The answers so far are very close, but unsatisfactory in my opinion. The problem is the following – after your for loop: myplots[[1]]$mapping #* x -> 1:dfmsize #* y -> dfrm[, i] myplots[[1]]$plot_env #<environment: R_GlobalEnv> myplots[[2]]$mapping #* x -> 1:dfmsize #* y -> dfrm[, i] myplots[[2]]$plot_env #<environment: R_GlobalEnv> i #[1] “B” As the other answers … Read more
Note: this is longer an issue in current releases of cowplot, where the default theme is not changed. Original answer below: You can use theme_get() to see the current “default” theme. You can use theme_set() to change the “default” theme. Theme settings do not carry over sessions. Usually, your default will be theme_grey, but cowplot … Read more
The idea is to create each plot individually (color, fill & size) then extract their legends and combine them in a desired way together with the main plot. See more about the cowplot package here & the patchwork package here library(ggplot2) library(cowplot) # get_legend() & plot_grid() functions library(patchwork) # blank plot: plot_spacer() data <- seq(1000, … Read more
You can use either egg, cowplot or patchwork packages to combine those two plots. See also this answer for more complex examples. library(dplyr) library(ggplot2) plt1 <- my_df %>% select(value) %>% ggplot(aes(x=””, y = value)) + geom_boxplot(fill = “lightblue”, color = “black”) + coord_flip() + theme_classic() + xlab(“”) + theme(axis.text.y=element_blank(), axis.ticks.y=element_blank()) plt2 <- my_df %>% select(id, … Read more
Here is one way to do it with cowplot::plot_grid. The plot_duo function uses tidyeval approach in ggplot2 v3.0.0 # install.packages(“ggplot2”, dependencies = TRUE) library(rlang) library(dplyr) library(ggplot2) library(cowplot) plot_duo <- function(df, plot_var_x, plot_var_y) { if (is.character(plot_var_x)) { print(‘character column names supplied, use ensym()’) plot_var_x <- ensym(plot_var_x) } else { print(‘bare column names supplied, use enquo()’) plot_var_x … Read more