Titanic
Nurudeen Hussain
2024-05-01
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#(1) and (2)
# Set the CRAN mirror
chooseCRANmirror(ind=1)
# Install the packages
install.packages("rpart")## Installing package into 'C:/Users/Home/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)## package 'rpart' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'rpart'## Warning in file.copy(savedcopy, lib, recursive = TRUE): problem copying
## C:\Users\Home\AppData\Local\R\win-library\4.3\00LOCK\rpart\libs\x64\rpart.dll
## to C:\Users\Home\AppData\Local\R\win-library\4.3\rpart\libs\x64\rpart.dll:
## Permission denied## Warning: restored 'rpart'##
## The downloaded binary packages are in
## C:\Users\Home\AppData\Local\Temp\RtmpqyR593\downloaded_packagesinstall.packages("caret")## Installing package into 'C:/Users/Home/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)## package 'caret' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'caret'## Warning in file.copy(savedcopy, lib, recursive = TRUE): problem copying
## C:\Users\Home\AppData\Local\R\win-library\4.3\00LOCK\caret\libs\x64\caret.dll
## to C:\Users\Home\AppData\Local\R\win-library\4.3\caret\libs\x64\caret.dll:
## Permission denied## Warning: restored 'caret'##
## The downloaded binary packages are in
## C:\Users\Home\AppData\Local\Temp\RtmpqyR593\downloaded_packagesinstall.packages("caret")## Installing package into 'C:/Users/Home/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)## package 'caret' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'caret'## Warning in file.copy(savedcopy, lib, recursive = TRUE): problem copying
## C:\Users\Home\AppData\Local\R\win-library\4.3\00LOCK\caret\libs\x64\caret.dll
## to C:\Users\Home\AppData\Local\R\win-library\4.3\caret\libs\x64\caret.dll:
## Permission denied## Warning: restored 'caret'##
## The downloaded binary packages are in
## C:\Users\Home\AppData\Local\Temp\RtmpqyR593\downloaded_packageslibrary(caret)## Warning: package 'caret' was built under R version 4.3.3## Loading required package: ggplot2## Loading required package: latticelibrary("rpart")## Warning: package 'rpart' was built under R version 4.3.3T3 <- read.csv("https://goo.gl/At238b",stringsAsFactors = FALSE)
View(T3)
#(3) Select the desired features from the original dataset T3 with adjusted column names
titanic <- T3[, c("survived", "embarked", "age", "sex", "sibsp", "parch", "fare")]
titanic## survived embarked age sex sibsp parch fare
## 1 1 S 29.00 female 0 0 211.3375
## 2 1 S 0.92 male 1 2 151.5500
## 3 0 S 2.00 female 1 2 151.5500
## 4 0 S 30.00 male 1 2 151.5500
## 5 0 S 25.00 female 1 2 151.5500
## 6 1 S 48.00 male 0 0 26.5500
## 7 1 S 63.00 female 1 0 77.9583
## 8 0 S 39.00 male 0 0 0.0000
## 9 1 S 53.00 female 2 0 51.4792
## 10 0 C 71.00 male 0 0 49.5042
## 11 0 C 47.00 male 1 0 227.5250
## 12 1 C 18.00 female 1 0 227.5250
## 13 1 C 24.00 female 0 0 69.3000
## 14 1 S 26.00 female 0 0 78.8500
## 15 1 S 80.00 male 0 0 30.0000
## 16 0 S NA male 0 0 25.9250
## 17 0 C 24.00 male 0 1 247.5208
## 18 1 C 50.00 female 0 1 247.5208
## 19 1 C 32.00 female 0 0 76.2917
## 20 0 C 36.00 male 0 0 75.2417
## 21 1 S 37.00 male 1 1 52.5542
## 22 1 S 47.00 female 1 1 52.5542
## 23 1 C 26.00 male 0 0 30.0000
## 24 1 C 42.00 female 0 0 227.5250
## 25 1 S 29.00 female 0 0 221.7792
## 26 0 C 25.00 male 0 0 26.0000
## 27 1 C 25.00 male 1 0 91.0792
## 28 1 C 19.00 female 1 0 91.0792
## 29 1 S 35.00 female 0 0 135.6333
## 30 1 S 28.00 male 0 0 26.5500
## 31 0 S 45.00 male 0 0 35.5000
## 32 1 C 40.00 male 0 0 31.0000
## 33 1 S 30.00 female 0 0 164.8667
## 34 1 S 58.00 female 0 0 26.5500
## 35 0 S 42.00 male 0 0 26.5500
## 36 1 C 45.00 female 0 0 262.3750
## 37 1 S 22.00 female 0 1 55.0000
## 38 1 S NA male 0 0 26.5500
## 39 0 S 41.00 male 0 0 30.5000
## 40 0 C 48.00 male 0 0 50.4958
## 41 0 C NA male 0 0 39.6000
## 42 1 C 44.00 female 0 0 27.7208
## 43 1 S 59.00 female 2 0 51.4792
## 44 1 C 60.00 female 0 0 76.2917
## 45 1 C 41.00 female 0 0 134.5000
## 46 0 S 45.00 male 0 0 26.5500
## 47 0 S NA male 0 0 31.0000
## 48 1 S 42.00 male 0 0 26.2875
## 49 1 C 53.00 female 0 0 27.4458
## 50 1 C 36.00 male 0 1 512.3292
## 51 1 C 58.00 female 0 1 512.3292
## 52 0 S 33.00 male 0 0 5.0000
## 53 0 S 28.00 male 0 0 47.1000
## 54 0 S 17.00 male 0 0 47.1000
## 55 1 S 11.00 male 1 2 120.0000
## 56 1 S 14.00 female 1 2 120.0000
## 57 1 S 36.00 male 1 2 120.0000
## 58 1 S 36.00 female 1 2 120.0000
## 59 0 S 49.00 male 0 0 26.0000
## 60 1 C NA female 0 0 27.7208
## 61 0 S 36.00 male 1 0 78.8500
## 62 1 S 76.00 female 1 0 78.8500
## 63 0 S 46.00 male 1 0 61.1750
## 64 1 S 47.00 female 1 0 61.1750
## 65 1 S 27.00 male 1 0 53.1000
## 66 1 S 33.00 female 1 0 53.1000
## 67 1 C 36.00 female 0 0 262.3750
## 68 1 S 30.00 female 0 0 86.5000
## 69 1 C 45.00 male 0 0 29.7000
## 70 1 S NA female 0 1 55.0000
## 71 0 S NA male 0 0 0.0000
## 72 0 C 27.00 male 1 0 136.7792
## 73 1 C 26.00 female 1 0 136.7792
## 74 1 S 22.00 female 0 0 151.5500
## 75 0 S NA male 0 0 52.0000
## 76 0 S 47.00 male 0 0 25.5875
## 77 1 C 39.00 female 1 1 83.1583
## 78 0 C 37.00 male 1 1 83.1583
## 79 1 C 64.00 female 0 2 83.1583
## 80 1 S 55.00 female 2 0 25.7000
## 81 0 S NA male 0 0 26.5500
## 82 0 S 70.00 male 1 1 71.0000
## 83 1 S 36.00 female 0 2 71.0000
## 84 1 S 64.00 female 1 1 26.5500
## 85 0 C 39.00 male 1 0 71.2833
## 86 1 C 38.00 female 1 0 71.2833
## 87 1 S 51.00 male 0 0 26.5500
## 88 1 S 27.00 male 0 0 30.5000
## 89 1 S 33.00 female 0 0 151.5500
## 90 0 S 31.00 male 1 0 52.0000
## 91 1 S 27.00 female 1 2 52.0000
## 92 1 S 31.00 male 1 0 57.0000
## 93 1 S 17.00 female 1 0 57.0000
## 94 1 S 53.00 male 1 1 81.8583
## 95 1 S 4.00 male 0 2 81.8583
## 96 1 S 54.00 female 1 1 81.8583
## 97 0 C 50.00 male 1 0 106.4250
## 98 1 C 27.00 female 1 1 247.5208
## 99 1 C 48.00 female 1 0 106.4250
## 100 1 C 48.00 female 1 0 39.6000
## 101 1 C 49.00 male 1 0 56.9292
## 102 0 C 39.00 male 0 0 29.7000
## 103 1 C 23.00 female 0 1 83.1583
## 104 1 C 38.00 female 0 0 227.5250
## 105 1 C 54.00 female 1 0 78.2667
## 106 0 C 36.00 female 0 0 31.6792
## 107 0 S NA male 0 0 221.7792
## 108 1 S NA female 0 0 31.6833
## 109 1 C NA female 0 0 110.8833
## 110 1 S 36.00 male 0 0 26.3875
## 111 0 C 30.00 male 0 0 27.7500
## 112 1 S 24.00 female 3 2 263.0000
## 113 1 S 28.00 female 3 2 263.0000
## 114 1 S 23.00 female 3 2 263.0000
## 115 0 S 19.00 male 3 2 263.0000
## 116 0 S 64.00 male 1 4 263.0000
## 117 1 S 60.00 female 1 4 263.0000
## 118 1 C 30.00 female 0 0 56.9292
## 119 0 S NA male 0 0 26.5500
## 120 1 S 50.00 male 2 0 133.6500
## 121 1 C 43.00 male 1 0 27.7208
## 122 1 S NA female 1 0 133.6500
## 123 1 C 22.00 female 0 2 49.5000
## 124 1 C 60.00 male 1 1 79.2000
## 125 1 C 48.00 female 1 1 79.2000
## 126 0 S NA male 0 0 0.0000
## 127 0 S 37.00 male 1 0 53.1000
## 128 1 S 35.00 female 1 0 53.1000
## 129 0 S 47.00 male 0 0 38.5000
## 130 1 C 35.00 female 0 0 211.5000
## 131 1 C 22.00 female 0 1 59.4000
## 132 1 C 45.00 female 0 1 59.4000
## 133 0 C 24.00 male 0 0 79.2000
## 134 1 C 49.00 male 1 0 89.1042
## 135 1 C NA female 1 0 89.1042
## 136 0 C 71.00 male 0 0 34.6542
## 137 1 C 53.00 male 0 0 28.5000
## 138 1 S 19.00 female 0 0 30.0000
## 139 0 S 38.00 male 0 1 153.4625
## 140 1 S 58.00 female 0 1 153.4625
## 141 1 C 23.00 male 0 1 63.3583
## 142 1 C 45.00 female 0 1 63.3583
## 143 0 C 46.00 male 0 0 79.2000
## 144 1 C 25.00 male 1 0 55.4417
## 145 1 C 25.00 female 1 0 55.4417
## 146 1 C 48.00 male 1 0 76.7292
## 147 1 C 49.00 female 1 0 76.7292
## 148 0 S NA male 0 0 42.4000
## 149 0 S 45.00 male 1 0 83.4750
## 150 1 S 35.00 female 1 0 83.4750
## 151 0 S 40.00 male 0 0 0.0000
## 152 1 C 27.00 male 0 0 76.7292
## 153 1 S NA male 0 0 30.0000
## 154 1 C 24.00 female 0 0 83.1583
## 155 0 S 55.00 male 1 1 93.5000
## 156 1 S 52.00 female 1 1 93.5000
## 157 0 S 42.00 male 0 0 42.5000
## 158 0 S NA male 0 0 51.8625
## 159 0 S 55.00 male 0 0 50.0000
## 160 1 C 16.00 female 0 1 57.9792
## 161 1 C 44.00 female 0 1 57.9792
## 162 1 S 51.00 female 1 0 77.9583
## 163 0 S 42.00 male 1 0 52.0000
## 164 1 S 35.00 female 1 0 52.0000
## 165 1 C 35.00 male 0 0 26.5500
## 166 1 S 38.00 male 1 0 90.0000
## 167 0 C NA male 0 0 30.6958
## 168 1 S 35.00 female 1 0 90.0000
## 169 1 <NA> 38.00 female 0 0 80.0000
## 170 0 C 50.00 female 0 0 28.7125
## 171 1 S 49.00 male 0 0 0.0000
## 172 0 S 46.00 male 0 0 26.0000
## 173 0 S 50.00 male 0 0 26.0000
## 174 0 C 32.50 male 0 0 211.5000
## 175 0 C 58.00 male 0 0 29.7000
## 176 0 S 41.00 male 1 0 51.8625
## 177 1 S NA female 1 0 51.8625
## 178 1 S 42.00 male 1 0 52.5542
## 179 1 S 45.00 female 1 0 52.5542
## 180 0 S NA male 0 0 26.5500
## 181 1 S 39.00 female 0 0 211.3375
## 182 1 S 49.00 female 0 0 25.9292
## 183 1 C 30.00 female 0 0 106.4250
## 184 1 C 35.00 male 0 0 512.3292
## 185 0 C NA male 0 0 27.7208
## 186 0 S 42.00 male 0 0 26.5500
## 187 1 C 55.00 female 0 0 27.7208
## 188 1 S 16.00 female 0 1 39.4000
## 189 1 S 51.00 female 0 1 39.4000
## 190 0 S 29.00 male 0 0 30.0000
## 191 1 S 21.00 female 0 0 77.9583
## 192 0 S 30.00 male 0 0 45.5000
## 193 1 C 58.00 female 0 0 146.5208
## 194 1 S 15.00 female 0 1 211.3375
## 195 0 S 30.00 male 0 0 26.0000
## 196 1 S 16.00 female 0 0 86.5000
## 197 1 C NA male 0 0 29.7000
## 198 0 S 19.00 male 1 0 53.1000
## 199 1 S 18.00 female 1 0 53.1000
## 200 1 C 24.00 female 0 0 49.5042
## 201 0 C 46.00 male 0 0 75.2417
## 202 0 S 54.00 male 0 0 51.8625
## 203 1 S 36.00 male 0 0 26.2875
## 204 0 C 28.00 male 1 0 82.1708
## 205 1 C NA female 1 0 82.1708
## 206 0 S 65.00 male 0 0 26.5500
## 207 0 Q 44.00 male 2 0 90.0000
## 208 1 Q 33.00 female 1 0 90.0000
## 209 1 Q 37.00 female 1 0 90.0000
## 210 1 C 30.00 male 1 0 57.7500
## 211 0 S 55.00 male 0 0 30.5000
## 212 0 S 47.00 male 0 0 42.4000
## 213 0 C 37.00 male 0 1 29.7000
## 214 1 C 31.00 female 1 0 113.2750
## 215 1 C 23.00 female 1 0 113.2750
## 216 0 C 58.00 male 0 2 113.2750
## 217 1 S 19.00 female 0 2 26.2833
## 218 0 S 64.00 male 0 0 26.0000
## 219 1 C 39.00 female 0 0 108.9000
## 220 1 C NA male 0 0 25.7417
## 221 1 C 22.00 female 0 1 61.9792
## 222 0 C 65.00 male 0 1 61.9792
## 223 0 C 28.50 male 0 0 27.7208
## 224 0 S NA male 0 0 0.0000
## 225 0 S 45.50 male 0 0 28.5000
## 226 0 S 23.00 male 0 0 93.5000
## 227 0 S 29.00 male 1 0 66.6000
## 228 1 S 22.00 female 1 0 66.6000
## 229 0 C 18.00 male 1 0 108.9000
## 230 1 C 17.00 female 1 0 108.9000
## 231 1 S 30.00 female 0 0 93.5000
## 232 1 S 52.00 male 0 0 30.5000
## 233 0 S 47.00 male 0 0 52.0000
## 234 1 C 56.00 female 0 1 83.1583
## 235 0 S 38.00 male 0 0 0.0000
## 236 1 S NA male 0 0 39.6000
## 237 0 C 22.00 male 0 0 135.6333
## 238 0 C NA male 0 0 227.5250
## 239 1 S 43.00 female 0 1 211.3375
## 240 0 S 31.00 male 0 0 50.4958
## 241 1 S 45.00 male 0 0 26.5500
## 242 0 S NA male 0 0 50.0000
## 243 1 C 33.00 female 0 0 27.7208
## 244 0 C 46.00 male 0 0 79.2000
## 245 0 C 36.00 male 0 0 40.1250
## 246 1 S 33.00 female 0 0 86.5000
## 247 0 C 55.00 male 1 0 59.4000
## 248 1 C 54.00 female 1 0 59.4000
## 249 0 S 33.00 male 0 0 26.5500
## 250 1 C 13.00 male 2 2 262.3750
## 251 1 C 18.00 female 2 2 262.3750
## 252 1 C 21.00 female 2 2 262.3750
## 253 0 C 61.00 male 1 3 262.3750
## 254 1 C 48.00 female 1 3 262.3750
## 255 1 S NA male 0 0 30.5000
## 256 1 C 24.00 female 0 0 69.3000
## 257 1 S NA male 0 0 26.0000
## 258 1 C 35.00 female 1 0 57.7500
## 259 1 C 30.00 female 0 0 31.0000
## 260 1 S 34.00 male 0 0 26.5500
## 261 1 S 40.00 female 0 0 153.4625
## 262 1 S 35.00 male 0 0 26.2875
## 263 0 S 50.00 male 1 0 55.9000
## 264 1 S 39.00 female 1 0 55.9000
## 265 1 C 56.00 male 0 0 35.5000
## 266 1 S 28.00 male 0 0 35.5000
## 267 0 S 56.00 male 0 0 26.5500
## 268 0 C 56.00 male 0 0 30.6958
## 269 0 S 24.00 male 1 0 60.0000
## 270 0 S NA male 0 0 26.0000
## 271 1 S 18.00 female 1 0 60.0000
## 272 1 S 24.00 male 1 0 82.2667
## 273 1 S 23.00 female 1 0 82.2667
## 274 1 C 6.00 male 0 2 134.5000
## 275 1 C 45.00 male 1 1 134.5000
## 276 1 C 40.00 female 1 1 134.5000
## 277 0 C 57.00 male 1 0 146.5208
## 278 1 C NA female 1 0 146.5208
## 279 1 C 32.00 male 0 0 30.5000
## 280 0 S 62.00 male 0 0 26.5500
## 281 1 C 54.00 male 1 0 55.4417
## 282 1 C 43.00 female 1 0 55.4417
## 283 1 C 52.00 female 1 0 78.2667
## 284 0 C NA male 0 0 27.7208
## 285 1 <NA> 62.00 female 0 0 80.0000
## 286 0 S 67.00 male 1 0 221.7792
## 287 0 S 63.00 female 1 0 221.7792
## 288 0 S 61.00 male 0 0 32.3208
## 289 1 S 48.00 female 0 0 25.9292
## 290 1 S 18.00 female 0 2 79.6500
## 291 0 S 52.00 male 1 1 79.6500
## 292 1 S 39.00 female 1 1 79.6500
## 293 1 S 48.00 male 1 0 52.0000
## 294 1 S NA female 1 0 52.0000
## 295 0 C 49.00 male 1 1 110.8833
## 296 1 C 17.00 male 0 2 110.8833
## 297 1 C 39.00 female 1 1 110.8833
## 298 1 C NA female 0 0 79.2000
## 299 1 C 31.00 male 0 0 28.5375
## 300 0 C 40.00 male 0 0 27.7208
## 301 0 S 61.00 male 0 0 33.5000
## 302 0 S 47.00 male 0 0 34.0208
## 303 1 C 35.00 female 0 0 512.3292
## 304 0 C 64.00 male 1 0 75.2500
## 305 1 C 60.00 female 1 0 75.2500
## 306 0 S 60.00 male 0 0 26.5500
## 307 0 S 54.00 male 0 1 77.2875
## 308 0 S 21.00 male 0 1 77.2875
## 309 1 C 55.00 female 0 0 135.6333
## 310 1 S 31.00 female 0 2 164.8667
## 311 0 S 57.00 male 1 1 164.8667
## 312 1 S 45.00 female 1 1 164.8667
## 313 0 C 50.00 male 1 1 211.5000
## 314 0 C 27.00 male 0 2 211.5000
## 315 1 C 50.00 female 1 1 211.5000
## 316 1 S 21.00 female 0 0 26.5500
## 317 0 C 51.00 male 0 1 61.3792
## 318 1 C 21.00 male 0 1 61.3792
## 319 0 S NA male 0 0 35.0000
## 320 1 C 31.00 female 0 0 134.5000
## 321 1 S NA male 0 0 35.5000
## 322 0 S 62.00 male 0 0 26.5500
## 323 1 C 36.00 female 0 0 135.6333
## 324 0 C 30.00 male 1 0 24.0000
## 325 1 C 28.00 female 1 0 24.0000
## 326 0 S 30.00 male 0 0 13.0000
## 327 0 S 18.00 male 0 0 11.5000
## 328 0 S 25.00 male 0 0 10.5000
## 329 0 S 34.00 male 1 0 26.0000
## 330 1 S 36.00 female 1 0 26.0000
## 331 0 S 57.00 male 0 0 13.0000
## 332 0 S 18.00 male 0 0 11.5000
## 333 0 S 23.00 male 0 0 10.5000
## 334 1 S 36.00 female 0 0 13.0000
## 335 0 S 28.00 male 0 0 10.5000
## 336 0 S 51.00 male 0 0 12.5250
## 337 1 S 32.00 male 1 0 26.0000
## 338 1 S 19.00 female 1 0 26.0000
## 339 0 S 28.00 male 0 0 26.0000
## 340 1 S 1.00 male 2 1 39.0000
## 341 1 S 4.00 female 2 1 39.0000
## 342 1 S 12.00 female 2 1 39.0000
## 343 1 S 36.00 female 0 3 39.0000
## 344 1 S 34.00 male 0 0 13.0000
## 345 1 S 19.00 female 0 0 13.0000
## 346 0 S 23.00 male 0 0 13.0000
## 347 0 S 26.00 male 0 0 13.0000
## 348 0 S 42.00 male 0 0 13.0000
## 349 0 S 27.00 male 0 0 13.0000
## 350 1 S 24.00 female 0 0 13.0000
## 351 1 S 15.00 female 0 2 39.0000
## 352 0 S 60.00 male 1 1 39.0000
## 353 1 S 40.00 female 1 1 39.0000
## 354 1 S 20.00 female 1 0 26.0000
## 355 0 S 25.00 male 1 0 26.0000
## 356 1 S 36.00 female 0 0 13.0000
## 357 0 S 25.00 male 0 0 13.0000
## 358 0 S 42.00 male 0 0 13.0000
## 359 1 S 42.00 female 0 0 13.0000
## 360 1 S 0.83 male 0 2 29.0000
## 361 1 S 26.00 male 1 1 29.0000
## 362 1 S 22.00 female 1 1 29.0000
## 363 1 S 35.00 female 0 0 21.0000
## 364 0 S NA male 0 0 0.0000
## 365 0 S 19.00 male 0 0 13.0000
## 366 0 S 44.00 female 1 0 26.0000
## 367 0 S 54.00 male 1 0 26.0000
## 368 0 S 52.00 male 0 0 13.5000
## 369 0 S 37.00 male 1 0 26.0000
## 370 0 S 29.00 female 1 0 26.0000
## 371 1 S 25.00 female 1 1 30.0000
## 372 1 S 45.00 female 0 2 30.0000
## 373 0 S 29.00 male 1 0 26.0000
## 374 1 S 28.00 female 1 0 26.0000
## 375 0 S 29.00 male 0 0 10.5000
## 376 0 S 28.00 male 0 0 13.0000
## 377 1 S 24.00 male 0 0 10.5000
## 378 1 S 8.00 female 0 2 26.2500
## 379 0 S 31.00 male 1 1 26.2500
## 380 1 S 31.00 female 1 1 26.2500
## 381 1 S 22.00 female 0 0 10.5000
## 382 0 S 30.00 female 0 0 13.0000
## 383 0 S NA female 0 0 21.0000
## 384 0 S 21.00 male 0 0 11.5000
## 385 0 S NA male 0 0 0.0000
## 386 1 S 8.00 male 1 1 36.7500
## 387 0 S 18.00 male 0 0 73.5000
## 388 1 S 48.00 female 0 2 36.7500
## 389 1 S 28.00 female 0 0 13.0000
## 390 0 S 32.00 male 0 0 13.0000
## 391 0 S 17.00 male 0 0 73.5000
## 392 0 C 29.00 male 1 0 27.7208
## 393 1 C 24.00 female 1 0 27.7208
## 394 0 S 25.00 male 0 0 31.5000
## 395 0 S 18.00 male 0 0 73.5000
## 396 1 S 18.00 female 0 1 23.0000
## 397 1 S 34.00 female 0 1 23.0000
## 398 0 S 54.00 male 0 0 26.0000
## 399 1 S 8.00 male 0 2 32.5000
## 400 0 S 42.00 male 1 1 32.5000
## 401 1 S 34.00 female 1 1 32.5000
## 402 1 C 27.00 female 1 0 13.8583
## 403 1 C 30.00 female 1 0 13.8583
## 404 0 S 23.00 male 0 0 13.0000
## 405 0 S 21.00 male 0 0 13.0000
## 406 0 S 18.00 male 0 0 13.0000
## 407 0 S 40.00 male 1 0 26.0000
## 408 1 S 29.00 female 1 0 26.0000
## 409 0 S 18.00 male 0 0 10.5000
## 410 0 S 36.00 male 0 0 13.0000
## 411 0 S NA male 0 0 0.0000
## 412 0 S 38.00 female 0 0 13.0000
## 413 0 S 35.00 male 0 0 26.0000
## 414 0 S 38.00 male 1 0 21.0000
## 415 0 S 34.00 male 1 0 21.0000
## 416 1 S 34.00 female 0 0 13.0000
## 417 0 S 16.00 male 0 0 26.0000
## 418 0 S 26.00 male 0 0 10.5000
## 419 0 S 47.00 male 0 0 10.5000
## 420 0 S 21.00 male 1 0 11.5000
## 421 0 S 21.00 male 1 0 11.5000
## 422 0 S 24.00 male 0 0 13.5000
## 423 0 S 24.00 male 0 0 13.0000
## 424 0 S 34.00 male 0 0 13.0000
## 425 0 S 30.00 male 0 0 13.0000
## 426 0 S 52.00 male 0 0 13.0000
## 427 0 S 30.00 male 0 0 13.0000
## 428 1 S 0.67 male 1 1 14.5000
## 429 1 S 24.00 female 0 2 14.5000
## 430 0 S 44.00 male 0 0 13.0000
## 431 1 S 6.00 female 0 1 33.0000
## 432 0 S 28.00 male 0 1 33.0000
## 433 1 S 62.00 male 0 0 10.5000
## 434 0 S 30.00 male 0 0 10.5000
## 435 1 S 7.00 female 0 2 26.2500
## 436 0 S 43.00 male 1 1 26.2500
## 437 1 S 45.00 female 1 1 26.2500
## 438 1 S 24.00 female 1 2 65.0000
## 439 1 S 24.00 female 1 2 65.0000
## 440 0 S 49.00 male 1 2 65.0000
## 441 1 S 48.00 female 1 2 65.0000
## 442 1 S 55.00 female 0 0 16.0000
## 443 0 S 24.00 male 2 0 73.5000
## 444 0 S 32.00 male 2 0 73.5000
## 445 0 S 21.00 male 2 0 73.5000
## 446 0 S 18.00 female 1 1 13.0000
## 447 1 S 20.00 female 2 1 23.0000
## 448 0 S 23.00 male 2 1 11.5000
## 449 0 S 36.00 male 0 0 13.0000
## 450 1 S 54.00 female 1 3 23.0000
## 451 0 S 50.00 male 0 0 13.0000
## 452 0 S 44.00 male 1 0 26.0000
## 453 1 S 29.00 female 1 0 26.0000
## 454 0 S 21.00 male 0 0 73.5000
## 455 1 S 42.00 male 0 0 13.0000
## 456 0 S 63.00 male 1 0 26.0000
## 457 0 S 60.00 female 1 0 26.0000
## 458 0 S 33.00 male 0 0 12.2750
## 459 1 S 17.00 female 0 0 10.5000
## 460 0 S 42.00 male 1 0 27.0000
## 461 1 S 24.00 female 2 1 27.0000
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## 972 0 Q NA male 0 0 7.8792
## 973 0 S 28.00 male 0 0 56.4958
## 974 0 S NA male 0 0 7.5500
## 975 0 S 30.00 male 1 0 16.1000
## 976 0 S 26.00 female 1 0 16.1000
## 977 0 S NA male 0 0 7.8792
## 978 0 S 20.50 male 0 0 7.2500
## 979 1 S 27.00 male 0 0 8.6625
## 980 0 S 51.00 male 0 0 7.0542
## 981 1 S 23.00 female 0 0 7.8542
## 982 1 S 32.00 male 0 0 7.5792
## 983 0 S NA male 0 0 7.8958
## 984 0 S NA male 0 0 7.5500
## 985 1 Q NA female 0 0 7.7500
## 986 1 S 24.00 male 0 0 7.1417
## 987 0 S 22.00 male 0 0 7.1250
## 988 0 Q NA female 0 0 7.8792
## 989 0 Q NA male 0 0 7.7500
## 990 0 S NA male 0 0 8.0500
## 991 0 S 29.00 male 0 0 7.9250
## 992 1 C NA male 0 0 7.2292
## 993 0 Q 30.50 female 0 0 7.7500
## 994 1 Q NA female 0 0 7.7375
## 995 0 C NA male 0 0 7.2292
## 996 0 C 35.00 male 0 0 7.8958
## 997 0 S 33.00 male 0 0 7.8958
## 998 1 C NA female 0 0 7.2250
## 999 0 C NA male 0 0 7.8958
## 1000 1 Q NA female 0 0 7.7500
## 1001 1 Q NA male 0 0 7.7500
## 1002 1 Q NA female 2 0 23.2500
## 1003 1 Q NA female 2 0 23.2500
## 1004 1 Q NA male 2 0 23.2500
## 1005 1 Q NA female 0 0 7.7875
## 1006 0 Q NA male 0 0 15.5000
## 1007 1 Q NA female 0 0 7.8792
## 1008 1 Q 15.00 female 0 0 8.0292
## 1009 0 Q 35.00 female 0 0 7.7500
## 1010 0 Q NA male 0 0 7.7500
## 1011 0 S 24.00 male 1 0 16.1000
## 1012 0 S 19.00 female 1 0 16.1000
## 1013 0 Q NA female 0 0 7.7500
## 1014 0 S NA female 0 0 8.0500
## 1015 0 S NA female 0 0 8.0500
## 1016 0 S 55.50 male 0 0 8.0500
## 1017 0 Q NA male 0 0 7.7500
## 1018 1 S 21.00 male 0 0 7.7750
## 1019 0 S NA male 0 0 8.0500
## 1020 0 S 24.00 male 0 0 7.8958
## 1021 0 S 21.00 male 0 0 7.8958
## 1022 0 S 28.00 male 0 0 7.8958
## 1023 0 S NA male 0 0 7.8958
## 1024 1 Q NA female 0 0 7.8792
## 1025 0 S 25.00 male 0 0 7.6500
## 1026 1 S 6.00 male 0 1 12.4750
## 1027 1 S 27.00 female 0 1 12.4750
## 1028 0 S NA male 0 0 8.0500
## 1029 1 Q NA female 1 0 24.1500
## 1030 0 Q NA male 1 0 24.1500
## 1031 0 Q NA male 0 0 8.4583
## 1032 0 S 34.00 male 0 0 8.0500
## 1033 0 Q NA male 0 0 7.7500
## 1034 1 S NA male 0 0 7.7750
## 1035 1 C NA male 1 1 15.2458
## 1036 1 C NA male 1 1 15.2458
## 1037 1 C NA female 0 2 15.2458
## 1038 1 C NA female 0 0 7.2292
## 1039 0 S NA male 0 0 8.0500
## 1040 1 Q NA female 0 0 7.7333
## 1041 1 Q 24.00 female 0 0 7.7500
## 1042 0 S NA male 0 0 8.0500
## 1043 1 Q NA female 1 0 15.5000
## 1044 1 Q NA female 1 0 15.5000
## 1045 1 Q NA female 0 0 15.5000
## 1046 0 S 18.00 male 0 0 7.7500
## 1047 0 S 22.00 male 0 0 7.8958
## 1048 1 C 15.00 female 0 0 7.2250
## 1049 1 C 1.00 female 0 2 15.7417
## 1050 1 C 20.00 male 1 1 15.7417
## 1051 1 C 19.00 female 1 1 15.7417
## 1052 0 S 33.00 male 0 0 8.0500
## 1053 0 S NA male 0 0 7.8958
## 1054 0 C NA male 0 0 7.2292
## 1055 0 Q NA female 0 0 7.7500
## 1056 0 S NA male 0 0 7.8958
## 1057 1 C 12.00 male 1 0 11.2417
## 1058 1 C 14.00 female 1 0 11.2417
## 1059 0 S 29.00 female 0 0 7.9250
## 1060 0 S 28.00 male 0 0 8.0500
## 1061 1 S 18.00 female 0 0 7.7750
## 1062 1 S 26.00 female 0 0 7.8542
## 1063 0 S 21.00 male 0 0 7.8542
## 1064 0 S 41.00 male 0 0 7.1250
## 1065 1 S 39.00 male 0 0 7.9250
## 1066 0 S 21.00 male 0 0 7.8000
## 1067 0 C 28.50 male 0 0 7.2292
## 1068 1 S 22.00 female 0 0 7.7500
## 1069 0 S 61.00 male 0 0 6.2375
## 1070 0 Q NA male 1 0 15.5000
## 1071 0 Q NA male 0 0 7.8292
## 1072 1 Q NA female 1 0 15.5000
## 1073 0 Q NA male 0 0 7.7333
## 1074 0 Q NA male 0 0 7.7500
## 1075 0 Q NA male 0 0 7.7500
## 1076 0 S 23.00 male 0 0 9.2250
## 1077 0 Q NA female 0 0 7.7500
## 1078 1 Q NA female 0 0 7.7500
## 1079 1 Q NA female 0 0 7.8792
## 1080 1 S 22.00 female 0 0 7.7750
## 1081 1 Q NA male 0 0 7.7500
## 1082 1 Q NA female 0 0 7.8292
## 1083 1 S 9.00 male 0 1 3.1708
## 1084 0 S 28.00 male 0 0 22.5250
## 1085 0 S 42.00 male 0 1 8.4042
## 1086 0 S NA male 0 0 7.3125
## 1087 0 S 31.00 female 0 0 7.8542
## 1088 0 S 28.00 male 0 0 7.8542
## 1089 1 S 32.00 male 0 0 7.7750
## 1090 0 S 20.00 male 0 0 9.2250
## 1091 0 S 23.00 female 0 0 8.6625
## 1092 0 S 20.00 female 0 0 8.6625
## 1093 0 S 20.00 male 0 0 8.6625
## 1094 0 S 16.00 male 0 0 9.2167
## 1095 1 S 31.00 female 0 0 8.6833
## 1096 0 Q NA female 0 0 7.6292
## 1097 0 S 2.00 male 3 1 21.0750
## 1098 0 S 6.00 male 3 1 21.0750
## 1099 0 S 3.00 female 3 1 21.0750
## 1100 0 S 8.00 female 3 1 21.0750
## 1101 0 S 29.00 female 0 4 21.0750
## 1102 0 S 1.00 male 4 1 39.6875
## 1103 0 S 7.00 male 4 1 39.6875
## 1104 0 S 2.00 male 4 1 39.6875
## 1105 0 S 16.00 male 4 1 39.6875
## 1106 0 S 14.00 male 4 1 39.6875
## 1107 0 S 41.00 female 0 5 39.6875
## 1108 0 S 21.00 male 0 0 8.6625
## 1109 0 S 19.00 male 0 0 14.5000
## 1110 0 C NA male 0 0 8.7125
## 1111 0 S 32.00 male 0 0 7.8958
## 1112 0 S 0.75 male 1 1 13.7750
## 1113 0 S 3.00 female 1 1 13.7750
## 1114 0 S 26.00 female 0 2 13.7750
## 1115 0 S NA male 0 0 7.0000
## 1116 0 S NA male 0 0 7.7750
## 1117 0 S NA male 0 0 8.0500
## 1118 0 S 21.00 male 0 0 7.9250
## 1119 0 S 25.00 male 0 0 7.9250
## 1120 0 S 22.00 male 0 0 7.2500
## 1121 1 S 25.00 male 1 0 7.7750
## 1122 1 C NA male 1 1 22.3583
## 1123 1 C NA female 1 1 22.3583
## 1124 1 C NA female 0 2 22.3583
## 1125 0 Q NA female 0 0 8.1375
## 1126 0 S 24.00 male 0 0 8.0500
## 1127 0 S 28.00 female 0 0 7.8958
## 1128 0 S 19.00 male 0 0 7.8958
## 1129 0 S NA male 0 0 7.8958
## 1130 0 S 25.00 male 1 0 7.7750
## 1131 0 S 18.00 female 0 0 7.7750
## 1132 1 S 32.00 male 0 0 8.0500
## 1133 0 S NA male 0 0 7.8958
## 1134 0 S 17.00 male 0 0 8.6625
## 1135 0 S 24.00 male 0 0 8.6625
## 1136 0 S NA male 0 0 7.8958
## 1137 0 S NA female 0 0 8.1125
## 1138 0 C NA male 0 0 7.2292
## 1139 0 S NA male 0 0 7.2500
## 1140 0 S 38.00 male 0 0 7.8958
## 1141 0 S 21.00 male 0 0 8.0500
## 1142 0 Q 10.00 male 4 1 29.1250
## 1143 0 Q 4.00 male 4 1 29.1250
## 1144 0 Q 7.00 male 4 1 29.1250
## 1145 0 Q 2.00 male 4 1 29.1250
## 1146 0 Q 8.00 male 4 1 29.1250
## 1147 0 Q 39.00 female 0 5 29.1250
## 1148 0 S 22.00 female 0 0 39.6875
## 1149 0 S 35.00 male 0 0 7.1250
## 1150 1 Q NA female 0 0 7.7208
## 1151 0 S NA male 0 0 14.5000
## 1152 0 S NA female 0 0 14.5000
## 1153 0 S 50.00 male 1 0 14.5000
## 1154 0 S 47.00 female 1 0 14.5000
## 1155 0 S NA male 0 0 8.0500
## 1156 0 S NA male 0 0 7.7750
## 1157 0 S 2.00 female 1 1 20.2125
## 1158 0 S 18.00 male 1 1 20.2125
## 1159 0 S 41.00 female 0 2 20.2125
## 1160 1 S NA female 0 0 8.0500
## 1161 0 S 50.00 male 0 0 8.0500
## 1162 0 S 16.00 male 0 0 8.0500
## 1163 1 Q NA male 0 0 7.7500
## 1164 0 Q NA male 0 0 24.1500
## 1165 0 C NA male 0 0 7.2292
## 1166 0 C 25.00 male 0 0 7.2250
## 1167 0 C NA male 0 0 7.2250
## 1168 0 Q NA male 0 0 7.7292
## 1169 0 S NA male 0 0 7.5750
## 1170 0 S 38.50 male 0 0 7.2500
## 1171 0 S NA male 8 2 69.5500
## 1172 0 S 14.50 male 8 2 69.5500
## 1173 0 S NA female 8 2 69.5500
## 1174 0 S NA female 8 2 69.5500
## 1175 0 S NA female 8 2 69.5500
## 1176 0 S NA female 8 2 69.5500
## 1177 0 S NA male 8 2 69.5500
## 1178 0 S NA male 8 2 69.5500
## 1179 0 S NA male 8 2 69.5500
## 1180 0 S NA male 1 9 69.5500
## 1181 0 S NA female 1 9 69.5500
## 1182 0 S 24.00 male 0 0 9.3250
## 1183 1 S 21.00 female 0 0 7.6500
## 1184 0 S 39.00 male 0 0 7.9250
## 1185 0 C NA male 2 0 21.6792
## 1186 0 C NA male 2 0 21.6792
## 1187 0 C NA male 2 0 21.6792
## 1188 1 S 1.00 female 1 1 16.7000
## 1189 1 S 24.00 female 0 2 16.7000
## 1190 1 S 4.00 female 1 1 16.7000
## 1191 1 S 25.00 male 0 0 9.5000
## 1192 0 S 20.00 male 0 0 8.0500
## 1193 0 S 24.50 male 0 0 8.0500
## 1194 0 Q NA male 0 0 7.7250
## 1195 0 S NA male 0 0 7.8958
## 1196 0 Q NA male 0 0 7.7500
## 1197 1 S 29.00 male 0 0 9.5000
## 1198 0 S NA male 0 0 15.1000
## 1199 1 Q NA female 0 0 7.7792
## 1200 0 S NA male 0 0 8.0500
## 1201 0 S NA male 0 0 8.0500
## 1202 0 C 22.00 male 0 0 7.2292
## 1203 0 S NA male 0 0 8.0500
## 1204 0 S 40.00 male 0 0 7.8958
## 1205 0 S 21.00 male 0 0 7.9250
## 1206 1 S 18.00 female 0 0 7.4958
## 1207 0 S 4.00 male 3 2 27.9000
## 1208 0 S 10.00 male 3 2 27.9000
## 1209 0 S 9.00 female 3 2 27.9000
## 1210 0 S 2.00 female 3 2 27.9000
## 1211 0 S 40.00 male 1 4 27.9000
## 1212 0 S 45.00 female 1 4 27.9000
## 1213 0 S NA male 0 0 7.8958
## 1214 0 S NA male 0 0 8.0500
## 1215 0 S NA male 0 0 8.6625
## 1216 0 Q NA male 0 0 7.7500
## 1217 1 Q NA female 0 0 7.7333
## 1218 0 S 19.00 male 0 0 7.6500
## 1219 0 S 30.00 male 0 0 8.0500
## 1220 0 S NA male 0 0 8.0500
## 1221 0 S 32.00 male 0 0 8.0500
## 1222 0 S NA male 0 0 7.8958
## 1223 0 C 33.00 male 0 0 8.6625
## 1224 1 S 23.00 female 0 0 7.5500
## 1225 0 S 21.00 male 0 0 8.0500
## 1226 0 S 60.50 male 0 0 NA
## 1227 0 S 19.00 male 0 0 7.8958
## 1228 0 S 22.00 female 0 0 9.8375
## 1229 1 S 31.00 male 0 0 7.9250
## 1230 0 S 27.00 male 0 0 8.6625
## 1231 0 S 2.00 female 0 1 10.4625
## 1232 0 S 29.00 female 1 1 10.4625
## 1233 1 S 16.00 male 0 0 8.0500
## 1234 1 S 44.00 male 0 0 7.9250
## 1235 0 S 25.00 male 0 0 7.0500
## 1236 0 S 74.00 male 0 0 7.7750
## 1237 1 S 14.00 male 0 0 9.2250
## 1238 0 S 24.00 male 0 0 7.7958
## 1239 1 S 25.00 male 0 0 7.7958
## 1240 0 S 34.00 male 0 0 8.0500
## 1241 1 C 0.42 male 0 1 8.5167
## 1242 0 C NA male 1 0 6.4375
## 1243 0 C NA male 0 0 6.4375
## 1244 0 C NA male 0 0 7.2250
## 1245 1 C 16.00 female 1 1 8.5167
## 1246 0 S NA male 0 0 8.0500
## 1247 0 S NA male 1 0 16.1000
## 1248 1 S NA female 1 0 16.1000
## 1249 0 S 32.00 male 0 0 7.9250
## 1250 0 Q NA male 0 0 7.7500
## 1251 0 S NA male 0 0 7.8958
## 1252 0 S 30.50 male 0 0 8.0500
## 1253 0 S 44.00 male 0 0 8.0500
## 1254 0 C NA male 0 0 7.2292
## 1255 1 S 25.00 male 0 0 0.0000
## 1256 0 C NA male 0 0 7.2292
## 1257 1 C 7.00 male 1 1 15.2458
## 1258 1 C 9.00 female 1 1 15.2458
## 1259 1 C 29.00 female 0 2 15.2458
## 1260 0 S 36.00 male 0 0 7.8958
## 1261 1 S 18.00 female 0 0 9.8417
## 1262 1 S 63.00 female 0 0 9.5875
## 1263 0 S NA male 1 1 14.5000
## 1264 0 S 11.50 male 1 1 14.5000
## 1265 0 S 40.50 male 0 2 14.5000
## 1266 0 S 10.00 female 0 2 24.1500
## 1267 0 S 36.00 male 1 1 24.1500
## 1268 0 S 30.00 female 1 1 24.1500
## 1269 0 S NA male 0 0 9.5000
## 1270 0 S 33.00 male 0 0 9.5000
## 1271 0 S 28.00 male 0 0 9.5000
## 1272 0 S 28.00 male 0 0 9.5000
## 1273 0 S 47.00 male 0 0 9.0000
## 1274 0 S 18.00 female 2 0 18.0000
## 1275 0 S 31.00 male 3 0 18.0000
## 1276 0 S 16.00 male 2 0 18.0000
## 1277 0 S 31.00 female 1 0 18.0000
## 1278 1 C 22.00 male 0 0 7.2250
## 1279 0 S 20.00 male 0 0 7.8542
## 1280 0 S 14.00 female 0 0 7.8542
## 1281 0 S 22.00 male 0 0 7.8958
## 1282 0 S 22.00 male 0 0 9.0000
## 1283 0 S NA male 0 0 8.0500
## 1284 0 S NA male 0 0 7.5500
## 1285 0 S NA male 0 0 8.0500
## 1286 0 S 32.50 male 0 0 9.5000
## 1287 1 C 38.00 female 0 0 7.2292
## 1288 0 S 51.00 male 0 0 7.7500
## 1289 0 S 18.00 male 1 0 6.4958
## 1290 0 S 21.00 male 1 0 6.4958
## 1291 1 S 47.00 female 1 0 7.0000
## 1292 0 S NA male 0 0 8.7125
## 1293 0 S NA male 0 0 7.5500
## 1294 0 S NA male 0 0 8.0500
## 1295 0 S 28.50 male 0 0 16.1000
## 1296 0 S 21.00 male 0 0 7.2500
## 1297 0 S 27.00 male 0 0 8.6625
## 1298 0 S NA male 0 0 7.2500
## 1299 0 S 36.00 male 0 0 9.5000
## 1300 0 C 27.00 male 1 0 14.4542
## 1301 1 C 15.00 female 1 0 14.4542
## 1302 0 C 45.50 male 0 0 7.2250
## 1303 0 C NA male 0 0 7.2250
## 1304 0 C NA male 0 0 14.4583
## 1305 0 C 14.50 female 1 0 14.4542
## 1306 0 C NA female 1 0 14.4542
## 1307 0 C 26.50 male 0 0 7.2250
## 1308 0 C 27.00 male 0 0 7.2250
## 1309 0 S 29.00 male 0 0 7.8750#4) Perform a statistical analysis of the titanic dataset.
View(titanic)#View the new table
# Display the first few rows of the new dataset
head(titanic)#displays the first few rows of a dataset## survived embarked age sex sibsp parch fare
## 1 1 S 29.00 female 0 0 211.3375
## 2 1 S 0.92 male 1 2 151.5500
## 3 0 S 2.00 female 1 2 151.5500
## 4 0 S 30.00 male 1 2 151.5500
## 5 0 S 25.00 female 1 2 151.5500
## 6 1 S 48.00 male 0 0 26.5500tail(titanic)#displays the last few rows of a dataset## survived embarked age sex sibsp parch fare
## 1304 0 C NA male 0 0 14.4583
## 1305 0 C 14.5 female 1 0 14.4542
## 1306 0 C NA female 1 0 14.4542
## 1307 0 C 26.5 male 0 0 7.2250
## 1308 0 C 27.0 male 0 0 7.2250
## 1309 0 S 29.0 male 0 0 7.8750summary(titanic)#provides a concise summary of the variables ## survived embarked age sex
## Min. :0.000 Length:1309 Min. : 0.17 Length:1309
## 1st Qu.:0.000 Class :character 1st Qu.:21.00 Class :character
## Median :0.000 Mode :character Median :28.00 Mode :character
## Mean :0.382 Mean :29.88
## 3rd Qu.:1.000 3rd Qu.:39.00
## Max. :1.000 Max. :80.00
## NA's :263
## sibsp parch fare
## Min. :0.0000 Min. :0.000 Min. : 0.000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.: 7.896
## Median :0.0000 Median :0.000 Median : 14.454
## Mean :0.4989 Mean :0.385 Mean : 33.295
## 3rd Qu.:1.0000 3rd Qu.:0.000 3rd Qu.: 31.275
## Max. :8.0000 Max. :9.000 Max. :512.329
## NA's :1str(titanic)## 'data.frame': 1309 obs. of 7 variables:
## $ survived: int 1 1 0 0 0 1 1 0 1 0 ...
## $ embarked: chr "S" "S" "S" "S" ...
## $ age : num 29 0.92 2 30 25 48 63 39 53 71 ...
## $ sex : chr "female" "male" "female" "male" ...
## $ sibsp : int 0 1 1 1 1 0 1 0 2 0 ...
## $ parch : int 0 2 2 2 2 0 0 0 0 0 ...
## $ fare : num 211 152 152 152 152 ...dim(titanic)#gives you the dimensions (number of rows and columns)## [1] 1309 7names(titanic)# retrieves the name of the variable## [1] "survived" "embarked" "age" "sex" "sibsp" "parch" "fare"install.packages("knitr")## Installing package into 'C:/Users/Home/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)## package 'knitr' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\Home\AppData\Local\Temp\RtmpqyR593\downloaded_packages# Remove rows with NA values
titanic <- na.omit(titanic)
knitr::kable(titanic) #displays the new datafram "titanic" separately in a proper and more bold view| survived | embarked | age | sex | sibsp | parch | fare | |
|---|---|---|---|---|---|---|---|
| 1 | 1 | S | 29.00 | female | 0 | 0 | 211.3375 |
| 2 | 1 | S | 0.92 | male | 1 | 2 | 151.5500 |
| 3 | 0 | S | 2.00 | female | 1 | 2 | 151.5500 |
| 4 | 0 | S | 30.00 | male | 1 | 2 | 151.5500 |
| 5 | 0 | S | 25.00 | female | 1 | 2 | 151.5500 |
| 6 | 1 | S | 48.00 | male | 0 | 0 | 26.5500 |
| 7 | 1 | S | 63.00 | female | 1 | 0 | 77.9583 |
| 8 | 0 | S | 39.00 | male | 0 | 0 | 0.0000 |
| 9 | 1 | S | 53.00 | female | 2 | 0 | 51.4792 |
| 10 | 0 | C | 71.00 | male | 0 | 0 | 49.5042 |
| 11 | 0 | C | 47.00 | male | 1 | 0 | 227.5250 |
| 12 | 1 | C | 18.00 | female | 1 | 0 | 227.5250 |
| 13 | 1 | C | 24.00 | female | 0 | 0 | 69.3000 |
| 14 | 1 | S | 26.00 | female | 0 | 0 | 78.8500 |
| 15 | 1 | S | 80.00 | male | 0 | 0 | 30.0000 |
| 17 | 0 | C | 24.00 | male | 0 | 1 | 247.5208 |
| 18 | 1 | C | 50.00 | female | 0 | 1 | 247.5208 |
| 19 | 1 | C | 32.00 | female | 0 | 0 | 76.2917 |
| 20 | 0 | C | 36.00 | male | 0 | 0 | 75.2417 |
| 21 | 1 | S | 37.00 | male | 1 | 1 | 52.5542 |
| 22 | 1 | S | 47.00 | female | 1 | 1 | 52.5542 |
| 23 | 1 | C | 26.00 | male | 0 | 0 | 30.0000 |
| 24 | 1 | C | 42.00 | female | 0 | 0 | 227.5250 |
| 25 | 1 | S | 29.00 | female | 0 | 0 | 221.7792 |
| 26 | 0 | C | 25.00 | male | 0 | 0 | 26.0000 |
| 27 | 1 | C | 25.00 | male | 1 | 0 | 91.0792 |
| 28 | 1 | C | 19.00 | female | 1 | 0 | 91.0792 |
| 29 | 1 | S | 35.00 | female | 0 | 0 | 135.6333 |
| 30 | 1 | S | 28.00 | male | 0 | 0 | 26.5500 |
| 31 | 0 | S | 45.00 | male | 0 | 0 | 35.5000 |
| 32 | 1 | C | 40.00 | male | 0 | 0 | 31.0000 |
| 33 | 1 | S | 30.00 | female | 0 | 0 | 164.8667 |
| 34 | 1 | S | 58.00 | female | 0 | 0 | 26.5500 |
| 35 | 0 | S | 42.00 | male | 0 | 0 | 26.5500 |
| 36 | 1 | C | 45.00 | female | 0 | 0 | 262.3750 |
| 37 | 1 | S | 22.00 | female | 0 | 1 | 55.0000 |
| 39 | 0 | S | 41.00 | male | 0 | 0 | 30.5000 |
| 40 | 0 | C | 48.00 | male | 0 | 0 | 50.4958 |
| 42 | 1 | C | 44.00 | female | 0 | 0 | 27.7208 |
| 43 | 1 | S | 59.00 | female | 2 | 0 | 51.4792 |
| 44 | 1 | C | 60.00 | female | 0 | 0 | 76.2917 |
| 45 | 1 | C | 41.00 | female | 0 | 0 | 134.5000 |
| 46 | 0 | S | 45.00 | male | 0 | 0 | 26.5500 |
| 48 | 1 | S | 42.00 | male | 0 | 0 | 26.2875 |
| 49 | 1 | C | 53.00 | female | 0 | 0 | 27.4458 |
| 50 | 1 | C | 36.00 | male | 0 | 1 | 512.3292 |
| 51 | 1 | C | 58.00 | female | 0 | 1 | 512.3292 |
| 52 | 0 | S | 33.00 | male | 0 | 0 | 5.0000 |
| 53 | 0 | S | 28.00 | male | 0 | 0 | 47.1000 |
| 54 | 0 | S | 17.00 | male | 0 | 0 | 47.1000 |
| 55 | 1 | S | 11.00 | male | 1 | 2 | 120.0000 |
| 56 | 1 | S | 14.00 | female | 1 | 2 | 120.0000 |
| 57 | 1 | S | 36.00 | male | 1 | 2 | 120.0000 |
| 58 | 1 | S | 36.00 | female | 1 | 2 | 120.0000 |
| 59 | 0 | S | 49.00 | male | 0 | 0 | 26.0000 |
| 61 | 0 | S | 36.00 | male | 1 | 0 | 78.8500 |
| 62 | 1 | S | 76.00 | female | 1 | 0 | 78.8500 |
| 63 | 0 | S | 46.00 | male | 1 | 0 | 61.1750 |
| 64 | 1 | S | 47.00 | female | 1 | 0 | 61.1750 |
| 65 | 1 | S | 27.00 | male | 1 | 0 | 53.1000 |
| 66 | 1 | S | 33.00 | female | 1 | 0 | 53.1000 |
| 67 | 1 | C | 36.00 | female | 0 | 0 | 262.3750 |
| 68 | 1 | S | 30.00 | female | 0 | 0 | 86.5000 |
| 69 | 1 | C | 45.00 | male | 0 | 0 | 29.7000 |
| 72 | 0 | C | 27.00 | male | 1 | 0 | 136.7792 |
| 73 | 1 | C | 26.00 | female | 1 | 0 | 136.7792 |
| 74 | 1 | S | 22.00 | female | 0 | 0 | 151.5500 |
| 76 | 0 | S | 47.00 | male | 0 | 0 | 25.5875 |
| 77 | 1 | C | 39.00 | female | 1 | 1 | 83.1583 |
| 78 | 0 | C | 37.00 | male | 1 | 1 | 83.1583 |
| 79 | 1 | C | 64.00 | female | 0 | 2 | 83.1583 |
| 80 | 1 | S | 55.00 | female | 2 | 0 | 25.7000 |
| 82 | 0 | S | 70.00 | male | 1 | 1 | 71.0000 |
| 83 | 1 | S | 36.00 | female | 0 | 2 | 71.0000 |
| 84 | 1 | S | 64.00 | female | 1 | 1 | 26.5500 |
| 85 | 0 | C | 39.00 | male | 1 | 0 | 71.2833 |
| 86 | 1 | C | 38.00 | female | 1 | 0 | 71.2833 |
| 87 | 1 | S | 51.00 | male | 0 | 0 | 26.5500 |
| 88 | 1 | S | 27.00 | male | 0 | 0 | 30.5000 |
| 89 | 1 | S | 33.00 | female | 0 | 0 | 151.5500 |
| 90 | 0 | S | 31.00 | male | 1 | 0 | 52.0000 |
| 91 | 1 | S | 27.00 | female | 1 | 2 | 52.0000 |
| 92 | 1 | S | 31.00 | male | 1 | 0 | 57.0000 |
| 93 | 1 | S | 17.00 | female | 1 | 0 | 57.0000 |
| 94 | 1 | S | 53.00 | male | 1 | 1 | 81.8583 |
| 95 | 1 | S | 4.00 | male | 0 | 2 | 81.8583 |
| 96 | 1 | S | 54.00 | female | 1 | 1 | 81.8583 |
| 97 | 0 | C | 50.00 | male | 1 | 0 | 106.4250 |
| 98 | 1 | C | 27.00 | female | 1 | 1 | 247.5208 |
| 99 | 1 | C | 48.00 | female | 1 | 0 | 106.4250 |
| 100 | 1 | C | 48.00 | female | 1 | 0 | 39.6000 |
| 101 | 1 | C | 49.00 | male | 1 | 0 | 56.9292 |
| 102 | 0 | C | 39.00 | male | 0 | 0 | 29.7000 |
| 103 | 1 | C | 23.00 | female | 0 | 1 | 83.1583 |
| 104 | 1 | C | 38.00 | female | 0 | 0 | 227.5250 |
| 105 | 1 | C | 54.00 | female | 1 | 0 | 78.2667 |
| 106 | 0 | C | 36.00 | female | 0 | 0 | 31.6792 |
| 110 | 1 | S | 36.00 | male | 0 | 0 | 26.3875 |
| 111 | 0 | C | 30.00 | male | 0 | 0 | 27.7500 |
| 112 | 1 | S | 24.00 | female | 3 | 2 | 263.0000 |
| 113 | 1 | S | 28.00 | female | 3 | 2 | 263.0000 |
| 114 | 1 | S | 23.00 | female | 3 | 2 | 263.0000 |
| 115 | 0 | S | 19.00 | male | 3 | 2 | 263.0000 |
| 116 | 0 | S | 64.00 | male | 1 | 4 | 263.0000 |
| 117 | 1 | S | 60.00 | female | 1 | 4 | 263.0000 |
| 118 | 1 | C | 30.00 | female | 0 | 0 | 56.9292 |
| 120 | 1 | S | 50.00 | male | 2 | 0 | 133.6500 |
| 121 | 1 | C | 43.00 | male | 1 | 0 | 27.7208 |
| 123 | 1 | C | 22.00 | female | 0 | 2 | 49.5000 |
| 124 | 1 | C | 60.00 | male | 1 | 1 | 79.2000 |
| 125 | 1 | C | 48.00 | female | 1 | 1 | 79.2000 |
| 127 | 0 | S | 37.00 | male | 1 | 0 | 53.1000 |
| 128 | 1 | S | 35.00 | female | 1 | 0 | 53.1000 |
| 129 | 0 | S | 47.00 | male | 0 | 0 | 38.5000 |
| 130 | 1 | C | 35.00 | female | 0 | 0 | 211.5000 |
| 131 | 1 | C | 22.00 | female | 0 | 1 | 59.4000 |
| 132 | 1 | C | 45.00 | female | 0 | 1 | 59.4000 |
| 133 | 0 | C | 24.00 | male | 0 | 0 | 79.2000 |
| 134 | 1 | C | 49.00 | male | 1 | 0 | 89.1042 |
| 136 | 0 | C | 71.00 | male | 0 | 0 | 34.6542 |
| 137 | 1 | C | 53.00 | male | 0 | 0 | 28.5000 |
| 138 | 1 | S | 19.00 | female | 0 | 0 | 30.0000 |
| 139 | 0 | S | 38.00 | male | 0 | 1 | 153.4625 |
| 140 | 1 | S | 58.00 | female | 0 | 1 | 153.4625 |
| 141 | 1 | C | 23.00 | male | 0 | 1 | 63.3583 |
| 142 | 1 | C | 45.00 | female | 0 | 1 | 63.3583 |
| 143 | 0 | C | 46.00 | male | 0 | 0 | 79.2000 |
| 144 | 1 | C | 25.00 | male | 1 | 0 | 55.4417 |
| 145 | 1 | C | 25.00 | female | 1 | 0 | 55.4417 |
| 146 | 1 | C | 48.00 | male | 1 | 0 | 76.7292 |
| 147 | 1 | C | 49.00 | female | 1 | 0 | 76.7292 |
| 149 | 0 | S | 45.00 | male | 1 | 0 | 83.4750 |
| 150 | 1 | S | 35.00 | female | 1 | 0 | 83.4750 |
| 151 | 0 | S | 40.00 | male | 0 | 0 | 0.0000 |
| 152 | 1 | C | 27.00 | male | 0 | 0 | 76.7292 |
| 154 | 1 | C | 24.00 | female | 0 | 0 | 83.1583 |
| 155 | 0 | S | 55.00 | male | 1 | 1 | 93.5000 |
| 156 | 1 | S | 52.00 | female | 1 | 1 | 93.5000 |
| 157 | 0 | S | 42.00 | male | 0 | 0 | 42.5000 |
| 159 | 0 | S | 55.00 | male | 0 | 0 | 50.0000 |
| 160 | 1 | C | 16.00 | female | 0 | 1 | 57.9792 |
| 161 | 1 | C | 44.00 | female | 0 | 1 | 57.9792 |
| 162 | 1 | S | 51.00 | female | 1 | 0 | 77.9583 |
| 163 | 0 | S | 42.00 | male | 1 | 0 | 52.0000 |
| 164 | 1 | S | 35.00 | female | 1 | 0 | 52.0000 |
| 165 | 1 | C | 35.00 | male | 0 | 0 | 26.5500 |
| 166 | 1 | S | 38.00 | male | 1 | 0 | 90.0000 |
| 168 | 1 | S | 35.00 | female | 1 | 0 | 90.0000 |
| 170 | 0 | C | 50.00 | female | 0 | 0 | 28.7125 |
| 171 | 1 | S | 49.00 | male | 0 | 0 | 0.0000 |
| 172 | 0 | S | 46.00 | male | 0 | 0 | 26.0000 |
| 173 | 0 | S | 50.00 | male | 0 | 0 | 26.0000 |
| 174 | 0 | C | 32.50 | male | 0 | 0 | 211.5000 |
| 175 | 0 | C | 58.00 | male | 0 | 0 | 29.7000 |
| 176 | 0 | S | 41.00 | male | 1 | 0 | 51.8625 |
| 178 | 1 | S | 42.00 | male | 1 | 0 | 52.5542 |
| 179 | 1 | S | 45.00 | female | 1 | 0 | 52.5542 |
| 181 | 1 | S | 39.00 | female | 0 | 0 | 211.3375 |
| 182 | 1 | S | 49.00 | female | 0 | 0 | 25.9292 |
| 183 | 1 | C | 30.00 | female | 0 | 0 | 106.4250 |
| 184 | 1 | C | 35.00 | male | 0 | 0 | 512.3292 |
| 186 | 0 | S | 42.00 | male | 0 | 0 | 26.5500 |
| 187 | 1 | C | 55.00 | female | 0 | 0 | 27.7208 |
| 188 | 1 | S | 16.00 | female | 0 | 1 | 39.4000 |
| 189 | 1 | S | 51.00 | female | 0 | 1 | 39.4000 |
| 190 | 0 | S | 29.00 | male | 0 | 0 | 30.0000 |
| 191 | 1 | S | 21.00 | female | 0 | 0 | 77.9583 |
| 192 | 0 | S | 30.00 | male | 0 | 0 | 45.5000 |
| 193 | 1 | C | 58.00 | female | 0 | 0 | 146.5208 |
| 194 | 1 | S | 15.00 | female | 0 | 1 | 211.3375 |
| 195 | 0 | S | 30.00 | male | 0 | 0 | 26.0000 |
| 196 | 1 | S | 16.00 | female | 0 | 0 | 86.5000 |
| 198 | 0 | S | 19.00 | male | 1 | 0 | 53.1000 |
| 199 | 1 | S | 18.00 | female | 1 | 0 | 53.1000 |
| 200 | 1 | C | 24.00 | female | 0 | 0 | 49.5042 |
| 201 | 0 | C | 46.00 | male | 0 | 0 | 75.2417 |
| 202 | 0 | S | 54.00 | male | 0 | 0 | 51.8625 |
| 203 | 1 | S | 36.00 | male | 0 | 0 | 26.2875 |
| 204 | 0 | C | 28.00 | male | 1 | 0 | 82.1708 |
| 206 | 0 | S | 65.00 | male | 0 | 0 | 26.5500 |
| 207 | 0 | Q | 44.00 | male | 2 | 0 | 90.0000 |
| 208 | 1 | Q | 33.00 | female | 1 | 0 | 90.0000 |
| 209 | 1 | Q | 37.00 | female | 1 | 0 | 90.0000 |
| 210 | 1 | C | 30.00 | male | 1 | 0 | 57.7500 |
| 211 | 0 | S | 55.00 | male | 0 | 0 | 30.5000 |
| 212 | 0 | S | 47.00 | male | 0 | 0 | 42.4000 |
| 213 | 0 | C | 37.00 | male | 0 | 1 | 29.7000 |
| 214 | 1 | C | 31.00 | female | 1 | 0 | 113.2750 |
| 215 | 1 | C | 23.00 | female | 1 | 0 | 113.2750 |
| 216 | 0 | C | 58.00 | male | 0 | 2 | 113.2750 |
| 217 | 1 | S | 19.00 | female | 0 | 2 | 26.2833 |
| 218 | 0 | S | 64.00 | male | 0 | 0 | 26.0000 |
| 219 | 1 | C | 39.00 | female | 0 | 0 | 108.9000 |
| 221 | 1 | C | 22.00 | female | 0 | 1 | 61.9792 |
| 222 | 0 | C | 65.00 | male | 0 | 1 | 61.9792 |
| 223 | 0 | C | 28.50 | male | 0 | 0 | 27.7208 |
| 225 | 0 | S | 45.50 | male | 0 | 0 | 28.5000 |
| 226 | 0 | S | 23.00 | male | 0 | 0 | 93.5000 |
| 227 | 0 | S | 29.00 | male | 1 | 0 | 66.6000 |
| 228 | 1 | S | 22.00 | female | 1 | 0 | 66.6000 |
| 229 | 0 | C | 18.00 | male | 1 | 0 | 108.9000 |
| 230 | 1 | C | 17.00 | female | 1 | 0 | 108.9000 |
| 231 | 1 | S | 30.00 | female | 0 | 0 | 93.5000 |
| 232 | 1 | S | 52.00 | male | 0 | 0 | 30.5000 |
| 233 | 0 | S | 47.00 | male | 0 | 0 | 52.0000 |
| 234 | 1 | C | 56.00 | female | 0 | 1 | 83.1583 |
| 235 | 0 | S | 38.00 | male | 0 | 0 | 0.0000 |
| 237 | 0 | C | 22.00 | male | 0 | 0 | 135.6333 |
| 239 | 1 | S | 43.00 | female | 0 | 1 | 211.3375 |
| 240 | 0 | S | 31.00 | male | 0 | 0 | 50.4958 |
| 241 | 1 | S | 45.00 | male | 0 | 0 | 26.5500 |
| 243 | 1 | C | 33.00 | female | 0 | 0 | 27.7208 |
| 244 | 0 | C | 46.00 | male | 0 | 0 | 79.2000 |
| 245 | 0 | C | 36.00 | male | 0 | 0 | 40.1250 |
| 246 | 1 | S | 33.00 | female | 0 | 0 | 86.5000 |
| 247 | 0 | C | 55.00 | male | 1 | 0 | 59.4000 |
| 248 | 1 | C | 54.00 | female | 1 | 0 | 59.4000 |
| 249 | 0 | S | 33.00 | male | 0 | 0 | 26.5500 |
| 250 | 1 | C | 13.00 | male | 2 | 2 | 262.3750 |
| 251 | 1 | C | 18.00 | female | 2 | 2 | 262.3750 |
| 252 | 1 | C | 21.00 | female | 2 | 2 | 262.3750 |
| 253 | 0 | C | 61.00 | male | 1 | 3 | 262.3750 |
| 254 | 1 | C | 48.00 | female | 1 | 3 | 262.3750 |
| 256 | 1 | C | 24.00 | female | 0 | 0 | 69.3000 |
| 258 | 1 | C | 35.00 | female | 1 | 0 | 57.7500 |
| 259 | 1 | C | 30.00 | female | 0 | 0 | 31.0000 |
| 260 | 1 | S | 34.00 | male | 0 | 0 | 26.5500 |
| 261 | 1 | S | 40.00 | female | 0 | 0 | 153.4625 |
| 262 | 1 | S | 35.00 | male | 0 | 0 | 26.2875 |
| 263 | 0 | S | 50.00 | male | 1 | 0 | 55.9000 |
| 264 | 1 | S | 39.00 | female | 1 | 0 | 55.9000 |
| 265 | 1 | C | 56.00 | male | 0 | 0 | 35.5000 |
| 266 | 1 | S | 28.00 | male | 0 | 0 | 35.5000 |
| 267 | 0 | S | 56.00 | male | 0 | 0 | 26.5500 |
| 268 | 0 | C | 56.00 | male | 0 | 0 | 30.6958 |
| 269 | 0 | S | 24.00 | male | 1 | 0 | 60.0000 |
| 271 | 1 | S | 18.00 | female | 1 | 0 | 60.0000 |
| 272 | 1 | S | 24.00 | male | 1 | 0 | 82.2667 |
| 273 | 1 | S | 23.00 | female | 1 | 0 | 82.2667 |
| 274 | 1 | C | 6.00 | male | 0 | 2 | 134.5000 |
| 275 | 1 | C | 45.00 | male | 1 | 1 | 134.5000 |
| 276 | 1 | C | 40.00 | female | 1 | 1 | 134.5000 |
| 277 | 0 | C | 57.00 | male | 1 | 0 | 146.5208 |
| 279 | 1 | C | 32.00 | male | 0 | 0 | 30.5000 |
| 280 | 0 | S | 62.00 | male | 0 | 0 | 26.5500 |
| 281 | 1 | C | 54.00 | male | 1 | 0 | 55.4417 |
| 282 | 1 | C | 43.00 | female | 1 | 0 | 55.4417 |
| 283 | 1 | C | 52.00 | female | 1 | 0 | 78.2667 |
| 286 | 0 | S | 67.00 | male | 1 | 0 | 221.7792 |
| 287 | 0 | S | 63.00 | female | 1 | 0 | 221.7792 |
| 288 | 0 | S | 61.00 | male | 0 | 0 | 32.3208 |
| 289 | 1 | S | 48.00 | female | 0 | 0 | 25.9292 |
| 290 | 1 | S | 18.00 | female | 0 | 2 | 79.6500 |
| 291 | 0 | S | 52.00 | male | 1 | 1 | 79.6500 |
| 292 | 1 | S | 39.00 | female | 1 | 1 | 79.6500 |
| 293 | 1 | S | 48.00 | male | 1 | 0 | 52.0000 |
| 295 | 0 | C | 49.00 | male | 1 | 1 | 110.8833 |
| 296 | 1 | C | 17.00 | male | 0 | 2 | 110.8833 |
| 297 | 1 | C | 39.00 | female | 1 | 1 | 110.8833 |
| 299 | 1 | C | 31.00 | male | 0 | 0 | 28.5375 |
| 300 | 0 | C | 40.00 | male | 0 | 0 | 27.7208 |
| 301 | 0 | S | 61.00 | male | 0 | 0 | 33.5000 |
| 302 | 0 | S | 47.00 | male | 0 | 0 | 34.0208 |
| 303 | 1 | C | 35.00 | female | 0 | 0 | 512.3292 |
| 304 | 0 | C | 64.00 | male | 1 | 0 | 75.2500 |
| 305 | 1 | C | 60.00 | female | 1 | 0 | 75.2500 |
| 306 | 0 | S | 60.00 | male | 0 | 0 | 26.5500 |
| 307 | 0 | S | 54.00 | male | 0 | 1 | 77.2875 |
| 308 | 0 | S | 21.00 | male | 0 | 1 | 77.2875 |
| 309 | 1 | C | 55.00 | female | 0 | 0 | 135.6333 |
| 310 | 1 | S | 31.00 | female | 0 | 2 | 164.8667 |
| 311 | 0 | S | 57.00 | male | 1 | 1 | 164.8667 |
| 312 | 1 | S | 45.00 | female | 1 | 1 | 164.8667 |
| 313 | 0 | C | 50.00 | male | 1 | 1 | 211.5000 |
| 314 | 0 | C | 27.00 | male | 0 | 2 | 211.5000 |
| 315 | 1 | C | 50.00 | female | 1 | 1 | 211.5000 |
| 316 | 1 | S | 21.00 | female | 0 | 0 | 26.5500 |
| 317 | 0 | C | 51.00 | male | 0 | 1 | 61.3792 |
| 318 | 1 | C | 21.00 | male | 0 | 1 | 61.3792 |
| 320 | 1 | C | 31.00 | female | 0 | 0 | 134.5000 |
| 322 | 0 | S | 62.00 | male | 0 | 0 | 26.5500 |
| 323 | 1 | C | 36.00 | female | 0 | 0 | 135.6333 |
| 324 | 0 | C | 30.00 | male | 1 | 0 | 24.0000 |
| 325 | 1 | C | 28.00 | female | 1 | 0 | 24.0000 |
| 326 | 0 | S | 30.00 | male | 0 | 0 | 13.0000 |
| 327 | 0 | S | 18.00 | male | 0 | 0 | 11.5000 |
| 328 | 0 | S | 25.00 | male | 0 | 0 | 10.5000 |
| 329 | 0 | S | 34.00 | male | 1 | 0 | 26.0000 |
| 330 | 1 | S | 36.00 | female | 1 | 0 | 26.0000 |
| 331 | 0 | S | 57.00 | male | 0 | 0 | 13.0000 |
| 332 | 0 | S | 18.00 | male | 0 | 0 | 11.5000 |
| 333 | 0 | S | 23.00 | male | 0 | 0 | 10.5000 |
| 334 | 1 | S | 36.00 | female | 0 | 0 | 13.0000 |
| 335 | 0 | S | 28.00 | male | 0 | 0 | 10.5000 |
| 336 | 0 | S | 51.00 | male | 0 | 0 | 12.5250 |
| 337 | 1 | S | 32.00 | male | 1 | 0 | 26.0000 |
| 338 | 1 | S | 19.00 | female | 1 | 0 | 26.0000 |
| 339 | 0 | S | 28.00 | male | 0 | 0 | 26.0000 |
| 340 | 1 | S | 1.00 | male | 2 | 1 | 39.0000 |
| 341 | 1 | S | 4.00 | female | 2 | 1 | 39.0000 |
| 342 | 1 | S | 12.00 | female | 2 | 1 | 39.0000 |
| 343 | 1 | S | 36.00 | female | 0 | 3 | 39.0000 |
| 344 | 1 | S | 34.00 | male | 0 | 0 | 13.0000 |
| 345 | 1 | S | 19.00 | female | 0 | 0 | 13.0000 |
| 346 | 0 | S | 23.00 | male | 0 | 0 | 13.0000 |
| 347 | 0 | S | 26.00 | male | 0 | 0 | 13.0000 |
| 348 | 0 | S | 42.00 | male | 0 | 0 | 13.0000 |
| 349 | 0 | S | 27.00 | male | 0 | 0 | 13.0000 |
| 350 | 1 | S | 24.00 | female | 0 | 0 | 13.0000 |
| 351 | 1 | S | 15.00 | female | 0 | 2 | 39.0000 |
| 352 | 0 | S | 60.00 | male | 1 | 1 | 39.0000 |
| 353 | 1 | S | 40.00 | female | 1 | 1 | 39.0000 |
| 354 | 1 | S | 20.00 | female | 1 | 0 | 26.0000 |
| 355 | 0 | S | 25.00 | male | 1 | 0 | 26.0000 |
| 356 | 1 | S | 36.00 | female | 0 | 0 | 13.0000 |
| 357 | 0 | S | 25.00 | male | 0 | 0 | 13.0000 |
| 358 | 0 | S | 42.00 | male | 0 | 0 | 13.0000 |
| 359 | 1 | S | 42.00 | female | 0 | 0 | 13.0000 |
| 360 | 1 | S | 0.83 | male | 0 | 2 | 29.0000 |
| 361 | 1 | S | 26.00 | male | 1 | 1 | 29.0000 |
| 362 | 1 | S | 22.00 | female | 1 | 1 | 29.0000 |
| 363 | 1 | S | 35.00 | female | 0 | 0 | 21.0000 |
| 365 | 0 | S | 19.00 | male | 0 | 0 | 13.0000 |
| 366 | 0 | S | 44.00 | female | 1 | 0 | 26.0000 |
| 367 | 0 | S | 54.00 | male | 1 | 0 | 26.0000 |
| 368 | 0 | S | 52.00 | male | 0 | 0 | 13.5000 |
| 369 | 0 | S | 37.00 | male | 1 | 0 | 26.0000 |
| 370 | 0 | S | 29.00 | female | 1 | 0 | 26.0000 |
| 371 | 1 | S | 25.00 | female | 1 | 1 | 30.0000 |
| 372 | 1 | S | 45.00 | female | 0 | 2 | 30.0000 |
| 373 | 0 | S | 29.00 | male | 1 | 0 | 26.0000 |
| 374 | 1 | S | 28.00 | female | 1 | 0 | 26.0000 |
| 375 | 0 | S | 29.00 | male | 0 | 0 | 10.5000 |
| 376 | 0 | S | 28.00 | male | 0 | 0 | 13.0000 |
| 377 | 1 | S | 24.00 | male | 0 | 0 | 10.5000 |
| 378 | 1 | S | 8.00 | female | 0 | 2 | 26.2500 |
| 379 | 0 | S | 31.00 | male | 1 | 1 | 26.2500 |
| 380 | 1 | S | 31.00 | female | 1 | 1 | 26.2500 |
| 381 | 1 | S | 22.00 | female | 0 | 0 | 10.5000 |
| 382 | 0 | S | 30.00 | female | 0 | 0 | 13.0000 |
| 384 | 0 | S | 21.00 | male | 0 | 0 | 11.5000 |
| 386 | 1 | S | 8.00 | male | 1 | 1 | 36.7500 |
| 387 | 0 | S | 18.00 | male | 0 | 0 | 73.5000 |
| 388 | 1 | S | 48.00 | female | 0 | 2 | 36.7500 |
| 389 | 1 | S | 28.00 | female | 0 | 0 | 13.0000 |
| 390 | 0 | S | 32.00 | male | 0 | 0 | 13.0000 |
| 391 | 0 | S | 17.00 | male | 0 | 0 | 73.5000 |
| 392 | 0 | C | 29.00 | male | 1 | 0 | 27.7208 |
| 393 | 1 | C | 24.00 | female | 1 | 0 | 27.7208 |
| 394 | 0 | S | 25.00 | male | 0 | 0 | 31.5000 |
| 395 | 0 | S | 18.00 | male | 0 | 0 | 73.5000 |
| 396 | 1 | S | 18.00 | female | 0 | 1 | 23.0000 |
| 397 | 1 | S | 34.00 | female | 0 | 1 | 23.0000 |
| 398 | 0 | S | 54.00 | male | 0 | 0 | 26.0000 |
| 399 | 1 | S | 8.00 | male | 0 | 2 | 32.5000 |
| 400 | 0 | S | 42.00 | male | 1 | 1 | 32.5000 |
| 401 | 1 | S | 34.00 | female | 1 | 1 | 32.5000 |
| 402 | 1 | C | 27.00 | female | 1 | 0 | 13.8583 |
| 403 | 1 | C | 30.00 | female | 1 | 0 | 13.8583 |
| 404 | 0 | S | 23.00 | male | 0 | 0 | 13.0000 |
| 405 | 0 | S | 21.00 | male | 0 | 0 | 13.0000 |
| 406 | 0 | S | 18.00 | male | 0 | 0 | 13.0000 |
| 407 | 0 | S | 40.00 | male | 1 | 0 | 26.0000 |
| 408 | 1 | S | 29.00 | female | 1 | 0 | 26.0000 |
| 409 | 0 | S | 18.00 | male | 0 | 0 | 10.5000 |
| 410 | 0 | S | 36.00 | male | 0 | 0 | 13.0000 |
| 412 | 0 | S | 38.00 | female | 0 | 0 | 13.0000 |
| 413 | 0 | S | 35.00 | male | 0 | 0 | 26.0000 |
| 414 | 0 | S | 38.00 | male | 1 | 0 | 21.0000 |
| 415 | 0 | S | 34.00 | male | 1 | 0 | 21.0000 |
| 416 | 1 | S | 34.00 | female | 0 | 0 | 13.0000 |
| 417 | 0 | S | 16.00 | male | 0 | 0 | 26.0000 |
| 418 | 0 | S | 26.00 | male | 0 | 0 | 10.5000 |
| 419 | 0 | S | 47.00 | male | 0 | 0 | 10.5000 |
| 420 | 0 | S | 21.00 | male | 1 | 0 | 11.5000 |
| 421 | 0 | S | 21.00 | male | 1 | 0 | 11.5000 |
| 422 | 0 | S | 24.00 | male | 0 | 0 | 13.5000 |
| 423 | 0 | S | 24.00 | male | 0 | 0 | 13.0000 |
| 424 | 0 | S | 34.00 | male | 0 | 0 | 13.0000 |
| 425 | 0 | S | 30.00 | male | 0 | 0 | 13.0000 |
| 426 | 0 | S | 52.00 | male | 0 | 0 | 13.0000 |
| 427 | 0 | S | 30.00 | male | 0 | 0 | 13.0000 |
| 428 | 1 | S | 0.67 | male | 1 | 1 | 14.5000 |
| 429 | 1 | S | 24.00 | female | 0 | 2 | 14.5000 |
| 430 | 0 | S | 44.00 | male | 0 | 0 | 13.0000 |
| 431 | 1 | S | 6.00 | female | 0 | 1 | 33.0000 |
| 432 | 0 | S | 28.00 | male | 0 | 1 | 33.0000 |
| 433 | 1 | S | 62.00 | male | 0 | 0 | 10.5000 |
| 434 | 0 | S | 30.00 | male | 0 | 0 | 10.5000 |
| 435 | 1 | S | 7.00 | female | 0 | 2 | 26.2500 |
| 436 | 0 | S | 43.00 | male | 1 | 1 | 26.2500 |
| 437 | 1 | S | 45.00 | female | 1 | 1 | 26.2500 |
| 438 | 1 | S | 24.00 | female | 1 | 2 | 65.0000 |
| 439 | 1 | S | 24.00 | female | 1 | 2 | 65.0000 |
| 440 | 0 | S | 49.00 | male | 1 | 2 | 65.0000 |
| 441 | 1 | S | 48.00 | female | 1 | 2 | 65.0000 |
| 442 | 1 | S | 55.00 | female | 0 | 0 | 16.0000 |
| 443 | 0 | S | 24.00 | male | 2 | 0 | 73.5000 |
| 444 | 0 | S | 32.00 | male | 2 | 0 | 73.5000 |
| 445 | 0 | S | 21.00 | male | 2 | 0 | 73.5000 |
| 446 | 0 | S | 18.00 | female | 1 | 1 | 13.0000 |
| 447 | 1 | S | 20.00 | female | 2 | 1 | 23.0000 |
| 448 | 0 | S | 23.00 | male | 2 | 1 | 11.5000 |
| 449 | 0 | S | 36.00 | male | 0 | 0 | 13.0000 |
| 450 | 1 | S | 54.00 | female | 1 | 3 | 23.0000 |
| 451 | 0 | S | 50.00 | male | 0 | 0 | 13.0000 |
| 452 | 0 | S | 44.00 | male | 1 | 0 | 26.0000 |
| 453 | 1 | S | 29.00 | female | 1 | 0 | 26.0000 |
| 454 | 0 | S | 21.00 | male | 0 | 0 | 73.5000 |
| 455 | 1 | S | 42.00 | male | 0 | 0 | 13.0000 |
| 456 | 0 | S | 63.00 | male | 1 | 0 | 26.0000 |
| 457 | 0 | S | 60.00 | female | 1 | 0 | 26.0000 |
| 458 | 0 | S | 33.00 | male | 0 | 0 | 12.2750 |
| 459 | 1 | S | 17.00 | female | 0 | 0 | 10.5000 |
| 460 | 0 | S | 42.00 | male | 1 | 0 | 27.0000 |
| 461 | 1 | S | 24.00 | female | 2 | 1 | 27.0000 |
| 462 | 0 | S | 47.00 | male | 0 | 0 | 15.0000 |
| 463 | 0 | S | 24.00 | male | 2 | 0 | 31.5000 |
| 464 | 0 | S | 22.00 | male | 2 | 0 | 31.5000 |
| 465 | 0 | S | 32.00 | male | 0 | 0 | 10.5000 |
| 466 | 1 | C | 23.00 | female | 0 | 0 | 13.7917 |
| 467 | 0 | S | 34.00 | male | 1 | 0 | 26.0000 |
| 468 | 1 | S | 24.00 | female | 1 | 0 | 26.0000 |
| 469 | 0 | S | 22.00 | female | 0 | 0 | 21.0000 |
| 471 | 0 | Q | 35.00 | male | 0 | 0 | 12.3500 |
| 472 | 1 | S | 45.00 | female | 0 | 0 | 13.5000 |
| 473 | 0 | Q | 57.00 | male | 0 | 0 | 12.3500 |
| 475 | 0 | S | 31.00 | male | 0 | 0 | 10.5000 |
| 476 | 0 | S | 26.00 | female | 1 | 1 | 26.0000 |
| 477 | 0 | S | 30.00 | male | 1 | 1 | 26.0000 |
| 479 | 1 | C | 1.00 | female | 1 | 2 | 41.5792 |
| 480 | 1 | C | 3.00 | female | 1 | 2 | 41.5792 |
| 481 | 0 | C | 25.00 | male | 1 | 2 | 41.5792 |
| 482 | 1 | C | 22.00 | female | 1 | 2 | 41.5792 |
| 483 | 1 | C | 17.00 | female | 0 | 0 | 12.0000 |
| 485 | 1 | S | 34.00 | female | 0 | 0 | 10.5000 |
| 486 | 0 | C | 36.00 | male | 0 | 0 | 12.8750 |
| 487 | 0 | S | 24.00 | male | 0 | 0 | 10.5000 |
| 488 | 0 | Q | 61.00 | male | 0 | 0 | 12.3500 |
| 489 | 0 | S | 50.00 | male | 1 | 0 | 26.0000 |
| 490 | 1 | S | 42.00 | female | 1 | 0 | 26.0000 |
| 491 | 0 | S | 57.00 | female | 0 | 0 | 10.5000 |
| 493 | 1 | C | 1.00 | male | 0 | 2 | 37.0042 |
| 494 | 0 | C | 31.00 | male | 1 | 1 | 37.0042 |
| 495 | 1 | C | 24.00 | female | 1 | 1 | 37.0042 |
| 497 | 0 | S | 30.00 | male | 0 | 0 | 13.0000 |
| 498 | 0 | S | 40.00 | male | 0 | 0 | 16.0000 |
| 499 | 0 | S | 32.00 | male | 0 | 0 | 13.5000 |
| 500 | 0 | S | 30.00 | male | 0 | 0 | 13.0000 |
| 501 | 0 | S | 46.00 | male | 0 | 0 | 26.0000 |
| 502 | 1 | S | 13.00 | female | 0 | 1 | 19.5000 |
| 503 | 1 | S | 41.00 | female | 0 | 1 | 19.5000 |
| 504 | 1 | S | 19.00 | male | 0 | 0 | 10.5000 |
| 505 | 0 | S | 39.00 | male | 0 | 0 | 13.0000 |
| 506 | 0 | S | 48.00 | male | 0 | 0 | 13.0000 |
| 507 | 0 | S | 70.00 | male | 0 | 0 | 10.5000 |
| 508 | 0 | S | 27.00 | male | 0 | 0 | 13.0000 |
| 509 | 0 | S | 54.00 | male | 0 | 0 | 14.0000 |
| 510 | 0 | S | 39.00 | male | 0 | 0 | 26.0000 |
| 511 | 0 | S | 16.00 | male | 0 | 0 | 10.5000 |
| 512 | 0 | Q | 62.00 | male | 0 | 0 | 9.6875 |
| 513 | 0 | C | 32.50 | male | 1 | 0 | 30.0708 |
| 514 | 1 | C | 14.00 | female | 1 | 0 | 30.0708 |
| 515 | 1 | S | 2.00 | male | 1 | 1 | 26.0000 |
| 516 | 1 | S | 3.00 | male | 1 | 1 | 26.0000 |
| 517 | 0 | S | 36.50 | male | 0 | 2 | 26.0000 |
| 518 | 0 | S | 26.00 | male | 0 | 0 | 13.0000 |
| 519 | 0 | S | 19.00 | male | 1 | 1 | 36.7500 |
| 520 | 0 | S | 28.00 | male | 0 | 0 | 13.5000 |
| 521 | 1 | C | 20.00 | male | 0 | 0 | 13.8625 |
| 522 | 1 | S | 29.00 | female | 0 | 0 | 10.5000 |
| 523 | 0 | S | 39.00 | male | 0 | 0 | 13.0000 |
| 524 | 1 | S | 22.00 | male | 0 | 0 | 10.5000 |
| 526 | 0 | S | 23.00 | male | 0 | 0 | 10.5000 |
| 527 | 1 | C | 29.00 | male | 0 | 0 | 13.8583 |
| 528 | 0 | S | 28.00 | male | 0 | 0 | 10.5000 |
| 530 | 1 | S | 50.00 | female | 0 | 1 | 26.0000 |
| 531 | 0 | S | 19.00 | male | 0 | 0 | 10.5000 |
| 533 | 0 | S | 41.00 | male | 0 | 0 | 13.0000 |
| 534 | 1 | S | 21.00 | female | 0 | 1 | 21.0000 |
| 535 | 1 | S | 19.00 | female | 0 | 0 | 26.0000 |
| 536 | 0 | S | 43.00 | male | 0 | 1 | 21.0000 |
| 537 | 1 | S | 32.00 | female | 0 | 0 | 13.0000 |
| 538 | 0 | S | 34.00 | male | 0 | 0 | 13.0000 |
| 539 | 1 | C | 30.00 | male | 0 | 0 | 12.7375 |
| 540 | 0 | C | 27.00 | male | 0 | 0 | 15.0333 |
| 541 | 1 | S | 2.00 | female | 1 | 1 | 26.0000 |
| 542 | 1 | S | 8.00 | female | 1 | 1 | 26.0000 |
| 543 | 1 | S | 33.00 | female | 0 | 2 | 26.0000 |
| 544 | 0 | S | 36.00 | male | 0 | 0 | 10.5000 |
| 545 | 0 | S | 34.00 | male | 1 | 0 | 21.0000 |
| 546 | 1 | S | 30.00 | female | 3 | 0 | 21.0000 |
| 547 | 1 | S | 28.00 | female | 0 | 0 | 13.0000 |
| 548 | 0 | C | 23.00 | male | 0 | 0 | 15.0458 |
| 549 | 1 | S | 0.83 | male | 1 | 1 | 18.7500 |
| 550 | 1 | S | 3.00 | male | 1 | 1 | 18.7500 |
| 551 | 1 | S | 24.00 | female | 2 | 3 | 18.7500 |
| 552 | 1 | S | 50.00 | female | 0 | 0 | 10.5000 |
| 553 | 0 | S | 19.00 | male | 0 | 0 | 10.5000 |
| 554 | 1 | S | 21.00 | female | 0 | 0 | 10.5000 |
| 555 | 0 | S | 26.00 | male | 0 | 0 | 13.0000 |
| 556 | 0 | S | 25.00 | male | 0 | 0 | 13.0000 |
| 557 | 0 | S | 27.00 | male | 0 | 0 | 26.0000 |
| 558 | 1 | S | 25.00 | female | 0 | 1 | 26.0000 |
| 559 | 1 | S | 18.00 | female | 0 | 2 | 13.0000 |
| 560 | 1 | S | 20.00 | female | 0 | 0 | 36.7500 |
| 561 | 1 | S | 30.00 | female | 0 | 0 | 13.0000 |
| 562 | 0 | S | 59.00 | male | 0 | 0 | 13.5000 |
| 563 | 1 | Q | 30.00 | female | 0 | 0 | 12.3500 |
| 564 | 0 | S | 35.00 | male | 0 | 0 | 10.5000 |
| 565 | 1 | S | 40.00 | female | 0 | 0 | 13.0000 |
| 566 | 0 | S | 25.00 | male | 0 | 0 | 13.0000 |
| 567 | 0 | C | 41.00 | male | 0 | 0 | 15.0458 |
| 568 | 0 | S | 25.00 | male | 0 | 0 | 10.5000 |
| 569 | 0 | S | 18.50 | male | 0 | 0 | 13.0000 |
| 570 | 0 | S | 14.00 | male | 0 | 0 | 65.0000 |
| 571 | 1 | S | 50.00 | female | 0 | 0 | 10.5000 |
| 572 | 0 | S | 23.00 | male | 0 | 0 | 13.0000 |
| 573 | 1 | S | 28.00 | female | 0 | 0 | 12.6500 |
| 574 | 1 | S | 27.00 | female | 0 | 0 | 10.5000 |
| 575 | 0 | S | 29.00 | male | 1 | 0 | 21.0000 |
| 576 | 0 | S | 27.00 | female | 1 | 0 | 21.0000 |
| 577 | 0 | S | 40.00 | male | 0 | 0 | 13.0000 |
| 578 | 1 | S | 31.00 | female | 0 | 0 | 21.0000 |
| 579 | 0 | S | 30.00 | male | 1 | 0 | 21.0000 |
| 580 | 0 | S | 23.00 | male | 1 | 0 | 10.5000 |
| 581 | 1 | S | 31.00 | female | 0 | 0 | 21.0000 |
| 583 | 1 | S | 12.00 | female | 0 | 0 | 15.7500 |
| 584 | 1 | S | 40.00 | female | 0 | 0 | 15.7500 |
| 585 | 1 | S | 32.50 | female | 0 | 0 | 13.0000 |
| 586 | 0 | S | 27.00 | male | 1 | 0 | 26.0000 |
| 587 | 1 | S | 29.00 | female | 1 | 0 | 26.0000 |
| 588 | 1 | S | 2.00 | male | 1 | 1 | 23.0000 |
| 589 | 1 | S | 4.00 | female | 1 | 1 | 23.0000 |
| 590 | 1 | S | 29.00 | female | 0 | 2 | 23.0000 |
| 591 | 1 | S | 0.92 | female | 1 | 2 | 27.7500 |
| 592 | 1 | S | 5.00 | female | 1 | 2 | 27.7500 |
| 593 | 0 | S | 36.00 | male | 1 | 2 | 27.7500 |
| 594 | 1 | S | 33.00 | female | 1 | 2 | 27.7500 |
| 595 | 0 | S | 66.00 | male | 0 | 0 | 10.5000 |
| 597 | 1 | S | 31.00 | male | 0 | 0 | 13.0000 |
| 599 | 1 | S | 26.00 | female | 0 | 0 | 13.5000 |
| 600 | 0 | S | 24.00 | female | 0 | 0 | 13.0000 |
| 601 | 0 | S | 42.00 | male | 0 | 0 | 7.5500 |
| 602 | 0 | S | 13.00 | male | 0 | 2 | 20.2500 |
| 603 | 0 | S | 16.00 | male | 1 | 1 | 20.2500 |
| 604 | 1 | S | 35.00 | female | 1 | 1 | 20.2500 |
| 605 | 1 | S | 16.00 | female | 0 | 0 | 7.6500 |
| 606 | 1 | S | 25.00 | male | 0 | 0 | 7.6500 |
| 607 | 1 | S | 20.00 | male | 0 | 0 | 7.9250 |
| 608 | 1 | C | 18.00 | female | 0 | 0 | 7.2292 |
| 609 | 0 | S | 30.00 | male | 0 | 0 | 7.2500 |
| 610 | 0 | S | 26.00 | male | 0 | 0 | 8.0500 |
| 611 | 0 | S | 40.00 | female | 1 | 0 | 9.4750 |
| 612 | 1 | S | 0.83 | male | 0 | 1 | 9.3500 |
| 613 | 1 | S | 18.00 | female | 0 | 1 | 9.3500 |
| 614 | 1 | C | 26.00 | male | 0 | 0 | 18.7875 |
| 615 | 0 | S | 26.00 | male | 0 | 0 | 7.8875 |
| 616 | 0 | S | 20.00 | male | 0 | 0 | 7.9250 |
| 617 | 0 | S | 24.00 | male | 0 | 0 | 7.0500 |
| 618 | 0 | S | 25.00 | male | 0 | 0 | 7.0500 |
| 619 | 0 | S | 35.00 | male | 0 | 0 | 8.0500 |
| 620 | 0 | S | 18.00 | male | 0 | 0 | 8.3000 |
| 621 | 0 | S | 32.00 | male | 0 | 0 | 22.5250 |
| 622 | 1 | S | 19.00 | female | 1 | 0 | 7.8542 |
| 623 | 0 | S | 4.00 | male | 4 | 2 | 31.2750 |
| 624 | 0 | S | 6.00 | female | 4 | 2 | 31.2750 |
| 625 | 0 | S | 2.00 | female | 4 | 2 | 31.2750 |
| 626 | 1 | S | 17.00 | female | 4 | 2 | 7.9250 |
| 627 | 0 | S | 38.00 | female | 4 | 2 | 7.7750 |
| 628 | 0 | S | 9.00 | female | 4 | 2 | 31.2750 |
| 629 | 0 | S | 11.00 | female | 4 | 2 | 31.2750 |
| 630 | 0 | S | 39.00 | male | 1 | 5 | 31.2750 |
| 631 | 1 | S | 27.00 | male | 0 | 0 | 7.7958 |
| 632 | 0 | S | 26.00 | male | 0 | 0 | 7.7750 |
| 633 | 0 | S | 39.00 | female | 1 | 5 | 31.2750 |
| 634 | 0 | S | 20.00 | male | 0 | 0 | 7.8542 |
| 635 | 0 | S | 26.00 | male | 0 | 0 | 7.8958 |
| 636 | 0 | S | 25.00 | male | 1 | 0 | 17.8000 |
| 637 | 0 | S | 18.00 | female | 1 | 0 | 17.8000 |
| 638 | 0 | S | 24.00 | male | 0 | 0 | 7.7750 |
| 639 | 0 | S | 35.00 | male | 0 | 0 | 7.0500 |
| 640 | 0 | S | 5.00 | male | 4 | 2 | 31.3875 |
| 641 | 0 | S | 9.00 | male | 4 | 2 | 31.3875 |
| 642 | 1 | S | 3.00 | male | 4 | 2 | 31.3875 |
| 643 | 0 | S | 13.00 | male | 4 | 2 | 31.3875 |
| 644 | 1 | S | 5.00 | female | 4 | 2 | 31.3875 |
| 645 | 0 | S | 40.00 | male | 1 | 5 | 31.3875 |
| 646 | 1 | S | 23.00 | male | 0 | 0 | 7.7958 |
| 647 | 1 | S | 38.00 | female | 1 | 5 | 31.3875 |
| 648 | 1 | C | 45.00 | female | 0 | 0 | 7.2250 |
| 649 | 0 | C | 21.00 | male | 0 | 0 | 7.2250 |
| 650 | 0 | S | 23.00 | male | 0 | 0 | 7.0500 |
| 651 | 0 | C | 17.00 | female | 0 | 0 | 14.4583 |
| 652 | 0 | C | 30.00 | male | 0 | 0 | 7.2250 |
| 653 | 0 | S | 23.00 | male | 0 | 0 | 7.8542 |
| 654 | 1 | C | 13.00 | female | 0 | 0 | 7.2292 |
| 655 | 0 | C | 20.00 | male | 0 | 0 | 7.2250 |
| 656 | 0 | S | 32.00 | male | 1 | 0 | 15.8500 |
| 657 | 1 | S | 33.00 | female | 3 | 0 | 15.8500 |
| 658 | 1 | C | 0.75 | female | 2 | 1 | 19.2583 |
| 659 | 1 | C | 0.75 | female | 2 | 1 | 19.2583 |
| 660 | 1 | C | 5.00 | female | 2 | 1 | 19.2583 |
| 661 | 1 | C | 24.00 | female | 0 | 3 | 19.2583 |
| 662 | 1 | S | 18.00 | female | 0 | 0 | 8.0500 |
| 663 | 0 | C | 40.00 | male | 0 | 0 | 7.2250 |
| 664 | 0 | S | 26.00 | male | 0 | 0 | 7.8958 |
| 665 | 1 | C | 20.00 | male | 0 | 0 | 7.2292 |
| 666 | 0 | C | 18.00 | female | 0 | 1 | 14.4542 |
| 667 | 0 | C | 45.00 | female | 0 | 1 | 14.4542 |
| 668 | 0 | Q | 27.00 | female | 0 | 0 | 7.8792 |
| 669 | 0 | S | 22.00 | male | 0 | 0 | 8.0500 |
| 670 | 0 | S | 19.00 | male | 0 | 0 | 8.0500 |
| 671 | 0 | S | 26.00 | male | 0 | 0 | 7.7750 |
| 672 | 0 | S | 22.00 | male | 0 | 0 | 9.3500 |
| 674 | 0 | C | 20.00 | male | 0 | 0 | 4.0125 |
| 675 | 1 | S | 32.00 | male | 0 | 0 | 56.4958 |
| 676 | 0 | S | 21.00 | male | 0 | 0 | 7.7750 |
| 677 | 0 | S | 18.00 | male | 0 | 0 | 7.7500 |
| 678 | 0 | S | 26.00 | male | 0 | 0 | 7.8958 |
| 679 | 0 | C | 6.00 | male | 1 | 1 | 15.2458 |
| 680 | 0 | C | 9.00 | female | 1 | 1 | 15.2458 |
| 684 | 0 | Q | 40.00 | male | 1 | 1 | 15.5000 |
| 685 | 0 | Q | 32.00 | female | 1 | 1 | 15.5000 |
| 686 | 0 | S | 21.00 | male | 0 | 0 | 16.1000 |
| 687 | 1 | Q | 22.00 | female | 0 | 0 | 7.7250 |
| 688 | 0 | S | 20.00 | female | 0 | 0 | 7.8542 |
| 689 | 0 | S | 29.00 | male | 1 | 0 | 7.0458 |
| 690 | 0 | S | 22.00 | male | 1 | 0 | 7.2500 |
| 691 | 0 | S | 22.00 | male | 0 | 0 | 7.7958 |
| 692 | 0 | S | 35.00 | male | 0 | 0 | 8.0500 |
| 693 | 0 | Q | 18.50 | female | 0 | 0 | 7.2833 |
| 694 | 1 | Q | 21.00 | male | 0 | 0 | 7.8208 |
| 695 | 0 | Q | 19.00 | male | 0 | 0 | 6.7500 |
| 696 | 0 | Q | 18.00 | female | 0 | 0 | 7.8792 |
| 697 | 0 | S | 21.00 | female | 0 | 0 | 8.6625 |
| 698 | 0 | S | 30.00 | female | 0 | 0 | 8.6625 |
| 699 | 0 | S | 18.00 | male | 0 | 0 | 8.6625 |
| 700 | 0 | S | 38.00 | male | 0 | 0 | 8.6625 |
| 701 | 0 | S | 17.00 | male | 0 | 0 | 8.6625 |
| 702 | 0 | S | 17.00 | male | 0 | 0 | 8.6625 |
| 703 | 0 | Q | 21.00 | female | 0 | 0 | 7.7500 |
| 704 | 0 | Q | 21.00 | male | 0 | 0 | 7.7500 |
| 705 | 0 | S | 21.00 | male | 0 | 0 | 8.0500 |
| 708 | 0 | S | 28.00 | male | 0 | 0 | 7.7958 |
| 709 | 0 | S | 24.00 | male | 0 | 0 | 7.8542 |
| 710 | 1 | Q | 16.00 | female | 0 | 0 | 7.7500 |
| 711 | 0 | Q | 37.00 | female | 0 | 0 | 7.7500 |
| 712 | 0 | S | 28.00 | male | 0 | 0 | 7.2500 |
| 713 | 0 | S | 24.00 | male | 0 | 0 | 8.0500 |
| 714 | 0 | Q | 21.00 | male | 0 | 0 | 7.7333 |
| 715 | 1 | S | 32.00 | male | 0 | 0 | 56.4958 |
| 716 | 0 | S | 29.00 | male | 0 | 0 | 8.0500 |
| 717 | 0 | C | 26.00 | male | 1 | 0 | 14.4542 |
| 718 | 0 | C | 18.00 | male | 1 | 0 | 14.4542 |
| 719 | 0 | S | 20.00 | male | 0 | 0 | 7.0500 |
| 720 | 1 | S | 18.00 | male | 0 | 0 | 8.0500 |
| 721 | 0 | Q | 24.00 | male | 0 | 0 | 7.2500 |
| 722 | 0 | S | 36.00 | male | 0 | 0 | 7.4958 |
| 723 | 0 | S | 24.00 | male | 0 | 0 | 7.4958 |
| 724 | 0 | Q | 31.00 | male | 0 | 0 | 7.7333 |
| 725 | 0 | Q | 31.00 | male | 0 | 0 | 7.7500 |
| 726 | 1 | Q | 22.00 | female | 0 | 0 | 7.7500 |
| 727 | 0 | Q | 30.00 | female | 0 | 0 | 7.6292 |
| 728 | 0 | Q | 70.50 | male | 0 | 0 | 7.7500 |
| 729 | 0 | S | 43.00 | male | 0 | 0 | 8.0500 |
| 730 | 0 | S | 35.00 | male | 0 | 0 | 7.8958 |
| 731 | 0 | S | 27.00 | male | 0 | 0 | 7.8958 |
| 732 | 0 | S | 19.00 | male | 0 | 0 | 7.8958 |
| 733 | 0 | S | 30.00 | male | 0 | 0 | 8.0500 |
| 734 | 1 | S | 9.00 | male | 1 | 1 | 15.9000 |
| 735 | 1 | S | 3.00 | male | 1 | 1 | 15.9000 |
| 736 | 1 | S | 36.00 | female | 0 | 2 | 15.9000 |
| 737 | 0 | S | 59.00 | male | 0 | 0 | 7.2500 |
| 738 | 0 | S | 19.00 | male | 0 | 0 | 8.1583 |
| 739 | 1 | S | 17.00 | female | 0 | 1 | 16.1000 |
| 740 | 0 | S | 44.00 | male | 0 | 1 | 16.1000 |
| 741 | 0 | S | 17.00 | male | 0 | 0 | 8.6625 |
| 742 | 0 | C | 22.50 | male | 0 | 0 | 7.2250 |
| 743 | 1 | S | 45.00 | male | 0 | 0 | 8.0500 |
| 744 | 0 | S | 22.00 | female | 0 | 0 | 10.5167 |
| 745 | 0 | S | 19.00 | male | 0 | 0 | 10.1708 |
| 746 | 1 | Q | 30.00 | female | 0 | 0 | 6.9500 |
| 747 | 1 | Q | 29.00 | male | 0 | 0 | 7.7500 |
| 748 | 0 | S | 0.33 | male | 0 | 2 | 14.4000 |
| 749 | 0 | S | 34.00 | male | 1 | 1 | 14.4000 |
| 750 | 0 | S | 28.00 | female | 1 | 1 | 14.4000 |
| 751 | 0 | S | 27.00 | male | 0 | 0 | 7.8958 |
| 752 | 0 | S | 25.00 | male | 0 | 0 | 7.8958 |
| 753 | 0 | S | 24.00 | male | 2 | 0 | 24.1500 |
| 754 | 0 | S | 22.00 | male | 0 | 0 | 8.0500 |
| 755 | 0 | S | 21.00 | male | 2 | 0 | 24.1500 |
| 756 | 0 | S | 17.00 | male | 2 | 0 | 8.0500 |
| 759 | 1 | S | 36.50 | male | 1 | 0 | 17.4000 |
| 760 | 1 | S | 36.00 | female | 1 | 0 | 17.4000 |
| 761 | 1 | S | 30.00 | male | 0 | 0 | 9.5000 |
| 762 | 0 | S | 16.00 | male | 0 | 0 | 9.5000 |
| 763 | 1 | S | 1.00 | male | 1 | 2 | 20.5750 |
| 764 | 1 | S | 0.17 | female | 1 | 2 | 20.5750 |
| 765 | 0 | S | 26.00 | male | 1 | 2 | 20.5750 |
| 766 | 1 | S | 33.00 | female | 1 | 2 | 20.5750 |
| 767 | 0 | S | 25.00 | male | 0 | 0 | 7.8958 |
| 770 | 0 | S | 22.00 | male | 0 | 0 | 7.2500 |
| 771 | 0 | S | 36.00 | male | 0 | 0 | 7.2500 |
| 772 | 1 | Q | 19.00 | female | 0 | 0 | 7.8792 |
| 773 | 0 | S | 17.00 | male | 0 | 0 | 7.8958 |
| 774 | 0 | S | 42.00 | male | 0 | 0 | 8.6625 |
| 775 | 0 | S | 43.00 | male | 0 | 0 | 7.8958 |
| 777 | 0 | Q | 32.00 | male | 0 | 0 | 7.7500 |
| 778 | 1 | S | 19.00 | male | 0 | 0 | 8.0500 |
| 779 | 1 | S | 30.00 | female | 0 | 0 | 12.4750 |
| 780 | 0 | Q | 24.00 | female | 0 | 0 | 7.7500 |
| 781 | 1 | S | 23.00 | female | 0 | 0 | 8.0500 |
| 782 | 0 | C | 33.00 | male | 0 | 0 | 7.8958 |
| 783 | 0 | Q | 65.00 | male | 0 | 0 | 7.7500 |
| 784 | 1 | S | 24.00 | male | 0 | 0 | 7.5500 |
| 785 | 0 | S | 23.00 | male | 1 | 0 | 13.9000 |
| 786 | 1 | S | 22.00 | female | 1 | 0 | 13.9000 |
| 787 | 0 | S | 18.00 | male | 0 | 0 | 7.7750 |
| 788 | 0 | S | 16.00 | male | 0 | 0 | 7.7750 |
| 789 | 0 | S | 45.00 | male | 0 | 0 | 6.9750 |
| 791 | 0 | C | 39.00 | male | 0 | 2 | 7.2292 |
| 792 | 0 | C | 17.00 | male | 1 | 1 | 7.2292 |
| 793 | 0 | C | 15.00 | male | 1 | 1 | 7.2292 |
| 794 | 0 | S | 47.00 | male | 0 | 0 | 7.2500 |
| 795 | 1 | S | 5.00 | female | 0 | 0 | 12.4750 |
| 797 | 0 | S | 40.50 | male | 0 | 0 | 15.1000 |
| 798 | 0 | Q | 40.50 | male | 0 | 0 | 7.7500 |
| 800 | 0 | S | 18.00 | male | 0 | 0 | 7.7958 |
| 804 | 0 | Q | 26.00 | male | 0 | 0 | 7.8792 |
| 807 | 0 | S | 21.00 | female | 2 | 2 | 34.3750 |
| 808 | 0 | S | 9.00 | female | 2 | 2 | 34.3750 |
| 810 | 0 | S | 18.00 | male | 2 | 2 | 34.3750 |
| 811 | 0 | S | 16.00 | male | 1 | 3 | 34.3750 |
| 812 | 0 | S | 48.00 | female | 1 | 3 | 34.3750 |
| 815 | 0 | Q | 25.00 | male | 0 | 0 | 7.7417 |
| 818 | 0 | S | 22.00 | male | 0 | 0 | 8.0500 |
| 819 | 1 | Q | 16.00 | female | 0 | 0 | 7.7333 |
| 821 | 1 | S | 9.00 | male | 0 | 2 | 20.5250 |
| 822 | 0 | S | 33.00 | male | 1 | 1 | 20.5250 |
| 823 | 0 | S | 41.00 | male | 0 | 0 | 7.8500 |
| 824 | 1 | S | 31.00 | female | 1 | 1 | 20.5250 |
| 825 | 0 | S | 38.00 | male | 0 | 0 | 7.0500 |
| 826 | 0 | S | 9.00 | male | 5 | 2 | 46.9000 |
| 827 | 0 | S | 1.00 | male | 5 | 2 | 46.9000 |
| 828 | 0 | S | 11.00 | male | 5 | 2 | 46.9000 |
| 829 | 0 | S | 10.00 | female | 5 | 2 | 46.9000 |
| 830 | 0 | S | 16.00 | female | 5 | 2 | 46.9000 |
| 831 | 0 | S | 14.00 | male | 5 | 2 | 46.9000 |
| 832 | 0 | S | 40.00 | male | 1 | 6 | 46.9000 |
| 833 | 0 | S | 43.00 | female | 1 | 6 | 46.9000 |
| 834 | 0 | S | 51.00 | male | 0 | 0 | 8.0500 |
| 835 | 0 | S | 32.00 | male | 0 | 0 | 8.3625 |
| 837 | 0 | S | 20.00 | male | 0 | 0 | 9.8458 |
| 838 | 0 | S | 37.00 | male | 2 | 0 | 7.9250 |
| 839 | 0 | S | 28.00 | male | 2 | 0 | 7.9250 |
| 840 | 0 | S | 19.00 | male | 0 | 0 | 7.7750 |
| 841 | 0 | S | 24.00 | female | 0 | 0 | 8.8500 |
| 842 | 0 | Q | 17.00 | female | 0 | 0 | 7.7333 |
| 845 | 0 | S | 28.00 | male | 1 | 0 | 15.8500 |
| 846 | 1 | S | 24.00 | female | 1 | 0 | 15.8500 |
| 847 | 0 | S | 20.00 | male | 0 | 0 | 9.5000 |
| 848 | 0 | C | 23.50 | male | 0 | 0 | 7.2292 |
| 849 | 0 | S | 41.00 | male | 2 | 0 | 14.1083 |
| 850 | 0 | S | 26.00 | male | 1 | 0 | 7.8542 |
| 851 | 0 | S | 21.00 | male | 0 | 0 | 7.8542 |
| 852 | 1 | S | 45.00 | female | 1 | 0 | 14.1083 |
| 854 | 0 | S | 25.00 | male | 0 | 0 | 7.2500 |
| 856 | 0 | C | 11.00 | male | 0 | 0 | 18.7875 |
| 858 | 1 | S | 27.00 | male | 0 | 0 | 6.9750 |
| 860 | 0 | Q | 18.00 | female | 0 | 0 | 6.7500 |
| 861 | 1 | S | 26.00 | female | 0 | 0 | 7.9250 |
| 862 | 0 | S | 23.00 | female | 0 | 0 | 7.9250 |
| 863 | 1 | S | 22.00 | female | 0 | 0 | 8.9625 |
| 864 | 0 | S | 28.00 | male | 0 | 0 | 7.8958 |
| 865 | 0 | S | 28.00 | female | 0 | 0 | 7.7750 |
| 867 | 1 | S | 2.00 | female | 0 | 1 | 12.2875 |
| 868 | 1 | S | 22.00 | female | 1 | 1 | 12.2875 |
| 869 | 0 | S | 43.00 | male | 0 | 0 | 6.4500 |
| 870 | 0 | S | 28.00 | male | 0 | 0 | 22.5250 |
| 871 | 1 | S | 27.00 | female | 0 | 0 | 7.9250 |
| 874 | 0 | S | 42.00 | male | 0 | 0 | 7.6500 |
| 876 | 0 | C | 30.00 | male | 0 | 0 | 7.2292 |
| 878 | 0 | S | 27.00 | female | 1 | 0 | 7.9250 |
| 879 | 0 | S | 25.00 | female | 1 | 0 | 7.9250 |
| 881 | 1 | C | 29.00 | male | 0 | 0 | 7.8958 |
| 882 | 1 | S | 21.00 | male | 0 | 0 | 7.7958 |
| 884 | 0 | S | 20.00 | male | 0 | 0 | 7.8542 |
| 885 | 0 | S | 48.00 | male | 0 | 0 | 7.8542 |
| 886 | 0 | S | 17.00 | male | 1 | 0 | 7.0542 |
| 889 | 0 | S | 34.00 | male | 0 | 0 | 6.4958 |
| 890 | 1 | S | 26.00 | male | 0 | 0 | 7.7750 |
| 891 | 0 | S | 22.00 | male | 0 | 0 | 7.7958 |
| 892 | 0 | S | 33.00 | male | 0 | 0 | 8.6542 |
| 893 | 0 | S | 31.00 | male | 0 | 0 | 7.7750 |
| 894 | 0 | S | 29.00 | male | 0 | 0 | 7.8542 |
| 895 | 1 | S | 4.00 | male | 1 | 1 | 11.1333 |
| 896 | 1 | S | 1.00 | female | 1 | 1 | 11.1333 |
| 897 | 0 | S | 49.00 | male | 0 | 0 | 0.0000 |
| 898 | 0 | S | 33.00 | male | 0 | 0 | 7.7750 |
| 899 | 0 | S | 19.00 | male | 0 | 0 | 0.0000 |
| 900 | 1 | S | 27.00 | female | 0 | 2 | 11.1333 |
| 905 | 0 | S | 23.00 | male | 0 | 0 | 7.8958 |
| 906 | 1 | S | 32.00 | male | 0 | 0 | 7.8542 |
| 907 | 0 | S | 27.00 | male | 0 | 0 | 7.8542 |
| 908 | 0 | S | 20.00 | female | 1 | 0 | 9.8250 |
| 909 | 0 | S | 21.00 | female | 1 | 0 | 9.8250 |
| 910 | 1 | S | 32.00 | male | 0 | 0 | 7.9250 |
| 911 | 0 | S | 17.00 | male | 0 | 0 | 7.1250 |
| 912 | 0 | S | 21.00 | male | 0 | 0 | 8.4333 |
| 913 | 0 | S | 30.00 | male | 0 | 0 | 7.8958 |
| 914 | 1 | S | 21.00 | male | 0 | 0 | 7.7958 |
| 915 | 0 | S | 33.00 | male | 0 | 0 | 7.8542 |
| 916 | 0 | S | 22.00 | male | 0 | 0 | 7.5208 |
| 917 | 1 | C | 4.00 | female | 0 | 1 | 13.4167 |
| 918 | 1 | C | 39.00 | male | 0 | 1 | 13.4167 |
| 920 | 0 | C | 18.50 | male | 0 | 0 | 7.2292 |
| 925 | 0 | Q | 34.50 | male | 0 | 0 | 7.8292 |
| 926 | 0 | S | 44.00 | male | 0 | 0 | 8.0500 |
| 933 | 0 | S | 22.00 | female | 2 | 0 | 8.6625 |
| 934 | 0 | S | 26.00 | male | 2 | 0 | 8.6625 |
| 935 | 1 | S | 4.00 | female | 0 | 2 | 22.0250 |
| 936 | 1 | S | 29.00 | male | 3 | 1 | 22.0250 |
| 937 | 1 | S | 26.00 | female | 1 | 1 | 22.0250 |
| 938 | 0 | S | 1.00 | female | 1 | 1 | 12.1833 |
| 939 | 0 | S | 18.00 | male | 1 | 1 | 7.8542 |
| 940 | 0 | S | 36.00 | female | 0 | 2 | 12.1833 |
| 942 | 1 | C | 25.00 | male | 0 | 0 | 7.2292 |
| 944 | 0 | S | 37.00 | female | 0 | 0 | 9.5875 |
| 948 | 1 | S | 22.00 | female | 0 | 0 | 7.2500 |
| 950 | 1 | S | 26.00 | male | 0 | 0 | 56.4958 |
| 951 | 0 | S | 29.00 | male | 0 | 0 | 9.4833 |
| 952 | 0 | S | 29.00 | male | 0 | 0 | 7.7750 |
| 953 | 0 | S | 22.00 | male | 0 | 0 | 7.7750 |
| 954 | 1 | C | 22.00 | male | 0 | 0 | 7.2250 |
| 960 | 0 | S | 32.00 | male | 0 | 0 | 7.9250 |
| 961 | 0 | C | 34.50 | male | 0 | 0 | 6.4375 |
| 964 | 0 | S | 36.00 | male | 0 | 0 | 0.0000 |
| 965 | 0 | S | 39.00 | male | 0 | 0 | 24.1500 |
| 966 | 0 | S | 24.00 | male | 0 | 0 | 9.5000 |
| 967 | 0 | S | 25.00 | female | 0 | 0 | 7.7750 |
| 968 | 0 | S | 45.00 | female | 0 | 0 | 7.7500 |
| 969 | 0 | S | 36.00 | male | 1 | 0 | 15.5500 |
| 970 | 0 | S | 30.00 | female | 1 | 0 | 15.5500 |
| 971 | 1 | S | 20.00 | male | 1 | 0 | 7.9250 |
| 973 | 0 | S | 28.00 | male | 0 | 0 | 56.4958 |
| 975 | 0 | S | 30.00 | male | 1 | 0 | 16.1000 |
| 976 | 0 | S | 26.00 | female | 1 | 0 | 16.1000 |
| 978 | 0 | S | 20.50 | male | 0 | 0 | 7.2500 |
| 979 | 1 | S | 27.00 | male | 0 | 0 | 8.6625 |
| 980 | 0 | S | 51.00 | male | 0 | 0 | 7.0542 |
| 981 | 1 | S | 23.00 | female | 0 | 0 | 7.8542 |
| 982 | 1 | S | 32.00 | male | 0 | 0 | 7.5792 |
| 986 | 1 | S | 24.00 | male | 0 | 0 | 7.1417 |
| 987 | 0 | S | 22.00 | male | 0 | 0 | 7.1250 |
| 991 | 0 | S | 29.00 | male | 0 | 0 | 7.9250 |
| 993 | 0 | Q | 30.50 | female | 0 | 0 | 7.7500 |
| 996 | 0 | C | 35.00 | male | 0 | 0 | 7.8958 |
| 997 | 0 | S | 33.00 | male | 0 | 0 | 7.8958 |
| 1008 | 1 | Q | 15.00 | female | 0 | 0 | 8.0292 |
| 1009 | 0 | Q | 35.00 | female | 0 | 0 | 7.7500 |
| 1011 | 0 | S | 24.00 | male | 1 | 0 | 16.1000 |
| 1012 | 0 | S | 19.00 | female | 1 | 0 | 16.1000 |
| 1016 | 0 | S | 55.50 | male | 0 | 0 | 8.0500 |
| 1018 | 1 | S | 21.00 | male | 0 | 0 | 7.7750 |
| 1020 | 0 | S | 24.00 | male | 0 | 0 | 7.8958 |
| 1021 | 0 | S | 21.00 | male | 0 | 0 | 7.8958 |
| 1022 | 0 | S | 28.00 | male | 0 | 0 | 7.8958 |
| 1025 | 0 | S | 25.00 | male | 0 | 0 | 7.6500 |
| 1026 | 1 | S | 6.00 | male | 0 | 1 | 12.4750 |
| 1027 | 1 | S | 27.00 | female | 0 | 1 | 12.4750 |
| 1032 | 0 | S | 34.00 | male | 0 | 0 | 8.0500 |
| 1041 | 1 | Q | 24.00 | female | 0 | 0 | 7.7500 |
| 1046 | 0 | S | 18.00 | male | 0 | 0 | 7.7500 |
| 1047 | 0 | S | 22.00 | male | 0 | 0 | 7.8958 |
| 1048 | 1 | C | 15.00 | female | 0 | 0 | 7.2250 |
| 1049 | 1 | C | 1.00 | female | 0 | 2 | 15.7417 |
| 1050 | 1 | C | 20.00 | male | 1 | 1 | 15.7417 |
| 1051 | 1 | C | 19.00 | female | 1 | 1 | 15.7417 |
| 1052 | 0 | S | 33.00 | male | 0 | 0 | 8.0500 |
| 1057 | 1 | C | 12.00 | male | 1 | 0 | 11.2417 |
| 1058 | 1 | C | 14.00 | female | 1 | 0 | 11.2417 |
| 1059 | 0 | S | 29.00 | female | 0 | 0 | 7.9250 |
| 1060 | 0 | S | 28.00 | male | 0 | 0 | 8.0500 |
| 1061 | 1 | S | 18.00 | female | 0 | 0 | 7.7750 |
| 1062 | 1 | S | 26.00 | female | 0 | 0 | 7.8542 |
| 1063 | 0 | S | 21.00 | male | 0 | 0 | 7.8542 |
| 1064 | 0 | S | 41.00 | male | 0 | 0 | 7.1250 |
| 1065 | 1 | S | 39.00 | male | 0 | 0 | 7.9250 |
| 1066 | 0 | S | 21.00 | male | 0 | 0 | 7.8000 |
| 1067 | 0 | C | 28.50 | male | 0 | 0 | 7.2292 |
| 1068 | 1 | S | 22.00 | female | 0 | 0 | 7.7500 |
| 1069 | 0 | S | 61.00 | male | 0 | 0 | 6.2375 |
| 1076 | 0 | S | 23.00 | male | 0 | 0 | 9.2250 |
| 1080 | 1 | S | 22.00 | female | 0 | 0 | 7.7750 |
| 1083 | 1 | S | 9.00 | male | 0 | 1 | 3.1708 |
| 1084 | 0 | S | 28.00 | male | 0 | 0 | 22.5250 |
| 1085 | 0 | S | 42.00 | male | 0 | 1 | 8.4042 |
| 1087 | 0 | S | 31.00 | female | 0 | 0 | 7.8542 |
| 1088 | 0 | S | 28.00 | male | 0 | 0 | 7.8542 |
| 1089 | 1 | S | 32.00 | male | 0 | 0 | 7.7750 |
| 1090 | 0 | S | 20.00 | male | 0 | 0 | 9.2250 |
| 1091 | 0 | S | 23.00 | female | 0 | 0 | 8.6625 |
| 1092 | 0 | S | 20.00 | female | 0 | 0 | 8.6625 |
| 1093 | 0 | S | 20.00 | male | 0 | 0 | 8.6625 |
| 1094 | 0 | S | 16.00 | male | 0 | 0 | 9.2167 |
| 1095 | 1 | S | 31.00 | female | 0 | 0 | 8.6833 |
| 1097 | 0 | S | 2.00 | male | 3 | 1 | 21.0750 |
| 1098 | 0 | S | 6.00 | male | 3 | 1 | 21.0750 |
| 1099 | 0 | S | 3.00 | female | 3 | 1 | 21.0750 |
| 1100 | 0 | S | 8.00 | female | 3 | 1 | 21.0750 |
| 1101 | 0 | S | 29.00 | female | 0 | 4 | 21.0750 |
| 1102 | 0 | S | 1.00 | male | 4 | 1 | 39.6875 |
| 1103 | 0 | S | 7.00 | male | 4 | 1 | 39.6875 |
| 1104 | 0 | S | 2.00 | male | 4 | 1 | 39.6875 |
| 1105 | 0 | S | 16.00 | male | 4 | 1 | 39.6875 |
| 1106 | 0 | S | 14.00 | male | 4 | 1 | 39.6875 |
| 1107 | 0 | S | 41.00 | female | 0 | 5 | 39.6875 |
| 1108 | 0 | S | 21.00 | male | 0 | 0 | 8.6625 |
| 1109 | 0 | S | 19.00 | male | 0 | 0 | 14.5000 |
| 1111 | 0 | S | 32.00 | male | 0 | 0 | 7.8958 |
| 1112 | 0 | S | 0.75 | male | 1 | 1 | 13.7750 |
| 1113 | 0 | S | 3.00 | female | 1 | 1 | 13.7750 |
| 1114 | 0 | S | 26.00 | female | 0 | 2 | 13.7750 |
| 1118 | 0 | S | 21.00 | male | 0 | 0 | 7.9250 |
| 1119 | 0 | S | 25.00 | male | 0 | 0 | 7.9250 |
| 1120 | 0 | S | 22.00 | male | 0 | 0 | 7.2500 |
| 1121 | 1 | S | 25.00 | male | 1 | 0 | 7.7750 |
| 1126 | 0 | S | 24.00 | male | 0 | 0 | 8.0500 |
| 1127 | 0 | S | 28.00 | female | 0 | 0 | 7.8958 |
| 1128 | 0 | S | 19.00 | male | 0 | 0 | 7.8958 |
| 1130 | 0 | S | 25.00 | male | 1 | 0 | 7.7750 |
| 1131 | 0 | S | 18.00 | female | 0 | 0 | 7.7750 |
| 1132 | 1 | S | 32.00 | male | 0 | 0 | 8.0500 |
| 1134 | 0 | S | 17.00 | male | 0 | 0 | 8.6625 |
| 1135 | 0 | S | 24.00 | male | 0 | 0 | 8.6625 |
| 1140 | 0 | S | 38.00 | male | 0 | 0 | 7.8958 |
| 1141 | 0 | S | 21.00 | male | 0 | 0 | 8.0500 |
| 1142 | 0 | Q | 10.00 | male | 4 | 1 | 29.1250 |
| 1143 | 0 | Q | 4.00 | male | 4 | 1 | 29.1250 |
| 1144 | 0 | Q | 7.00 | male | 4 | 1 | 29.1250 |
| 1145 | 0 | Q | 2.00 | male | 4 | 1 | 29.1250 |
| 1146 | 0 | Q | 8.00 | male | 4 | 1 | 29.1250 |
| 1147 | 0 | Q | 39.00 | female | 0 | 5 | 29.1250 |
| 1148 | 0 | S | 22.00 | female | 0 | 0 | 39.6875 |
| 1149 | 0 | S | 35.00 | male | 0 | 0 | 7.1250 |
| 1153 | 0 | S | 50.00 | male | 1 | 0 | 14.5000 |
| 1154 | 0 | S | 47.00 | female | 1 | 0 | 14.5000 |
| 1157 | 0 | S | 2.00 | female | 1 | 1 | 20.2125 |
| 1158 | 0 | S | 18.00 | male | 1 | 1 | 20.2125 |
| 1159 | 0 | S | 41.00 | female | 0 | 2 | 20.2125 |
| 1161 | 0 | S | 50.00 | male | 0 | 0 | 8.0500 |
| 1162 | 0 | S | 16.00 | male | 0 | 0 | 8.0500 |
| 1166 | 0 | C | 25.00 | male | 0 | 0 | 7.2250 |
| 1170 | 0 | S | 38.50 | male | 0 | 0 | 7.2500 |
| 1172 | 0 | S | 14.50 | male | 8 | 2 | 69.5500 |
| 1182 | 0 | S | 24.00 | male | 0 | 0 | 9.3250 |
| 1183 | 1 | S | 21.00 | female | 0 | 0 | 7.6500 |
| 1184 | 0 | S | 39.00 | male | 0 | 0 | 7.9250 |
| 1188 | 1 | S | 1.00 | female | 1 | 1 | 16.7000 |
| 1189 | 1 | S | 24.00 | female | 0 | 2 | 16.7000 |
| 1190 | 1 | S | 4.00 | female | 1 | 1 | 16.7000 |
| 1191 | 1 | S | 25.00 | male | 0 | 0 | 9.5000 |
| 1192 | 0 | S | 20.00 | male | 0 | 0 | 8.0500 |
| 1193 | 0 | S | 24.50 | male | 0 | 0 | 8.0500 |
| 1197 | 1 | S | 29.00 | male | 0 | 0 | 9.5000 |
| 1202 | 0 | C | 22.00 | male | 0 | 0 | 7.2292 |
| 1204 | 0 | S | 40.00 | male | 0 | 0 | 7.8958 |
| 1205 | 0 | S | 21.00 | male | 0 | 0 | 7.9250 |
| 1206 | 1 | S | 18.00 | female | 0 | 0 | 7.4958 |
| 1207 | 0 | S | 4.00 | male | 3 | 2 | 27.9000 |
| 1208 | 0 | S | 10.00 | male | 3 | 2 | 27.9000 |
| 1209 | 0 | S | 9.00 | female | 3 | 2 | 27.9000 |
| 1210 | 0 | S | 2.00 | female | 3 | 2 | 27.9000 |
| 1211 | 0 | S | 40.00 | male | 1 | 4 | 27.9000 |
| 1212 | 0 | S | 45.00 | female | 1 | 4 | 27.9000 |
| 1218 | 0 | S | 19.00 | male | 0 | 0 | 7.6500 |
| 1219 | 0 | S | 30.00 | male | 0 | 0 | 8.0500 |
| 1221 | 0 | S | 32.00 | male | 0 | 0 | 8.0500 |
| 1223 | 0 | C | 33.00 | male | 0 | 0 | 8.6625 |
| 1224 | 1 | S | 23.00 | female | 0 | 0 | 7.5500 |
| 1225 | 0 | S | 21.00 | male | 0 | 0 | 8.0500 |
| 1227 | 0 | S | 19.00 | male | 0 | 0 | 7.8958 |
| 1228 | 0 | S | 22.00 | female | 0 | 0 | 9.8375 |
| 1229 | 1 | S | 31.00 | male | 0 | 0 | 7.9250 |
| 1230 | 0 | S | 27.00 | male | 0 | 0 | 8.6625 |
| 1231 | 0 | S | 2.00 | female | 0 | 1 | 10.4625 |
| 1232 | 0 | S | 29.00 | female | 1 | 1 | 10.4625 |
| 1233 | 1 | S | 16.00 | male | 0 | 0 | 8.0500 |
| 1234 | 1 | S | 44.00 | male | 0 | 0 | 7.9250 |
| 1235 | 0 | S | 25.00 | male | 0 | 0 | 7.0500 |
| 1236 | 0 | S | 74.00 | male | 0 | 0 | 7.7750 |
| 1237 | 1 | S | 14.00 | male | 0 | 0 | 9.2250 |
| 1238 | 0 | S | 24.00 | male | 0 | 0 | 7.7958 |
| 1239 | 1 | S | 25.00 | male | 0 | 0 | 7.7958 |
| 1240 | 0 | S | 34.00 | male | 0 | 0 | 8.0500 |
| 1241 | 1 | C | 0.42 | male | 0 | 1 | 8.5167 |
| 1245 | 1 | C | 16.00 | female | 1 | 1 | 8.5167 |
| 1249 | 0 | S | 32.00 | male | 0 | 0 | 7.9250 |
| 1252 | 0 | S | 30.50 | male | 0 | 0 | 8.0500 |
| 1253 | 0 | S | 44.00 | male | 0 | 0 | 8.0500 |
| 1255 | 1 | S | 25.00 | male | 0 | 0 | 0.0000 |
| 1257 | 1 | C | 7.00 | male | 1 | 1 | 15.2458 |
| 1258 | 1 | C | 9.00 | female | 1 | 1 | 15.2458 |
| 1259 | 1 | C | 29.00 | female | 0 | 2 | 15.2458 |
| 1260 | 0 | S | 36.00 | male | 0 | 0 | 7.8958 |
| 1261 | 1 | S | 18.00 | female | 0 | 0 | 9.8417 |
| 1262 | 1 | S | 63.00 | female | 0 | 0 | 9.5875 |
| 1264 | 0 | S | 11.50 | male | 1 | 1 | 14.5000 |
| 1265 | 0 | S | 40.50 | male | 0 | 2 | 14.5000 |
| 1266 | 0 | S | 10.00 | female | 0 | 2 | 24.1500 |
| 1267 | 0 | S | 36.00 | male | 1 | 1 | 24.1500 |
| 1268 | 0 | S | 30.00 | female | 1 | 1 | 24.1500 |
| 1270 | 0 | S | 33.00 | male | 0 | 0 | 9.5000 |
| 1271 | 0 | S | 28.00 | male | 0 | 0 | 9.5000 |
| 1272 | 0 | S | 28.00 | male | 0 | 0 | 9.5000 |
| 1273 | 0 | S | 47.00 | male | 0 | 0 | 9.0000 |
| 1274 | 0 | S | 18.00 | female | 2 | 0 | 18.0000 |
| 1275 | 0 | S | 31.00 | male | 3 | 0 | 18.0000 |
| 1276 | 0 | S | 16.00 | male | 2 | 0 | 18.0000 |
| 1277 | 0 | S | 31.00 | female | 1 | 0 | 18.0000 |
| 1278 | 1 | C | 22.00 | male | 0 | 0 | 7.2250 |
| 1279 | 0 | S | 20.00 | male | 0 | 0 | 7.8542 |
| 1280 | 0 | S | 14.00 | female | 0 | 0 | 7.8542 |
| 1281 | 0 | S | 22.00 | male | 0 | 0 | 7.8958 |
| 1282 | 0 | S | 22.00 | male | 0 | 0 | 9.0000 |
| 1286 | 0 | S | 32.50 | male | 0 | 0 | 9.5000 |
| 1287 | 1 | C | 38.00 | female | 0 | 0 | 7.2292 |
| 1288 | 0 | S | 51.00 | male | 0 | 0 | 7.7500 |
| 1289 | 0 | S | 18.00 | male | 1 | 0 | 6.4958 |
| 1290 | 0 | S | 21.00 | male | 1 | 0 | 6.4958 |
| 1291 | 1 | S | 47.00 | female | 1 | 0 | 7.0000 |
| 1295 | 0 | S | 28.50 | male | 0 | 0 | 16.1000 |
| 1296 | 0 | S | 21.00 | male | 0 | 0 | 7.2500 |
| 1297 | 0 | S | 27.00 | male | 0 | 0 | 8.6625 |
| 1299 | 0 | S | 36.00 | male | 0 | 0 | 9.5000 |
| 1300 | 0 | C | 27.00 | male | 1 | 0 | 14.4542 |
| 1301 | 1 | C | 15.00 | female | 1 | 0 | 14.4542 |
| 1302 | 0 | C | 45.50 | male | 0 | 0 | 7.2250 |
| 1305 | 0 | C | 14.50 | female | 1 | 0 | 14.4542 |
| 1307 | 0 | C | 26.50 | male | 0 | 0 | 7.2250 |
| 1308 | 0 | C | 27.00 | male | 0 | 0 | 7.2250 |
| 1309 | 0 | S | 29.00 | male | 0 | 0 | 7.8750 |
View(titanic)
# Round fractional ages to the nearest integer
titanic$age <- round(titanic$age) # for cases like value that are 0.92,0.42 are rouded up to the nearest interger
#reason this makes the data more consistent and easy to work with and well interpretable
View(titanic)
#We need to install Packages for better visualization and some statistical analysis
install.packages("tidyverse", repos = "https://cloud.r-project.org/")# tells R where to download packages from## Installing package into 'C:/Users/Home/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)## package 'tidyverse' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\Home\AppData\Local\Temp\RtmpqyR593\downloaded_packageslibrary(tidyverse)## Warning: package 'tidyverse' was built under R version 4.3.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::lift() masks caret::lift()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggplot2)
# Mean of age
mean_age <- mean(titanic$age, na.rm = TRUE)
mean_age## [1] 29.80345# Median of age
median_age <- median(titanic$age, na.rm = TRUE)
median_age## [1] 28# Mode of age
mode_age <- as.numeric(names(sort(table(titanic$age), decreasing = TRUE)[1]))
mode_age## [1] 24# Calculate total number of passengers
total_passengers <- nrow(titanic)
total_passengers## [1] 1043# Calculate number of male passengers
male_passengers <- sum(titanic$sex == "male", na.rm = TRUE)
male_passengers## [1] 657# Calculate number of female passengers
female_passengers <- sum(titanic$sex == "female", na.rm = TRUE)
female_passengers ## [1] 386# Calculate number of male passengers who survived
male_survived <- sum(titanic$sex == "male" & titanic$survived == 1, na.rm = TRUE)
male_survived## [1] 135#Calculate number of female passengers who survived
female_survived <- sum(titanic$sex == "female" & titanic$survived == 1, na.rm = TRUE)
female_survived ## [1] 290# Calculate percentage of male passengers who survived
male_survival_percentage <- (male_survived / male_passengers) * 100
male_survival_percentage## [1] 20.54795# Calculate percentage of female passengers who survived
female_survival_percentage <- (female_survived / female_passengers) * 100
female_survival_percentage## [1] 75.12953#Visualization Analysis
# Create a data frame for visualization
survival_data <- data.frame(Gender = c("Male", "Female"),Survived = c(male_survived, female_survived))
survival_data ## Gender Survived
## 1 Male 135
## 2 Female 290# Plot the data
ggplot(survival_data, aes(x = Gender, y = Survived, fill = Gender)) +
geom_bar(stat = "identity") +
labs(title = "Number of Passengers Who Survived by Gender",
x = "Gender",
y = "Number of Passengers") +
theme_minimal() +
theme(legend.position = "none") # Remove the legend
ggplot(data = titanic) +geom_point(mapping = aes(x = sex, y = age))
#Each point on the plot represents a passenger from the Titanic dataset,
#with its position determined by its gender and survival status.
ggplot(data = titanic) +geom_point(mapping = aes(x = sex, y = survived))
ggplot(data = titanic) +
stat_summary(mapping = aes(x = sex, y = survived),
fun = "mean",
geom = "point",
size = 10)
ggplot(data = titanic) +
geom_point(mapping = aes(x = sex, y = age, color = factor(survived))) +
labs(title = "Survival by Age and Sex", x = "sex", y = "age",color = "Survived")# this displays the colour of those who survived=1 and those that didnt survive=0
ggplot(data = titanic) +
geom_point(mapping = aes(x = embarked, y = age, color = sex)) +
scale_color_manual(values = c("male" = "blue", "female" = "pink"),
labels = c("male" = "Male", "female" = "Female")) +
labs(x = "Embarked", y = "Age")
library(scales)## Warning: package 'scales' was built under R version 4.3.3##
## Attaching package: 'scales'
##
## The following object is masked from 'package:purrr':
##
## discard
##
## The following object is masked from 'package:readr':
##
## col_factorggplot(data = titanic) +stat_summary(mapping = aes(x = sex, y = survived), fun = "mean", geom = "point", size = 5) + scale_y_continuous(labels = scales::percent_format())
ggplot(data = titanic) +geom_point(mapping = aes(x = sex, y = survived))
# Plot the data
ggplot(titanic, aes(x = embarked, y = fare, color = sex)) +
geom_point() +
labs(title = "Number of Passengers by Sex and Fare",
x = "Embarked",
y = "Fare") +
theme_minimal()
#Plot the data
ggplot(titanic, aes(x = embarked, fill = factor(sibsp))) +
geom_bar(position = "dodge", color = "black") +
labs(title = "Number of Passengers by Embarked and Sibsp",
x = "Embarked",
y = "Number of Passengers",
fill = "Number of Sibsp") +
theme_minimal()
# Plot the data
ggplot(data = titanic) +
geom_point(mapping = aes(x = sex, y = age, color = factor(survived))) +
facet_wrap(~ embarked, nrow = 2) 
#density plot
ggplot(data = titanic) +
geom_density(mapping = aes(x = age, fill = sex), alpha = 0.5) +
labs(title = "Density Plot of Passenger Ages by Sex",
x = "Age",
y = "Density") +
theme_minimal()
# Histogram
ggplot(data = titanic) +
geom_histogram(mapping = aes(x = age)) +
labs(title = "Histogram of Passenger Ages",
x = "Age",
y = "Frequency") +
theme_minimal()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(data = titanic) +
geom_histogram(mapping = aes(x = age), bins = 20, fill = "lightblue", color = "black") +
labs(title = "Histogram of Passenger Ages",
x = "Age",
y = "Count") +
theme_minimal()
#Histogram of "Fare":
ggplot(data = titanic) +
geom_histogram(mapping = aes(x = fare), bins = 20, fill = "lightgreen", color = "pink") +
labs(title = "Histogram of Passenger Fares",
x = "Fare",
y = "Count") +
theme_minimal()
#Histogram of "Siblings/Spouses Aboard" (sibsp
ggplot(data = titanic) +
geom_histogram(mapping = aes(x = sibsp), bins = 10, fill = "lightcoral", color = "black") +
labs(title = "Histogram of Siblings/Spouses Aboard",
x = "Sibsp",
y = "Count") +
theme_minimal()
# Boxplot
ggplot(data = titanic) +
geom_boxplot(mapping = aes(x = sex, y = age)) +
labs(title = "Boxplot of Passenger Ages by Sex",
x = "Sex",
y = "Age") +
theme_minimal()
#Remove non-numeric columns
numeric_titanic <- titanic[, sapply(titanic, is.numeric)]
# Calculate correlation matrix
correlation_matrix <- cor(numeric_titanic)
# Plot heatmap
ggplot(data = reshape2::melt(correlation_matrix), aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "steelblue") +
labs(title = "Correlation Heatmap of Titanic Dataset",
x = "Variable",
y = "Variable") +
theme_minimal()
###5) Survived is the dependent variable, find its proportion in the dataset.
# Calculate the proportion of passengers who survived
survived_proportion <- mean(titanic$survived == 1, na.rm = TRUE) * 100
# Print the proportion
survived_proportion## [1] 40.74784#(7)Make Survived embarked and sex as factors.
titanic$survived <- factor(titanic$survived)
titanic$survived## [1] 1 1 0 0 0 1 1 0 1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 0
## [38] 0 1 1 1 1 0 1 1 1 1 0 0 0 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 0 1 0 1 1 0 1
## [75] 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 0 1 0 1 1 1 0 0 1 1 1 1 1 1 1
## [112] 0 1 0 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 1 1 0 1 1 1 1
## [149] 0 1 0 0 0 0 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 0 1 0 1 1 0 0 1 0 0 0 1 1 1 0
## [186] 0 0 1 1 0 1 0 1 1 0 0 0 0 0 1 0 1 1 1 0 1 0 0 1 0 1 1 0 0 1 0 1 0 1 1 1 0
## [223] 1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 0 1 0 1 1 1 0 0 0 1 1 0 1 1 0 1 1
## [260] 1 0 0 0 1 0 1 0 0 0 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 1
## [297] 1 0 1 1 1 1 1 1 0 0 0 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 0 1 0
## [334] 0 1 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 0 1 1 0 1 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0
## [371] 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 1 0 1 1 1 0 1 1 0 0 0 0 1 0 0 1 0 0
## [408] 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0
## [445] 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1 1
## [482] 0 1 0 1 0 1 1 1 0 0 1 1 0 1 1 1 1 0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0 0 0 1 0
## [519] 1 1 0 0 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0 1 1 1 1 1 0 0 0 1
## [556] 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0
## [593] 0 0 0 0 1 0 0 1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0
## [630] 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0
## [667] 0 0 0 0 1 1 1 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 1 0 0
## [704] 0 1 0 0 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0
## [741] 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1
## [778] 1 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0
## [815] 1 1 0 0 0 0 0 1 1 1 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0
## [852] 1 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 1 1 0 1 1 0 0 1 1 0 0
## [889] 1 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [926] 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1
## [963] 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 1 0 1 1 0
## [1000] 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0
## [1037] 0 1 0 0 0 0 0
## Levels: 0 1titanic$embarked <- factor(titanic$embarked)
titanic$embarked ## [1] S S S S S S S S S C C C C S S C C C C S S C C S C C C S S S C S S S C S S
## [38] C C S C C S S C C C S S S S S S S S S S S S S S C S C C C S S C C C S S S
## [75] S C C S S S S S S S S S S C C C C C C C C C C S C S S S S S S C S C C C C
## [112] S S S C C C C C C C S S S C C C C C C C S S S C C S S S S C C S S S C S S
## [149] C S S S C C S S S S S C C S C S S S S S C S S S S S C C S S C S Q Q Q C S
## [186] S C C C C S S C C C C S S S S C C S S S C S C S S S C C C S C C S C C C C
## [223] C C C C S S S S S C S S C S S S S C C C C C S C C C S S S S S S S S C C C
## [260] C C S S C C C S S S C S S S C C C S C C C S C C C S S S S S S S S S S S S
## [297] S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S
## [334] S S S S S S S S S S S S S S C C S S S S S S S S C C S S S S S S S S S S S
## [371] S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S
## [408] S S S S S S S S S S S S S C S S S Q S Q S S S C C C C C S C S Q S S S C C
## [445] C S S S S S S S S S S S S S S S Q C C S S S S S S C S S S S C S S S S S S
## [482] S S S C C S S S S S S S C S S S S S S S S S S S S S S Q S S S C S S S S S
## [519] S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S C S S S S
## [556] S C S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S C C
## [593] S C C S C C S S C C C C S C S C C C Q S S S S C S S S S C C Q Q S Q S S S
## [630] S S Q Q Q Q S S S S S S Q Q S S S Q Q S S Q S S C C S S Q S S Q Q Q Q Q S
## [667] S S S S S S S S S S S S C S S S Q Q S S S S S S S S S S S S S S S S S S S
## [704] S Q S S S Q S S Q S C Q S S S S S S C C C S S S Q S Q S S S S S Q S Q S S
## [741] S S S S S S S S S S S S S S S S S S Q S S S C S S S S S C S Q S S S S S S
## [778] S S S S S C S S C S S S S S S S S S S S S S S S S S S S S S S S S S S S S
## [815] C C C Q S S S S S S S S S C S S S S S S C S C S S S S S S S S S S S S S S
## [852] S S S S S Q C S Q Q S S S S S S S S S S S Q S S C C C C S C C S S S S S S
## [889] S S C S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S
## [926] S S S S S S S S S S S S S Q Q Q Q Q Q S S S S S S S S S C S S S S S S S S
## [963] S S S S C S S S S S S S S S S S S C S S S S S S S S S S S S S S S S C C S
## [1000] S S S C C C S S S S S S S S S S S S S S S S C S S S S S C S S S S S S S S
## [1037] C C C C C C S
## Levels: C Q Stitanic$sex <- factor(titanic$sex)
titanic$sex ## [1] female male female male female male female male female male
## [11] male female female female male male female female male male
## [21] female male female female male male female female male male
## [31] male female female male female female male male female female
## [41] female female male male female male female male male male
## [51] male female male female male male female male female male
## [61] female female female male male female female male female male
## [71] female female male female female male female male male female
## [81] male female male female male male female male female female
## [91] female male male female female female female male male female
## [101] female female male male female female male male female male
## [111] female male female male female female female male male male
## [121] male female male female male female male male female male
## [131] female male female male male female male female male male
## [141] female female female male female male male female female male
## [151] male male male male male male female female female female
## [161] male male female female female male female male female female
## [171] male female male female female male male male male male
## [181] male female female male male male male female female male
## [191] female male female female male male male male male female
## [201] male female female male male female male male female male
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## [221] female male female female female female male female male male
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## [241] male female male male male male female female male female
## [251] male female female male female male male male female male
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## [301] female female male female male male male male female female
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## [381] male male male female male female male male male female
## [391] male female female female male female female male male male
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## [501] male male male female female female female male female male
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## [521] male female male female male male female female female female
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## [601] female female female female female male male male female female
## [611] female male male male male male male male male male
## [621] male female male female male female female male male male
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## [641] male female male male male male female female male male
## [651] male male male male male male male male male male
## [661] male male female female male male male male male male
## [671] male male female male male female male male male male
## [681] female male female male male male female male male male
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## [721] male male male male male female male male male male
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## [741] male female male male male male female female male male
## [751] female male male male male male male female female male
## [761] female male male male male male female male male male
## [771] female female female female male female female female male male
## [781] female male male female female male male male male male
## [791] male male male male male male male female male male
## [801] male female male male male female female male male male
## [811] male male male male female male male male male female
## [821] male female male female female male female male female female
## [831] male male male male male male male male male male
## [841] female female male female male male male female male male
## [851] male female male male male male female male male female
## [861] female male female male male male male male male male
## [871] female male female male male female female male female male
## [881] male female female male female female male male male male
## [891] male female male male female male male male female male
## [901] male male female female male male female male male female
## [911] female female male male male male male female male male
## [921] male male female female male male male male male female
## [931] male male female male male male male male male male
## [941] male male male female female male male female female male
## [951] female male male male male male male female male female
## [961] female female male male male male male male male female
## [971] male male female female male female male male male male
## [981] female male male female male male female female male male
## [991] male male male male male male male female male male
## [1001] male male male female female male female female male male
## [1011] female male female male male male male female male male
## [1021] female male male female male male male female male male
## [1031] male female male male male male male female male female
## [1041] male male male
## Levels: female male#(8)Find the correlation matrix between survival and the other features
# Compute correlation matrix
# Convert factors to numeric
titanic_numeric <- as.data.frame(sapply(titanic, function(x) if(is.factor(x)) as.numeric(x) else x))
# Compute correlation matrix
correlation_matrix <- cor(titanic_numeric)
# Correlation with survival
survival_correlation <- correlation_matrix["survived", ]
# Display correlation matrix
#Scatter plot between survival and age:
print(correlation_matrix)## survived embarked age sex sibsp parch
## survived 1.00000000 -0.20225751 -0.05686779 -0.5363321 -0.01140343 0.11543601
## embarked -0.20225751 1.00000000 -0.08288529 0.1094254 0.04550984 0.01122982
## age -0.05686779 -0.08288529 1.00000000 0.0657101 -0.24222445 -0.14912595
## sex -0.53633212 0.10942541 0.06571010 1.0000000 -0.09646420 -0.22253083
## sibsp -0.01140343 0.04550984 -0.24222445 -0.0964642 1.00000000 0.37395967
## parch 0.11543601 0.01122982 -0.14912595 -0.2225308 0.37395967 1.00000000
## fare 0.24785762 -0.30145454 0.17739013 -0.1864003 0.14213054 0.21764954
## fare
## survived 0.2478576
## embarked -0.3014545
## age 0.1773901
## sex -0.1864003
## sibsp 0.1421305
## parch 0.2176495
## fare 1.0000000#(9)Plot survival with other features to see if any correlation exists
# Scatter plot of survival vs. age
ggplot(titanic, aes(x = age, y = survived)) +
geom_point() +
labs(title = "Survived vs. Age", x = "Age", y = "survived")
# Bar plot of survival by gender
ggplot(titanic, aes(x = sex, fill = factor(survived))) +
geom_bar() +
labs(title = "Survival by Gender", x = "Gender", y = "Count") +
scale_fill_manual(values = c("red", "blue")) # Customize fill colors
# Box plot of survival by passenger class
ggplot(titanic, aes(x = factor(parch), y = survived)) +
geom_boxplot() +
labs(title = "Survival by Passenger Class", x = "Passenger Class", y = "Survival")
#pairwise scatter plot matrix to visualize the relationships between multiple numerical #attributes in the dataset.
pairs(~ age + fare + sibsp + parch + survived, data = titanic)
#plot to visualize the distribution of survival outcomes within each category of a categorical variable
ggplot(titanic, aes(x = embarked, fill = factor(survived))) +
geom_bar() +
labs(title = "Survival by Embarked", x = "Embarked", y = "Count", fill = "Survived")
#faceted scatter plot to visualize the relationship between survival and other numerical
#attributes, such as fare and age, across different categories (e.g., sex, passenger class).
ggplot(titanic, aes(x = fare, y = age, color = factor(survived))) +
geom_point() +
facet_wrap(~ sex, ncol = 2) +
labs(title = "Survival by Fare and Age", x = "Fare", y = "Age", color = "Survived") #
#---------------------A pie chart representation
# Calculate the count of passengers by survival status and sex
survival_counts <- table(titanic$survived, titanic$sex)
# Convert to data frame for plotting
survival_data <- data.frame(Survived = rep(c("Not Survived", "Survived"), each = nlevels(titanic$sex)),
Sex = rep(levels(titanic$sex), 2),
Count = as.numeric(survival_counts))
# Create pie chart with facets
ggplot(survival_data, aes(x = "", y = Count, fill = Survived)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0) +
facet_wrap(~Sex) +
labs(title = "Survival Status of Titanic Passengers by Sex",
fill = "Survived",
x = NULL,
y = NULL) +
theme_void() +
theme(legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
plot.title = element_text(size = 14, hjust = 0.5))
# (10)Set seed for reproducibility
# Load and preprocess data
data(titanic)## Warning in data(titanic): data set 'titanic' not foundset.seed(1000)
#(11) Build your training (till index 1046) and test (till index 1308) datasets
# Split data into training and testing sets
train_index <- sample(1:nrow(titanic), 0.7 * nrow(titanic))
titanic_train <- titanic[train_index, ]
titanic_test <- titanic[-train_index, ]
#(12)
library(rpart.plot)## Warning: package 'rpart.plot' was built under R version 4.3.3# (13)Train the model
fit <- rpart(survived ~ sex + age + sibsp + parch + fare + embarked, data = titanic_train, method = "class")
fit## n= 730
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 730 304 0 (0.5835616 0.4164384)
## 2) sex=male 454 93 0 (0.7951542 0.2048458)
## 4) age>=9.5 419 73 0 (0.8257757 0.1742243) *
## 5) age< 9.5 35 15 1 (0.4285714 0.5714286)
## 10) sibsp>=2 12 0 0 (1.0000000 0.0000000) *
## 11) sibsp< 2 23 3 1 (0.1304348 0.8695652) *
## 3) sex=female 276 65 1 (0.2355072 0.7644928) *#14) Plot your regression tree and save plot into an image file
# Plot the regression tree
rpart.plot(fit, main="Regression Tree", extra=101)
# Save the plot as an image file (e.g., PNG)
png("regression_tree.png", width=800, height=600)
rpart.plot(fit, main="Regression Tree", extra=101)
dev.off()## png
## 2#(15) Fit the regression tree model
fit <- rpart(survived ~ sex + age + sibsp + parch + fare + embarked, data = titanic_train, method = "class")
library(rattle)## Warning: package 'rattle' was built under R version 4.3.3## Loading required package: bitops
## Rattle: A free graphical interface for data science with R.
## Version 5.5.1 Copyright (c) 2006-2021 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.library(RColorBrewer)
#(16)
fancyRpartPlot(fit) 
# Make predictions on the training set
train_predictions <- predict(fit, titanic_train, type = "class")
# Make predictions on the test set
test_predictions <- predict(fit, titanic_test, type = "class")
#(17) Fit the regression tree model
fit <- rpart(survived ~ sex + age + sibsp + parch + fare + embarked, data = titanic_train, method = "class")
# Examine the tree
summary(fit)## Call:
## rpart(formula = survived ~ sex + age + sibsp + parch + fare +
## embarked, data = titanic_train, method = "class")
## n= 730
##
## CP nsplit rel error xerror xstd
## 1 0.48026316 0 1.0000000 1.0000000 0.04381336
## 2 0.02796053 1 0.5197368 0.5197368 0.03660088
## 3 0.01000000 3 0.4638158 0.4967105 0.03599919
##
## Variable importance
## sex fare sibsp age parch embarked
## 69 9 9 7 4 2
##
## Node number 1: 730 observations, complexity param=0.4802632
## predicted class=0 expected loss=0.4164384 P(node) =1
## class counts: 426 304
## probabilities: 0.584 0.416
## left son=2 (454 obs) right son=3 (276 obs)
## Primary splits:
## sex splits as RL, improve=107.522700, (0 missing)
## fare < 50.7396 to the left, improve= 28.335920, (0 missing)
## embarked splits as RLL, improve= 21.813940, (0 missing)
## parch < 0.5 to the left, improve= 13.911990, (0 missing)
## age < 8.5 to the right, improve= 7.238396, (0 missing)
## Surrogate splits:
## fare < 77.6229 to the left, agree=0.656, adj=0.091, (0 split)
## parch < 0.5 to the left, agree=0.647, adj=0.065, (0 split)
## embarked splits as LRL, agree=0.625, adj=0.007, (0 split)
##
## Node number 2: 454 observations, complexity param=0.02796053
## predicted class=0 expected loss=0.2048458 P(node) =0.6219178
## class counts: 361 93
## probabilities: 0.795 0.205
## left son=4 (419 obs) right son=5 (35 obs)
## Primary splits:
## age < 9.5 to the right, improve=10.192580, (0 missing)
## embarked splits as RLL, improve= 6.087304, (0 missing)
## parch < 0.5 to the left, improve= 4.717559, (0 missing)
## fare < 79.025 to the left, improve= 3.061952, (0 missing)
## sibsp < 1.5 to the right, improve= 1.395124, (0 missing)
## Surrogate splits:
## sibsp < 2.5 to the left, agree=0.938, adj=0.2, (0 split)
##
## Node number 3: 276 observations
## predicted class=1 expected loss=0.2355072 P(node) =0.3780822
## class counts: 65 211
## probabilities: 0.236 0.764
##
## Node number 4: 419 observations
## predicted class=0 expected loss=0.1742243 P(node) =0.5739726
## class counts: 346 73
## probabilities: 0.826 0.174
##
## Node number 5: 35 observations, complexity param=0.02796053
## predicted class=1 expected loss=0.4285714 P(node) =0.04794521
## class counts: 15 20
## probabilities: 0.429 0.571
## left son=10 (12 obs) right son=11 (23 obs)
## Primary splits:
## sibsp < 2 to the right, improve=11.9254700, (0 missing)
## fare < 26.95 to the right, improve= 3.1559290, (0 missing)
## age < 3.5 to the right, improve= 0.6722689, (0 missing)
## parch < 1.5 to the right, improve= 0.2380952, (0 missing)
## Surrogate splits:
## fare < 26.95 to the right, agree=0.800, adj=0.417, (0 split)
## embarked splits as RLR, agree=0.714, adj=0.167, (0 split)
##
## Node number 10: 12 observations
## predicted class=0 expected loss=0 P(node) =0.01643836
## class counts: 12 0
## probabilities: 1.000 0.000
##
## Node number 11: 23 observations
## predicted class=1 expected loss=0.1304348 P(node) =0.03150685
## class counts: 3 20
## probabilities: 0.130 0.870# the most important feature over which the tree first splits is the "sex" variable
#The node splits into two branches based on gender: one branch for females and another for
#males. This aligns with the historical practice of "Women and children first!" during
#emergencies like the Titanic disaster, where priority was given to women and children
#for evacuation.Therefore, the analysis of the regression tree model supports the notion
#that gender played a crucial role in survival on the Titanic, reflecting the well-known
#principle of prioritizing women and children during emergencies.
#(18)
predictions <- predict(fit, newdata = titanic_test, type = "class")
predictions## 11 12 14 17 18 19 20 21 32 39 40 42 46 48 54 55
## 0 1 1 0 1 1 0 0 0 0 0 1 0 0 0 0
## 65 66 67 68 73 76 79 83 84 87 89 95 101 102 105 106
## 0 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1
## 110 112 115 116 118 120 121 128 132 133 139 143 144 149 151 152
## 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0
## 156 157 162 170 172 174 175 176 186 188 189 194 195 203 210 215
## 1 0 1 1 0 0 0 0 0 1 1 1 0 0 0 1
## 227 229 230 232 233 237 240 243 260 261 262 265 275 280 293 309
## 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 1
## 312 315 320 328 330 331 336 340 344 348 353 370 372 374 381 387
## 1 1 1 0 1 0 0 0 0 0 1 1 1 1 1 0
## 392 393 396 401 403 404 406 407 408 409 418 421 422 426 429 430
## 0 1 1 1 1 0 0 0 1 0 0 0 0 0 1 0
## 432 433 435 438 439 440 441 442 444 460 468 469 471 473 477 481
## 0 0 1 1 1 0 1 1 0 0 1 1 0 0 0 0
## 482 483 485 486 489 490 494 495 501 502 507 509 513 522 528 533
## 1 1 1 0 0 1 0 1 0 1 0 0 0 1 0 0
## 535 537 539 540 544 546 548 553 557 559 561 563 566 573 575 578
## 1 1 0 0 0 1 0 0 0 1 1 1 0 1 0 1
## 587 593 601 605 608 610 611 617 618 619 621 625 631 632 633 638
## 1 0 0 1 1 0 1 0 0 0 0 1 0 0 1 0
## 642 644 647 649 651 656 659 663 666 672 674 675 678 685 686 690
## 0 1 1 0 1 0 1 0 1 0 0 0 0 1 0 0
## 692 694 695 698 702 708 711 712 719 721 733 735 737 745 747 752
## 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0
## 754 759 762 766 767 770 773 774 777 779 780 783 793 797 811 812
## 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1
## 828 829 831 834 845 849 850 854 861 867 870 871 881 891 892 893
## 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0
## 898 899 900 906 907 908 914 938 939 944 948 950 951 954 960 965
## 0 0 1 0 0 1 0 1 0 1 1 0 0 0 0 0
## 970 973 975 978 986 993 996 997 1008 1018 1021 1022 1047 1049 1060 1064
## 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0
## 1069 1083 1089 1090 1092 1098 1100 1101 1106 1107 1113 1114 1121 1130 1134 1143
## 0 1 0 0 1 0 1 1 0 1 1 1 0 0 0 0
## 1145 1148 1153 1170 1172 1189 1202 1219 1221 1228 1236 1238 1249 1265 1267 1268
## 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1
## 1274 1275 1279 1286 1288 1297 1305 1308 1309
## 1 0 0 0 0 0 1 0 0
## Levels: 0 1#(19) Create a data frame with PassengerSex and Survived columns
Results <- data.frame(PassengerSex = titanic_test$sex, Survived = as.factor(predictions))
# Print the first few rows of the Results data frame
print(head(Results))## PassengerSex Survived
## 11 male 0
## 12 female 1
## 14 female 1
## 17 male 0
## 18 female 1
## 19 female 1# Calculate accuracy on training and test sets
train_accuracy <- sum(train_predictions == titanic_train$survived) / nrow(titanic_train)
test_accuracy <- sum(test_predictions == titanic_test$survived) / nrow(titanic_test)
# Print accuracies
print(paste("Training Accuracy:", train_accuracy))## [1] "Training Accuracy: 0.806849315068493"print(paste("Test Accuracy:", test_accuracy))## [1] "Test Accuracy: 0.776357827476038"# Perform cross-validation to assess overfitting
fit_cv <- train(
survived ~ sex + age + sibsp + parch + fare + embarked,
data = titanic_train,
method = "rpart",
trControl = trainControl(method = "cv", number = 5)
)
# Print cross-validated accuracy
print(paste("Cross-Validated Accuracy:", fit_cv$results$Accuracy))## [1] "Cross-Validated Accuracy: 0.798645175595037"
## [2] "Cross-Validated Accuracy: 0.791833264245708"
## [3] "Cross-Validated Accuracy: 0.650425611907493"#(20) Save the Results data frame to a CSV file
write.csv(Results, file = "Titanicdtree.csv", row.names = FALSE)