e Binary files /dev/null and b/DataVizProj3/travel_b_processed.PNG differ diff --git a/DataVizProj3/travel_b_processed.csv b/DataVizProj3/travel_b_processed.csv new file mode 100644 index 0000000..c3fdfcd --- /dev/null +++ b/DataVizProj3/travel_b_processed.csv @@ -0,0 +1,98 @@ +rise +omicron +may +launched +new +year +chaos +travelers +planning +trips +cautious +optimism +according +industry +insiders +consumers +navigating +recent +variant +hurdle +either +booking +trips +far +far +advance +think +seizing +lastminute +opportunities +luke +charny +cofounder +food +tour +company +chefs +tour +says +almost +percent +bookings +made +hours +tour +departure +vs +percent +prepandemic +people +continue +make +travel +plans +last +minute +know +available +today +might +tomorrow +says +justin +smith +owner +luxury +travelplanning +company +evolved +traveler +travelers +decide +book +going +spoke +travel +advisers +tour +operators +find +popular +destinations +coronavirus +outlook +destinations +unknown +make +sure +check +recommendations +centers +disease +control +prevention +evaluate +risk +planning +trip diff --git a/DataVizProj3/travel_c_processed.PNG b/DataVizProj3/travel_c_processed.PNG new file mode 100644 index 0000000..015fbcb Binary files /dev/null and b/DataVizProj3/travel_c_processed.PNG differ diff --git a/DataVizProj3/travel_c_processed.csv b/DataVizProj3/travel_c_processed.csv new file mode 100644 index 0000000..df79261 --- /dev/null +++ b/DataVizProj3/travel_c_processed.csv @@ -0,0 +1,104 @@ +international +travel +taken +hit +past +two +years +nonetheless +annual +list +top +travel +destinations +back +better +ever +always +aim +highlight +places +offer +exciting +reasons +architecture +design +lovers +visit +rest +major +museum +openings +burgeoning +trends +worth +checking +surveyed +experts +gathered +intel +exciting +cultural +attractions +international +travel +around +world +new +frank +lloyd +wright +road +trip +james +turrell +skyspace +uruguay +read +picks +top +picks +sure +get +planning +valencia +spain +port +city +spains +southeastern +coast +named +world +design +capital +thanks +longstanding +legacy +design +innovative +public +policy +distinction +comes +years +worth +public +programming +celebrating +design +ability +improve +lives +citys +inhabitants +go +sure +check +turia +gardens +one +spains +largest +urban +parks diff --git a/Process_text.ipynb b/Process_text.ipynb new file mode 100644 index 0000000..27e303a --- /dev/null +++ b/Process_text.ipynb @@ -0,0 +1,257 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "8ff55f55", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package stopwords to\n", + "[nltk_data] C:\\Users\\jcaza\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n" + ] + } + ], + "source": [ + "import nltk\n", + "nltk.download('stopwords')\n", + "from nltk.corpus import stopwords\n", + "from nltk.tokenize import word_tokenize\n", + "import re\n", + "import string\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "021f21c0", + "metadata": {}, + "outputs": [], + "source": [ + "def text_processor(file):\n", + "\n", + " stop_words = set(stopwords.words('english'))\n", + " txt_name = str(file) + '_processed'\n", + " csv_name = str(file) + '_processed'\n", + "\n", + " with open(file, encoding = 'utf-8') as f:\n", + " text = f.read()\n", + " text = text.lower()\n", + "\n", + "\n", + " # Remove urls\n", + " text = re.sub(r\"http\\S+|www\\S+|https\\S+\", '', text, flags=re.MULTILINE)\n", + "\n", + " # Remove user @ references and '#' \n", + " text = re.sub(r'\\@\\w+|\\#', '', text)\n", + "\n", + " # Remove punctuations\n", + " text = text.translate(str.maketrans('', '', string.punctuation))\n", + "\n", + " # Manually remove weird punctuation that is apparently not caught by the previous thing\n", + " # I honestly have no idea why some dashes look the same but don't remove all dashes\n", + " text = text.replace(\"‘\",\"\").replace('’', '').replace('\"', '').replace('“', '').replace('”', '').replace(\"''\", '')\n", + " text = text.replace('—', '').replace('≥', '').replace('≤', '').replace('−', '').replace('–', '').replace('…', '')\n", + " \n", + " # Remove numbers\n", + " text = re.sub(r'[0-9]+', '', text)\n", + " \n", + " # Remove stopwords\n", + " text_tokens = word_tokenize(text)\n", + " filtered_text = [w for w in text_tokens if not w in stop_words]\n", + "\n", + " # Save to text file\n", + " textfile = open(str(file)[:-4] + '_processed.txt', \"w\")\n", + " for word in filtered_text:\n", + " print(word)\n", + " textfile.write(word + \"\\n\")\n", + " textfile.close()\n", + " \n", + " # Save to csv file\n", + " textdf = pd.DataFrame(filtered_text)\n", + " textdf.to_csv( str(file)[:-4] + '_processed.csv', index = False, header = False)\n", + " \n", + " print(filtered_text)\n", + "\n", + " # COMMENTS ABOUT HOW MANY WORDS ARE IN EACH WORD CLOUD\n", + "\n", + " # COVID ARTICLE - 133\n", + " # KARDASHION ARTICLE - 98\n", + " # MUSIC TRENDS - 112\n", + " # PLANTS DRUG DISCOVERY - 137\n", + " # TRAVEL - 84\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "87e09125", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "kim\n", + "kardashian\n", + "seemingly\n", + "explained\n", + "choosing\n", + "put\n", + "first\n", + "caused\n", + "kanye\n", + "wests\n", + "divorce\n", + "west\n", + "shared\n", + "collage\n", + "family\n", + "photos\n", + "keeping\n", + "kardashians\n", + "star\n", + "four\n", + "kids\n", + "instagram\n", + "account\n", + "wrote\n", + "accompanying\n", + "caption\n", + "read\n", + "god\n", + "please\n", + "bring\n", + "family\n", + "back\n", + "together\n", + "days\n", + "since\n", + "post\n", + "daily\n", + "mail\n", + "reported\n", + "west\n", + "publicly\n", + "shared\n", + "series\n", + "texts\n", + "sent\n", + "estranged\n", + "wife\n", + "social\n", + "media\n", + "messages\n", + "seemingly\n", + "convey\n", + "entrepreneurs\n", + "wishes\n", + "west\n", + "stop\n", + "putting\n", + "current\n", + "rumoured\n", + "boyfriend\n", + "pete\n", + "davidson\n", + "danger\n", + "comments\n", + "comedian\n", + "rapper\n", + "recently\n", + "split\n", + "actor\n", + "julia\n", + "fox\n", + "reportedly\n", + "took\n", + "instagram\n", + "share\n", + "message\n", + "monday\n", + "february\n", + "appears\n", + "read\n", + "upon\n", + "wifes\n", + "request\n", + "please\n", + "nobody\n", + "anything\n", + "physical\n", + "skete\n", + "im\n", + "going\n", + "handle\n", + "situation\n", + "reported\n", + "texts\n", + "sent\n", + "skims\n", + "founder\n", + "west\n", + "shared\n", + "screenshots\n", + "kardashian\n", + "said\n", + "west\n", + "creating\n", + "dangerous\n", + "scary\n", + "environment\n", + "someone\n", + "hurt\n", + "pete\n", + "fault\n", + "dangerous\n", + "people\n", + "scary\n", + "doesnt\n", + "seemingly\n", + "added\n", + "['kim', 'kardashian', 'seemingly', 'explained', 'choosing', 'put', 'first', 'caused', 'kanye', 'wests', 'divorce', 'west', 'shared', 'collage', 'family', 'photos', 'keeping', 'kardashians', 'star', 'four', 'kids', 'instagram', 'account', 'wrote', 'accompanying', 'caption', 'read', 'god', 'please', 'bring', 'family', 'back', 'together', 'days', 'since', 'post', 'daily', 'mail', 'reported', 'west', 'publicly', 'shared', 'series', 'texts', 'sent', 'estranged', 'wife', 'social', 'media', 'messages', 'seemingly', 'convey', 'entrepreneurs', 'wishes', 'west', 'stop', 'putting', 'current', 'rumoured', 'boyfriend', 'pete', 'davidson', 'danger', 'comments', 'comedian', 'rapper', 'recently', 'split', 'actor', 'julia', 'fox', 'reportedly', 'took', 'instagram', 'share', 'message', 'monday', 'february', 'appears', 'read', 'upon', 'wifes', 'request', 'please', 'nobody', 'anything', 'physical', 'skete', 'im', 'going', 'handle', 'situation', 'reported', 'texts', 'sent', 'skims', 'founder', 'west', 'shared', 'screenshots', 'kardashian', 'said', 'west', 'creating', 'dangerous', 'scary', 'environment', 'someone', 'hurt', 'pete', 'fault', 'dangerous', 'people', 'scary', 'doesnt', 'seemingly', 'added']\n" + ] + } + ], + "source": [ + "text_processor('kanye_c.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4751987", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/README.md b/README.md index 2afa6c2..2f84166 100644 --- a/README.md +++ b/README.md @@ -1,106 +1,94 @@ Assignment 3 - Replicating a Classic Experiment === +--- +Data Visualization +Professor Harrison +A3 - 3/4/2022 +Jules Cazaubiel, Elaine Chen, Johvanni Perez, & Nick Tourtillott +a3-JulesCazaubiel-ElaineChen-JohvanniPerez-NickTourtillott -For the scope of this project, assume the role of a scientist who runs experiments for a living. +LINK TO SURVEY SITE: https://wpi.qualtrics.com/jfe/form/SV_aVtNFyOlFsN68om +--- -Q: How do we know that bar charts are "better" than pie charts? -A: Controlled experiments! +Description of Experiment: -In this assignment you'll implement a simple controlled experiment using some of the visualizations you’ve been building in this class. -You'll need to develop support code for the experiment sequence, results file output, and other experiment components. -(These are all simple with Javascript buttons and forms.) -The main goals for you are to a) test three competing visualizations, b) implement data generation and error calculation functions inspired by Cleveland and McGill's 1984 paper, c) run the experiment with 10 participants (or a trial equivalent), and d) do some basic analysis and reporting of the results. +The goal of our experiment is to test the effectiveness of word clouds and their capability of being utilized to identify main points and summarizations of written articles. The experiment utilized five different articles, all of different topics to see if the topic impacted how individuals would be able to identify the corresponding word cloud. Participants were asked to read an excerpt of an article, and we would present 3 different word clouds. The participants had to select the word cloud that was associated with the excerpt they just read. The word cloud choices were all about similar topics, but generated using different articles. The participants would be presented an excerpt of an article. After reading it, they would be presented the word cloud choices and select the correct one. This was done for each of the five articles. The participants were only informed that they would complete a study about word clouds, but they did not have any prior knowledge on what the articles would be about or what they were be expected to do. -For this assignment you should aim to write everything from scratch. For experimentation it is often necessary to control all elements of the chart. -You should definitely *reference* demo programs from books or the web, and if you do please provide a References section with links at the end of your Readme. +In order to produce word clouds uncluttered by commonly used words that are ultimately not relevant to any theme (= stopwords), such as “the” or “and”, we decided to preprocess the texts. We used NLTK to do so (Natural Language Toolkit), a widely used python package used for natural language processing. We used the basic stopword list from NLTK to remove these unnecessary words, along with other functions to remove all punctuation and numbers from the texts. This left us with a list of all relevant words for each text, outputted to a csv/text file for ease of use. Utlizing these csv files, we were able to use the D3 library to create the word clouds for each article. -Going Beyond Cleveland-McGill ---- -Several have expressed interest in conducting surveys of various sorts. I encourage you go move beyond Cleveland and McGill if you can think of other interesting visualization experiment designs and corresponding analyses. +Screenshots of Experiment: -You might study how people interpret COVID visualizations, for example. -If you decide to go in a custom route, simply contact staff so we can help you set acceptable parameters. -Basically, we still want you to do a multi-trial study with each participant, to raise the chance that you get solid results. -How you measure "error" and similar facets also matter. But you can't go wrong with finding a visualization study online to start from :) +Visualization 1: COVID-19 Article -Requirements ---- +Correct Answer: +![covid-19 correct word cloud](DataVizProj3/covid_a_processed.PNG) +Incorrect Answers: +![covid-19 incorrect word cloud](DataVizProj3/covid_b_processed.PNG) -- Look it over Cleveland and McGill's original experiment (see the section below) and [watch this video](experiment-example.mp4) to get a sense of the experiment structure and where your visualizations will go. -- When viewing the example experiment video, note the following: - - Trials are in random order. - - Each trial has a randomly generated set of 5-10 data points. - - Two of these data points are marked. - - (Note: the experiment UI and User Experience could be better. Plenty of design achievements here). -- Implement the data generation code **as described in the Cleveland & McGill paper**. - - The goal is to generate a set of random datapoints (usually 5 or 10, with values be between 0 and 100) and to mark two of them for comparison in the trial. -- Add 3 visualizations (i.e. conditions) to your experiment. When you are adding these visualizations, think about *why* these visualizations are interesting to test. In other words, keep in mind a *testable hypothesis* for each of the added visualization. Some good options include bar charts, pie charts, stacked-bar charts, and treemaps. You can also rotate your bar chart to be horizontal or upside-down as one of your conditions. You are encouraged to test unorthodox charts -- radar charts come to mind, but there are MANY possibilities here-- feel free to be creative! - - Follow the style from Cleveland and McGill closely (e.g. no color, simple lines) unless you are specifically testing a hypothesis (e.g. color versus no color). Pay attention to spacing between elements like bars. Do not mark bars for comparison using color-- this makes the perceptual task too easy. -- After each trial, implement code that grades and stores participant’s responses. -- At the end of the experiment, to get the data, one easy option use Javascript to show the data from the current experiment\* (i.e. a comma separated list in a text box) and copy it into your master datafile. See the Background section below for an example of what this file should look like. (\*Alternately implement a server, if you're experienced with that sort of thing.) - -- Figure out how to calculate "Error", the difference between the true percentage and the reported percentage. -- Scale this error using Cleveland and McGill’s log-base-2 error equation. For details, see the background section (there’s a figure with the equation). This becomes your “Error” column in the output. Make sure you use whole percentages (not decimal) in the log-base-2 equation. Make sure you handle the case of when a person gets the exact percentage correct (log-base-2 of 1/8 is -3, it is better to set this to 0). -- Run your experiment with 10 or more participants, or-- make sure you get at least 200 trials **per visualization type** in total. - - Grab friends or people in the class. - - Run at least 20 trials per visualization type, per participant. This is to ensure that you cover the range of possible answers (e.g. 5%, 10%, ..., 95%) -- Make sure to save the resulting CSV after each participant. Compile the results into a master csv file (all participants, all trials). -- Produce a README with figures that shows the visualizations you tested and results, ordered by best performance to worst performance. Follow the modern Cleveland-McGill figure below -- though note that using names instead of icons is fine. -- To obtain the ranking, calculate and report the average log2Error for each visualization across all trials and participants. This should be straightforward to do in a spreadsheet. -- Use Bootstrapped 95\% confidence intervals for your error upper and lower bounds. Include these in your figures. Bootstrapped confidence intervals are easily implemented in R + ggplot2 using the `stat_summary` geom. You can also use Excel, Python, or many many other tools. Bootstrapped 95% CIs are **very** useful in modern experiment practice. -- Include example images of each visualization as they appeared in your experiment (i.e. if you used a pie chart show the actual pie chart you used in the experiment along with the markings, not an example from Google Images). - -## General Requirements - -0. Your code should be forked from the GitHub repo and linked using GitHub pages. -2. Your project should use d3 to build visualizations. -3. Your writeup (readme.md in the repo) should contain the following: - -- Working link to the experiment hosted on gh-pages or some other site. -- Concise description and screenshot of your experiment. -- Description of the technical achievements you attempted with this project. -- Description of the design achievements you attempted with this project. - -Background ---- +![covid-19 incorrect word cloud](DataVizProj3/covid_c_processed.PNG) -In 1984, William Cleveland and Robert McGill published the results of several controlled experiments that pitted bar charts against pies and stacked-bar variants. -Their paper (http://www.cs.ubc.ca/~tmm/courses/cpsc533c-04-spr/readings/cleveland.pdf) (http://info.slis.indiana.edu/~katy/S637-S11/cleveland84.pdf) is considered a seminal paper in data visualization. -In particular, they ran a psychology-style experiment where users were shown a series of randomly-generated charts with two graphical elements marked like this: +Visualization 2: Kardashian Drama Article -![cleveland bar chart](img/cleveland-bar.png) +Correct Answer: +![kanye correct word cloud](DataVizProj3/kanye_c_processed_wc.PNG) -Participants were then asked, "What percentage is the smaller of the larger?". -This was repeated hundreds of time with varying data and charts. -By the end of the study, Cleveland and McGill had amassed a large dataset that looked like this: +Incorrect Answers: +![kanye incorrect word cloud](DataVizProj3/kanye_a_processed.PNG) -![cleveland table](img/cleveland-table.png) +![kanye incorrect word cloud](DataVizProj3/kanye_b_processed.PNG) -__Log-base-2 or "cm-error"__: The true percent is the actual percentage of the smaller to the larger, while the reported percent is what participants reported. -Cleveland and McGill recognized that their analyses would be biased if they took `abs(ReportedPercent – TruePercent)` as their score for error. -To compensate, they came up with a logarithmic scale for error with this equation: +Visualization 3: Music Industry Trends Article -![cleveland equation](img/cleveland-equation.png) +Correct Answer: +![music correct word cloud](DataVizProj3/music_a_processed.PNG) -You’ll be implementing this error score as part of the lab. -(Hint: it’s not a trick question, this is just to familiarize you with the experiment protocol). -With this Cleveland-McGill error score you can better compare the performance of the charts you test to figure out which one performs the best. +Incorrect Answers: +![music incorrect word cloud](DataVizProj3/music_b_processed.PNG) -As a baseline, compare your average Error scores to the following chart, which include both Cleveland and McGill’s results as well as more recent extensions of this experiment (lower error indicates better performance, and error bars are bootstrapped 95% confidence intervals (`http://en.wikipedia.org/wiki/Confidence_interval#Meaning_and_interpretation`)): +![music incorrect word cloud](DataVizProj3/music_c_processed.PNG) -![cleveland results](img/cleveland-results.png) +Visualization 4: Plant Based Drug Discovery Article -GitHub Details ---- +Correct Answer: +![plant correct word cloud](DataVizProj3/plant_c_processed.PNG) + + +Incorrect Answers: +![plant incorrect word cloud](DataVizProj3/plant_a_processed.PNG) + +![plant incorrect word cloud](DataVizProj3/plant_b_processed.PNG) + + +Visualization 5: Travel Trends Article + +Correct Answer: +![travel correct word cloud](DataVizProj3/travel_b_processed.PNG) + +Incorrect Answers: +![travel incorrect word cloud](DataVizProj3/travel_a_processed.PNG) + +![travel incorrect word cloud](DataVizProj3/travel_c_processed.PNG) + +Results from Experiment: + +![accuracy_gender](results/accuracy_gender.png) +![accuracy_gender_distro](results/accuracy_gender_distro.png) +![accuracy_major](results/accuracy_major.png) +![accuracy_major_distro](results/accuracy_major_distro.png) +![accuracy_major_text](results/accuracy_major_text.png) +![accuracy_participants_text](results/accuracy_participants_text.png) +![accuracy_word_count](results/accuracy_word_count.png) + +Technical Achievements: +- utilized D3 to create word clouds presented in Qualtrics Survey +- utilized word processing software in Python to create CSV files for word clouds +- utilized python script to create results visualizations +- utilized qualtrics to create survey and sent it out to students -- Fork the GitHub Repository. You now have a copy associated with your username. -- Make changes to index.html to fulfill the project requirements. -- Make sure your "master" branch matches your "gh-pages" branch. See the GitHub Guides referenced above if you need help. -- Edit this README.md with a link to your gh-pages site: e.g. http://YourUsernameGoesHere.github.io/Experiment/index.html -- Replace this file (README.md) with your writeup and Design/Technical achievements. -- To submit, make a [Pull Request](https://help.github.com/articles/using-pull-requests/) on the original repository. -- Name your submission using the following scheme: -``` -a3-FirstLastnameMember1-FirstLastnameMember2-FirstLastnameMember3-... -``` +Design Achievements: +- utilized D3 library to create word cloud that was only one color and created similar shapes so there wouldn't be alternative factors contributing to the effectiveness of the word cloud +- D3 API generated random word clouds and utilized random positioning +- D3 API caused styling all of the word clouds to be created in a similar manner +- designed the experiment so that the user would be able to read an article and then identify the associated word cloud right after, so that they would have the most accurate memory of the article +- each results visualization is utilizing color to differentiate between the different variables, like the relationship between accuracy and gender or the relationship between accuracy and major \ No newline at end of file diff --git a/Survey Results Viz.ipynb b/Survey Results Viz.ipynb new file mode 100644 index 0000000..36b01c2 --- /dev/null +++ b/Survey Results Viz.ipynb @@ -0,0 +1,830 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a23f4f9a", + "metadata": {}, + "source": [ + "## CS 573 Assignment 3 Survey Results\n", + "## Word Whales\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "831b4f2e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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StartDateEndDateStatusIPAddressProgressDuration (in seconds)FinishedRecordedDateResponseIdRecipientLastName...LocationLongitudeDistributionChannelUserLanguageQ21Q22Q19Q20Q15Word cloud 5
0Start DateEnd DateResponse TypeIP AddressProgressDuration (in seconds)FinishedRecorded DateResponse IDRecipient Last Name...Location LongitudeDistribution ChannelUser LanguageWhat's your major?Can you please share your gender identity?Which word cloud do you think was built using ...Which word cloud do you think was built using ...Which word cloud do you think was built using ...Which word cloud do you think was built using ...Which word cloud do you think was built using ...
1{\"ImportId\":\"startDate\",\"timeZone\":\"America/De...{\"ImportId\":\"endDate\",\"timeZone\":\"America/Denv...{\"ImportId\":\"status\"}{\"ImportId\":\"ipAddress\"}{\"ImportId\":\"progress\"}{\"ImportId\":\"duration\"}{\"ImportId\":\"finished\"}{\"ImportId\":\"recordedDate\",\"timeZone\":\"America...{\"ImportId\":\"_recordId\"}{\"ImportId\":\"recipientLastName\"}...{\"ImportId\":\"locationLongitude\"}{\"ImportId\":\"distributionChannel\"}{\"ImportId\":\"userLanguage\"}{\"ImportId\":\"QID24\"}{\"ImportId\":\"QID25\"}{\"ImportId\":\"QID22\"}{\"ImportId\":\"QID23\"}{\"ImportId\":\"QID14\"}{\"ImportId\":\"QID18\"}{\"ImportId\":\"QID21\"}
22022-02-27 15:00:452022-02-27 15:03:37IP Address68.187.220.222100171True2022-02-27 15:03:37R_SUVNjoueIeAVUZjNaN...-71.826202392578125anonymousENNaNNaNNaNNaNNaNNaNNaN
32022-02-28 08:53:312022-02-28 08:53:59IP Address130.215.168.18510028True2022-02-28 08:54:00R_yQOZdFQQmdWZ2QVNaN...-71.826202392578125anonymousENNaNNaNNaNNaNNaNNaNNaN
42022-02-28 08:54:022022-02-28 09:35:53IP Address130.215.168.1851002511True2022-02-28 09:35:54R_3Ebu4IWgVXRVRkYNaN...-71.826202392578125anonymousENNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " StartDate \\\n", + "0 Start Date \n", + "1 {\"ImportId\":\"startDate\",\"timeZone\":\"America/De... \n", + "2 2022-02-27 15:00:45 \n", + "3 2022-02-28 08:53:31 \n", + "4 2022-02-28 08:54:02 \n", + "\n", + " EndDate Status \\\n", + "0 End Date Response Type \n", + "1 {\"ImportId\":\"endDate\",\"timeZone\":\"America/Denv... {\"ImportId\":\"status\"} \n", + "2 2022-02-27 15:03:37 IP Address \n", + "3 2022-02-28 08:53:59 IP Address \n", + "4 2022-02-28 09:35:53 IP Address \n", + "\n", + " IPAddress Progress Duration (in seconds) \\\n", + "0 IP Address Progress Duration (in seconds) \n", + "1 {\"ImportId\":\"ipAddress\"} {\"ImportId\":\"progress\"} {\"ImportId\":\"duration\"} \n", + "2 68.187.220.222 100 171 \n", + "3 130.215.168.185 100 28 \n", + "4 130.215.168.185 100 2511 \n", + "\n", + " Finished RecordedDate \\\n", + "0 Finished Recorded Date \n", + "1 {\"ImportId\":\"finished\"} {\"ImportId\":\"recordedDate\",\"timeZone\":\"America... \n", + "2 True 2022-02-27 15:03:37 \n", + "3 True 2022-02-28 08:54:00 \n", + "4 True 2022-02-28 09:35:54 \n", + "\n", + " ResponseId RecipientLastName ... \\\n", + "0 Response ID Recipient Last Name ... \n", + "1 {\"ImportId\":\"_recordId\"} {\"ImportId\":\"recipientLastName\"} ... \n", + "2 R_SUVNjoueIeAVUZj NaN ... \n", + "3 R_yQOZdFQQmdWZ2QV NaN ... \n", + "4 R_3Ebu4IWgVXRVRkY NaN ... \n", + "\n", + " LocationLongitude DistributionChannel \\\n", + "0 Location Longitude Distribution Channel \n", + "1 {\"ImportId\":\"locationLongitude\"} {\"ImportId\":\"distributionChannel\"} \n", + "2 -71.826202392578125 anonymous \n", + "3 -71.826202392578125 anonymous \n", + "4 -71.826202392578125 anonymous \n", + "\n", + " UserLanguage Q21 \\\n", + "0 User Language What's your major? \n", + "1 {\"ImportId\":\"userLanguage\"} {\"ImportId\":\"QID24\"} \n", + "2 EN NaN \n", + "3 EN NaN \n", + "4 EN NaN \n", + "\n", + " Q22 \\\n", + "0 Can you please share your gender identity? \n", + "1 {\"ImportId\":\"QID25\"} \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " Q19 \\\n", + "0 Which word cloud do you think was built using ... \n", + "1 {\"ImportId\":\"QID22\"} \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " Q20 \\\n", + "0 Which word cloud do you think was built using ... \n", + "1 {\"ImportId\":\"QID23\"} \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " \\\n", + "0 Which word cloud do you think was built using ... \n", + "1 {\"ImportId\":\"QID14\"} \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " Q15 \\\n", + "0 Which word cloud do you think was built using ... \n", + "1 {\"ImportId\":\"QID18\"} \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " Word cloud 5 \n", + "0 Which word cloud do you think was built using ... \n", + "1 {\"ImportId\":\"QID21\"} \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Import all the things\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "data = pd.read_csv('A3 - CS573_March 3, 2022_15.37.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7aac87cf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Q21 Q22 Q19 Q20 \\\n", + "0 Engineering Man Word cloud a Word cloud c Word cloud a \n", + "1 Engineering Woman Word cloud a Word cloud c Word cloud a \n", + "2 Life Sciences Woman Word cloud a Word cloud c Word cloud c \n", + "3 Computer Science Woman Word cloud a Word cloud c Word cloud a \n", + "4 Computer Science Woman Word cloud a Word cloud c Word cloud b \n", + "\n", + " Q15 Word cloud 5 \n", + "0 Word cloud c Word cloud b \n", + "1 Word cloud c Word cloud b \n", + "2 Word cloud c Word cloud b \n", + "3 Word cloud c Word cloud b \n", + "4 Word cloud c Word cloud b \n" + ] + }, + { + "data": { + "text/plain": [ + "(24, 7)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Clean it up a bit\n", + "\n", + "# first rows are useless (either headers or blank responses)\n", + "data = data.drop([0, 1, 2, 3, 4, 5,6])\n", + "data = data.reset_index()\n", + "\n", + "# get rid of useless columns\n", + "data = data.drop(['index','StartDate', 'EndDate', 'Status', 'IPAddress', 'Progress', 'Duration (in seconds)', 'Finished', 'RecordedDate', 'ResponseId', 'RecipientLastName'], axis = 1)\n", + "data = data.drop(['RecipientFirstName', 'RecipientEmail', 'ExternalReference', 'LocationLatitude', 'LocationLongitude', 'DistributionChannel', 'UserLanguage'], axis = 1)\n", + "\n", + "print(data.head())\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2f9ef147", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Q21Q22Q19Q20Q15Word cloud 5WC1WC2WC3WC4WC5acc
0EngineeringManWord cloud aWord cloud cWord cloud aWord cloud cWord cloud b111111.0
1EngineeringWomanWord cloud aWord cloud cWord cloud aWord cloud cWord cloud b111111.0
2Life SciencesWomanWord cloud aWord cloud cWord cloud cWord cloud cWord cloud b110110.8
3Computer ScienceWomanWord cloud aWord cloud cWord cloud aWord cloud cWord cloud b111111.0
4Computer ScienceWomanWord cloud aWord cloud cWord cloud bWord cloud cWord cloud b110110.8
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" + ], + "text/plain": [ + " Q21 Q22 Q19 Q20 \\\n", + "0 Engineering Man Word cloud a Word cloud c Word cloud a \n", + "1 Engineering Woman Word cloud a Word cloud c Word cloud a \n", + "2 Life Sciences Woman Word cloud a Word cloud c Word cloud c \n", + "3 Computer Science Woman Word cloud a Word cloud c Word cloud a \n", + "4 Computer Science Woman Word cloud a Word cloud c Word cloud b \n", + "\n", + " Q15 Word cloud 5 WC1 WC2 WC3 WC4 WC5 acc \n", + "0 Word cloud c Word cloud b 1 1 1 1 1 1.0 \n", + "1 Word cloud c Word cloud b 1 1 1 1 1 1.0 \n", + "2 Word cloud c Word cloud b 1 1 0 1 1 0.8 \n", + "3 Word cloud c Word cloud b 1 1 1 1 1 1.0 \n", + "4 Word cloud c Word cloud b 1 1 0 1 1 0.8 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check for right answers and get participant accuracy\n", + "# 1 = right answer, 0 = wrong answer\n", + "\n", + "acc = []\n", + "doc1 = []\n", + "doc2 = []\n", + "doc3 = []\n", + "doc4 = []\n", + "doc5 = []\n", + "rubric = ['Word cloud a', 'Word cloud c', 'Word cloud a', 'Word cloud c', 'Word cloud b']\n", + "\n", + " \n", + "for i in range(24): ## be sure to change this based off of csv shape cuz I'm lazy\n", + " di = data.iloc[i,]\n", + " check = di.tolist()\n", + " \n", + " if rubric[0] == check[2]:\n", + " doc1.append(1)\n", + " elif rubric[0] != check[2]:\n", + " doc1.append(0)\n", + " \n", + " if rubric[1] == check[3]:\n", + " doc2.append(1)\n", + " elif rubric[1] != check[3]:\n", + " doc2.append(0)\n", + " \n", + " if rubric[2] == check[4]:\n", + " doc3.append(1)\n", + " elif rubric[2] != check[4]:\n", + " doc3.append(0)\n", + " \n", + " if rubric[3] == check[5]:\n", + " doc4.append(1)\n", + " elif rubric[3] != check[5]:\n", + " doc4.append(0)\n", + " \n", + " if rubric[4] == check[6]:\n", + " doc5.append(1)\n", + " elif rubric[4] != check[6]:\n", + " doc5.append(0)\n", + " \n", + " acc.append( (doc1[i] + doc2[i] + doc3[i] + doc4[i] + doc5[i])/5 ) \n", + " \n", + "data['WC1'] = doc1\n", + "data['WC2'] = doc2\n", + "data['WC3'] = doc3\n", + "data['WC4'] = doc4\n", + "data['WC5'] = doc5\n", + "data['acc'] = acc\n", + " \n", + " \n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ae0a672e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\jcaza\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Viz Time\n", + "\n", + "sns.scatterplot(data.index, data['acc'], hue = data['Q22'])\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "plt.title('Accuracy of each Participant by Gender')\n", + "plt.xlabel('Participant')\n", + "plt.show()\n", + "\n", + "sns.scatterplot(data.index, data['acc'], hue = data['Q21'])\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "plt.title('Accuracy of each Participant by Major')\n", + "plt.xlabel('Participant')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "24882d59", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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nj7rppps2HzduXOd22233wujRo7te/epXrwY46aSTnrn44ou3ARYBvPvd734W4FWvetWqlStXNk2YMKFrwoQJXaNHj+5asmTJiC222KLrE5/4xPa33HJLc1NTE4sWLRr15JNPjgRoaWlZfcABB7QD7LPPPqsee+yx0f2t3Z64JGmTNnv27LY//vGPzfPmzRvz2te+tv2www5ru+2225pvueWW5r333nudJ3JsttlmCdDU1PSSnyVtampizZo18c1vfnOrZ555ZuQ999wzd968efdvvfXWa9rb25vg5T9j2tHR4XC6JEn9ceihh7b99re/HT9+/PjOkSNHsu2223auWLFixJ133tl82mmnLVmwYMGoe++9dzTAZZddtvXBBx/8XL3rXr58+YiJEyeuGT16dF599dVbPPXUUwN20h44nC5JGkaax2/9Ql9nlPd3fX0ts++++7Y/++yzI9/5znc+0z1tjz32aF+5cuWIXXbZZc03vvGNx4499thdOjs7mTFjxqpPfepTi+t9/VNPPXXpkUceOXX69OnT9tprr1U77bTT8+vblt74U6Qlf4pUGlxvectbaHv+Bdpec0KjS1Gp+bbvMKIpuP769fv9bn+KdGj4U6SSJG2EDHFJkirKEJckNVJXV1dXv8/K3lSU22atV+QxxCVJjXTv4sWLxxnkL9fV1RWLFy8eB9y7tmU8O12S1DAdHR2nLly48JKFCxdOx45lT13AvR0dHaeubQFDXJLUMDNnzlwEvL3RdVSVez2SJFWUIS5JUkUZ4pIkVZQhLklSRRnikiRVlCEuSVJFGeKSJFWUIS5JUkUZ4pIkVZQhLmlIjBkzhmx6RaPLUK0ImpqMgSrz3ZM0JFpaWujabMtGl6FaTSMYPXp0o6vQBjDEJUmqKENckqSKMsQlSaooQ1ySpIoyxCVJqihDXJKkihq2IR4RGRH/WfN4ZEQsjohrGlmXJEnDxchGF7AOK4HpETEmM9uBNwILGlyTpA0wYtVSxsz7ZaPLULfODmBUo6vQBhjOIQ7wK+AtwBXA8cCPgIMBImJf4OvAGKAdODkzH4iIk4C3A2OBXYCfZ+ZnhrxySS8xderURpegHhYs6GD8+PGNLkMbYLiH+I+Bz5dD6K8GLqUMcWAecEhmdkTEG4BzgWPKeXsD+wCrgQci4qLMfKLnyiPiw8CHAXbcccfBbIe0yTvjjDMaXYK00RnWIZ6Zf46IKRS98J5jcOOA70XErkACtRdlvi4zlwNExP3AZOBlIZ6Z3wK+BTBr1qwc8AZIkjSIhu2JbTWuAs6nGEqv9S/ADZk5HXgbsFnNvNU19zsZ5jsrkiStjyqE26XA8sy8JyIOq5k+jr+e6HbSENckSVLDDfueeGY+mZkX9DLrS8D/i4ibgBFDXJYkSQ0XmR4KhuKY+O23397oMiSpMiJiTmbOanQdm7Jh3xOXJEm9M8QlSaooQ1ySpIoyxCVJqihDXJKkijLEJUmqKENckqSKMsQlSaooQ1ySpIoyxCVJqihDXJKkijLEJUmqKENckqSKMsQlSaooQ1ySpIoyxCVJqihDXJKkijLEJUmqKENckqSKMsQlSaooQ1ySpIoyxCVJqihDXJKkijLEJUmqKENckqSKMsQlSaooQ1ySpIoyxCVJqihDXJKkijLEJUmqKENckqSKMsQlSaooQ1ySpIoyxCVJqihDXJKkijLEJUmqKENckqSKMsQlSaooQ1ySpIoyxCVJqihDXJKkiuozxCPizRFxSkRM6TH9g4NWlSRJ6tM6QzwizgX+AXgVcF1EnFEz+/TBLEySJK1bXz3xtwGvz8xPADOBIyPia+W8GMzCJEnSuvUV4iMzswMgM5+lCPUtI+KnwKhBrk2SJK1DXyH+cEQc2v0gMzsz8xTgAWDaoFYmSZLWqa8QPxa4tefEzPxHYIdBqUiSJNVl5LpmZmY7QES8IjPX9Ji9etCqkiRJferr7PTXRcSTwFMR8eseXzP79aBWJkmS1qmv4fQvAW/OzEnAt4DfRMT+5TzPTpckqYHWOZwOjMrM+wAy84qImAv8LCL+DshBr06SJK1VXyG+JiK2y8yFAJl5X0QcDlwD7DLo1UmSpLXqK8T/Dtg2IlYAU8tpDwCH4hXbJElqqL5C/HcUx8VPBB6lOIa+DXBRZn4hIvbJzDsHuUZJktSLvkL8fGAMMDkznwOIiC2B8yPi34EjgJ0Gt0RJktSbvkL8KGDXzHzxJLbMXBERHwGWAEcOZnGSJGnt+vqKWVdtgHfLzE5gcWbeMjhlSZKkvvQV4vdHxIk9J0bE+4G5g1OSJEmqR1/D6R+j+F74B4E5FN8Nfy3FcfJ3DHJtkiRpHfq6dvoCYL+IeD2wF8VV2n6VmdcNRXGSJGnt+uqJA5CZ1wPXD3ItkiSpH/o6Ji5JkoYpQ1ySpIoyxCVJqihDXJKkijLEJUmqKENckqSKMsQlSaooQ1ySpIoyxCVJqihDXJKkijLEJUmqKENckqSKMsQlSaooQ1ySpIoyxCVJqihDXJKkijLEJUmqKENckqSKMsQlSaooQ1ySpIoyxCVJqihDXJKkijLEJUmqKENckqSKMsQlSaooQ1ySpIoyxCVJqihDXJKkijLEJUmqKENckqSKMsQlSaooQ1ySpIoyxCVJqihDXJKkijLEJUmqKENckqSKMsQlSaooQ1ySpIoatBCPiK9FxCdqHv9PRFxS8/grEfG3g/X6kiRt7AazJ/5H4ACAiGgCJgJ71cw/ALhpEF9fkqSN2mCG+E2UIU4R3vcCz0XEhIgYDUwDxkfEnRFxT0RcWk4nIh6LiHMj4uaIuD0iXlP25B+OiNPKZZoj4rqIuKN8/tHl9CkRMTcivh0R90XEryNizCC2U5Kkhhg5WCvOzKcioiMidqQI85uBFmA2sBx4ELgEODwzH4yIy4CPAF8vV/FEZs6OiK8B3wUOBDYD7gO+ATwPvCMzV0TEROCWiLiqfO6uwPGZ+aGI+AlwDPD9njVGxIeBDwPsuOOOA70JpA120UUX0dra2ugyBsSCBQsAaGlpaXAlqjV16lTOOOOMRpeh9TRoIV7q7o0fAHyVIsQPoAjxBcDzmflguez3gI/x1xDvDuR7gObMfI6iJ/98RIwHVgLnRsQhQFe57m3L5zyamXeV9+cAU3orLjO/BXwLYNasWblhTZUGXmtrKw/ddyc7Nnc2upQNtvK5EQCs7vhLgytRt/ltIxpdgjbQYId493HxV1EMpz8BfBJYAdwBvHEdz11d/ttVc7/78UjgfcAkYGZmromIxyh66vRYvhNwOF2VtWNzJ599zYpGl7HBzr1jS4CNoi0bi+73RNU12F8xuwl4K7A0MzszcykwnmJI/TvAlIiYWi57AnBjP9Y9DlhUBvjrgMkDV7YkScPfYIf4PRRnpd/SY9ryzHwSOBn4aUTcQ9HD/kY/1v0DYFZE3E7RK583MCVLklQNgzqcnpmdwJY9pp1Uc/86YJ9enjel5v53KU5se9k8ih59b6bXLH9+f2qWJKkqvGKbJEkVZYhLklRRhrgkSRVliEuSVFGGuCRJFTXYF3vZ6F100UUAXrZQUuU8vaqJEeXlcFVNhvgG2liuay1p0/N8Z9DU3t7oMrQBHE6XJKmiDHFJkirKEJckqaIMcUmSKsoQlySpogxxSZIqyhCXJKmiDHFJkirKi71soAULFtDe3s6ZZ57Z6FK0EWptbWXUGve1JfXOvw6SJFWUPfEN1NLSAsAFF1zQ4Eq0MTrzzDNZ/fjtjS5D0jBlT1ySpIoyxCVJqihDXJKkijLEJUmqKENckqSKMsQlSaooQ1ySpIoyxCVJqihDXJKkivKKbRto6tSpjS5BktbLZiOSEWPGNLoMbQBDfAOdccYZjS5BktbLtmO7GF1eOlrV5HC6JEkVZYhLklRRhrgkSRVliEuSVFGGuCRJFWWIS5JUUYa4JEkV5ffEpWFuftsIzr1jy0aXscEef24EwEbRlo3F/LYR7NroIrRBDHFpGNuYrgi4+YIFAF5cZBjZlY3rM7YpMsSlYcwrAkpaF4+JS5JUUYa4JEkVZYhLklRRhrgkSRVliEuSVFGGuCRJFWWIS5JUUYa4JEkVZYhLklRRhrgkSRUVmdnoGoaFiFgMPL6eT58ILBnAchppY2nLxtIOsC3D0cbSDtiwtkzOzEkDWYz6xxAfABFxe2bOanQdA2FjacvG0g6wLcPRxtIO2LjasilyOF2SpIoyxCVJqihDfGB8q9EFDKCNpS0bSzvAtgxHG0s7YONqyybHY+KSJFWUPXFJkirKEJckqaIM8TpFxBER8UBEtEbE361juddGRGdEvGso6+uPvtoSEYdFxPKIuKu8fb4RddajnvelbM9dEXFfRNw41DXWq4735dM178m95edsq0bUui51tGNcRFwdEXeX78nJjaizHnW0ZUJE/Dwi/hwRt0bE9EbU2ZeIuDQiFkXEvWuZHxFxYdnOP0fEa4a6Rq2nzPTWxw0YATwM7AyMAu4G9lzLctcDvwTe1ei617ctwGHANY2udYDaMh64H9ixfLxNo+vekM9YzfJvA65vdN3r+Z58FjivvD8JWAqManTt69mWLwNnl/f3AK5rdN1racshwGuAe9cy/yjgV0AA+wN/anTN3uq72ROvz75Aa2Y+kpkvAD8Gju5luTOA/wIWDWVx/VRvW6qgnra8F/hZZs4HyMzh+t709305HvjRkFTWP/W0I4EtIiKAZooQ7xjaMutST1v2BK4DyMx5wJSI2HZoy+xbZv6OYjuvzdHAZVm4BRgfEa8cmuq0IQzx+rQAT9Q8frKc9qKIaAHeAXxjCOtaH322pTS7HO78VUTsNTSl9Vs9bdkNmBAR/xsRcyLixCGrrn/qfV+IiLHAERQ7jMNNPe34V2Aa8BRwD3BmZnYNTXn9Uk9b7gbeCRAR+wKTge2HpLqBVffnT8PLyEYXUBHRy7Se3837OnBWZnYWHYxhq5623EFxTeS2iDgKuBLYdbALWw/1tGUkMBM4HBgD3BwRt2Tmg4NdXD/V05ZubwNuysx19awapZ52vBm4C3g9sAvwm4j4fWauGOTa+quetnwRuCAi7qLYIbmT4Tmq0Jf+fP40jBji9XkS2KHm8fYUvYhas4AflwE+ETgqIjoy88ohqbB+fbal9o9pZv4yIv4tIiZm5nD7wYd63pcngSWZuRJYGRG/A2YAwy3E62lLt+MYnkPpUF87Tga+mJkJtEbEoxTHk28dmhLrVu//lZOhODkMeLS8VU1/Pn8aRhxOr89twK4RsVNEjKL4I3pV7QKZuVNmTsnMKcAVwEeHYYBDHW2JiO3KP0jdQ4RNwDNDXmnf+mwL8Avg4IgYWQ5D7wfMHeI661FPW4iIccChFO0ajuppx3yKkRHK48e7A48MaZX1qef/yvhyHsCpwO+G4YhCPa4CTizPUt8fWJ6Zf2l0UeqbPfE6ZGZHRJwO/A/FGauXZuZ9EXFaOX+4Hwd/UZ1teRfwkYjoANqB48pe07BST1syc25E/DfwZ6ALuCQze/2aTSP14zP2DuDX5cjCsFNnO/4F+G5E3EMxjHvWMBzlqbct04DLIqKT4lsQpzSs4HWIiB9RfOtkYkQ8CZwNvAJebMcvKc5QbwVWUY4uaPjzsquSJFWUw+mSJFWUIS5JUkUZ4pIkVZQhLklSRRnikiRVlCEuSVJFGeKSJFWUIS4NAxFxZfkDLfdFxIfLaUdExB3lD9FcV05rjojvRMQ95e8+H9PYyiU1khd7kYaBiNgqM5dGxBiKy30eDtwOHJKZj9bMPw8YnZmfKJ83ITOXNa5ySY3kZVel4eHjEfGO8v4OwIcprsP9KEDNL5a9geIa3pTTDXBpE+ZwutRgEXEYRTjPzswZFD9neTe9/xRkrGW6pE2QIS413jhgWWauiog9gP2B0cChEbETFMPt5bK/Bk7vfmJETBjqYiUNHx4TlxosIkYDVwItwAPAJOAcYAxwLsXO9qLMfGNENAMXAzOBTuCfMvNnDShb0jBgiEuSVFEOp0uSVFGGuCRJFWWIS5JUUYa4JEkVZYhLklRRhrgkSRVliEuSVFH/H49+xg01oLoLAAAAAElFTkSuQmCC\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(x = 'acc', y = 'Q22', data = data, hue = 'Q22', orient = 'h')\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "plt.title('Gender Accuracy Distributions')\n", + "plt.show()\n", + "\n", + "\n", + "sns.boxplot(x = 'acc', y = 'Q21', data = data, hue = 'Q21', orient = 'h')\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "plt.title('Major Accuracy Distributions')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "17fdf25a", + "metadata": {}, + "outputs": [], + "source": [ + "# Calc each word cloud acc\n", + "wcacc = []\n", + "\n", + "wc1 = data['WC1'].tolist()\n", + "wcacc.append( (sum(wc1))/len(wc1) ) \n", + "\n", + "\n", + "wc2 = data['WC2'].tolist()\n", + "wcacc.append( (sum(wc2))/len(wc2) ) \n", + "\n", + "\n", + "wc3 = data['WC3'].tolist()\n", + "wcacc.append( (sum(wc3))/len(wc3) ) \n", + "\n", + "\n", + "wc4 = data['WC4'].tolist()\n", + "wcacc.append( (sum(wc4))/len(wc4) ) \n", + "\n", + "wc5 = data['WC5'].tolist()\n", + "wcacc.append( (sum(wc5))/len(wc5) ) \n", + "\n", + "WC = ['Text 1', 'Text 2', 'Text 3', 'Text 4', 'Text 5']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "71f5a38b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Accuracy of Participants per Text')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.barplot(x = WC, y = wcacc)\n", + "plt.title('Accuracy of Participants per Text')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4c2fc6fa", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "wordcounts = [133, 98, 112, 137, 84]\n", + "\n", + "sns.scatterplot(x = wordcounts, y = wcacc, size = wordcounts)\n", + "plt.title('Accuracy by Word Count in Word Cloud')\n", + "plt.xlabel('Word Count')\n", + "plt.ylabel('Accuracy')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "59b91276", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot question accuracy within majors\n", + "majors = data.groupby(['Q21']).mean()\n", + "\n", + "for x in range(5):\n", + " b = majors.iloc[0,].tolist()\n", + " c = majors.iloc[1,].tolist()\n", + " e = majors.iloc[2,].tolist()\n", + " l = majors.iloc[3,].tolist()\n", + " m = majors.iloc[4,].tolist()\n", + " \n", + "WC = ['Text 1', 'Text 2', 'Text 3', 'Text 4', 'Text 5', 'Overall Accuracy']\n", + "\n", + "plt.plot(b,label ='Business/Humanities' )\n", + "plt.plot(c, label ='CS')\n", + "plt.plot(e , label ='Engineering')\n", + "plt.plot( l, label = 'Life Science')\n", + "plt.plot(m, label = 'Math/Physics')\n", + "plt.legend()\n", + "plt.ylabel('Accuracy')\n", + "plt.xticks([0,1,2,3,4,5], WC, rotation=20)\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "plt.title('Accuracy of Each Major per Text')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "068e8607", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/default-030f7bff-dcfd-4f55-af8e-12123d1c0727.ipynb b/default-030f7bff-dcfd-4f55-af8e-12123d1c0727.ipynb new file mode 100644 index 0000000..7fec515 --- /dev/null +++ b/default-030f7bff-dcfd-4f55-af8e-12123d1c0727.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/default-c6220cb9-524f-4153-8a71-497f267e456b.ipynb b/default-c6220cb9-524f-4153-8a71-497f267e456b.ipynb new file mode 100644 index 0000000..7fec515 --- /dev/null +++ b/default-c6220cb9-524f-4153-8a71-497f267e456b.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/default-e7df73b7-8d27-45e2-9910-8cc42472274e.ipynb b/default-e7df73b7-8d27-45e2-9910-8cc42472274e.ipynb new file mode 100644 index 0000000..7fec515 --- /dev/null +++ b/default-e7df73b7-8d27-45e2-9910-8cc42472274e.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/results/accuracy_gender.png b/results/accuracy_gender.png new file mode 100644 index 0000000..c44db8a Binary files /dev/null and b/results/accuracy_gender.png differ diff --git a/results/accuracy_gender_distro.png b/results/accuracy_gender_distro.png new file mode 100644 index 0000000..2aa7fdb Binary files /dev/null and b/results/accuracy_gender_distro.png differ diff --git a/results/accuracy_major.png b/results/accuracy_major.png new file mode 100644 index 0000000..80392b2 Binary files /dev/null and b/results/accuracy_major.png differ diff --git a/results/accuracy_major_distro.png b/results/accuracy_major_distro.png new file mode 100644 index 0000000..103b9c4 Binary files /dev/null and b/results/accuracy_major_distro.png differ diff --git a/results/accuracy_major_text.png b/results/accuracy_major_text.png new file mode 100644 index 0000000..ec167ac Binary files /dev/null and b/results/accuracy_major_text.png differ diff --git a/results/accuracy_participants_text.png b/results/accuracy_participants_text.png new file mode 100644 index 0000000..0e3d4b1 Binary files /dev/null and b/results/accuracy_participants_text.png differ diff --git a/results/accuracy_word_count.png b/results/accuracy_word_count.png new file mode 100644 index 0000000..55db91f Binary files /dev/null and b/results/accuracy_word_count.png differ