Dplyr penguins data basics filter na (solution)

NoteExercise

If the file penguins.csv is not already in your working directory then download it into your working directory.

Load the data using read_csv() and use dplyr to complete the following tasks.

  1. Remove rows with null values in the body_mass_g column.
  2. Remove rows with null values in the sex column.
  3. Create a new data frame called penguin_masses that includes all of the original data with no null body masses and a new column containing the body mass in kilograms (body mass in grams / 1000).
CautionOutput solution 

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
Rows: 344 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): species, island, sex
dbl (5): bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, year

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.
  1. Remove rows with null values in the body_mass_g column.
# A tibble: 342 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <chr>   <chr>              <dbl>         <dbl>             <dbl>       <dbl>
 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
# ℹ 332 more rows
# ℹ 2 more variables: sex <chr>, year <dbl>
  1. Remove rows with null values in the sex column.
# A tibble: 333 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <chr>   <chr>              <dbl>         <dbl>             <dbl>       <dbl>
 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           41.1          17.6               182        3200
 9 Adelie  Torgersen           38.6          21.2               191        3800
10 Adelie  Torgersen           34.6          21.1               198        4400
# ℹ 323 more rows
# ℹ 2 more variables: sex <chr>, year <dbl>
  1. Create a new data frame called penguin_masses that includes all of the original data with no null body masses and a new column containing the body mass in kilograms (body mass in grams / 1000).
# A tibble: 342 × 9
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <chr>   <chr>              <dbl>         <dbl>             <dbl>       <dbl>
 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
# ℹ 332 more rows
# ℹ 3 more variables: sex <chr>, year <dbl>, body_mass_kg <dbl>