Exploring data with dplyr
Manipolazione dei dati con dplyr
James Chapman
Curriculum Manager, DataCamp
The dplyr package
- Part of the
tidyversecollection - Specializes in data manipulation
- Install
dplyronly:install.packages("dplyr")
- Install entire
tidyverse, inc.dplyr:install.packages("tidyverse")
1 Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, et al. (2019). "Welcome to the tidyverse." Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686.
Chapter 1 verbs
select()filter()arrange()mutate()
2015 United States Census
counties dataset
counties
# A tibble: 3,138 x 40
census_id state county region metro population men women hispanic white black native asian pacific
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1001 Alab… Autau… South Metro 55221 26745 28476 2.6 75.8 18.5 0.4 1 0
2 1003 Alab… Baldw… South Metro 195121 95314 99807 4.5 83.1 9.5 0.6 0.7 0
3 1005 Alab… Barbo… South Nonm… 26932 14497 12435 4.6 46.2 46.7 0.2 0.4 0
4 1007 Alab… Bibb South Metro 22604 12073 10531 2.2 74.5 21.4 0.4 0.1 0
5 1009 Alab… Blount South Metro 57710 28512 29198 8.6 87.9 1.5 0.3 0.1 0
6 1011 Alab… Bullo… South Nonm… 10678 5660 5018 4.4 22.2 70.7 1.2 0.2 0
7 1013 Alab… Butler South Nonm… 20354 9502 10852 1.2 53.3 43.8 0.1 0.4 0
8 1015 Alab… Calho… South Metro 116648 56274 60374 3.5 73 20.3 0.2 0.9 0
9 1017 Alab… Chamb… South Nonm… 34079 16258 17821 0.4 57.3 40.3 0.2 0.8 0
10 1019 Alab… Chero… South Nonm… 26008 12975 13033 1.5 91.7 4.8 0.6 0.3 0
# … with 3,128 more rows, and 26 more variables: citizens <dbl>, income <dbl>, 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>
glimpse(counties)
Observations: 3,138
Variables: 40
$ census_id <chr> "1001", "1003", "1005", "1007", "1009", "1011", "1013", …
$ state <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "…
$ county <chr> "Autauga", "Baldwin", "Barbour", "Bibb", "Blount", "Bull…
$ region <chr> "South", "South", "South", "South", "South", "South", "S…
$ metro <chr> "Metro", "Metro", "Nonmetro", "Metro", "Metro", "Nonmetr…
$ population <dbl> 55221, 195121, 26932, 22604, 57710, 10678, 20354, 116648…
$ men <dbl> 26745, 95314, 14497, 12073, 28512, 5660, 9502, 56274, 16…
$ women <dbl> 28476, 99807, 12435, 10531, 29198, 5018, 10852, 60374, 1…
$ hispanic <dbl> 2.6, 4.5, 4.6, 2.2, 8.6, 4.4, 1.2, 3.5, 0.4, 1.5, 7.6, 0…
$ white <dbl> 75.8, 83.1, 46.2, 74.5, 87.9, 22.2, 53.3, 73.0, 57.3, 91…
$ black <dbl> 18.5, 9.5, 46.7, 21.4, 1.5, 70.7, 43.8, 20.3, 40.3, 4.8,…
$ native <dbl> 0.4, 0.6, 0.2, 0.4, 0.3, 1.2, 0.1, 0.2, 0.2, 0.6, 0.4, 0…
$ asian <dbl> 1.0, 0.7, 0.4, 0.1, 0.1, 0.2, 0.4, 0.9, 0.8, 0.3, 0.3, 0…
$ pacific <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…
$ citizens <dbl> 40725, 147695, 20714, 17495, 42345, 8057, 15581, 88612, …
$ income <dbl> 51281, 50254, 32964, 38678, 45813, 31938, 32229, 41703, …
...
select() verb
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
Creating a new table
counties_selected <- counties %>%
select(state, county, population, unemployment)
counties_selected
# 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
Let's practice!
Manipolazione dei dati con dplyr
