Data Science — R

R basics

# Vectors
x <- c(1, 2, 3, 4, 5)
mean(x)
sd(x)

# Data frames
df <- data.frame(name = c("A", "B"), value = c(1, 2))

# Read CSV
df <- read.csv("data.csv")

ggplot2

library(ggplot2)

# Scatter plot
ggplot(df, aes(x = x, y = y)) +
  geom_point()

# Line plot
ggplot(df, aes(x = date, y = value)) +
  geom_line()

# Bar plot
ggplot(df, aes(x = category)) +
  geom_bar()

dplyr

library(dplyr)

# Filter
df %>% filter(value > 5)

# Select
df %>% select(name, value)

# Arrange
df %>% arrange(desc(value))

# Summarize
df %>% group_by(category) %>% summarize(mean_value = mean(value))

Statistics

# T-test
t.test(group1, group2)

# Linear regression
model <- lm(y ~ x, data = df)
summary(model)

Mini Practice

Create data frames
Visualize with ggplot2
Manipulate with dplyr
Perform statistical tests

Up Next

Continue with Tableau - Data visualization tool.

Frequently Asked Questions about R

What is R in Data Science?

R is a fundamental concept in Data Science. This lesson explains it step by step with clear examples, making it easy for beginners to understand.

How do I learn R?

Start by reading the explanation above, then try the code examples. Practice by modifying the examples and experimenting with different values. Hands-on practice is the best way to learn R.

Why is R important in Data Science?

R is essential for Data Science development. Understanding this concept will help you write better code and solve real-world problems more effectively.