Let's start by installing and then loading a nice graphing package called ggplot2.
install.packages("ggplot2")
## Error: trying to use CRAN without setting a mirror
library(ggplot2)
Depending on how you opened the files, you might need to set your working directory. Let's check.
getwd()
## [1] "/Users/ericpgreen/Desktop"
If the result does not point to your desktop, you'll need to change the working directory.
setwd("/Users/ericpgreen/Desktop")
You can check to make sure it's correct by running getwd() again. Assuming it is, we can import some data. The population data we are going to use come from the UN (2012 revision).
dat <- read.csv("popdata.csv")
You can open the data frame or just run the following command to get a sense of the structure.
head(dat)
## year value variant who
## 1 2010 6916183 high world
## 2 2011 7005845 high world
## 3 2012 7099195 high world
## 4 2013 7195452 high world
## 5 2014 7293436 high world
## 6 2015 7392233 high world
The data are organized in a long format, so every year appears six times: variant (low, medium, high) times who (world, ssa). We can limit the dataset to just the world projection as follows:
dat.w <- subset(dat, dat$who == "world")
Let's plot the world data.
ggplot(dat.w, aes(x = year, y = value, group = variant, colour = variant)) +
geom_line()
It's OK, but we can clean up our labels a bit.
ggplot(dat.w, aes(x = year, y = value, group = variant, colour = variant)) +
geom_line() + ylab("Total Population, Both Sexes Combined") + xlab("Year")
We should divide by 10 million to make the y-axis easier to read.
ggplot(dat.w, aes(x = year, y = value/1e+07, group = variant, colour = variant)) +
geom_line() + ylab("Total Population, Both Sexes Combined (in billions)") +
xlab("Year")
Maybe we prefer a minimalist style…
ggplot(dat.w, aes(x = year, y = value/1e+07, group = variant, colour = variant)) +
geom_line() + ylab("Total Population, Both Sexes Combined (in billions)") +
xlab("Year") + theme_bw()
