# 6 Great R Packages Every Beginners Should Know

Getting started with data science? The R language is your companion, offering a plethora of functionalities for statistical computing, data analysis, and visualization. Dive into our beginner-friendly tutorial on R and discover six key packages that enhance your data science journey.

## Why Choose R for Data Science?

R has established its position as a top choice for data scientists. Not only is it open-source, but it also provides a comprehensive environment for data analysis and graphics.

## How to Set Up R for Maximum Efficiency

Before delving into the intricacies of R, it's pivotal to have the right setup:

### 1. R Installation

Visit CRAN (Comprehensive R Archive Network) (opens in a new tab) and ensure you're working with the latest R version.

### 2. RStudio: Your Friendly IDE

RStudio isn't just an integrated development environment; it's a gateway to efficient R programming. Get the free desktop version from RStudio's official website (opens in a new tab).

## The Cornerstone: Basics of R Programming

To wield R's power, get a grip on its foundational aspects:

• Variable Assignment: With `<-`, variable declaration becomes intuitive.

``x <- 5``
• Function Calls: Invoke a function with its name followed by `()`.

``print(x)``
• Grasping Data Structures: Know your vectors, matrices, data frames, and lists. They're the backbone of data handling in R.

## Top 6 R Packages Every Data Scientist Should Know

To supercharge your R capabilities, embrace these packages:

### 1. dplyr: Mastering Data Manipulation

From filtering data rows to modifying variables, `dplyr` is your toolkit. Familiarize yourself with functions like `filter()`, `select()`, and `mutate()`.

``````install.packages("dplyr")
library(dplyr)``````

### 2. ggplot2: Elevate Your Data Visualization Game

With `ggplot2`, your data doesn't just speak; it sings! Understand the `aes()` function and learn to layer your plots for richer visualizations.

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

### 3. GWalkR (opens in a new tab): Turn Your Data into Interactive Visualization App

GWalkR (opens in a new tab) is an interactive Exploratory Data Analysis (EDA) Tool in R build by Kanaries (opens in a new tab). It integrates the htmlwidgets with Graphic Walker. It can simplify your R data analysis and data visualization workflow, by turning your data frame into a tableau-alternative User Interface for visual exploration.

``````install.packages("GWalkR")
library(GWalkR)``````

### 4. tidyr (opens in a new tab): The Art of Data Tidying

`tidyr` ensures your data is neat and accessible. Explore `spread()`, `gather()`, and more to structure your data perfectly.

``````install.packages("tidyr")
library(tidyr)``````

### 5. readr: Streamline Data Input and Output

Whether you're reading a CSV or outputting data, `readr` simplifies it all. Dive into functions like `read_csv()` and `write_csv()`.

``````install.packages("readr")
For model training and machine learning in R, `caret` is indispensable. Train models, make predictions, and evaluate performance seamlessly.
``````install.packages("caret")