Computation Boot Camp

Day 1 Solutions

Patrick Cahan

Assignment

  1. Use facets to plot distributions of teaching scores, one plot per year
  2. Scatter plot to explore relationship between the teaching score and the income (facet by year)
  3. Add a regression line to the above plot
  4. Same as 2 and 3 but exploring the relationship between total_score and income
  5. Same as 2 and 3 but exploring relationship between world_rank and research
  6. Does there appear to be an effect of female_male_ratio and total_score? Use boxplot to visualize

Load and clean the data

require(devtools)
library(slidify)
library(ggplot2)

tdat<-read.csv("../../misc/timesData.csv", header=1)
cclasses<-rep("numeric", ncol(tdat))
cclasses[c(1, 2,3, 10,12, 13)]<-"character"
tdat<-read.csv("../../misc/timesData.csv", header=1, colClass=cclasses, na.strings="-")
# na.strings defines what should be treated as NA

Load and clean the data Part II

Fix World Rank

wor<-tdat$world_rank
table(wor[grep("-", wor)])[1:2]

## 
## 201-225 201-250 
##     103      53

Fix World Rank

newWOR<-rep(0, length(wor));
badWOR<-names(table(wor[grep("-", wor)]))
indexBad<-vector();
for(bbad in badWOR){
  xi<-which(wor==bbad);
  indexBad<-append(indexBad, xi);
  valPair<-as.numeric(strsplit(bbad, "-")[[1]]);
  newChar<-mean(valPair);
  newWOR[xi]<-newChar;
}
goodInd<-setdiff(1:length(wor), indexBad);
newWOR[goodInd]<-as.numeric(wor[goodInd]);

tdat$world_rank<-newWOR

Problem 1: Use facets to plot distributions of teaching scores, one plot per year

plot1 <- ggplot(tdat, aes(x=teaching)) + geom_histogram() + facet_grid( .~ year) + theme_bw()
plot1 + xlab("Teaching score")

plot of chunk unnamed-chunk-4

Problem 2-3: Scatter plot to explore relationship between the teaching score and the income (facet by year), add regression lines

plot2<-ggplot(tdat, aes(x=income, y=teaching)) + geom_point(colour="blue",alpha=0.15) +
geom_smooth(method=lm, colour='red') +
facet_grid( .~ year) +
theme_bw()
plot2 + ylab("Teaching score")

plot of chunk unnamed-chunk-5

Problem 2-3: If you want to run stats on the associations:

summary(lm(teaching~income, data=subset(tdat, year==2011)))

## 
## Call:
## lm(formula = teaching ~ income, data = subset(tdat, year == 2011))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.066  -9.665  -2.810   6.263  50.445 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 40.71592    2.81008  14.489  < 2e-16 ***
## income       0.24751    0.04954   4.996 1.73e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.7 on 139 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.1522, Adjusted R-squared:  0.1461 
## F-statistic: 24.96 on 1 and 139 DF,  p-value: 1.728e-06

Problem 4: relationship between total_score and income

plot4<-ggplot(tdat, aes(x=income, y=total_score)) + geom_point(colour="blue",alpha=0.15) +
geom_smooth(method=lm, colour='red') +
facet_grid( .~ year) +
theme_bw()
plot4 + ylab("Total score")

plot of chunk unnamed-chunk-7

Problem 5: world_rank and research

plot5<-ggplot(tdat, aes(x=research, y=world_rank)) + geom_point(colour="blue",alpha=0.15) +
geom_smooth(method=lm, colour='red') +
facet_grid( .~ year) +
theme_bw()
plot5 + ylab("World rank")

plot of chunk unnamed-chunk-8

Problem 5: world_rank and research take II

thresh<-200
plot5v2<-ggplot(tdat[tdat$world_rank<thresh,], aes(x=research, y=world_rank)) + geom_point(colour="blue",alpha=0.15) +
geom_smooth(method=lm, colour='red') +
facet_grid( .~ year) +
theme_bw()
plot5v2 + ylab("World rank")

plot of chunk unnamed-chunk-9

Problem 6: female_male_ratio and total_score?

plot6<-ggplot(tdat, aes(x=female_male_ratio, y=total_score)) +
geom_boxplot() +
facet_grid( .~ year) +
theme_bw()
plot6 + xlab("F-M ratio")

plot of chunk unnamed-chunk-10