dplyr & tidyr Cheat Sheet

Covers dplyr's data manipulation verbs, table joins, and tidyr's pivot_longer/pivot_wider reshaping functions for tidy data workflows.

2 PagesIntermediateFeb 25, 2026

Core dplyr Verbs

Filter, select, mutate, and summarize a data frame.

library(dplyr)sales %>%  filter(region == "US", amount > 0) %>%  select(customer_id, order_date, amount) %>%  mutate(amount_usd = amount * 1.0) %>%  arrange(desc(amount_usd))sales %>%  group_by(region) %>%  summarise(    total_sales = sum(amount),    avg_sales = mean(amount),    n_orders = n()  )

Joins

Combine tables using dplyr's join family.

orders %>%  left_join(customers, by = "customer_id")orders %>%  inner_join(products, by = c("product_id" = "id"))# anti_join: rows in orders with no match in customersorders %>% anti_join(customers, by = "customer_id")

Reshaping with tidyr

Pivot between long and wide formats and clean up columns.

library(tidyr)# Wide to longlong_df <- wide_df %>%  pivot_longer(cols = jan:dec, names_to = "month", values_to = "sales")# Long to widewide_df <- long_df %>%  pivot_wider(names_from = month, values_from = sales)# Split/combine columnsdf %>% separate(full_name, into = c("first", "last"), sep = " ")df %>% unite(full_name, first, last, sep = " ")df %>% drop_na(amount)          # remove rows with NA in amountdf %>% replace_na(list(amount = 0))

Key Concepts

The verbs and pipes that define tidyverse-style data manipulation.

filter()- Keeps rows matching a logical condition
select()- Chooses/reorders columns by name or helper (starts_with(), everything())
mutate()- Creates or modifies columns, computed row-wise
summarise()/summarize()- Collapses grouped data into one row per group with aggregate statistics
group_by()- Groups rows so subsequent verbs (summarise, mutate) operate per group
pivot_longer/pivot_wider- tidyr's modern reshaping functions, replacing the older gather()/spread()
%>% / |>- Pipe operator passing the left-hand result as the first argument to the right-hand function

Pro Tip

Always call ungroup() after a group_by() %>% summarise() chain if you plan further row-wise mutate() calls - a lingering grouping silently changes how later verbs compute results.

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