Question

How do I create a variable in R Studio and have it sort data?

The professor gave an assignment that basically expects us all to be pros at this language. I'm not a programmer and this is not a programming class, it's accounting. Whatsmore, the professor has yet to tell us how to do anything except calculate a mean. Somehow we are just expected to learn this.
**The manuals and videos do not help me. I need someone to explain something. I'm not even sure where to start with figuring this out. **
Here's what we need to do: import data from Excel and then classify it into categories based on ranges of numbers. All of the data is a number from 1 to 6. We are supposed to put it into three categories (1 and 2 in one, 3 and 4 in another, 5 and 6 in another). These are results from a survey. I know how to get the spreadsheet in, but how do I do this? I need to somehow create a variable and then have it sort data into categories.

Answer 1

To sort data into categories in R Studio, import the Excel data, then use the mutate() and case_when() functions from the dplyr package to create a new variable that classifies the data into categories based on specified ranges.

Step-by-step explanation:

To create a variable in R Studio that sorts data into categories, you would first import your Excel data using a function such as readxl::read_excel(). Assuming your data is now in a data frame, you can use the mutate() and case_when() functions from the dplyr package to create a new variable that categorizes your data based on the specified ranges. Here's an example code snippet:

# Assuming your data frame is named df
library(dplyr)
df <- df %>%
mutate(category = case_when(
your_variable %in% 1:2 ~ 'Category 1',
your_variable %in% 3:4 ~ 'Category 2',
your_variable %in% 5:6 ~ 'Category 3'
))

This code will create a new column in your data frame, category, where each number from 1 to 6 is classified into 'Category 1', 'Category 2', or 'Category 3'. Replace your_variable with the actual name of your imported data column containing the numbers 1 to 6.

Remember that in descriptive statistics, organizing and summarizing data is imperative, and this is a practical example of turning numerical variables into categorical variables. Such transformation helps in better understanding the distribution and characteristics of survey results or other data types.