1
R User Notebook
2
Basic Concepts
2.1
Vectors
2.2
Matrices
2.3
Dataframes
2.4
Tibbles
2.5
Extracting data from Dataframes/Tibbles
2.6
Lists
2.7
Factors
2.8
Dates
2.9
Style Guide
3
Importing & Exporting
3.1
Importing with Base R
3.2
Importing with other packages
3.2.1
Importing a CSV
3.2.2
Importing a TSV
3.2.3
Importing a R data file
3.2.4
Importing a Excel file
3.2.5
Importing a SAS file
3.2.6
Importing JSON (Newline Delimited JSON)
3.3
Exporting
3.3.1
Write to an R dataset
3.3.2
Write to an CSV file
4
Manipulating data
4.1
Basic data wrangling
4.2
Summary statistics
4.3
Conditional statements
4.4
Recode
4.5
Appending columns and rows
4.6
Joins
4.7
Tidy Data
4.8
tibbles
5
Functions
5.1
Function Basics - an example using str()
5.2
Common (base) functions
5.2.1
Strings
5.2.2
Mathematical
5.2.3
Properties & Lookups
5.2.4
Transformation
5.2.5
Other useful functions
5.3
Stringr - more string functions
5.4
Lubridate - more date functions
5.5
Converting data to other formats
5.5.1
JSON
5.6
User Defined Functions
5.7
Chaining/Piping
6
Loops
6.1
Basic for Loop structure
6.2
Looping through a vector
6.3
Creating a List in a Loop
6.4
Looping with apply functions
7
Tables
7.1
Displaying tables - htmlTable
7.2
Displaying Interactive tables - DT
7.3
Creating Tabulations
8
Charts
8.0.1
Static charts with ggplot2
8.0.2
Changing Axes
8.0.3
Chart Themes & Styles
8.0.4
Line Charts (geom_line)
8.0.5
Bar Charts (geom_bar & geom_histogram)
8.0.6
Adding features
8.0.7
Heatmaps
8.0.8
Boxplots
8.0.9
Combination Charts
8.0.10
Scales
8.0.11
ggplot wizard
8.0.12
Interactive charts with ggiraph
8.0.13
Interactive charts with dygraph
9
Maps
9.1
Leaflet
9.1.1
Display a basic map
9.1.2
Add markers, shapes and popups to a map
9.1.3
Add Boundaries
9.1.4
Add Interactivity
9.1.5
Attach extra information to your boundary dataset
9.1.6
Add colour based on area data
9.2
Inserting HTML & Javascript directly into R
10
RMarkdown & Knitr
10.1
Knitr
10.2
YAML
10.3
Modular coding
11
Machine Learning & Predictive Analytics
11.0.1
Decision Trees
12
Text Mining & NLP
12.0.1
Text data
12.0.2
Tidy text
12.0.3
Stemming
12.0.4
Basic text stats
12.0.5
N-Grams analysis
12.0.6
Text Preparation with
tm
12.1
Create a Term Document Matrix
12.1.1
From a Corpus (by default words are converted to lowercase less than 3 characters are excluded)
12.1.2
Matrix Managment
12.2
Topic Modelling & Latent Dirichlet Allocation (LDA)
12.3
Clustering - Similarity between topics
12.4
Calculating distance between objects
12.5
Non-Euclidian Distances - Jensen Shannon
12.6
Scaling - Principal Components
12.7
Eigen vectors
12.8
K-Means
12.9
Naive Bayes Classifiers
12.10
TF-IDF Classifiers (Supervised)
12.11
Sentiment Analysis
12.12
Word Bubble
13
Shiny
13.1
Basic Shiny Structure
13.2
User Interface
13.2.1
Layout
13.2.2
Inputs
13.2.3
Outputs
13.3
Server
13.3.1
Linking to UI outputs
13.3.2
Linking to UI inputs
13.4
Shiny Dashboard
14
Bookdown
14.1
Set-up
14.2
Create the book
15
Creating Packages
15.1
Setup Folders
15.2
R folder
15.3
man folder
15.4
Package details
15.5
Package Dependencies
15.6
Build the package
15.7
Making changes
16
Git - Version Control
16.1
Set-up
16.2
Link Git to a GitHub account
16.3
Starting a project with Github
16.4
Hosting a site on Github
17
Other Languages
17.1
SQL
17.2
Python
R User Notebook
R User Notebook
Kieran Driscoll
2019-02-11
1
R User Notebook