I Putu Eddy Irawan 07/06/2020
Install the Gapminder data from CRAN
install.packages("gapminder")Now load the package:
library(gapminder)Get an overview of the Gapminder data structure
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...The tidyverse offers a special case of R’s default data frame: the
“tibble”, which is a nod to the actual class of these objects,
tbl_df.
If you have not already done so, install the tidyverse meta-package now
install.packages("tidyverse")Now load it:
library(tidyverse)
## ── Attaching packages ───────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.1 ✓ purrr 0.3.4
## ✓ tibble 3.0.1 ✓ dplyr 1.0.0
## ✓ tidyr 1.1.0 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ──────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Prints the names of classes
class(gapminder)
## [1] "tbl_df" "tbl" "data.frame"Now we can print the gapminder to screen. It is a tibble (and also a regular data frame)
gapminder
## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # … with 1,694 more rowsSince we are dealing with plain vanilla data frames, we can rein in data
frame printing explicity with head() and tail(). Or turn it into a
tibble with as_tibble()
head(gapminder)
## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
tail(gapminder)
## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Zimbabwe Africa 1982 60.4 7636524 789.
## 2 Zimbabwe Africa 1987 62.4 9216418 706.
## 3 Zimbabwe Africa 1992 60.4 10704340 693.
## 4 Zimbabwe Africa 1997 46.8 11404948 792.
## 5 Zimbabwe Africa 2002 40.0 11926563 672.
## 6 Zimbabwe Africa 2007 43.5 12311143 470.
as_tibble(iris)
## # A tibble: 150 x 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <fct>
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## # … with 140 more rowsMore ways to query basic info on a data frame
names(gapminder)
## [1] "country" "continent" "year" "lifeExp" "pop" "gdpPercap"
ncol(gapminder)
## [1] 6
length(gapminder)
## [1] 6
dim(gapminder)
## [1] 1704 6
nrow(gapminder)
## [1] 1704A statistical overview can be obtained with summary()
summary(gapminder)
## country continent year lifeExp
## Afghanistan: 12 Africa :624 Min. :1952 Min. :23.60
## Albania : 12 Americas:300 1st Qu.:1966 1st Qu.:48.20
## Algeria : 12 Asia :396 Median :1980 Median :60.71
## Angola : 12 Europe :360 Mean :1980 Mean :59.47
## Argentina : 12 Oceania : 24 3rd Qu.:1993 3rd Qu.:70.85
## Australia : 12 Max. :2007 Max. :82.60
## (Other) :1632
## pop gdpPercap
## Min. :6.001e+04 Min. : 241.2
## 1st Qu.:2.794e+06 1st Qu.: 1202.1
## Median :7.024e+06 Median : 3531.8
## Mean :2.960e+07 Mean : 7215.3
## 3rd Qu.:1.959e+07 3rd Qu.: 9325.5
## Max. :1.319e+09 Max. :113523.1
## Here we use only base R graphics, which are very basic.
plot(lifeExp ~ year, gapminder)
plot(lifeExp ~ gdpPercap, gapminder)
plot(lifeExp ~ log(gdpPercap), gapminder)
To specify a single variable from a data frame, use the dollar sign $.
Let’s explore the numeric variable of life expectancy.
head(gapminder$lifeExp)
## [1] 28.801 30.332 31.997 34.020 36.088 38.438
summary(gapminder$lifeExp)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 23.60 48.20 60.71 59.47 70.85 82.60
hist(gapminder$lifeExp)
The year variable is an integer variable, but since there are so few unique values it also functions a bit like a categorical variable.
summary(gapminder$year)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1952 1966 1980 1980 1993 2007
table(gapminder$year)
##
## 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
## 142 142 142 142 142 142 142 142 142 142 142 142The variables for country and continent hold truly categorical information, which is stored as a factor in R.
class(gapminder$continent)
## [1] "factor"
summary(gapminder$continent)
## Africa Americas Asia Europe Oceania
## 624 300 396 360 24
levels(gapminder$continent)
## [1] "Africa" "Americas" "Asia" "Europe" "Oceania"
nlevels(gapminder$continent)
## [1] 5The levels of the factor continent in R is stored as integer codes
1, 2, 3, etc.
str(gapminder$continent)
## Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...Here we count how many observations are associated with each continent and, as usual, try to portray that info visually.
table(gapminder$continent)
##
## Africa Americas Asia Europe Oceania
## 624 300 396 360 24
barplot(table(gapminder$continent))
In the figures below, we see how factors can be put to work in figures. The continent factor is easily mapped into “facets” or colors and a legend by the ggplot2 package.
## we exploit the fact that ggplot2 was installed and loaded via tidyversep <- ggplot(filter(gapminder, continent != "Oceania"),
aes(x = gdpPercap, y = lifeExp)) # initialize
p <- p + scale_x_log10() # log the x axis the right way
p + geom_point() # scatterplot
p + geom_point(aes(color = continent)) # map continent color
p + geom_point(alpha = (1/3), size = 3) + geom_smooth(lwd = 3, se = FALSE) # geom_smooth() using method = 'gam' and formula 'y ~ s(x, bs = "cs)'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
p + geom_point(alpha = (1/3), size = 3) + facet_wrap(~ continent) +
geom_smooth(lwd = 1.5, se = FALSE) # geom_smooth() using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'