how-to-ggplot2.R

# How to ggplot2 efficiently
# CJ Brown 2020-10-27


library(ggplot2)

#
# Make some data 
#
n <- 20 #Sample size per group
sd <- 5 #SD for errors

#All this is just to make some data, using
# a cubic regression equation 
X <- cbind(rep(1, n), 1:n, (1:n)^2, (1:n)^3)
b <- matrix(c(-10,  0, 50, 
              1,   0.1, -1,
              2,    0.4, -0.1,
              -0.1, -0.01, 0), nrow = 4, byrow = T)
Y1 <- X %*% b[,1] + rnorm(n, sd = sd)
Y2 <- X %*% b[,2] + rnorm(n, sd = sd)
Y3 <- X %*% b[,3] + rnorm(n, sd = sd)

#Put data into a dataframe
dat <- data.frame(
  yobs = c(Y1, Y2, Y3),
  xvar = rep(1:n,  3), 
  grp = rep(LETTERS[1:3], each = n))

#
# Plot the data 
#

#A base R plot, but fiddly to customize!
plot(dat$xvar, dat$yobs)

#A ggplot

#base of the plot , name the data
ggplot(dat) + 
  #add axes
  aes(x = xvar, y = yobs, color = grp) + 
  #add data to axes (here points)
  geom_point() + 
  #add a smoothing trend
  stat_smooth(method = "gam",
              formula = y ~ s(x, k = 4)) + 
  #individual panels for each group
  facet_wrap(~grp) + 
  #change the theme (overall look)
  theme_classic() + 
  #Modify the colour palette 
  scale_color_brewer(palette = "Dark2")
#colorbrewer2.org

#
# Making snippets
#
#Navigate to the menu bar then: 
# Tools > Global Options > Code > 'edit snippets' 
# (the button way down the bottom)

# Here's my snippet: 

# snippet gg
# ggplot(${1:data}) + 
#   aes(x = ${2:x}, y = ${3:y}, color = NULL) +
#   geom_${4:geomtype}()
#The ${1:data} etc... just 

#once that's set up, type 'gg' and hit tab, away you go: 
ggplot(data) + 
  aes(x = x, y = y, color = NULL) +
  geom_geomtype()