Data Manipulation in R: A Step-by-Step Guide to Conditional Formatting with Tables

R is a powerful programming language and software environment for statistical computing and graphics. It’s widely used by data analysts, scientists, and researchers to manipulate and analyze data. One of the most common tasks when working with data in R is conditional formatting, where you need to extract specific rows or columns based on certain conditions.

In this article, we’ll explore how to achieve conditional formatting for tables in R. We’ll use a sample dataset as an example, which contains information about time, class, type, value1, and value2.

Introduction to the Sample Dataset

Let’s start by looking at the sample dataset:

Time Class  Type Value1 Value2
2005  A      KS    5      6
2005  B      KS    3      3
2005  C      CS    6      6
2006  A      CS    5      3
2006  A      KS    9      2
2006  B      KS    6      9
2006  C      KS   39      6
2007  C      CS   10      20
2007  A      KS   26      23

This dataset contains information about three classes (A, B, and C) with two types (KS and CS). Each row represents a single observation with values for time, class, type, value1, and value2.

Step 1: Data Manipulation in R

To manipulate data in R, you’ll need to use the data.frame function to create a dataset. The data.frame() function takes one or more vectors as arguments and returns an object of class “data.frame”.

In this example, we have a vector df that contains the sample dataset.

# Create a data frame from the sample dataset
df <- structure(list(Time = c(2005L, 2005L, 2005L, 2006L, 2006L, 2006L, 
                           2006L, 2007L, 2007L), Class = c("A", "B", "C", "A", "A", "B", 
                                                           "C", "C", "A"), Type = c("KS", "KS", "CS", "CS", "KS", "KS", 
                                                                   "KS", "CS", "KS"), Value1 = c(5L, 3L, 6L, 5L, 9L, 6L, 39L, 10L, 26L), 
                                               Value2 = c(6L, 3L, 6L, 3L, 2L, 9L, 6L, 20L, 23L)), .Names = c("Time", 
                                                           "Class", "Type", "Value1", "Value2"), class = "data.frame", row.names =
                                                           c(NA, -9L))

Step 2: Splitting the Dataset by Time

To extract rows with the same class and type for each year, we need to split the dataset into separate data frames based on time.

We can use the split() function in R to divide a vector or a data frame into two or more parts. In this case, we’ll use it to split the dataset by time.

# Split the dataset by time
df_split <- lapply(split(df, df$Time), function(x) as.character(interaction(x[, c("Class", "Type")]))))

df_split

The interaction() function is used to extract a subset of columns from the data frame. In this case, we’re extracting the class and type columns.

Step 3: Reducing the Dataset by Intersection

To find the rows with the same class and type for each year, we need to reduce the dataset by intersection.

The reduce() function in R is used to apply a function across a list of values. In this case, we’re using it to intersect the lists generated by splitting the dataset by time.

# Reduce the dataset by intersection
indx1 <- Reduce(intersect, df_split)

indx1

Step 4: Filtering the Dataset

Finally, we can filter the dataset to extract rows with the same class and type for each year using the intersect() function.

We’ll use the df data frame and intersect it with the reduced dataset generated in step 3.

# Filter the dataset
final_df <- df[as.character(interaction(df[, c("Class", "Type")])) %in% indx1,]

final_df

The final dataset will contain only rows with the same class and type for each year.

Conclusion

In this article, we’ve explored how to achieve conditional formatting for tables in R. We’ve used a sample dataset as an example and walked through the steps of data manipulation, including splitting the dataset by time, reducing it by intersection, and filtering it using the intersect() function.

By following these steps, you can easily extract rows with the same class and type for each year from your own dataset.

Additional Resources

For more information on R programming language and its applications in data analysis, visit the following resources:

Example Use Case

Here’s an example use case for the code provided above:

Suppose you have a dataset containing information about students’ grades over several years. You want to extract rows where the student has the same grade level and subject for each year.

You can follow the steps outlined in this article to achieve this goal.

# Load the required libraries
library(dplyr)

# Create a sample dataset
grades <- data.frame(Student = c("John", "Jane", "Bob"),
                     Year = c(2018, 2019, 2020),
                     GradeLevel = c(1, 2, 3),
                     Subject = c("Math", "English", "Science"))

# Split the dataset by year
grades_split <- lapply(split(grades, grades$Year), function(x) x)

# Reduce the dataset by intersection
indx1 <- Reduce(intersect, df_split)

# Filter the dataset
final_grades <- grades[as.character(interaction(grades[, c("GradeLevel", "Subject")])) %in% indx1,]

# Print the final results
print(final_grades)

Last modified on 2024-02-10