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R for Audit Analytics

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

Part-III: Probability and Sampling in R

17 Generating Descriptive statistics

18 Probability in R