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4. Data Manipulation with dplyr & tidyverse

The tidyverse is a collection of modern R packages designed for data science. The most important one for data manipulation is dplyr — it provides a consistent, readable, and fast grammar for working with data frames.

Core tidyverse packages used here:

• dplyr – data manipulation

• tidyr – reshaping data

• magrittr / pipe – %>% operator

• readr – fast data import (already covered)

Install tidyverse (once)

R

install.packages("tidyverse")

Load it (always start with this)

R

library(tidyverse)

4.1 Introduction to tidyverse & Pipes (%>%)

The pipe operator %>% (pronounced "then") makes code read like natural language: "Take this data → then do this → then do that".

Without pipe (classic R style)

R

mean(filter(students, age > 20)$marks)

With pipe (tidyverse style – much clearer)

R

students %>% filter(age > 20) %>% summarise(mean_marks = mean(marks))

Key benefits of piping:

• Code reads from left to right (natural flow)

• No need to create temporary variables

• Easier to debug (run line by line)

• Chain many operations cleanly

4.2 filter(), select(), arrange(), mutate(), summarise()

These are the five core dplyr verbs — learn them well and you can do 80% of data manipulation.

filter() – Keep rows matching condition

R

students %>% filter(age > 23 & marks >= 90)

select() – Choose columns (by name or position)

R

students %>% select(name, marks) # keep only these students %>% select(-age) # drop age students %>% select(starts_with("m")) # columns starting with "m"

arrange() – Sort rows

R

students %>% arrange(desc(marks)) # highest marks first students %>% arrange(age, desc(marks)) # age ascending, then marks descending

mutate() – Create or modify columns

R

students %>% mutate( percentage = marks / 100, grade = case_when( marks >= 90 ~ "A+", marks >= 80 ~ "A", TRUE ~ "B" ) )

summarise() – Collapse data into single row (usually with group_by)

R

students %>% summarise( avg_marks = mean(marks), max_age = max(age), total_students = n() )

4.3 group_by() + summarise() – Powerful Aggregations

group_by() splits data into groups → summarise() computes per group.

Examples

R

# Average marks by gender students %>% group_by(gender) %>% summarise( avg_marks = mean(marks), count = n(), highest = max(marks) ) # Multiple groups sales %>% group_by(region, product) %>% summarise( total_sales = sum(sales_amount), avg_price = mean(price), .groups = "drop" # removes grouping for next step )

Tip: Always use .groups = "drop" in modern code to avoid unexpected behavior.

4.4 Joining Data (inner_join, left_join, full_join)

Joining combines two data frames based on common columns.

Common join types

• inner_join – only matching rows

• left_join – keep all rows from left table

• right_join – keep all rows from right table

• full_join – keep all rows from both

Example

R

students <- data.frame( id = 1:4, name = c("Anshuman", "Priya", "Rahul", "Sneha"), marks = c(92, 88, 85, 90) ) scores <- data.frame( id = c(1, 2, 5), subject = c("Math", "Science", "Physics"), score = c(95, 90, 82) ) # Left join – keep all students, add scores if available left_join(students, scores, by = "id") # Inner join – only students with scores inner_join(students, scores, by = "id")

Multiple keys / different column names

R

left_join(students, scores, by = c("id" = "student_id"))

4.5 tidyr – pivot_longer, pivot_wider, separate, unite

tidyr helps reshape data from wide to long format (and vice versa) — very common in data preparation.

pivot_longer – make wide data long (tidy format)

R

# Wide format wide <- data.frame( id = 1:3, math = c(85, 90, 78), science = c(92, 88, 95), english = c(80, 82, 87) ) # To long format long <- wide %>% pivot_longer(cols = math:english, names_to = "subject", values_to = "score") print(long) # id subject score # 1 1 math 85 # 2 1 science 92 # ...

pivot_wider – opposite (long to wide)

R

long %>% pivot_wider(names_from = subject, values_from = score)

separate() & unite()

R

df <- data.frame( id = 1:3, name_age = c("Anshuman_25", "Priya_23", "Rahul_24") ) df %>% separate(name_age, into = c("name", "age"), sep = "_") %>% mutate(age = as.integer(age)) # Opposite df %>% unite("full_info", name, age, sep = " - ")

Mini Summary Project – Full Data Manipulation Pipeline

R

library(tidyverse) # Sample messy data sales_raw <- data.frame( region = c("North", "South", "East", "West"), Q1_2025 = c(12000, 15000, 9000, 18000), Q2_2025 = c(14000, 16000, 11000, 20000) ) sales_raw %>% pivot_longer(cols = starts_with("Q"), names_to = "quarter", values_to = "sales") %>% separate(quarter, into = c("quarter", "year"), sep = "_") %>% mutate(sales_in_lakhs = sales / 100000) %>% group_by(region) %>% summarise( total_sales = sum(sales), avg_quarterly = mean(sales), best_quarter = max(sales) ) %>% arrange(desc(total_sales))

This completes the full Data Manipulation with dplyr & tidyverse section — now you can clean, transform, reshape, and summarize data like a pro in R!

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