17: Iteration
Load the Data
unscaled_cancer <- read_csv("https://raw.githubusercontent.com/UBC-DSCI/introduction-to-datascience/refs/heads/main/data/wdbc_unscaled.csv")
unscaled_cancer# A tibble: 569 × 12
ID Class Radius Texture Perimeter Area Smoothness Compactness Concavity
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 8.42e5 M 18.0 10.4 123. 1001 0.118 0.278 0.300
2 8.43e5 M 20.6 17.8 133. 1326 0.0847 0.0786 0.0869
3 8.43e7 M 19.7 21.2 130 1203 0.110 0.160 0.197
4 8.43e7 M 11.4 20.4 77.6 386. 0.142 0.284 0.241
5 8.44e7 M 20.3 14.3 135. 1297 0.100 0.133 0.198
6 8.44e5 M 12.4 15.7 82.6 477. 0.128 0.17 0.158
7 8.44e5 M 18.2 20.0 120. 1040 0.0946 0.109 0.113
8 8.45e7 M 13.7 20.8 90.2 578. 0.119 0.164 0.0937
9 8.45e5 M 13 21.8 87.5 520. 0.127 0.193 0.186
10 8.45e7 M 12.5 24.0 84.0 476. 0.119 0.240 0.227
# ℹ 559 more rows
# ℹ 3 more variables: Concave_Points <dbl>, Symmetry <dbl>,
# Fractal_Dimension <dbl>Your turn: For loop
Load the palmerpenguins package.
Write a for loop that calculates the mean of the numeric variables in the penguins data set and stores the means in a named vector.
Your turn: summary/table for loop
Revisit the {palmerpenguins} penguins data.
Write a for loop that calculates the summary() of a numeric variable and the table() of a factor variable.
Store the results in a list (it will have length 8).
Your turn: across
Use summarize and across to find the range of any quantitative variables, and the number of levels of any factor variables in the penguins dataset.