#' ---

#' author: "Jenny Bryan"

#' output:

#' html_document:

#' keep_md: TRUE

#' ---

#+ setup, include = FALSE

library(knitr)

opts_chunk$set(fig.path = 'figure/scatterplot-', error = TRUE)

#' Note: this HTML is made by applying `knitr::spin()` to an R script. So the

#' narrative is very minimal.

library(ggplot2)

#' pick a way to load the data

#gdURL <- "http://tiny.cc/gapminder"

#gapminder <- read.delim(file = gdURL)

#gapminder <- read.delim("gapminderDataFiveYear.tsv")

library(gapminder)

str(gapminder)

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

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

#' scatterplot

p + geom_point()

#p + layer(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))

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

geom_point() + scale_x_log10() # in full detail, up to now

#' 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

#' * make mini-plots, split out by continent

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

#' * 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()