dplyr Non-Standard Evaluation Exercises

dplyr is a great package for interactive data wrangling and exploration. One of key aspects that makes it so great is that it uses non-standard evaluation so a user does not have to repeat data frame name and quote names all the time. On the other hand this feature makes programming with dplyr a non-trivial task. For example passing a name of a grouping column, say grp_var = 'Species' to a function that utilizes dplyr‘s group_by() will fail as grp_var will be taken for a column name. Fortunately author’s of dplyr provided rlang package that comes with solutions to such problems.

The reader is assumed to know basics of dplyr. If you want to practice dplyr first there is great series of exercises Data wrangling: Transforming available. For quick introduction to programming with dplyr I recommend this vignette.

Answers to the exercises are available here.

If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.

All answers should be written the dplyr way.

Exercise 1
Load packages dplyr and rlang. Write a function my_fun_1(df) that takes a data frame and adds a column sum being a sum of columns x and y. Make sure it will not mistake a variable for a column!

Test it with code:

df_1 <- data.frame(x = 1:5, y = 6:10)
my_fun_1(df_1)

df_2 <- data.frame(x = 1:5)
y <- 1
my_fun_1(df_2)


Exercise 2
Write a function my_fun_1(df) that takes a data frame and adds a column sum being a sum of a column x and a value y passed to function. Make sure it will not mistake a variable for a column!

Test it with code:

df_1 <- data.frame(x = 1:5, y = 6:10)
my_fun_2(df_1)


Exercise 3
Write a function my_fun_3(x) that quotes its input. Check type and environment of the object returned.

Exercise 4
Get value of an input from the object from previous exercise.

Exercise 5
Given obj <- my_fun_3(100) modify x to make value under obj equal to 200.

Exercise 6
Write a function that groups data frame by a column passed as an argument and counts number of rows in each group. Test it with code: my_group_by_1(iris, Species).

Exercise 7
Modify the function from previous exercise so that it takes string as a grouping variable. Test it with code: my_group_by_2(iris, "Species").

Exercise 8
Write a function that groups data frame by multiple columns passed as arguments and counts number of rows in each group. Test it with code: my_group_by_3(iris, Species, sl_g_5=Sepal.Length > 5).

Exercise 9
Modify a function from exercise 6 so that additionally takes names of columns to be aggregated by mean() function. Test it with code: my_group_by_4(iris, Species, Sepal.Length, Sepal.Width).

Exercise 10
Write a function that takes a data frame and for a column passed as an argument and creates a column **column_name**_sqrt.

Related exercise sets:

Parallel Computing Exercises: Foreach and DoParallel (Part-2) Parallel Computing Exercises: Snow and Rmpi (Part-3) Building Shiny App exercises part 4 Scripting Loops In R Building Shiny App exercises part 3