by 365 Careers | Released October 2018 | ISBN: 9781789950298

R Programming for data science and data analysis. Apply R for statistics and data visualization with GGplot2 in RAbout This VideoIntroductory guide to statistics - descriptive statistics and the fundamentals of inferential statisticsEssentials of R-based programming - soar above the average data scientist and boost the productivity of your operations.Data manipulation and analysis techniques - learn to work with R's most comprehensive collection of tools and create meaning-heavy data visualizations and plots.In DetailR Programming is a skill you'll need if you want to work as a data analyst or a data scientist in your industry of choice. And why wouldn't you - data scientist is the hottest ranked profession in the US. But to do that, you need the tools and the skillset to handle data. R is one of the top languages to get you where you want to be. Combine that with statistical know-how, and you will be well on your way to your dream job. This course packs all of this, and more, in one easy-to-handle bundle, and it's the perfect start to your journey. So, welcome to R Programming for Statistics and Data Science, the course that will get you from a complete beginner in programming with R to a professional who can complete data manipulation on demand. It gives you the complete skillset to tackle any new data science project with confidence and critically assess your work and other people's.Practicability is the key to this course, Using R, you have a wide variety of options where you can take the code provided within this course and expand on it in any number of directions. You'll reinforce your learning through numerous practical exercises. Show and hide more Publisher Resources Download Example Code

1. Chapter 1 : Introduction
   • Ten Things You Will Learn in This Course 00:04:48
2. Chapter 2 : Getting started
   • Intro 00:00:53
   • Downloading and installing R & RStudio 00:03:20
   • Quick guide to the RStudio user interface 00:07:37
   • Changing the appearance in RStudio 00:01:47
   • Installing packages in R and using the library 00:05:11
3. Chapter 3 : The building blocks of R
   • Creating an object in R 00:05:21
   • Data types in R - Integers and doubles 00:04:40
   • Data types in R - Characters and logicals 00:03:18
   • Coercion rules in R 00:02:39
   • Functions in R 00:03:23
   • Functions and arguments 00:02:31
   • Building a function in R (basics) 00:08:06
   • Using the script vs. using the console 00:02:56
4. Chapter 4 : Vectors and vector operations
   • Intro 00:01:10
   • Introduction to vectors 00:03:31
   • Vector recycling 00:01:40
   • Naming a vector in R 00:03:22
   • Getting help with R 00:06:38
   • Slicing and indexing a vector in R 00:07:01
   • Changing the dimensions of an object in R 00:04:50
5. Chapter 5 : Matrices
   • Creating a matrix in R 00:06:52
   • Faster code: creating a matrix in a single line of code 00:02:46
   • Do matrices recycle? 00:01:36
   • Indexing an element from a matrix 00:04:37
   • Slicing a matrix in R 00:03:33
   • Matrix arithmetic 00:07:07
   • Matrix operations in R 00:04:18
   • Categorical data 00:03:29
   • Creating a factor in R 00:06:01
   • Lists in R 00:06:01
6. Chapter 6 : Fundamentals of programming with R
   • Relational operators in R 00:05:07
   • Logical operators in R 00:03:22
   • Vectors and logicals operators 00:02:30
   • If, else, else if statements in R 00:05:48
   • If, else, else if statements - Keep-In-Mind's 00:03:50
   • For loops in R 00:06:20
   • While loops in R 00:04:06
   • Repeat loops in R 00:03:06
   • Building a function in R 2.0 00:04:34
   • Building a function in R 2.0 - Scoping 00:05:16
7. Chapter 7 : Data frames
   • Intro 00:00:55
   • Creating a data frame in R 00:05:54
   • The Tidyverse package 00:03:19
   • Data import in R 00:03:29
   • Importing a CSV in R 00:03:14
   • Data export in R 00:02:31
   • Getting a sense of your data frame 00:03:58
   • Indexing and slicing a data frame in R 00:04:10
   • Extending a data frame in R 00:04:20
   • Dealing with missing data in R 00:04:48
8. Chapter 8 : Manipulating data
   • Intro 00:01:15
   • Data transformation with R - the Dplyr package - Part I 00:05:44
   • Data transformation with R - the Dplyr package - Part II 00:03:22
   • Sampling data with the Dplyr package 00:01:44
   • Using the pipe operator in R 00:03:28
   • Tidying data in R - gather() and separate() 00:07:27
   • Tidying data in R - unite() and spread() 00:02:44
9. Chapter 9 : Visualizing data
   • Intro 00:01:01
   • Intro to data visualization 00:04:00
   • Intro to ggplot2 00:06:43
   • Variables: revisited 00:05:51
   • Building a histogram with ggplot2 00:06:32
   • Building a bar chart with ggplot2 00:06:29
   • Building a box and whiskers plot with ggplot2 00:05:58
   • Building a scatterplot with ggplot2 00:05:21
10. Chapter 10 : Exploratory data analysis
    • Population vs. sample 00:04:03
    • Mean, median, mode 00:05:05
    • Skewness 00:03:22
    • Variance, standard deviation, and coefficient of variability 00:06:12
    • Covariance and correlation 00:06:41
11. Chapter 11 : Hypothesis Testing
    • Distributions 00:06:32
    • Standard Error and Confidence Intervals 00:08:37
    • Hypothesis testing 00:08:03
    • Type I and Type II errors 00:03:23
    • Test for the mean - population variance known 00:07:01
    • The P-value 00:04:45
    • Test for the mean - Population variance unknown 00:05:10
    • Comparing two means - Dependent samples 00:06:40
    • Comparing two means - Independent samples 00:05:30
12. Chapter 12 : Linear Regression Analysis
    • The linear regression model 00:05:26
    • Correlation vs regression 00:01:37
    • Geometrical representation 00:01:37
    • First regression in R 00:04:18
    • How to interpret the regression table 00:04:25
    • Decomposition of variability: SST, SSR, SSE 00:03:15
    • R-squared 00:05:02
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