The dplyr package in R is a powerful tool for data manipulation. It provides a set of functions that are easy to use and efficient for transforming and summarizing data. In this section, we will cover the key functions of dplyr and how to use them to manipulate data frames.

• Installation and Loading: To use dplyr, you need to install and load the package.

  install.packages("dplyr")
  library(dplyr)
  

• select(): Select columns from a data frame.
• filter(): Filter rows based on conditions.
• mutate(): Create new columns or modify existing ones.
• arrange(): Arrange rows in a specific order.
• summarize(): Summarize data by creating summary statistics.
• group_by(): Group data by one or more variables.

Let's start with a sample data frame to demonstrate the dplyr functions.

The select() function is used to choose specific columns from a data frame.

The filter() function is used to filter rows based on specific conditions.

The mutate() function is used to add new columns or modify existing ones.

The arrange() function is used to sort rows in a specific order.

The summarize() function is used to create summary statistics, often used with group_by().

1. Task: Select the 'id' and 'age' columns and filter rows where the score is greater than 80.
2. Solution:

   selected_filtered_data <- data %>%
     select(id, age) %>%
     filter(score > 80)
   print(selected_filtered_data)
   

1. Task: Add a new column 'score_category' based on the score (e.g., "High" if score > 85, otherwise "Low") and arrange the data by 'score_category'.
2. Solution:

   mutated_arranged_data <- data %>%
     mutate(score_category = ifelse(score > 85, "High", "Low")) %>%
     arrange(score_category)
   print(mutated_arranged_data)
   

1. Task: Group the data by 'age_group' and calculate the total score for each group.
2. Solution:

   grouped_summarized_data <- data %>%
     mutate(age_group = ifelse(age > 30, "Senior", "Junior")) %>%
     group_by(age_group) %>%
     summarize(total_score = sum(score))
   print(grouped_summarized_data)
   

• Common Mistake: Forgetting to use the %>% (pipe) operator to chain functions.
  • Tip: Always use %>% to pass the data frame from one function to the next.
• Common Mistake: Using incorrect column names.
  • Tip: Double-check column names for typos and ensure they match exactly.

In this section, we covered the basics of data manipulation using the dplyr package in R. We learned how to select, filter, mutate, arrange, and summarize data. These functions are essential for transforming and analyzing data efficiently. In the next section, we will explore more advanced data structures like matrices and arrays.