R Data Pre-Processing Data Management Shape your Data!

R Data Pre-Processing & Data Management – Shape your Data!, available at $59.99, has an average rating of 4.55, with 71 lectures, based on 658 reviews, and has 4880 subscribers.

You will learn about 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 This course is ideal for individuals who are Data pre-processing is a crucial step of data related work – therefore this course is intended for all R users It is particularly useful for Data pre-processing is a crucial step of data related work – therefore this course is intended for all R users.

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Summary

Title: R Data Pre-Processing & Data Management – Shape your Data!

Price: $59.99

Average Rating: 4.55

Number of Lectures: 71

Number of Published Lectures: 64

Number of Curriculum Items: 71

Number of Published Curriculum Objects: 64

Original Price: $94.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Managing Expectations and Course Orientation

Lecture 3: Data Pre-Processing as Integral Part of Data Science

Lecture 4: Lets See an R Example of Data Pre-Processing

Lecture 5: Lures Example Script

Chapter 2: Data Import and Data Structuring

Lecture 1: Script: Data import

Lecture 2: Importing Data and Snippets

Lecture 3: Using fread to handle big data fast

Lecture 4: Choosing the right class for your data

Lecture 5: Further R Exercises

Chapter 3: Cleaning Your Data

Lecture 1: Script: Data cleaning

Lecture 2: tidyr – How tidy data looks like

Lecture 3: Wide to long data format

Lecture 4: Splitting columns

Lecture 5: Long to wide data format

Chapter 4: Querying and Filtering Data with data.table

Lecture 1: Script: Querying with data.table

Lecture 2: What is data.table?

Lecture 3: Basic queries

Lecture 4: Queries at column level

Lecture 5: The by paramater for queries

Lecture 6: Update on recycle queries

Lecture 7: Keys

Lecture 8: Data.table exercises

Lecture 9: Data.table solutions

Chapter 5: Queries and Filtering Exercises

Lecture 1: Query exercises INTRO

Lecture 2: 10 Exercises on data.frame

Lecture 3: Data.frame Exercise Script

Lecture 4: Data.frame Solutions 1-4

Lecture 5: Data.frame Solutions 5-10

Lecture 6: 10 Exercises on data.table

Lecture 7: Data.table Exercise Script

Lecture 8: Data.table Solutions 1-4

Lecture 9: Data.table Solutions 5 – 10

Chapter 6: Using dplyr on one and multiple Datasets

Lecture 1: Script: dplyr

Lecture 2: Single Table Verbs in dplyr

Lecture 3: Two Table Verbs – Mutating Joins

Lecture 4: Two Table Verbs – Filtering Joins and handling of ID mismatches

Lecture 5: Two Table Verbs – Set Operations

Chapter 7: Integrate SQL into R

Lecture 1: Script: Integrate SQL

Lecture 2: Get package dbplyr

Lecture 3: R to SQL Translator

Lecture 4: Using SQL within R

Lecture 5: Set Up a SQLite Database in R

Chapter 8: Detecting Outliers

Lecture 1: Outlier Script

Lecture 2: Introduction to Outlier Detection

Lecture 3: Detecting Outliers in Univariate Datasets

Lecture 4: Detecting Outliers in Multivariate Datasets

Chapter 9: Working with Strings – Regular Expressions

Lecture 1: Script: Working with Strings

Lecture 2: Regular Expressions and Gsub

Lecture 3: What You Should Know about Strings in R

Lecture 4: The Gsub Family of Functions and Regular Expressions

Lecture 5: Regular Expressions Syntax

Lecture 6: A Great Add On Package

Lecture 7: Working with Strings in R: Exercise with Solution

Chapter 10: Working with Dates and Time

Lecture 1: Data management and time series INTRO

Lecture 2: Importing a Time Series From Excel

Lecture 3: Section Script

Lecture 4: Classes POSIXt, Date and Chron

Lecture 5: Lubridate: Input and Time Zones

Lecture 6: Lubridate: Weekdays and Intervals

Lecture 7: Lubridate: Exercise Data Frame

Lecture 8: Lubridate: Calculations and Leap Years

Lecture 9: Lubridate: Data Handling Exercise

Lecture 10: Further R Exercises

Instructors

R-Tutorials Training
Data Science Education

Rating Distribution

1 stars: 8 votes

2 stars: 9 votes

3 stars: 83 votes

4 stars: 247 votes

5 stars: 309 votes

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