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gapminder-ggplot2-scatterplot.r

Jenny Bryan Mon Oct 3 23:50:31 2016

Note: this report is made by rendering an R script. So the narrative is very minimal.

library(tibble)
library(ggplot2)

Load the gapminder data package.

library(gapminder)
gapminder
## # A tibble: 1,704 × 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.801 8425333 779.4453
## 2 Afghanistan Asia 1957 30.332 9240934 820.8530
## 3 Afghanistan Asia 1962 31.997 10267083 853.1007
## 4 Afghanistan Asia 1967 34.020 11537966 836.1971
## 5 Afghanistan Asia 1972 36.088 13079460 739.9811
## 6 Afghanistan Asia 1977 38.438 14880372 786.1134
## 7 Afghanistan Asia 1982 39.854 12881816 978.0114
## 8 Afghanistan Asia 1987 40.822 13867957 852.3959
## 9 Afghanistan Asia 1992 41.674 16317921 649.3414
## 10 Afghanistan Asia 1997 41.763 22227415 635.3414
## # ... with 1,694 more rows

ggplot(gapminder, aes(x=gdpPercap, y=lifeExp)) # nothing to plot yet!

ggplot(gapminder, aes(x=gdpPercap, y=lifeExp)) +
geom_point()

p<- ggplot(gapminder, aes(x=gdpPercap, y=lifeExp)) # just initializes

scatterplot

p+ geom_point()

log transformation ... quick and dirty

ggplot(gapminder, aes(x= log10(gdpPercap), y=lifeExp)) +
geom_point()

a better way to log transform

p+ geom_point() + scale_x_log10()

let's make that stick

p<-p+ scale_x_log10()

common workflow: gradually build up the plot you want re-define the object 'p' as you develop "keeper" commands convey continent by color: MAP continent variable to aesthetic color

p+ geom_point(aes(color=continent))

## add summary(p)!
plot(gapminder, aes(x=gdpPercap, y=lifeExp, color=continent)) +
geom_point() + scale_x_log10() # in full detail, up to now
## Error in plot(gapminder, aes(x = gdpPercap, y = lifeExp, color = continent)) + : non-numeric argument to binary operator

address overplotting: SET alpha transparency and size to a value

p+ geom_point(alpha= (1/3), size=3)

add a fitted curve or line

p+ geom_point() + geom_smooth()

p+ geom_point() + geom_smooth(lwd=3, se=FALSE)

p+ geom_point() + geom_smooth(lwd=3, se=FALSE, method="lm")

revive our interest in continents!

p+ aes(color=continent) + geom_point() +
geom_smooth(lwd=3, se=FALSE)

facetting: another way to exploit a factor

p+ geom_point(alpha= (1/3), size=3) +
facet_wrap(~continent)

p+ geom_point(alpha= (1/3), size=3) +
facet_wrap(~continent) +
geom_smooth(lwd=2, se=FALSE)

exercises: * plot lifeExp against year

ggplot(gapminder, aes(x=year, y=lifeExp,
color=continent)) +
geom_jitter(alpha=1/3, size=3)

  • make mini-plots, split out by continent HINT: use facet_wrap()
ggplot(gapminder, aes(x=year, y=lifeExp,
color=continent)) +
facet_wrap(~continent, scales="free_x") +
geom_jitter(alpha=1/3, size=3) +
scale_color_manual(values=continent_colors)

ggplot(subset(gapminder, continent!="Oceania"),
aes(x=year, y=lifeExp, group=country, color=country)) +
geom_line(lwd=1, show_guide=FALSE) + facet_wrap(~continent) +
scale_color_manual(values=country_colors) +#scale_color_brewer()+
theme_bw() + theme(strip.text= element_text(size= rel(1.1)))
## Warning: `show_guide` has been deprecated. Please use `show.legend`
## instead.

  • add a fitted smooth and/or linear regression, w/ or w/o facetting
ggplot(gapminder, aes(x=year, y=lifeExp,
color=continent)) +
facet_wrap(~continent, scales="free_x") +
geom_jitter(alpha=1/3, size=3) +
scale_color_manual(values=continent_colors) +
geom_smooth(lwd=2)

  • use dplyr::filter() to plot lifeExp against year for just one country or continent
jc<-"Cambodia"gapminder %>% filter(country==jc) %>% ggplot(aes(x=year, y=lifeExp)) +
labs(title=jc) +
geom_line()
## Error in eval(expr, envir, enclos): could not find function "%>%"

rwanda<-gapminder %>%
filter(country=="Rwanda")
## Error in eval(expr, envir, enclos): could not find function "%>%"

p<- ggplot(rwanda, aes(x=year, y=lifeExp)) +
labs(title="Rwanda") +
geom_line()
## Error in ggplot(rwanda, aes(x = year, y = lifeExp)): object 'rwanda' not found

print(p)

ggsave("rwanda.pdf")
## Saving 7 x 5 in image

ggsave("rwanda.pdf",plot=p)
## Saving 7 x 5 in image
  • other ideas? plot lifeExp against year
(y<- ggplot(gapminder, aes(x=year, y=lifeExp)) + geom_point())

make mini-plots, split out by continent

y+ facet_wrap(~continent)

add a fitted smooth and/or linear regression, w/ or w/o facetting

y+ geom_smooth(se=FALSE, lwd=2) +
geom_smooth(se=FALSE, method="lm", color="orange", lwd=2)

y+ geom_smooth(se=FALSE, lwd=2) +
facet_wrap(~continent)

last bit on scatterplots how can we "connect the dots" for one country? i.e. make a spaghetti plot?

y+ facet_wrap(~continent) + geom_line() # uh, no

y+ facet_wrap(~continent) + geom_line(aes(group=country)) # yes!

y+ facet_wrap(~continent) + geom_line(aes(group=country)) +
geom_smooth(se=FALSE, lwd=2)

note about subsetting data sadly, ggplot() does not have a 'subset =' argument so do that 'on the fly' with subset(..., subset = ...)

ggplot(subset(gapminder, country=="Zimbabwe"),
aes(x=year, y=lifeExp)) + geom_line() + geom_point()

or could do with dplyr::filter

suppressPackageStartupMessages(library(dplyr))
ggplot(gapminder %>% filter(country=="Zimbabwe"),
aes(x=year, y=lifeExp)) + geom_line() + geom_point()

let just look at four countries

jCountries<- c("Canada", "Rwanda", "Cambodia", "Mexico")
ggplot(subset(gapminder, country%in%jCountries),
aes(x=year, y=lifeExp, color=country)) + geom_line() + geom_point()

when you really care, make your legend easy to navigate this means visual order = data order = factor level order

ggplot(subset(gapminder, country%in%jCountries),
aes(x=year, y=lifeExp, color= reorder(country, -1*lifeExp, max))) +
geom_line() + geom_point()

another approach to overplotting ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +

ggplot(gapminder, aes(x=gdpPercap, y=lifeExp)) +
scale_x_log10() + geom_bin2d()

sessionInfo()
## R version 3.3.1 (2016-06-21)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.11.6 (El Capitan)
## ## locale:
## [1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8
## ## attached base packages:
## [1] stats graphics grDevices utils datasets methods base ## ## other attached packages:
## [1] dplyr_0.5.0 gapminder_0.2.0 ggplot2_2.1.0 tibble_1.2 ## [5] knitr_1.14.2 ## ## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.7 magrittr_1.5 munsell_0.4.3 ## [4] colorspace_1.2-6 lattice_0.20-33 R6_2.1.3 ## [7] stringr_1.1.0 plyr_1.8.4 tools_3.3.1 ## [10] grid_3.3.1 gtable_0.2.0 nlme_3.1-128 ## [13] mgcv_1.8-13 DBI_0.4-1 htmltools_0.3.5 ## [16] lazyeval_0.2.0 yaml_2.1.13 assertthat_0.1 ## [19] digest_0.6.10 Matrix_1.2-6 formatR_1.4 ## [22] evaluate_0.9 rmarkdown_1.0.9014 labeling_0.3 ## [25] stringi_1.1.1 scales_0.4.0