29 Tidyverse Cheatsheet

Huaizhi Ge

library(tidyverse)

29.1 Introduction

Usually, before we plot the data, we need to preprocess the data first. And we use tidyverse to process data commonly. And I find myself can not remember all the tidyverse functions to deal with the data. So I create a cheat sheet that includes some commonly used functions in tidyverse to process the data.

reference: https://www.rdocumentation.org/, https://dplyr.tidyverse.org/index.html

29.2 select()

To select some columns in the data, we only need to add the names of columns to the Select() function. (Use dataset “mpg” as an example dataset.)

mpg %>% 
  select(year,manufacturer,model)

## # A tibble: 234 × 3
##     year manufacturer model     
##    <int> <chr>        <chr>     
##  1  1999 audi         a4        
##  2  1999 audi         a4        
##  3  2008 audi         a4        
##  4  2008 audi         a4        
##  5  1999 audi         a4        
##  6  1999 audi         a4        
##  7  2008 audi         a4        
##  8  1999 audi         a4 quattro
##  9  1999 audi         a4 quattro
## 10  2008 audi         a4 quattro
## # … with 224 more rows

Also, we can use start_col:end_col.

mpg %>% 
  select(year:drv)

## # A tibble: 234 × 4
##     year   cyl trans      drv  
##    <int> <int> <chr>      <chr>
##  1  1999     4 auto(l5)   f    
##  2  1999     4 manual(m5) f    
##  3  2008     4 manual(m6) f    
##  4  2008     4 auto(av)   f    
##  5  1999     6 auto(l5)   f    
##  6  1999     6 manual(m5) f    
##  7  2008     6 auto(av)   f    
##  8  1999     4 manual(m5) 4    
##  9  1999     4 auto(l5)   4    
## 10  2008     4 manual(m6) 4    
## # … with 224 more rows

We can select columns that have the same structure using starts_with(),end_with() or contains().

mpg %>% 
  select(starts_with("m"),contains("c"),ends_with("y"))

## # A tibble: 234 × 6
##    manufacturer model        cyl   cty class     hwy
##    <chr>        <chr>      <int> <int> <chr>   <int>
##  1 audi         a4             4    18 compact    29
##  2 audi         a4             4    21 compact    29
##  3 audi         a4             4    20 compact    31
##  4 audi         a4             4    21 compact    30
##  5 audi         a4             6    16 compact    26
##  6 audi         a4             6    18 compact    26
##  7 audi         a4             6    18 compact    27
##  8 audi         a4 quattro     4    18 compact    26
##  9 audi         a4 quattro     4    16 compact    25
## 10 audi         a4 quattro     4    20 compact    28
## # … with 224 more rows

select_if() can be used to select columns that have a specific data type. For example, select_if(is.character) can select all the character type columns. Similarly, we can use is.numeric, is.integer, is.factor and so on.

mpg %>% 
  select_if(is.numeric)

## # A tibble: 234 × 5
##    displ  year   cyl   cty   hwy
##    <dbl> <int> <int> <int> <int>
##  1   1.8  1999     4    18    29
##  2   1.8  1999     4    21    29
##  3   2    2008     4    20    31
##  4   2    2008     4    21    30
##  5   2.8  1999     6    16    26
##  6   2.8  1999     6    18    26
##  7   3.1  2008     6    18    27
##  8   1.8  1999     4    18    26
##  9   1.8  1999     4    16    25
## 10   2    2008     4    20    28
## # … with 224 more rows

select_if() can select the columns that meet some conditions.

mpg %>% select(where(is.numeric)) %>% 
  select(where(~mean(.)<100))

## # A tibble: 234 × 4
##    displ   cyl   cty   hwy
##    <dbl> <int> <int> <int>
##  1   1.8     4    18    29
##  2   1.8     4    21    29
##  3   2       4    20    31
##  4   2       4    21    30
##  5   2.8     6    16    26
##  6   2.8     6    18    26
##  7   3.1     6    18    27
##  8   1.8     4    18    26
##  9   1.8     4    16    25
## 10   2       4    20    28
## # … with 224 more rows

29.3 filter()

We can use filter() to choose certain rows.

mpg %>% 
  filter(year==1999)

## # A tibble: 117 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
##  2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
##  3 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
##  4 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
##  5 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
##  6 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
##  7 audi         a4 quattro   2.8  1999     6 auto… 4        15    25 p     comp…
##  8 audi         a4 quattro   2.8  1999     6 manu… 4        17    25 p     comp…
##  9 audi         a6 quattro   2.8  1999     6 auto… 4        15    24 p     mids…
## 10 chevrolet    c1500 sub…   5.7  1999     8 auto… r        13    17 r     suv  
## # … with 107 more rows

mpg %>% 
  filter(year!=1999)

## # A tibble: 117 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4           2    2008     4 manu… f        20    31 p     comp…
##  2 audi         a4           2    2008     4 auto… f        21    30 p     comp…
##  3 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
##  4 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
##  5 audi         a4 quattro   2    2008     4 auto… 4        19    27 p     comp…
##  6 audi         a4 quattro   3.1  2008     6 auto… 4        17    25 p     comp…
##  7 audi         a4 quattro   3.1  2008     6 manu… 4        15    25 p     comp…
##  8 audi         a6 quattro   3.1  2008     6 auto… 4        17    25 p     mids…
##  9 audi         a6 quattro   4.2  2008     8 auto… 4        16    23 p     mids…
## 10 chevrolet    c1500 sub…   5.3  2008     8 auto… r        14    20 r     suv  
## # … with 107 more rows

filter(condition1,condition2) will return the rows that meet the two conditions.

mpg %>% 
  filter(year==1999,cyl==4)

## # A tibble: 45 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
##  2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
##  3 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
##  4 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
##  5 chevrolet    malibu       2.4  1999     4 auto… f        19    27 r     mids…
##  6 dodge        caravan 2…   2.4  1999     4 auto… f        18    24 r     mini…
##  7 honda        civic        1.6  1999     4 manu… f        28    33 r     subc…
##  8 honda        civic        1.6  1999     4 auto… f        24    32 r     subc…
##  9 honda        civic        1.6  1999     4 manu… f        25    32 r     subc…
## 10 honda        civic        1.6  1999     4 manu… f        23    29 p     subc…
## # … with 35 more rows

filter(condition1|condition2) will return the rows that meet condition1 or condition2.

mpg %>% 
  filter(year==1999|cyl==4)

## # A tibble: 153 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
##  2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
##  3 audi         a4           2    2008     4 manu… f        20    31 p     comp…
##  4 audi         a4           2    2008     4 auto… f        21    30 p     comp…
##  5 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
##  6 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
##  7 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
##  8 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
##  9 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
## 10 audi         a4 quattro   2    2008     4 auto… 4        19    27 p     comp…
## # … with 143 more rows

filter(xor(condition1,condition2)) will return rows that only meet one condition.

mpg %>% 
  filter(xor(year==1999,cyl==4))

## # A tibble: 108 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4           2    2008     4 manu… f        20    31 p     comp…
##  2 audi         a4           2    2008     4 auto… f        21    30 p     comp…
##  3 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
##  4 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
##  5 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
##  6 audi         a4 quattro   2    2008     4 auto… 4        19    27 p     comp…
##  7 audi         a4 quattro   2.8  1999     6 auto… 4        15    25 p     comp…
##  8 audi         a4 quattro   2.8  1999     6 manu… 4        17    25 p     comp…
##  9 audi         a6 quattro   2.8  1999     6 auto… 4        15    24 p     mids…
## 10 chevrolet    c1500 sub…   5.7  1999     8 auto… r        13    17 r     suv  
## # … with 98 more rows

filter_all() will filter all the columns.

mpg %>% 
  filter_all(any_vars(.==4))

## # A tibble: 164 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
##  2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
##  3 audi         a4           2    2008     4 manu… f        20    31 p     comp…
##  4 audi         a4           2    2008     4 auto… f        21    30 p     comp…
##  5 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
##  6 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
##  7 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
##  8 audi         a4 quattro   2    2008     4 auto… 4        19    27 p     comp…
##  9 audi         a4 quattro   2.8  1999     6 auto… 4        15    25 p     comp…
## 10 audi         a4 quattro   2.8  1999     6 manu… 4        17    25 p     comp…
## # … with 154 more rows

filter_if() first find the columns that meet some condition then filter.

mpg %>% 
  filter_if(is.character,any_vars(.==4))

## # A tibble: 103 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
##  2 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
##  3 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
##  4 audi         a4 quattro   2    2008     4 auto… 4        19    27 p     comp…
##  5 audi         a4 quattro   2.8  1999     6 auto… 4        15    25 p     comp…
##  6 audi         a4 quattro   2.8  1999     6 manu… 4        17    25 p     comp…
##  7 audi         a4 quattro   3.1  2008     6 auto… 4        17    25 p     comp…
##  8 audi         a4 quattro   3.1  2008     6 manu… 4        15    25 p     comp…
##  9 audi         a6 quattro   2.8  1999     6 auto… 4        15    24 p     mids…
## 10 audi         a6 quattro   3.1  2008     6 auto… 4        17    25 p     mids…
## # … with 93 more rows

filter_at() need to select columns in vars().

mpg %>% 
  filter_at(vars(displ),all_vars(.>3))

## # A tibble: 126 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
##  2 audi         a4 quattro   3.1  2008     6 auto… 4        17    25 p     comp…
##  3 audi         a4 quattro   3.1  2008     6 manu… 4        15    25 p     comp…
##  4 audi         a6 quattro   3.1  2008     6 auto… 4        17    25 p     mids…
##  5 audi         a6 quattro   4.2  2008     8 auto… 4        16    23 p     mids…
##  6 chevrolet    c1500 sub…   5.3  2008     8 auto… r        14    20 r     suv  
##  7 chevrolet    c1500 sub…   5.3  2008     8 auto… r        11    15 e     suv  
##  8 chevrolet    c1500 sub…   5.3  2008     8 auto… r        14    20 r     suv  
##  9 chevrolet    c1500 sub…   5.7  1999     8 auto… r        13    17 r     suv  
## 10 chevrolet    c1500 sub…   6    2008     8 auto… r        12    17 r     suv  
## # … with 116 more rows

29.4 mutate()

mutate() can add a new column based on the existing column.

mpg %>% 
  select(manufacturer,model,year) %>% 
  mutate(year2=year-2000)

## # A tibble: 234 × 4
##    manufacturer model       year year2
##    <chr>        <chr>      <int> <dbl>
##  1 audi         a4          1999    -1
##  2 audi         a4          1999    -1
##  3 audi         a4          2008     8
##  4 audi         a4          2008     8
##  5 audi         a4          1999    -1
##  6 audi         a4          1999    -1
##  7 audi         a4          2008     8
##  8 audi         a4 quattro  1999    -1
##  9 audi         a4 quattro  1999    -1
## 10 audi         a4 quattro  2008     8
## # … with 224 more rows

We can use ifelse() to choose data.

mpg %>% 
  select(manufacturer,model,year) %>% 
  mutate(year2=ifelse(year>2000,0,NA))

## # A tibble: 234 × 4
##    manufacturer model       year year2
##    <chr>        <chr>      <int> <dbl>
##  1 audi         a4          1999    NA
##  2 audi         a4          1999    NA
##  3 audi         a4          2008     0
##  4 audi         a4          2008     0
##  5 audi         a4          1999    NA
##  6 audi         a4          1999    NA
##  7 audi         a4          2008     0
##  8 audi         a4 quattro  1999    NA
##  9 audi         a4 quattro  1999    NA
## 10 audi         a4 quattro  2008     0
## # … with 224 more rows

mutate_all() can change all the columns.

mpg %>% 
  mutate_all(toupper)

## # A tibble: 234 × 11
##    manufacturer model      displ year  cyl   trans drv   cty   hwy   fl    class
##    <chr>        <chr>      <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
##  1 AUDI         A4         1.8   1999  4     AUTO… F     18    29    P     COMP…
##  2 AUDI         A4         1.8   1999  4     MANU… F     21    29    P     COMP…
##  3 AUDI         A4         2     2008  4     MANU… F     20    31    P     COMP…
##  4 AUDI         A4         2     2008  4     AUTO… F     21    30    P     COMP…
##  5 AUDI         A4         2.8   1999  6     AUTO… F     16    26    P     COMP…
##  6 AUDI         A4         2.8   1999  6     MANU… F     18    26    P     COMP…
##  7 AUDI         A4         3.1   2008  6     AUTO… F     18    27    P     COMP…
##  8 AUDI         A4 QUATTRO 1.8   1999  4     MANU… 4     18    26    P     COMP…
##  9 AUDI         A4 QUATTRO 1.8   1999  4     AUTO… 4     16    25    P     COMP…
## 10 AUDI         A4 QUATTRO 2     2008  4     MANU… 4     20    28    P     COMP…
## # … with 224 more rows

mutate_if() can select certain columns and mutate.

mpg %>% 
  mutate_if(is.numeric,round)

## # A tibble: 234 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr>
##  1 audi         a4             2  1999     4 auto… f        18    29 p     comp…
##  2 audi         a4             2  1999     4 manu… f        21    29 p     comp…
##  3 audi         a4             2  2008     4 manu… f        20    31 p     comp…
##  4 audi         a4             2  2008     4 auto… f        21    30 p     comp…
##  5 audi         a4             3  1999     6 auto… f        16    26 p     comp…
##  6 audi         a4             3  1999     6 manu… f        18    26 p     comp…
##  7 audi         a4             3  2008     6 auto… f        18    27 p     comp…
##  8 audi         a4 quattro     2  1999     4 manu… 4        18    26 p     comp…
##  9 audi         a4 quattro     2  1999     4 auto… 4        16    25 p     comp…
## 10 audi         a4 quattro     2  2008     4 manu… 4        20    28 p     comp…
## # … with 224 more rows

mutate_at() need to select columns in vars().

mpg %>% 
  select(manufacturer,model,displ,cyl) %>% 
  mutate_at(vars(displ,cyl),~(.*2))

## # A tibble: 234 × 4
##    manufacturer model      displ   cyl
##    <chr>        <chr>      <dbl> <dbl>
##  1 audi         a4           3.6     8
##  2 audi         a4           3.6     8
##  3 audi         a4           4       8
##  4 audi         a4           4       8
##  5 audi         a4           5.6    12
##  6 audi         a4           5.6    12
##  7 audi         a4           6.2    12
##  8 audi         a4 quattro   3.6     8
##  9 audi         a4 quattro   3.6     8
## 10 audi         a4 quattro   4       8
## # … with 224 more rows

case_when() can create categorical data.

mpg %>% 
  select(manufacturer,model,year) %>% 
  mutate(year2=case_when(
    year<2000~"very old",
    year<2005~"old",
    TRUE~"new"
  ))

## # A tibble: 234 × 4
##    manufacturer model       year year2   
##    <chr>        <chr>      <int> <chr>   
##  1 audi         a4          1999 very old
##  2 audi         a4          1999 very old
##  3 audi         a4          2008 new     
##  4 audi         a4          2008 new     
##  5 audi         a4          1999 very old
##  6 audi         a4          1999 very old
##  7 audi         a4          2008 new     
##  8 audi         a4 quattro  1999 very old
##  9 audi         a4 quattro  1999 very old
## 10 audi         a4 quattro  2008 new     
## # … with 224 more rows

29.5 rowwise()

rowwise() can process data by row.

mpg %>% 
  select_if(is.numeric) %>% 
  rowwise() %>% 
  mutate(mean = mean(c_across(displ:hwy)))

## # A tibble: 234 × 6
## # Rowwise: 
##    displ  year   cyl   cty   hwy  mean
##    <dbl> <int> <int> <int> <int> <dbl>
##  1   1.8  1999     4    18    29  410.
##  2   1.8  1999     4    21    29  411.
##  3   2    2008     4    20    31  413 
##  4   2    2008     4    21    30  413 
##  5   2.8  1999     6    16    26  410.
##  6   2.8  1999     6    18    26  410.
##  7   3.1  2008     6    18    27  412.
##  8   1.8  1999     4    18    26  410.
##  9   1.8  1999     4    16    25  409.
## 10   2    2008     4    20    28  412.
## # … with 224 more rows

Or, we can do this by another way.

mpg %>% 
  rowwise() %>% 
  mutate(mean=rowMeans(across(where(is.numeric))))

## # A tibble: 234 × 12
## # Rowwise: 
##    manufactu…¹ model displ  year   cyl trans drv     cty   hwy fl    class  mean
##    <chr>       <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr> <dbl>
##  1 audi        a4      1.8  1999     4 auto… f        18    29 p     comp…  410.
##  2 audi        a4      1.8  1999     4 manu… f        21    29 p     comp…  411.
##  3 audi        a4      2    2008     4 manu… f        20    31 p     comp…  413 
##  4 audi        a4      2    2008     4 auto… f        21    30 p     comp…  413 
##  5 audi        a4      2.8  1999     6 auto… f        16    26 p     comp…  410.
##  6 audi        a4      2.8  1999     6 manu… f        18    26 p     comp…  410.
##  7 audi        a4      3.1  2008     6 auto… f        18    27 p     comp…  412.
##  8 audi        a4 q…   1.8  1999     4 manu… 4        18    26 p     comp…  410.
##  9 audi        a4 q…   1.8  1999     4 auto… 4        16    25 p     comp…  409.
## 10 audi        a4 q…   2    2008     4 manu… 4        20    28 p     comp…  412.
## # … with 224 more rows, and abbreviated variable name ¹​manufacturer

We can use rowwise() to get the max/min/sum/sd too.

mpg %>% 
  rowwise() %>% 
  mutate(sum=sum(across(where(is.numeric))))

## # A tibble: 234 × 12
## # Rowwise: 
##    manufactu…¹ model displ  year   cyl trans drv     cty   hwy fl    class   sum
##    <chr>       <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr> <dbl>
##  1 audi        a4      1.8  1999     4 auto… f        18    29 p     comp… 2052.
##  2 audi        a4      1.8  1999     4 manu… f        21    29 p     comp… 2055.
##  3 audi        a4      2    2008     4 manu… f        20    31 p     comp… 2065 
##  4 audi        a4      2    2008     4 auto… f        21    30 p     comp… 2065 
##  5 audi        a4      2.8  1999     6 auto… f        16    26 p     comp… 2050.
##  6 audi        a4      2.8  1999     6 manu… f        18    26 p     comp… 2052.
##  7 audi        a4      3.1  2008     6 auto… f        18    27 p     comp… 2062.
##  8 audi        a4 q…   1.8  1999     4 manu… 4        18    26 p     comp… 2049.
##  9 audi        a4 q…   1.8  1999     4 auto… 4        16    25 p     comp… 2046.
## 10 audi        a4 q…   2    2008     4 manu… 4        20    28 p     comp… 2062 
## # … with 224 more rows, and abbreviated variable name ¹​manufacturer

29.6 arrange()

arrange() can sort the data.

mpg %>% 
  arrange(displ)

## # A tibble: 234 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 honda        civic        1.6  1999     4 manu… f        28    33 r     subc…
##  2 honda        civic        1.6  1999     4 auto… f        24    32 r     subc…
##  3 honda        civic        1.6  1999     4 manu… f        25    32 r     subc…
##  4 honda        civic        1.6  1999     4 manu… f        23    29 p     subc…
##  5 honda        civic        1.6  1999     4 auto… f        24    32 r     subc…
##  6 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
##  7 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
##  8 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
##  9 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
## 10 honda        civic        1.8  2008     4 manu… f        26    34 r     subc…
## # … with 224 more rows

desc() can help sort data by descending order.

mpg %>% 
  arrange(desc(displ))

## # A tibble: 234 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 chevrolet    corvette     7    2008     8 manu… r        15    24 p     2sea…
##  2 chevrolet    k1500 tah…   6.5  1999     8 auto… 4        14    17 d     suv  
##  3 chevrolet    corvette     6.2  2008     8 manu… r        16    26 p     2sea…
##  4 chevrolet    corvette     6.2  2008     8 auto… r        15    25 p     2sea…
##  5 jeep         grand che…   6.1  2008     8 auto… 4        11    14 p     suv  
##  6 chevrolet    c1500 sub…   6    2008     8 auto… r        12    17 r     suv  
##  7 dodge        durango 4…   5.9  1999     8 auto… 4        11    15 r     suv  
##  8 dodge        ram 1500 …   5.9  1999     8 auto… 4        11    15 r     pick…
##  9 chevrolet    c1500 sub…   5.7  1999     8 auto… r        13    17 r     suv  
## 10 chevrolet    corvette     5.7  1999     8 manu… r        16    26 p     2sea…
## # … with 224 more rows

29.7 distinct()

distinct() can help delete the duplicate rows.

df <- tibble(
  x=sample(5,20,rep=TRUE),
  y=sample(5,20,rep=TRUE)
)
df %>% 
  distinct()

## # A tibble: 15 × 2
##        x     y
##    <int> <int>
##  1     4     2
##  2     3     3
##  3     2     1
##  4     3     4
##  5     4     5
##  6     4     4
##  7     4     3
##  8     1     4
##  9     4     1
## 10     2     2
## 11     2     4
## 12     2     3
## 13     3     1
## 14     5     4
## 15     5     3

29.8 rename()

rename() can change the column name.

mpg %>% 
  rename(YEAR=year)

## # A tibble: 234 × 11
##    manufacturer model      displ  YEAR   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
##  2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
##  3 audi         a4           2    2008     4 manu… f        20    31 p     comp…
##  4 audi         a4           2    2008     4 auto… f        21    30 p     comp…
##  5 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
##  6 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
##  7 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
##  8 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
##  9 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
## 10 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
## # … with 224 more rows

29.9 relocate()

relocate() can help change the order of the columns.

mpg %>% 
  relocate(year)

## # A tibble: 234 × 11
##     year manufacturer model      displ   cyl trans drv     cty   hwy fl    class
##    <int> <chr>        <chr>      <dbl> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1  1999 audi         a4           1.8     4 auto… f        18    29 p     comp…
##  2  1999 audi         a4           1.8     4 manu… f        21    29 p     comp…
##  3  2008 audi         a4           2       4 manu… f        20    31 p     comp…
##  4  2008 audi         a4           2       4 auto… f        21    30 p     comp…
##  5  1999 audi         a4           2.8     6 auto… f        16    26 p     comp…
##  6  1999 audi         a4           2.8     6 manu… f        18    26 p     comp…
##  7  2008 audi         a4           3.1     6 auto… f        18    27 p     comp…
##  8  1999 audi         a4 quattro   1.8     4 manu… 4        18    26 p     comp…
##  9  1999 audi         a4 quattro   1.8     4 auto… 4        16    25 p     comp…
## 10  2008 audi         a4 quattro   2       4 manu… 4        20    28 p     comp…
## # … with 224 more rows

mpg %>% 
  relocate(year,.after = manufacturer)

## # A tibble: 234 × 11
##    manufacturer  year model      displ   cyl trans drv     cty   hwy fl    class
##    <chr>        <int> <chr>      <dbl> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi          1999 a4           1.8     4 auto… f        18    29 p     comp…
##  2 audi          1999 a4           1.8     4 manu… f        21    29 p     comp…
##  3 audi          2008 a4           2       4 manu… f        20    31 p     comp…
##  4 audi          2008 a4           2       4 auto… f        21    30 p     comp…
##  5 audi          1999 a4           2.8     6 auto… f        16    26 p     comp…
##  6 audi          1999 a4           2.8     6 manu… f        18    26 p     comp…
##  7 audi          2008 a4           3.1     6 auto… f        18    27 p     comp…
##  8 audi          1999 a4 quattro   1.8     4 manu… 4        18    26 p     comp…
##  9 audi          1999 a4 quattro   1.8     4 auto… 4        16    25 p     comp…
## 10 audi          2008 a4 quattro   2       4 manu… 4        20    28 p     comp…
## # … with 224 more rows

mpg %>% 
  relocate(year,.before = displ)

## # A tibble: 234 × 11
##    manufacturer model       year displ   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <int> <dbl> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4          1999   1.8     4 auto… f        18    29 p     comp…
##  2 audi         a4          1999   1.8     4 manu… f        21    29 p     comp…
##  3 audi         a4          2008   2       4 manu… f        20    31 p     comp…
##  4 audi         a4          2008   2       4 auto… f        21    30 p     comp…
##  5 audi         a4          1999   2.8     6 auto… f        16    26 p     comp…
##  6 audi         a4          1999   2.8     6 manu… f        18    26 p     comp…
##  7 audi         a4          2008   3.1     6 auto… f        18    27 p     comp…
##  8 audi         a4 quattro  1999   1.8     4 manu… 4        18    26 p     comp…
##  9 audi         a4 quattro  1999   1.8     4 auto… 4        16    25 p     comp…
## 10 audi         a4 quattro  2008   2       4 manu… 4        20    28 p     comp…
## # … with 224 more rows

29.10 drop_na()

drop_na can help drop the missing value.

df <- tibble(
  x=c(1,NA,2,3),
  y=c(4,5,NA,6)
)
df %>% 
  drop_na()

## # A tibble: 2 × 2
##       x     y
##   <dbl> <dbl>
## 1     1     4
## 2     3     6

29.11 pull()

pull() can help get a single column.

mpg %>% 
  pull(year)

##   [1] 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 2008 1999 1999 2008 2008
##  [16] 1999 2008 2008 2008 2008 2008 1999 2008 1999 1999 2008 2008 2008 2008 2008
##  [31] 1999 1999 1999 2008 1999 2008 2008 1999 1999 1999 1999 2008 2008 2008 1999
##  [46] 1999 2008 2008 2008 2008 1999 1999 2008 2008 2008 1999 1999 1999 2008 2008
##  [61] 2008 1999 2008 1999 2008 2008 2008 2008 2008 2008 1999 1999 2008 1999 1999
##  [76] 1999 2008 1999 1999 1999 2008 2008 1999 1999 1999 1999 1999 2008 1999 2008
##  [91] 1999 1999 2008 2008 1999 1999 2008 2008 2008 1999 1999 1999 1999 1999 2008
## [106] 2008 2008 2008 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 2008 2008
## [121] 2008 2008 2008 2008 1999 1999 2008 2008 2008 2008 1999 2008 2008 1999 1999
## [136] 1999 2008 1999 2008 2008 1999 1999 1999 2008 2008 2008 2008 1999 1999 2008
## [151] 1999 1999 2008 2008 1999 1999 1999 2008 2008 1999 1999 2008 2008 2008 2008
## [166] 1999 1999 1999 1999 2008 2008 2008 2008 1999 1999 1999 1999 2008 2008 1999
## [181] 1999 2008 2008 1999 1999 2008 1999 1999 2008 2008 1999 1999 2008 1999 1999
## [196] 1999 2008 2008 1999 2008 1999 1999 2008 1999 1999 2008 2008 1999 1999 2008
## [211] 2008 1999 1999 1999 1999 2008 2008 2008 2008 1999 1999 1999 1999 1999 1999
## [226] 2008 2008 1999 1999 2008 2008 1999 1999 2008

29.12 unite()

unite() can help unite the columns.

mpg %>% 
  unite(col="newcolumn",manufacturer:model,sep=",")->newdf

newdf

## # A tibble: 234 × 10
##    newcolumn       displ  year   cyl trans      drv     cty   hwy fl    class  
##    <chr>           <dbl> <int> <int> <chr>      <chr> <int> <int> <chr> <chr>  
##  1 audi,a4           1.8  1999     4 auto(l5)   f        18    29 p     compact
##  2 audi,a4           1.8  1999     4 manual(m5) f        21    29 p     compact
##  3 audi,a4           2    2008     4 manual(m6) f        20    31 p     compact
##  4 audi,a4           2    2008     4 auto(av)   f        21    30 p     compact
##  5 audi,a4           2.8  1999     6 auto(l5)   f        16    26 p     compact
##  6 audi,a4           2.8  1999     6 manual(m5) f        18    26 p     compact
##  7 audi,a4           3.1  2008     6 auto(av)   f        18    27 p     compact
##  8 audi,a4 quattro   1.8  1999     4 manual(m5) 4        18    26 p     compact
##  9 audi,a4 quattro   1.8  1999     4 auto(l5)   4        16    25 p     compact
## 10 audi,a4 quattro   2    2008     4 manual(m6) 4        20    28 p     compact
## # … with 224 more rows

29.13 separate()

separate() can help separate one column to several columns.

newdf %>% 
  separate("newcolumn",into=c("manufacturer","model"),sep=",")

## # A tibble: 234 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
##  2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
##  3 audi         a4           2    2008     4 manu… f        20    31 p     comp…
##  4 audi         a4           2    2008     4 auto… f        21    30 p     comp…
##  5 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
##  6 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
##  7 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
##  8 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
##  9 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
## 10 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
## # … with 224 more rows

29.14 pivot_longer()

pivot_longer() “lengthens” data, increasing the number of rows and decreasing the number of columns.

df <- tibble(index=c(1,2),x=c('a','b'),y=c('c','d'),z=c('e','f'))
df

## # A tibble: 2 × 4
##   index x     y     z    
##   <dbl> <chr> <chr> <chr>
## 1     1 a     c     e    
## 2     2 b     d     f

df2 <- df %>% 
  pivot_longer(cols=!index,names_to="key",values_to="val")
df2

## # A tibble: 6 × 3
##   index key   val  
##   <dbl> <chr> <chr>
## 1     1 x     a    
## 2     1 y     c    
## 3     1 z     e    
## 4     2 x     b    
## 5     2 y     d    
## 6     2 z     f

29.15 pivot_wider()

pivot_wider() “widens” data, increasing the number of columns and decreasing the number of rows.

df2 %>% 
  pivot_wider(names_from=key,values_from=val)

## # A tibble: 2 × 4
##   index x     y     z    
##   <dbl> <chr> <chr> <chr>
## 1     1 a     c     e    
## 2     2 b     d     f

29.16 count()

count() can count the number of occurences.

mpg %>% 
  count(model,sort=TRUE)

## # A tibble: 38 × 2
##    model                   n
##    <chr>               <int>
##  1 caravan 2wd            11
##  2 ram 1500 pickup 4wd    10
##  3 civic                   9
##  4 dakota pickup 4wd       9
##  5 jetta                   9
##  6 mustang                 9
##  7 a4 quattro              8
##  8 grand cherokee 4wd      8
##  9 impreza awd             8
## 10 a4                      7
## # … with 28 more rows

29.17 summarize()

summarise() is typically used on grouped data created by group_by(). The output will have one row for each group.

mpg %>% 
  group_by(model) %>% 
  summarise(count=n(),averagedispl=mean(displ),maxdispl=max(displ))

## # A tibble: 38 × 4
##    model              count averagedispl maxdispl
##    <chr>              <int>        <dbl>    <dbl>
##  1 4runner 4wd            6         3.48      4.7
##  2 a4                     7         2.33      3.1
##  3 a4 quattro             8         2.42      3.1
##  4 a6 quattro             3         3.37      4.2
##  5 altima                 6         2.8       3.5
##  6 c1500 suburban 2wd     5         5.52      6  
##  7 camry                  7         2.67      3.5
##  8 camry solara           7         2.64      3.3
##  9 caravan 2wd           11         3.39      4  
## 10 civic                  9         1.71      2  
## # … with 28 more rows

summarise_all() applies the functions to all (non-grouping) columns.

mpg %>% 
  group_by(model) %>% 
  select_if(is.numeric) %>% 
  summarise_all(mean)

## # A tibble: 38 × 6
##    model              displ  year   cyl   cty   hwy
##    <chr>              <dbl> <dbl> <dbl> <dbl> <dbl>
##  1 4runner 4wd         3.48 2002   5.67  15.2  18.8
##  2 a4                  2.33 2003.  4.86  18.9  28.3
##  3 a4 quattro          2.42 2004.  5     17.1  25.8
##  4 a6 quattro          3.37 2005   6.67  16    24  
##  5 altima              2.8  2005   4.67  20.7  28.7
##  6 c1500 suburban 2wd  5.52 2006.  8     12.8  17.8
##  7 camry               2.67 2003.  4.86  19.9  28.3
##  8 camry solara        2.64 2003.  4.86  19.9  28.1
##  9 caravan 2wd         3.39 2003.  5.82  15.8  22.4
## 10 civic               1.71 2003   4     24.4  32.6
## # … with 28 more rows

summarise_if() operate on columns for which a predicate returns TRUE.

mpg %>% 
  group_by(model) %>% 
  summarise_if(is.numeric,mean)

## # A tibble: 38 × 6
##    model              displ  year   cyl   cty   hwy
##    <chr>              <dbl> <dbl> <dbl> <dbl> <dbl>
##  1 4runner 4wd         3.48 2002   5.67  15.2  18.8
##  2 a4                  2.33 2003.  4.86  18.9  28.3
##  3 a4 quattro          2.42 2004.  5     17.1  25.8
##  4 a6 quattro          3.37 2005   6.67  16    24  
##  5 altima              2.8  2005   4.67  20.7  28.7
##  6 c1500 suburban 2wd  5.52 2006.  8     12.8  17.8
##  7 camry               2.67 2003.  4.86  19.9  28.3
##  8 camry solara        2.64 2003.  4.86  19.9  28.1
##  9 caravan 2wd         3.39 2003.  5.82  15.8  22.4
## 10 civic               1.71 2003   4     24.4  32.6
## # … with 28 more rows

summarise_at() allow you to select columns.

mpg %>% 
  group_by(model) %>% 
  summarise_at(vars(displ,cyl),mean)

## # A tibble: 38 × 3
##    model              displ   cyl
##    <chr>              <dbl> <dbl>
##  1 4runner 4wd         3.48  5.67
##  2 a4                  2.33  4.86
##  3 a4 quattro          2.42  5   
##  4 a6 quattro          3.37  6.67
##  5 altima              2.8   4.67
##  6 c1500 suburban 2wd  5.52  8   
##  7 camry               2.67  4.86
##  8 camry solara        2.64  4.86
##  9 caravan 2wd         3.39  5.82
## 10 civic               1.71  4   
## # … with 28 more rows