7.2 select
select will select (and optionally rename) columns/variables in a data frame / tibble.
Select column “name” only from the presidential2 object:
| name | start | end | party | duration_days |
|---|---|---|---|---|
| Eisenhower | 1953-01-20 | 1961-01-20 | Republican | 2922 days |
| Kennedy | 1961-01-20 | 1963-11-22 | Democratic | 1036 days |
| Johnson | 1963-11-22 | 1969-01-20 | Democratic | 1886 days |
| Nixon | 1969-01-20 | 1974-08-09 | Republican | 2027 days |
| Ford | 1974-08-09 | 1977-01-20 | Republican | 895 days |
| Carter | 1977-01-20 | 1981-01-20 | Democratic | 1461 days |
| Reagan | 1981-01-20 | 1989-01-20 | Republican | 2922 days |
| Bush | 1989-01-20 | 1993-01-20 | Republican | 1461 days |
| Clinton | 1993-01-20 | 2001-01-20 | Democratic | 2922 days |
| Bush | 2001-01-20 | 2009-01-20 | Republican | 2922 days |
| Obama | 2009-01-20 | 2017-01-20 | Democratic | 2922 days |
select(presidential2,
name)Select 2 columns: party and name (in that order):
select(presidential2,
party, name)Rename a column as you select it:
select(presidential2,
party, President=name)Select all columns except party with the - sign:
select(presidential2,
-party)Select all columns between start and party (with both columns included)
select(presidential2,
start:party)## # A tibble: 11 x 3
## start end party
## <date> <date> <chr>
## 1 1953-01-20 1961-01-20 Republican
## 2 1961-01-20 1963-11-22 Democratic
## 3 1963-11-22 1969-01-20 Democratic
## 4 1969-01-20 1974-08-09 Republican
## 5 1974-08-09 1977-01-20 Republican
## 6 1977-01-20 1981-01-20 Democratic
## 7 1981-01-20 1989-01-20 Republican
## 8 1989-01-20 1993-01-20 Republican
## 9 1993-01-20 2001-01-20 Democratic
## 10 2001-01-20 2009-01-20 Republican
## 11 2009-01-20 2017-01-20 Democratic7.2.1 select_if
Select only columns containing characters with select_if():
# select columns containing characters:
select_if(presidential2,
is.character)## # A tibble: 11 x 2
## name party
## <chr> <chr>
## 1 Eisenhower Republican
## 2 Kennedy Democratic
## 3 Johnson Democratic
## 4 Nixon Republican
## 5 Ford Republican
## 6 Carter Democratic
## 7 Reagan Republican
## 8 Bush Republican
## 9 Clinton Democratic
## 10 Bush Republican
## 11 Obama DemocraticSelect only columns containing a date with the is.Date function from the lubridate package:
select_if(presidential2,
lubridate::is.Date)## # A tibble: 11 x 2
## start end
## <date> <date>
## 1 1953-01-20 1961-01-20
## 2 1961-01-20 1963-11-22
## 3 1963-11-22 1969-01-20
## 4 1969-01-20 1974-08-09
## 5 1974-08-09 1977-01-20
## 6 1977-01-20 1981-01-20
## 7 1981-01-20 1989-01-20
## 8 1989-01-20 1993-01-20
## 9 1993-01-20 2001-01-20
## 10 2001-01-20 2009-01-20
## 11 2009-01-20 2017-01-20HANDS-ON
For the next hands-on, we will use th starwars dataset: it contains information about the Star Wars movie characters:
| name | height | mass | hair_color | skin_color | eye_color | birth_year | sex | gender |
|---|---|---|---|---|---|---|---|---|
| Luke Skywalker | 172 | 77 | blond | fair | blue | 19.0 | male | masculine |
| C-3PO | 167 | 75 | NA | gold | yellow | 112.0 | none | masculine |
| R2-D2 | 96 | 32 | NA | white, blue | red | 33.0 | none | masculine |
| Darth Vader | 202 | 136 | none | white | yellow | 41.9 | male | masculine |
| Leia Organa | 150 | 49 | brown | light | brown | 19.0 | female | feminine |
- Create a new column BMI that contains the BMI of each character (Body Mass Index, calculated as
weight in kg / (height in m)^2: we will assume that theheightcolumn is expressed as cm and themasscolumn is expressed as kg) - Rename column
nametocharacter_name. - Remove columns
vehiclesandstarships. - Save all the changes into the new tibble
starwarsBMI(use the%>%!).
Answer
# Create a new column BMI that contains the BMI of each character
mutate(starwars, BMI=mass/(height*0.01)^2)
# Rename column name to character_name.
rename(starwars, character_name=name)
# Remove columns `vehicles` and `starships`.
select(starwars, -c(vehicles, starships))
# Save all the changes into the new tibble starwarsBMI (use the %>% operator)
starwarsBMI <- starwars %>%
mutate(BMI=mass/(height*0.01)^2) %>%
select(-c(vehicles, starships)) %>%
rename(character_name=name)7.2.2 select using patterns
Some select helpers are available, that help you select columns given certain patterns in their names:
| name | description |
|---|---|
| starts_with | starts with a prefix |
| ends_with | ends with a suffix |
| contains | contains a literal string |
| matches | matches a regular expression |
| num_range | matches a numerical range like x01, x02, x03 |
For example, select only columns from the starwars dataset which name end with “color”:
select(starwars,
ends_with("color"))## # A tibble: 87 x 3
## hair_color skin_color eye_color
## <chr> <chr> <chr>
## 1 blond fair blue
## 2 <NA> gold yellow
## 3 <NA> white, blue red
## 4 none white yellow
## 5 brown light brown
## 6 brown, grey light blue
## 7 brown light blue
## 8 <NA> white, red red
## 9 black light brown
## 10 auburn, white fair blue-gray
## # … with 77 more rowsOr which start with the letter h:
select(starwars,
starts_with("h"))## # A tibble: 87 x 3
## height hair_color homeworld
## <int> <chr> <chr>
## 1 172 blond Tatooine
## 2 167 <NA> Tatooine
## 3 96 <NA> Naboo
## 4 202 none Tatooine
## 5 150 brown Alderaan
## 6 178 brown, grey Tatooine
## 7 165 brown Tatooine
## 8 97 <NA> Tatooine
## 9 183 black Tatooine
## 10 182 auburn, white Stewjon
## # … with 77 more rowsIf you are familiar with regular expressions, you can also use them within the matches function:
select(starwars,
matches("^h")) # same as starts_with("h")## # A tibble: 87 x 3
## height hair_color homeworld
## <int> <chr> <chr>
## 1 172 blond Tatooine
## 2 167 <NA> Tatooine
## 3 96 <NA> Naboo
## 4 202 none Tatooine
## 5 150 brown Alderaan
## 6 178 brown, grey Tatooine
## 7 165 brown Tatooine
## 8 97 <NA> Tatooine
## 9 183 black Tatooine
## 10 182 auburn, white Stewjon
## # … with 77 more rowsFinally, you can select columns which name match a numerical range with num_range.
For example, let’s take the billboard dataset that contains column names wk1, wk2, wk3 … up to wk76, and select only columns from wk18 to wk22:
select(billboard,
num_range("wk", 18:22))## # A tibble: 317 x 5
## wk18 wk19 wk20 wk21 wk22
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 18 18 14 12 7
## 4 67 73 70 NA NA
## 5 96 NA NA NA NA
## 6 84 93 94 NA NA
## 7 NA NA NA NA NA
## 8 67 83 86 NA NA
## 9 2 3 4 5 5
## 10 71 79 89 NA NA
## # … with 307 more rows