The post is about multiple-choice questions about the package dplyr in R Language. There are 20 MCQs about the package and its use. Let us start with the Quiz on dplyr in R Language.
Online Multiple Choice Questions about R and dplyr package
MCQs dplyr in R Language
- What is the function of the dplyr verb Filter?
- What is the function of the dplyr verb Select?
- What is the function of the dplyr verb Group By?
- How does Summarise work?
- What does the dplyr verb mutate do?
- The dplyr verb Arrange is responsible for what action?
- The dplyr verb ‘Filter‘ does what to a data frame?
- The dplyr verb ‘Select‘ does?
- What does the dplyr verb ‘Group By‘ do?
- What does the dplyr verb ‘Arrange‘ do?
- What does the dplyr verb ‘Mutate‘ do?
- What symbol is used in dplyr that holds verbs together in a single phrase?
- Example tools for reproducible report writing are:
- Reproducibility tools for reports like knitr help with:
- What is the purpose of the distinct() function in dplyr?
- In dplyr, what is the purpose of the %>% operator (known as pipe operator)
- ———– function is similar to the existing subset() function in R but is quite a bit faster.
- What is the purpose of ungroup() function in dplyr?
- In dplyr, what does the slice() function do?
- How can a new column/variable (total_price) be created in dplyr with the sum of two existing columns/variables price1 and price2?
An Introduction to dplyr Package
The dplyr package is used for data manipulation and transformation. It gives a set of functions that make it easy to perform common data manipulation tasks, which include (1) filtering, (2) grouping, (3) summarizing, (4) arranging, and (5) joining data frames.
The package is part of the tidyverse, a collection of R packages designed to work together seamlessly for data analysis and visualization.
Some key functions available in dplyr R Package include:
filter(): Used to subset rows based on specified conditions.select(): Used to choose specific columns from a data frame.arrange(): Used to reorder rows based on one or more columns.mutate(): Used to create new columns or modify existing ones.group_by(): Used to group data by one or more variables.summarize(): Used to compute summary statistics for groups of data.join(): Used to merge data frames based on common keys.
The dplyr package provides a powerful and efficient toolkit for data manipulation in R.
