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gapminder %>%
group_by(continent,country) %>%
# is as the same as nest(gapminder,group_by(continet,country))
nest()## # A tibble: 142 x 3
## continent country data
## <fct> <fct> <list>
## 1 Asia Afghanistan <tibble [12 x 4]>
## 2 Europe Albania <tibble [12 x 4]>
## 3 Africa Algeria <tibble [12 x 4]>
## 4 Africa Angola <tibble [12 x 4]>
## 5 Americas Argentina <tibble [12 x 4]>
## 6 Oceania Australia <tibble [12 x 4]>
## 7 Europe Austria <tibble [12 x 4]>
## 8 Asia Bahrain <tibble [12 x 4]>
## 9 Asia Bangladesh <tibble [12 x 4]>
## 10 Europe Belgium <tibble [12 x 4]>
## # ... with 132 more rowsmeans <- map_dbl(mtcars,mean)
medians <- map_dbl(mtcars,median)
summary(means)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.4062 3.0149 3.6875 39.6085 18.9697 230.7219summary(medians)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.663 4.000 34.112 18.455 196.300x <- c()
seq_along(x)## integer(0)1:length(x)## [1] 1 0#source("gapminder-shiny.R")
colnames(gapminder)## [1] "country" "continent" "year" "lifeExp" "pop" "gdpPercap"#model <- lm(country ~ year, data = gapminder)
#models <- models %>%
# mutate(
# glance = model %>% map(broom::glance),
# rsq = glance %>% map_dbl("r.squared"),
# tidy = model %>% map(broom::tidy),
# augment = model %>% map(broom::augment)
# )
#modelsfit <- lm(mtcars, hp ~.)
summary(fit)##
## Call:
## stats::lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.681 -15.558 0.799 18.106 34.718
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 79.0484 184.5041 0.428 0.67270
## mpg -2.0631 2.0906 -0.987 0.33496
## cyl 8.2037 10.0861 0.813 0.42513
## disp 0.4390 0.1492 2.942 0.00778 **
## drat -4.6185 16.0829 -0.287 0.77680
## wt -27.6600 19.2704 -1.435 0.16591
## qsec -1.7844 7.3639 -0.242 0.81089
## vs 25.8129 19.8512 1.300 0.20758
## am 9.4863 20.7599 0.457 0.65240
## gear 7.2164 14.6160 0.494 0.62662
## carb 18.7487 7.0288 2.667 0.01441 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.97 on 21 degrees of freedom
## Multiple R-squared: 0.9028, Adjusted R-squared: 0.8565
## F-statistic: 19.5 on 10 and 21 DF, p-value: 1.898e-08library(dplyr)
mtcars %>% lm(hp ~ .) %>% summary()##
## Call:
## stats::lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.681 -15.558 0.799 18.106 34.718
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 79.0484 184.5041 0.428 0.67270
## mpg -2.0631 2.0906 -0.987 0.33496
## cyl 8.2037 10.0861 0.813 0.42513
## disp 0.4390 0.1492 2.942 0.00778 **
## drat -4.6185 16.0829 -0.287 0.77680
## wt -27.6600 19.2704 -1.435 0.16591
## qsec -1.7844 7.3639 -0.242 0.81089
## vs 25.8129 19.8512 1.300 0.20758
## am 9.4863 20.7599 0.457 0.65240
## gear 7.2164 14.6160 0.494 0.62662
## carb 18.7487 7.0288 2.667 0.01441 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.97 on 21 degrees of freedom
## Multiple R-squared: 0.9028, Adjusted R-squared: 0.8565
## F-statistic: 19.5 on 10 and 21 DF, p-value: 1.898e-08# why there is error when write like:
# mtcars %>% lm(hp ~ .)
# %>% summary()fit <- mtcars %>% lm(hp ~ .)
glance(fit)## r.squared adj.r.squared sigma statistic p.value df logLik
## 1 0.9027993 0.8565132 25.97138 19.50477 1.89833e-08 11 -142.8905
## AIC BIC deviance df.residual
## 1 309.7809 327.3697 14164.76 21tidy(fit)## term estimate std.error statistic p.value
## 1 (Intercept) 79.0483879 184.5040756 0.4284371 0.672695339
## 2 mpg -2.0630545 2.0905650 -0.9868407 0.334955314
## 3 cyl 8.2037204 10.0861425 0.8133655 0.425134929
## 4 disp 0.4390024 0.1492007 2.9423609 0.007779725
## 5 drat -4.6185488 16.0829171 -0.2871711 0.776795845
## 6 wt -27.6600472 19.2703681 -1.4353668 0.165910518
## 7 qsec -1.7843654 7.3639133 -0.2423121 0.810889101
## 8 vs 25.8128774 19.8512410 1.3003156 0.207583411
## 9 am 9.4862914 20.7599371 0.4569518 0.652397317
## 10 gear 7.2164047 14.6160152 0.4937327 0.626619355
## 11 carb 18.7486691 7.0287674 2.6674192 0.014412403augment(fit) %>% head()## .rownames hp mpg cyl disp drat wt qsec vs am gear carb
## 1 Mazda RX4 110 21.0 6 160 3.90 2.620 16.46 0 1 4 4
## 2 Mazda RX4 Wag 110 21.0 6 160 3.90 2.875 17.02 0 1 4 4
## 3 Datsun 710 93 22.8 4 108 3.85 2.320 18.61 1 1 4 1
## 4 Hornet 4 Drive 110 21.4 6 258 3.08 3.215 19.44 1 0 3 1
## 5 Hornet Sportabout 175 18.7 8 360 3.15 3.440 17.02 0 0 3 2
## 6 Valiant 105 18.1 6 225 2.76 3.460 20.22 1 0 3 1
## .fitted .resid .hat .sigma .cooksd .std.resid
## 1 148.68122 -38.681220 0.2142214 24.75946 0.069964902 -1.6801773
## 2 140.62866 -30.628664 0.2323739 25.43881 0.049861042 -1.3460408
## 3 79.99158 13.008418 0.3075987 26.38216 0.014633059 0.6019364
## 4 125.75448 -15.754483 0.2103960 26.31579 0.011288712 -0.6826601
## 5 183.21756 -8.217565 0.2016137 26.53317 0.002878707 -0.3541128
## 6 111.38490 -6.384902 0.3147448 26.55680 0.003682813 -0.2969840mtcars %>%
lm(hp ~ .) %>%
glance()## r.squared adj.r.squared sigma statistic p.value df logLik
## 1 0.9027993 0.8565132 25.97138 19.50477 1.89833e-08 11 -142.8905
## AIC BIC deviance df.residual
## 1 309.7809 327.3697 14164.76 21iris %>%
select(-Species) %>%
kmeans(centers = 3) %>%
tidy()## x1 x2 x3 x4 size withinss cluster
## 1 5.901613 2.748387 4.393548 1.433871 62 39.82097 1
## 2 6.850000 3.073684 5.742105 2.071053 38 23.87947 2
## 3 5.006000 3.428000 1.462000 0.246000 50 15.15100 3# dependancy
library(glmnet)## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:tidyr':
##
## expand## Loading required package: foreach##
## Attaching package: 'foreach'## The following objects are masked from 'package:purrr':
##
## accumulate, when## Loaded glmnet 2.0-16##
## Attaching package: 'glmnet'## The following objects are masked from 'package:twidlr':
##
## cv.glmnet, glmnet, predict.cv.glmnet, predict.glmnet#mtcars %>%
# cv.glmnet(am ~ ., alpha = 0) %>%
# glance()