Short R Tutorial: Data Analysis Example

R is a powerful tool for data analysis, statistics, and visualization. In this short tutorial, we will walk through a simple example that covers:

• Loading data from a CSV file
• Cleaning missing values
• Summarizing data by category
• Creating a bar chart

Step 1: Install and Load Required Packages

We will use the dplyr package for data manipulation and ggplot2 for plotting. Install them (once) and then load them into your R session.

install.packages("dplyr")
install.packages("ggplot2")

library(dplyr)
library(ggplot2)

Step 2: Load Your Dataset

We’ll assume you have a file named data.csv in your working directory.

df <- read.csv("data.csv")
head(df)

Step 3: Clean the Data

Remove rows with missing values in key columns.

df_clean <- df %>%
  filter(!is.na(category), !is.na(value))

Step 4: Summarize by Group

We calculate the mean value for each category.

summary_tbl <- df_clean %>%
  group_by(category) %>%
  summarize(mean_value = mean(value, na.rm = TRUE),
            count = n())
summary_tbl

Step 5: Create a Bar Chart

Visualize the mean value by category using ggplot2.

ggplot(summary_tbl, aes(x = category, y = mean_value)) +
  geom_col(fill = "steelblue") +
  labs(title = "Mean Value by Category",
       x = "Category",
       y = "Mean Value") +
  theme_minimal()

Output

The result will be a bar chart showing the average value for each category in your dataset.

Next Steps

From here, you can:

• Try different plot types (scatter, line, histogram)
• Filter your data for specific conditions
• Export results to a CSV or PDF