Loop to plot boxplot with ggplot - r

Question

I am using diamonds df,
I would like to plot a boxplot for each numerical column by category,
In this case category would be defined by "cut" column.
I am using a for-loop to accomplish this task,
Here's the code I am using:
##################################################################################
# Data #
# #
##################################################################################
data("diamonds")
basePlot <- diamonds[ names(diamonds)[!names(diamonds) %in% c("color", "clarity")] ]
##################################################################################
## set Plot view to 4 boxplots ##
par(mfrow = c(2,2))
## for-loop to boxplot all numerical columns ##
for (i in 1:(ncol(basePlot)-1)){
print(ggplot(basePlot, aes(as.factor(cut),
basePlot[c(i)],color=as.factor(cut)))
+ geom_boxplot(outlier.colour="black",outlier.shape=16,outlier.size=1,notch=FALSE)
+ xlab("Diamond Cut")
+ ylab(colnames(basePlot)[i])
)
}
Console output:
Don't know how to automatically pick scale for object of type data.frame. Defaulting to continuous.
Error in is.finite(x) : default method not implemented for type 'list'
Is there any other way to accomplish this task?

Answer 1

Instead of multiple plots, I suggest facets. To do this, though, we need to convert the data from "wide" format to "longer" format, and the canonical way in the tidyverse is with tidyr::pivot_longer.
> basePlot
# A tibble: 53,940 x 8
carat cut depth table price x y z
<dbl> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.23 Ideal 61.5 55 326 3.95 3.98 2.43
2 0.21 Premium 59.8 61 326 3.89 3.84 2.31
3 0.23 Good 56.9 65 327 4.05 4.07 2.31
4 0.290 Premium 62.4 58 334 4.2 4.23 2.63
5 0.31 Good 63.3 58 335 4.34 4.35 2.75
6 0.24 Very Good 62.8 57 336 3.94 3.96 2.48
7 0.24 Very Good 62.3 57 336 3.95 3.98 2.47
8 0.26 Very Good 61.9 55 337 4.07 4.11 2.53
9 0.22 Fair 65.1 61 337 3.87 3.78 2.49
10 0.23 Very Good 59.4 61 338 4 4.05 2.39
# ... with 53,930 more rows
> pivot_longer(basePlot, -cut, names_to="var", values_to="val")
# A tibble: 377,580 x 3
cut var val
<ord> <chr> <dbl>
1 Ideal carat 0.23
2 Ideal depth 61.5
3 Ideal table 55
4 Ideal price 326
5 Ideal x 3.95
6 Ideal y 3.98
7 Ideal z 2.43
8 Premium carat 0.21
9 Premium depth 59.8
10 Premium table 61
# ... with 377,570 more rows
With this, we only have to tell ggplot2 to worry about val for the values, and var for the x-axis.
library(ggplot2)
library(tidyr) # pivot_longer
ggplot(pivot_longer(basePlot, -cut, names_to="var", values_to="val"),
aes(cut, val, color=cut)) +
geom_boxplot(outlier.colour="black", outlier.shape=16, outlier.size=1, notch=FALSE) +
xlab("Diamond Cut") +
facet_wrap(~var, nrow=2, scales="free") +
scale_x_discrete(guide=guide_axis(n.dodge=2))
The reason you have cut both in the x-axis and in the legend is because color= will add the legend. Since it's redundant, we could either remove the color aesthetic (which would also remove the legend) or we could just suppress the legend (by adding + scale_color_discrete(guide=FALSE)).
There are two ways of faceting: facet_wrap and facet_grid. The latter is well tuned for multiple variables (one facet variable on the x, one on the y) and many other configurations. Granted, you can use facet_grid with just one variable (which is similar to facet_wrap(nrow=1) or ncol=1), but there are some styling distinctions between them.