Skip to main content
R for Audit Analytics
Show table of contents
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
41
Finding Anamolous Outliers
The content is under development
40
Anomaly Detection
42
Duplicate Detection
On this page
41
Finding Anamolous Outliers