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Development Data Science R

R Data Pre-Processing & Data Management - Shape your Data!

Learn how to prepare your data for great analytics in R.
Bestseller
Rating: 4.5 out of 54.5 (526 ratings)
3,790 students
Created by R-Tutorials Training
Last updated 11/2018
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • import data into R in several ways while also beeing able to identify a suitable import tool
  • select and implement a proper object class (data.frame, data.table, data_frame)
  • convert your data into (and understand) a tidy data format
  • filter and query your data based on a wide range of parameters
  • join 2 data tables together with dplyr 2 table verb syntax
  • use SQL code within R
  • translate basic R into SQL
  • work with dates and time
  • work with strings using regular expressions
  • detecting outliers in datasets
Curated for the Udemy for Business collection

Course content

10 sections • 64 lectures • 6h 25m total length

  • Preview03:18
  • Preview08:28
  • Data Pre-Processing as Integral Part of Data Science
    11:50
  • Let's See an R Example of Data Pre-Processing
    22:34
  • Lures Example Script
    00:22

  • Script: Data import
    00:35
  • Importing Data and Snippets
    14:17
  • Using fread to handle big data fast
    07:53
  • Choosing the right class for your data
    09:46
  • Further R Exercises
    02:39

  • Script: Data cleaning
    01:19
  • tidyr - How tidy data looks like
    03:56
  • Wide to long data format
    09:20
  • Splitting columns
    03:04
  • Long to wide data format
    04:12

  • Script: Querying with data.table
    03:00
  • What is data.table?
    06:04
  • Basic queries
    05:52
  • Preview07:05
  • The by paramater for queries
    08:51
  • Update on recycle queries
    00:16
  • Keys
    05:18
  • Data.table exercises
    05:43
  • Data.table solutions
    09:28

  • Query exercises INTRO
    01:42
  • 10 Exercises on 'data.frame'
    11:50
  • Data.frame Exercise Script
    02:59
  • Data.frame Solutions 1-4
    09:05
  • Data.frame Solutions 5-10
    11:28
  • 10 Exercises on 'data.table'
    15:06
  • Data.table Exercise Script
    02:32
  • Data.table Solutions 1-4
    15:42
  • Data.table Solutions 5 - 10
    12:12

  • Script: dplyr
    01:08
  • Single Table Verbs in 'dplyr'
    10:19
  • Two Table Verbs - Mutating Joins
    09:20
  • Two Table Verbs - Filtering Joins and handling of ID mismatches
    04:22
  • Preview06:09

  • Script: Integrate SQL
    00:34
  • Get package dbplyr
    00:10
  • R to SQL Translator
    07:59
  • Preview03:52
  • Set Up a SQLite Database in R
    06:11

  • Outlier Script
    00:21
  • Introduction to Outlier Detection
    11:02
  • Detecting Outliers in Univariate Datasets
    09:08
  • Detecting Outliers in Multivariate Datasets
    05:46

  • Script: Working with Strings
    02:02
  • Regular Expressions and Gsub
    00:02
  • What You Should Know about Strings in R
    05:34
  • The Gsub Family of Functions and Regular Expressions
    07:42
  • Regular Expressions Syntax
    05:17
  • A Great Add On Package
    05:10
  • Working with Strings in R: Exercise with Solution
    03:46

  • Data management and time series INTRO
    02:23
  • Importing a Time Series From Excel
    05:55
  • Section Script
    02:05
  • Classes POSIXt, Date and Chron
    08:50
  • Lubridate: Input and Time Zones
    05:15
  • Lubridate: Weekdays and Intervals
    02:57
  • Lubridate: Exercise Data Frame
    03:18
  • Lubridate: Calculations and Leap Years
    04:47
  • Lubridate: Data Handling Exercise
    03:43
  • Further R Exercises
    02:39

Requirements

  • Computer with R and RStudio ready to use
  • You should have basic R / RStudio knowledge
  • Required add on packages will be listed in the course orientation video

Description

Let’s get your data in shape!

Data Pre-Processing is the very first step in data analytics. You cannot escape it, it is too important. Unfortunately this topic is widely overlooked and information is hard to find.

With this course I will change this!

Data Pre-Processing as taught in this course has the following steps:

1.       Data Import: this might sound trivial but if you consider all the different data formats out there you can imagine that this can be confusing. In the course we will take a look at a standard way of importing csv files, we will learn about the very fast fread method and I will show you what you can do if you have more exotic file formats to handle.

2.       Selecting the object class: a standard data.frame might be fine for easy standard tasks, but there are more advanced classes out there like the data.table. Especially with those huge datasets nowadays, a data.frame might not do it anymore. Alternatives will be demonstrated in this course.

3.       Getting your data in a tidy form: a tidy dataset has 1 row for each observation and 1 column for each variable. This might sound trivial, but in your daily work you will find instances where this simple rule is not followed. Often times you will not even notice that the dataset is not tidy in its layout. We will learn how tidyr can help you in getting your data into a clean and tidy format.

4.       Querying and filtering: when you have a huge dataset you need to filter for the desired parameters. We will learn about the combination of parameters and implementation of advanced filtering methods. Especially data.table has proven effective for that sort of querying on huge datasets, therefore we will focus on this package in the querying section.

5.       Data joins: when your data is spread over 2 different tables but you want to join them together based on given criteria, you will need joins for that. There are several methods of data joins in R, but here we will take a look at dplyr and the 2 table verbs which are such a great tool to work with 2 tables at the same time.

6.       Integrating and interacting with SQL: R is great at interacting with SQL. And SQL is of course the leading database language, which you will have to learn sooner or later as a data scientist. I will show you how to use SQL code within R and there is even a R to SQL translator for standard R code. And we will set up a SQLite database from within R. 

7.  Outlier detection: Datasets often contain values outside a plausible range. Faulty data generation or entry happens regularly. Statistical methods of outlier detection help to identify these values. We will take a look at the implemention of these.

8. Character strings as well as dates and time have their own rules when it comes to pre-processing. In this course we will also take a look at these types of data and how to effectively handle it in R.

How do you best prepare yourself for this course?

You only need a basic knowledge of R to fully benefit from this course. Once you know the basics of RStudio and R you are ready to follow along with the course material. Of course you will also get the R scripts which makes it even easier.

The screencasts are made in RStudio so you should get this program on top of R. Add on packages required are listed in the course.

Again, if you want to make sure that you have proper data with a tidy format, take a look at this course. It will make your analytics with R much easier!

Who this course is for:

  • Data pre-processing is a crucial step of data related work - therefore this course is intended for all R users

Featured review

Maria Caro
Maria Caro
26 courses
9 reviews
Rating: 4.5 out of 5a year ago
Well structured course with clear explanations and examples. In my opinion this is how an online course should be taught. However, you need to have some knowledge of R to better follow up the instructor.

Instructor

R-Tutorials Training
Data Science Education
R-Tutorials Training
  • 4.4 Instructor Rating
  • 27,077 Reviews
  • 220,128 Students
  • 23 Courses

  R-Tutorials is your provider of choice when it comes to analytics training courses! Try it out – our 100,000+ students love it. 

        We focus on Data Science tutorials. Offering several R courses for every skill level, we are among Udemy's top R training provider. On top of that courses on Tableau, Excel and a Data Science career guide are available.

        All of our courses contain exercises to give you the opportunity to try out the material on your own. You will also get downloadable script pdfs to recap the lessons. 

        The courses are taught by our main instructor Martin – trained biostatistician and enthusiastic data scientist / R user. 

        Should you have any questions, you are invited to check out our website, you can open a discussion in the course or you can simply drop us a pm. 

        We are here to help you boost your career with analytics training – Just learn and enjoy. 

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