Selecting
Data Manipulation with dplyr
James Chapman
Curriculum Manager, DataCamp
Select
counties %>%
select(state, county, population, unemployment)
# A tibble: 3,138 x 4
state county population unemployment
<chr> <chr> <dbl> <dbl>
1 Alabama Autauga 55221 7.6
2 Alabama Baldwin 195121 7.5
3 Alabama Barbour 26932 17.6
4 Alabama Bibb 22604 8.3
5 Alabama Blount 57710 7.7
6 Alabama Bullock 10678 18
7 Alabama Butler 20354 10.9
8 Alabama Calhoun 116648 12.3
9 Alabama Chambers 34079 8.9
10 Alabama Cherokee 26008 7.9
# … with 3,128 more rows
Select a range
counties %>%
select(state, county, drive:work_at_home)
# A tibble: 3,138 x 8
state county drive carpool transit walk other_transp work_at_home
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Alabama Autauga 87.5 8.8 0.1 0.5 1.3 1.8
2 Alabama Baldwin 84.7 8.8 0.1 1 1.4 3.9
3 Alabama Barbour 83.8 10.9 0.4 1.8 1.5 1.6
4 Alabama Bibb 83.2 13.5 0.5 0.6 1.5 0.7
5 Alabama Blount 84.9 11.2 0.4 0.9 0.4 2.3
6 Alabama Bullock 74.9 14.9 0.7 5 1.7 2.8
7 Alabama Butler 84.5 12.4 0 0.8 0.6 1.7
8 Alabama Calhoun 85.3 9.4 0.2 1.2 1.2 2.7
9 Alabama Chambers 85.1 11.9 0.2 0.3 0.4 2.1
10 Alabama Cherokee 83.9 12.1 0.2 0.6 0.7 2.5
# … with 3,128 more rows
Select and arrange
counties %>%
select(state, county, drive:work_at_home) %>%
arrange(drive)
# A tibble: 3,138 x 8
state county drive carpool transit walk other_transp work_at_home
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 New York New York 6.1 1.9 59.2 20.7 5.4 6.8
2 Alaska Northwest Arctic Borough 16.5 10.4 0.4 46.9 21.2 4.6
3 Alaska Aleutians East Borough 18.4 4.9 0.5 71.2 2.2 2.8
4 New York Kings 18.6 4.4 61.7 8.8 2.5 3.9
5 Alaska North Slope Borough 20.1 17 2.8 37.9 7.9 14.3
6 Alaska Lake and Peninsula Borough 21.2 6.8 1.1 36.2 32.4 2.4
7 New York Bronx 22.5 4.7 59.7 8 1.8 3.3
8 Alaska Nome Census Area 25.8 10 0.3 36.9 22.7 4.3
9 Alaska Bethel Census Area 26.5 12.7 0.5 33 22.6 4.8
10 Alaska Yukon-Koyukuk Census Area 28.7 8.1 0.2 38.1 20.1 4.9
# … with 3,128 more rows
Contains
counties %>%
select(state, county, contains("work"))
# A tibble: 3,138 x 6
state county work_at_home private_work public_work family_work
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Alabama Autauga 1.8 73.6 20.9 0
2 Alabama Baldwin 3.9 81.5 12.3 0.4
3 Alabama Barbour 1.6 71.8 20.8 0.1
4 Alabama Bibb 0.7 76.8 16.1 0.4
5 Alabama Blount 2.3 82 13.5 0.4
6 Alabama Bullock 2.8 79.5 15.1 0
7 Alabama Butler 1.7 77.4 16.2 0.2
8 Alabama Calhoun 2.7 74.1 20.8 0.1
9 Alabama Chambers 2.1 85.1 12.1 0
10 Alabama Cherokee 2.5 73.1 18.5 0.5
# … with 3,128 more rows
Starts with
counties %>%
select(state, county, starts_with("income"))
# A tibble: 3,138 x 6
state county income income_err income_per_cap income_per_cap_err
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Alabama Autauga 51281 2391 24974 1080
2 Alabama Baldwin 50254 1263 27317 711
3 Alabama Barbour 32964 2973 16824 798
4 Alabama Bibb 38678 3995 18431 1618
5 Alabama Blount 45813 3141 20532 708
6 Alabama Bullock 31938 5884 17580 2055
7 Alabama Butler 32229 1793 18390 714
8 Alabama Calhoun 41703 925 21374 489
9 Alabama Chambers 34177 2949 21071 1366
10 Alabama Cherokee 36296 1710 21811 1556
# … with 3,128 more rows
Other helpers
contains()starts_with()ends_with()last_col()matches()
For more:
?select_helpers
Removing a variable
counties %>%
select(-census_id)
# A tibble: 3,138 x 39
state county region metro population men women hispanic white black native asian pacific citizens income
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Alab… Autau… South Metro 55221 26745 28476 2.6 75.8 18.5 0.4 1 0 40725 51281
2 Alab… Baldw… South Metro 195121 95314 99807 4.5 83.1 9.5 0.6 0.7 0 147695 50254
3 Alab… Barbo… South Nonm… 26932 14497 12435 4.6 46.2 46.7 0.2 0.4 0 20714 32964
4 Alab… Bibb South Metro 22604 12073 10531 2.2 74.5 21.4 0.4 0.1 0 17495 38678
5 Alab… Blount South Metro 57710 28512 29198 8.6 87.9 1.5 0.3 0.1 0 42345 45813
6 Alab… Bullo… South Nonm… 10678 5660 5018 4.4 22.2 70.7 1.2 0.2 0 8057 31938
7 Alab… Butler South Nonm… 20354 9502 10852 1.2 53.3 43.8 0.1 0.4 0 15581 32229
8 Alab… Calho… South Metro 116648 56274 60374 3.5 73 20.3 0.2 0.9 0 88612 41703
9 Alab… Chamb… South Nonm… 34079 16258 17821 0.4 57.3 40.3 0.2 0.8 0 26462 34177
10 Alab… Chero… South Nonm… 26008 12975 13033 1.5 91.7 4.8 0.6 0.3 0 20600 36296
# … with 3,128 more rows, and 24 more variables: income_err <dbl>, income_per_cap <dbl>,
# income_per_cap_err <dbl>, poverty <dbl>, child_poverty <dbl>, professional <dbl>, service <dbl>,
# office <dbl>, construction <dbl>, production <dbl>, drive <dbl>, carpool <dbl>, transit <dbl>, walk <dbl>,
# other_transp <dbl>, work_at_home <dbl>, mean_commute <dbl>, employed <dbl>, private_work <dbl>,
# public_work <dbl>, self_employed <dbl>, family_work <dbl>, unemployment <dbl>, land_area <dbl>
Let's practice!
Data Manipulation with dplyr
