{"id":32,"date":"2026-03-03T06:47:56","date_gmt":"2026-03-03T06:47:56","guid":{"rendered":"https:\/\/gigz.pk\/r\/?post_type=lesson&p=32"},"modified":"2026-04-01T10:51:36","modified_gmt":"2026-04-01T10:51:36","slug":"using-dplyr-for-data-manipulation","status":"publish","type":"lesson","link":"https:\/\/gigz.pk\/r\/lesson\/using-dplyr-for-data-manipulation\/","title":{"rendered":"Using dplyr for Data Manipulation"},"content":{"rendered":"\n

dplyr<\/code> is one of the most popular R packages for data manipulation. It provides a set of intuitive functions that allow you to filter, arrange, select, mutate, and summarize datasets efficiently. Learning dplyr<\/code> is essential for modern data analysis in R.<\/p>\n\n\n\n

1. Installing and Loading dplyr<\/strong><\/h1>\n\n\n\n

Before using dplyr<\/code>, install and load the package:<\/p>\n\n\n\n

install.packages(\"dplyr\")   # Install dplyr
library(dplyr)              # Load dplyr<\/pre>\n\n\n\n
2. Core dplyr Functions<\/strong><\/h1>\n\n\n\n
dplyr<\/code> provides several key verbs to manipulate data frames or tibbles:<\/p>\n\n\n\n
a) select()<\/strong><\/h2>\n\n\n\n
select()<\/code> is used to choose specific columns from a dataset:<\/p>\n\n\n\n
data <- data.frame(Name=c(\"Alice\",\"Bob\",\"Charlie\"),
                   Age=c(25,30,28),
                   Score=c(90,85,88))select(data, Name, Score)   # Select only Name and Score columns<\/pre>\n\n\n\n
b) filter()<\/strong><\/h2>\n\n\n\n
filter()<\/code> is used to filter rows based on conditions:<\/p>\n\n\n\n
filter(data, Age > 26)      # Returns rows where Age > 26
filter(data, Score >= 88)   # Rows with Score 88 or higher<\/pre>\n\n\n\n
c) arrange()<\/strong><\/h2>\n\n\n\n
arrange()<\/code> is used to sort rows by one or more columns:<\/p>\n\n\n\n
arrange(data, Age)           # Sort by Age ascending
arrange(data, desc(Score))   # Sort by Score descending<\/pre>\n\n\n\n
d) mutate()<\/strong><\/h2>\n\n\n\n
mutate()<\/code> adds new columns or modifies existing ones:<\/p>\n\n\n\n
mutate(data, Passed = Score >= 85)   # Adds a logical column Passed
mutate(data, ScoreBonus = Score + 5) # Adds 5 points to Score<\/pre>\n\n\n\n
e) summarise() and group_by()<\/strong><\/h2>\n\n\n\n
summarise()<\/code> is used to calculate summary statistics, often combined with group_by()<\/code>:<\/p>\n\n\n\n
group_by(data, Passed = Score >= 85) %>%
  summarise(AverageAge = mean(Age), MaxScore = max(Score))<\/pre>\n\n\n\n
This groups the data by the Passed<\/code> status and calculates the average age and maximum score for each group.<\/p>\n\n\n\n
3. The Pipe Operator %>%<\/code><\/strong><\/h1>\n\n\n\n
The pipe operator %>%<\/code> allows chaining multiple operations together in a readable way:<\/p>\n\n\n\n
data %>%
  filter(Age > 25) %>%
  arrange(desc(Score)) %>%
  select(Name, Score)<\/pre>\n\n\n\n
This filters, sorts, and selects columns in a single, readable statement.<\/p>\n\n\n\n
4. Advantages of Using dplyr<\/strong><\/h1>\n\n\n\n