R Programming Interview Questions and Answers Programming

We can use the following approach using separate to distribute date field into three separate columns for year, month and day values.

# This will load the tidyr package
library(tidyr)
# This will reshape the data in desired format
separate(my_df, Col4, c("year","month","day"),sep = "-")

separate() function will use the parameters appropriately to display data in desired format.

• First parameter used here is the data frame which is my_df.
• Second parameter used here is the date column. We can use any column to split up as per need.
• Third parameter used here is the names of new columns to make.
• Fourth parameter is the string to split on. Basically this is the separation criteria. By default, separate() will split on any non-alphanumeric characters.

The output data will not be same as that of input.

Output will look like below.

The difference is in the format of Col4 which is the date value.
Separate() function splits into 3 different parts of this date column.
Unite() function unites these 3 different parts into one column which is Col4.
However the format is slightly different as mentioned in the code.

Here we are converting non-tidy format to tidy format and again back to non-tidy format.

This is one of the most frequently asked R programming interview questions and answers for freshers in recent times.

The differences are the following:

• apply(): Use as an alternative to for() loop
• lapply(): Applies function to every item and returns the result as a list
• sapply(): function will be executed column wise
• tapply(): Similar to aggregate() function

These are NOT same. Flights_mutate1 will perform appropriately. Where as

flights_mutate2 will throw an error. We can not use select because the derived variables “speed” does not exist. It has to be created first using mutate() function and then select() function can be used to extract specific variables from the data frame.

We can use the summarise() function from R in the dplyr package which will provide the mean and variance values as per below.

If can include the below parameter to get the number of observations information as well.

Expect to come across this, one of the most important R programming interview questions for experienced professionals in programming, in your next R interviews.

The n() provides the number of values in a vector, where as n_distinct() provides number of distinct values in a vector. For example, if we take the sample “flights” dataset in R, then we see the following characteristic:

We first remove the NA values from air_time and distance before using the summarise function.

The n() function performs a count of total number of flights or rows in the dataset. The

n_distinct() function captures the number of distinct carriers / airlines in the dataset which is 16.

Data set comes in many formats but R prefers just one format and that is tidy data. Tidyr package in R does this. For example if you look at below dataset of pollution:

Each variable is saved in its own column, each observation is saved in its own row and each “type” observation stored in a single table (here it is in “pollution” shown above). It automatically preserve observations.

Library(tidyr) can be used to load the required package in R if not installed already.

The mutate() function in dplyr package in R is used to derive new variables from existing variables (not from existing observations). For existing observations, one needs to use summarise() function instead. Below is an example:

If we take a sample data from “nycflights13” dataset, and try to view top few records, it looks like as below.

Now, if we use the mutate() function to derive a new variable and use select() function to fetch selected columns from above data frame.

flights <- as.data.frame(flights)
flights_mutate <- flights %>% mutate(speed=distance/air_time*60) %>% select(carrier,arr_delay,speed)

This will give below desired result. (again, few records shown from the data frame). Here the new derived variable is “speed” which is computed and derived based on the formula [distance / air_time*60]

kable() function is used to explore entirety of a data frame. This is from the knitr() package in R. When we execute above two statements from R console, the kable() statement produces output which is much more legible. It is used in the R markdown where documentation can be clearer.

Below are snapshot of differences while executing from R console.

We need to groupby data from source to destination using a group_by() function and then summarize it find number of records in each grouped by set. That will provide us the desired result. Please refer below.

We would like to convert it into below format which can be tidy format and will be easily analyzed in R.

A staple in senior R language interview questions with answers, be prepared to answer this one using your hands-on experience. This is also one of the top interview questions to ask an R programmer.

We need to use gather() function to reshape the dataset into tidy format in R so that desired / expected output can be achieved. Please see below.

The first parameter in gather()function takes the data frame name that needs to be reshaped, second parameter is the name of the new key column which is “year” here since we want to show number of cases by year, by country, third parameter is the name of new value column which is count here, fourth parameter is the names or numeric indexes of columns to collapse. There could be different ways to achieve, but important aspect to think about the approach and see how we can leverage powerful packages such as “tidyr” package in R to accomplish this.

Both code snippet will yield the same result output.

This is because we are arranging by country, year, sex and age in both cases.

The 4:6 and child:elderly portion will pick based on column indexes or column names. Post that reshaping by arrange() will provide in desired / expected organized fashion.

This, along with other basic R interview questions for freshers, is a regular feature in R programming interviews, be ready to tackle it with the approach mentioned.