Assigning Values to Different Columns Based on Lookup Values in R

Introduction

R is a popular programming language for statistical computing and data visualization. It provides an extensive range of libraries and functions for data manipulation, analysis, and visualization. In this article, we will explore how to assign values to different columns based on lookup values using the tidyverse package in R.

Background

In many real-world applications, we have datasets with multiple variables or columns, each representing a variable of interest. We often want to add additional variables or transform existing ones based on certain conditions or rules. This is where lookups come into play – a lookup table can be used to assign values to the corresponding columns in our dataset.

The Problem

In this article, we will use an example from the Stack Overflow post provided. We have a dataframe mydata with multiple variables (columns) representing item scores for different participants. We also have a lookup table lookup that maps each item score to its corresponding value. The goal is to create new columns in our dataframe that assign values based on these lookups.

Solution

We will use the tidyverse package, which provides an efficient and expressive way of data manipulation in R.

Step 1: Load the necessary libraries

First, we need to load the necessary libraries. In this case, we only need the tidyverse library.

library(tidyverse)

Step 2: Create a sample dataframe

Next, let’s create a sample dataframe with item scores for different participants.

item1 <- c(NA, 1, NA, 4)
item2 <- c(NA, 2, NA, 3)
item3 <- c(NA, 3, NA, NA)
item57 <- c(NA, 4, 4, 1)

mydata <- data.frame(item1, item2, item3, item57)

Step 3: Create a lookup table

Now, let’s create a lookup table that maps each item score to its corresponding value.

lookup <- data.frame(score = 1:4, value = c(6, 7, 8, 10))

Step 4: Use rowid_to_column() to create an id variable

To facilitate the lookups later on, we need to create an id variable that can be used as a key in our lookups. We will use rowid_to_column() function from tidyverse package.

mydata <- mydata %>% 
  rowid_to_column(var = "participant")

Step 5: Gather all score columns

Next, we need to gather all the score columns into a single column called score. We will use gather() function from tidyverse package.

mydata <- mydata %>% 
  gather(items, score, starts_with("item"))

Step 6: Left join with lookup table

Now, we can left join our dataframe with the lookup table. This will allow us to assign values based on the item scores in our dataframe.

mydata <- mydata %>% 
  left_join(lookup)

Step 7: Gather all column types

Next, we need to gather all the column types into a single column called coltype. We will use gather() function from tidyverse package again.

mydata <- mydata %>% 
  gather(coltype, val, score:value)

Step 8: Unite column name and coltype

Now that we have all the values in a single column called val, we need to unite it with its corresponding coltype column.

mydata <- mydata %>% 
  unite(colname, coltype, items)

Step 9: Spread out the columns

Finally, we can spread out the columns using spread() function from tidyverse package.

mydata <- mydata %>% 
  spread(colname, val)

The Final Result

After running all these steps, our dataframe will look like this:

   participant score_item1 score_item2 score_item3 score_item57 value_item1 value_item2 value_item3 value_item57
1           1             NA             NA             NA            NA          NA          NA            NA
2           2             1             2             3             4             6          10
3           3             NA             NA             NA             4          NA          10
4           4             4             3             NA             1             8             6

Conclusion

In this article, we demonstrated how to assign values to different columns based on lookup values using the tidyverse package in R. By following these steps, you can create new columns or transform existing ones in your dataframe based on certain conditions or rules.

Last modified on 2023-05-21