Functional Programming Key

Check out this purrr tutorial by Jenny Bryan: https://jennybc.github.io/purrr-tutorial/ls02_map-extraction-advanced.html

Additional content to read up on: https://jennybc.github.io/purrr-tutorial/ls01_map-name-position-shortcuts.html

0. Create a new dataset from iris called iris_lab. Use this for the lab.

iris_lab <- iris

1. Working with the iris_lab data, use mutate and across to change the numeric variables to be rounded with the round function. Take a look with head()- do this for the rest of the questions in which you create an output of data.

iris_lab %>% 
  mutate(across(.cols = !Species, round)) %>%
  head()

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1            5           4            1           0  setosa
## 2            5           3            1           0  setosa
## 3            5           3            1           0  setosa
## 4            5           3            2           0  setosa
## 5            5           4            1           0  setosa
## 6            5           4            2           0  setosa

# OR 
iris_lab %>% 
  mutate(across(.cols = where(is.numeric), round)) %>%
  head()

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1            5           4            1           0  setosa
## 2            5           3            1           0  setosa
## 3            5           3            1           0  setosa
## 4            5           3            2           0  setosa
## 5            5           4            1           0  setosa
## 6            5           4            2           0  setosa

2. Now do the same thing as question one, but this time set the number of digits for rounding to be 1.

iris_lab %>% 
  mutate(across(.cols = !Species, ~ round(.x, digits = 1))) %>%
  head()

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

3. Use if_all() to check if there are iris flowers with all measurements greater than 2 centimeters (which is the unit, if you type in ?iris you can learn more about the data). Hint - use filter. If any are, how many?

iris_lab %>%
filter(if_all(starts_with(c("Petal", "Sepal")), ~.x > 2))  %>%
  head()

##   Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 1          6.3         3.3          6.0         2.5 virginica
## 2          7.1         3.0          5.9         2.1 virginica
## 3          6.5         3.0          5.8         2.2 virginica
## 4          7.6         3.0          6.6         2.1 virginica
## 5          7.2         3.6          6.1         2.5 virginica
## 6          6.8         3.0          5.5         2.1 virginica

4. The function format() with the argument scientific = TRUE can be used to format numbers in scientific notation. For example:

format(10000, scientific = TRUE)

## [1] "1e+04"

Use modify_if to convert numeric columns of iris_lab to be in scientific notation.

iris_lab <- iris_lab %>%
  modify_if(is.numeric, ~ format(.x, scientific = TRUE))

head(iris_lab)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1      5.1e+00     3.5e+00      1.4e+00     2.0e-01  setosa
## 2      4.9e+00     3.0e+00      1.4e+00     2.0e-01  setosa
## 3      4.7e+00     3.2e+00      1.3e+00     2.0e-01  setosa
## 4      4.6e+00     3.1e+00      1.5e+00     2.0e-01  setosa
## 5      5.0e+00     3.6e+00      1.4e+00     2.0e-01  setosa
## 6      5.4e+00     3.9e+00      1.7e+00     4.0e-01  setosa