Bar Charts with Diamonds Dataset

Rachel Saidi

Access Library package - Tidyverse

library(tidyverse)

Load the pre-built dataset, Diamonds, and view it in the global environment

head(diamonds)  # shows the first few lines of the dataset

# A tibble: 6 × 10
  carat cut       color clarity depth table price     x     y     z
  <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48

data(diamonds)   # places the dataset in the global environment

Statistical transformations (from R for Data Science)

Bar charts seem simple, but they are interesting because they reveal something subtle about plots. Consider a basic bar chart, as drawn with geom_bar(). The following chart displays the total number of diamonds in the diamonds dataset, grouped by cut. The diamonds dataset comes in ggplot2 and contains information about ~54,000 diamonds, including the price, carat, color, clarity, and cut of each diamond. The bar graph shows that more diamonds are available with high quality cuts than with low quality cuts.

First Bar Plot

ggplot(data = diamonds) + 
  geom_bar(aes(x = cut))

How do bar charts work with 2 variables?

Bar graphs are EASY when you have a single categorical variable that defines several levels for each observation. Ex: “cut” has levels: fair, good, very good, premium, and ideal. Each observation is categorized this way. But what if you have a table of aggregated data: x = cut vs y = frequency?

Here is a tibble to show this table and how you can create a bar graph from this data

A Tibble/Tribble (think of this like a dataframe)

We will create a frequency table of the types of cuts that mimick the calculations done to create geom_bar

demo <- tribble(
      ~cut,         ~freq,
      "Fair",       1610,
      "Good",       4906,
      "Very Good",  12082,
      "Premium",    13791,
      "Ideal",      21551
    )

Demo Tibble Bar Plot Looks just like our other bar graphs

ggplot(data = demo) +
  geom_bar(mapping = aes(x = cut, y = freq), stat = "identity")

(Don’t worry that you haven’t seen tibble() or tribble() before. You might be able to guess at their meaning from the context, and you’ll learn exactly what they do soon!)

Creating Proportional Bars

You might want to override the default mapping from transformed variables to aesthetics. For example, you might want to display a bar chart of proportion, rather than count:

Proportional Bar Graph (Relative Frequencies)

You need “group=1” when plotting proportions (try to omit it and see)

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, y = stat(prop), group = 1)) +
  labs(x = "Diamond Cut", y = "Proportion", 
       title = "Proportional Bar Graph of Diamond Cuts")

Some details about proportional plots

To find the variables computed by the stat, look for the help section titled “computed variables”.

This is a different type of plot that shows a line with min, max, and median values

You might want to draw greater attention to the statistical transformation in your code. For example, you might use stat_summary(), which summarises the y values for each unique x value, to draw attention to the summary that you’re computing:

Line Plot

This is a different way of visualizing center and spread of cuts and depth

ggplot(data = diamonds) + 
     stat_summary(
        mapping = aes(x = cut, y = depth),
        fun.min = min,
        fun.max = max,
        fun = median
  )

Fill vs Color

There’s one more piece of magic associated with bar charts. You can color a bar chart using either the color aesthetic, or, more usefully, fill:

Notice that “fill=” fills the inside of the bar, whereas “color=” draws a color outline of the bar. Alpha gives a level of transparency, with alpha = 0 is invisible and alpha = 1 is fully saturated

Bar Plot with Alpha Transparency

ggplot(data = diamonds, aes(x=cut, fill = cut)) + 
  geom_bar(alpha = 0.5)+  # try replacing alpha = 0.5 with 0.8 to see how it changes
  labs(x = "Diamond Cut", y = "Frequency", 
       title = "Frequency Bar Graph of Diamond Cuts")

Try stacking bar graphs with position = “stack”

ggplot(data = diamonds) + 
  geom_bar(aes(x = cut, fill = clarity), position = "stack") +
    labs(x = "Diamond Cut", y = "Frequency", 
       title = "Stacked Bar Graph of Diamond Cuts by Clarity")

Note what happens if you map the fill aesthetic to another variable, like clarity: the bars are automatically stacked. Each colored rectangle represents a combination of cut and clarity.

Note position = “dodge” or “identity” or “fill”

The identity position adjustment is more useful for 2d geoms, like points, where it is the default. position = “fill” works like stacking, but makes each set of stacked bars the same height. This makes it easier to compare proportions across groups.

Using position = “fill” the bars fill the vertical space proportionally

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "fill") +
    labs(x = "Diamond Cut", y = "Proportion", 
       title = "Proportional Bar Graph of Diamond Cuts by Clarity")

Position = “dodge” will get side-by-side bars

ggplot(data = diamonds, aes(x = cut, fill = clarity)) + 
      geom_bar(alpha = .7, position = "dodge") +
    labs(x = "Diamond Cut", y = "Frequency", 
       title = "Side-by-Side Bar Graph of Diamond Cuts by Clarity")

Change the angle of the x-axis labels

When x-axis labels are too long, they may overlap. You can change the text angle with axis.text.x = element_text(angle = 45))

ggplot(data = diamonds, aes(x = cut, fill = clarity)) +
  geom_bar(alpha = .7, position = "dodge") +
    labs(x = "Diamond Cut", y = "Frequency", 
       title = "Side-by-Side Bar Graph of Diamond Cuts by Clarity") +
  theme(axis.text.x = element_text(angle = 45))

Finally, make the x-axis labels fit in a narrow width

Here is another option for dealing with x-axis labels when they are long. You can use this function to break words into 2 lines.

ggplot(data = diamonds, aes(x = cut, fill = clarity)) +
  geom_bar(alpha = .7, position = "dodge") +
  labs(x = "Diamond Cut", y = "Frequency", 
       title = "Side-by-Side Bar Graph of Diamond Cuts by Clarity") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 5))

Notice “very good” will fit on two lines instead of one line

Bonus “bar-like” graph - Polar Plot

bar <- ggplot(data = diamonds) + 
  geom_bar(aes(x = clarity, fill = clarity), 
           show.legend = FALSE, width = 1) + 
  theme(aspect.ratio = 1)

bar + coord_polar()