Again, for your convenience, you can load the data for this exercise using the following code:
library(dplyr)
library(haven)
gp_covid <-
read_sav(
"./data/ZA5667_v1-1-0.sav"
) %>%
sjlabelled::set_na(na = c(-1:-99, 97, 98))
par()
function in combination with its mfrow
option.
tab_1 <- table(gp_covid$hzcy001a)
tab_2 <- table(gp_covid$hzcy002a)
tab_3 <- table(gp_covid$hzcy003a)
tab_4 <- table(gp_covid$hzcy004a)
par(mfrow = c(2, 2))
barplot(tab_1)
barplot(tab_2)
barplot(tab_3)
barplot(tab_4)
That’s interesting. One may wonder what the median is in each of these distributions. You know what’s perfect for visualizing this statistic? Boxplots!
par(mfrow = c(2, 2))
boxplot(gp_covid$hzcy001a)
boxplot(gp_covid$hzcy002a)
boxplot(gp_covid$hzcy003a)
boxplot(gp_covid$hzcy004a)
png
image.
png("Boxlots.png")
par(mfrow = c(2, 2))
boxplot(gp_covid$hzcy001a)
boxplot(gp_covid$hzcy002a)
boxplot(gp_covid$hzcy003a)
boxplot(gp_covid$hzcy004a)
dev.off()
## RStudioGD
## 2
Before we later start with the other exercises, you should clean your graphics device with dev.off()
dev.off()
## RStudioGD
## 2