Data Wrangling with R Dr Thiyanga TalagalaIASSL - 21/ 25 Feb, 20221

Data Wrangling/ Data Munging

Reshaping data

pivot_wider
pivot_longer
seperate
unite

Tidy Data

Each variable is placed in its column
Each observation is placed in its own row
Each value is placed in its own cell

packages

library(tidyverse) #or library(tidyr)
library(magrittr)

`tidyr` package

knitrhex

Hadley Wickham, Chief Scientist at RStudio explaining tidyr at WOMBAT organized by Monash University, Australia.

Image taken by Thiyanga S Talagala at WOMBAT Melbourne, Australia, December-2019

tidyr verbs

Main verbs

pivot_longer (gather)
pivot_wider (spread)

Input and Output

Main input: data frame or tibble.

Output: tibble

pivot_longer9

`pivot_longer()`

Turns columns into rows.
From wide format to long format.

pivot_longer()dengue <- tibble( dist = c("Colombo", "Gampaha", "Kalutara"), 
                  '2017' = c(20718, 10258, 34274), 
                  '2018' = c(16573, 5857, 31647), 
                  '2019' = c(8395, 3155, 10961)); 
dengue

# A tibble: 3 × 4
  dist     `2017` `2018` `2019`
  <chr>     <dbl>  <dbl>  <dbl>
1 Colombo   20718  16573   8395
2 Gampaha   10258   5857   3155
3 Kalutara  34274  31647  10961
dengue %>% 
  pivot_longer(2:4,
               names_to="Year", 
               values_to = "Dengue counts")

# A tibble: 9 × 3
  dist     Year  `Dengue counts`
  <chr>    <chr>           <dbl>
1 Colombo  2017            20718
2 Colombo  2018            16573
3 Colombo  2019             8395
4 Gampaha  2017            10258
5 Gampaha  2018             5857
6 Gampaha  2019             3155
7 Kalutara 2017            34274
8 Kalutara 2018            31647
9 Kalutara 2019            10961
11

pivot_wider12

`pivot_wider()`

From long to wide format.

`pivot_wider()`

Corona <- tibble(
country = rep(c("USA", "Brazil", "Russia"), each=2),
status = rep(c("Death", "Recovered"), 3),
count = c(99381, 451745, 22746, 149911, 3633, 118798))

Corona

# A tibble: 6 × 3
  country status     count
  <chr>   <chr>      <dbl>
1 USA     Death      99381
2 USA     Recovered 451745
3 Brazil  Death      22746
4 Brazil  Recovered 149911
5 Russia  Death       3633
6 Russia  Recovered 118798

pivot_wider()Corona

# A tibble: 6 × 3
  country status     count
  <chr>   <chr>      <dbl>
1 USA     Death      99381
2 USA     Recovered 451745
3 Brazil  Death      22746
4 Brazil  Recovered 149911
5 Russia  Death       3633
6 Russia  Recovered 118798
Corona %>% 
  pivot_wider(names_from=status, 
              values_from=count)

# A tibble: 3 × 3
  country Death Recovered
  <chr>   <dbl>     <dbl>
1 USA     99381    451745
2 Brazil  22746    149911
3 Russia   3633    118798
15

Assign a name:

corona_wide_format <- Corona %>% 
  pivot_wider(names_from=status, 
  values_from=count)
corona_wide_format

# A tibble: 3 × 3
  country Death Recovered
  <chr>   <dbl>     <dbl>
1 USA     99381    451745
2 Brazil  22746    149911
3 Russia   3633    118798

`pivot_longer` vs `pivot_wider`

class: duke-orange, center, middle

`separate`

`separate()`

Separate one column into several columns.

Melbourne <- 
  tibble(Date = c("10-5-2020", "11-5-2020", "12-5-2020","13-5-2020"),
         Tmin = c(5, 9, 9, 7), Tmax = c(18, 16, 16, 17),
         Rainfall= c(30, 40, 10, 5)); Melbourne

# A tibble: 4 × 4
  Date       Tmin  Tmax Rainfall
  <chr>     <dbl> <dbl>    <dbl>
1 10-5-2020     5    18       30
2 11-5-2020     9    16       40
3 12-5-2020     9    16       10
4 13-5-2020     7    17        5

separate(): Separate one column into several columns.

# A tibble: 4 × 4
  Date       Tmin  Tmax Rainfall
  <chr>     <dbl> <dbl>    <dbl>
1 10-5-2020     5    18       30
2 11-5-2020     9    16       40
3 12-5-2020     9    16       10
4 13-5-2020     7    17        5

Melbourne %>% separate(Date, into=c("day", "month", "year"), sep="-")

# A tibble: 4 × 6
  day   month year   Tmin  Tmax Rainfall
  <chr> <chr> <chr> <dbl> <dbl>    <dbl>
1 10    5     2020      5    18       30
2 11    5     2020      9    16       40
3 12    5     2020      9    16       10
4 13    5     2020      7    17        5

separate()

df <- data.frame(x = c(NA, "a.b", "a.d", "b.c"))
df

     x
1 <NA>
2  a.b
3  a.d
4  b.c

df %>% separate(x, c("Text1", "Text2"))

  Text1 Text2
1  <NA>  <NA>
2     a     b
3     a     d
4     b     c

separate()

tbl <- tibble(input = c("a", "a b", "a-b c", NA))
tbl

# A tibble: 4 × 1
  input
  <chr>
1 a    
2 a b  
3 a-b c
4 <NA>

separate()

tbl <- tibble(input = c("a", "a b", "a-b c", NA))
tbl

# A tibble: 4 × 1
  input
  <chr>
1 a    
2 a b  
3 a-b c
4 <NA>

tbl %>% 
  separate(input, c("Input1", "Input2"))

# A tibble: 4 × 2
  Input1 Input2
  <chr>  <chr> 
1 a      <NA>  
2 a      b     
3 a      b     
4 <NA>   <NA>

separate()

tbl <- tibble(input = c("a", "a b", "a-b c", NA)); tbl

# A tibble: 4 × 1
  input
  <chr>
1 a    
2 a b  
3 a-b c
4 <NA>

separate()

tbl <- tibble(input = c("a", "a b", "a-b c", NA)); tbl

# A tibble: 4 × 1
  input
  <chr>
1 a    
2 a b  
3 a-b c
4 <NA>

tbl %>% separate(input, c("Input1", "Input2", "Input3"))

# A tibble: 4 × 3
  Input1 Input2 Input3
  <chr>  <chr>  <chr> 
1 a      <NA>   <NA>  
2 a      b      <NA>  
3 a      b      c     
4 <NA>   <NA>   <NA>

unite25

unite()Unite several columns into one.
projects <- tibble(
  Country = c("USA", "USA", "AUS", "AUS"),
  State = c("LA", "CO", "VIC", "NSW"),
  Cost = c(1000, 11000, 20000,30000)
)
projects

# A tibble: 4 × 3
  Country State  Cost
  <chr>   <chr> <dbl>
1 USA     LA     1000
2 USA     CO    11000
3 AUS     VIC   20000
4 AUS     NSW   30000
26

unite()

# A tibble: 4 × 3
  Country State  Cost
  <chr>   <chr> <dbl>
1 USA     LA     1000
2 USA     CO    11000
3 AUS     VIC   20000
4 AUS     NSW   30000

projects %>% unite("Location", c("State", "Country"))

# A tibble: 4 × 2
  Location  Cost
  <chr>    <dbl>
1 LA_USA    1000
2 CO_USA   11000
3 VIC_AUS  20000
4 NSW_AUS  30000

unite()

# A tibble: 4 × 3
  Country State  Cost
  <chr>   <chr> <dbl>
1 USA     LA     1000
2 USA     CO    11000
3 AUS     VIC   20000
4 AUS     NSW   30000

projects %>% unite("Location", c("State", "Country"),
                    sep="-")

# A tibble: 4 × 2
  Location  Cost
  <chr>    <dbl>
1 LA-USA    1000
2 CO-USA   11000
3 VIC-AUS  20000
4 NSW-AUS  30000

filter
select
mutate
summarise
arrange
group_by
rename

packages

library(tidyverse) # To obtain dplyr
library(magrittr)

knitrhex rmarkdown

Dataset

library(palmerpenguins)
data(penguins)
head(penguins)

# A tibble: 6 × 8
  species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex  
  <fct>   <fct>           <dbl>         <dbl>            <int>       <int> <fct>
1 Adelie  Torge…           39.1          18.7              181        3750 male 
2 Adelie  Torge…           39.5          17.4              186        3800 fema…
3 Adelie  Torge…           40.3          18                195        3250 fema…
4 Adelie  Torge…           NA            NA                 NA          NA <NA> 
5 Adelie  Torge…           36.7          19.3              193        3450 fema…
6 Adelie  Torge…           39.3          20.6              190        3650 male 
# … with 1 more variable: year <int>

summary(penguins)

      species          island    bill_length_mm  bill_depth_mm  
 Adelie   :152   Biscoe   :168   Min.   :32.10   Min.   :13.10  
 Chinstrap: 68   Dream    :124   1st Qu.:39.23   1st Qu.:15.60  
 Gentoo   :124   Torgersen: 52   Median :44.45   Median :17.30  
                                 Mean   :43.92   Mean   :17.15  
                                 3rd Qu.:48.50   3rd Qu.:18.70  
                                 Max.   :59.60   Max.   :21.50  
                                 NA's   :2       NA's   :2      
 flipper_length_mm  body_mass_g       sex           year     
 Min.   :172.0     Min.   :2700   female:165   Min.   :2007  
 1st Qu.:190.0     1st Qu.:3550   male  :168   1st Qu.:2007  
 Median :197.0     Median :4050   NA's  : 11   Median :2008  
 Mean   :200.9     Mean   :4202                Mean   :2008  
 3rd Qu.:213.0     3rd Qu.:4750                3rd Qu.:2009  
 Max.   :231.0     Max.   :6300                Max.   :2009  
 NA's   :2         NA's   :2

`dplyr` verbs

filter
select
mutate
summarise

arrange
group_by
rename

`filter`: Picks observations by their values.

Takes logical expressions and returns the rows for which all are TRUE.

filter(penguins, flipper_length_mm > 180)

# A tibble: 329 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           36.7          19.3               193        3450
 5 Adelie  Torgersen           39.3          20.6               190        3650
 6 Adelie  Torgersen           38.9          17.8               181        3625
 7 Adelie  Torgersen           39.2          19.6               195        4675
 8 Adelie  Torgersen           34.1          18.1               193        3475
 9 Adelie  Torgersen           42            20.2               190        4250
10 Adelie  Torgersen           37.8          17.1               186        3300
# … with 319 more rows, and 2 more variables: sex <fct>, year <int>

`filter` (cont)

# penguins %>% filter(species == "Chinstrap")
filter(penguins,  species == "Chinstrap")

# A tibble: 68 × 8
   species   island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>     <fct>           <dbl>         <dbl>             <int>       <int>
 1 Chinstrap Dream            46.5          17.9               192        3500
 2 Chinstrap Dream            50            19.5               196        3900
 3 Chinstrap Dream            51.3          19.2               193        3650
 4 Chinstrap Dream            45.4          18.7               188        3525
 5 Chinstrap Dream            52.7          19.8               197        3725
 6 Chinstrap Dream            45.2          17.8               198        3950
 7 Chinstrap Dream            46.1          18.2               178        3250
 8 Chinstrap Dream            51.3          18.2               197        3750
 9 Chinstrap Dream            46            18.9               195        4150
10 Chinstrap Dream            51.3          19.9               198        3700
# … with 58 more rows, and 2 more variables: sex <fct>, year <int>

`select`: Picks variables by their names.

head(penguins, 3)

# A tibble: 3 × 8
  species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex  
  <fct>   <fct>           <dbl>         <dbl>            <int>       <int> <fct>
1 Adelie  Torge…           39.1          18.7              181        3750 male 
2 Adelie  Torge…           39.5          17.4              186        3800 fema…
3 Adelie  Torge…           40.3          18                195        3250 fema…
# … with 1 more variable: year <int>

select(penguins, bill_length_mm:body_mass_g)

# A tibble: 344 × 4
   bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
            <dbl>         <dbl>             <int>       <int>
 1           39.1          18.7               181        3750
 2           39.5          17.4               186        3800
 3           40.3          18                 195        3250
 4           NA            NA                  NA          NA
 5           36.7          19.3               193        3450
 6           39.3          20.6               190        3650
 7           38.9          17.8               181        3625
 8           39.2          19.6               195        4675
 9           34.1          18.1               193        3475
10           42            20.2               190        4250
# … with 334 more rows

`select` (cont.)

head(penguins, 3)

# A tibble: 3 × 8
  species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex  
  <fct>   <fct>           <dbl>         <dbl>            <int>       <int> <fct>
1 Adelie  Torge…           39.1          18.7              181        3750 male 
2 Adelie  Torge…           39.5          17.4              186        3800 fema…
3 Adelie  Torge…           40.3          18                195        3250 fema…
# … with 1 more variable: year <int>

select(penguins, species, body_mass_g)

# A tibble: 344 × 2
   species body_mass_g
   <fct>         <int>
 1 Adelie         3750
 2 Adelie         3800
 3 Adelie         3250
 4 Adelie           NA
 5 Adelie         3450
 6 Adelie         3650
 7 Adelie         3625
 8 Adelie         4675
 9 Adelie         3475
10 Adelie         4250
# … with 334 more rows

`select` (cont.)

head(penguins, 3)

# A tibble: 3 × 8
  species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex  
  <fct>   <fct>           <dbl>         <dbl>            <int>       <int> <fct>
1 Adelie  Torge…           39.1          18.7              181        3750 male 
2 Adelie  Torge…           39.5          17.4              186        3800 fema…
3 Adelie  Torge…           40.3          18                195        3250 fema…
# … with 1 more variable: year <int>

select(penguins, -c(island, year))

# A tibble: 344 × 6
   species bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex   
   <fct>            <dbl>         <dbl>             <int>       <int> <fct> 
 1 Adelie            39.1          18.7               181        3750 male  
 2 Adelie            39.5          17.4               186        3800 female
 3 Adelie            40.3          18                 195        3250 female
 4 Adelie            NA            NA                  NA          NA <NA>  
 5 Adelie            36.7          19.3               193        3450 female
 6 Adelie            39.3          20.6               190        3650 male  
 7 Adelie            38.9          17.8               181        3625 female
 8 Adelie            39.2          19.6               195        4675 male  
 9 Adelie            34.1          18.1               193        3475 <NA>  
10 Adelie            42            20.2               190        4250 <NA>  
# … with 334 more rows

`mutate`

Creates new variables with functions of existing variables

penguins <- penguins %>% mutate(BMI = body_mass_g / 1000)
penguins

# A tibble: 344 × 9
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# … with 334 more rows, and 3 more variables: sex <fct>, year <int>, BMI <dbl>

`summarise`(British) or `summarize` (US)

Collapse many values down to a single summary

penguins %>%
  summarise(
    bill_length_mm_mean=mean(bill_length_mm),
    bill_length_mm_median=median(bill_length_mm),
    BMI_mean=mean(BMI))

# A tibble: 1 × 3
  bill_length_mm_mean bill_length_mm_median BMI_mean
                <dbl>                 <dbl>    <dbl>
1                  NA                    NA       NA

`summarise`(British) or `summarize` (US)

Collapse many values down to a single summary

penguins %>%
  summarise(
    bill_length_mm_mean=mean(bill_length_mm, na.rm=TRUE),
    bill_length_mm_median=median(bill_length_mm, na.rm=TRUE),
    BMI_mean=mean(BMI, na.rm=TRUE))

# A tibble: 1 × 3
  bill_length_mm_mean bill_length_mm_median BMI_mean
                <dbl>                 <dbl>    <dbl>
1                43.9                  44.4     4.20

`arrange`

Reorder the rows

arrange(penguins, desc(body_mass_g))

# A tibble: 344 × 9
   species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>           <dbl>         <dbl>             <int>       <int>
 1 Gentoo  Biscoe           49.2          15.2               221        6300
 2 Gentoo  Biscoe           59.6          17                 230        6050
 3 Gentoo  Biscoe           51.1          16.3               220        6000
 4 Gentoo  Biscoe           48.8          16.2               222        6000
 5 Gentoo  Biscoe           45.2          16.4               223        5950
 6 Gentoo  Biscoe           49.8          15.9               229        5950
 7 Gentoo  Biscoe           48.4          14.6               213        5850
 8 Gentoo  Biscoe           49.3          15.7               217        5850
 9 Gentoo  Biscoe           55.1          16                 230        5850
10 Gentoo  Biscoe           49.5          16.2               229        5800
# … with 334 more rows, and 3 more variables: sex <fct>, year <int>, BMI <dbl>

group_byTakes an existing tibble and converts it into a grouped tibble where operations are performed "by group". ungroup() removes grouping.
47

`group_by`

penguins_grouped <- penguins %>% group_by(species)
penguins_grouped

# A tibble: 344 × 9
# Groups:   species [3]
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# … with 334 more rows, and 3 more variables: sex <fct>, year <int>, BMI <dbl>

`group_by` (cont.)

penguins %>% summarise(BMI_mean=mean(BMI, na.rm=TRUE))

# A tibble: 1 × 1
  BMI_mean
     <dbl>
1     4.20

penguins_grouped %>% summarise(BMI_mean=mean(BMI, na.rm=TRUE))

# A tibble: 3 × 2
  species   BMI_mean
  <fct>        <dbl>
1 Adelie        3.70
2 Chinstrap     3.73
3 Gentoo        5.08

`rename`

Rename variables

head(penguins, 3)

# A tibble: 3 × 9
  species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex  
  <fct>   <fct>           <dbl>         <dbl>            <int>       <int> <fct>
1 Adelie  Torge…           39.1          18.7              181        3750 male 
2 Adelie  Torge…           39.5          17.4              186        3800 fema…
3 Adelie  Torge…           40.3          18                195        3250 fema…
# … with 2 more variables: year <int>, BMI <dbl>

`rename`

penguins <- rename(penguins, `Bill length`=bill_length_mm,
       Location=island) # new_name = old_name
penguins

# A tibble: 344 × 9
   species Location  `Bill length` bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>             <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen          39.1          18.7               181        3750
 2 Adelie  Torgersen          39.5          17.4               186        3800
 3 Adelie  Torgersen          40.3          18                 195        3250
 4 Adelie  Torgersen          NA            NA                  NA          NA
 5 Adelie  Torgersen          36.7          19.3               193        3450
 6 Adelie  Torgersen          39.3          20.6               190        3650
 7 Adelie  Torgersen          38.9          17.8               181        3625
 8 Adelie  Torgersen          39.2          19.6               195        4675
 9 Adelie  Torgersen          34.1          18.1               193        3475
10 Adelie  Torgersen          42            20.2               190        4250
# … with 334 more rows, and 3 more variables: sex <fct>, year <int>, BMI <dbl>

Combine multiple operations

penguins %>%
filter(species == 'Gentoo') %>% head(2)

# A tibble: 2 × 9
  species Location `Bill length` bill_depth_mm flipper_length_mm body_mass_g
  <fct>   <fct>            <dbl>         <dbl>             <int>       <int>
1 Gentoo  Biscoe            46.1          13.2               211        4500
2 Gentoo  Biscoe            50            16.3               230        5700
# … with 3 more variables: sex <fct>, year <int>, BMI <dbl>

penguins %>%
filter(species == 'Gentoo') %>% summarise(BMI_mean=mean(BMI, na.rm=TRUE))

# A tibble: 1 × 1
  BMI_mean
     <dbl>
1     5.08

Combine multiple operations

penguins %>%
filter(species == 'Adelie') %>%
group_by(Location) %>%
filter(bill_depth_mm > 20) %>%
arrange(desc(flipper_length_mm))

# A tibble: 14 × 9
# Groups:   Location [3]
   species Location  `Bill length` bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>             <dbl>         <dbl>             <int>       <int>
 1 Adelie  Dream              40.2          20.1               200        3975
 2 Adelie  Torgersen          37.3          20.5               199        3775
 3 Adelie  Torgersen          34.6          21.1               198        4400
 4 Adelie  Torgersen          42.5          20.7               197        4500
 5 Adelie  Dream              39.2          21.1               196        4150
 6 Adelie  Biscoe             41.3          21.1               195        4400
 7 Adelie  Torgersen          46            21.5               194        4200
 8 Adelie  Dream              41.3          20.3               194        3550
 9 Adelie  Torgersen          38.6          21.2               191        3800
10 Adelie  Dream              42.3          21.2               191        4150
11 Adelie  Biscoe             45.6          20.3               191        4600
12 Adelie  Biscoe             39.6          20.7               191        3900
13 Adelie  Torgersen          39.3          20.6               190        3650
14 Adelie  Torgersen          42            20.2               190        4250
# … with 3 more variables: sex <fct>, year <int>, BMI <dbl>

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Data Wrangling with R

Dr Thiyanga Talagala

IASSL - 21/ 25 Feb, 2022

Data Wrangling/ Data Munging

Reshaping data

Tidy Data

packages

tidyr package

tidyr verbs

Main verbs

Input and Output

pivot_longer

pivot_longer()

pivot_longer()

pivot_wider

pivot_wider()

pivot_wider()

pivot_wider()

Assign a name:

pivot_longer vs pivot_wider

separate

separate()

unite

unite()

packages

Dataset

dplyr verbs

filter: Picks observations by their values.

filter (cont)

select: Picks variables by their names.

select (cont.)

select (cont.)

mutate

summarise(British) or summarize (US)

summarise(British) or summarize (US)

arrange

group_by

group_by

group_by (cont.)

rename

rename

Combine multiple operations

Combine multiple operations

Data Wrangling/ Data Munging

Help

`tidyr` package

`pivot_longer`

`pivot_longer()`

`pivot_longer()`

`pivot_wider`

`pivot_wider()`

`pivot_wider()`

`pivot_wider()`

`pivot_longer` vs `pivot_wider`

`separate`

`separate()`

`unite`

`unite()`

`dplyr` verbs

`filter`: Picks observations by their values.

`filter` (cont)

`select`: Picks variables by their names.

`select` (cont.)

`select` (cont.)

`mutate`

`summarise`(British) or `summarize` (US)

`summarise`(British) or `summarize` (US)

`arrange`

`group_by`

`group_by`

`group_by` (cont.)

`rename`

`rename`