1.1 filter_all()函数

filter_all()函数可以对所有的列进行判断从而对行进行筛选.

比如我想对mtcars数据集进行筛选,对于每一行,只有每一行的值都大于150才保留下来.

library(tidyverse)

## Warning: package 'tidyverse' was built under R version 3.6.1

## -- Attaching packages ------------------------------------------------------------------------------------ tidyverse 1.2.1 --

## v ggplot2 3.2.1     v purrr   0.3.2
## v tibble  2.1.3     v dplyr   0.8.3
## v tidyr   1.0.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.4.0

## Warning: package 'ggplot2' was built under R version 3.6.1

## Warning: package 'tibble' was built under R version 3.6.1

## Warning: package 'tidyr' was built under R version 3.6.1

## Warning: package 'dplyr' was built under R version 3.6.1

## -- Conflicts --------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

filter_all(mtcars, all_vars(. > 150))

##  [1] mpg  cyl  disp hp   drat wt   qsec vs   am   gear carb
## <0 rows> (or 0-length row.names)

如果只要任意一行大于150,就保留下来,那么可以这么写:

filter_all(mtcars, any_vars(. > 150))

##     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## 1  21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## 2  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## 3  21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## 4  18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## 5  18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## 6  14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## 7  19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## 8  17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## 9  16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## 10 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## 11 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## 12 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## 13 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## 14 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## 15 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## 16 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## 17 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## 18 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## 19 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## 20 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## 21 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8

可以看到,ffilter_all()经常和all_vars()和any_vals()函数搭配使用.