Histograms with ggplot2

Let's go over how to create histograms with ggplot2. Refer to the video for the full explanation! Also a quick note, we are going to be showing a lot of what ggplot can do, but not what you should do!

Load Data

We'll use the movie dataset that comes with ggplot:

library(ggplot2)
df <- movies <- movies[sample(nrow(movies), 1000), ]

head(df)

	title	year	length	budget	rating	votes	r1	r2	r3	r4	r5	r6	r7	r8	r9	r10	mpaa	Action	Animation	Comedy	Drama	Documentary	Romance	Short
21569	Gunan il guerriero	1983	81	NA	2	38	44.5	14.5	4.5	14.5	14.5	0	4.5	4.5	4.5	4.5		1	0	0	0	0	0	0
39820	Phantom Brother	1988	92	NA	2	10	45.5	14.5	0	0	0	24.5	14.5	0	0	14.5		0	0	0	0	0	0	0
47662	Snow	1996	81	NA	7.2	10	0	0	14.5	0	0	44.5	14.5	24.5	0	24.5		0	0	0	1	0	0	0
41080	Prelude	2000	6	NA	6.3	18	4.5	0	0	14.5	4.5	0	24.5	24.5	4.5	24.5		0	0	0	0	0	0	1
58741	Zwaarmoedige verhalen voor bij de centrale verwarming	1975	95	NA	5	27	14.5	0	0	4.5	14.5	14.5	14.5	14.5	14.5	24.5		0	0	1	1	0	0	0
47430	Sleepy-Time Tom	1951	7	NA	6.7	32	0	0	0	14.5	4.5	4.5	24.5	14.5	24.5	4.5		0	1	1	0	0	0	1

Using qplot()

Basics

qplot(rating,data=df,geom='histogram',binwidth=0.1,alpha=0.8)

Using ggplot()

Let's see how we can really expand on this by using ggplot! They syntax starts off with the base plot:

# ggplot(data, aesthetics)
pl <- ggplot(df,aes(x=rating))

# Add Histogram Geometry
pl + geom_histogram()

stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

Adding Color

pl <- ggplot(df,aes(x=rating))
pl + geom_histogram(binwidth=0.1,color='red',fill='pink')

Adding Labels

pl <- ggplot(df,aes(x=rating))
pl + geom_histogram(binwidth=0.1,color='red',fill='pink') + xlab('Movie Ratings')+ ylab('Occurences') + ggtitle(' Movie Ratings')

Change Alpha (Transparency)

pl <- ggplot(df,aes(x=rating))
pl + geom_histogram(binwidth=0.1,fill='blue',alpha=0.4) + xlab('Movie Ratings')+ ylab('Occurences')

Linetypes

We have the options: "blank", "solid", "dashed", "dotted", "dotdash", "longdash", and "twodash". You would never really use these with a histogram, but just to show your options:

pl <- ggplot(df,aes(x=rating))
pl + geom_histogram(binwidth=0.1,color='blue',fill='pink',linetype='dotted') + xlab('Movie Ratings')+ ylab('Occurences')

Advanced Aesthetics

We can add a aes() argument to the geom_histogram for some more advanced features. We won't go too deep into these, but ggplot gives you the ability to edit color and fill scales.

# Adding Labels
pl <- ggplot(df,aes(x=rating))
pl + geom_histogram(binwidth=0.1,aes(fill=..count..)) + xlab('Movie Ratings')+ ylab('Occurences')

You can further edit this by adding the scale_fill_gradient() function to your ggplot objects:

# Adding Labels
pl <- ggplot(df,aes(x=rating))
pl2 <- pl + geom_histogram(binwidth=0.1,aes(fill=..count..)) + xlab('Movie Ratings')+ ylab('Occurences')

# scale_fill_gradient('Label',low=color1,high=color2)
pl2 + scale_fill_gradient('Count',low='blue',high='red')

# scale_fill_gradient('Label',low=color1,high=color2)
pl2 + scale_fill_gradient('Count',low='darkgreen',high='lightblue')

Adding density plot

You can add a kernel density estimation plot

# Adding Labels
pl <- ggplot(df,aes(x=rating))
pl + geom_histogram(aes(y=..density..)) + geom_density(color='red')

stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

Alright! That's all for now concerning histograms. We've shown that ggplot has amazing customization capabilities, however it definitely takes time to get used to!