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To reorder the facets accordingly of the given ggplot2 plot, the user needs to reorder the levels of our grouping variable accordingly with the help of the levels function and required parameter passed into it, further it will lead to the reordering of the facets accordingly in the R programming language.
While facet_grid shows the labels at the margins of the facet plot, facet_wrap creates a label for each plot panel.
facet_grid() forms a matrix of panels defined by row and column faceting variables. It is most useful when you have two discrete variables, and all combinations of the variables exist in the data.
facet_wrap() makes a long ribbon of panels (generated by any number of variables) and wraps it into 2d. This is useful if you have a single variable with many levels and want to arrange the plots in a more space efficient manner. You can control how the ribbon is wrapped into a grid with ncol , nrow , as.
I don't think I can really satisfy your "without making a new data frame" requirement, but you can create the new data frame on the fly:
ggplot(transform(iris,
Species=factor(Species,levels=c("virginica","setosa","versicolor")))) +
geom_histogram(aes(Petal.Width))+ facet_grid(Species~.)
or, in tidyverse idiom:
iris %>%
mutate(across(Species, factor, levels=c("virginica","setosa","versicolor"))) %>%
ggplot() +
geom_histogram(aes(Petal.Width))+
facet_grid(Species~.)
I agree it would be nice if there were another way to control this, but ggplot is already a pretty powerful (and complicated) engine ...
Note that the order of (1) the rows in the data set is independent of the order of (2) the levels of the factor. #2 is what factor(...,levels=...) changes, and what ggplot looks at to determine the order of the facets. Doing #1 (sorting the rows of the data frame in a specified order) is an interesting challenge. I think I would actually achieve this by doing #2 first, and then using order() or arrange() to sort according to the numeric values of the factor:
neworder <- c("virginica","setosa","versicolor")
library(plyr) ## or dplyr (transform -> mutate)
iris2 <- arrange(transform(iris,
Species=factor(Species,levels=neworder)),Species)
I can't immediately see a quick way to do this without changing the order of the factor levels (you could do it and then reset the order of the factor levels accordingly).
In general, functions in R that depend on the order of levels of a categorical variable are based on factor level order, not the order of the rows in the dataset: the answer above applies more generally.
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