6 Chart: Histogram
This section covers how to make histograms.
Gimme a full-fledged example!
Here’s an application of histograms that looks at how the beaks of Galapagos finches changed due to external factors:
And here’s the code:
library(Sleuth3) # data library(ggplot2) # plotting # load data <- Sleuth3::case0201 finches # finch histograms by year with overlayed density curves ggplot(finches, aes(x = Depth, y = ..density..)) + # plotting geom_histogram(bins = 20, colour = "#80593D", fill = "#9FC29F", boundary = 0) + geom_density(color = "#3D6480") + facet_wrap(~Year) + # formatting ggtitle("Severe Drought Led to Finches with Bigger Chompers", subtitle = "Beak Depth Density of Galapagos Finches by Year") + labs(x = "Beak Depth (mm)", caption = "Source: Sleuth3::case0201") + theme(plot.title = element_text(face = "bold")) + theme(plot.subtitle = element_text(face = "bold", color = "grey35")) + theme(plot.caption = element_text(color = "grey68"))
For more info on this dataset, type
?Sleuth3::case0201 into the console.
6.3 Simple examples
Whoa whoa whoa! Much simpler please!
Let’s use a very simple dataset:
# store data <- c(50, 51, 53, 55, 56, 60, 65, 65, 68)x
6.3.1 Histogram using base R
# plot data hist(x, col = "lightblue", main = "Base R Histogram of x")
For the Base R histogram, it’s advantages are in it’s ease to setup. In truth, all you need to plot the data
x in question is
hist(x), but we included a little color and a title to make it more presentable.
Full documentation on
hist() can be found here
6.3.2 Histogram using ggplot2
# import ggplot library(ggplot2) # must store data as dataframe <- data.frame(x) df # plot data ggplot(df, aes(x)) + geom_histogram(color = "grey", fill = "lightBlue", binwidth = 5, center = 52.5) + ggtitle("ggplot2 histogram of x")
The ggplot version is a little more complicated on the surface, but you get more power and control as a result. Note: as shown above, ggplot expects a dataframe, so if you are getting an error where “R doesn’t know what to do” like this:
make sure you are using a dataframe.
Generally speaking, the histogram is one of many options for displaying continuous data.
The histogram is clear and quick to make. Histograms are relatively self-explanatory: they show your data’s empirical distribution within a set of intervals. Histograms can be employed on raw data to quickly show the distribution without much manipulation. Use a histogram to get a basic sense of the distribution with minimal processing necessary.
- For more info about histograms and continuous variables, check out Chapter 3 of the textbook.
6.5 Types of histograms
Use a histogram to show the distribution of one continuous variable. The y-scale can be represented in a variety of ways to express different results:
6.5.1 Frequency or count
y = number of values that fall in each bin
6.5.2 Relative frequency historgram
y = number of values that fall in each bin / total number of values
6.5.3 Cumulative frequency histogram
y = total number of values <= (or <) right boundary of bin
y = relative frequency / binwidth
6.6.1 Bin boundaries
Be mindful of the boundaries of the bins and whether a point will fall into the left or right bin if it is on a boundary.
# format layout <- par(mfrow = c(1, 2), las = 1) op # right closed hist(x, col = "lightblue", ylim = c(0, 4), xlab = "right closed ex. (55, 60]", font.lab = 2) # right open hist(x, col = "lightblue", right = FALSE, ylim = c(0, 4), xlab = "right open ex. [55, 60)", font.lab = 2)
6.6.2 Bin number
The default bin number of 30 in ggplot2 is not always ideal, so consider altering it if things are looking strange. You can specify the width explicitly with
binwidth or provide the desired number of bins with
# default...note the pop-up about default bin number ggplot(finches, aes(x = Depth)) + geom_histogram() + ggtitle("Default with pop-up about bin number")
Here are examples of changing the bins using the two ways described above:
# using binwidth <- ggplot(finches, aes(x = Depth)) + p1 geom_histogram(binwidth = 0.5, boundary = 6) + ggtitle("Changed binwidth value") # using bins <- ggplot(finches, aes(x = Depth)) + p2 geom_histogram(bins = 48, boundary = 6) + ggtitle("Changed bins value") # format plot layout library(gridExtra) grid.arrange(p1, p2, ncol = 2)
6.6.3 Bin alignment
Make sure the axes reflect the true boundaries of the histogram. You can use
boundary to specify the endpoint of any bin or
center to specify the center of any bin.
ggplot2 will be able to calculate where to place the rest of the bins (Also, notice that when the boundary was changed, the number of bins got smaller by one. This is because by default the bins are centered and go over/under the range of the data.)
<- data.frame(x) df # default alignment ggplot(df, aes(x)) + geom_histogram(binwidth = 5, fill = "lightBlue", col = "black") + ggtitle("Default Bin Alignment")
# specify alignment with boundary <- ggplot(df, aes(x)) + p3 geom_histogram(binwidth = 5, boundary = 60, fill = "lightBlue", col = "black") + ggtitle("Bin Alignment Using boundary") # specify alignment with center <- ggplot(df, aes(x)) + p4 geom_histogram(binwidth = 5, center = 67.5, fill = "lightBlue", col = "black") + ggtitle("Bin Alignment Using center") # format layout library(gridExtra) grid.arrange(p3, p4, ncol = 2)
Note: Don’t use both
center for bin alignment. Just pick one.
6.7 Interactive histograms with
ggvis package is not currently in development, but does certain things very well, such as adjusting parameters of a histogram interactively while coding.
Since images cannot be shared by knitting (as with other packages, such as
plotly), we present the code here, but not the output. To try them out, copy and paste into an R session.
6.7.1 Change binwidth interactively
library(tidyverse) library(ggvis) %>% ggvis(~eruptions) %>% faithful layer_histograms(fill := "lightblue", width = input_slider(0.1, 2, value = .1, step = .1, label = "width"))
6.7.2 GDP example
<-read.csv("countries2012.csv") df %>% ggvis(~GDP) %>% df layer_histograms(fill := "green", width = input_slider(500, 10000, value = 5000, step = 500, label = "width"))
6.7.3 Change center interactively
<- data.frame(x = c(50, 51, 53, 55, 56, 60, 65, 65, 68)) df %>% ggvis(~x) %>% df layer_histograms(fill := "red", width = input_slider(1, 10, value = 5, step = 1, label = "width"), center = input_slider(50, 55, value = 52.5, step = .5, label = "center"))
6.7.4 Change center (with data values shown)
<- data.frame(x = c(50, 51, 53, 55, 56, 60, 65, 65, 68), df y = c(.5, .5, .5, .5, .5, .5, .5, 1.5, .5)) %>% ggvis(~x, ~y) %>% df layer_histograms(fill := "lightcyan", width = 5, center = input_slider(45, 55, value = 45, step = 1, label = "center")) %>% layer_points(fill := "blue", size := 200) %>% add_axis("x", properties = axis_props(labels = list(fontSize = 20))) %>% scale_numeric("x", domain = c(46, 72)) %>% add_axis("y", values = 0:3, properties = axis_props(labels = list(fontSize = 20)))
6.7.5 Change boundary interactively
%>% ggvis(~x) %>% df layer_histograms(fill := "red", width = input_slider(1, 10, value = 5, step = 1, label = "width"), boundary = input_slider(47.5, 50, value = 50, step = .5, label = "boundary"))