library(wooldridge)
library(rmarkdown)
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, uniondata(smoke)“smoke” adlı datayı kullanmadan önce library() komutuyla önce “rmarkdown” paketini ardından “wooldridge” paketini en son ise data() komutuyla “smoke” adlı datamı yazıyorum.
head(smoke)## educ cigpric white age income cigs restaurn lincome agesq lcigpric
## 1 16.0 60.506 1 46 20000 0 0 9.903487 2116 4.102743
## 2 16.0 57.883 1 40 30000 0 0 10.308952 1600 4.058424
## 3 12.0 57.664 1 58 30000 3 0 10.308952 3364 4.054633
## 4 13.5 57.883 1 30 20000 0 0 9.903487 900 4.058424
## 5 10.0 58.320 1 17 20000 0 0 9.903487 289 4.065945
## 6 6.0 59.340 1 86 6500 0 0 8.779557 7396 4.083283tail(smoke)## educ cigpric white age income cigs restaurn lincome agesq lcigpric
## 802 16 60.995 1 23 12500 0 0 9.433484 529 4.110792
## 803 18 61.818 0 52 30000 20 0 10.308952 2704 4.124195
## 804 18 61.676 1 31 12500 0 0 9.433484 961 4.121895
## 805 16 60.707 1 30 20000 0 0 9.903487 900 4.106059
## 806 10 59.988 1 18 20000 0 0 9.903487 324 4.094144
## 807 10 59.652 1 47 30000 20 0 10.308952 2209 4.088528head() komutuyla “smoke” verisinin ilk altı gözlemine, tail() komutuyla ise son altı gözlemine ulaşıyoruz.
paged_table(smoke)paged_table() komutu sayesinde smoke datasının tüm gözlemlerini görebiliyoruz.
summary(smoke)## educ cigpric white age
## Min. : 6.00 Min. :44.00 Min. :0.0000 Min. :17.00
## 1st Qu.:10.00 1st Qu.:58.14 1st Qu.:1.0000 1st Qu.:28.00
## Median :12.00 Median :61.05 Median :1.0000 Median :38.00
## Mean :12.47 Mean :60.30 Mean :0.8786 Mean :41.24
## 3rd Qu.:13.50 3rd Qu.:63.18 3rd Qu.:1.0000 3rd Qu.:54.00
## Max. :18.00 Max. :70.13 Max. :1.0000 Max. :88.00
## income cigs restaurn lincome
## Min. : 500 Min. : 0.000 Min. :0.0000 Min. : 6.215
## 1st Qu.:12500 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 9.433
## Median :20000 Median : 0.000 Median :0.0000 Median : 9.903
## Mean :19305 Mean : 8.686 Mean :0.2466 Mean : 9.687
## 3rd Qu.:30000 3rd Qu.:20.000 3rd Qu.:0.0000 3rd Qu.:10.309
## Max. :30000 Max. :80.000 Max. :1.0000 Max. :10.309
## agesq lcigpric
## Min. : 289 Min. :3.784
## 1st Qu.: 784 1st Qu.:4.063
## Median :1444 Median :4.112
## Mean :1990 Mean :4.096
## 3rd Qu.:2916 3rd Qu.:4.146
## Max. :7744 Max. :4.250summary() komutu bize datamızın özetini verecektir.
grup <- smoke %>%
group_by(income) %>%
summarise('yıllık gelire göre sigara içme oranı' = mean(cigs))
paged_table(grup)“mean” komutu sayesinde ortalamaya ulaştık. Gelir arttıkça yıllık sigara içme oranının da değişkenliğini gözlemlemekteyiz.
summary(lm(age ~ educ + cigpric + white + income + cigs + restaurn + lincome + white + agesq + lcigpric , data= smoke ))##
## Call:
## lm(formula = age ~ educ + cigpric + white + income + cigs + restaurn +
## lincome + white + agesq + lcigpric, data = smoke)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.0130 -1.5288 0.6493 2.1084 7.4872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.122e+01 5.659e+01 -0.552 0.581352
## educ 1.293e-01 3.648e-02 3.545 0.000415 ***
## cigpric -2.125e-01 3.249e-01 -0.654 0.513172
## white 5.616e-01 3.170e-01 1.772 0.076858 .
## income 1.108e-05 2.903e-05 0.382 0.702689
## cigs 3.680e-02 7.591e-03 4.848 1.5e-06 ***
## restaurn -4.631e-02 2.465e-01 -0.188 0.851056
## lincome 1.028e+00 3.698e-01 2.779 0.005579 **
## agesq 1.075e-02 6.750e-05 159.301 < 2e-16 ***
## lcigpric 1.252e+01 1.859e+01 0.674 0.500824
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.916 on 797 degrees of freedom
## Multiple R-squared: 0.971, Adjusted R-squared: 0.9707
## F-statistic: 2964 on 9 and 797 DF, p-value: < 2.2e-16educ’un t istatistiği 3.545’dir ve istatistiksel olarak anlamlıdır.Bunun karşılığında “signif. codes” sıfır olma ihtimali sıfır olduğu için üç yıldız vermiştir.
cigpric’in t istatistiği -0.654’dür ve istatistiksel olarak anlamsızdır.
white’ın t istatistiği 1.772’dir ve istatistiksel olarak anlamsızdır ki bu değerin karşılığında “signif. codes” %5’ten küçük olduğunda “.” vermiş.
summary(lm(age ~ educ + cigpric + white + income + cigs + restaurn + lincome + white + agesq + lcigpric -1 , data= smoke ))##
## Call:
## lm(formula = age ~ educ + cigpric + white + income + cigs + restaurn +
## lincome + white + agesq + lcigpric - 1, data = smoke)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.0807 -1.5189 0.6769 2.1106 7.4665
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## educ 1.293e-01 3.646e-02 3.547 0.000413 ***
## cigpric -3.426e-02 3.328e-02 -1.029 0.303584
## white 5.607e-01 3.169e-01 1.769 0.077213 .
## income 1.128e-05 2.901e-05 0.389 0.697537
## cigs 3.662e-02 7.580e-03 4.831 1.63e-06 ***
## restaurn -2.811e-02 2.442e-01 -0.115 0.908385
## lincome 1.025e+00 3.696e-01 2.772 0.005697 **
## agesq 1.075e-02 6.719e-05 159.988 < 2e-16 ***
## lcigpric 2.283e+00 1.081e+00 2.113 0.034942 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.915 on 798 degrees of freedom
## Multiple R-squared: 0.9958, Adjusted R-squared: 0.9957
## F-statistic: 2.091e+04 on 9 and 798 DF, p-value: < 2.2e-16Intercept’siz fonksiyon oluşturmak için yazdığımız fonksiyonun sonuna “-1” yazmamız yeterli olacaktır.
yenidata <- smoke %>%
mutate(benimverim = 6) %>%
mutate(yenidata = ifelse(income== 3, 5 , 9))“yenidata” isimli bir data oluşturduk ve “benimverim” isimli bir değişken ekledik.Bu değişkenin de bütün değerlerini 6 yaptık. yenidata’ya bir de ifelse ekledik,ifelse’un kullanım amacı, bir şart belirlenir ve o şartın gerçekleşmesi durumunda hangi işlemler, gerçekleşmemesi durumunda hangi işlemlerin yapılacağı ayrı ayrı yazılır.Buna göre income verisini 3 olduğunda 5 değerini 5 olduğunda 9 değerini verecektir.
max(smoke$age)## [1] 88En yüksek yaşın 88 olduğunu görüyoruz.
min(smoke$age)## [1] 17En küçük yaşın 17 olduğunu görüyoruz.
mean(smoke$age)## [1] 41.23792Genel ortalamanın 41.23 olduğunu görüyoruz.
yenitablo <- smoke %>% select(educ, cigpric, age)
yenitablo## educ cigpric age
## 1 16.0 60.506 46
## 2 16.0 57.883 40
## 3 12.0 57.664 58
## 4 13.5 57.883 30
## 5 10.0 58.320 17
## 6 6.0 59.340 86
## 7 12.0 57.883 35
## 8 15.0 57.883 48
## 9 12.0 59.195 48
## 10 12.0 58.028 31
## 11 13.5 57.883 27
## 12 12.0 60.288 24
## 13 10.0 57.883 71
## 14 6.0 58.028 44
## 15 16.0 60.746 22
## 16 12.0 60.071 29
## 17 12.0 60.282 33
## 18 12.0 60.704 34
## 19 13.5 59.860 30
## 20 10.0 60.324 48
## 21 6.0 59.045 61
## 22 18.0 60.294 49
## 23 12.0 59.045 33
## 24 16.0 60.632 24
## 25 16.0 61.744 36
## 26 15.0 60.862 39
## 27 18.0 61.399 38
## 28 12.0 60.823 29
## 29 18.0 62.012 23
## 30 16.0 61.322 51
## 31 13.5 62.089 28
## 32 10.0 61.015 47
## 33 10.0 62.489 80
## 34 10.0 61.667 62
## 35 10.0 62.012 51
## 36 10.0 61.667 51
## 37 12.0 61.053 69
## 38 12.0 61.092 28
## 39 18.0 61.974 38
## 40 15.0 61.168 30
## 41 18.0 60.862 32
## 42 8.0 61.629 71
## 43 10.0 62.489 18
## 44 13.5 62.089 26
## 45 10.0 61.744 30
## 46 12.0 60.670 19
## 47 12.0 61.168 19
## 48 13.5 61.245 21
## 49 16.0 62.089 44
## 50 10.0 60.555 69
## 51 16.0 60.555 53
## 52 8.0 62.241 53
## 53 6.0 61.705 38
## 54 10.0 60.555 49
## 55 18.0 61.437 24
## 56 16.0 61.322 57
## 57 15.0 61.168 28
## 58 12.0 61.207 32
## 59 13.5 60.823 55
## 60 13.5 60.632 47
## 61 13.5 60.785 26
## 62 10.0 60.555 26
## 63 16.0 60.708 61
## 64 10.0 60.670 48
## 65 18.0 62.736 32
## 66 16.0 62.089 29
## 67 18.0 62.571 47
## 68 10.0 60.708 62
## 69 16.0 62.159 30
## 70 13.5 61.667 21
## 71 12.0 61.207 28
## 72 12.0 61.053 50
## 73 12.0 61.475 69
## 74 13.5 61.629 37
## 75 12.0 61.744 40
## 76 6.0 61.437 47
## 77 10.0 61.936 78
## 78 18.0 61.744 38
## 79 13.5 62.489 25
## 80 12.0 61.322 67
## 81 13.5 60.900 50
## 82 13.5 61.552 47
## 83 8.0 62.489 76
## 84 8.0 60.785 28
## 85 12.0 61.514 32
## 86 12.0 61.897 28
## 87 12.0 61.168 53
## 88 12.0 60.823 57
## 89 18.0 61.590 42
## 90 15.0 61.092 49
## 91 18.0 61.782 31
## 92 12.0 61.437 20
## 93 18.0 61.897 45
## 94 18.0 60.555 63
## 95 15.0 61.705 61
## 96 13.5 61.859 35
## 97 6.0 61.705 59
## 98 10.0 62.654 17
## 99 18.0 62.736 38
## 100 13.5 61.245 41
## 101 12.0 61.897 35
## 102 12.0 61.590 40
## 103 13.5 62.571 37
## 104 12.0 62.406 27
## 105 12.0 60.862 48
## 106 12.0 62.089 18
## 107 12.0 60.862 43
## 108 10.0 62.012 20
## 109 8.0 60.823 19
## 110 13.5 60.708 22
## 111 16.0 60.708 60
## 112 13.5 61.744 47
## 113 10.0 61.629 77
## 114 13.5 61.360 26
## 115 12.0 62.654 37
## 116 13.5 62.159 40
## 117 6.0 61.284 44
## 118 6.0 62.012 65
## 119 16.0 60.900 26
## 120 8.0 60.862 77
## 121 12.0 61.207 73
## 122 10.0 60.938 64
## 123 13.5 61.629 28
## 124 13.5 60.862 26
## 125 10.0 61.092 64
## 126 12.0 61.667 29
## 127 12.0 61.974 55
## 128 12.0 62.736 54
## 129 15.0 61.207 42
## 130 13.5 62.736 70
## 131 16.0 61.284 71
## 132 12.0 61.744 24
## 133 10.0 61.859 38
## 134 15.0 61.936 73
## 135 18.0 60.823 29
## 136 13.5 60.555 24
## 137 12.0 61.437 25
## 138 15.0 62.089 22
## 139 12.0 61.475 36
## 140 12.0 53.624 28
## 141 13.5 53.313 27
## 142 13.5 55.185 28
## 143 12.0 53.382 53
## 144 13.5 54.984 23
## 145 12.0 53.969 19
## 146 15.0 54.314 25
## 147 13.5 54.648 34
## 148 13.5 54.141 20
## 149 10.0 54.314 17
## 150 16.0 66.587 51
## 151 12.0 65.290 44
## 152 12.0 65.290 29
## 153 8.0 66.088 65
## 154 12.0 65.290 53
## 155 8.0 67.933 46
## 156 18.0 65.290 51
## 157 18.0 65.540 66
## 158 8.0 64.991 42
## 159 16.0 65.141 32
## 160 13.5 66.437 53
## 161 8.0 66.038 70
## 162 10.0 65.889 30
## 163 18.0 65.889 42
## 164 12.0 66.537 69
## 165 10.0 66.437 18
## 166 12.0 68.184 22
## 167 6.0 66.687 38
## 168 10.0 66.437 52
## 169 10.0 65.141 17
## 170 12.0 65.041 46
## 171 16.0 66.587 46
## 172 12.0 66.038 35
## 173 12.0 66.338 22
## 174 12.0 61.546 54
## 175 12.0 61.184 58
## 176 10.0 62.012 27
## 177 18.0 61.184 47
## 178 16.0 62.012 53
## 179 12.0 65.082 29
## 180 13.5 67.820 29
## 181 13.5 68.200 47
## 182 8.0 69.402 67
## 183 18.0 69.402 31
## 184 12.0 67.124 27
## 185 12.0 68.263 34
## 186 10.0 68.516 25
## 187 12.0 67.820 46
## 188 16.0 67.820 50
## 189 6.0 67.757 69
## 190 13.5 67.820 32
## 191 12.0 67.693 64
## 192 15.0 67.947 30
## 193 8.0 68.073 78
## 194 13.5 69.149 46
## 195 13.5 69.529 49
## 196 12.0 70.014 30
## 197 10.0 68.833 23
## 198 12.0 69.402 27
## 199 18.0 68.390 28
## 200 10.0 69.592 26
## 201 6.0 67.693 44
## 202 13.5 69.402 54
## 203 12.0 69.841 54
## 204 12.0 69.899 66
## 205 18.0 67.883 49
## 206 16.0 67.440 77
## 207 12.0 68.896 74
## 208 18.0 69.465 69
## 209 13.5 68.643 78
## 210 13.5 68.390 57
## 211 8.0 70.129 53
## 212 16.0 67.314 88
## 213 12.0 67.187 79
## 214 12.0 57.473 21
## 215 18.0 58.094 30
## 216 18.0 57.473 39
## 217 15.0 59.852 25
## 218 10.0 59.130 58
## 219 13.5 59.130 23
## 220 16.0 59.233 30
## 221 10.0 57.939 31
## 222 16.0 57.473 31
## 223 16.0 58.094 50
## 224 8.0 58.508 20
## 225 13.5 57.110 24
## 226 12.0 61.988 33
## 227 16.0 63.556 39
## 228 13.5 57.466 19
## 229 12.0 59.430 31
## 230 13.5 60.042 40
## 231 16.0 59.492 39
## 232 15.0 59.676 23
## 233 8.0 58.632 71
## 234 6.0 58.080 54
## 235 10.0 58.510 33
## 236 18.0 60.221 30
## 237 12.0 57.835 31
## 238 18.0 60.518 46
## 239 12.0 59.921 75
## 240 8.0 58.264 40
## 241 12.0 59.430 66
## 242 18.0 59.492 45
## 243 8.0 59.246 73
## 244 12.0 57.712 31
## 245 13.5 58.264 27
## 246 10.0 58.632 50
## 247 12.0 57.712 45
## 248 12.0 58.264 22
## 249 13.5 59.246 44
## 250 10.0 58.510 51
## 251 6.0 58.816 59
## 252 10.0 58.448 59
## 253 12.0 57.650 66
## 254 13.5 59.246 35
## 255 10.0 57.650 17
## 256 12.0 59.553 35
## 257 16.0 59.614 30
## 258 12.0 59.430 23
## 259 8.0 58.141 60
## 260 18.0 59.246 27
## 261 16.0 57.650 37
## 262 15.0 57.957 42
## 263 8.0 59.921 87
## 264 13.5 59.614 26
## 265 12.0 57.405 20
## 266 10.0 58.878 65
## 267 12.0 59.614 35
## 268 18.0 59.001 38
## 269 10.0 59.185 64
## 270 10.0 58.264 62
## 271 12.0 58.694 47
## 272 10.0 58.080 52
## 273 13.5 58.571 32
## 274 16.0 58.448 26
## 275 18.0 59.553 35
## 276 16.0 57.712 35
## 277 10.0 59.062 24
## 278 8.0 59.430 77
## 279 13.5 58.141 24
## 280 8.0 53.325 63
## 281 18.0 53.049 58
## 282 10.0 52.888 69
## 283 13.5 54.381 62
## 284 12.0 53.670 27
## 285 13.5 53.049 30
## 286 10.0 52.796 51
## 287 12.0 53.118 32
## 288 10.0 52.819 51
## 289 6.0 53.647 53
## 290 13.5 53.509 20
## 291 13.5 53.279 22
## 292 16.0 52.819 24
## 293 12.0 53.256 23
## 294 12.0 52.819 65
## 295 8.0 53.233 21
## 296 8.0 52.980 19
## 297 10.0 53.509 24
## 298 12.0 53.325 34
## 299 10.0 52.819 54
## 300 18.0 58.142 50
## 301 13.5 57.912 65
## 302 12.0 56.900 33
## 303 8.0 57.682 69
## 304 15.0 59.235 26
## 305 8.0 57.682 69
## 306 12.0 56.900 59
## 307 12.0 57.677 35
## 308 6.0 58.378 54
## 309 13.5 55.596 28
## 310 12.0 55.596 28
## 311 10.0 45.283 77
## 312 12.0 44.059 64
## 313 10.0 45.561 52
## 314 10.0 45.283 21
## 315 10.0 45.839 46
## 316 12.0 45.227 24
## 317 12.0 45.839 31
## 318 12.0 44.004 26
## 319 10.0 45.784 79
## 320 12.0 46.521 53
## 321 12.0 58.149 32
## 322 15.0 60.235 21
## 323 15.0 58.983 18
## 324 13.5 59.428 30
## 325 12.0 59.372 20
## 326 8.0 56.904 69
## 327 8.0 58.053 59
## 328 12.0 57.069 56
## 329 18.0 57.179 43
## 330 10.0 57.434 40
## 331 16.0 58.795 37
## 332 10.0 58.255 36
## 333 13.5 58.795 28
## 334 12.0 57.871 33
## 335 16.0 57.288 63
## 336 8.0 57.835 51
## 337 12.0 58.795 33
## 338 13.5 57.069 39
## 339 12.0 57.762 21
## 340 13.5 58.255 37
## 341 18.0 58.885 70
## 342 12.0 57.871 20
## 343 16.0 57.069 27
## 344 16.0 58.885 28
## 345 16.0 66.761 30
## 346 13.5 65.767 52
## 347 18.0 65.199 24
## 348 12.0 65.767 71
## 349 12.0 67.115 39
## 350 12.0 68.335 19
## 351 13.5 66.973 21
## 352 16.0 67.399 33
## 353 13.5 66.122 51
## 354 8.0 65.767 54
## 355 13.5 67.541 44
## 356 12.0 67.115 43
## 357 6.0 65.767 68
## 358 12.0 66.761 22
## 359 10.0 65.838 54
## 360 16.0 67.825 37
## 361 13.5 66.548 22
## 362 18.0 58.381 65
## 363 12.0 60.224 30
## 364 12.0 60.224 19
## 365 15.0 60.349 35
## 366 10.0 59.986 59
## 367 12.0 58.202 18
## 368 12.0 58.262 44
## 369 16.0 58.856 37
## 370 12.0 60.522 37
## 371 12.0 58.202 19
## 372 12.0 58.975 25
## 373 16.0 58.916 31
## 374 12.0 59.986 19
## 375 10.0 58.499 18
## 376 16.0 59.867 28
## 377 18.0 58.381 46
## 378 12.0 58.262 27
## 379 12.0 57.845 58
## 380 12.0 60.522 19
## 381 13.5 60.283 30
## 382 13.5 58.737 58
## 383 6.0 58.024 42
## 384 13.5 58.083 45
## 385 10.0 60.436 18
## 386 12.0 60.551 40
## 387 13.5 59.986 66
## 388 12.0 60.224 64
## 389 12.0 59.986 43
## 390 10.0 60.551 53
## 391 16.0 58.797 36
## 392 13.5 60.493 43
## 393 18.0 59.570 25
## 394 12.0 60.464 35
## 395 16.0 62.102 29
## 396 12.0 62.465 18
## 397 12.0 63.199 20
## 398 12.0 62.344 19
## 399 10.0 62.948 17
## 400 18.0 61.498 29
## 401 8.0 61.458 65
## 402 12.0 61.498 42
## 403 16.0 63.253 32
## 404 18.0 62.948 36
## 405 16.0 63.253 57
## 406 12.0 62.465 31
## 407 10.0 62.264 17
## 408 10.0 62.948 17
## 409 8.0 59.376 45
## 410 12.0 59.621 19
## 411 8.0 54.338 74
## 412 12.0 53.653 46
## 413 13.5 54.925 27
## 414 12.0 56.294 66
## 415 16.0 54.925 44
## 416 12.0 55.022 33
## 417 12.0 54.142 64
## 418 13.5 57.485 54
## 419 10.0 57.585 27
## 420 6.0 57.485 75
## 421 10.0 53.262 19
## 422 6.0 56.979 69
## 423 12.0 55.707 30
## 424 12.0 56.098 64
## 425 13.5 57.336 37
## 426 12.0 54.436 31
## 427 12.0 57.585 30
## 428 6.0 55.015 38
## 429 12.0 59.653 20
## 430 12.0 59.653 21
## 431 12.0 56.804 19
## 432 18.0 58.464 71
## 433 10.0 63.103 49
## 434 10.0 55.168 42
## 435 8.0 55.007 54
## 436 13.5 55.168 24
## 437 12.0 54.322 60
## 438 12.0 55.168 54
## 439 10.0 53.879 28
## 440 8.0 64.994 67
## 441 12.0 65.550 56
## 442 16.0 64.048 36
## 443 12.0 64.438 25
## 444 13.5 65.828 34
## 445 10.0 65.605 20
## 446 13.5 64.493 21
## 447 12.0 64.938 42
## 448 13.5 64.549 28
## 449 18.0 65.439 29
## 450 8.0 64.994 70
## 451 12.0 66.328 32
## 452 13.5 65.772 42
## 453 10.0 65.161 17
## 454 12.0 66.784 24
## 455 15.0 66.328 64
## 456 12.0 65.717 57
## 457 16.0 64.104 51
## 458 13.5 65.717 29
## 459 10.0 64.438 66
## 460 13.5 64.382 62
## 461 16.0 66.328 29
## 462 6.0 65.605 58
## 463 15.0 65.161 53
## 464 13.5 65.439 38
## 465 12.0 64.104 24
## 466 8.0 66.384 59
## 467 16.0 66.217 30
## 468 8.0 64.382 37
## 469 12.0 65.995 43
## 470 12.0 66.162 60
## 471 10.0 64.994 24
## 472 12.0 64.549 60
## 473 8.0 62.040 82
## 474 12.0 64.192 61
## 475 10.0 64.507 66
## 476 15.0 61.850 33
## 477 10.0 64.564 26
## 478 8.0 64.192 85
## 479 13.5 62.104 32
## 480 10.0 63.369 27
## 481 12.0 64.129 30
## 482 12.0 62.673 30
## 483 13.5 63.179 47
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## 807 10.0 59.652 47select() komutuyla sadece sütunları görürüz.
yeniveri <- smoke%>% select(age)
yeniveri1 <- yeniveri %>% filter(age> 22)Data içerisinden yaş ortalaması 22 den büyük olanları alalım.
require(stargazer)## Loading required package: stargazer##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazerrequire komutu() library komutu yerine kullanabileceğimiz bir komuttur. Kullandığımız paketi, daha önce yükleyip yüklemediğimizi hatırlamıyorsak, ve eğer yazdığımız paket yüklü değilse önce yükler sonra library yapar.
stargazer(yeniveri, yeniveri1, type = 'text')##
## =====================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------
## age 807 41.238 17.027 17 88
## -------------------------------------
##
## =====================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------
## age 693 44.827 15.682 23 88
## -------------------------------------