Some R Tips
Using data from an R package
To use data from an R package, for instance, the bechdel data from the fivethirtyeight package, you can simply load the relevant package with library and then the data frame will be available
library(fivethirtyeight)
bechdel# A tibble: 1,794 x 15
year imdb title test clean_test binary budget domgross
<int> <chr> <chr> <chr> <ord> <chr> <int> <dbl>
1 2013 tt17~ 21 &~ nota~ notalk FAIL 1.3 e7 25682380
2 2012 tt13~ Dred~ ok-d~ ok PASS 4.5 e7 13414714
3 2013 tt20~ 12 Y~ nota~ notalk FAIL 2 e7 53107035
4 2013 tt12~ 2 Gu~ nota~ notalk FAIL 6.1 e7 75612460
5 2013 tt04~ 42 men men FAIL 4 e7 95020213
6 2013 tt13~ 47 R~ men men FAIL 2.25e8 38362475
7 2013 tt16~ A Go~ nota~ notalk FAIL 9.2 e7 67349198
8 2013 tt21~ Abou~ ok-d~ ok PASS 1.2 e7 15323921
9 2013 tt18~ Admi~ ok ok PASS 1.3 e7 18007317
10 2013 tt18~ Afte~ nota~ notalk FAIL 1.3 e8 60522097
# ... with 1,784 more rows, and 7 more variables:
# intgross <dbl>, code <chr>, budget_2013 <int>,
# domgross_2013 <dbl>, intgross_2013 <dbl>,
# period_code <int>, decade_code <int>Using read_rds to read in an R data set
We have provided the nnyfs.Rds data file on the course data page.
Suppose you have downloaded this data file into a directory on your computer called data which is a sub-directory of the directory where you plan to do your work, perhaps called 431-nnyfs.
Open RStudio and create a new project into the 431-nnyfs directory on your computer. You should see a data subdirectory in the Files window in RStudio after the project is created.
Now, read in the nnyfs.Rds file to a new tibble in R called nnyfs_new with the following command:
nnyfs_new <- read_rds("data/nnyfs.Rds")Here are the results…
nnyfs_new# A tibble: 1,518 x 45
SEQN sex age_child race_eth educ_child language
<dbl> <fct> <dbl> <fct> <dbl> <fct>
1 71917 Female 15 3_Black Non-H~ 9 English
2 71918 Female 8 3_Black Non-H~ 2 English
3 71919 Female 14 2_White Non-H~ 8 English
4 71920 Female 15 2_White Non-H~ 8 English
5 71921 Male 3 2_White Non-H~ NA English
6 71922 Male 12 1_Hispanic 6 English
7 71923 Male 12 2_White Non-H~ 5 English
8 71924 Female 8 4_Other Race/~ 2 English
9 71925 Male 7 1_Hispanic 0 English
10 71926 Male 8 3_Black Non-H~ 2 English
# ... with 1,508 more rows, and 39 more variables:
# sampling_wt <dbl>, income_pov <dbl>, age_adult <dbl>,
# educ_adult <fct>, respondent <fct>, salt_used <fct>,
# energy <dbl>, protein <dbl>, sugar <dbl>, fat <dbl>,
# diet_yesterday <fct>, water <dbl>, plank_time <dbl>,
# height <dbl>, weight <dbl>, bmi <dbl>, bmi_cat <fct>,
# arm_length <dbl>, waist <dbl>, arm_circ <dbl>, ...Using read_csv to read in a comma-separated version of a data file
We have provided the nnyfs.csv data file on the course data page.
Suppose you have downloaded this data file into a directory on your computer called data which is a sub-directory of the directory where you plan to do your work, perhaps called 431-nnyfs.
Open RStudio and create a new project into the 431-nnyfs directory on your computer. You should see a data subdirectory in the Files window in RStudio after the project is created.
Now, read in the nnyfs.csv file to a new tibble in R called nnyfs_new2 with the following command:
nnyfs_new2 <- read_csv("data/nnyfs.csv")Rows: 1518 Columns: 45-- Column specification ------------------------------------
Delimiter: ","
chr (18): sex, race_eth, language, educ_adult, responden...
dbl (27): SEQN, age_child, educ_child, sampling_wt, inco...
i Use `spec()` to retrieve the full column specification for this data.
i Specify the column types or set `show_col_types = FALSE` to quiet this message.
nnyfs_new2# A tibble: 1,518 x 45
SEQN sex age_child race_eth educ_child language
<dbl> <chr> <dbl> <chr> <dbl> <chr>
1 71917 Female 15 3_Black Non-H~ 9 English
2 71918 Female 8 3_Black Non-H~ 2 English
3 71919 Female 14 2_White Non-H~ 8 English
4 71920 Female 15 2_White Non-H~ 8 English
5 71921 Male 3 2_White Non-H~ NA English
6 71922 Male 12 1_Hispanic 6 English
7 71923 Male 12 2_White Non-H~ 5 English
8 71924 Female 8 4_Other Race/~ 2 English
9 71925 Male 7 1_Hispanic 0 English
10 71926 Male 8 3_Black Non-H~ 2 English
# ... with 1,508 more rows, and 39 more variables:
# sampling_wt <dbl>, income_pov <dbl>, age_adult <dbl>,
# educ_adult <chr>, respondent <chr>, salt_used <chr>,
# energy <dbl>, protein <dbl>, sugar <dbl>, fat <dbl>,
# diet_yesterday <chr>, water <dbl>, plank_time <dbl>,
# height <dbl>, weight <dbl>, bmi <dbl>, bmi_cat <chr>,
# arm_length <dbl>, waist <dbl>, arm_circ <dbl>, ...If you also want to convert the character variables to factors, as you will often want to do before analyzing the results, you should instead use:
Rows: 1518 Columns: 45-- Column specification ------------------------------------
Delimiter: ","
chr (18): sex, race_eth, language, educ_adult, responden...
dbl (27): SEQN, age_child, educ_child, sampling_wt, inco...
i Use `spec()` to retrieve the full column specification for this data.
i Specify the column types or set `show_col_types = FALSE` to quiet this message.
nnyfs_new3# A tibble: 1,518 x 45
SEQN sex age_child race_eth educ_child language
<dbl> <fct> <dbl> <fct> <dbl> <fct>
1 71917 Female 15 3_Black Non-H~ 9 English
2 71918 Female 8 3_Black Non-H~ 2 English
3 71919 Female 14 2_White Non-H~ 8 English
4 71920 Female 15 2_White Non-H~ 8 English
5 71921 Male 3 2_White Non-H~ NA English
6 71922 Male 12 1_Hispanic 6 English
7 71923 Male 12 2_White Non-H~ 5 English
8 71924 Female 8 4_Other Race/~ 2 English
9 71925 Male 7 1_Hispanic 0 English
10 71926 Male 8 3_Black Non-H~ 2 English
# ... with 1,508 more rows, and 39 more variables:
# sampling_wt <dbl>, income_pov <dbl>, age_adult <dbl>,
# educ_adult <fct>, respondent <fct>, salt_used <fct>,
# energy <dbl>, protein <dbl>, sugar <dbl>, fat <dbl>,
# diet_yesterday <fct>, water <dbl>, plank_time <dbl>,
# height <dbl>, weight <dbl>, bmi <dbl>, bmi_cat <fct>,
# arm_length <dbl>, waist <dbl>, arm_circ <dbl>, ...Note that, for example, sex and race_eth are now listed as factor (fctr) variables. One place where this distinction between character and factor variables matters is when you summarize the data.
summary(nnyfs_new2$race_eth) Length Class Mode
1518 character character
summary(nnyfs_new3$race_eth) 3_Black Non-Hispanic 2_White Non-Hispanic
338 610
1_Hispanic 4_Other Race/Ethnicity
450 120 Converting Character Variables into Factors
The command you want to create newdata from olddata is:
newdata <- olddata %>%
mutate(across(where(is.character), as_factor))For more on factors, visit https://r4ds.had.co.nz/factors.html
Converting Data Frames to Tibbles
Use as_tibble() or simply tibble() to assign the attributes of a tibble to a data frame. Note that read_rds and read_csv automatically create tibbles.
For more on tibbles, visit https://r4ds.had.co.nz/tibbles.html.