R Language MCQs Test 33

Test your R programming expertise with this 20-question MCQ quiz! R Language MCQs Test designed for both learners and professionals, this quiz covers essential topics like data wrangling with dplyr (group_by, summarize, pipes), string manipulation, lubridate, tidymodels, and predictive modeling. Perfect for preparing for data scientist job interviews, brushing up on core R concepts, and mastering the tidyverse ecosystem. Let us start with the R Language MCQs Test now.

Online R Language MCQs Test with Answers

• What’s the point of using group_by()?
• Which tidymodels function do you use to create the grid for a grid search?
• What is the purpose of the pipe (%>%) operator?
• You are checking your data using the glimpse() function before beginning your analysis, and determine that the data type of a variable called TimeStamp is in a character format. What should you do next?
• How can the factor() function be used to map R onto a relational database management system (RDBMS)?
• Which function can you use to read a text file that uses the “%” character as a delimiter?
• What is the main similarity between the summarize() and group_by() functions?
• What is the result of the following statement?
  sub_airline %>% map(~sum(is.na(.)))
• Which functions do you use together to correct data types in all columns of your dataset?
• You have a variable called “Status” that contains a status code in the format “error_type-severity_level”, for example “10-07”, and you want to reformat the column so that the “error_type” and “severity_level” are in different columns. What is the correct function to do this?
• Which of the following can you accomplish using the spread() function?
• When grouping data and calculating the mean of each group as part of your exploratory data analysis, you typically use the group_by() function with which other function?
• Assume you have a dataset called “new_dataset”, a predictor variable called X, and a target called Y, and you want to fit a simple linear regression model. Which command should you use?
• When using the predict() function in R, what is the default confidence level?
• Assume you have a dataset called “new_dataset”, two predictor variables called X and Y, and a target variable called Z, and you want to fit a multiple linear regression model. Which command should you use?
• You’ve got some messy data that looks like this:
  my_strings<-c( “xyztiger”, ” i33tiger”, “898natiger ” )
  You want to use a function to do a logical test for whether the character string “tiger” is present in any of the items in this vector. What is the correct function?
• You’ve still got this same messy data:
  my_strings<-c( “xyztiger”, ” i33tiger”, “898natiger ” )
  You want to use a function to take this data and create a column of data that looks like this:
  “tiger”
  “tiger”
  “tiger”
  What is the correct function?
• Say you want to split a character vector and split the strings, so you have a matrix with two columns, splitting the string as indicated. Your character vector looks like
  this: my_strings<-c( “paper_store1”, “pens_store1”, “pencils_store1”)
  You want to split the strings at the underscore. What function do you use?
• You have a character vector that looks like this:
  my_dates<-c( “05-28-1984”, “07-15-1981”, “9-12-1986”, “1-15-1982”)
  You want to extract the year values from this vector, using the tools in lubridate. Which is correct?
• Let’s say you want to calculate how many days passed from 14 July, 1789 until 1 December 1941. How can you calculate that?

Try Online Correlation Regression Quiz