Hw3-TinLizzie58 (Elizabeth Mulvey)

2014__10_23_Mulvey_Elizabeth .R

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+setwd("/Users/elizabethmulvey/qmssviz/hw3")
+
+load('WV5_Data_rdata_v_2014_04_28_2.rdata')
+load('WV6_Data_rdata_v_2014_06_04.rdata')
+d <- WV5_Data_spss_v_2014_04_28
+d2 <- WV6_Data_spss_v_2014_06_04
+
+subsetd<-subset(d, V2A== 840 | V2A== 484)
+
+require(ggplot2)
+
+
+bar1 <- ggplot(subsetd, aes(x=V68, xlab= "Countries")) + 
+  geom_histogram(binwidth=1, color="white", fill="blue") + 
+  coord_cartesian(xlim=c(0,10)) +
+  xlab("Countries (Mexico and the U.S.)") + ggtitle("Financial Satisfaction on a Scale from 0 to 10") 
+bar1
+bar1 + facet_grid(. ~ V2)
+
+
+density1 <- ggplot(data = subsetd, aes(x = V68, col=V2)) + geom_density(aes(group = V2, color=V2)) + ggtitle("Financial Satisfaction 2005-2009: Mexico (dark blue) v. US (light blue)")  + xlab("0= Dissatisfied, 10=Satisfied")   
+density1
+
+
+
+subsetd2<-subset(d2, V2A== 840 | V2A== 484)
+
+
+bar2 <- ggplot(subsetd2, aes(x=V59, xlab= "Countries")) + 
+  geom_bar(binwidth=1, color="white", fill="green") + 
+  coord_cartesian(xlim=c(0,10)) +
+  xlab("Countries (Mexico and the U.S.)") + ggtitle("Financial Satisfaction: 2010-2014)") 
+bar2
+bar2 + facet_grid(. ~ V2)
+
+density2 <- ggplot(data = subsetd2, aes(x = V59, col=V2)) + geom_density(aes(group = V2, color=V2)) + ggtitle("Financial Satisfaction 2010-2014: Mexico (dark blue) v. US (light blue)")  + xlab("0= Dissatisfied, 10=Satisfied")    
+density2
+
+

Lab1/Mulvey_Lab2.Rmd

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+---
+title: "Mulvey_Lab2"
+output: html_document
+---
+
+This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
+
+When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
+
+```{r}
+
+setwd("~/Desktop/QMSS_Semester_1/Data Viz")
+getwd()
+setwd("~/Desktop/QMSS_Semester_1/Data Viz")
+d <- read.csv('lgas.csv')
+str(d)
+df <- data.frame()
+nd <- read.csv('sample_health_facilities.csv')
+nd
+
+
+newdata <- subset(nd, zone == "South-South" | zone == "Southwest" | zone == "Southeast")
+newdata
+newd <- c("lga_id", "pop_2006")
+newd
+newdata
+newd <- subset(d, select=c(lga_id, pop_2006, area_sq_km, pop_density_2006))
+newd
+
+south <- merge(newdata, newd, by = "lga_id")
+
+merge(newdata, newd, by = "lga_id", all=FALSE)
+
+south
+south$Dperarea <-south$area_sq_km / south$num_doctors_fulltime
+south$Dpercapita <-south$pop_2006 / south$num_doctors_fulltime
+south$Nperarea <- south$area_sq_km / south$num_nurses_fulltime
+south$Npercapita <-south$pop_2006 / south$num_nurses_fulltime
+south 
+
+south$Dperarea <- as.character(south$Dperarea)
+south$Dperarea[south$Dperarea == "Inf"] <- "0"
+
+south$Dpercapita <- as.character(south$Dpercapita)
+south$Dpercapita[south$Dpercapita == "Inf"] <- "0"
+
+south$Nperarea <- as.character(south$Nperarea)
+south$Nperarea[south$Nperarea == "Inf"] <- "0"
+
+south$Npercapita <- as.character(south$Npercapita)
+south$Npercapita[south$Npercapita == "Inf"] <- "0"
+
+south 
+south$Dperarea <- as.numeric(south$Dperarea)
+south$Dpercapita <- as.numeric(south$Dpercapita)
+south$Nperarea <- as.numeric(south$Nperarea)
+south$Npercapita <- as.numeric(south$Npercapita)
+south
+
+#south <- transform(south, Dperarea = area_sq_km/ num_doctors_fulltime)
+
+#aggregate( cbind( num_nurses_fulltime , num_doctors_fulltime ) ~ state , data = south , FUN=sum )
+
+
+summing <-  data.frame(south$state,south$num_doctors_fulltime,south$num_nurses_fulltime, south$pop_2006, south$area_sq_km, south$pop_density_2006, south$Dperarea, south$Dpercapita, south$Nperarea, south$Npercapita)
+summing
+
+
+
+#summary(summing)
+#sum(summing$Delta$south.num_nurses_fulltime)
+#aggregate(x = summing, list(state = south.state), FUN = "sum")
+#aggregate(south.num_doctors_fulltime ~ south.state, data = summing, sum)
+
+
+#install.packages("plyr")
+#install.packages("reshape2")
+library(plyr)
+library(reshape2)
+
+summing2 <- ddply(summing, .(south.state), numcolwise(sum), na.rm=TRUE)
+summing2
+
+summing2[order(summing2$south.pop_2006, decreasing=TRUE), ]
+
+```
+