R Tutorial

Fundamentals of R

Variables

Input and Output

Decision Making

Control Flow

Functions

Strings

Vectors

Lists

Arrays

Matrices

Factors

DataFrames

Object Oriented Programming

Error Handling

File Handling

Packages in R

Data Interfaces

Data Visualization

Statistics

Machine Learning with R

What Are the Tidyverse Packages in R?

The tidyverse is a collection of R packages designed for data science. All packages share an underlying philosophy and common APIs. The main packages included in the tidyverse are:

ggplot2 - Data visualization.
dplyr - Data manipulation.
tidyr - Data tidying.
readr - Data import.
purrr - Functional programming.
tibble - Tidy data structure.
stringr - String manipulation.
forcats - Factor manipulation.

Let's go through a quick tutorial for some of these packages:

Installing and Loading the tidyverse:

install.packages("tidyverse")
library(tidyverse)

dplyr Basics:

Select columns with select():

starwars %>% select(name, height, species)

Filter rows with filter():

starwars %>% filter(species == "Droid")

Arrange rows with arrange():

starwars %>% arrange(desc(height))

Create new columns with mutate():

starwars %>% mutate(height_in_meters = height / 100)

Summarize data with summarise():

starwars %>% group_by(species) %>% summarise(avg_mass = mean(mass, na.rm = TRUE))

tidyr Basics:

Gather columns into rows:

data <- tibble(x = 1, y = 2, z = 3)
data %>% gather("key", "value")

Spread rows into columns:

data <- tibble(key = c("x", "y", "z"), value = c(1, 2, 3))
data %>% spread(key, value)

ggplot2 Basics:

A simple plot:

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy))

Add layers:

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = class)) +
  geom_smooth(mapping = aes(x = displ, y = hwy))

readr Basics:

To read in a CSV file:

data <- read_csv("path_to_file.csv")

stringr Basics:

Find length of a string:

str_length("Hello, World!")

Replace in a string:

str_replace("I like cats.", "cats", "dogs")

purrr Basics:

Map a function to a list:

list(1, 2, 3) %>% map(~ .x * 2)

Core Packages of Tidyverse in R:
- Tidyverse is a collection of R packages designed for data science and analysis, promoting a tidy data approach.
```
# Example: Loading Tidyverse core packages
library(tidyverse)
```
R Packages Included in Tidyverse:
- Tidyverse includes several packages for various data tasks, providing a cohesive and integrated data science environment.
```
# Example: List of Tidyverse packages
tidyverse_packages <- c("dplyr", "ggplot2", "tidyr", "readr", "purrr", "stringr", "forcats", ...)
```
dplyr Package in Tidyverse:
- dplyr is a powerful package for data manipulation tasks, providing functions for filtering, arranging, summarizing, and more.
```
# Example: Using dplyr functions
my_data %>%
  filter(condition) %>%
  group_by(variable) %>%
  summarise(mean_value = mean(value))
```

ggplot2 Package in Tidyverse:

ggplot2 is a versatile package for creating data visualizations using a grammar of graphics approach.

# Example: Creating a ggplot2 plot
ggplot(data = my_data, aes(x = x_variable, y = y_variable)) +
  geom_point() +
  labs(title = "My Plot")

Tibble and tidyr in Tidyverse:

Tibble provides an improved data frame, and tidyr offers functions for data tidying and reshaping.

# Example: Using Tibble and tidyr
my_tibble <- as_tibble(my_data)
tidied_data <- gather(my_data, key = "variable", value = "value", -id_column)

Stringr and forcats in Tidyverse:

Stringr is for string manipulation, and forcats is for handling factors in Tidyverse.

# Example: Using Stringr and forcats
modified_string <- str_replace(my_string, pattern, replacement)
my_data$factor_variable <- fct_relevel(my_data$factor_variable, "desired_level")

Working with Factors in Tidyverse:

Tidyverse provides tools for effective handling and manipulation of factors.

# Example: Working with factors in Tidyverse
my_data$factor_variable <- fct_explicit_na(my_data$factor_variable, na_level = "NA")

Tidyverse Data Manipulation in R:

Tidyverse packages collectively enable seamless data manipulation workflows.

# Example: Tidyverse data manipulation
my_data %>%
  filter(condition) %>%
  select(columns) %>%
  mutate(new_variable = transform_function(existing_variable))

Tidyverse Data Visualization in R:

Leverage Tidyverse packages for creating expressive and customizable data visualizations.

# Example: Tidyverse data visualization
my_data %>%
  ggplot(aes(x = x_variable, y = y_variable)) +
  geom_point() +
  facet_wrap(~facet_variable)

R purrr Package in Tidyverse:
- purrr provides functional programming tools for working with data in a vectorized manner.
```
# Example: Using purrr functions
my_list %>% map(function)
```
Tidyverse readr for Data Import in R:
- readr is a Tidyverse package for efficient and user-friendly data import.
```
# Example: Using readr for data import
my_data <- read_csv("my_data.csv")
```

Combining Tidyverse Packages for Data Analysis in R:

Combine Tidyverse packages to streamline and enhance your data analysis workflow.

# Example: Comprehensive Tidyverse data analysis
my_data %>%
  filter(condition) %>%
  ggplot(aes(x = x_variable, y = y_variable)) +
  geom_point() +
  labs(title = "Analyzing My Data")