Data Visualization - Part One
Miao Yu
2024-01-22
Create scatter plots in R
Now let’s learn from creating scatter plots, which is one of the most
commonly used graphs in scientific research. Let’s plot the
cty variable against the hwy variable in the
mpg data set, which is given below.
“jitter” positioning to avoid overplotting (Optional)
We can add the option position = "jitter" into the
geom_point function to avoid overplotting problem. By doing
this, we add a small amount of random noise to each point, which spreads
the points out.
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy), position = "jitter")Here the position argument controls position adjustments, which determines how to arrange geoms that would otherwise occupy the same space.
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy), position = "jitter")
Create a bar plot
Next, let’s learn how to create a bar plot for one variable with
ggplot2. The code template is very similar to that for
scatter plots. But it must be for a categorical variable and we don’t
need the y variable in the mapping. Now let’s
plot the bar plot for the variable drv in the
mpg data set.
ggplot(data = mpg) +
geom_bar(mapping = aes(x = drv))Here we use the function geom_bar to create a bar
plot.
ggplot(data = mpg) +
geom_bar(mapping = aes(x = drv))
A bar plot summarizes the count (or frequency) of each category in the data set.
Create a colored bar plot
We can use the fill argument in the aes
function to make a bar plot colored.
ggplot(data = mpg) +
geom_bar(mapping = aes(x = drv, fill = drv))
Stacked bar plot
Here we use the same value of fill and x
argument, which means “filled by colors of different x values”. If we
use different values, it becomes a stacked bar
plot.
ggplot(data = mpg) +
geom_bar(mapping = aes(x = drv, fill = class))
Dodged bar plot
Another way to show the distribution among combination of two categorical variables is using dodged bar plot.
ggplot(data = mpg) +
geom_bar(mapping = aes(x = drv, fill = class), position = "dodge")
Again, we use the position argument in geom
function to adjust the position of bars.
Box plots
Next, let’s learn how to create a box plot. A box plot summarizes key information about the center, spread and potential outliers of a numeric variable.
First, let’s review how to read a box plot.
Create a box plot
We use the function geom_boxplot to create a box
plot.
ggplot(data = mpg) +
geom_boxplot(mapping = aes(x = displ)) +
scale_y_discrete(breaks = NULL) # Remove the y scales
ggplot(data = mpg) +
geom_boxplot(mapping = aes(y = displ)) + # The plot can also be vertical
scale_x_discrete(breaks = NULL) # Remove the x scales
Add whisker lines
The looking of boxplots created by ggplot2 does not have
the whisker lines. There is a trick to add them onto our plot:
ggplot(data = mpg, mapping = aes(y = displ)) +
stat_boxplot(geom = "errorbar", width = 0.5) + # The "width" controls the line size
geom_boxplot() +
scale_x_discrete(breaks = NULL)
Create multiple boxplots
More often, multiple boxplots are used to compare the effect of a
categorical variable on a numeric variable. It’s very easy to do this
with ggplot. We simply use both x and
y arguments.
For example, we hope to study the effect of drive train types on fuel
economy measured by hwy. We can create the plots with the
following code.
ggplot(data = mpg, mapping = aes(x = drv, y = hwy)) +
stat_boxplot(geom = "errorbar", width = 0.5) +
geom_boxplot()
Creating a smooth line graph
How are these two plots similar?
Both plots use the same data set, but different visual objects (which we call geoms).
Multiple-layer plot
What if we hope to combine the two plots into one plot? It is very
simple to do it with ggplot2 - we just apply two
geom functions which add two layers to the same plot.
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
geom_smooth(mapping = aes(x = displ, y = hwy))
We can put mapping into the ggplot function
to avoid redundant codes
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() + geom_smooth()