Statistical graphics Exercise
Exercise 1
The distribution of personal disposable income in Taiwan in 2015 has a story to tell. Revise the following plot to enhance that message
loading data and check data structure
## 'data.frame': 41 obs. of 2 variables:
## $ Income: Factor w/ 41 levels "1,000,000 to 1,069,999",..: 7 8 9 11 12 13 14 15 16 17 ...
## $ Count : int 807160 301650 313992 329290 369583 452671 495387 517779 557786 584497 ...## Income Count
## 1 160,000 and under 807160
## 2 160,000 to 179,999 301650
## 3 180,000 to 199,999 313992
## 4 200,000 to 219,999 329290
## 5 220,000 to 239,999 369583
## 6 240,000 to 259,999 452671data manipulation
library(dplyr)
dta$Income <- factor(dta$Income, levels = dta$Income)
dta2<-dta%>%mutate(group=ifelse(Count>=mean(Count), "M", "L"))plot
library(ggplot2)
ggplot(dta2, aes(Count/10000, y=Income, col=group))+
geom_point()+
geom_segment(aes(xend=mean(Count)/10000 , yend=Income))+
labs(x="Number of person (x 10,000)", y=" ")+
theme(legend.position=" ")
Exercise 3
Sarah Leo at the Economist magazine published a data set to accompany the story about how scientific publishing is dominated by men. The plot on the left panel below is the orignal graph that appeared in the article. Help her find a better plot.
loading data and check data structure
dta<-read.csv("C:/Users/USER/Desktop/R_data management/0420/data/Economist_women-research.csv", header=T)
names(dta)<-c("Country", "Health sciences", "Physical science", "Engineering","Computer science, maths", "women inventores")
str(dta)## 'data.frame': 19 obs. of 6 variables:
## $ Country : Factor w/ 18 levels "","Australia",..: 6 12 5 17 18 13 7 8 10 4 ...
## $ Health sciences : Factor w/ 13 levels "","0.24","0.43",..: 11 2 3 4 5 5 6 7 7 8 ...
## $ Physical science : Factor w/ 11 levels "","0.11","0.2",..: 11 2 6 4 3 8 5 8 7 4 ...
## $ Engineering : Factor w/ 9 levels "","0.11","0.22",..: 9 2 3 3 3 6 4 5 5 3 ...
## $ Computer science, maths: Factor w/ 9 levels "","0.11","0.16",..: 9 2 3 5 6 6 4 6 6 6 ...
## $ women inventores : Factor w/ 10 levels "","% of women inventores",..: 2 3 9 4 6 8 5 4 7 5 ...## Country Health sciences Physical science Engineering
## 1 Country Health sciences Physical sciences Engineering
## 2 Japan 0.24 0.11 0.11
## 3 Chile 0.43 0.23 0.22
## 4 United Kingdom 0.45 0.21 0.22
## 5 United States 0.46 0.2 0.22
## 6 Mexico 0.46 0.25 0.26
## Computer science, maths women inventores
## 1 Computer science, maths % of women inventores
## 2 0.11 0.08
## 3 0.16 0.19
## 4 0.21 0.12
## 5 0.22 0.14
## 6 0.22 0.18data manipulation
## [1] "factor"m<-as.numeric(levels(dta.melt$value))[dta.melt$value]
dta.melt$newv<-m
dta.melt$menv<-1-dta.melt$newv
dta.melt$diff<-dta.melt$newv-dta.melt$menv
dta.melt<-na.omit(dta.melt)ggplot
I think this plot tried to demonstrate how scientific publishing is dominated by men, hence show the difference (gap) between female and male researchers could easy to catch the point and use the facet_wrap to avoid too much information in the same plot
library(ggplot2)
ggplot(dta.melt, aes(y=diff, x=Country))+
geom_point()+
facet_wrap(variable~., ncol=5)+
geom_segment(aes(yend=0, xend=Country))+
geom_hline(aes(yintercept=0), color="gray")+
theme(legend.position=" ")+
theme(axis.text.x=element_text(angle=90, vjust=0.5))+
labs(x="Country", y="Difference in paper published (%, F-M)")
ggplot(dta.melt, aes(y=diff, x=variable))+
geom_point()+
facet_wrap(Country~., ncol=3)+
geom_segment(aes(yend=0, xend=variable))+
geom_hline(aes(yintercept=0), color="gray")+
theme(legend.position=" ")+
theme(axis.text.x=element_text(angle=90, vjust=0.5))+
labs(x="Country", y="Difference in paper published (%, F-M)")