R: Data Visualization with ggplot2

Create Beautiful & Informative Plots in R

Data visualization helps you see patterns, trends, and relationships in your data. In R, the most powerful and flexible plotting system is ggplot2.

In this tutorial, you will learn how to:

• Make basic plots
• Customize colors and themes
• Create bar charts, line charts, scatter plots
• Add labels and titles
• Visualize grouped data

1. Install & Load ggplot2

install.packages("ggplot2")
library(ggplot2)

2. Create Example Data

ggplot(data, aeset.seed(123)

data <- data.frame(
  age    = sample(20:60, 50, replace = TRUE),
  income = round(rnorm(50, mean = 50000, sd = 10000)),
  gender = sample(c("Male", "Female"), 50, replace = TRUE),
  city   = sample(c("Jakarta", "Bandung", "Surabaya"), 50, replace = TRUE),
  year   = rep(2016:2020, each = 10),
  sales  = round(runif(50, 100, 500))
)

head(data)s(x = age, y = income)) +
  geom_point()

3. Basic Structure of ggplot

ggplot(data, aes(x = age, y = income)) +
  geom_point()

4. Scatter Plot

ggplot(data, aes(x = age, y = income)) +
  geom_point() +
  labs(title = "Income vs Age",
       x = "Age",
       y = "Income")

5. Line Chart

ggplot(data, aes(x = year, y = sales)) +
  geom_line() +
  geom_point()

6. Bar Chart

ggplot(data, aes(x = city)) +
  geom_bar()

With values:

ggplot(data, aes(x = city, y = income)) +
  geom_col()

7. Grouped Data

ggplot(data, aes(x = age, y = income, color = gender)) +
  geom_point()

8. Histograms

ggplot(data, aes(x = income)) +
  geom_histogram(bins = 20)

9. Themes & Styling

ggplot(data, aes(x = age, y = income)) +
  geom_point() +
  theme_minimal()

Other themes:

theme_bw()
theme_classic()
theme_dark()

10. Save Plots

ggsave("plot.png", width = 6, height = 4)

Conclusion

With ggplot2, you can build layered plots, customize everything, communicate insights visually.