Chapter 7: Exploratory Data Analysis
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
## # ℹ 53,930 more rowsdiamonds %>%
ggplot(aes(x = cut)) +
geom_bar()
diamonds %>%
ggplot(aes(x = carat)) +
geom_histogram(binwidth = 0.5)
diamonds %>%
filter(carat < 3) %>%
ggplot(aes(x = carat)) +
geom_histogram(binwidth = 0.5)
diamonds %>%
ggplot(aes(x = carat, color = cut)) +
geom_freqpoly(binwidth = 0.5)
diamonds %>%
filter(carat < 3) %>%
ggplot(aes(x = carat)) +
geom_histogram(binwidth = 0.01)
faithful## eruptions waiting
## 1 3.600 79
## 2 1.800 54
## 3 3.333 74
## 4 2.283 62
## 5 4.533 85
## 6 2.883 55
## 7 4.700 88
## 8 3.600 85
## 9 1.950 51
## 10 4.350 85
## 11 1.833 54
## 12 3.917 84
## 13 4.200 78
## 14 1.750 47
## 15 4.700 83
## 16 2.167 52
## 17 1.750 62
## 18 4.800 84
## 19 1.600 52
## 20 4.250 79
## 21 1.800 51
## 22 1.750 47
## 23 3.450 78
## 24 3.067 69
## 25 4.533 74
## 26 3.600 83
## 27 1.967 55
## 28 4.083 76
## 29 3.850 78
## 30 4.433 79
## 31 4.300 73
## 32 4.467 77
## 33 3.367 66
## 34 4.033 80
## 35 3.833 74
## 36 2.017 52
## 37 1.867 48
## 38 4.833 80
## 39 1.833 59
## 40 4.783 90
## 41 4.350 80
## 42 1.883 58
## 43 4.567 84
## 44 1.750 58
## 45 4.533 73
## 46 3.317 83
## 47 3.833 64
## 48 2.100 53
## 49 4.633 82
## 50 2.000 59
## 51 4.800 75
## 52 4.716 90
## 53 1.833 54
## 54 4.833 80
## 55 1.733 54
## 56 4.883 83
## 57 3.717 71
## 58 1.667 64
## 59 4.567 77
## 60 4.317 81
## 61 2.233 59
## 62 4.500 84
## 63 1.750 48
## 64 4.800 82
## 65 1.817 60
## 66 4.400 92
## 67 4.167 78
## 68 4.700 78
## 69 2.067 65
## 70 4.700 73
## 71 4.033 82
## 72 1.967 56
## 73 4.500 79
## 74 4.000 71
## 75 1.983 62
## 76 5.067 76
## 77 2.017 60
## 78 4.567 78
## 79 3.883 76
## 80 3.600 83
## 81 4.133 75
## 82 4.333 82
## 83 4.100 70
## 84 2.633 65
## 85 4.067 73
## 86 4.933 88
## 87 3.950 76
## 88 4.517 80
## 89 2.167 48
## 90 4.000 86
## 91 2.200 60
## 92 4.333 90
## 93 1.867 50
## 94 4.817 78
## 95 1.833 63
## 96 4.300 72
## 97 4.667 84
## 98 3.750 75
## 99 1.867 51
## 100 4.900 82
## 101 2.483 62
## 102 4.367 88
## 103 2.100 49
## 104 4.500 83
## 105 4.050 81
## 106 1.867 47
## 107 4.700 84
## 108 1.783 52
## 109 4.850 86
## 110 3.683 81
## 111 4.733 75
## 112 2.300 59
## 113 4.900 89
## 114 4.417 79
## 115 1.700 59
## 116 4.633 81
## 117 2.317 50
## 118 4.600 85
## 119 1.817 59
## 120 4.417 87
## 121 2.617 53
## 122 4.067 69
## 123 4.250 77
## 124 1.967 56
## 125 4.600 88
## 126 3.767 81
## 127 1.917 45
## 128 4.500 82
## 129 2.267 55
## 130 4.650 90
## 131 1.867 45
## 132 4.167 83
## 133 2.800 56
## 134 4.333 89
## 135 1.833 46
## 136 4.383 82
## 137 1.883 51
## 138 4.933 86
## 139 2.033 53
## 140 3.733 79
## 141 4.233 81
## 142 2.233 60
## 143 4.533 82
## 144 4.817 77
## 145 4.333 76
## 146 1.983 59
## 147 4.633 80
## 148 2.017 49
## 149 5.100 96
## 150 1.800 53
## 151 5.033 77
## 152 4.000 77
## 153 2.400 65
## 154 4.600 81
## 155 3.567 71
## 156 4.000 70
## 157 4.500 81
## 158 4.083 93
## 159 1.800 53
## 160 3.967 89
## 161 2.200 45
## 162 4.150 86
## 163 2.000 58
## 164 3.833 78
## 165 3.500 66
## 166 4.583 76
## 167 2.367 63
## 168 5.000 88
## 169 1.933 52
## 170 4.617 93
## 171 1.917 49
## 172 2.083 57
## 173 4.583 77
## 174 3.333 68
## 175 4.167 81
## 176 4.333 81
## 177 4.500 73
## 178 2.417 50
## 179 4.000 85
## 180 4.167 74
## 181 1.883 55
## 182 4.583 77
## 183 4.250 83
## 184 3.767 83
## 185 2.033 51
## 186 4.433 78
## 187 4.083 84
## 188 1.833 46
## 189 4.417 83
## 190 2.183 55
## 191 4.800 81
## 192 1.833 57
## 193 4.800 76
## 194 4.100 84
## 195 3.966 77
## 196 4.233 81
## 197 3.500 87
## 198 4.366 77
## 199 2.250 51
## 200 4.667 78
## 201 2.100 60
## 202 4.350 82
## 203 4.133 91
## 204 1.867 53
## 205 4.600 78
## 206 1.783 46
## 207 4.367 77
## 208 3.850 84
## 209 1.933 49
## 210 4.500 83
## 211 2.383 71
## 212 4.700 80
## 213 1.867 49
## 214 3.833 75
## 215 3.417 64
## 216 4.233 76
## 217 2.400 53
## 218 4.800 94
## 219 2.000 55
## 220 4.150 76
## 221 1.867 50
## 222 4.267 82
## 223 1.750 54
## 224 4.483 75
## 225 4.000 78
## 226 4.117 79
## 227 4.083 78
## 228 4.267 78
## 229 3.917 70
## 230 4.550 79
## 231 4.083 70
## 232 2.417 54
## 233 4.183 86
## 234 2.217 50
## 235 4.450 90
## 236 1.883 54
## 237 1.850 54
## 238 4.283 77
## 239 3.950 79
## 240 2.333 64
## 241 4.150 75
## 242 2.350 47
## 243 4.933 86
## 244 2.900 63
## 245 4.583 85
## 246 3.833 82
## 247 2.083 57
## 248 4.367 82
## 249 2.133 67
## 250 4.350 74
## 251 2.200 54
## 252 4.450 83
## 253 3.567 73
## 254 4.500 73
## 255 4.150 88
## 256 3.817 80
## 257 3.917 71
## 258 4.450 83
## 259 2.000 56
## 260 4.283 79
## 261 4.767 78
## 262 4.533 84
## 263 1.850 58
## 264 4.250 83
## 265 1.983 43
## 266 2.250 60
## 267 4.750 75
## 268 4.117 81
## 269 2.150 46
## 270 4.417 90
## 271 1.817 46
## 272 4.467 74faithful %>%
ggplot(aes(x = eruptions)) +
geom_histogram(binwidth = 0.25)
diamonds %>%
ggplot(aes(x = y)) +
geom_histogram(binwidth = 0.5) +
coord_cartesian(ylim = c(0, 50))
diamonds %>%
filter(y < 3 | y > 20)## # A tibble: 9 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 1 Very Good H VS2 63.3 53 5139 0 0 0
## 2 1.14 Fair G VS1 57.5 67 6381 0 0 0
## 3 2 Premium H SI2 58.9 57 12210 8.09 58.9 8.06
## 4 1.56 Ideal G VS2 62.2 54 12800 0 0 0
## 5 1.2 Premium D VVS1 62.1 59 15686 0 0 0
## 6 2.25 Premium H SI2 62.8 59 18034 0 0 0
## 7 0.51 Ideal E VS1 61.8 55 2075 5.15 31.8 5.12
## 8 0.71 Good F SI2 64.1 60 2130 0 0 0
## 9 0.71 Good F SI2 64.1 60 2130 0 0 0diamonds2 <- diamonds %>%
mutate(y = if_else(y < 3 | y > 20, NA_real_, y))
diamonds2 %>%
ggplot(aes(x = x, y = y)) +
geom_point()## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).
diamonds %>%
ggplot(aes(x = cut, y = price)) +
geom_boxplot()
diamonds %>%
count(color, cut) %>%
ggplot(aes(x = color, y = cut, fill = n)) +
geom_tile()
diamonds %>%
filter(carat < 3) %>%
ggplot(aes(x = carat, y = price)) +
geom_hex(bins = 50)
## 4. Workflow: Projects Example
# salaries <- read.csv("../data/salaries.csv")
# head(salaries)