How to Create a ggplot Frequency Plot in R

Intro

A Frequency plot is similar to a Histogram as it bins the count of continuous data. However, instead of using bars to display, it will use a line plot. In this article, we will learn how to create ggplot frequency plots in R.

For those who are in a Hurry

For those with little time, here is a quick snippet of box plots. Read on for more details.

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.0     v dplyr   1.0.5
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

data(diamonds)

ggplot(diamonds, aes(x = price, colour = cut)) +
  geom_freqpoly(binwidth = 50)

Loading the Data

For our tutorial, we will use the diamonds data set that comes with the ggplot package.

library(tidyverse)

data(diamonds)

glimpse(diamonds)

## Rows: 53,940
## Columns: 10
## $ carat   <dbl> 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, 0.23, 0.~
## $ cut     <ord> Ideal, Premium, Good, Premium, Good, Very Good, Very Good, Ver~
## $ color   <ord> E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, J, J, I,~
## $ clarity <ord> SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI1, VS1, ~
## $ depth   <dbl> 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4, 64~
## $ table   <dbl> 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54, 62, 58~
## $ price   <int> 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339, 340, 34~
## $ x       <dbl> 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, 4.00, 4.~
## $ y       <dbl> 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, 4.05, 4.~
## $ z       <dbl> 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, 2.39, 2.~

The Basic ggplot Frequency Plot

We can create a frequency plot by adding the geom_freqpoly geom to our ggplot. Below is an example of plotting the price of diamonds.

library(ggplot2)

ggplot(diamonds, aes(x = price)) +
  geom_freqpoly()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Frequency plot will automatically select bins for your continous varaibles and plot how many observations fall into those bins. If you would like to control the bin width, you can pass the bins paramter to the geom.

ggplot(diamonds, aes(x = price)) +
  geom_freqpoly(bins=10)

Customizing the ggplot Frequency Plot

We can customize our box plots using some parameters on the geom_freqpoly method. For example, we can change the color using the color named parameter. Here is an example.

ggplot(diamonds, aes(x = price)) +
  geom_freqpoly(
    color = 4,
    lwd = 1,
    linetype = 2
  )

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Customize the labels on the ggplot Frequency Plot

We can adjust the title, x-label, and y-label of our box plot using the labs method. We then pass the title, x and y parameters.

ggplot(diamonds, aes(x = price)) +
  geom_freqpoly() +
  labs(
    title = "Frequency of Diamonds Price",
    x = "Observation",
    y = "Price"
  )

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Limiting X and Y on a ggplot Frequency Plot

If we would like to limit the y values of our plots, we can use the ylimit function

ggplot(diamonds, aes(x = price)) +
  geom_freqpoly() +
  xlim(10, 10000) +
  ylim(0, 5000)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 5222 rows containing non-finite values (stat_bin).

## Warning: Removed 3 row(s) containing missing values (geom_path).

Scaling X and Y

We can also scale the y axis using the scale_ function from ggplot. Here are some example of a log10 and sqrt scale of the y axis.

ggplot(diamonds, aes(x = price)) +
  geom_freqpoly() +
  scale_y_log10()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Transformation introduced infinite values in continuous y-axis

ggplot(diamonds, aes(x = price)) +
  geom_freqpoly() +
  scale_y_sqrt()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Groups by Color

We can color the separate groups of our violin plots by using the fill or colour aesthetic properties. Here is an example of using the fill to assign colors to each factor.

library(ggplot2)

ggplot(diamonds, aes(x = price, colour = cut, fill = cut)) +
  geom_freqpoly()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Facets

If we prefer to have separate plots, we can use the facet_ methods in ggplot. For example, here are plots separated by each cut.

library(ggplot2)

ggplot(diamonds, aes(x = price, colour = cut, fill = cut)) +
  geom_freqpoly() +
  facet_grid(~cut)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Color and Fill Scales

There are many color options in ggplot. We can use scale_ methods like scale_fill_brewer() to have ggplot automatically assign different themes based on our data set.

library(ggplot2)

ggplot(diamonds, aes(x = price, colour = cut, fill = cut)) +
  geom_freqpoly() +
  scale_fill_brewer()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Customizing the Legend

When we have groups, ggplot will add a legend to the plot. We can customize the position of this legend using the theme method and the legend.position parameter. Here are example of moving the legend to the top, bottom, and hiding it.

ggplot(diamonds, aes(x = price, colour = cut, fill = cut)) +
  geom_freqpoly() +
  theme(legend.position="top")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(diamonds, aes(x = price, colour = cut, fill = cut)) +
  geom_freqpoly() +
  theme(legend.position="bottom")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(diamonds, aes(x = price, colour = cut, fill = cut)) +
  geom_freqpoly() +
  theme(legend.position="none")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Using Themes

If we want to use built in styles for the full plot, ggplot provides themes to add to our plot. Here is an example of adding the theme_classic to our plot.

ggplot(diamonds, aes(x = price, colour = cut, fill = cut)) +
  geom_freqpoly() +
  theme_classic()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.