R ggplot // Multiple Grouping in X-axis - r

Question

I am trying to implement the diagram 1 from Excel to Shiny. So far I got this code with the resulting diagram 2.
ggplot(filteredData(), aes(x=interaction(month, year), y=sum))
+ geom_bar(stat="identity") + facet_grid(. ~ X) + theme(legend.position="none")
I want to group month and year like in the Excel example, so hat you have only the month counter ("1", "2", ...) in the first row of the legend and the year ("2016", "2017", ...) in the second. The number of months can vary.
The data set looks like:
X year month sum
10 2016 1 450
10 2016 2 670
... ... ... ...
10 2017 1 200
11 2016 1 460

Answer 1

I slightly changed the data set, this is the closest I got to your specs:
df <- read.table(text = "X year month sum
10 2016 1 450
10 2016 2 670
10 2017 1 200
11 2016 1 460
11 2017 2 500", header = T)
# Notice the variable type for month and year
df$month <- as.factor(df$month)
df$year <- as.factor(df$year)
df$X <- as.factor(df$X)
ggplot(df, aes(x = month, y = sum)) + geom_bar(stat = "identity") +
facet_grid(.~X + year,
switch = "x", # Moves the labels from the top to the bottom
labeller = label_both # Adds the labels to the year and X variables
) +
xlab("") # Removes the month label
Result:
Or if you want to drop unused levels:
ggplot(df, aes(x = month, y = sum)) + geom_bar(stat = "identity") +
facet_grid(.~X + year,
switch = "x", # Moves the labels from the top to the bottom
labeller = label_both, # Adds the labels to the year and X variables
scales = "free_x") +
xlab("") # Removes the month legend

Answer 2

You can get a little more complex and use cowplot to merge the plots together. You could automate this using lapply to loop through your unique values, though that is probably overkill for just two groups.
library(ggplot2)
library(cowplot)
library(dplyr)
# Return to default theme, as cowplot sets its own
theme_set(theme_gray())
# Save y limits to get same scale
myYlims <- c(0, ceiling(max(df$sum)/100)*100)
# Generate each plot
x10 <-
ggplot(df %>%
filter(X == 10)
, aes(x = month, y = sum)) + geom_bar(stat = "identity") +
facet_grid(~ year,
switch = "x") +
panel_border() +
coord_cartesian(ylim = myYlims) +
xlab("X = 10")
x11 <-
ggplot(df %>%
filter(X == 11)
, aes(x = month, y = sum)) + geom_bar(stat = "identity") +
facet_grid(~ year,
switch = "x") +
panel_border() +
coord_cartesian(ylim = myYlims) +
xlab("X = 11")
# Put the plots together
plot_grid(x10
, x11 +
theme(axis.title.y = element_blank()
, axis.text.y = element_blank()
, axis.ticks.y = element_blank())
, rel_widths = c(1.1,1)
)
Here is an approach to automate this, including more complex data to justify the automation. Note that you will need to play with the aspect ratio of your output and with the rel_widths option to make it look decent:
df <-
data.frame(
X = rep(1:6, each = 9)
, year = rep(rep(2016:2018, each = 3),3)
, month = rep(1:3, 6)
, sum = rnorm(9*6, 700, 100)
)
# Notice the variable type for month and year
df$month <- as.factor(df$month)
df$year <- as.factor(df$year)
df$X <- as.factor(df$X)
# Save y limits to get same scale
myYlims <- c(0, ceiling(max(df$sum)/100)*100)
# Generate each plot
eachPlot <- lapply(levels(df$X), function(thisX){
ggplot(df %>%
filter(X == thisX)
, aes(x = month, y = sum)) +
geom_bar(stat = "identity") +
facet_grid(~ year,
switch = "x") +
panel_border() +
coord_cartesian(ylim = myYlims) +
xlab(paste("X =", thisX))
})
# Remove axes from all but the first
eachPlot[-1] <- lapply(eachPlot[-1], function(x){
x +
theme(axis.title.y = element_blank()
, axis.text.y = element_blank()
, axis.ticks.y = element_blank()
)
})
# Put the plots together
plot_grid(plotlist = eachPlot
, rel_widths = c(1.4, rep(1, length(eachPlot)-1))
, nrow = 1
)