ggplot2
Anthony Hung
2020-02-20
Last updated: 2020-02-20
Checks: 6 1
Knit directory: MSTPsummerstatistics/
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Data visualization in many dimensions with ggplot2
Load data and libraries
library(ggplot2)
cars <- mtcars
?mtcarsExamine structure of data
These data contain 32 observations on cars across 11 different variables. Some of these variables (e.g. mpg) are numeric, while others (e.g. cyl) are factors. Notice that these data are in “tidy” format, meaning that:
Each variable forms a column.
Each observation forms a row.
Each type of observational unit forms a table.
dim(cars)[1] 32 11head(cars) mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1Plot showing 2 dimensions
Let’s examine the relationship between two variables, mpg and wt:
ggplot(data = cars) + #data layer
geom_point(aes(x = wt, y = mpg)) #geom layer, aesthetics layer
Plot showing 3 dimensions
Now let’s color by cylinder number. What is important to take into account for the cylinder variable?
ggplot(data = cars) + #data layer
geom_point(aes(x = wt, y = mpg, color = cyl)) #geom layer, aesthetics layer
ggplot(data = cars) + #data layer
geom_point(aes(x = wt, y = mpg, size = factor(cyl))) #geom layer, aesthetics layer, colorWarning: Using size for a discrete variable is not advised.
ggplot(data = cars) + #data layer
geom_point(aes(x = wt, y = mpg, shape = factor(cyl))) #geom layer, aesthetics layer, color
ggplot(data = cars) + #data layer
geom_point(aes(x = wt, y = mpg, color = factor(cyl))) #geom layer, aesthetics layer, color
Plot showing 4 dimensions
What if we’re interested in the same plot as above, but separated by automatic vs manual transmissions?
ggplot(data = cars) + #data layer
geom_point(aes(x = wt, y = mpg, color = factor(cyl))) + #geom layer, aesthetics layer, color
facet_wrap(~factor(am)) #facet
#rename factor labels
cars$am <- as.factor(cars$am)
levels(cars$am) <- c("automatic", "manual")
ggplot(data = cars) + #data layer
geom_point(aes(x = wt, y = mpg, color = factor(cyl))) + #geom layer, aesthetics layer, color
facet_wrap(~am) #facet
Plot showing 5 dimensions
cars$vs <- as.factor(cars$vs)
levels(cars$vs) <- c("V-shaped engine", "straight engine")
ggplot(data = cars) + #data layer
geom_point(aes(x = wt, y = mpg, color = factor(cyl))) + #geom layer, aesthetics layer, color
facet_grid(vs~am) #facet
Gone too far?
ggplot(data = cars) + #data layer
geom_point(aes(x = wt, y = mpg, color = factor(cyl), size = disp, shape = factor(gear))) + #geom layer, aesthetics layer, color
facet_grid(vs~am)
Additional points
Adding labels
labeled_plot <- ggplot(data = cars) + #data layer
geom_point(aes(x = wt, y = mpg, color = factor(cyl))) + #geom layer, aesthetics layer, color
ggtitle("Relationship between mpg and weight \n in mtcars") +
xlab("weight (1000 lbs)") +
ylab("miles per gallon (mpg)") +
labs(color = "Cylinder type")
labeled_plot
Choosing colors
cylinder_colors <- c("#035AA6", "#F2AE2E", "#F23D3D")
labeled_plot +
scale_color_manual(values=cylinder_colors)
Reordering factors
cars$cyl <- factor(cars$cyl)
cars$cyl <- factor(cars$cyl, levels(cars$cyl)[c(3,2,1)])
ggplot(data = cars) + #data layer
geom_point(aes(x = wt, y = mpg, color = cyl)) + #geom layer, aesthetics layer, color
ggtitle("Relationship between mpg and weight \n in mtcars") +
xlab("weight (1000 lbs)") +
ylab("miles per gallon (mpg)") +
labs(color = "Cylinder type") +
scale_color_manual(values=cylinder_colors)
Adding statistics
Remember, we are working with the grammar of graphics so we can add as many geoms as we want/need to our plot!
labeled_plot +
geom_smooth(aes(x = wt, y = mpg, color = factor(cyl)), method = "lm", se = FALSE) #additional geoms inherit data and aesthetics from the predefined plot
#we can also define a new dataset for an additional geom
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionmeans <- cars %>%
group_by(cyl) %>%
summarise(mean.wt = mean(wt), mean.mpg = mean(mpg))
means# A tibble: 3 x 3
cyl mean.wt mean.mpg
<fct> <dbl> <dbl>
1 8 4.00 15.1
2 6 3.12 19.7
3 4 2.29 26.7labeled_plot +
geom_point(data = means, aes(x = mean.wt, y = mean.mpg, color = cyl), size = 10, alpha = 0.5) +
scale_color_manual(values=c(cylinder_colors))
Themes
library(ggthemes)
labeled_plot +
theme_fivethirtyeight()
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggthemes_4.2.0 dplyr_0.8.4 ggplot2_3.2.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.3 plyr_1.8.5 compiler_3.6.1 pillar_1.4.3
[5] later_1.0.0 git2r_0.26.1 workflowr_1.5.0 tools_3.6.1
[9] digest_0.6.23 evaluate_0.14 lifecycle_0.1.0 tibble_2.1.3
[13] gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.4 cli_2.0.1
[17] yaml_2.2.1 xfun_0.12 withr_2.1.2 stringr_1.4.0
[21] knitr_1.26 vctrs_0.2.2 fs_1.3.1 rprojroot_1.3-2
[25] grid_3.6.1 tidyselect_1.0.0 glue_1.3.1 R6_2.4.1
[29] fansi_0.4.1 rmarkdown_1.18 reshape2_1.4.3 farver_2.0.3
[33] purrr_0.3.3 magrittr_1.5 backports_1.1.5 scales_1.1.0
[37] promises_1.1.0 htmltools_0.4.0 assertthat_0.2.1 colorspace_1.4-1
[41] httpuv_1.5.2 labeling_0.3 utf8_1.1.4 stringi_1.4.5
[45] lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4