R for Excel Users
R for Excel Users
An Introduction to R for Excel Analysts
About the Book
While Excel might continue to be your bread-and-butter, knowing R and applying it at the right times can make you a more productive and effective analyst. This book is for beginners, and the goal is to get you started. R is known to have a steep learning curve, but I really think it has three separate curves: (1) data management, (2) statistics and (3) visualization.
The focus of this book will be on data management: how to import, manipulate, transform and summarize data through the use of functions. Once you understand and are able to work with data structures, applying analytical techniques is a relative breeze.
For example, running a linear regression is a simple command that looks like this:
lm(y ~ x1 + x2 + ... xn, data = dataset_name)
That's easy, and if you understand regression, then interpreting the output will also be easy. But getting your data set in the right format, combining it with other data sets, manipulating columns, filtering rows, etc -- that is the hurdle I will help you overcome.
About the Author
Table of Contents
-
- Preface
- About me
-
Part 1 - Introduction & Set Up
-
1. Getting Set Up
- 1.1 Getting mentally ready to write code
- 1.2 Downloading the software
- 1.3 Navigating the software
- 1.4 Using other Software
- 1.5 Libraries
- 1.6 Console tips
- 1.7 Getting help
-
2. Programming Basics
- 2.1 Assigning variables
- 2.2 Testing conditions
- 2.3 Data structures
- 2.4 Special values
- 2.5 Commenting your code
- 2.6 Control Structures
-
3. Quick Start - Analysis Examples
- 3.1 Example 1: General data analysis
- 3.2 Example 2: Using SQL in R
- 3.3 Example 3: Multiple regression model
- 3.4 Summary
-
1. Getting Set Up
-
Part 2 - Building Blocks: Cells and Formulas
-
4. Cells are Vectors
- 4.1 Individual cells
- 4.2 Cell ranges
- 4.3 They are all vectors
- 4.4 Why vectors matter
- 4.5 Working with vectors
- 4.6 How vector operations work
- 4.7 Summary
-
5. Formulas are Functions
- 5.1 Inputs
- 5.2 Outputs
- 5.3 Getting help on functions
- 5.4 Some base functions
- 5.5 Create your own functions
- 5.6 Summary
-
4. Cells are Vectors
-
Part 3 - Data Frames
-
6. Import and Create Data Sets
- 6.1 Importing from .csv and .txt files
- 6.2 Importing from .xlsx files
- 6.3 Creating a data frame
- 6.4 Deleting a data frame
- 6.5 Summary
-
7. Inspect Your Data
- 7.1 Sneak a peek with brackets
-
7.2 Sneak another peek with
head()andtail() -
7.3 Data structure -
str() -
7.4 Column names -
names() -
7.5 Summary stats -
summary() -
7.6 Inspecting columns -
unique(),table()and others - 7.7 Visually understand your data
- 7.8 Other quick inspection functions
- 7.9 Summary
-
8. Working with Columns
- 8.1 Refer to columns
- 8.2 Create columns
- 8.3 Reformat columns
- 8.4 Rename columns
- 8.5 Remove columns
- 8.6 Unbound columns
- 8.7 Summary
-
9. Working with Rows
- 9.1 Filtering with brackets
-
9.2 Filtering with
subset() -
9.3 Filtering uniques with
unique() -
9.4 Sorting with
order()
-
10. Manipulating Rows and Columns with dplyr
- 10.1 sqldf
- 10.2 dplyr - manipulation verbs
- 10.3 Application: by-group processing with dplyr
- 10.4 Summary
-
6. Import and Create Data Sets
-
Part 4 - Shape your Dataset
-
11. Combine Data Tables
-
11.1
merge()- vlookup on steroids -
11.2
merge()options -
11.3 Merging with
sqldf() - 11.4 Merging with dplyr
- 11.5 Summary
-
11.1
-
12. PivotTables - Summarize and Transpose your Data
- 12.1 Making a simple PivotTable
- 12.2 Reshaping the table
- 12.3 Summary
-
11. Combine Data Tables
-
Part 5 - Advanced Topics
-
13. Working with lists
- 13.1 What is a list?
- 13.2 Two common uses
- 13.3 How to get things from lists
- 13.4 Creating and modifying lists
- 13.5 Basic list functions
- 13.6 Summary
-
14. Programming: Loops and Control Flow
-
14.1
while()loop -
14.2
for()loop - 14.3 Breaking out of or skipping loops
-
14.4
if() - 14.5 Summary
-
14.1
-
15. Writing your own functions
- 15.1 Simple function - Custom table summary
- 15.2 More advanced function
- 15.3 Summary
-
16. Apply Family of Functions
-
16.1
lapply(),sapply()andmapply() -
16.2
apply() - 16.3 Summary
-
16.1
-
17. Text / String Extraction
-
17.1 Using
substr()to mimic LEFT() and RIGHT() -
17.2 Using
strsplit()and apply functions - 17.3 Regular Expression Text Extraction
- 17.4 Summary
-
17.1 Using
- Next Steps
-
13. Working with lists
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