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load("social-service-agency.RData") # simulated dataBackground
ggplot2 is a powerful graphing library that can make beautiful graphs. ggplot2 can also help us to understand ideas of an underlying “grammar of graphics”.
However, ggplot can be difficult to learn. I am thinking that one way to better understand ggplot2 might be to see how this graphing library could be applied to a concrete example of comparing program outcomes.
In this example, program is a factor and mental health at time 2 is numeric.
Load the Library
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library(ggplot2) # beautiful graphsFirst Approach (x is program; y is mental health)
There is a lot of code below. This is where we are setting up the grammatical logic of the graphing approach.
Devoting some time to setting up the initial logic of the plot will pay dividends in terms of exploring multiple geometries later on.
Note that I am adding optional
scale_...andtheme...arguments just to make the graphs look a little nicer, but these are not an essential part of the code.
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myplot1 <- ggplot(clients, # the data I am using
aes(x = program, # x is program
y = mental_health_T2, # y is mental health
color = program, # color is also program
fill = program)) + # fill is also program
labs(y = "mental health at time 2") + # labels
scale_color_viridis_d() + # beautiful colors
scale_fill_viridis_d() + # beautiful fills
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(size = rel(.75))) # smaller labelsAdd Geometries That Show The Average
Now that we have devoted a lot of code to setting up the grammar of the graph, it is a relatively simple matter to try out different geometries. The geometries show the average value.
Bar Chart
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myplot1 +
stat_summary(fun = "mean", # summarize at mean
geom = "bar") + # bar geometry
labs(title = "Bar Chart")
Horizontal Bar Chart
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myplot1 +
stat_summary(fun = "mean", # summarize at mean
geom = "bar") + # bar geometry
coord_flip() + # flip coordinates
labs(title = "Horizontal Bar Chart")
Point Chart
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myplot1 +
stat_summary(fun = "mean", # summarize at mean
geom = "point", size = 5) + # point geometry
labs(title = "Point Chart") +
ylim(90, 105) # manually adjust y limits
“Lollipop” Chart
The segments connecting the x axis with the points, require their own geometry that has its own aesthetic.
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myplot1 +
stat_summary(fun = "mean",
geom = "point",
size = 5) +
geom_segment(aes(x = program, # x starts at
xend = program, # x ends at
y = 0, # y starts at
yend = mean(mental_health_T2))) + # y ends at
labs(title = "Lollipop Chart") +
ylim(0, 105) # manually adjust y limits
Line Chart
An extra element of the aesthetic is required for lines.
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myplot1 +
stat_summary(aes(group = 1), # line geom needs group aesthetic
color = "black", # consistent color
fun = "mean",
geom = "line") +
labs(title = "Line Chart")
A line chart is likely not an appropriate way to show these program outcomes as a line chart is more appropriate when the x axis represents some kind of time trend.
Add Geometries That Show the Distribution
Now that we have devoted a lot of code to setting up the grammar of the graph, it is a relatively simple matter to try out different geometries. The geometries show the distribution of all values.
Boxplot
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myplot1 +
geom_boxplot(fill="white") + # boxplot geometry
labs(title = "Boxplot")
Violin Plot
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myplot1 +
geom_violin() + # violinplot geometry
labs(title = "Violin Plot")
Points
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myplot1 +
geom_point() + # point geometry
labs(title = "Points")
Jittered Points
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myplot1 +
geom_jitter() + # jittered point geometry
labs(title = "Jittered Points")
Beeswarm Plot
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library(ggbeeswarm) # beeswarm geometry
myplot1 +
geom_beeswarm() + # beeswarm geometry
labs(title = "Beeswarm Plot")
Dotplots
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library(ggdist) # dotplot geometry
myplot1 +
stat_dots() + # dotplot geometry
labs(title = "Dotlot")
Second Approach (x is mental health; facet wrap on program)
Again, there is a lot of code below. This is where we are setting up the grammatical logic of the graphing approach.
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myplot2 <- ggplot(clients, # the data I am using
aes(x = mental_health_T2, # x is mental health
fill = program)) + # fill is program
facet_wrap(~program) + # facet on this variable
labs(x = "mental health at time 2") + # labels
scale_color_viridis_d() + # beautiful colors
scale_fill_viridis_d() + # beautiful fills
theme_bw() # bw theme makes facets more clearAdd Geometries
However, now that we have devoted a lot of code to setting up the grammar of the graph, it is again a relatively simple matter to try out different geometries.
Histogram
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myplot2 +
geom_histogram() + # histogram geometry
labs(title = "Histogram")
Density
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myplot2 +
geom_density() + # density geometry
labs(title = "Density")
Third Approach (x is mental health; transparent geometries)
One last time, there is a lot of code below. This is where we are setting up the grammatical logic of the graphing approach.
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myplot3 <- ggplot(clients, # the data I am using
aes(x = mental_health_T2, # x is mental health
fill = program)) + # fill is program
labs(x = "mental health at time 2") + # labels
scale_color_viridis_d() + # beautiful colors
scale_fill_viridis_d() + # beautiful fills
theme_minimal() # minimal themeAdd Geometries
And again, now that we have devoted a lot of code to setting up the grammar of the graph, it is again a relatively simple matter to try out different geometries.1
Histogram
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myplot3 +
geom_histogram(alpha = .5) + # histogram geometry (transparent)
labs(title="Histogram")
Density
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myplot3 +
geom_density(alpha = .5) + # density geometry (transparent)
labs(title = "Density")
Footnotes
It is important to use
(alpha = ...)to create transparency with thesegeoms.↩︎