Understanding how to prep your data is an essential skill when working in R. It’s what you have to do before you can reveal the insights that matter. In this track, you’ll learn how to import your data from a variety of sources, including .csv, .xls, text files, and more. You'll then gain the skills you'll need to prepare your data for analysis, including converting data types, filling in missing values, and using fuzzy string matching. Throughout the track, you'll have the chance to apply your skills to real-world data such as customer asset portfolios and restaurant reviews. Start this track and gain the data prepping skills you need to clean your dirty data.

Gain the real-world skills you need to import and clean your data when working in R—making it possible for you to reveal the insights that matter.

with R

Importing & Cleaning Data 

A lot of data comes in the form of flat files: simple tabular text files. Learn how to import the common formats of flat file data with base R functions.

Importing data from flat files with utils

In addition to base R, there are dedicated packages to easily and efficiently import flat file data. We'll talk about two such packages: readr and data.table.

readr & data.table

Excel is a widely used data analysis tool. If you prefer to do your analyses in R, though, you'll need an understanding of how to import  .csv data into R. This chapter will show you how to use readxl and gdata to do so.

Importing Excel data

Beyond importing data from Excel, you can take things one step further with XLConnect. Learn all about it and bridge the gap between R and Excel.

Reproducible Excel work with XLConnect

Hotdogs

Potatoes (CSV)

Potatoes (TSV)

Swimming pools

Urban population (XLS)

Urban population (XLSX)

Importing data into R should be the easiest step in your analysis. Unfortunately, that is almost never the case. Data can come in many formats, ranging from .csv and text files, to statistical software files, to databases and HTML data. Knowing which approach to use is key to getting started with the actual analysis.
In this course, you’ll start by learning how to read .csv and text files in R. You will then cover the readr and data.table packages to easily and efficiently import flat file data. After that, you will learn how to read .xls files in R using readxl and gdata.

Introduction to R

In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.

Introduction to Importing Data in R

Importing & Cleaning Data

Many companies store their information in relational databases. The R community has also developed R packages to get data from these architectures. You'll learn how to connect to a database and how to retrieve data from it.

Importing data from databases (Part 1)

Importing an entire table from a database while you might only need a tiny bit of information seems like a lot of unncessary work. In this chapter, you'll learn about SQL queries, which will help you make things more efficient by performing some computations on the database side.

Importing data from databases (Part 2)

More and more of the information that data scientists are using resides on the web. Importing this data into R requires an understanding of the protocols used on the web. In this chapter, you'll get a crash course in HTTP and learn to perform your own HTTP requests from inside R.

Importing data from the web (Part 1)

Importing data from the web is one thing; actually being able to extract useful information is another. Learn more about the JSON format to get one step closer to web domination.

Importing data from the web (Part 2)

Next to R, there are also other commonly used statistical software packages: SAS, STATA and SPSS. Each of them has their own file format. Learn how to use the haven and foreign packages to get them into R with remarkable ease!

Importing data from statistical software packages

Education equality data

Employee data

Florida election data

International socio-economic data

Latitude (XLS)

Latitude (XLSX)

Big Five data

Potatoes

Sales data

Sugar import data

Water data

Wine data

In this course, you will take a deeper dive into the wide range of data formats out there. More specifically, you'll learn how to import data from relational databases and how to import and work with data coming from the web. Finally, you'll get hands-on experience with importing data from statistical software packages such as SAS, STATA, and SPSS.

Learn how to parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

Intermediate Importing Data in R

Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

In this chapter, you'll learn how to overcome some of the most common dirty data problems. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double-counting.

Common Data Problems

Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.

Categorical and Text Data

In this chapter, you’ll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. You’ll also gain invaluable skills that will help you verify that values have been added correctly and that missing values don’t negatively impact your analyses.

Advanced Data Problems

Record linkage is a powerful technique used to merge multiple datasets together, used when values have typos or different spellings. In this chapter, you'll learn how to link records by calculating the similarity between strings—you’ll then use your new skills to join two restaurant review datasets into one clean master dataset.

Record Linkage

Zagat

Fodor's

Bike Sharing

SFO Satisfaction Survey

Customer Accounts

It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time analyzing it. The time spent cleaning is vital since analyzing dirty data can lead you to draw inaccurate conclusions.
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In this course, you'll learn how to clean dirty data. Using R, you'll learn how to identify values that don't look right and fix dirty data by converting data types, filling in missing values, and using fuzzy string matching. As you learn, you’ll brush up on your skills by working with real-world datasets, including bike-share trips, customer asset portfolios, and restaurant reviews—developing the skills you need to go from raw data to awesome insights as quickly and accurately as possible!

Joining Data with dplyr

Introduction to Data Visualization with ggplot2

Develop the skills you need to go from raw data to awesome insights as quickly and accurately as possible.

Cleaning Data in R

You will start this course by learning how to read data into R. We'll begin with the readr package, and use it to read in data files organized in rows and columns. In the rest of the chapter, you'll learn how to explore your data using tools to help you view, summarize, and count values effectively. You'll see how each of these steps gives you more insights into your data. 

Explore your data

In this chapter, you will learn some basics of data taming, like how to tame your variable types, names, and values.

Tame your data

Now that your data has been tamed, it is time to get tidy. In this chapter, you will get hands-on experience tidying data and combining multiple tidying functions together in a chain using the pipe operator. 

Tidy your data

In this chapter, you will learn how to tame specific types of variables that are known to be tricky to work with, such as dates, strings, and factors. 

Transform your data

Bakeoff

Bakers

Desserts

Ratings

In this course, you'll learn to work with data using tools from the tidyverse in R. By data, we mean your own data, other people's data, messy data, big data, small data - any data with rows and columns that comes your way! By work, we mean doing most of the things that sound hard to do with R, and that need to happen before you can analyze or visualize your data. But work doesn't mean that it is not fun - you will see why so many people love working in the tidyverse as you learn how to explore, tame, tidy, and transform your data. Throughout this course, you'll work with data from a popular television baking competition called "The Great British Bake Off."

Learn to work with data using tools from the tidyverse, and master the important skills of taming and tidying your data.

Learning Experience

Business Features

Academics

Importing & Cleaning Data with R

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Join over .css-w0wpus-TrackShowPage{color:#03ef62;}7,460,000 learners and start Importing & Cleaning Data with R today!

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Join over 7,460,000 learners and start Importing & Cleaning Data with R today!