Hey guys, welcome back to my R-tips newsletter. In today’s R-tip, I’m sharing the top 9 R packages that I use almost every day… You’re getting the cheat code to learning these R packages. Plus, I’m sharing a 200 lines of R code that shows how you can use my 9 R code templates for ANY company. Let’s go!

Table of Contents

Today I share how to use my Top 9 R Packages . Here’s what you’re learning today:

• Top 9 R Packages: We’ll go through each of the top 9 R packages that I use almost every day.
• 9 Code Templates: How I use each of these R packages to complete business analysis and data science tasks.
• Shiny App Bonus: I’m sharing my Shiny App: Interactive Store Locator.

Matt’s Top R Packages

This is the Bonus Shiny App you’re getting today!

Bonus Shiny App!

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R-Tips Weekly

This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks. Pretty cool, right?

Here are the links to get set up. 👇

• Get the Code
• YouTube Tutorial

This Tutorial is Available in Video

I have a companion video that walks you through all of the code templates for my Top 9 R packages that every Data Scientist must know. 👇

I used to struggle at Data Science

Let’s be honest. I used to suck at Data Science.

In 2014, I was learning R. I was a beginner. I was struggling. I was frustrated. And I was stuck.

So if you’re in the same shoes now, I get it. I’ve been there. But here’s what changed for me.

I found out about an early version of the tidyverse. And it changed everything.

Over the course of the next two years, I went from a struggling Mechanical Engineer to a Director of Sales, Engineering, and Forecasting. And I had a dirty little secret.

R was behind everything.

In fact, I was using R to automate my job. I was using R to automate my team’s jobs. I was using R to automate my boss’s job.

And this led to a promotion. And then another promotion. And then another promotion.

My point is that R is a superpower. And I want to share with you the 9 R packages that I use almost every day.

Tutorial: Top 9 R Packages (With 9 Code Templates)

This tutorial is excellent. You’ll learn how to use my Top 9 R Packages with short code templates that you can use for almost ANY company:

1. tidyverse – Meta R package for data analysis
2. dplyr – Data wrangling and manipulation
3. ggplot2 – Data visualization
4. tidyr – Data wrangling and manipulation
5. timetk – Time series analysis
6. readr – Data import
7. tidymodels – Machine learning
8. leaflet – Interactive maps and geospatial analysis
9. shiny – Interactive web apps

1: Tidyverse

The tidyverse is the package that loads all of the other packages that I use.

Code Template #1: Load the tidyverse

Get the Code.

Here’s what happens when you run the code:

It attaches (or loads):

• dplyr – Data wrangling and manipulation
• ggplot2 – Data visualization
• tidyr – Data wrangling and manipulation
• readr – Data import
• purrr – Functional programming and iteration
• tibble – Tidy data structure
• stringr – String manipulation
• forcats – Factor manipulation
• lubridate – Date manipulation

In 1 line of code, now we have most of the R packages that we need to do our day-to-day work. Let’s get started with an example analysis. For that we’ll use the dplyr package.

2: dplyr

The dplyr package is the workhorse of the tidyverse. It’s the package that I use to manipulate data.

• Purpose: Data manipulation.
• Features: Enables filtering, grouping, summarizing data, and more.
• Usefulness: Offers a more readable and concise syntax for data manipulation.

Code Template #2: Group by and summarize

Get the Code.

First, we make a sample sales data with products and their respective sales numbers.

Then we use group_by() and summarize() to get the Total Sales by Product.

3: ggplot2

The ggplot2 package is a data visualization package. It’s the package that I use to visualize data for static plots that go into Executive reports.

• Purpose: Data visualization.
• Features: Creates complex multi-plot layouts and produces elegant graphics.
• Usefulness: It has a consistent syntax and is good for creating high-quality visualizations.

Code Template #3: Create a ggplot2 plot

Get the Code.

First, we make a sample monthly revenue data. And produce a ggplot2 data visualization with revenue by month.

4: tidyr

The tidyr package is a data wrangling package. It’s the package that I use to reshape data (also called pivoting).

• Purpose: Pivoting data (also nesting).
• Features: Enables data reshaping and tidying.
• Usefulness: Helps in organizing messy data for easier analysis.

Code Template #4: Pivot data from wide to long format

Get the Code.

First, we make a sample data with sales data for 2 products by month. This is in “wide format”.

Then we use pivot_longer() to convert the data to “long format”. Long format is needed for most “tidy” data analysis including making plots with ggplot2 and summarizing data with dplyr.

5: timetk

The timetk package is a time series package. I am the creator of this R package. And it’s the package that I use to analyze time series data analysis problems.

• Purpose: Time series analysis.
• Features: Enables time series data wrangling and manipulation.
• Usefulness: Helps in exploring and manipulating time series data for easier analysis.

Code Template #5: Create a time series trelliscope visualization for multiple time series

Get the Code.

We’ll use the FANG stock data to create a trelliscope visualization which is great for visualizing 10+ time series.

6: readr

The readr package is a data input/output package. It’s the package that I use to read and write data.

• Purpose: Data input/output.
• Features: Provides functions to read and write data.
• Usefulness: Efficiently handles large datasets and supports various data formats.

Code Template #6: Read data from a CSV file

We’ll use the read_csv() function to read data from a CSV file. This produces the following output:

7: tidymodels

The tidymodels package is a machine learning package. It’s the package that I use to build machine learning models.

• Purpose: Machine learning.
• Features: Provides a consistent interface for modeling and machine learning.
• Usefulness: Helps in building and evaluating machine learning models fast.

Code Template #7: Fit and predict sales with a linear regression model

Get the Code.

We’ll use the linear_reg() function to fit a linear regression model to predict sales. Then we use the predict() function to predict sales for a Marketing_Spend of $4,000. The prediction is $8,000.

8: leaflet

The leaflet package is a geospatial package. It’s the package that I use to create interactive maps for Shiny web apps and Exploratory Data Analysis.

• Purpose: Interactive maps and geospatial analysis.
• Features: Provides functions to create interactive maps.
• Usefulness: Helps in visualizing geospatial data.

Code Template #8: Create an interactive map

Get the Code.

We’ll use the leaflet() function to create an interactive map for 2 Store Locations. This produces the following output:

9: shiny (BIG BONUS)

The shiny package is a web application package. It’s the package that I use to create interactive web apps for use in production.

• Purpose: Interactive web apps.
• Features: Allows the creation of interactive web applications directly from R.
• Usefulness: Good for sharing analyses and visualizations in a user-friendly way. This is called “Production”.

Code Template #9: Create a Shiny App

This is a bonus and the code template is too. You’ll need to join the R-Tips newsletter to get the code.

Click here to get the Bonus Shiny App.

It produces this Shiny App:

Click here to get the Bonus Shiny App.

Conclusion

In this article, I shared 9 R packages that have helped me the most.

• You now have 9 code templates that you can use to perform data analysis and data science tasks for almost any company.
• This should give you a leg up in your Data Science career.

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