graphics
graphics examples
Based on examples in R Graphics Cookbook
See also: https://www.rstudio.com/wp-content/uploads/2015/12/ggplot2-cheatsheet-2.0.pdf
#install.packages(c("ggplot2","gcookbook"))
library(ggplot2)
library(gcookbook)
data(package="gcookbook")
data(package="MASS")
vignette(package="ggplot2")
vignette(package="gcookbook")## no vignettes foundCreating a Histogram
Use histograms to view the distribution of one-dimensional data
str(mtcars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...#View(mtcars)
ggplot(mtcars, aes(x=mpg)) + geom_histogram(binwidth=4)
Creating a Box Plot
box plots compare distributions pass in: factor of x values, and vector of y values
#View(ToothGrowth)
ggplot(ToothGrowth, aes(x=supp, y=len)) + geom_boxplot()
Making a Basic Scatter Plot
Use geom_point(), and map one variable to x and one to y.
default shape: #16 - solid circles default size: 2
str(heightweight)## 'data.frame': 236 obs. of 5 variables:
## $ sex : Factor w/ 2 levels "f","m": 1 1 1 1 1 1 1 1 1 1 ...
## $ ageYear : num 11.9 12.9 12.8 13.4 15.9 ...
## $ ageMonth: int 143 155 153 161 191 171 185 142 160 140 ...
## $ heightIn: num 56.3 62.3 63.3 59 62.5 62.5 59 56.5 62 53.8 ...
## $ weightLb: num 85 105 108 92 112 ...heightweight[,c("ageYear","heightIn")]## ageYear heightIn
## 1 11.92 56.3
## 2 12.92 62.3
## 3 12.75 63.3
## 4 13.42 59.0
## 5 15.92 62.5
## 6 14.25 62.5
## 7 15.42 59.0
## 8 11.83 56.5
## 9 13.33 62.0
## 10 11.67 53.8
## 11 11.58 61.5
## 12 14.83 61.5
## 13 13.08 64.5
## 14 12.42 58.3
## 15 11.92 51.3
## 16 12.08 58.8
## 17 15.92 65.3
## 18 12.50 59.5
## 19 12.25 61.3
## 20 15.00 63.3
## 21 11.75 61.8
## 22 11.67 53.5
## 23 13.67 58.0
## 24 14.67 61.3
## 25 15.42 63.3
## 26 13.83 61.5
## 27 14.58 60.8
## 28 15.00 59.0
## 29 17.50 65.5
## 30 12.17 56.3
## 31 14.17 64.3
## 32 13.50 58.0
## 33 12.42 64.3
## 34 11.58 57.5
## 35 15.50 57.8
## 36 16.42 61.5
## 37 14.08 62.3
## 38 14.75 61.8
## 39 15.42 65.3
## 40 15.17 58.3
## 41 14.42 62.8
## 42 13.83 59.3
## 43 14.00 61.5
## 44 14.08 62.0
## 45 12.50 61.3
## 46 15.33 62.3
## 47 11.58 52.8
## 48 12.25 59.8
## 49 12.00 59.5
## 50 14.75 61.3
## 51 14.83 63.5
## 52 16.42 64.8
## 53 12.17 60.0
## 54 12.08 59.0
## 55 12.25 55.8
## 56 12.08 57.8
## 57 12.92 61.3
## 58 13.92 62.3
## 59 15.25 64.3
## 60 11.92 55.5
## 61 15.25 64.5
## 62 15.42 60.0
## 63 12.33 56.3
## 64 12.25 58.3
## 65 12.83 60.0
## 66 13.00 54.5
## 67 12.00 55.8
## 68 12.83 62.8
## 69 12.67 60.5
## 70 15.92 63.3
## 71 15.83 66.8
## 72 11.67 60.0
## 73 12.33 60.5
## 74 15.75 64.3
## 75 11.92 58.3
## 76 14.83 66.5
## 77 13.67 65.3
## 78 13.08 60.5
## 79 12.25 59.5
## 80 12.33 59.0
## 81 14.75 61.3
## 82 14.25 61.5
## 83 14.33 64.8
## 84 15.83 56.8
## 85 15.25 66.5
## 86 11.92 61.5
## 87 14.92 63.0
## 88 15.50 57.0
## 89 15.17 65.5
## 90 15.17 62.0
## 91 11.83 56.0
## 92 13.75 61.3
## 93 13.75 55.5
## 94 12.83 61.0
## 95 12.50 54.5
## 96 12.92 66.0
## 97 13.58 56.5
## 98 11.75 56.0
## 99 12.25 51.5
## 100 17.50 62.0
## 101 14.25 63.0
## 102 13.92 61.0
## 103 15.17 64.0
## 104 12.00 61.0
## 105 16.08 59.8
## 106 11.75 61.3
## 107 13.67 63.3
## 108 15.50 63.5
## 109 14.08 61.5
## 110 14.58 60.3
## 111 15.00 61.3
## 112 13.75 64.8
## 113 13.08 60.5
## 114 12.00 57.3
## 115 12.50 59.5
## 116 12.50 60.8
## 117 11.58 60.5
## 118 15.75 67.0
## 119 15.25 64.8
## 120 12.25 50.5
## 121 12.17 57.5
## 122 13.33 60.5
## 123 13.00 61.8
## 124 14.42 61.3
## 125 12.58 66.3
## 126 11.75 53.3
## 127 12.50 59.0
## 128 13.67 57.8
## 129 12.75 60.0
## 130 17.17 68.3
## 132 14.67 63.8
## 133 14.67 65.0
## 134 11.67 59.5
## 135 15.42 66.0
## 136 15.00 61.8
## 137 12.17 57.3
## 138 15.25 66.0
## 139 11.67 56.5
## 140 12.58 58.3
## 141 12.58 61.0
## 142 12.00 62.8
## 143 13.33 59.3
## 144 14.83 67.3
## 145 16.08 66.3
## 146 13.50 64.5
## 147 13.67 60.5
## 148 15.50 66.0
## 149 11.92 57.5
## 150 14.58 64.0
## 151 14.58 68.0
## 152 14.58 63.5
## 153 14.42 69.0
## 154 14.17 63.8
## 155 14.50 66.0
## 156 13.67 63.5
## 157 12.00 59.5
## 158 13.00 66.3
## 159 12.42 57.0
## 160 12.00 60.0
## 161 12.25 57.0
## 162 15.67 67.3
## 163 14.08 62.0
## 164 14.33 65.0
## 165 12.50 59.5
## 166 16.08 67.8
## 167 13.08 58.0
## 168 14.00 60.0
## 169 11.67 58.5
## 170 13.00 58.3
## 171 13.00 61.5
## 172 13.17 65.0
## 173 15.33 66.5
## 174 13.00 68.5
## 175 12.00 57.0
## 176 14.67 61.5
## 177 14.00 66.5
## 178 12.42 52.5
## 179 11.83 55.0
## 180 15.67 71.0
## 181 16.92 66.5
## 182 11.83 58.8
## 183 15.75 66.3
## 184 15.67 65.8
## 185 16.67 71.0
## 186 12.67 59.5
## 187 14.50 69.8
## 188 13.83 62.5
## 189 12.08 56.5
## 190 11.92 57.5
## 191 13.58 65.3
## 192 13.83 67.3
## 193 15.17 67.0
## 194 14.42 66.0
## 195 12.92 61.8
## 196 13.50 60.0
## 197 14.75 63.0
## 198 14.75 60.5
## 199 14.58 65.5
## 200 13.83 62.0
## 201 12.50 59.0
## 202 12.50 61.8
## 203 15.67 63.3
## 204 13.58 66.0
## 205 14.25 61.8
## 206 13.50 63.0
## 207 11.75 57.5
## 208 14.50 63.0
## 209 11.83 56.0
## 210 12.33 60.5
## 211 11.67 56.8
## 212 13.33 64.0
## 213 12.00 60.0
## 214 17.17 69.5
## 215 13.25 63.3
## 216 12.42 56.3
## 217 16.08 72.0
## 218 16.17 65.3
## 219 12.67 60.8
## 220 12.17 55.0
## 221 11.58 55.0
## 222 15.50 66.5
## 223 13.42 56.8
## 224 12.75 64.8
## 225 16.33 64.5
## 226 13.67 58.0
## 227 13.25 62.8
## 228 14.83 63.8
## 229 12.75 57.8
## 230 12.92 57.3
## 231 14.83 63.5
## 232 11.83 55.0
## 233 13.67 66.5
## 234 15.75 65.0
## 235 13.67 61.5
## 236 13.92 62.0
## 237 12.58 59.3ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point()
Group Data Points by a Variable Using Shape or Color
map the grouping variable to shape or colour grouping variable must be categorical (factor or character vector)
heightweight[,c("sex","ageYear","heightIn")]## sex ageYear heightIn
## 1 f 11.92 56.3
## 2 f 12.92 62.3
## 3 f 12.75 63.3
## 4 f 13.42 59.0
## 5 f 15.92 62.5
## 6 f 14.25 62.5
## 7 f 15.42 59.0
## 8 f 11.83 56.5
## 9 f 13.33 62.0
## 10 f 11.67 53.8
## 11 f 11.58 61.5
## 12 f 14.83 61.5
## 13 f 13.08 64.5
## 14 f 12.42 58.3
## 15 f 11.92 51.3
## 16 f 12.08 58.8
## 17 f 15.92 65.3
## 18 f 12.50 59.5
## 19 f 12.25 61.3
## 20 f 15.00 63.3
## 21 f 11.75 61.8
## 22 f 11.67 53.5
## 23 f 13.67 58.0
## 24 f 14.67 61.3
## 25 f 15.42 63.3
## 26 f 13.83 61.5
## 27 f 14.58 60.8
## 28 f 15.00 59.0
## 29 f 17.50 65.5
## 30 f 12.17 56.3
## 31 f 14.17 64.3
## 32 f 13.50 58.0
## 33 f 12.42 64.3
## 34 f 11.58 57.5
## 35 f 15.50 57.8
## 36 f 16.42 61.5
## 37 f 14.08 62.3
## 38 f 14.75 61.8
## 39 f 15.42 65.3
## 40 f 15.17 58.3
## 41 f 14.42 62.8
## 42 f 13.83 59.3
## 43 f 14.00 61.5
## 44 f 14.08 62.0
## 45 f 12.50 61.3
## 46 f 15.33 62.3
## 47 f 11.58 52.8
## 48 f 12.25 59.8
## 49 f 12.00 59.5
## 50 f 14.75 61.3
## 51 f 14.83 63.5
## 52 f 16.42 64.8
## 53 f 12.17 60.0
## 54 f 12.08 59.0
## 55 f 12.25 55.8
## 56 f 12.08 57.8
## 57 f 12.92 61.3
## 58 f 13.92 62.3
## 59 f 15.25 64.3
## 60 f 11.92 55.5
## 61 f 15.25 64.5
## 62 f 15.42 60.0
## 63 f 12.33 56.3
## 64 f 12.25 58.3
## 65 f 12.83 60.0
## 66 f 13.00 54.5
## 67 f 12.00 55.8
## 68 f 12.83 62.8
## 69 f 12.67 60.5
## 70 f 15.92 63.3
## 71 f 15.83 66.8
## 72 f 11.67 60.0
## 73 f 12.33 60.5
## 74 f 15.75 64.3
## 75 f 11.92 58.3
## 76 f 14.83 66.5
## 77 f 13.67 65.3
## 78 f 13.08 60.5
## 79 f 12.25 59.5
## 80 f 12.33 59.0
## 81 f 14.75 61.3
## 82 f 14.25 61.5
## 83 f 14.33 64.8
## 84 f 15.83 56.8
## 85 f 15.25 66.5
## 86 f 11.92 61.5
## 87 f 14.92 63.0
## 88 f 15.50 57.0
## 89 f 15.17 65.5
## 90 f 15.17 62.0
## 91 f 11.83 56.0
## 92 f 13.75 61.3
## 93 f 13.75 55.5
## 94 f 12.83 61.0
## 95 f 12.50 54.5
## 96 f 12.92 66.0
## 97 f 13.58 56.5
## 98 f 11.75 56.0
## 99 f 12.25 51.5
## 100 f 17.50 62.0
## 101 f 14.25 63.0
## 102 f 13.92 61.0
## 103 f 15.17 64.0
## 104 f 12.00 61.0
## 105 f 16.08 59.8
## 106 f 11.75 61.3
## 107 f 13.67 63.3
## 108 f 15.50 63.5
## 109 f 14.08 61.5
## 110 f 14.58 60.3
## 111 f 15.00 61.3
## 112 m 13.75 64.8
## 113 m 13.08 60.5
## 114 m 12.00 57.3
## 115 m 12.50 59.5
## 116 m 12.50 60.8
## 117 m 11.58 60.5
## 118 m 15.75 67.0
## 119 m 15.25 64.8
## 120 m 12.25 50.5
## 121 m 12.17 57.5
## 122 m 13.33 60.5
## 123 m 13.00 61.8
## 124 m 14.42 61.3
## 125 m 12.58 66.3
## 126 m 11.75 53.3
## 127 m 12.50 59.0
## 128 m 13.67 57.8
## 129 m 12.75 60.0
## 130 m 17.17 68.3
## 132 m 14.67 63.8
## 133 m 14.67 65.0
## 134 m 11.67 59.5
## 135 m 15.42 66.0
## 136 m 15.00 61.8
## 137 m 12.17 57.3
## 138 m 15.25 66.0
## 139 m 11.67 56.5
## 140 m 12.58 58.3
## 141 m 12.58 61.0
## 142 m 12.00 62.8
## 143 m 13.33 59.3
## 144 m 14.83 67.3
## 145 m 16.08 66.3
## 146 m 13.50 64.5
## 147 m 13.67 60.5
## 148 m 15.50 66.0
## 149 m 11.92 57.5
## 150 m 14.58 64.0
## 151 m 14.58 68.0
## 152 m 14.58 63.5
## 153 m 14.42 69.0
## 154 m 14.17 63.8
## 155 m 14.50 66.0
## 156 m 13.67 63.5
## 157 m 12.00 59.5
## 158 m 13.00 66.3
## 159 m 12.42 57.0
## 160 m 12.00 60.0
## 161 m 12.25 57.0
## 162 m 15.67 67.3
## 163 m 14.08 62.0
## 164 m 14.33 65.0
## 165 m 12.50 59.5
## 166 m 16.08 67.8
## 167 m 13.08 58.0
## 168 m 14.00 60.0
## 169 m 11.67 58.5
## 170 m 13.00 58.3
## 171 m 13.00 61.5
## 172 m 13.17 65.0
## 173 m 15.33 66.5
## 174 m 13.00 68.5
## 175 m 12.00 57.0
## 176 m 14.67 61.5
## 177 m 14.00 66.5
## 178 m 12.42 52.5
## 179 m 11.83 55.0
## 180 m 15.67 71.0
## 181 m 16.92 66.5
## 182 m 11.83 58.8
## 183 m 15.75 66.3
## 184 m 15.67 65.8
## 185 m 16.67 71.0
## 186 m 12.67 59.5
## 187 m 14.50 69.8
## 188 m 13.83 62.5
## 189 m 12.08 56.5
## 190 m 11.92 57.5
## 191 m 13.58 65.3
## 192 m 13.83 67.3
## 193 m 15.17 67.0
## 194 m 14.42 66.0
## 195 m 12.92 61.8
## 196 m 13.50 60.0
## 197 m 14.75 63.0
## 198 m 14.75 60.5
## 199 m 14.58 65.5
## 200 m 13.83 62.0
## 201 m 12.50 59.0
## 202 m 12.50 61.8
## 203 m 15.67 63.3
## 204 m 13.58 66.0
## 205 m 14.25 61.8
## 206 m 13.50 63.0
## 207 m 11.75 57.5
## 208 m 14.50 63.0
## 209 m 11.83 56.0
## 210 m 12.33 60.5
## 211 m 11.67 56.8
## 212 m 13.33 64.0
## 213 m 12.00 60.0
## 214 m 17.17 69.5
## 215 m 13.25 63.3
## 216 m 12.42 56.3
## 217 m 16.08 72.0
## 218 m 16.17 65.3
## 219 m 12.67 60.8
## 220 m 12.17 55.0
## 221 m 11.58 55.0
## 222 m 15.50 66.5
## 223 m 13.42 56.8
## 224 m 12.75 64.8
## 225 m 16.33 64.5
## 226 m 13.67 58.0
## 227 m 13.25 62.8
## 228 m 14.83 63.8
## 229 m 12.75 57.8
## 230 m 12.92 57.3
## 231 m 14.83 63.5
## 232 m 11.83 55.0
## 233 m 13.67 66.5
## 234 m 15.75 65.0
## 235 m 13.67 61.5
## 236 m 13.92 62.0
## 237 m 12.58 59.3ggplot(heightweight, aes(x=ageYear, y=heightIn, shape=sex )) + geom_point()
ggplot(heightweight, aes(x=ageYear, y=heightIn, colour=sex )) + geom_point()
ggplot(heightweight, aes(x=ageYear, y=heightIn, shape=sex, colour=sex )) + geom_point()
ggplot(heightweight, aes(x=ageYear, y=heightIn, shape=sex, colour=sex )) +
geom_point() +
scale_shape_manual(values=c(1,2)) +
scale_colour_brewer(palette="Set1")
facets
p <- ggplot(heightweight, aes(x=ageYear, y=heightIn )) + geom_point()
p + facet_grid( sex ~ .)
mpg dataset (included with ggplot2 package)
dim(mpg)## [1] 234 11str(mpg)## Classes 'tbl_df', 'tbl' and 'data.frame': 234 obs. of 11 variables:
## $ manufacturer: chr "audi" "audi" "audi" "audi" ...
## $ model : chr "a4" "a4" "a4" "a4" ...
## $ displ : num 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
## $ year : int 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
## $ cyl : int 4 4 4 4 6 6 6 4 4 4 ...
## $ trans : chr "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
## $ drv : chr "f" "f" "f" "f" ...
## $ cty : int 18 21 20 21 16 18 18 18 16 20 ...
## $ hwy : int 29 29 31 30 26 26 27 26 25 28 ...
## $ fl : chr "p" "p" "p" "p" ...
## $ class : chr "compact" "compact" "compact" "compact" ...#View(mpg)mtcars dataset (included with base R datasets)
https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/mtcars.html
dim(mtcars)## [1] 32 11str(mtcars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...#View(mtcars)diamonds dataset (included with ggplot2 package)
dim(diamonds)## [1] 53940 10str(diamonds)## Classes 'tbl_df', 'tbl' and 'data.frame': 53940 obs. of 10 variables:
## $ carat : num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
## $ depth : num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
## $ table : num 55 61 65 58 58 57 57 55 61 61 ...
## $ price : int 326 326 327 334 335 336 336 337 337 338 ...
## $ x : num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
## $ y : num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
## $ z : num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...#View(diamonds)Making a Cleveland dot plot
See also: https://www.perceptualedge.com/articles/b-eye/dot_plots.pdf
tophit <- tophitters2001[1:25,c("name","lg","avg")]
ggplot(tophit,aes(x=avg,y=name)) + geom_point()
ggplot(tophit,aes(x=avg,y=reorder(name,avg))) + geom_point()
ggplot(tophit,aes(x=avg,y=reorder(name,avg))) + geom_point(size=3) +
theme_bw() +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_line(colour="grey60", linetype = "dashed"))
ggplot(tophit,aes(x=reorder(name,avg), y=avg)) + geom_point(size=3) +
theme_bw() +
theme(axis.text.x = element_text(angle=60, hjust=1),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(colour="grey60", linetype = "dashed"))
nameorder <- tophit$name[order(tophit$lg, tophit$avg)]
# turn name into a factor, with levels in the order of nameorder
tophit$name <- factor(tophit$name, levels=nameorder)
ggplot(tophit, aes(x=avg, y=name)) +
geom_segment(aes(yend=name), xend=0, colour="grey50") +
geom_point(size=3, aes(colour=lg)) +
scale_colour_brewer(palette="Set1", limits=c("NL","AL")) +
theme_bw() +
theme(panel.grid.major.y = element_blank(), # No horizontal grid lines
legend.position=c(1, 0.55), # Put legend inside plot area
legend.justification=c(1, 0.5))
# using facets
ggplot(tophit, aes(x=avg, y=name)) +
geom_segment(aes(yend=name), xend=0, colour="grey50") +
geom_point(size=3, aes(colour=lg)) +
scale_colour_brewer(palette="Set1", limits=c("NL","AL"), guide=FALSE) +
theme_bw() +
theme(panel.grid.major.y = element_blank()) +
facet_grid(lg ~ ., scales="free_y", space="free_y")