data(iris)
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
names(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
names(iris)<-tolower(names(iris))
iris<-iris[1:4]
head(iris)
## sepal.length sepal.width petal.length petal.width
## 1 5.1 3.5 1.4 0.2
## 2 4.9 3.0 1.4 0.2
## 3 4.7 3.2 1.3 0.2
## 4 4.6 3.1 1.5 0.2
## 5 5.0 3.6 1.4 0.2
## 6 5.4 3.9 1.7 0.4
names(iris)<-c('sl','sw','pl','pw')
names(iris)
## [1] "sl" "sw" "pl" "pw"
1:4
## [1] 1 2 3 4
m4<-lm(sl~.,data=iris)
summary(m4)
##
## Call:
## lm(formula = sl ~ ., data = iris)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.82816 -0.21989 0.01875 0.19709 0.84570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.85600 0.25078 7.401 9.85e-12 ***
## sw 0.65084 0.06665 9.765 < 2e-16 ***
## pl 0.70913 0.05672 12.502 < 2e-16 ***
## pw -0.55648 0.12755 -4.363 2.41e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3145 on 146 degrees of freedom
## Multiple R-squared: 0.8586, Adjusted R-squared: 0.8557
## F-statistic: 295.5 on 3 and 146 DF, p-value: < 2.2e-16
options(scipen = 999)
options(scipen = -999)
m1<-lm(sl~pl+sw,data=iris)
summary(m1)
##
## Call:
## lm(formula = sl ~ pl + sw, data = iris)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.616e-01 -2.349e-01 7.700e-04 2.145e-01 7.856e-01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.249e+00 2.480e-01 9.070e+00 7.04e-16 ***
## pl 4.719e-01 1.712e-02 2.757e+01 < 2e-16 ***
## sw 5.955e-01 6.933e-02 8.590e+00 1.16e-14 ***
## ---
## Signif. codes: 0e+00 '***' 1e-03 '**' 1e-02 '*' 5e-02 '.' 1e-01 ' ' 1e+00
##
## Residual standard error: 3.333e-01 on 147 degrees of freedom
## Multiple R-squared: 0.8402, Adjusted R-squared: 0.838
## F-statistic: 386.4 on 2e+00 and 1.47e+02 DF, p-value: < 2.2e-16
m2<-lm(sl~pl+pw,data=iris)
summary(m2)
##
## Call:
## lm(formula = sl ~ pl + pw, data = iris)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.185e+00 -2.984e-01 -2.763e-02 2.893e-01 1.023e+00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.191e+00 9.705e-02 4.318e+01 < 2e-16 ***
## pl 5.418e-01 6.928e-02 7.820e+00 9.41e-13 ***
## pw -3.196e-01 1.605e-01 -1.992e+00 4.83e-02 *
## ---
## Signif. codes: 0e+00 '***' 1e-03 '**' 1e-02 '*' 5e-02 '.' 1e-01 ' ' 1e+00
##
## Residual standard error: 4.031e-01 on 147 degrees of freedom
## Multiple R-squared: 0.7663, Adjusted R-squared: 0.7631
## F-statistic: 241 on 2e+00 and 1.47e+02 DF, p-value: < 2.2e-16
data(iris)
str(iris)
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3.0 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 2e-01 2e-01 2e-01 2e-01 2e-01 4e-01 3e-01 2e-01 2e-01 1e-01 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
names(iris)<-tolower(names(iris))
m3<-lm(sepal.length~.,data=iris)
summary(m3)
##
## Call:
## lm(formula = sepal.length ~ ., data = iris)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.942e-01 -2.187e-01 8.990e-03 2.026e-01 7.310e-01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.171e+00 2.798e-01 7.760e+00 1.43e-12 ***
## sepal.width 4.959e-01 8.607e-02 5.761e+00 4.87e-08 ***
## petal.length 8.292e-01 6.853e-02 1.210e+01 < 2e-16 ***
## petal.width -3.152e-01 1.512e-01 -2.084e+00 3.89e-02 *
## speciesversicolor -7.236e-01 2.402e-01 -3.013e+00 3.06e-03 **
## speciesvirginica -1.024e+00 3.337e-01 -3.067e+00 2.58e-03 **
## ---
## Signif. codes: 0e+00 '***' 1e-03 '**' 1e-02 '*' 5e-02 '.' 1e-01 ' ' 1e+00
##
## Residual standard error: 3.068e-01 on 144 degrees of freedom
## Multiple R-squared: 0.8673, Adjusted R-squared: 0.8627
## F-statistic: 188.3 on 5e+00 and 1.44e+02 DF, p-value: < 2.2e-16
step(m3,direction = "both")
## Start: AIC=-3.4857e+02
## sepal.length ~ sepal.width + petal.length + petal.width + species
##
## Df Sum of Sq RSS AIC
## <none> 1.3556e+01 -3.4857e+02
## - petal.width 1e+00 4.0900e-01 1.3966e+01 -3.4611e+02
## - species 2e+00 8.8890e-01 1.4445e+01 -3.4304e+02
## - sepal.width 1e+00 3.1250e+00 1.6681e+01 -3.1945e+02
## - petal.length 1e+00 1.3785e+01 2.7342e+01 -2.4533e+02
##
## Call:
## lm(formula = sepal.length ~ sepal.width + petal.length + petal.width +
## species, data = iris)
##
## Coefficients:
## (Intercept) sepal.width petal.length petal.width
## 2.171e+00 4.959e-01 8.292e-01 -3.152e-01
## speciesversicolor speciesvirginica
## -7.236e-01 -1.023e+00
step(m3,direction = "forward")
## Start: AIC=-3.4857e+02
## sepal.length ~ sepal.width + petal.length + petal.width + species
##
## Call:
## lm(formula = sepal.length ~ sepal.width + petal.length + petal.width +
## species, data = iris)
##
## Coefficients:
## (Intercept) sepal.width petal.length petal.width
## 2.171e+00 4.959e-01 8.292e-01 -3.152e-01
## speciesversicolor speciesvirginica
## -7.236e-01 -1.023e+00
step(m3,direction = "backward")
## Start: AIC=-3.4857e+02
## sepal.length ~ sepal.width + petal.length + petal.width + species
##
## Df Sum of Sq RSS AIC
## <none> 1.3556e+01 -3.4857e+02
## - petal.width 1e+00 4.0900e-01 1.3966e+01 -3.4611e+02
## - species 2e+00 8.8890e-01 1.4445e+01 -3.4304e+02
## - sepal.width 1e+00 3.1250e+00 1.6681e+01 -3.1945e+02
## - petal.length 1e+00 1.3785e+01 2.7342e+01 -2.4533e+02
##
## Call:
## lm(formula = sepal.length ~ sepal.width + petal.length + petal.width +
## species, data = iris)
##
## Coefficients:
## (Intercept) sepal.width petal.length petal.width
## 2.171e+00 4.959e-01 8.292e-01 -3.152e-01
## speciesversicolor speciesvirginica
## -7.236e-01 -1.023e+00
library(mlbench)
data("BostonHousing")
head(BostonHousing)
## crim zn indus chas nox rm age dis rad
## 1 6.320e-03 1.8e+01 2.31e+00 0 5.38e-01 6.575e+00 6.52e+01 4.0900e+00 1e+00
## 2 2.731e-02 0.0e+00 7.07e+00 0 4.69e-01 6.421e+00 7.89e+01 4.9671e+00 2e+00
## 3 2.729e-02 0.0e+00 7.07e+00 0 4.69e-01 7.185e+00 6.11e+01 4.9671e+00 2e+00
## 4 3.237e-02 0.0e+00 2.18e+00 0 4.58e-01 6.998e+00 4.58e+01 6.0622e+00 3e+00
## 5 6.905e-02 0.0e+00 2.18e+00 0 4.58e-01 7.147e+00 5.42e+01 6.0622e+00 3e+00
## 6 2.985e-02 0.0e+00 2.18e+00 0 4.58e-01 6.430e+00 5.87e+01 6.0622e+00 3e+00
## tax ptratio b lstat medv
## 1 2.96e+02 1.53e+01 3.9690e+02 4.98e+00 2.40e+01
## 2 2.42e+02 1.78e+01 3.9690e+02 9.14e+00 2.16e+01
## 3 2.42e+02 1.78e+01 3.9283e+02 4.03e+00 3.47e+01
## 4 2.22e+02 1.87e+01 3.9463e+02 2.94e+00 3.34e+01
## 5 2.22e+02 1.87e+01 3.9690e+02 5.33e+00 3.62e+01
## 6 2.22e+02 1.87e+01 3.9412e+02 5.21e+00 2.87e+01
m5<-lm(medv~.,data=BostonHousing)
step(m5,direction = "forward")
## Start: AIC=1.58964e+03
## medv ~ crim + zn + indus + chas + nox + rm + age + dis + rad +
## tax + ptratio + b + lstat
##
## Call:
## lm(formula = medv ~ crim + zn + indus + chas + nox + rm + age +
## dis + rad + tax + ptratio + b + lstat, data = BostonHousing)
##
## Coefficients:
## (Intercept) crim zn indus chas1 nox
## 3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
## rm age dis rad tax ptratio
## 3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
## b lstat
## 9.312e-03 -5.248e-01
step(m5,direction = "backward")
## Start: AIC=1.58964e+03
## medv ~ crim + zn + indus + chas + nox + rm + age + dis + rad +
## tax + ptratio + b + lstat
##
## Df Sum of Sq RSS AIC
## - age 1e+00 6.0000e-02 1.1079e+04 1.5877e+03
## - indus 1e+00 2.5200e+00 1.1081e+04 1.5878e+03
## <none> 1.1079e+04 1.5896e+03
## - chas 1e+00 2.1897e+02 1.1298e+04 1.5975e+03
## - tax 1e+00 2.4226e+02 1.1321e+04 1.5986e+03
## - crim 1e+00 2.4322e+02 1.1322e+04 1.5986e+03
## - zn 1e+00 2.5749e+02 1.1336e+04 1.5993e+03
## - b 1e+00 2.7063e+02 1.1349e+04 1.5998e+03
## - rad 1e+00 4.7915e+02 1.1558e+04 1.6091e+03
## - nox 1e+00 4.8716e+02 1.1566e+04 1.6094e+03
## - ptratio 1e+00 1.1942e+03 1.2273e+04 1.6394e+03
## - dis 1e+00 1.2324e+03 1.2311e+04 1.6410e+03
## - rm 1e+00 1.8713e+03 1.2950e+04 1.6666e+03
## - lstat 1e+00 2.4108e+03 1.3490e+04 1.6873e+03
##
## Step: AIC=1.58765e+03
## medv ~ crim + zn + indus + chas + nox + rm + dis + rad + tax +
## ptratio + b + lstat
##
## Df Sum of Sq RSS AIC
## - indus 1e+00 2.5200e+00 1.1081e+04 1.5858e+03
## <none> 1.1079e+04 1.5877e+03
## - chas 1e+00 2.1991e+02 1.1299e+04 1.5956e+03
## - tax 1e+00 2.4224e+02 1.1321e+04 1.5966e+03
## - crim 1e+00 2.4320e+02 1.1322e+04 1.5966e+03
## - zn 1e+00 2.6032e+02 1.1339e+04 1.5974e+03
## - b 1e+00 2.7226e+02 1.1351e+04 1.5979e+03
## - rad 1e+00 4.8109e+02 1.1560e+04 1.6072e+03
## - nox 1e+00 5.2087e+02 1.1600e+04 1.6089e+03
## - ptratio 1e+00 1.2002e+03 1.2279e+04 1.6377e+03
## - dis 1e+00 1.3523e+03 1.2431e+04 1.6439e+03
## - rm 1e+00 1.9595e+03 1.3038e+04 1.6680e+03
## - lstat 1e+00 2.7189e+03 1.3798e+04 1.6967e+03
##
## Step: AIC=1.58576e+03
## medv ~ crim + zn + chas + nox + rm + dis + rad + tax + ptratio +
## b + lstat
##
## Df Sum of Sq RSS AIC
## <none> 1.1081e+04 1.5858e+03
## - chas 1e+00 2.2721e+02 1.1309e+04 1.5940e+03
## - crim 1e+00 2.4537e+02 1.1327e+04 1.5948e+03
## - zn 1e+00 2.5782e+02 1.1339e+04 1.5954e+03
## - b 1e+00 2.7082e+02 1.1352e+04 1.5960e+03
## - tax 1e+00 2.7362e+02 1.1355e+04 1.5961e+03
## - rad 1e+00 5.0092e+02 1.1582e+04 1.6061e+03
## - nox 1e+00 5.4191e+02 1.1623e+04 1.6079e+03
## - ptratio 1e+00 1.2065e+03 1.2288e+04 1.6360e+03
## - dis 1e+00 1.4489e+03 1.2530e+04 1.6459e+03
## - rm 1e+00 1.9637e+03 1.3045e+04 1.6663e+03
## - lstat 1e+00 2.7235e+03 1.3805e+04 1.6950e+03
##
## Call:
## lm(formula = medv ~ crim + zn + chas + nox + rm + dis + rad +
## tax + ptratio + b + lstat, data = BostonHousing)
##
## Coefficients:
## (Intercept) crim zn chas1 nox rm
## 3.634e+01 -1.084e-01 4.584e-02 2.719e+00 -1.738e+01 3.802e+00
## dis rad tax ptratio b lstat
## -1.493e+00 2.996e-01 -1.178e-02 -9.465e-01 9.291e-03 -5.226e-01
library(leaps)
m6<-regsubsets(medv~.,data=BostonHousing)
summary(m6)
## Subset selection object
## Call: regsubsets.formula(medv ~ ., data = BostonHousing)
## 13 Variables (and intercept)
## Forced in Forced out
## crim FALSE FALSE
## zn FALSE FALSE
## indus FALSE FALSE
## chas1 FALSE FALSE
## nox FALSE FALSE
## rm FALSE FALSE
## age FALSE FALSE
## dis FALSE FALSE
## rad FALSE FALSE
## tax FALSE FALSE
## ptratio FALSE FALSE
## b FALSE FALSE
## lstat FALSE FALSE
## 1 subsets of each size up to 8
## Selection Algorithm: exhaustive
## crim zn indus chas1 nox rm age dis rad tax ptratio b lstat
## 1 ( 1 ) " " " " " " " " " " " " " " " " " " " " " " " " "*"
## 2 ( 1 ) " " " " " " " " " " "*" " " " " " " " " " " " " "*"
## 3 ( 1 ) " " " " " " " " " " "*" " " " " " " " " "*" " " "*"
## 4 ( 1 ) " " " " " " " " " " "*" " " "*" " " " " "*" " " "*"
## 5 ( 1 ) " " " " " " " " "*" "*" " " "*" " " " " "*" " " "*"
## 6 ( 1 ) " " " " " " "*" "*" "*" " " "*" " " " " "*" " " "*"
## 7 ( 1 ) " " " " " " "*" "*" "*" " " "*" " " " " "*" "*" "*"
## 8 ( 1 ) " " "*" " " "*" "*" "*" " " "*" " " " " "*" "*" "*"
library(magrittr)
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x tidyr::extract() masks magrittr::extract()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x purrr::set_names() masks magrittr::set_names()
x<-1:10
mean(x)
## [1] 5.5e+00
x %>% mean
## [1] 5.5e+00
z<-c(1,2,NA,8,3,NA,3)
is.na(z)
## [1] FALSE FALSE TRUE FALSE FALSE TRUE FALSE
sum(is.na(z))
## [1] 2
data("airquality")
e<-airquality
sum(is.na(airquality))
## [1] 44
names(airquality)
## [1] "Ozone" "Solar.R" "Wind" "Temp" "Month" "Day"
dim(airquality)
## [1] 153 6
table(is.na(airquality))
##
## FALSE TRUE
## 874 44
z %>% is.na %>% sum
## [1] 2
library(tidyverse)
data("diamonds")
head(diamonds)
## # A tibble: 6 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
diamonds %>% head
## # A tibble: 6 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
diamonds %>% select(color, cut)
## # A tibble: 53,940 x 2
## color cut
## <ord> <ord>
## 1 E Ideal
## 2 E Premium
## 3 E Good
## 4 I Premium
## 5 J Good
## 6 J Very Good
## 7 I Very Good
## 8 H Very Good
## 9 E Fair
## 10 H Very Good
## # with 53,930 more rows
diamonds %>% select(-color)
## # A tibble: 53,940 x 9
## carat cut clarity depth table price x y z
## <dbl> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good VS1 59.4 61 338 4 4.05 2.39
## # with 53,930 more rows
data(iris)
iris %>% select(-Species)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.1e+00 3.5e+00 1.4e+00 2.0e-01
## 2 4.9e+00 3.0e+00 1.4e+00 2.0e-01
## 3 4.7e+00 3.2e+00 1.3e+00 2.0e-01
## 4 4.6e+00 3.1e+00 1.5e+00 2.0e-01
## 5 5.0e+00 3.6e+00 1.4e+00 2.0e-01
## 6 5.4e+00 3.9e+00 1.7e+00 4.0e-01
## 7 4.6e+00 3.4e+00 1.4e+00 3.0e-01
## 8 5.0e+00 3.4e+00 1.5e+00 2.0e-01
## 9 4.4e+00 2.9e+00 1.4e+00 2.0e-01
## 10 4.9e+00 3.1e+00 1.5e+00 1.0e-01
## 11 5.4e+00 3.7e+00 1.5e+00 2.0e-01
## 12 4.8e+00 3.4e+00 1.6e+00 2.0e-01
## 13 4.8e+00 3.0e+00 1.4e+00 1.0e-01
## 14 4.3e+00 3.0e+00 1.1e+00 1.0e-01
## 15 5.8e+00 4.0e+00 1.2e+00 2.0e-01
## 16 5.7e+00 4.4e+00 1.5e+00 4.0e-01
## 17 5.4e+00 3.9e+00 1.3e+00 4.0e-01
## 18 5.1e+00 3.5e+00 1.4e+00 3.0e-01
## 19 5.7e+00 3.8e+00 1.7e+00 3.0e-01
## 20 5.1e+00 3.8e+00 1.5e+00 3.0e-01
## 21 5.4e+00 3.4e+00 1.7e+00 2.0e-01
## 22 5.1e+00 3.7e+00 1.5e+00 4.0e-01
## 23 4.6e+00 3.6e+00 1.0e+00 2.0e-01
## 24 5.1e+00 3.3e+00 1.7e+00 5.0e-01
## 25 4.8e+00 3.4e+00 1.9e+00 2.0e-01
## 26 5.0e+00 3.0e+00 1.6e+00 2.0e-01
## 27 5.0e+00 3.4e+00 1.6e+00 4.0e-01
## 28 5.2e+00 3.5e+00 1.5e+00 2.0e-01
## 29 5.2e+00 3.4e+00 1.4e+00 2.0e-01
## 30 4.7e+00 3.2e+00 1.6e+00 2.0e-01
## 31 4.8e+00 3.1e+00 1.6e+00 2.0e-01
## 32 5.4e+00 3.4e+00 1.5e+00 4.0e-01
## 33 5.2e+00 4.1e+00 1.5e+00 1.0e-01
## 34 5.5e+00 4.2e+00 1.4e+00 2.0e-01
## 35 4.9e+00 3.1e+00 1.5e+00 2.0e-01
## 36 5.0e+00 3.2e+00 1.2e+00 2.0e-01
## 37 5.5e+00 3.5e+00 1.3e+00 2.0e-01
## 38 4.9e+00 3.6e+00 1.4e+00 1.0e-01
## 39 4.4e+00 3.0e+00 1.3e+00 2.0e-01
## 40 5.1e+00 3.4e+00 1.5e+00 2.0e-01
## 41 5.0e+00 3.5e+00 1.3e+00 3.0e-01
## 42 4.5e+00 2.3e+00 1.3e+00 3.0e-01
## 43 4.4e+00 3.2e+00 1.3e+00 2.0e-01
## 44 5.0e+00 3.5e+00 1.6e+00 6.0e-01
## 45 5.1e+00 3.8e+00 1.9e+00 4.0e-01
## 46 4.8e+00 3.0e+00 1.4e+00 3.0e-01
## 47 5.1e+00 3.8e+00 1.6e+00 2.0e-01
## 48 4.6e+00 3.2e+00 1.4e+00 2.0e-01
## 49 5.3e+00 3.7e+00 1.5e+00 2.0e-01
## 50 5.0e+00 3.3e+00 1.4e+00 2.0e-01
## 51 7.0e+00 3.2e+00 4.7e+00 1.4e+00
## 52 6.4e+00 3.2e+00 4.5e+00 1.5e+00
## 53 6.9e+00 3.1e+00 4.9e+00 1.5e+00
## 54 5.5e+00 2.3e+00 4.0e+00 1.3e+00
## 55 6.5e+00 2.8e+00 4.6e+00 1.5e+00
## 56 5.7e+00 2.8e+00 4.5e+00 1.3e+00
## 57 6.3e+00 3.3e+00 4.7e+00 1.6e+00
## 58 4.9e+00 2.4e+00 3.3e+00 1.0e+00
## 59 6.6e+00 2.9e+00 4.6e+00 1.3e+00
## 60 5.2e+00 2.7e+00 3.9e+00 1.4e+00
## 61 5.0e+00 2.0e+00 3.5e+00 1.0e+00
## 62 5.9e+00 3.0e+00 4.2e+00 1.5e+00
## 63 6.0e+00 2.2e+00 4.0e+00 1.0e+00
## 64 6.1e+00 2.9e+00 4.7e+00 1.4e+00
## 65 5.6e+00 2.9e+00 3.6e+00 1.3e+00
## 66 6.7e+00 3.1e+00 4.4e+00 1.4e+00
## 67 5.6e+00 3.0e+00 4.5e+00 1.5e+00
## 68 5.8e+00 2.7e+00 4.1e+00 1.0e+00
## 69 6.2e+00 2.2e+00 4.5e+00 1.5e+00
## 70 5.6e+00 2.5e+00 3.9e+00 1.1e+00
## 71 5.9e+00 3.2e+00 4.8e+00 1.8e+00
## 72 6.1e+00 2.8e+00 4.0e+00 1.3e+00
## 73 6.3e+00 2.5e+00 4.9e+00 1.5e+00
## 74 6.1e+00 2.8e+00 4.7e+00 1.2e+00
## 75 6.4e+00 2.9e+00 4.3e+00 1.3e+00
## 76 6.6e+00 3.0e+00 4.4e+00 1.4e+00
## 77 6.8e+00 2.8e+00 4.8e+00 1.4e+00
## 78 6.7e+00 3.0e+00 5.0e+00 1.7e+00
## 79 6.0e+00 2.9e+00 4.5e+00 1.5e+00
## 80 5.7e+00 2.6e+00 3.5e+00 1.0e+00
## 81 5.5e+00 2.4e+00 3.8e+00 1.1e+00
## 82 5.5e+00 2.4e+00 3.7e+00 1.0e+00
## 83 5.8e+00 2.7e+00 3.9e+00 1.2e+00
## 84 6.0e+00 2.7e+00 5.1e+00 1.6e+00
## 85 5.4e+00 3.0e+00 4.5e+00 1.5e+00
## 86 6.0e+00 3.4e+00 4.5e+00 1.6e+00
## 87 6.7e+00 3.1e+00 4.7e+00 1.5e+00
## 88 6.3e+00 2.3e+00 4.4e+00 1.3e+00
## 89 5.6e+00 3.0e+00 4.1e+00 1.3e+00
## 90 5.5e+00 2.5e+00 4.0e+00 1.3e+00
## 91 5.5e+00 2.6e+00 4.4e+00 1.2e+00
## 92 6.1e+00 3.0e+00 4.6e+00 1.4e+00
## 93 5.8e+00 2.6e+00 4.0e+00 1.2e+00
## 94 5.0e+00 2.3e+00 3.3e+00 1.0e+00
## 95 5.6e+00 2.7e+00 4.2e+00 1.3e+00
## 96 5.7e+00 3.0e+00 4.2e+00 1.2e+00
## 97 5.7e+00 2.9e+00 4.2e+00 1.3e+00
## 98 6.2e+00 2.9e+00 4.3e+00 1.3e+00
## 99 5.1e+00 2.5e+00 3.0e+00 1.1e+00
## 100 5.7e+00 2.8e+00 4.1e+00 1.3e+00
## 101 6.3e+00 3.3e+00 6.0e+00 2.5e+00
## 102 5.8e+00 2.7e+00 5.1e+00 1.9e+00
## 103 7.1e+00 3.0e+00 5.9e+00 2.1e+00
## 104 6.3e+00 2.9e+00 5.6e+00 1.8e+00
## 105 6.5e+00 3.0e+00 5.8e+00 2.2e+00
## 106 7.6e+00 3.0e+00 6.6e+00 2.1e+00
## 107 4.9e+00 2.5e+00 4.5e+00 1.7e+00
## 108 7.3e+00 2.9e+00 6.3e+00 1.8e+00
## 109 6.7e+00 2.5e+00 5.8e+00 1.8e+00
## 110 7.2e+00 3.6e+00 6.1e+00 2.5e+00
## 111 6.5e+00 3.2e+00 5.1e+00 2.0e+00
## 112 6.4e+00 2.7e+00 5.3e+00 1.9e+00
## 113 6.8e+00 3.0e+00 5.5e+00 2.1e+00
## 114 5.7e+00 2.5e+00 5.0e+00 2.0e+00
## 115 5.8e+00 2.8e+00 5.1e+00 2.4e+00
## 116 6.4e+00 3.2e+00 5.3e+00 2.3e+00
## 117 6.5e+00 3.0e+00 5.5e+00 1.8e+00
## 118 7.7e+00 3.8e+00 6.7e+00 2.2e+00
## 119 7.7e+00 2.6e+00 6.9e+00 2.3e+00
## 120 6.0e+00 2.2e+00 5.0e+00 1.5e+00
## 121 6.9e+00 3.2e+00 5.7e+00 2.3e+00
## 122 5.6e+00 2.8e+00 4.9e+00 2.0e+00
## 123 7.7e+00 2.8e+00 6.7e+00 2.0e+00
## 124 6.3e+00 2.7e+00 4.9e+00 1.8e+00
## 125 6.7e+00 3.3e+00 5.7e+00 2.1e+00
## 126 7.2e+00 3.2e+00 6.0e+00 1.8e+00
## 127 6.2e+00 2.8e+00 4.8e+00 1.8e+00
## 128 6.1e+00 3.0e+00 4.9e+00 1.8e+00
## 129 6.4e+00 2.8e+00 5.6e+00 2.1e+00
## 130 7.2e+00 3.0e+00 5.8e+00 1.6e+00
## 131 7.4e+00 2.8e+00 6.1e+00 1.9e+00
## 132 7.9e+00 3.8e+00 6.4e+00 2.0e+00
## 133 6.4e+00 2.8e+00 5.6e+00 2.2e+00
## 134 6.3e+00 2.8e+00 5.1e+00 1.5e+00
## 135 6.1e+00 2.6e+00 5.6e+00 1.4e+00
## 136 7.7e+00 3.0e+00 6.1e+00 2.3e+00
## 137 6.3e+00 3.4e+00 5.6e+00 2.4e+00
## 138 6.4e+00 3.1e+00 5.5e+00 1.8e+00
## 139 6.0e+00 3.0e+00 4.8e+00 1.8e+00
## 140 6.9e+00 3.1e+00 5.4e+00 2.1e+00
## 141 6.7e+00 3.1e+00 5.6e+00 2.4e+00
## 142 6.9e+00 3.1e+00 5.1e+00 2.3e+00
## 143 5.8e+00 2.7e+00 5.1e+00 1.9e+00
## 144 6.8e+00 3.2e+00 5.9e+00 2.3e+00
## 145 6.7e+00 3.3e+00 5.7e+00 2.5e+00
## 146 6.7e+00 3.0e+00 5.2e+00 2.3e+00
## 147 6.3e+00 2.5e+00 5.0e+00 1.9e+00
## 148 6.5e+00 3.0e+00 5.2e+00 2.0e+00
## 149 6.2e+00 3.4e+00 5.4e+00 2.3e+00
## 150 5.9e+00 3.0e+00 5.1e+00 1.8e+00
iris %>% select(-3)
## Sepal.Length Sepal.Width Petal.Width Species
## 1 5.1e+00 3.5e+00 2.0e-01 setosa
## 2 4.9e+00 3.0e+00 2.0e-01 setosa
## 3 4.7e+00 3.2e+00 2.0e-01 setosa
## 4 4.6e+00 3.1e+00 2.0e-01 setosa
## 5 5.0e+00 3.6e+00 2.0e-01 setosa
## 6 5.4e+00 3.9e+00 4.0e-01 setosa
## 7 4.6e+00 3.4e+00 3.0e-01 setosa
## 8 5.0e+00 3.4e+00 2.0e-01 setosa
## 9 4.4e+00 2.9e+00 2.0e-01 setosa
## 10 4.9e+00 3.1e+00 1.0e-01 setosa
## 11 5.4e+00 3.7e+00 2.0e-01 setosa
## 12 4.8e+00 3.4e+00 2.0e-01 setosa
## 13 4.8e+00 3.0e+00 1.0e-01 setosa
## 14 4.3e+00 3.0e+00 1.0e-01 setosa
## 15 5.8e+00 4.0e+00 2.0e-01 setosa
## 16 5.7e+00 4.4e+00 4.0e-01 setosa
## 17 5.4e+00 3.9e+00 4.0e-01 setosa
## 18 5.1e+00 3.5e+00 3.0e-01 setosa
## 19 5.7e+00 3.8e+00 3.0e-01 setosa
## 20 5.1e+00 3.8e+00 3.0e-01 setosa
## 21 5.4e+00 3.4e+00 2.0e-01 setosa
## 22 5.1e+00 3.7e+00 4.0e-01 setosa
## 23 4.6e+00 3.6e+00 2.0e-01 setosa
## 24 5.1e+00 3.3e+00 5.0e-01 setosa
## 25 4.8e+00 3.4e+00 2.0e-01 setosa
## 26 5.0e+00 3.0e+00 2.0e-01 setosa
## 27 5.0e+00 3.4e+00 4.0e-01 setosa
## 28 5.2e+00 3.5e+00 2.0e-01 setosa
## 29 5.2e+00 3.4e+00 2.0e-01 setosa
## 30 4.7e+00 3.2e+00 2.0e-01 setosa
## 31 4.8e+00 3.1e+00 2.0e-01 setosa
## 32 5.4e+00 3.4e+00 4.0e-01 setosa
## 33 5.2e+00 4.1e+00 1.0e-01 setosa
## 34 5.5e+00 4.2e+00 2.0e-01 setosa
## 35 4.9e+00 3.1e+00 2.0e-01 setosa
## 36 5.0e+00 3.2e+00 2.0e-01 setosa
## 37 5.5e+00 3.5e+00 2.0e-01 setosa
## 38 4.9e+00 3.6e+00 1.0e-01 setosa
## 39 4.4e+00 3.0e+00 2.0e-01 setosa
## 40 5.1e+00 3.4e+00 2.0e-01 setosa
## 41 5.0e+00 3.5e+00 3.0e-01 setosa
## 42 4.5e+00 2.3e+00 3.0e-01 setosa
## 43 4.4e+00 3.2e+00 2.0e-01 setosa
## 44 5.0e+00 3.5e+00 6.0e-01 setosa
## 45 5.1e+00 3.8e+00 4.0e-01 setosa
## 46 4.8e+00 3.0e+00 3.0e-01 setosa
## 47 5.1e+00 3.8e+00 2.0e-01 setosa
## 48 4.6e+00 3.2e+00 2.0e-01 setosa
## 49 5.3e+00 3.7e+00 2.0e-01 setosa
## 50 5.0e+00 3.3e+00 2.0e-01 setosa
## 51 7.0e+00 3.2e+00 1.4e+00 versicolor
## 52 6.4e+00 3.2e+00 1.5e+00 versicolor
## 53 6.9e+00 3.1e+00 1.5e+00 versicolor
## 54 5.5e+00 2.3e+00 1.3e+00 versicolor
## 55 6.5e+00 2.8e+00 1.5e+00 versicolor
## 56 5.7e+00 2.8e+00 1.3e+00 versicolor
## 57 6.3e+00 3.3e+00 1.6e+00 versicolor
## 58 4.9e+00 2.4e+00 1.0e+00 versicolor
## 59 6.6e+00 2.9e+00 1.3e+00 versicolor
## 60 5.2e+00 2.7e+00 1.4e+00 versicolor
## 61 5.0e+00 2.0e+00 1.0e+00 versicolor
## 62 5.9e+00 3.0e+00 1.5e+00 versicolor
## 63 6.0e+00 2.2e+00 1.0e+00 versicolor
## 64 6.1e+00 2.9e+00 1.4e+00 versicolor
## 65 5.6e+00 2.9e+00 1.3e+00 versicolor
## 66 6.7e+00 3.1e+00 1.4e+00 versicolor
## 67 5.6e+00 3.0e+00 1.5e+00 versicolor
## 68 5.8e+00 2.7e+00 1.0e+00 versicolor
## 69 6.2e+00 2.2e+00 1.5e+00 versicolor
## 70 5.6e+00 2.5e+00 1.1e+00 versicolor
## 71 5.9e+00 3.2e+00 1.8e+00 versicolor
## 72 6.1e+00 2.8e+00 1.3e+00 versicolor
## 73 6.3e+00 2.5e+00 1.5e+00 versicolor
## 74 6.1e+00 2.8e+00 1.2e+00 versicolor
## 75 6.4e+00 2.9e+00 1.3e+00 versicolor
## 76 6.6e+00 3.0e+00 1.4e+00 versicolor
## 77 6.8e+00 2.8e+00 1.4e+00 versicolor
## 78 6.7e+00 3.0e+00 1.7e+00 versicolor
## 79 6.0e+00 2.9e+00 1.5e+00 versicolor
## 80 5.7e+00 2.6e+00 1.0e+00 versicolor
## 81 5.5e+00 2.4e+00 1.1e+00 versicolor
## 82 5.5e+00 2.4e+00 1.0e+00 versicolor
## 83 5.8e+00 2.7e+00 1.2e+00 versicolor
## 84 6.0e+00 2.7e+00 1.6e+00 versicolor
## 85 5.4e+00 3.0e+00 1.5e+00 versicolor
## 86 6.0e+00 3.4e+00 1.6e+00 versicolor
## 87 6.7e+00 3.1e+00 1.5e+00 versicolor
## 88 6.3e+00 2.3e+00 1.3e+00 versicolor
## 89 5.6e+00 3.0e+00 1.3e+00 versicolor
## 90 5.5e+00 2.5e+00 1.3e+00 versicolor
## 91 5.5e+00 2.6e+00 1.2e+00 versicolor
## 92 6.1e+00 3.0e+00 1.4e+00 versicolor
## 93 5.8e+00 2.6e+00 1.2e+00 versicolor
## 94 5.0e+00 2.3e+00 1.0e+00 versicolor
## 95 5.6e+00 2.7e+00 1.3e+00 versicolor
## 96 5.7e+00 3.0e+00 1.2e+00 versicolor
## 97 5.7e+00 2.9e+00 1.3e+00 versicolor
## 98 6.2e+00 2.9e+00 1.3e+00 versicolor
## 99 5.1e+00 2.5e+00 1.1e+00 versicolor
## 100 5.7e+00 2.8e+00 1.3e+00 versicolor
## 101 6.3e+00 3.3e+00 2.5e+00 virginica
## 102 5.8e+00 2.7e+00 1.9e+00 virginica
## 103 7.1e+00 3.0e+00 2.1e+00 virginica
## 104 6.3e+00 2.9e+00 1.8e+00 virginica
## 105 6.5e+00 3.0e+00 2.2e+00 virginica
## 106 7.6e+00 3.0e+00 2.1e+00 virginica
## 107 4.9e+00 2.5e+00 1.7e+00 virginica
## 108 7.3e+00 2.9e+00 1.8e+00 virginica
## 109 6.7e+00 2.5e+00 1.8e+00 virginica
## 110 7.2e+00 3.6e+00 2.5e+00 virginica
## 111 6.5e+00 3.2e+00 2.0e+00 virginica
## 112 6.4e+00 2.7e+00 1.9e+00 virginica
## 113 6.8e+00 3.0e+00 2.1e+00 virginica
## 114 5.7e+00 2.5e+00 2.0e+00 virginica
## 115 5.8e+00 2.8e+00 2.4e+00 virginica
## 116 6.4e+00 3.2e+00 2.3e+00 virginica
## 117 6.5e+00 3.0e+00 1.8e+00 virginica
## 118 7.7e+00 3.8e+00 2.2e+00 virginica
## 119 7.7e+00 2.6e+00 2.3e+00 virginica
## 120 6.0e+00 2.2e+00 1.5e+00 virginica
## 121 6.9e+00 3.2e+00 2.3e+00 virginica
## 122 5.6e+00 2.8e+00 2.0e+00 virginica
## 123 7.7e+00 2.8e+00 2.0e+00 virginica
## 124 6.3e+00 2.7e+00 1.8e+00 virginica
## 125 6.7e+00 3.3e+00 2.1e+00 virginica
## 126 7.2e+00 3.2e+00 1.8e+00 virginica
## 127 6.2e+00 2.8e+00 1.8e+00 virginica
## 128 6.1e+00 3.0e+00 1.8e+00 virginica
## 129 6.4e+00 2.8e+00 2.1e+00 virginica
## 130 7.2e+00 3.0e+00 1.6e+00 virginica
## 131 7.4e+00 2.8e+00 1.9e+00 virginica
## 132 7.9e+00 3.8e+00 2.0e+00 virginica
## 133 6.4e+00 2.8e+00 2.2e+00 virginica
## 134 6.3e+00 2.8e+00 1.5e+00 virginica
## 135 6.1e+00 2.6e+00 1.4e+00 virginica
## 136 7.7e+00 3.0e+00 2.3e+00 virginica
## 137 6.3e+00 3.4e+00 2.4e+00 virginica
## 138 6.4e+00 3.1e+00 1.8e+00 virginica
## 139 6.0e+00 3.0e+00 1.8e+00 virginica
## 140 6.9e+00 3.1e+00 2.1e+00 virginica
## 141 6.7e+00 3.1e+00 2.4e+00 virginica
## 142 6.9e+00 3.1e+00 2.3e+00 virginica
## 143 5.8e+00 2.7e+00 1.9e+00 virginica
## 144 6.8e+00 3.2e+00 2.3e+00 virginica
## 145 6.7e+00 3.3e+00 2.5e+00 virginica
## 146 6.7e+00 3.0e+00 2.3e+00 virginica
## 147 6.3e+00 2.5e+00 1.9e+00 virginica
## 148 6.5e+00 3.0e+00 2.0e+00 virginica
## 149 6.2e+00 3.4e+00 2.3e+00 virginica
## 150 5.9e+00 3.0e+00 1.8e+00 virginica
head(diamonds)
## # A tibble: 6 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
diamonds %>% filter(cut=="Ideal")
## # A tibble: 21,551 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.23 Ideal J VS1 62.8 56 340 3.93 3.9 2.46
## 3 0.31 Ideal J SI2 62.2 54 344 4.35 4.37 2.71
## 4 0.3 Ideal I SI2 62 54 348 4.31 4.34 2.68
## 5 0.33 Ideal I SI2 61.8 55 403 4.49 4.51 2.78
## 6 0.33 Ideal I SI2 61.2 56 403 4.49 4.5 2.75
## 7 0.33 Ideal J SI1 61.1 56 403 4.49 4.55 2.76
## 8 0.23 Ideal G VS1 61.9 54 404 3.93 3.95 2.44
## 9 0.32 Ideal I SI1 60.9 55 404 4.45 4.48 2.72
## 10 0.3 Ideal I SI2 61 59 405 4.3 4.33 2.63
## # with 21,541 more rows
str(diamonds)
## tibble [53,940 x 10] (S3: tbl_df/tbl/data.frame)
## $ carat : num [1:53940] 2.3e-01 2.1e-01 2.3e-01 2.9e-01 3.1e-01 2.4e-01 2.4e-01 2.6e-01 2.2e-01 2.3e-01 ...
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
## $ depth : num [1:53940] 6.15e+01 5.98e+01 5.69e+01 6.24e+01 6.33e+01 6.28e+01 6.23e+01 6.19e+01 6.51e+01 5.94e+01 ...
## $ table : num [1:53940] 5.5e+01 6.1e+01 6.5e+01 5.8e+01 5.8e+01 5.7e+01 5.7e+01 5.5e+01 6.1e+01 6.1e+01 ...
## $ price : int [1:53940] 326 326 327 334 335 336 336 337 337 338 ...
## $ x : num [1:53940] 3.95 3.89 4.05 4.20 4.34 3.94 3.95 4.07 3.87 4.00 ...
## $ y : num [1:53940] 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
## $ z : num [1:53940] 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1e+00 3.5e+00 1.4e+00 2e-01 setosa
## 2 4.9e+00 3.0e+00 1.4e+00 2e-01 setosa
## 3 4.7e+00 3.2e+00 1.3e+00 2e-01 setosa
## 4 4.6e+00 3.1e+00 1.5e+00 2e-01 setosa
## 5 5.0e+00 3.6e+00 1.4e+00 2e-01 setosa
## 6 5.4e+00 3.9e+00 1.7e+00 4e-01 setosa
iris %>% filter(Sepal.Length>=2.0&Species=="setosa")
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1e+00 3.5e+00 1.4e+00 2e-01 setosa
## 2 4.9e+00 3.0e+00 1.4e+00 2e-01 setosa
## 3 4.7e+00 3.2e+00 1.3e+00 2e-01 setosa
## 4 4.6e+00 3.1e+00 1.5e+00 2e-01 setosa
## 5 5.0e+00 3.6e+00 1.4e+00 2e-01 setosa
## 6 5.4e+00 3.9e+00 1.7e+00 4e-01 setosa
## 7 4.6e+00 3.4e+00 1.4e+00 3e-01 setosa
## 8 5.0e+00 3.4e+00 1.5e+00 2e-01 setosa
## 9 4.4e+00 2.9e+00 1.4e+00 2e-01 setosa
## 10 4.9e+00 3.1e+00 1.5e+00 1e-01 setosa
## 11 5.4e+00 3.7e+00 1.5e+00 2e-01 setosa
## 12 4.8e+00 3.4e+00 1.6e+00 2e-01 setosa
## 13 4.8e+00 3.0e+00 1.4e+00 1e-01 setosa
## 14 4.3e+00 3.0e+00 1.1e+00 1e-01 setosa
## 15 5.8e+00 4.0e+00 1.2e+00 2e-01 setosa
## 16 5.7e+00 4.4e+00 1.5e+00 4e-01 setosa
## 17 5.4e+00 3.9e+00 1.3e+00 4e-01 setosa
## 18 5.1e+00 3.5e+00 1.4e+00 3e-01 setosa
## 19 5.7e+00 3.8e+00 1.7e+00 3e-01 setosa
## 20 5.1e+00 3.8e+00 1.5e+00 3e-01 setosa
## 21 5.4e+00 3.4e+00 1.7e+00 2e-01 setosa
## 22 5.1e+00 3.7e+00 1.5e+00 4e-01 setosa
## 23 4.6e+00 3.6e+00 1.0e+00 2e-01 setosa
## 24 5.1e+00 3.3e+00 1.7e+00 5e-01 setosa
## 25 4.8e+00 3.4e+00 1.9e+00 2e-01 setosa
## 26 5.0e+00 3.0e+00 1.6e+00 2e-01 setosa
## 27 5.0e+00 3.4e+00 1.6e+00 4e-01 setosa
## 28 5.2e+00 3.5e+00 1.5e+00 2e-01 setosa
## 29 5.2e+00 3.4e+00 1.4e+00 2e-01 setosa
## 30 4.7e+00 3.2e+00 1.6e+00 2e-01 setosa
## 31 4.8e+00 3.1e+00 1.6e+00 2e-01 setosa
## 32 5.4e+00 3.4e+00 1.5e+00 4e-01 setosa
## 33 5.2e+00 4.1e+00 1.5e+00 1e-01 setosa
## 34 5.5e+00 4.2e+00 1.4e+00 2e-01 setosa
## 35 4.9e+00 3.1e+00 1.5e+00 2e-01 setosa
## 36 5.0e+00 3.2e+00 1.2e+00 2e-01 setosa
## 37 5.5e+00 3.5e+00 1.3e+00 2e-01 setosa
## 38 4.9e+00 3.6e+00 1.4e+00 1e-01 setosa
## 39 4.4e+00 3.0e+00 1.3e+00 2e-01 setosa
## 40 5.1e+00 3.4e+00 1.5e+00 2e-01 setosa
## 41 5.0e+00 3.5e+00 1.3e+00 3e-01 setosa
## 42 4.5e+00 2.3e+00 1.3e+00 3e-01 setosa
## 43 4.4e+00 3.2e+00 1.3e+00 2e-01 setosa
## 44 5.0e+00 3.5e+00 1.6e+00 6e-01 setosa
## 45 5.1e+00 3.8e+00 1.9e+00 4e-01 setosa
## 46 4.8e+00 3.0e+00 1.4e+00 3e-01 setosa
## 47 5.1e+00 3.8e+00 1.6e+00 2e-01 setosa
## 48 4.6e+00 3.2e+00 1.4e+00 2e-01 setosa
## 49 5.3e+00 3.7e+00 1.5e+00 2e-01 setosa
## 50 5.0e+00 3.3e+00 1.4e+00 2e-01 setosa
diamonds %>% filter(carat>2&price<14000)
## # A tibble: 644 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 2.06 Premium J I1 61.2 58 5203 8.1 8.07 4.95
## 2 2.14 Fair J I1 69.4 57 5405 7.74 7.7 5.36
## 3 2.15 Fair J I1 65.5 57 5430 8.01 7.95 5.23
## 4 2.22 Fair J I1 66.7 56 5607 8.04 8.02 5.36
## 5 2.01 Fair I I1 67.4 58 5696 7.71 7.64 5.17
## 6 2.01 Fair I I1 55.9 64 5696 8.48 8.39 4.71
## 7 2.27 Fair J I1 67.6 55 5733 8.05 8 5.43
## 8 2.03 Fair H I1 64.4 59 6002 7.91 7.85 5.07
## 9 2.03 Fair H I1 66.6 57 6002 7.81 7.75 5.19
## 10 2.06 Good H I1 64.3 58 6091 8.03 7.99 5.15
## # with 634 more rows
diamonds %>% slice(1:5)
## # A tibble: 5 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
diamonds %>% slice(1:5,8,20)
## # A tibble: 7 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 7 0.3 Very Good J SI1 62.7 59 351 4.21 4.27 2.66
diamonds %>% select(carat,price) %>%
mutate(Ratio=price/carat,Double=Ratio*2)
## # A tibble: 53,940 x 4
## carat price Ratio Double
## <dbl> <int> <dbl> <dbl>
## 1 0.23 326 1417. 2835.
## 2 0.21 326 1552. 3105.
## 3 0.23 327 1422. 2843.
## 4 0.29 334 1152. 2303.
## 5 0.31 335 1081. 2161.
## 6 0.24 336 1400 2800
## 7 0.24 336 1400 2800
## 8 0.26 337 1296. 2592.
## 9 0.22 337 1532. 3064.
## 10 0.23 338 1470. 2939.
## # with 53,930 more rows
diamonds %>% summarise(AvgPrice=mean(price), MediaPrice=median(price))
## # A tibble: 1 x 2
## AvgPrice MediaPrice
## <dbl> <dbl>
## 1 3933. 2401
diamonds %>% group_by(cut) %>%
summarise(AvgPrice=mean(price))
## # A tibble: 5 x 2
## cut AvgPrice
## <ord> <dbl>
## 1 Fair 4359.
## 2 Good 3929.
## 3 Very Good 3982.
## 4 Premium 4584.
## 5 Ideal 3458.
diamonds %>% group_by(cut) %>%
summarise(AvgPrice=mean(price)) %>% arrange(AvgPrice)
## # A tibble: 5 x 2
## cut AvgPrice
## <ord> <dbl>
## 1 Ideal 3458.
## 2 Good 3929.
## 3 Very Good 3982.
## 4 Fair 4359.
## 5 Premium 4584.
diamonds %>% group_by(cut) %>%
summarise(AvgPrice=mean(price)) %>% arrange(desc(AvgPrice))
## # A tibble: 5 x 2
## cut AvgPrice
## <ord> <dbl>
## 1 Premium 4584.
## 2 Fair 4359.
## 3 Very Good 3982.
## 4 Good 3929.
## 5 Ideal 3458.