change of axis style

motivation

• change the default ggplot style into a style close to publication ready

library(tidyverse, quietly=TRUE)
library(grid)

define my_theme

my_theme <- function(){
  list(  
    theme_classic(base_size=10) +
      theme(line = element_line(size = 0.25),
            axis.ticks.length=unit(-1, "mm"),
            axis.text.x = element_text(margin=unit(c(1,1,1,1), "mm")),
            axis.text.y = element_text(margin=unit(c(1,1,1,1), "mm")),
            legend.position = "none",
            strip.background = element_blank()
      ))
}

prepare data and ggplot

d <- tibble(t=1:100, 
            y=sin(seq(0, 10*pi, length.out=100)) + rnorm(100, 0, 0.5),
            y_sys=sin(seq(0, 10*pi, length.out=100)))

g1 <- ggplot(d) + 
  geom_point(aes(t, y), pch=1) +
  geom_line(aes(t, y_sys), alpha=0.5) +
  geom_line(aes(t, y_sys), lty=3)

g1

modify plot using my_theme

g2 <- g1 + my_theme()
g2

compare the two plots

my_subplot <- function(nrow=3, ncol=1, 
                       height=7, width=7,
                       ggs) {
  # height and width are in unit inch
  # ggs: ggplot objects wrapped as list
  # order of ggs is like matrix(1:length(ggs), nrow, ncol)
  # todo: spanning
  my_layout <- grid.layout(nrow, ncol,
                           heights=unit(rep(height, nrow)/nrow, 
                                        rep("inch", nrow)),
                           widths=unit(rep(width, ncol)/ncol, 
                                       rep("inch", ncol)))
  
  fg <- frameGrob(my_layout)
  n <- length(ggs)
  for (i in 1:n) {
    my_row <- ifelse(i %% nrow == 0, nrow, i %% nrow)
    my_col <- ifelse(i %% nrow == 0, i %/% nrow, (i %/% nrow) + 1)
    fg <- placeGrob(fg, 
                    ggplotGrob(ggs[[i]]),
                    row=my_row, col=my_col)
  }
  fg
}

c.f. https://rpubs.com/katzkagaya/851632

grid.newpage()
grid.draw(
  my_subplot(
    2, 1,
    3.5, 5,
    ggs=list(g1, g2)
  )
)

references

• Murrell, P. (2005). R graphics. Chapman and Hall/CRC.
• Murrell, P. (2009). R graphics. Wiley Interdisciplinary Reviews: Computational Statistics, 1(2), 216-220.
• Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. ” O’Reilly Media, Inc.”.