odev II-Palmer Penguins
Uslu C
15.10.2025
gerekli kütüphaneleri aktifleştirme ile başlıyoruz.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(openintro)## Loading required package: airports## Loading required package: cherryblossom## Loading required package: usdatalibrary(stats)palmerpenguins veri setindeki değişkenleri inceleme işlemi için öncelikle palmerpenguins paket yüklemesi yapılır
install.packages("palmerpenguins", repos = "http://cran.us.r-project.org")## Installing package into 'C:/Users/uslu_/AppData/Local/R/win-library/4.5'
## (as 'lib' is unspecified)## package 'palmerpenguins' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\uslu_\AppData\Local\Temp\RtmpkX5qED\downloaded_packagespaket yüklemesi ardından; penguins veri setini environmentte gorunmesi icin data fonksiyonu, değskenlerine goz atmak icin glimpse fonksiyonu kullanılır.
data(penguins)
glimpse(penguins)## Rows: 344
## Columns: 8
## $ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Ad…
## $ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Tor…
## $ bill_len <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, 42.0, …
## $ bill_dep <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, 20.2, …
## $ flipper_len <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186, 180,…
## $ body_mass <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, 4250, …
## $ sex <fct> male, female, female, NA, female, male, female, male, NA, …
## $ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…penguins veri setinin 8 değiskenli 344 satırdan olustugu gorulmekte. veri setindeki değskenler; penguen turu, ada, gaga uzunluğu, gaga derinlgi, yuzgec uzunlugu, vucut kutlesi, cinsiyet yıl
islemler öncesi veri setindeki degiskenlerin isimleri Turkcelestilmek istenirse;
colnames(penguins)<- c("tur", "ada", "gaga_uzunlugu",
"gaga_derinligi", "yuzgec uzunlugu",
"vucut_kutle" , "cinsiyet" , "yıl")eksik verileri değisken bazında gormek icin
is.na(penguins)## tur ada gaga_uzunlugu gaga_derinligi yuzgec uzunlugu vucut_kutle
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## cinsiyet yıl
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## [304,] FALSE FALSE
## [305,] FALSE FALSE
## [306,] FALSE FALSE
## [307,] FALSE FALSE
## [308,] FALSE FALSE
## [309,] FALSE FALSE
## [310,] FALSE FALSE
## [311,] FALSE FALSE
## [312,] FALSE FALSE
## [313,] FALSE FALSE
## [314,] FALSE FALSE
## [315,] FALSE FALSE
## [316,] FALSE FALSE
## [317,] FALSE FALSE
## [318,] FALSE FALSE
## [319,] FALSE FALSE
## [320,] FALSE FALSE
## [321,] FALSE FALSE
## [322,] FALSE FALSE
## [323,] FALSE FALSE
## [324,] FALSE FALSE
## [325,] FALSE FALSE
## [326,] FALSE FALSE
## [327,] FALSE FALSE
## [328,] FALSE FALSE
## [329,] FALSE FALSE
## [330,] FALSE FALSE
## [331,] FALSE FALSE
## [332,] FALSE FALSE
## [333,] FALSE FALSE
## [334,] FALSE FALSE
## [335,] FALSE FALSE
## [336,] FALSE FALSE
## [337,] FALSE FALSE
## [338,] FALSE FALSE
## [339,] FALSE FALSE
## [340,] FALSE FALSE
## [341,] FALSE FALSE
## [342,] FALSE FALSE
## [343,] FALSE FALSE
## [344,] FALSE FALSEeksik veri olan değişkenler; gaga_uzunlugu, gaga_derinligi, yuzgec uzunlugu, vucut_kutle, cinsiyet
Uzun veri setinde eksik veri degisken ve sayılarını tek tek incelemek yerine daha ozet olarak gormek icin; Eksik verileri değişken bazında görmek için sum fonksiyonu kullanılır.
summary(penguins)## tur ada gaga_uzunlugu gaga_derinligi
## Adelie :152 Biscoe :168 Min. :32.10 Min. :13.10
## Chinstrap: 68 Dream :124 1st Qu.:39.23 1st Qu.:15.60
## Gentoo :124 Torgersen: 52 Median :44.45 Median :17.30
## Mean :43.92 Mean :17.15
## 3rd Qu.:48.50 3rd Qu.:18.70
## Max. :59.60 Max. :21.50
## NA's :2 NA's :2
## yuzgec uzunlugu vucut_kutle cinsiyet yıl
## Min. :172.0 Min. :2700 female:165 Min. :2007
## 1st Qu.:190.0 1st Qu.:3550 male :168 1st Qu.:2007
## Median :197.0 Median :4050 NA's : 11 Median :2008
## Mean :200.9 Mean :4202 Mean :2008
## 3rd Qu.:213.0 3rd Qu.:4750 3rd Qu.:2009
## Max. :231.0 Max. :6300 Max. :2009
## NA's :2 NA's :2sum fonksiyonu çıktılarına göre; gaga uzunluğu değiskeninde 2, gaga_derinligi değiskeninde 2, yuzgec uzunlugu değiskeninde 2, vucut_kutle değiskeninde 2, cinsiyet değiskeninde 11, eksik veri olduğu gorulmekte.
eksik verinin olduğu veri seti boyutları: 344* 8
eksik verileri silme icin
(na.omit(penguins))## tur ada gaga_uzunlugu gaga_derinligi yuzgec uzunlugu
## 1 Adelie Torgersen 39.1 18.7 181
## 2 Adelie Torgersen 39.5 17.4 186
## 3 Adelie Torgersen 40.3 18.0 195
## 5 Adelie Torgersen 36.7 19.3 193
## 6 Adelie Torgersen 39.3 20.6 190
## 7 Adelie Torgersen 38.9 17.8 181
## 8 Adelie Torgersen 39.2 19.6 195
## 13 Adelie Torgersen 41.1 17.6 182
## 14 Adelie Torgersen 38.6 21.2 191
## 15 Adelie Torgersen 34.6 21.1 198
## 16 Adelie Torgersen 36.6 17.8 185
## 17 Adelie Torgersen 38.7 19.0 195
## 18 Adelie Torgersen 42.5 20.7 197
## 19 Adelie Torgersen 34.4 18.4 184
## 20 Adelie Torgersen 46.0 21.5 194
## 21 Adelie Biscoe 37.8 18.3 174
## 22 Adelie Biscoe 37.7 18.7 180
## 23 Adelie Biscoe 35.9 19.2 189
## 24 Adelie Biscoe 38.2 18.1 185
## 25 Adelie Biscoe 38.8 17.2 180
## 26 Adelie Biscoe 35.3 18.9 187
## 27 Adelie Biscoe 40.6 18.6 183
## 28 Adelie Biscoe 40.5 17.9 187
## 29 Adelie Biscoe 37.9 18.6 172
## 30 Adelie Biscoe 40.5 18.9 180
## 31 Adelie Dream 39.5 16.7 178
## 32 Adelie Dream 37.2 18.1 178
## 33 Adelie Dream 39.5 17.8 188
## 34 Adelie Dream 40.9 18.9 184
## 35 Adelie Dream 36.4 17.0 195
## 36 Adelie Dream 39.2 21.1 196
## 37 Adelie Dream 38.8 20.0 190
## 38 Adelie Dream 42.2 18.5 180
## 39 Adelie Dream 37.6 19.3 181
## 40 Adelie Dream 39.8 19.1 184
## 41 Adelie Dream 36.5 18.0 182
## 42 Adelie Dream 40.8 18.4 195
## 43 Adelie Dream 36.0 18.5 186
## 44 Adelie Dream 44.1 19.7 196
## 45 Adelie Dream 37.0 16.9 185
## 46 Adelie Dream 39.6 18.8 190
## 47 Adelie Dream 41.1 19.0 182
## 49 Adelie Dream 36.0 17.9 190
## 50 Adelie Dream 42.3 21.2 191
## 51 Adelie Biscoe 39.6 17.7 186
## 52 Adelie Biscoe 40.1 18.9 188
## 53 Adelie Biscoe 35.0 17.9 190
## 54 Adelie Biscoe 42.0 19.5 200
## 55 Adelie Biscoe 34.5 18.1 187
## 56 Adelie Biscoe 41.4 18.6 191
## 57 Adelie Biscoe 39.0 17.5 186
## 58 Adelie Biscoe 40.6 18.8 193
## 59 Adelie Biscoe 36.5 16.6 181
## 60 Adelie Biscoe 37.6 19.1 194
## 61 Adelie Biscoe 35.7 16.9 185
## 62 Adelie Biscoe 41.3 21.1 195
## 63 Adelie Biscoe 37.6 17.0 185
## 64 Adelie Biscoe 41.1 18.2 192
## 65 Adelie Biscoe 36.4 17.1 184
## 66 Adelie Biscoe 41.6 18.0 192
## 67 Adelie Biscoe 35.5 16.2 195
## 68 Adelie Biscoe 41.1 19.1 188
## 69 Adelie Torgersen 35.9 16.6 190
## 70 Adelie Torgersen 41.8 19.4 198
## 71 Adelie Torgersen 33.5 19.0 190
## 72 Adelie Torgersen 39.7 18.4 190
## 73 Adelie Torgersen 39.6 17.2 196
## 74 Adelie Torgersen 45.8 18.9 197
## 75 Adelie Torgersen 35.5 17.5 190
## 76 Adelie Torgersen 42.8 18.5 195
## 77 Adelie Torgersen 40.9 16.8 191
## 78 Adelie Torgersen 37.2 19.4 184
## 79 Adelie Torgersen 36.2 16.1 187
## 80 Adelie Torgersen 42.1 19.1 195
## 81 Adelie Torgersen 34.6 17.2 189
## 82 Adelie Torgersen 42.9 17.6 196
## 83 Adelie Torgersen 36.7 18.8 187
## 84 Adelie Torgersen 35.1 19.4 193
## 85 Adelie Dream 37.3 17.8 191
## 86 Adelie Dream 41.3 20.3 194
## 87 Adelie Dream 36.3 19.5 190
## 88 Adelie Dream 36.9 18.6 189
## 89 Adelie Dream 38.3 19.2 189
## 90 Adelie Dream 38.9 18.8 190
## 91 Adelie Dream 35.7 18.0 202
## 92 Adelie Dream 41.1 18.1 205
## 93 Adelie Dream 34.0 17.1 185
## 94 Adelie Dream 39.6 18.1 186
## 95 Adelie Dream 36.2 17.3 187
## 96 Adelie Dream 40.8 18.9 208
## 97 Adelie Dream 38.1 18.6 190
## 98 Adelie Dream 40.3 18.5 196
## 99 Adelie Dream 33.1 16.1 178
## 100 Adelie Dream 43.2 18.5 192
## 101 Adelie Biscoe 35.0 17.9 192
## 102 Adelie Biscoe 41.0 20.0 203
## 103 Adelie Biscoe 37.7 16.0 183
## 104 Adelie Biscoe 37.8 20.0 190
## 105 Adelie Biscoe 37.9 18.6 193
## 106 Adelie Biscoe 39.7 18.9 184
## 107 Adelie Biscoe 38.6 17.2 199
## 108 Adelie Biscoe 38.2 20.0 190
## 109 Adelie Biscoe 38.1 17.0 181
## 110 Adelie Biscoe 43.2 19.0 197
## 111 Adelie Biscoe 38.1 16.5 198
## 112 Adelie Biscoe 45.6 20.3 191
## 113 Adelie Biscoe 39.7 17.7 193
## 114 Adelie Biscoe 42.2 19.5 197
## 115 Adelie Biscoe 39.6 20.7 191
## 116 Adelie Biscoe 42.7 18.3 196
## 117 Adelie Torgersen 38.6 17.0 188
## 118 Adelie Torgersen 37.3 20.5 199
## 119 Adelie Torgersen 35.7 17.0 189
## 120 Adelie Torgersen 41.1 18.6 189
## 121 Adelie Torgersen 36.2 17.2 187
## 122 Adelie Torgersen 37.7 19.8 198
## 123 Adelie Torgersen 40.2 17.0 176
## 124 Adelie Torgersen 41.4 18.5 202
## 125 Adelie Torgersen 35.2 15.9 186
## 126 Adelie Torgersen 40.6 19.0 199
## 127 Adelie Torgersen 38.8 17.6 191
## 128 Adelie Torgersen 41.5 18.3 195
## 129 Adelie Torgersen 39.0 17.1 191
## 130 Adelie Torgersen 44.1 18.0 210
## 131 Adelie Torgersen 38.5 17.9 190
## 132 Adelie Torgersen 43.1 19.2 197
## 133 Adelie Dream 36.8 18.5 193
## 134 Adelie Dream 37.5 18.5 199
## 135 Adelie Dream 38.1 17.6 187
## 136 Adelie Dream 41.1 17.5 190
## 137 Adelie Dream 35.6 17.5 191
## 138 Adelie Dream 40.2 20.1 200
## 139 Adelie Dream 37.0 16.5 185
## 140 Adelie Dream 39.7 17.9 193
## 141 Adelie Dream 40.2 17.1 193
## 142 Adelie Dream 40.6 17.2 187
## 143 Adelie Dream 32.1 15.5 188
## 144 Adelie Dream 40.7 17.0 190
## 145 Adelie Dream 37.3 16.8 192
## 146 Adelie Dream 39.0 18.7 185
## 147 Adelie Dream 39.2 18.6 190
## 148 Adelie Dream 36.6 18.4 184
## 149 Adelie Dream 36.0 17.8 195
## 150 Adelie Dream 37.8 18.1 193
## 151 Adelie Dream 36.0 17.1 187
## 152 Adelie Dream 41.5 18.5 201
## 153 Gentoo Biscoe 46.1 13.2 211
## 154 Gentoo Biscoe 50.0 16.3 230
## 155 Gentoo Biscoe 48.7 14.1 210
## 156 Gentoo Biscoe 50.0 15.2 218
## 157 Gentoo Biscoe 47.6 14.5 215
## 158 Gentoo Biscoe 46.5 13.5 210
## 159 Gentoo Biscoe 45.4 14.6 211
## 160 Gentoo Biscoe 46.7 15.3 219
## 161 Gentoo Biscoe 43.3 13.4 209
## 162 Gentoo Biscoe 46.8 15.4 215
## 163 Gentoo Biscoe 40.9 13.7 214
## 164 Gentoo Biscoe 49.0 16.1 216
## 165 Gentoo Biscoe 45.5 13.7 214
## 166 Gentoo Biscoe 48.4 14.6 213
## 167 Gentoo Biscoe 45.8 14.6 210
## 168 Gentoo Biscoe 49.3 15.7 217
## 169 Gentoo Biscoe 42.0 13.5 210
## 170 Gentoo Biscoe 49.2 15.2 221
## 171 Gentoo Biscoe 46.2 14.5 209
## 172 Gentoo Biscoe 48.7 15.1 222
## 173 Gentoo Biscoe 50.2 14.3 218
## 174 Gentoo Biscoe 45.1 14.5 215
## 175 Gentoo Biscoe 46.5 14.5 213
## 176 Gentoo Biscoe 46.3 15.8 215
## 177 Gentoo Biscoe 42.9 13.1 215
## 178 Gentoo Biscoe 46.1 15.1 215
## 180 Gentoo Biscoe 47.8 15.0 215
## 181 Gentoo Biscoe 48.2 14.3 210
## 182 Gentoo Biscoe 50.0 15.3 220
## 183 Gentoo Biscoe 47.3 15.3 222
## 184 Gentoo Biscoe 42.8 14.2 209
## 185 Gentoo Biscoe 45.1 14.5 207
## 186 Gentoo Biscoe 59.6 17.0 230
## 187 Gentoo Biscoe 49.1 14.8 220
## 188 Gentoo Biscoe 48.4 16.3 220
## 189 Gentoo Biscoe 42.6 13.7 213
## 190 Gentoo Biscoe 44.4 17.3 219
## 191 Gentoo Biscoe 44.0 13.6 208
## 192 Gentoo Biscoe 48.7 15.7 208
## 193 Gentoo Biscoe 42.7 13.7 208
## 194 Gentoo Biscoe 49.6 16.0 225
## 195 Gentoo Biscoe 45.3 13.7 210
## 196 Gentoo Biscoe 49.6 15.0 216
## 197 Gentoo Biscoe 50.5 15.9 222
## 198 Gentoo Biscoe 43.6 13.9 217
## 199 Gentoo Biscoe 45.5 13.9 210
## 200 Gentoo Biscoe 50.5 15.9 225
## 201 Gentoo Biscoe 44.9 13.3 213
## 202 Gentoo Biscoe 45.2 15.8 215
## 203 Gentoo Biscoe 46.6 14.2 210
## 204 Gentoo Biscoe 48.5 14.1 220
## 205 Gentoo Biscoe 45.1 14.4 210
## 206 Gentoo Biscoe 50.1 15.0 225
## 207 Gentoo Biscoe 46.5 14.4 217
## 208 Gentoo Biscoe 45.0 15.4 220
## 209 Gentoo Biscoe 43.8 13.9 208
## 210 Gentoo Biscoe 45.5 15.0 220
## 211 Gentoo Biscoe 43.2 14.5 208
## 212 Gentoo Biscoe 50.4 15.3 224
## 213 Gentoo Biscoe 45.3 13.8 208
## 214 Gentoo Biscoe 46.2 14.9 221
## 215 Gentoo Biscoe 45.7 13.9 214
## 216 Gentoo Biscoe 54.3 15.7 231
## 217 Gentoo Biscoe 45.8 14.2 219
## 218 Gentoo Biscoe 49.8 16.8 230
## 220 Gentoo Biscoe 49.5 16.2 229
## 221 Gentoo Biscoe 43.5 14.2 220
## 222 Gentoo Biscoe 50.7 15.0 223
## 223 Gentoo Biscoe 47.7 15.0 216
## 224 Gentoo Biscoe 46.4 15.6 221
## 225 Gentoo Biscoe 48.2 15.6 221
## 226 Gentoo Biscoe 46.5 14.8 217
## 227 Gentoo Biscoe 46.4 15.0 216
## 228 Gentoo Biscoe 48.6 16.0 230
## 229 Gentoo Biscoe 47.5 14.2 209
## 230 Gentoo Biscoe 51.1 16.3 220
## 231 Gentoo Biscoe 45.2 13.8 215
## 232 Gentoo Biscoe 45.2 16.4 223
## 233 Gentoo Biscoe 49.1 14.5 212
## 234 Gentoo Biscoe 52.5 15.6 221
## 235 Gentoo Biscoe 47.4 14.6 212
## 236 Gentoo Biscoe 50.0 15.9 224
## 237 Gentoo Biscoe 44.9 13.8 212
## 238 Gentoo Biscoe 50.8 17.3 228
## 239 Gentoo Biscoe 43.4 14.4 218
## 240 Gentoo Biscoe 51.3 14.2 218
## 241 Gentoo Biscoe 47.5 14.0 212
## 242 Gentoo Biscoe 52.1 17.0 230
## 243 Gentoo Biscoe 47.5 15.0 218
## 244 Gentoo Biscoe 52.2 17.1 228
## 245 Gentoo Biscoe 45.5 14.5 212
## 246 Gentoo Biscoe 49.5 16.1 224
## 247 Gentoo Biscoe 44.5 14.7 214
## 248 Gentoo Biscoe 50.8 15.7 226
## 249 Gentoo Biscoe 49.4 15.8 216
## 250 Gentoo Biscoe 46.9 14.6 222
## 251 Gentoo Biscoe 48.4 14.4 203
## 252 Gentoo Biscoe 51.1 16.5 225
## 253 Gentoo Biscoe 48.5 15.0 219
## 254 Gentoo Biscoe 55.9 17.0 228
## 255 Gentoo Biscoe 47.2 15.5 215
## 256 Gentoo Biscoe 49.1 15.0 228
## 258 Gentoo Biscoe 46.8 16.1 215
## 259 Gentoo Biscoe 41.7 14.7 210
## 260 Gentoo Biscoe 53.4 15.8 219
## 261 Gentoo Biscoe 43.3 14.0 208
## 262 Gentoo Biscoe 48.1 15.1 209
## 263 Gentoo Biscoe 50.5 15.2 216
## 264 Gentoo Biscoe 49.8 15.9 229
## 265 Gentoo Biscoe 43.5 15.2 213
## 266 Gentoo Biscoe 51.5 16.3 230
## 267 Gentoo Biscoe 46.2 14.1 217
## 268 Gentoo Biscoe 55.1 16.0 230
## 270 Gentoo Biscoe 48.8 16.2 222
## 271 Gentoo Biscoe 47.2 13.7 214
## 273 Gentoo Biscoe 46.8 14.3 215
## 274 Gentoo Biscoe 50.4 15.7 222
## 275 Gentoo Biscoe 45.2 14.8 212
## 276 Gentoo Biscoe 49.9 16.1 213
## 277 Chinstrap Dream 46.5 17.9 192
## 278 Chinstrap Dream 50.0 19.5 196
## 279 Chinstrap Dream 51.3 19.2 193
## 280 Chinstrap Dream 45.4 18.7 188
## 281 Chinstrap Dream 52.7 19.8 197
## 282 Chinstrap Dream 45.2 17.8 198
## 283 Chinstrap Dream 46.1 18.2 178
## 284 Chinstrap Dream 51.3 18.2 197
## 285 Chinstrap Dream 46.0 18.9 195
## 286 Chinstrap Dream 51.3 19.9 198
## 287 Chinstrap Dream 46.6 17.8 193
## 288 Chinstrap Dream 51.7 20.3 194
## 289 Chinstrap Dream 47.0 17.3 185
## 290 Chinstrap Dream 52.0 18.1 201
## 291 Chinstrap Dream 45.9 17.1 190
## 292 Chinstrap Dream 50.5 19.6 201
## 293 Chinstrap Dream 50.3 20.0 197
## 294 Chinstrap Dream 58.0 17.8 181
## 295 Chinstrap Dream 46.4 18.6 190
## 296 Chinstrap Dream 49.2 18.2 195
## 297 Chinstrap Dream 42.4 17.3 181
## 298 Chinstrap Dream 48.5 17.5 191
## 299 Chinstrap Dream 43.2 16.6 187
## 300 Chinstrap Dream 50.6 19.4 193
## 301 Chinstrap Dream 46.7 17.9 195
## 302 Chinstrap Dream 52.0 19.0 197
## 303 Chinstrap Dream 50.5 18.4 200
## 304 Chinstrap Dream 49.5 19.0 200
## 305 Chinstrap Dream 46.4 17.8 191
## 306 Chinstrap Dream 52.8 20.0 205
## 307 Chinstrap Dream 40.9 16.6 187
## 308 Chinstrap Dream 54.2 20.8 201
## 309 Chinstrap Dream 42.5 16.7 187
## 310 Chinstrap Dream 51.0 18.8 203
## 311 Chinstrap Dream 49.7 18.6 195
## 312 Chinstrap Dream 47.5 16.8 199
## 313 Chinstrap Dream 47.6 18.3 195
## 314 Chinstrap Dream 52.0 20.7 210
## 315 Chinstrap Dream 46.9 16.6 192
## 316 Chinstrap Dream 53.5 19.9 205
## 317 Chinstrap Dream 49.0 19.5 210
## 318 Chinstrap Dream 46.2 17.5 187
## 319 Chinstrap Dream 50.9 19.1 196
## 320 Chinstrap Dream 45.5 17.0 196
## 321 Chinstrap Dream 50.9 17.9 196
## 322 Chinstrap Dream 50.8 18.5 201
## 323 Chinstrap Dream 50.1 17.9 190
## 324 Chinstrap Dream 49.0 19.6 212
## 325 Chinstrap Dream 51.5 18.7 187
## 326 Chinstrap Dream 49.8 17.3 198
## 327 Chinstrap Dream 48.1 16.4 199
## 328 Chinstrap Dream 51.4 19.0 201
## 329 Chinstrap Dream 45.7 17.3 193
## 330 Chinstrap Dream 50.7 19.7 203
## 331 Chinstrap Dream 42.5 17.3 187
## 332 Chinstrap Dream 52.2 18.8 197
## 333 Chinstrap Dream 45.2 16.6 191
## 334 Chinstrap Dream 49.3 19.9 203
## 335 Chinstrap Dream 50.2 18.8 202
## 336 Chinstrap Dream 45.6 19.4 194
## 337 Chinstrap Dream 51.9 19.5 206
## 338 Chinstrap Dream 46.8 16.5 189
## 339 Chinstrap Dream 45.7 17.0 195
## 340 Chinstrap Dream 55.8 19.8 207
## 341 Chinstrap Dream 43.5 18.1 202
## 342 Chinstrap Dream 49.6 18.2 193
## 343 Chinstrap Dream 50.8 19.0 210
## 344 Chinstrap Dream 50.2 18.7 198
## vucut_kutle cinsiyet yıl
## 1 3750 male 2007
## 2 3800 female 2007
## 3 3250 female 2007
## 5 3450 female 2007
## 6 3650 male 2007
## 7 3625 female 2007
## 8 4675 male 2007
## 13 3200 female 2007
## 14 3800 male 2007
## 15 4400 male 2007
## 16 3700 female 2007
## 17 3450 female 2007
## 18 4500 male 2007
## 19 3325 female 2007
## 20 4200 male 2007
## 21 3400 female 2007
## 22 3600 male 2007
## 23 3800 female 2007
## 24 3950 male 2007
## 25 3800 male 2007
## 26 3800 female 2007
## 27 3550 male 2007
## 28 3200 female 2007
## 29 3150 female 2007
## 30 3950 male 2007
## 31 3250 female 2007
## 32 3900 male 2007
## 33 3300 female 2007
## 34 3900 male 2007
## 35 3325 female 2007
## 36 4150 male 2007
## 37 3950 male 2007
## 38 3550 female 2007
## 39 3300 female 2007
## 40 4650 male 2007
## 41 3150 female 2007
## 42 3900 male 2007
## 43 3100 female 2007
## 44 4400 male 2007
## 45 3000 female 2007
## 46 4600 male 2007
## 47 3425 male 2007
## 49 3450 female 2007
## 50 4150 male 2007
## 51 3500 female 2008
## 52 4300 male 2008
## 53 3450 female 2008
## 54 4050 male 2008
## 55 2900 female 2008
## 56 3700 male 2008
## 57 3550 female 2008
## 58 3800 male 2008
## 59 2850 female 2008
## 60 3750 male 2008
## 61 3150 female 2008
## 62 4400 male 2008
## 63 3600 female 2008
## 64 4050 male 2008
## 65 2850 female 2008
## 66 3950 male 2008
## 67 3350 female 2008
## 68 4100 male 2008
## 69 3050 female 2008
## 70 4450 male 2008
## 71 3600 female 2008
## 72 3900 male 2008
## 73 3550 female 2008
## 74 4150 male 2008
## 75 3700 female 2008
## 76 4250 male 2008
## 77 3700 female 2008
## 78 3900 male 2008
## 79 3550 female 2008
## 80 4000 male 2008
## 81 3200 female 2008
## 82 4700 male 2008
## 83 3800 female 2008
## 84 4200 male 2008
## 85 3350 female 2008
## 86 3550 male 2008
## 87 3800 male 2008
## 88 3500 female 2008
## 89 3950 male 2008
## 90 3600 female 2008
## 91 3550 female 2008
## 92 4300 male 2008
## 93 3400 female 2008
## 94 4450 male 2008
## 95 3300 female 2008
## 96 4300 male 2008
## 97 3700 female 2008
## 98 4350 male 2008
## 99 2900 female 2008
## 100 4100 male 2008
## 101 3725 female 2009
## 102 4725 male 2009
## 103 3075 female 2009
## 104 4250 male 2009
## 105 2925 female 2009
## 106 3550 male 2009
## 107 3750 female 2009
## 108 3900 male 2009
## 109 3175 female 2009
## 110 4775 male 2009
## 111 3825 female 2009
## 112 4600 male 2009
## 113 3200 female 2009
## 114 4275 male 2009
## 115 3900 female 2009
## 116 4075 male 2009
## 117 2900 female 2009
## 118 3775 male 2009
## 119 3350 female 2009
## 120 3325 male 2009
## 121 3150 female 2009
## 122 3500 male 2009
## 123 3450 female 2009
## 124 3875 male 2009
## 125 3050 female 2009
## 126 4000 male 2009
## 127 3275 female 2009
## 128 4300 male 2009
## 129 3050 female 2009
## 130 4000 male 2009
## 131 3325 female 2009
## 132 3500 male 2009
## 133 3500 female 2009
## 134 4475 male 2009
## 135 3425 female 2009
## 136 3900 male 2009
## 137 3175 female 2009
## 138 3975 male 2009
## 139 3400 female 2009
## 140 4250 male 2009
## 141 3400 female 2009
## 142 3475 male 2009
## 143 3050 female 2009
## 144 3725 male 2009
## 145 3000 female 2009
## 146 3650 male 2009
## 147 4250 male 2009
## 148 3475 female 2009
## 149 3450 female 2009
## 150 3750 male 2009
## 151 3700 female 2009
## 152 4000 male 2009
## 153 4500 female 2007
## 154 5700 male 2007
## 155 4450 female 2007
## 156 5700 male 2007
## 157 5400 male 2007
## 158 4550 female 2007
## 159 4800 female 2007
## 160 5200 male 2007
## 161 4400 female 2007
## 162 5150 male 2007
## 163 4650 female 2007
## 164 5550 male 2007
## 165 4650 female 2007
## 166 5850 male 2007
## 167 4200 female 2007
## 168 5850 male 2007
## 169 4150 female 2007
## 170 6300 male 2007
## 171 4800 female 2007
## 172 5350 male 2007
## 173 5700 male 2007
## 174 5000 female 2007
## 175 4400 female 2007
## 176 5050 male 2007
## 177 5000 female 2007
## 178 5100 male 2007
## 180 5650 male 2007
## 181 4600 female 2007
## 182 5550 male 2007
## 183 5250 male 2007
## 184 4700 female 2007
## 185 5050 female 2007
## 186 6050 male 2007
## 187 5150 female 2008
## 188 5400 male 2008
## 189 4950 female 2008
## 190 5250 male 2008
## 191 4350 female 2008
## 192 5350 male 2008
## 193 3950 female 2008
## 194 5700 male 2008
## 195 4300 female 2008
## 196 4750 male 2008
## 197 5550 male 2008
## 198 4900 female 2008
## 199 4200 female 2008
## 200 5400 male 2008
## 201 5100 female 2008
## 202 5300 male 2008
## 203 4850 female 2008
## 204 5300 male 2008
## 205 4400 female 2008
## 206 5000 male 2008
## 207 4900 female 2008
## 208 5050 male 2008
## 209 4300 female 2008
## 210 5000 male 2008
## 211 4450 female 2008
## 212 5550 male 2008
## 213 4200 female 2008
## 214 5300 male 2008
## 215 4400 female 2008
## 216 5650 male 2008
## 217 4700 female 2008
## 218 5700 male 2008
## 220 5800 male 2008
## 221 4700 female 2008
## 222 5550 male 2008
## 223 4750 female 2008
## 224 5000 male 2008
## 225 5100 male 2008
## 226 5200 female 2008
## 227 4700 female 2008
## 228 5800 male 2008
## 229 4600 female 2008
## 230 6000 male 2008
## 231 4750 female 2008
## 232 5950 male 2008
## 233 4625 female 2009
## 234 5450 male 2009
## 235 4725 female 2009
## 236 5350 male 2009
## 237 4750 female 2009
## 238 5600 male 2009
## 239 4600 female 2009
## 240 5300 male 2009
## 241 4875 female 2009
## 242 5550 male 2009
## 243 4950 female 2009
## 244 5400 male 2009
## 245 4750 female 2009
## 246 5650 male 2009
## 247 4850 female 2009
## 248 5200 male 2009
## 249 4925 male 2009
## 250 4875 female 2009
## 251 4625 female 2009
## 252 5250 male 2009
## 253 4850 female 2009
## 254 5600 male 2009
## 255 4975 female 2009
## 256 5500 male 2009
## 258 5500 male 2009
## 259 4700 female 2009
## 260 5500 male 2009
## 261 4575 female 2009
## 262 5500 male 2009
## 263 5000 female 2009
## 264 5950 male 2009
## 265 4650 female 2009
## 266 5500 male 2009
## 267 4375 female 2009
## 268 5850 male 2009
## 270 6000 male 2009
## 271 4925 female 2009
## 273 4850 female 2009
## 274 5750 male 2009
## 275 5200 female 2009
## 276 5400 male 2009
## 277 3500 female 2007
## 278 3900 male 2007
## 279 3650 male 2007
## 280 3525 female 2007
## 281 3725 male 2007
## 282 3950 female 2007
## 283 3250 female 2007
## 284 3750 male 2007
## 285 4150 female 2007
## 286 3700 male 2007
## 287 3800 female 2007
## 288 3775 male 2007
## 289 3700 female 2007
## 290 4050 male 2007
## 291 3575 female 2007
## 292 4050 male 2007
## 293 3300 male 2007
## 294 3700 female 2007
## 295 3450 female 2007
## 296 4400 male 2007
## 297 3600 female 2007
## 298 3400 male 2007
## 299 2900 female 2007
## 300 3800 male 2007
## 301 3300 female 2007
## 302 4150 male 2007
## 303 3400 female 2008
## 304 3800 male 2008
## 305 3700 female 2008
## 306 4550 male 2008
## 307 3200 female 2008
## 308 4300 male 2008
## 309 3350 female 2008
## 310 4100 male 2008
## 311 3600 male 2008
## 312 3900 female 2008
## 313 3850 female 2008
## 314 4800 male 2008
## 315 2700 female 2008
## 316 4500 male 2008
## 317 3950 male 2008
## 318 3650 female 2008
## 319 3550 male 2008
## 320 3500 female 2008
## 321 3675 female 2009
## 322 4450 male 2009
## 323 3400 female 2009
## 324 4300 male 2009
## 325 3250 male 2009
## 326 3675 female 2009
## 327 3325 female 2009
## 328 3950 male 2009
## 329 3600 female 2009
## 330 4050 male 2009
## 331 3350 female 2009
## 332 3450 male 2009
## 333 3250 female 2009
## 334 4050 male 2009
## 335 3800 male 2009
## 336 3525 female 2009
## 337 3950 male 2009
## 338 3650 female 2009
## 339 3650 female 2009
## 340 4000 male 2009
## 341 3400 female 2009
## 342 3775 male 2009
## 343 4100 male 2009
## 344 3775 female 2009eksik verilerin silinmesi ardından veri seti boyutları: 333* 8
veri setimizin temizlenmis hali ile yeni veri seti olarak atama;
temiz_penguins<- (na.omit(penguins))Yeni bir değişken oluşturma
temiz_penguins2<-temiz_penguins %>%
mutate(bmi= vucut_kutle/ `yuzgec uzunlugu`)veri seti boyutlarını gorme; eksik veri ile;
dim(penguins)## [1] 344 8eksik veriler temizlendikten sonra;
dim(temiz_penguins)## [1] 333 8Turlere gore veri setini gruplama
temiz_penguins2 %>%
group_by(tur)## # A tibble: 333 × 9
## # Groups: tur [3]
## tur ada gaga_uzunlugu gaga_derinligi `yuzgec uzunlugu` vucut_kutle
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750
## 2 Adelie Torgersen 39.5 17.4 186 3800
## 3 Adelie Torgersen 40.3 18 195 3250
## 4 Adelie Torgersen 36.7 19.3 193 3450
## 5 Adelie Torgersen 39.3 20.6 190 3650
## 6 Adelie Torgersen 38.9 17.8 181 3625
## 7 Adelie Torgersen 39.2 19.6 195 4675
## 8 Adelie Torgersen 41.1 17.6 182 3200
## 9 Adelie Torgersen 38.6 21.2 191 3800
## 10 Adelie Torgersen 34.6 21.1 198 4400
## # ℹ 323 more rows
## # ℹ 3 more variables: cinsiyet <fct>, yıl <int>, bmi <dbl>library(tidyr)
temiz_penguins2 %>%
count(bmi,tur) %>%
pivot_wider(names_from = tur, values_from = n)## # A tibble: 291 × 4
## bmi Chinstrap Adelie Gentoo
## <dbl> <int> <int> <int>
## 1 14.1 1 NA NA
## 2 15.2 NA 1 NA
## 3 15.4 NA 1 NA
## 4 15.5 NA 1 NA
## 5 15.5 1 1 NA
## 6 15.6 NA 1 NA
## 7 15.7 NA 1 NA
## 8 16.0 NA 1 NA
## 9 16.1 NA 1 NA
## 10 16.2 NA 1 NA
## # ℹ 281 more rowsquantile(temiz_penguins2$bmi)## 0% 25% 50% 75% 100%
## 14.06250 18.81188 20.51282 22.74882 28.50679bmi indeksini belirli kategorik hale getirme
temiz_penguins3 <- temiz_penguins2 %>% mutate(bmi_3cat= if_else( bmi>=23, "yuksek",
if_else(bmi <23 & bmi>19, "orta", "alt")))ture gore bmi değiskeninin durumu
library(tidyr)
temiz_penguins3 %>%
count(tur,bmi_3cat) %>%
pivot_wider(names_from = bmi_3cat, values_from = n)## # A tibble: 3 × 4
## tur alt orta yuksek
## <fct> <int> <int> <int>
## 1 Adelie 62 76 8
## 2 Chinstrap 34 34 NA
## 3 Gentoo 1 51 67bmi degerlerine bakıldığında; adelie türündeki penguenlerin bmi degerlerinin orta duzeyde daha cok olduğu, Chinstrap turunde yuksek bmi degerinde penguen bulunmazken alt ve orta bmi duzeyindeki penguenlerin esit sayıda oldugu, Gentoo turunde penguenlerin ise bmi degerinin yuksek oldugu gorulmektedir.
turlere gore bmi degerlerinin ortalama, st.sapma ve min. max degerleri
temiz_penguins2%>%
group_by(tur) %>%
summarise(mean_bmi = mean(bmi))## # A tibble: 3 × 2
## tur mean_bmi
## <fct> <dbl>
## 1 Adelie 19.5
## 2 Chinstrap 19.0
## 3 Gentoo 23.4Adelie türü penguenlerin bmi ortalaması 19,480 iken, Chinstrap türü penguenlerin bmi ortalaması 19,043, en yüksek bmi ortalaması ise Gentoo türü penguenlerin oldugu gorulmektedir.
18.chunkta yapılan islemin bir baska alternatifi:
temiz_penguins2%>%
group_by(tur) %>%
summarise(N=n(), ort=mean(bmi))## # A tibble: 3 × 3
## tur N ort
## <fct> <int> <dbl>
## 1 Adelie 146 19.5
## 2 Chinstrap 68 19.0
## 3 Gentoo 119 23.4library(ggplot2)
ggplot(temiz_penguins3, aes(x=tur, fill= bmi)) +
geom_bar()## Warning: The following aesthetics were dropped during statistical transformation: fill.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
degiskenlerin standart sapmaları icin:
bminin ture gore st. sapmasının alınması
library(datasets)
tapply(temiz_penguins3$bmi, temiz_penguins3$tur, sd)## Adelie Chinstrap Gentoo
## 2.179467 1.598395 1.88154321.chunkın alternatifi by() fonksiyonu ile de yapılabilir.
by(temiz_penguins3$bmi, temiz_penguins3$tur, sd)## temiz_penguins3$tur: Adelie
## [1] 2.179467
## ------------------------------------------------------------
## temiz_penguins3$tur: Chinstrap
## [1] 1.598395
## ------------------------------------------------------------
## temiz_penguins3$tur: Gentoo
## [1] 1.881543- ve 22.Chunkın çıktılarında; Adelie türünün bmi değerlerinin standart sapmasının 2,179 değeri ile en büyük; Chinstrap penguen türünün bmi değerinin standart sapmasının 1,598 değeri ile en küçük olduğu görülmektedir.
bmi degerlerinin min-max degerleri
summarise(temiz_penguins3, min=min(bmi), max=max(bmi))## min max
## 1 14.0625 28.50679hem ortalama, hem standart sapma hem de min ve max degerlerin hepsinin tek bir komutla hesaplanması
temiz_penguins3 %>%
group_by(tur) %>%
summarise(
ortalama = mean(bmi),
std_sapma = sd(bmi),
min_bmi = min(bmi),
max_bmi = max(bmi)
)## # A tibble: 3 × 5
## tur ortalama std_sapma min_bmi max_bmi
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Adelie 19.5 2.18 15.2 25.3
## 2 Chinstrap 19.0 1.60 14.1 22.9
## 3 Gentoo 23.4 1.88 19.0 28.5turlere gore bmi grafigi
library(ggplot2)
ggplot(temiz_penguins3, aes(x = tur, y = bmi, fill = tur)) +
geom_boxplot() +
labs(
title = "Turlerin bmi Dagilimi",
x = "Tur",
y = "bmi"
) +
theme_minimal()
Türlerin bmi değerleri grafikleri incelendiğinde bmi degerlerinin
turlere gore farklılık gosterdigi, en yuksek bmi degerinin Gentoo
turunde oldugu gorulmektedir. Bİrden çok grup arasındaki farkın anlamlı
olup olmadığına ANOVA ile bakıldığında;
ANOVA
FARK <- aov(bmi ~ tur, data = temiz_penguins3)
summary(FARK)## Df Sum Sq Mean Sq F value Pr(>F)
## tur 2 1277 638.6 164.9 <2e-16 ***
## Residuals 330 1278 3.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ANOVA testi sonucları; farklı turler arasındaki bmi degerlerinin istatistiksel olarak anlamlı düzeyde farklılık gosterdigi soylenebilir. (p< ,01)
#GOREV 1-Toplam Duzeyde Analiz
Gaga uzunluğu (bill_length_mm) ile gaga derinliği (bill_depth_mm) arasındaki ilişki;
lm(formula= gaga_uzunlugu ~ gaga_derinligi, data= temiz_penguins3)##
## Call:
## lm(formula = gaga_uzunlugu ~ gaga_derinligi, data = temiz_penguins3)
##
## Coefficients:
## (Intercept) gaga_derinligi
## 54.8909 -0.6349Tum grupta; gaga_uzunlugu ile gaga_derinligi arasındaki korelasyon 0,63 değerinde ve negatif yonlu cıkmıstır.
geom_point() kullanarak scatter plot oluşturma; Tum grupta gaga_uzunlugu gaga_derinligi iliskisi:
ggplot(temiz_penguins3, aes(x=gaga_uzunlugu,y=gaga_derinligi))+
geom_point() +
geom_smooth(method="lm")## `geom_smooth()` using formula = 'y ~ x'
grafik incelendiginde; tum grupta gaga_uzunlugu arttıkca gaga_derinligi azalmakta, negatif yonlu korelasyon gorulmektedir.
Bu ilişkiyi doğrulamak için basit bir doğrusal regresyon modeli kurma:
regresyon <- lm(formula = gaga_uzunlugu ~ gaga_derinligi, data = temiz_penguins3)
summary(regresyon)##
## Call:
## lm(formula = gaga_uzunlugu ~ gaga_derinligi, data = temiz_penguins3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.9498 -3.9530 -0.3657 3.7327 15.5025
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.8909 2.5673 21.380 < 2e-16 ***
## gaga_derinligi -0.6349 0.1486 -4.273 2.53e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.332 on 331 degrees of freedom
## Multiple R-squared: 0.05227, Adjusted R-squared: 0.04941
## F-statistic: 18.26 on 1 and 331 DF, p-value: 2.528e-05Gaga uzunlugunun %5’inin gaga derinliği tarafından yordanmaktadır.gaga_uzunlugu ~ gaga_derinligi arasındaki iliski istatistiksel olarak anlamlıdır. (p<,01)
#GOREV 2- Tur Bazında Analiz
Veriyi türlere göre ayırın (group_by(species) veya filter() kullanabilirsiniz):
adelie <- temiz_penguins3 %>% filter(tur == "Adelie")
chinstrap <- temiz_penguins3 %>% filter(tur == "Chinstrap")
gentoo <- temiz_penguins3 %>% filter(tur == "Gentoo")turlere gore gaga_uzunlugu gaga_derinligi iliskisi;
ggplot(temiz_penguins3, aes(x = gaga_uzunlugu, y = gaga_derinligi, color = tur)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(
title = "Gaga Uzunluğu ve Gaga Derinliği İlişkisi",
x = "Gaga Uzunluğu (mm)",
y = "Gaga Derinliği (mm)"
) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
grafik incelendiginde; alt gruplar bazında incelendiginde gaga_uzunlugu ile gaga_derinliginin pozitif korelasyon gosterdigi gorulmektedir.
Tür bazında regresyon modelleri kurma: Adelie turu icin model;
regresyon_turA <- lm(formula = gaga_uzunlugu ~ gaga_derinligi,
data =adelie)
summary(regresyon_turA)##
## Call:
## lm(formula = gaga_uzunlugu ~ gaga_derinligi, data = adelie)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.5431 -1.8369 0.0158 1.7181 6.5104
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.3668 3.0869 7.570 4.10e-12 ***
## gaga_derinligi 0.8425 0.1679 5.018 1.51e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.465 on 144 degrees of freedom
## Multiple R-squared: 0.1489, Adjusted R-squared: 0.1429
## F-statistic: 25.18 on 1 and 144 DF, p-value: 1.515e-06Gaga uzunlugunun %14’ü gaga derinliği tarafından yordanmaktadır.gaga_uzunlugu ~ gaga_derinligi arasındaki iliski istatistiksel olarak anlamlıdır. (p<,01)
chinstrap turu icin model;
regresyon_turC <- lm(formula = gaga_uzunlugu ~ gaga_derinligi,
data =chinstrap)
summary(regresyon_turC)##
## Call:
## lm(formula = gaga_uzunlugu ~ gaga_derinligi, data = chinstrap)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1163 -1.2641 -0.1254 1.4807 10.3590
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.428 5.057 2.655 0.00992 **
## gaga_derinligi 1.922 0.274 7.015 1.53e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.547 on 66 degrees of freedom
## Multiple R-squared: 0.4271, Adjusted R-squared: 0.4184
## F-statistic: 49.21 on 1 and 66 DF, p-value: 1.526e-09Gaga uzunlugunun % 42’si gaga derinliği tarafından yordanmaktadır.gaga_uzunlugu ~ gaga_derinligi arasındaki iliski istatistiksel olarak anlamlıdır. (p<,01)
gentoo turu icin model;
regresyon_turG <- lm(formula = gaga_uzunlugu ~ gaga_derinligi,
data =gentoo)
summary(regresyon_turG)##
## Call:
## lm(formula = gaga_uzunlugu ~ gaga_derinligi, data = gentoo)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.914 -1.445 0.125 1.315 7.904
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.6702 3.3110 5.035 1.75e-06 ***
## gaga_derinligi 2.0603 0.2203 9.352 7.34e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.36 on 117 degrees of freedom
## Multiple R-squared: 0.4277, Adjusted R-squared: 0.4229
## F-statistic: 87.45 on 1 and 117 DF, p-value: 7.337e-16Gaga uzunlugunun % 42’si gaga derinliği tarafından yordanmaktadır.gaga_uzunlugu ~ gaga_derinligi arasındaki iliski istatistiksel olarak anlamlıdır. (p<,01)
Sonucların akrsılastırılması
karsılastırma <- data.frame(
Tur = c("Adelie", "Chinstrap", "Gentoo"),
Eğim = c(coef(regresyon_turA)[2],
coef(regresyon_turC)[2],
coef(regresyon_turG)[2]),
P_degeri = c(summary(regresyon_turA)$coefficients[2,4],
summary(regresyon_turC)$coefficients[2,4],
summary(regresyon_turG)$coefficients[2,4])
)
print(karsılastırma )## Tur Eğim P_degeri
## 1 Adelie 0.8424775 1.514901e-06
## 2 Chinstrap 1.9220839 1.525539e-09
## 3 Gentoo 2.0603208 7.336624e-16Her bir türde eğim pozitif. Tür düzeyinde elde edilen ilişki, toplam düzeydeki ilişkiyle çelişmekte.Tüm grupta gaga uzunlugu ve gaga derinligi negatif iliski gosterirken turlere gore degiskenler arasındaki iliski pozitif.
#GOREV 3- Yorum ve Tartısma
Toplam veri setinde neden negatif ilişki gözlemlediniz? Toplam veride uc farklı turde penguen turune ait veriler olmakal birlikte penguen turlerindeki beslenme alıskanlıkları, genetik faktorler, kullanım sekilleri gibi sebeplerle farklılk gostermesi ile veri setinde incelenen penguen turlerinin gaga uzunlugu ve derinligi negatiff cıkmıs olabilir.
Tür bilgisi eklendiğinde ilişkinin yön değiştirmesi; Tur bilgisi eklenmeden yapılan tum grup analizinde farklı ture ait tum penguenler birarada degerlendirilerek buna gore bir sonuc elde edilmektedir. Ancak her bir turu gruplandırarak veriler incelendiginde her bir alt grubun bireyleri kendi icinde degerlendirilmekte ve aynı turdeki bireyin ozellikleri degerlendirilmektedir.
Bu durum Simpson Paradoksu çerçevesinde acıklanacak olursa; Simpson Paradoksunda; bir veri setindeki incelemelerin genel ve belirli ölcute gore yapıldıgında sonucların ve anlamlılıkların degisebileceği durumu soz konusudur. İncelenen penguins veri setinde de incelenen degiskenler arasındaki iliski turlerin ayrımı yapılmadan farklı sonuc vermekte, turler alt gruplar olarak incelendiginde farklı sonuc elde edilmektedir.
Bu örnek, verileri alt gruplara göre incelemenin neden önemli olduğunu nasıl göstermektedir? Elde edilen sonucların yorumlanmasında, iliskilerin ortaya konulmasında veri setinin iyi anlasılması bakımından genel grup ve alt grup analizlerinin ayrı ayrı ve birlikte degerlendirilmesi sonucların mevcut durumu dogru yansıtabilmesi bakımından önemlidir.
##R OGRENME GUNLUGUM R ile veri analizinde; grafiklerin ozelliklerinden ; bir veri setindeki degişken isimlerini degistirebilmenin rename fonksiyonu ile, tablodaki oranların virgül sonrasını azaltmak için round fonksiyonu ile, RMarkdown olusturabilme RStudio>File>New File>R Markdown sekmeleri ile Markdown dosyasını yayınlayabilme; R sTUDİO ana konsolda Publish fonksiyonu ile, baslık ve duz metin yazım farkında # isaretinin kullanımı RMarkdown dosyasında ilk Chunk olan belgenin künye bilgilerinden; toc: bilgisi ile Outlineda gezinme secenegi aktiflesmekte, echo= FALSE komutu RMarkdown dosyasında kod cıktısını ekler ancak kodu vermez. anscomb veri setinde x’lerin ve y lerin bazı degerleri icin;
library(datasets) anscombe colMeans(anscombe) sapply(anscombe, mean) #bir veri setindeki aynı sütundaki verilerle hesap için sapply(anscombe, sd) #st.sapmayı hesaplamak için round(sapply(anscombe, mean), 3) #virgül sonrası basamk sayısını deştrmk için round(cor(anscombe[,1:4], anscombe[5:8]),3) #korelasyon hesabı {r} lm(y1 ~ x1 , anscombe) lm(y2 ~ x2 , anscombe) lm(y3 ~ x3 , anscombe) lm(y4 ~ x4 , anscombe)