Data visualization with ggplot2
MATH-7360 Data Analysis
Dr. Xiang Ji @ Tulane University
Oct 4, 2021
rm(list = ls()) # clean-up workspace
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## [53] R6_2.5.1 compiler_4.1.1Announcement
Mid-term evaluation (voluntary, anonymous, ~ 10 min)
Lab 3 solutions posted
HW1 deadline extended to this Wednesday 10/06
HW2 will be posted on Friday
Data visualization
“The simple graph has brought more information to the data analyst’s mind than any other device.”
John Tukey
mpg data
mpgdata is available from theggplot2package:mpg %>% print(width = Inf)## # A tibble: 234 × 11 ## manufacturer model displ year cyl trans drv cty hwy fl ## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> ## 1 audi a4 1.8 1999 4 auto(l5) f 18 29 p ## 2 audi a4 1.8 1999 4 manual(m5) f 21 29 p ## 3 audi a4 2 2008 4 manual(m6) f 20 31 p ## 4 audi a4 2 2008 4 auto(av) f 21 30 p ## 5 audi a4 2.8 1999 6 auto(l5) f 16 26 p ## 6 audi a4 2.8 1999 6 manual(m5) f 18 26 p ## 7 audi a4 3.1 2008 6 auto(av) f 18 27 p ## 8 audi a4 quattro 1.8 1999 4 manual(m5) 4 18 26 p ## 9 audi a4 quattro 1.8 1999 4 auto(l5) 4 16 25 p ## 10 audi a4 quattro 2 2008 4 manual(m6) 4 20 28 p ## class ## <chr> ## 1 compact ## 2 compact ## 3 compact ## 4 compact ## 5 compact ## 6 compact ## 7 compact ## 8 compact ## 9 compact ## 10 compact ## # … with 224 more rowsTibbles are a generalized form of data frames, which are extensively used in tidyverse.
displ: engine displacement, in litres.
hwy: highway fuel efficiency, in mile per gallen (mpg).
Aesthetic mappings | r4ds chapter 3.3
A graphing template
ggplot(data = <DATA>) +
<GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))Scatter plot
hwyvsdisplggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy))
An aesthetic maps data to a specifc feature of plot.
Check available aesthetics for a geometric object by
?geom_point.
Color of points
Color points according to
class:ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class))
Size of points
Assign different sizes to points according to
class:ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, size = class))
#> Warning: Using size for a discrete variable is not advised.
Transparency of points
Assign different transparency levels to points according to
class:ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, alpha = class))## Warning: Using alpha for a discrete variable is not advised.
Shape of points
Assign different shapes to points according to
class:ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, shape = class))
Maximum of 6 shapes at a time. By default, additional groups will go unplotted.
Manual setting of an aesthetic
Set the color of all points to be blue:
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy), color = "blue")
Facets | r4ds chapter 3.5
Facets
Facets divide a plot into subplots based on the values of one or more discrete variables.
A subplot for each car type:
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy)) + facet_wrap(~ class, nrow = 2)
A subplot for each car type and drive:
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy)) + facet_grid(drv ~ class)
Geometric objects | r4ds chapter 3.6
geom_smooth(): smooth line
How are these two plots similar?
hwyvsdisplline:ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy))
ggplot(data = mpg) + geom_smooth(mapping = aes(x = displ, y = hwy))
Different line types
Different line types according to
drv:ggplot(data = mpg) + geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv))
Different line colors
Different line colors according to
drv:ggplot(data = mpg) + geom_smooth(mapping = aes(x = displ, y = hwy, color = drv))
Points and lines
Lines overlaid over scatter plot:
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy)) + geom_smooth(mapping = aes(x = displ, y = hwy))
Same as
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + geom_point() + geom_smooth()
Aesthetics for each geometric object
Different aesthetics in different layers:
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + geom_point(mapping = aes(color = class)) + geom_smooth(data = filter(mpg, class == "subcompact"), se = FALSE)
Bar plots | r4ds chapter 3.7
diamonds data
diamondsdata:diamonds## # A tibble: 53,940 × 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 ## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47 ## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53 ## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 ## 10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39 ## # … with 53,930 more rows
Bar plot
geom_bar()creates bar chart:ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut))
Bar charts, like histograms, frequency polygons, smoothers, and boxplots, plot some computed variables instead of raw data.
Check available computed variables for a geometric object via help:
?geom_bar
Use
stat_count()directly:ggplot(data = diamonds) + stat_count(mapping = aes(x = cut))stat_count()has a default geomgeom_bar().
Display frequency instead of counts:
ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, y = stat(prop), group = 1))
Note the aesthetics mapping group=1overwrites the default grouping (bycut) by considering all observations as a group. Without this we getggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, y = stat(prop)))
geom_bar() vs geom_col()
geom_bar()makes the height of the bar proportional to the number of cases in each group (or if the weight aesthetic is supplied, the sum of the weights).ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut)) The height of bar is the number of diamonds in each cut category.geom_col()makes the heights of the bars to represent values in the data.ggplot(data = diamonds) + geom_col(mapping = aes(x = cut, y = carat))
The height of bar is total carat in each cut category.ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, weight = carat))
Positional adjustments | r4ds chapter 3.8
Color bar:
ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, colour = cut))
Fill color:
ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, fill = cut))
Fill color according to another variable:
ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, fill = clarity))
The stacking is performed automatically by the position adjustment specified by the
positionargument.If you don’t want a stacked bar chart, you can use one of three other options:
"identity""dodge""fill""stack"(default)
position = "identity"will place each object exactly where it falls in the context of the graph.ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + geom_bar(alpha = 1/5, position = "identity")
ggplot(data = diamonds, mapping = aes(x = cut, colour = clarity)) + geom_bar(fill = NA, position = "identity")
setting
alphato a small value makes the bars slightly transparentidentityposition adjustment is more useful (default) for 2d geoms
position = "fill"works like stacking, but makes each set of stacked bars the same height.- easier to compare proportions across groups.
ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, fill = clarity), position = "fill")
position = "dodge"places overlapping objects directly beside one another.ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge")
position_jitter()add random noise to X and Y position of each element to avoid overplotting:ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy), position = "jitter")
geom_jitter()is similar:ggplot(data = mpg) + geom_jitter(mapping = aes(x = displ, y = hwy))