DATA624_Homework4
Silma
2025-02-25
DATA 624 Data Acquisition & Management
Homework 4
Silma Khan SPRING 2025
Question 3.1:
library(mlbench)## Warning: package 'mlbench' was built under R version 4.4.3data(Glass)
str(Glass)## 'data.frame': 214 obs. of 10 variables:
## $ RI : num 1.52 1.52 1.52 1.52 1.52 ...
## $ Na : num 13.6 13.9 13.5 13.2 13.3 ...
## $ Mg : num 4.49 3.6 3.55 3.69 3.62 3.61 3.6 3.61 3.58 3.6 ...
## $ Al : num 1.1 1.36 1.54 1.29 1.24 1.62 1.14 1.05 1.37 1.36 ...
## $ Si : num 71.8 72.7 73 72.6 73.1 ...
## $ K : num 0.06 0.48 0.39 0.57 0.55 0.64 0.58 0.57 0.56 0.57 ...
## $ Ca : num 8.75 7.83 7.78 8.22 8.07 8.07 8.17 8.24 8.3 8.4 ...
## $ Ba : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Fe : num 0 0 0 0 0 0.26 0 0 0 0.11 ...
## $ Type: Factor w/ 6 levels "1","2","3","5",..: 1 1 1 1 1 1 1 1 1 1 ...summary(Glass)## RI Na Mg Al
## Min. :1.511 Min. :10.73 Min. :0.000 Min. :0.290
## 1st Qu.:1.517 1st Qu.:12.91 1st Qu.:2.115 1st Qu.:1.190
## Median :1.518 Median :13.30 Median :3.480 Median :1.360
## Mean :1.518 Mean :13.41 Mean :2.685 Mean :1.445
## 3rd Qu.:1.519 3rd Qu.:13.82 3rd Qu.:3.600 3rd Qu.:1.630
## Max. :1.534 Max. :17.38 Max. :4.490 Max. :3.500
## Si K Ca Ba
## Min. :69.81 Min. :0.0000 Min. : 5.430 Min. :0.000
## 1st Qu.:72.28 1st Qu.:0.1225 1st Qu.: 8.240 1st Qu.:0.000
## Median :72.79 Median :0.5550 Median : 8.600 Median :0.000
## Mean :72.65 Mean :0.4971 Mean : 8.957 Mean :0.175
## 3rd Qu.:73.09 3rd Qu.:0.6100 3rd Qu.: 9.172 3rd Qu.:0.000
## Max. :75.41 Max. :6.2100 Max. :16.190 Max. :3.150
## Fe Type
## Min. :0.00000 1:70
## 1st Qu.:0.00000 2:76
## Median :0.00000 3:17
## Mean :0.05701 5:13
## 3rd Qu.:0.10000 6: 9
## Max. :0.51000 7:29library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsGlass %>%
keep(is.numeric) %>%
gather() %>%
ggplot(aes(value)) +
geom_histogram(bins = 15) +
facet_wrap(~key, scales = 'free')
(b) Taking alook at the predictor variables and seeing their
distributions, it seems like there are some outliers in some predictor
variavles such as for “K” we can see that there seems
to be a very small count of maybe less than 10 around the 6 area. Many
of the predictor variables seem to be very dispersed along their
values
- Since some of the predictor variables show some skewness either to the left or right, for exable B, Fe, and K show very strong right skewness in their data, so maybe a log transformation could improve the classification model and allow the distribution of the data to be a little more symmetric
Question 3.2:
data(Soybean)
?Soybean## starting httpd help server ... doneSoybean## Class date plant.stand precip temp hail crop.hist
## 1 diaporthe-stem-canker 6 0 2 1 0 1
## 2 diaporthe-stem-canker 4 0 2 1 0 2
## 3 diaporthe-stem-canker 3 0 2 1 0 1
## 4 diaporthe-stem-canker 3 0 2 1 0 1
## 5 diaporthe-stem-canker 6 0 2 1 0 2
## 6 diaporthe-stem-canker 5 0 2 1 0 3
## 7 diaporthe-stem-canker 5 0 2 1 0 2
## 8 diaporthe-stem-canker 4 0 2 1 1 1
## 9 diaporthe-stem-canker 6 0 2 1 0 3
## 10 diaporthe-stem-canker 4 0 2 1 0 2
## 11 charcoal-rot 6 0 0 2 0 1
## 12 charcoal-rot 4 0 0 1 1 1
## 13 charcoal-rot 3 0 0 1 0 1
## 14 charcoal-rot 6 0 0 1 1 3
## 15 charcoal-rot 6 0 0 2 0 1
## 16 charcoal-rot 5 0 0 2 1 3
## 17 charcoal-rot 6 0 0 2 1 0
## 18 charcoal-rot 4 0 0 1 0 2
## 19 charcoal-rot 3 0 0 2 0 2
## 20 charcoal-rot 5 0 0 2 1 2
## 21 rhizoctonia-root-rot 1 1 2 0 0 2
## 22 rhizoctonia-root-rot 1 1 2 0 0 1
## 23 rhizoctonia-root-rot 3 0 2 0 1 3
## 24 rhizoctonia-root-rot 0 1 2 0 0 0
## 25 rhizoctonia-root-rot 0 1 2 0 0 1
## 26 rhizoctonia-root-rot 1 1 2 0 0 3
## 27 rhizoctonia-root-rot 1 1 2 0 0 0
## 28 rhizoctonia-root-rot 2 1 2 0 0 2
## 29 rhizoctonia-root-rot 1 1 2 0 0 1
## 30 rhizoctonia-root-rot 2 1 2 0 0 1
## 31 phytophthora-rot 0 1 2 1 1 1
## 32 phytophthora-rot 1 1 2 1 <NA> 3
## 33 phytophthora-rot 2 1 2 2 <NA> 2
## 34 phytophthora-rot 1 1 2 0 0 2
## 35 phytophthora-rot 2 1 2 2 <NA> 2
## 36 phytophthora-rot 3 1 2 1 <NA> 2
## 37 phytophthora-rot 0 1 1 1 0 1
## 38 phytophthora-rot 3 1 2 0 0 2
## 39 phytophthora-rot 2 1 1 1 <NA> 0
## 40 phytophthora-rot 2 1 2 0 0 1
## 41 phytophthora-rot 2 1 2 1 <NA> 1
## 42 phytophthora-rot 1 1 2 1 <NA> 1
## 43 phytophthora-rot 0 1 2 1 0 3
## 44 phytophthora-rot 0 1 1 1 1 2
## 45 phytophthora-rot 3 1 2 0 0 1
## 46 phytophthora-rot 2 1 2 2 <NA> 3
## 47 phytophthora-rot 0 1 2 1 0 2
## 48 phytophthora-rot 2 1 1 2 <NA> 2
## 49 phytophthora-rot 2 1 2 1 1 1
## 50 phytophthora-rot 0 1 2 1 0 3
## 51 phytophthora-rot 1 1 2 1 0 0
## 52 phytophthora-rot 1 1 2 1 <NA> 0
## 53 phytophthora-rot 3 1 2 1 <NA> 1
## 54 phytophthora-rot 2 1 2 1 <NA> 1
## 55 phytophthora-rot 3 1 2 2 <NA> 2
## 56 phytophthora-rot 1 1 2 1 1 3
## 57 phytophthora-rot 3 1 1 1 <NA> 3
## 58 phytophthora-rot 2 1 2 2 <NA> 1
## 59 phytophthora-rot 3 1 1 2 <NA> 2
## 60 phytophthora-rot 1 1 2 2 <NA> 1
## 61 phytophthora-rot 2 1 2 2 <NA> 3
## 62 phytophthora-rot 3 1 1 1 <NA> 0
## 63 phytophthora-rot 2 1 2 0 0 1
## 64 phytophthora-rot 3 1 1 1 <NA> 1
## 65 phytophthora-rot 2 1 2 2 <NA> 1
## 66 phytophthora-rot 1 1 2 0 0 0
## 67 phytophthora-rot 3 1 2 1 <NA> 2
## 68 phytophthora-rot 3 1 2 1 <NA> 3
## 69 phytophthora-rot 3 1 1 0 0 2
## 70 phytophthora-rot 3 1 2 2 <NA> 2
## 71 brown-stem-rot 4 0 0 1 0 1
## 72 brown-stem-rot 4 0 0 1 0 1
## 73 brown-stem-rot 3 1 0 0 0 3
## 74 brown-stem-rot 5 0 0 2 0 1
## 75 brown-stem-rot 5 0 0 2 0 2
## 76 brown-stem-rot 4 0 0 1 0 3
## 77 brown-stem-rot 5 0 0 1 1 3
## 78 brown-stem-rot 6 0 1 1 1 2
## 79 brown-stem-rot 5 1 0 0 0 3
## 80 brown-stem-rot 5 1 0 1 0 1
## 81 brown-stem-rot 4 0 1 0 1 2
## 82 brown-stem-rot 4 1 0 0 0 3
## 83 brown-stem-rot 4 0 0 1 0 2
## 84 brown-stem-rot 3 1 0 0 0 2
## 85 brown-stem-rot 5 0 0 1 0 3
## 86 brown-stem-rot 4 0 0 1 0 3
## 87 brown-stem-rot 4 0 0 1 0 3
## 88 brown-stem-rot 4 0 0 1 0 1
## 89 brown-stem-rot 4 0 0 1 0 1
## 90 brown-stem-rot 3 0 0 1 0 3
## 91 powdery-mildew 5 0 0 1 1 3
## 92 powdery-mildew 6 0 1 0 1 0
## 93 powdery-mildew 1 1 0 1 0 3
## 94 powdery-mildew 6 1 1 0 0 2
## 95 powdery-mildew 4 1 1 0 0 2
## 96 powdery-mildew 6 0 0 1 1 1
## 97 powdery-mildew 2 1 1 0 0 2
## 98 powdery-mildew 6 1 0 1 0 1
## 99 powdery-mildew 5 1 0 1 0 1
## 100 powdery-mildew 1 1 0 1 0 1
## 101 downy-mildew 6 0 2 0 1 2
## 102 downy-mildew 2 0 2 1 1 1
## 103 downy-mildew 1 0 2 1 1 3
## 104 downy-mildew 4 1 2 2 0 2
## 105 downy-mildew 1 0 2 0 1 0
## 106 downy-mildew 2 1 2 0 0 3
## 107 downy-mildew 2 1 2 1 0 2
## 108 downy-mildew 4 1 2 2 0 2
## 109 downy-mildew 4 1 2 0 0 1
## 110 downy-mildew 5 1 2 1 0 3
## 111 brown-spot 1 1 2 2 1 3
## 112 brown-spot 2 0 2 1 0 2
## 113 brown-spot 2 0 2 1 0 2
## 114 brown-spot 2 0 2 1 0 1
## 115 brown-spot 1 1 2 2 1 3
## 116 brown-spot 1 1 2 1 0 2
## 117 brown-spot 0 1 2 2 1 3
## 118 brown-spot 2 0 2 1 0 2
## 119 brown-spot 1 0 2 1 0 2
## 120 brown-spot 2 1 2 1 0 3
## 121 brown-spot 5 0 2 1 0 2
## 122 brown-spot 1 1 2 1 0 3
## 123 brown-spot 1 0 2 1 0 3
## 124 brown-spot 4 0 2 1 0 1
## 125 brown-spot 1 0 2 1 0 2
## 126 brown-spot 4 1 2 1 0 3
## 127 brown-spot 2 0 2 1 0 3
## 128 brown-spot 0 1 1 1 1 2
## 129 brown-spot 1 1 1 1 1 2
## 130 brown-spot 1 1 2 1 0 1
## 131 brown-spot 1 0 2 1 0 1
## 132 brown-spot 2 0 2 1 0 3
## 133 brown-spot 3 0 2 1 0 2
## 134 brown-spot 2 1 2 2 1 3
## 135 brown-spot 1 0 2 1 0 2
## 136 brown-spot 1 1 2 1 0 2
## 137 brown-spot 5 0 2 1 0 1
## 138 brown-spot 4 1 1 1 1 2
## 139 brown-spot 3 1 2 1 0 1
## 140 brown-spot 1 0 2 1 0 3
## 141 brown-spot 4 0 2 1 0 2
## 142 brown-spot 2 1 2 1 0 2
## 143 brown-spot 2 1 1 1 1 0
## 144 brown-spot 3 1 2 1 0 3
## 145 brown-spot 3 0 2 1 0 3
## 146 brown-spot 2 0 2 1 0 2
## 147 brown-spot 3 0 2 1 0 3
## 148 brown-spot 3 1 2 1 0 3
## 149 brown-spot 2 1 2 1 0 3
## 150 brown-spot 5 1 2 1 0 3
## 151 bacterial-blight 5 0 2 1 1 3
## 152 bacterial-blight 4 0 2 2 1 2
## 153 bacterial-blight 2 0 1 1 0 0
## 154 bacterial-blight 3 0 1 1 0 1
## 155 bacterial-blight 3 0 1 1 0 3
## 156 bacterial-blight 3 0 2 1 1 2
## 157 bacterial-blight 3 0 1 1 0 1
## 158 bacterial-blight 4 0 2 1 1 0
## 159 bacterial-blight 2 0 1 1 0 3
## 160 bacterial-blight 4 1 2 2 1 2
## 161 bacterial-pustule 2 1 1 2 0 2
## 162 bacterial-pustule 3 0 2 0 1 2
## 163 bacterial-pustule 2 0 1 0 0 0
## 164 bacterial-pustule 4 1 2 1 0 3
## 165 bacterial-pustule 3 0 2 1 1 1
## 166 bacterial-pustule 3 1 1 0 0 2
## 167 bacterial-pustule 3 0 1 1 1 2
## 168 bacterial-pustule 3 1 2 1 0 0
## 169 bacterial-pustule 4 0 1 1 1 1
## 170 bacterial-pustule 5 1 1 1 0 2
## 171 purple-seed-stain 6 0 2 0 1 2
## 172 purple-seed-stain 6 0 2 0 0 2
## 173 purple-seed-stain 4 0 2 1 1 1
## 174 purple-seed-stain 4 0 2 1 1 0
## 175 purple-seed-stain 4 0 2 0 0 0
## 176 purple-seed-stain 6 0 2 2 0 2
## 177 purple-seed-stain 3 0 2 0 1 0
## 178 purple-seed-stain 3 0 2 1 1 3
## 179 purple-seed-stain 5 0 2 1 0 1
## 180 purple-seed-stain 4 0 2 1 0 0
## 181 anthracnose 5 1 2 1 0 3
## 182 anthracnose 5 1 2 2 1 2
## 183 anthracnose 6 0 2 1 0 1
## 184 anthracnose 2 1 2 2 1 0
## 185 anthracnose 3 0 2 1 0 3
## 186 anthracnose 4 1 2 2 1 2
## 187 anthracnose 6 0 2 1 0 2
## 188 anthracnose 1 0 2 1 0 1
## 189 anthracnose 6 1 2 1 0 2
## 190 anthracnose 5 0 2 1 0 1
## 191 anthracnose 5 1 2 2 1 3
## 192 anthracnose 0 0 2 1 0 3
## 193 anthracnose 6 0 2 1 0 2
## 194 anthracnose 5 1 2 1 0 1
## 195 anthracnose 5 0 2 1 0 2
## 196 anthracnose 6 1 2 2 1 0
## 197 anthracnose 5 0 2 1 0 1
## 198 anthracnose 6 1 2 2 1 3
## 199 anthracnose 5 1 2 1 0 3
## 200 anthracnose 5 1 2 1 0 2
## 201 phyllosticta-leaf-spot 3 1 1 1 0 0
## 202 phyllosticta-leaf-spot 3 0 0 1 1 0
## 203 phyllosticta-leaf-spot 3 1 1 1 0 0
## 204 phyllosticta-leaf-spot 3 0 0 1 1 2
## 205 phyllosticta-leaf-spot 3 1 1 2 0 3
## 206 phyllosticta-leaf-spot 2 0 0 1 1 0
## 207 phyllosticta-leaf-spot 1 0 0 2 1 3
## 208 phyllosticta-leaf-spot 2 1 1 1 0 2
## 209 phyllosticta-leaf-spot 2 0 0 2 1 3
## 210 phyllosticta-leaf-spot 2 1 1 2 0 3
## 211 alternarialeaf-spot 4 1 2 1 0 1
## 212 alternarialeaf-spot 4 0 1 1 0 3
## 213 alternarialeaf-spot 3 0 2 1 0 0
## 214 alternarialeaf-spot 6 0 2 2 0 3
## 215 alternarialeaf-spot 6 0 1 1 1 2
## 216 alternarialeaf-spot 5 0 2 2 0 3
## 217 alternarialeaf-spot 6 0 1 1 0 3
## 218 alternarialeaf-spot 5 1 2 2 0 3
## 219 alternarialeaf-spot 6 0 2 2 0 3
## 220 alternarialeaf-spot 6 0 2 2 0 3
## 221 alternarialeaf-spot 5 0 2 2 0 2
## 222 alternarialeaf-spot 4 1 2 1 0 3
## 223 alternarialeaf-spot 6 0 2 1 0 1
## 224 alternarialeaf-spot 5 0 2 2 0 2
## 225 alternarialeaf-spot 5 1 2 1 0 0
## 226 alternarialeaf-spot 4 0 2 1 0 2
## 227 alternarialeaf-spot 4 0 2 1 0 1
## 228 alternarialeaf-spot 5 0 2 1 0 2
## 229 alternarialeaf-spot 6 0 2 2 0 3
## 230 alternarialeaf-spot 6 1 2 2 0 1
## 231 alternarialeaf-spot 5 1 2 2 0 3
## 232 alternarialeaf-spot 5 1 2 2 0 3
## 233 alternarialeaf-spot 4 1 2 1 0 2
## 234 alternarialeaf-spot 6 1 1 2 0 2
## 235 alternarialeaf-spot 4 1 2 1 0 1
## 236 alternarialeaf-spot 6 1 2 2 0 2
## 237 alternarialeaf-spot 4 1 2 1 0 0
## 238 alternarialeaf-spot 4 0 2 2 0 3
## 239 alternarialeaf-spot 5 0 2 2 0 2
## 240 alternarialeaf-spot 3 0 2 1 0 0
## 241 alternarialeaf-spot 5 0 2 1 0 1
## 242 alternarialeaf-spot 5 0 2 2 0 1
## 243 alternarialeaf-spot 4 0 2 2 0 1
## 244 alternarialeaf-spot 5 1 2 1 0 3
## 245 alternarialeaf-spot 6 0 2 1 0 2
## 246 alternarialeaf-spot 5 0 2 1 0 0
## 247 alternarialeaf-spot 6 0 2 1 0 0
## 248 alternarialeaf-spot 5 1 2 2 0 2
## 249 alternarialeaf-spot 5 0 2 1 0 3
## 250 alternarialeaf-spot 6 0 2 1 0 1
## 251 frog-eye-leaf-spot 6 0 1 2 0 3
## 252 frog-eye-leaf-spot 4 0 1 2 0 1
## 253 frog-eye-leaf-spot 5 0 1 1 0 2
## 254 frog-eye-leaf-spot 5 1 2 1 0 3
## 255 frog-eye-leaf-spot 6 1 2 2 0 3
## 256 frog-eye-leaf-spot 4 0 1 1 0 3
## 257 frog-eye-leaf-spot 3 0 2 1 0 2
## 258 frog-eye-leaf-spot 5 0 2 2 0 2
## 259 frog-eye-leaf-spot 5 0 2 1 0 1
## 260 frog-eye-leaf-spot 5 0 2 2 0 2
## 261 frog-eye-leaf-spot 5 0 2 1 0 0
## 262 frog-eye-leaf-spot 4 0 2 1 0 2
## 263 frog-eye-leaf-spot 4 0 2 2 0 1
## 264 frog-eye-leaf-spot 4 0 2 1 0 2
## 265 frog-eye-leaf-spot 3 1 2 1 0 3
## 266 frog-eye-leaf-spot 5 0 2 1 0 3
## 267 frog-eye-leaf-spot 5 0 2 2 0 1
## 268 frog-eye-leaf-spot 4 0 2 2 0 1
## 269 frog-eye-leaf-spot 5 0 2 2 0 2
## 270 frog-eye-leaf-spot 5 0 2 1 0 3
## 271 frog-eye-leaf-spot 3 0 2 1 0 1
## 272 frog-eye-leaf-spot 6 0 1 2 0 3
## 273 frog-eye-leaf-spot 5 0 1 1 0 1
## 274 frog-eye-leaf-spot 5 0 2 1 0 3
## 275 frog-eye-leaf-spot 5 1 2 1 0 3
## 276 frog-eye-leaf-spot 3 1 2 1 0 3
## 277 frog-eye-leaf-spot 6 1 2 2 0 3
## 278 frog-eye-leaf-spot 4 0 2 1 0 1
## 279 frog-eye-leaf-spot 4 0 2 2 0 1
## 280 frog-eye-leaf-spot 6 1 2 2 0 3
## 281 frog-eye-leaf-spot 5 1 2 2 0 3
## 282 frog-eye-leaf-spot 4 0 2 1 0 0
## 283 frog-eye-leaf-spot 4 0 2 1 0 2
## 284 frog-eye-leaf-spot 4 1 1 2 0 1
## 285 frog-eye-leaf-spot 4 0 2 1 0 2
## 286 frog-eye-leaf-spot 5 1 2 1 0 1
## 287 frog-eye-leaf-spot 4 0 2 2 0 1
## 288 frog-eye-leaf-spot 5 0 2 1 0 1
## 289 frog-eye-leaf-spot 5 0 2 2 0 2
## 290 frog-eye-leaf-spot 5 1 2 1 0 2
## 291 diaporthe-pod-&-stem-blight 5 0 2 2 <NA> 3
## 292 diaporthe-pod-&-stem-blight 6 0 2 2 <NA> 2
## 293 diaporthe-pod-&-stem-blight 5 0 2 2 <NA> 3
## 294 diaporthe-pod-&-stem-blight 1 1 1 2 <NA> 3
## 295 diaporthe-pod-&-stem-blight 5 <NA> 2 2 <NA> 2
## 296 diaporthe-pod-&-stem-blight 5 0 2 2 <NA> 2
## 297 cyst-nematode 2 <NA> <NA> <NA> <NA> 2
## 298 cyst-nematode 3 <NA> <NA> <NA> <NA> 3
## 299 cyst-nematode 4 <NA> <NA> <NA> <NA> 3
## 300 cyst-nematode 3 <NA> <NA> <NA> <NA> 2
## 301 cyst-nematode 3 <NA> <NA> <NA> <NA> 2
## 302 cyst-nematode 4 <NA> <NA> <NA> <NA> 2
## 303 2-4-d-injury <NA> <NA> <NA> <NA> <NA> <NA>
## 304 herbicide-injury 1 1 <NA> 0 <NA> 1
## 305 herbicide-injury 0 1 <NA> 0 <NA> 0
## 306 herbicide-injury 1 1 <NA> 0 <NA> 0
## 307 herbicide-injury 1 1 <NA> 0 <NA> 1
## 308 diaporthe-stem-canker 6 0 2 1 0 1
## 309 diaporthe-stem-canker 3 0 2 1 0 2
## 310 diaporthe-stem-canker 4 0 2 1 0 3
## 311 diaporthe-stem-canker 5 0 2 1 0 1
## 312 diaporthe-stem-canker 3 0 2 1 0 3
## 313 diaporthe-stem-canker 5 0 2 1 0 2
## 314 diaporthe-stem-canker 5 0 2 1 0 3
## 315 diaporthe-stem-canker 3 0 2 1 0 2
## 316 diaporthe-stem-canker 4 0 2 1 0 3
## 317 diaporthe-stem-canker 6 0 2 1 0 3
## 318 charcoal-rot 4 0 0 1 0 2
## 319 charcoal-rot 5 0 0 2 0 3
## 320 charcoal-rot 4 0 0 2 0 3
## 321 charcoal-rot 5 0 0 2 0 0
## 322 charcoal-rot 5 0 0 2 1 2
## 323 charcoal-rot 3 0 0 2 1 0
## 324 charcoal-rot 4 0 0 2 1 1
## 325 charcoal-rot 5 0 0 2 1 2
## 326 charcoal-rot 6 0 0 2 1 3
## 327 charcoal-rot 6 0 0 2 1 3
## 328 rhizoctonia-root-rot 0 1 2 0 0 0
## 329 rhizoctonia-root-rot 0 1 2 0 0 3
## 330 rhizoctonia-root-rot 0 1 2 0 0 2
## 331 rhizoctonia-root-rot 2 1 2 0 0 0
## 332 rhizoctonia-root-rot 1 1 2 0 0 2
## 333 rhizoctonia-root-rot 2 1 2 0 0 3
## 334 rhizoctonia-root-rot 2 1 2 0 0 2
## 335 rhizoctonia-root-rot 4 0 2 0 1 0
## 336 rhizoctonia-root-rot 0 1 2 0 0 1
## 337 rhizoctonia-root-rot 2 1 2 0 0 3
## 338 phytophthora-rot 2 1 1 0 0 3
## 339 phytophthora-rot 1 1 2 0 0 3
## 340 phytophthora-rot 2 1 2 1 1 3
## 341 phytophthora-rot 1 1 2 1 1 2
## 342 phytophthora-rot 3 1 1 1 <NA> 2
## 343 phytophthora-rot 3 1 1 1 <NA> 3
## 344 phytophthora-rot 0 1 2 2 <NA> 3
## 345 phytophthora-rot 1 1 2 1 <NA> 2
## 346 phytophthora-rot 1 1 2 1 <NA> 0
## 347 phytophthora-rot 4 1 1 2 <NA> 3
## 348 phytophthora-rot 1 1 2 2 <NA> 2
## 349 phytophthora-rot 2 1 2 1 <NA> 3
## 350 phytophthora-rot 3 1 2 2 <NA> 2
## 351 phytophthora-rot 4 1 1 1 <NA> 3
## 352 phytophthora-rot 1 1 2 1 <NA> 2
## 353 phytophthora-rot 1 1 2 1 <NA> 3
## 354 phytophthora-rot 1 1 2 2 <NA> 1
## 355 phytophthora-rot 2 1 1 2 <NA> 2
## 356 phytophthora-rot 3 1 1 1 <NA> 3
## 357 phytophthora-rot 3 1 1 1 <NA> 1
## 358 phytophthora-rot 1 1 2 2 <NA> 1
## 359 phytophthora-rot 2 1 2 2 <NA> 2
## 360 phytophthora-rot 3 1 1 1 <NA> 3
## 361 phytophthora-rot 4 1 1 1 <NA> 2
## 362 phytophthora-rot 1 1 2 2 <NA> 3
## 363 phytophthora-rot 2 1 2 2 <NA> 2
## 364 phytophthora-rot 3 1 1 1 <NA> 3
## 365 phytophthora-rot 4 1 1 1 <NA> 2
## 366 phytophthora-rot 1 1 2 2 <NA> 3
## 367 phytophthora-rot 2 1 2 2 <NA> 2
## 368 phytophthora-rot 3 1 1 1 <NA> 3
## 369 phytophthora-rot 1 1 2 2 <NA> 2
## 370 phytophthora-rot 2 1 2 1 <NA> 3
## 371 phytophthora-rot 3 1 2 1 <NA> 1
## 372 phytophthora-rot 1 1 2 2 <NA> 2
## 373 phytophthora-rot 2 1 2 2 <NA> 3
## 374 phytophthora-rot 3 1 1 1 <NA> 2
## 375 phytophthora-rot 4 1 1 1 <NA> 3
## 376 phytophthora-rot 1 1 2 1 <NA> 2
## 377 phytophthora-rot 2 1 2 1 <NA> 3
## 378 phytophthora-rot 3 1 1 1 <NA> 1
## 379 phytophthora-rot 4 1 1 1 <NA> 1
## 380 phytophthora-rot 1 1 2 2 <NA> 2
## 381 phytophthora-rot 2 1 2 2 <NA> 3
## 382 phytophthora-rot 3 1 1 1 <NA> 2
## 383 phytophthora-rot 2 1 1 1 <NA> 3
## 384 phytophthora-rot 3 1 1 1 <NA> 2
## 385 phytophthora-rot 3 1 1 1 <NA> 3
## 386 brown-stem-rot 3 0 0 0 1 1
## 387 brown-stem-rot 4 0 1 0 1 2
## 388 brown-stem-rot 3 0 0 0 1 3
## 389 brown-stem-rot 5 0 0 1 1 3
## 390 brown-stem-rot 6 0 0 1 1 2
## 391 brown-stem-rot 4 0 1 0 1 3
## 392 brown-stem-rot 5 0 0 2 0 2
## 393 brown-stem-rot 5 0 0 1 0 1
## 394 brown-stem-rot 4 0 0 1 0 3
## 395 brown-stem-rot 5 0 0 1 0 2
## 396 brown-stem-rot 5 0 0 1 0 2
## 397 brown-stem-rot 3 1 1 0 0 2
## 398 brown-stem-rot 5 1 1 1 0 2
## 399 brown-stem-rot 4 1 0 0 0 2
## 400 brown-stem-rot 5 1 1 0 0 3
## 401 brown-stem-rot 4 1 1 0 0 3
## 402 brown-stem-rot 6 1 1 1 0 3
## 403 brown-stem-rot 3 0 0 1 0 2
## 404 brown-stem-rot 5 0 0 2 0 3
## 405 brown-stem-rot 3 0 0 1 0 2
## 406 brown-stem-rot 4 0 0 1 0 1
## 407 brown-stem-rot 5 0 0 2 0 2
## 408 brown-stem-rot 4 0 0 1 0 1
## 409 brown-stem-rot 5 0 0 2 0 3
## 410 powdery-mildew 1 0 0 0 1 0
## 411 powdery-mildew 2 0 1 1 1 1
## 412 powdery-mildew 3 0 2 0 1 2
## 413 powdery-mildew 4 0 0 1 1 3
## 414 powdery-mildew 5 0 1 0 1 0
## 415 powdery-mildew 4 0 1 0 1 2
## 416 powdery-mildew 3 1 0 1 0 3
## 417 powdery-mildew 4 1 1 0 0 0
## 418 powdery-mildew 2 1 1 0 0 0
## 419 powdery-mildew 5 1 0 1 0 3
## 420 downy-mildew 3 0 2 0 1 2
## 421 downy-mildew 4 0 2 1 1 3
## 422 downy-mildew 5 0 2 1 1 1
## 423 downy-mildew 2 0 2 0 1 0
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## 655 diaporthe-pod-&-stem-blight 5 0 2 2 <NA> 3
## 656 diaporthe-pod-&-stem-blight 6 0 2 2 <NA> 3
## 657 cyst-nematode 2 <NA> <NA> <NA> <NA> 1
## 658 cyst-nematode 3 <NA> <NA> <NA> <NA> 2
## 659 cyst-nematode 4 <NA> <NA> <NA> <NA> 1
## 660 cyst-nematode 2 <NA> <NA> <NA> <NA> 2
## 661 cyst-nematode 3 <NA> <NA> <NA> <NA> 2
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## 663 cyst-nematode 4 <NA> <NA> <NA> <NA> 3
## 664 cyst-nematode 3 <NA> <NA> <NA> <NA> 3
## 665 2-4-d-injury 5 <NA> <NA> <NA> <NA> <NA>
## 666 2-4-d-injury 0 <NA> <NA> <NA> <NA> <NA>
## 667 2-4-d-injury 1 <NA> <NA> <NA> <NA> <NA>
## 668 2-4-d-injury 2 <NA> <NA> <NA> <NA> <NA>
## 669 2-4-d-injury 3 <NA> <NA> <NA> <NA> <NA>
## 670 2-4-d-injury 4 <NA> <NA> <NA> <NA> <NA>
## 671 2-4-d-injury 6 <NA> <NA> <NA> <NA> <NA>
## 672 2-4-d-injury 0 <NA> <NA> <NA> <NA> <NA>
## 673 2-4-d-injury 1 <NA> <NA> <NA> <NA> <NA>
## 674 2-4-d-injury 2 <NA> <NA> <NA> <NA> <NA>
## 675 2-4-d-injury 3 <NA> <NA> <NA> <NA> <NA>
## 676 2-4-d-injury 4 <NA> <NA> <NA> <NA> <NA>
## 677 2-4-d-injury 5 <NA> <NA> <NA> <NA> <NA>
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## 679 2-4-d-injury 0 <NA> <NA> <NA> <NA> <NA>
## 680 herbicide-injury 0 1 <NA> 0 <NA> 0
## 681 herbicide-injury 2 1 <NA> 0 <NA> 0
## 682 herbicide-injury 0 1 <NA> 0 <NA> 1
## 683 herbicide-injury 2 1 <NA> 0 <NA> 1
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## 368 1
## 369 1
## 370 1
## 371 1
## 372 1
## 373 1
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## 648 <NA>
## 649 <NA>
## 650 <NA>
## 651 <NA>
## 652 <NA>
## 653 <NA>
## 654 <NA>
## 655 <NA>
## 656 <NA>
## 657 2
## 658 2
## 659 2
## 660 2
## 661 2
## 662 2
## 663 2
## 664 2
## 665 <NA>
## 666 <NA>
## 667 <NA>
## 668 <NA>
## 669 <NA>
## 670 <NA>
## 671 <NA>
## 672 <NA>
## 673 <NA>
## 674 <NA>
## 675 <NA>
## 676 <NA>
## 677 <NA>
## 678 <NA>
## 679 <NA>
## 680 1
## 681 1
## 682 1
## 683 1summary(Soybean)## Class date plant.stand precip temp
## brown-spot : 92 5 :149 0 :354 0 : 74 0 : 80
## alternarialeaf-spot: 91 4 :131 1 :293 1 :112 1 :374
## frog-eye-leaf-spot : 91 3 :118 NA's: 36 2 :459 2 :199
## phytophthora-rot : 88 2 : 93 NA's: 38 NA's: 30
## anthracnose : 44 6 : 90
## brown-stem-rot : 44 (Other):101
## (Other) :233 NA's : 1
## hail crop.hist area.dam sever seed.tmt germ plant.growth
## 0 :435 0 : 65 0 :123 0 :195 0 :305 0 :165 0 :441
## 1 :127 1 :165 1 :227 1 :322 1 :222 1 :213 1 :226
## NA's:121 2 :219 2 :145 2 : 45 2 : 35 2 :193 NA's: 16
## 3 :218 3 :187 NA's:121 NA's:121 NA's:112
## NA's: 16 NA's: 1
##
##
## leaves leaf.halo leaf.marg leaf.size leaf.shread leaf.malf leaf.mild
## 0: 77 0 :221 0 :357 0 : 51 0 :487 0 :554 0 :535
## 1:606 1 : 36 1 : 21 1 :327 1 : 96 1 : 45 1 : 20
## 2 :342 2 :221 2 :221 NA's:100 NA's: 84 2 : 20
## NA's: 84 NA's: 84 NA's: 84 NA's:108
##
##
##
## stem lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 :296 0 :520 0 :379 0 :320 0 :473 0 :497
## 1 :371 1 : 42 1 : 39 1 : 83 1 :104 1 :135
## NA's: 16 NA's:121 2 : 36 2 :177 NA's:106 2 : 13
## 3 :191 3 : 65 NA's: 38
## NA's: 38 NA's: 38
##
##
## mycelium int.discolor sclerotia fruit.pods fruit.spots seed
## 0 :639 0 :581 0 :625 0 :407 0 :345 0 :476
## 1 : 6 1 : 44 1 : 20 1 :130 1 : 75 1 :115
## NA's: 38 2 : 20 NA's: 38 2 : 14 2 : 57 NA's: 92
## NA's: 38 3 : 48 4 :100
## NA's: 84 NA's:106
##
##
## mold.growth seed.discolor seed.size shriveling roots
## 0 :524 0 :513 0 :532 0 :539 0 :551
## 1 : 67 1 : 64 1 : 59 1 : 38 1 : 86
## NA's: 92 NA's:106 NA's: 92 NA's:106 2 : 15
## NA's: 31
##
##
## columns <- colnames(Soybean)
lapply(columns,
function(col) {
ggplot(Soybean,
aes_string(col)) + geom_bar() + coord_flip() + ggtitle(col)})## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
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Taking a look at each of these graphs, the distribution that seems most like a degenerate would be the mycelium one
library(naniar)## Warning: package 'naniar' was built under R version 4.4.3gg_miss_var(Soybean) +
ggtitle("Missing Data")
By using this gg_miss_var() function, we are able to
produce a bar plot that shows the number of missing values in each of
the variables, so we are able to quickly see whcih variables have the
most missing data
- A strategy for handling missing data in either this case or any, we can use the Mice package to help us use the imputation method when dealing with missing data. The imputation method allows us to enter some value depending on some variable into the missing data fields. For example, we can choose to impute the average into the missing data fields.
library(mice)## Warning: package 'mice' was built under R version 4.4.3##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbindmd.pattern(Soybean)
## Class leaves date area.dam crop.hist plant.growth stem temp roots
## 562 1 1 1 1 1 1 1 1 1
## 13 1 1 1 1 1 1 1 1 1
## 55 1 1 1 1 1 1 1 1 1
## 8 1 1 1 1 1 1 1 1 1
## 9 1 1 1 1 1 1 1 1 0
## 6 1 1 1 1 1 1 1 1 0
## 14 1 1 1 1 1 1 1 0 1
## 15 1 1 1 1 0 0 0 0 0
## 1 1 1 0 0 0 0 0 0 0
## 0 0 1 1 16 16 16 30 31
## plant.stand precip stem.cankers canker.lesion ext.decay mycelium
## 562 1 1 1 1 1 1
## 13 1 1 1 1 1 1
## 55 1 1 1 1 1 1
## 8 1 0 0 0 0 0
## 9 1 1 1 1 1 1
## 6 0 1 1 1 1 1
## 14 0 0 0 0 0 0
## 15 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 36 38 38 38 38 38
## int.discolor sclerotia leaf.halo leaf.marg leaf.size leaf.malf fruit.pods
## 562 1 1 1 1 1 1 1
## 13 1 1 1 1 1 1 0
## 55 1 1 0 0 0 0 0
## 8 0 0 1 1 1 1 1
## 9 1 1 0 0 0 0 1
## 6 1 1 0 0 0 0 1
## 14 0 0 0 0 0 0 1
## 15 0 0 1 1 1 1 0
## 1 0 0 1 1 1 1 0
## 38 38 84 84 84 84 84
## seed mold.growth seed.size leaf.shread fruiting.bodies fruit.spots
## 562 1 1 1 1 1 1
## 13 0 0 0 1 0 0
## 55 0 0 0 0 0 0
## 8 0 0 0 1 0 0
## 9 1 1 1 0 1 1
## 6 1 1 1 0 1 1
## 14 1 1 1 0 0 0
## 15 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 92 92 92 100 106 106
## seed.discolor shriveling leaf.mild germ hail sever seed.tmt lodging
## 562 1 1 1 1 1 1 1 1 0
## 13 0 0 1 0 0 0 0 0 13
## 55 0 0 0 0 0 0 0 0 19
## 8 0 0 0 0 0 0 0 0 20
## 9 1 1 0 1 0 0 0 0 11
## 6 1 1 0 0 0 0 0 0 13
## 14 0 0 0 0 0 0 0 0 24
## 15 0 0 0 0 0 0 0 0 28
## 1 0 0 0 0 0 0 0 0 30
## 106 106 108 112 121 121 121 121 2337#imputed_results <- mice(Soybean, m = 5, maxit = 50, seed = 123)
#Soybean_imputed <- complete(imputed_results)By using this method, I am able to generate 5 different imputed datasets with up to 50 iterations to create stable imputation methods. After fully imputing data for the missing values, it then extracts a new dataset with the imputed values to use.