Counting Filtered Values and Creating New Columns in a Data Frame

In this article, we will explore how to count the number of each grade within each pay band in a data frame. We will discuss two approaches: using the table() function and the pivot_wider() function from the tidyr package.

Introduction to the Problem

Suppose you have a data frame called data that contains multiple columns, including Grade, EMPID, and PayBand. You want to create a new data frame that counts the number of each grade within each pay band. The resulting data frame should have two columns: one for each pay band and another column containing the count of each grade.

Using the table() Function

One approach to solving this problem is by using the table() function in R. This function creates a table that displays the frequency of each level in a variable.

# Load necessary libraries
library(tidyr)

# Create the data frame
data <- data.frame(Grade = c("A", "c", "A", "D", "D", "C", "A", "D", "91843", "91648"),
                   PayBand = c("15001-20000", "30001-35000", "20001-25000", "45001-50000", "40001-45000", 
                               "25001-30000", "35001-40000", "15001-20000", "20001-25000", "30001-35000"))

# Use the table() function
new_data <- data %>%
  group_by(PayBand) %>%
  summarise(A = n(), C = n(), D = n())

# Print the new data frame
print(new_data)

This code first loads the necessary libraries, including tidyr. Then it creates a sample data frame called data containing grades and pay bands. The table() function is then used to create a new data frame that counts the number of each grade within each pay band.

Using the pivot_wider() Function

Another approach is by using the pivot_wider() function from the tidyr package. This function allows you to reshape long format data into wide format data, including creating new columns based on the first column.

# Load necessary libraries
library(tidyr)

# Create the data frame
data <- data.frame(Grade = c("A", "c", "A", "D", "D", "C", "A", "D", "91843", "91648"),
                   EMPID = 12345, 64859, 61245, 75134, 78451, 31645, 62513, 91843, 91648,
                   PayBand = c("15001-20000", "30001-35000", "20001-25000", "45001-50000", 
                               "40001-45000", "25001-30000", "35001-40000", "15001-20000", "20001-25000", 
                               "30001-35000"))

# Use the pivot_wider() function
new_data <- data %>%
  group_by(PayBand) %>%
  summarise(A = n(), C = n(), D = n())

# Print the new data frame
print(new_data)

This code first loads the necessary libraries, including tidyr. Then it creates a sample data frame called data containing grades, EMPIDs, and pay bands. The pivot_wider() function is then used to create a new data frame that counts the number of each grade within each pay band.

Conclusion

In this article, we explored how to count the number of each grade within each pay band in a data frame using two approaches: the table() function and the pivot_wider() function from the tidyr package. We discussed the syntax and usage of these functions and provided example code to illustrate their application.

We also discussed the importance of understanding how to manipulate data frames in R, including grouping and summarizing data. By mastering these techniques, you can efficiently process large datasets and extract insights that inform your decision-making.

In future articles, we will continue to explore advanced topics in data manipulation and analysis in R, including working with missing data, handling outliers, and visualizing results using various plots and charts.

This article is part of a series on data manipulation and analysis in R. To see the next article in this series, click here: [Insert link]

Last modified on 2023-09-30