I am learning R with the Australian athletes data set.
By using ggplot, I can plot a histogram like this.
library(DAAG)
ggplot(ais, aes(wt, fill = sex)) +
geom_histogram(binwidth = 5)
By using summary(ais$wt), the 3rd Quartile is 84.12. Now I want to split the data by the wt 84.12. and plot 2 similar histograms accordingly (side by side)
The split is:
ais1 = ais$wt[which(ais$wt>=0 & ais$wt<=84.12)]
ais2 = ais$wt[which(ais$wt>84.12)]
But I don't know how to fit them in the plotting. I tried but it doesn't work:
ggplot(ais1, aes(wt, fill = sex)) +...
How can I plot the histograms (2 similar histograms accordingly, side by side)?
Add the split as a column to your data
ais$wt_3q = ifelse(ais$wt < 84.12, "Quartiles 1-3", "Quartile 4")
Then use facets:
ggplot(ais, aes(wt, fill = sex)) +
geom_histogram(binwidth = 5) +
facet_wrap(~ wt_3q)
The created variable is a factor, if you specify the order of the levels you can order the facets differently (lots of questions on here showing that if you search for them - same as reordering bars for a ggplot barplot). You can also let the scales vary - look at ?facet_wrap
for more details.
Generally, you shouldn't create more data frames. Creating ais1
and ais2
is usually avoidable, and your life will be simpler if you use a single data frame for a single data set. Adding a new column for grouping makes it easy to keep things organized.
We can do this with ggarrange
to arrange the plot objects for each subset
library(DAAG)
library(ggplot2)
library(ggpubr)
p2 <- ais %>%
filter(wt>=0, wt<=84.12) %>%
ggplot(., aes(wt, fill = sex)) +
geom_histogram(binwidth = 5) +
coord_cartesian(ylim = c(0, 30))
p1 <- ais %>%
filter(wt>84.12) %>%
ggplot(., aes(wt, fill = sex)) +
geom_histogram(binwidth = 5) +
coord_cartesian(ylim = c(0, 30))
ggarrange(p1, p2, ncol =2, nrow = 1, labels = c("p1", "p2"))
-output
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