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R for Audit Analytics
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Table of contents
Welcome to R for Audit Analytics
Preface
About author
Gearing up
Part-I: Basic R Programming Concepts
1
R Programming Language
2
Subsetting R objects or accesing specific elements
3
Functions and operations in R
4
Existing and useful functions in base R
5
Pipes in R
6
Control statements
7
Functional Programming
8
File handling operations in R
9
Getting data in and out of R
10
Data Cleaning in R
11
Merging large number of similar datasets into one
Part-II: Exploratory Data Analysis
12
Visualisations in Base R
13
Visualising data with ggplot2
14
Data Transformation in dplyr
15
Combining Tables/tabular data
16
Data Wrangling in tidyr
17
Generating Descriptive statistics
Part-III: Probability and Sampling in R
18
Probability in R
19
Random sampling in R
Part-IV: Machine Learning in R
20
Linear Regression
21
Principal Component Analysis in R
22
Clustering in R (Using Kmeans algorithm)
23
Association Rule Mining in R (Apriori)
Part-V: Time Series
24
Date and Time calculations
25
Time Series Analysis
Part VI: Network Analytics
26
Network Analytics/Graph theory in R
27
Applying network analysis in audit/fraud detection
Part-VII: Text Analytics in R
28
String manipulation in stringr
29
Regex - A quick introduction
30
Regex in human readble format using rebus
31
Factors
32
Text Analytics in R
33
Sentiment Analysis
34
Visualising Text analytics through Wordcloud, etc.
35
Finding string similarity
Part-VIII: Geo computation in R
36
Maps in R
37
Geo-Coding in R
38
Reverse geo-coding
Part-IX: Identifying anamolous observations for audit
39
Benford Tests/Analysis
40
Anomaly Detection
41
Finding Anamolous Outliers
42
Duplicate Detection
43
Detecting gaps in sequences
44
Data Envelopment Analysis
Appendix
A
Colors in R
B
Various Datasets, in base R, used in this book
References
23
Association Rule Mining in R (Apriori)
The content is under development
22
Clustering in R (Using Kmeans algorithm)
Part-V: Time Series
On this page
23
Association Rule Mining in R (Apriori)