12.2.1.1: Data Wrangling and Visualization in R
- Page ID
- 53429
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Data Wrangling and Visualization in R
Below is an R snippet that reads data, manipulates it with dplyr, and plots with ggplot2:
library(dplyr)
library(ggplot2)
# Load data
df <- read.csv("sales_data.csv")
# Filter and transform
df2 <- df %>%
filter(region == "North") %>%
mutate(profit_margin = profit / revenue)
# Summarize by product
summary <- df2 %>%
group_by(product) %>%
summarise(total_revenue = sum(revenue), total_units = sum(units_sold))
# Simple bar chart
ggplot(summary, aes(x = product, y = total_revenue)) +
geom_bar(stat = "identity") +
labs(title = "Revenue by Product in North Region")
This R code uses the tidyverse style: %>% pipes data through a sequence of verbs. It mirrors the earlier Python example. First, read.csv loads data (base R function). Then filter selects rows, mutate creates a new column, and group_by/summarise aggregates by product. Finally, ggplot builds a bar chart of total revenue. The syntax is intuitive for many statisticians: each step reads like a data-processing step, and ggplot builds plots by layers.