20 dplyr Interview Questions and Answers

Dplyr is a popular R package that provides a suite of tools for data manipulation, analysis and visualization. If you’re applying for a position that involves data analysis, it’s likely that you’ll be asked dplyr questions during your interview. Knowing how to answer these questions can help you demonstrate your skills and knowledge, and impress the hiring manager. In this article, we discuss some commonly asked dplyr questions and how you should respond.

dplyr Interview Questions and Answers

Here are 20 commonly asked dplyr interview questions and answers to prepare you for your interview:

1. What is dplyr?

dplyr is a package for making data manipulation in R easier, faster, and more consistent with the tidyverse. It provides a set of tools that are particularly well-suited for working with data frames, including functions for filtering, grouping, and summarizing data. dplyr also makes it easy to work with data from different sources, such as databases and spreadsheets, by providing a unified interface for data import and export.

2. Can you explain the different types of verbs used in dplyr?

The five main types of verbs used in dplyr are: select, mutate, filter, summarise, and arrange. Select is used to choose certain columns from a data frame, mutate is used to create new columns based on existing columns, filter is used to choose certain rows based on certain conditions, summarise is used to collapse many rows into one summary row, and arrange is used to reorder the rows of a data frame.

3. How do you use mutate to add a new variable?

You can use mutate to add a new variable by using the following code:

mutate(data, new_variable = old_variable * 2)

This will create a new variable in your data set that is equal to twice the value of the old variable.

4. How can you find out which columns are available on your data frame?

You can use the colnames() function to find out which columns are available on your data frame.

5. Can you give me an example of how you would use select, filter and arrange together to get specific information from a dataset?

Let’s say we have a dataset with information on different countries. We want to select only the countries in Europe, filter out any that have a population less than 10 million, and arrange them in order of population size. We would do this by chaining together the select, filter and arrange functions:

countries %>%
select(country, continent, population) %>%
filter(continent == “Europe” & population >= 10 million) %>%
arrange(population)

6. Why should I consider using dplyr over base R?

dplyr is a package for data manipulation, and it offers a number of advantages over base R. First, it is much easier to use, especially for complex operations. Second, it is much faster, both in terms of execution time and in terms of memory usage. Finally, it is more flexible, allowing you to easily modify existing code to suit your needs.

7. What’s the difference between count and nrow? When should one be used over the other?

The difference between count and nrow is that count will count the number of observations in a data frame while nrow will count the number of rows. In general, nrow should be used over count because it is more accurate.

8. How do you rename variables in dplyr?

You can rename variables in dplyr using the “rename” function. For example, if you wanted to rename the variable “x” to “y”, you would use the following code:

rename(x = y)

9. How do you join two datasets with dplyr?

Joining two datasets with dplyr is simple. The first step is to make sure that both datasets have a common column, which will be used as the key for the join. Then, you can use the dplyr function ‘join’ to specify which type of join you want to perform. The most common types of joins are inner joins, left joins, and right joins, but there are also full joins and anti-joins.

10. How do you sort rows by values in a column?

You can sort rows by values in a column using the “arrange” function. For example, if you have a data frame with a column called “name”, you can sort the rows by alphabetical order of the “name” column using the following code:

df <- df %>%
arrange(name)

11. Is it possible to group multiple columns at once in dplyr? If yes, then how?

Yes, it is possible to group multiple columns at once in dplyr. You can do this by using the group_by() function and passing in a vector of column names. For example, if you have a dataframe with columns A, B, and C, and you want to group by A and B, you would do the following:

df %>% group_by(A, B)

12. Can you explain what pipes are and why they’re so useful?

Pipes are a way of chaining together multiple commands in R, so that the output of one command becomes the input of the next. This can be really useful for complex data analysis, because it allows you to break down a complicated task into a series of smaller, more manageable steps. Pipes also make your code more readable, because they show the flow of data from one step to the next.

13. What’s wrong with this code: “data %>% summarise(mean_val = mean(value))”?

The code will produce an error because the summarise function can only be used on data frames, and the mean function can only be used on vectors.

14. What are some common sources of confusion when working with dplyr?

One common source of confusion when working with dplyr is the difference between the mutate and transmute functions. Both of these functions are used to create new columns in a data frame, but the mutate function will keep all of the existing columns while transmute will only keep the columns that are explicitly specified. Another common confusion is between the filter and select functions. Both of these functions are used to subset a data frame, but filter is used to subset based on row values while select is used to subset based on column values.

15. How does dplyr handle NA or null values?

dplyr has a number of functions for working with missing values, including:

is.na() which returns a logical vector indicating which values are missing
na.omit() which returns a version of the data frame with missing values removed
na.fill() which fills in missing values with a specified value

16. In context with dplyr, what is a tibble?

A tibble is a type of data frame that is optimized for working with the dplyr package. Tibbles are data frames that are lazy and surly, meaning that they only compute values when they absolutely need to, and they never change values without your explicit permission. This makes them much easier to work with than traditional data frames.

17. How do you convert data frames into tibbles?

Tibbles are a type of data frame in R that are particularly well-suited for working with data that is both tidy and rectangular. To convert a data frame into a tibble, you can use the as_tibble() function.

18. What is a method chaining?

Method chaining is the process of connecting a series of method calls together with the “.” operator in order to execute all of the methods in sequence on the same data. This can be a convenient way to write code, but it can also lead to problems if the methods are not carefully chosen.

19. What are some ways to improve performance when using dplyr?

There are a few ways to improve performance when using dplyr:

– Use the ‘compact’ argument to remove unused levels from factors
– Use the ‘group_by’ function to group data by a particular column
– Use the ‘summarize’ function to create summaries of data by groups
– Use the ‘mutate’ function to create new columns based on existing data

20. Can you explain the difference between functions like distinct() and duplicated()?

The distinct() function will return only the unique values in a vector, while duplicated() will return a logical vector indicating which values are duplicates.