R Programming

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R Programming

R is a strong language for machine learning, statistics, data visualization, data analysis. In Data science R is one of popular language that basically developed for statistical programming.

  • R language for statistical programming
  • The statistical packages
  • Learning to deploy them in various scenarios
  • Components of R Studio like code editor
  • Familiarity with different data types and functions
  • Learn about R-bind
  • The various features of R, introduction to R Studio
  • Visualization and debugging tools
  • Use SQL to apply ‘join’ function

Part I Getting started:
1 Introduction to R :
1.1 Why use R?
1.2 Obtaining and installing R
1.3 Working with R
Getting started ¦ Getting help ¦ The workspace
Input and output
1.4 Packages
What are packages? ¦ Installing a package
Loading a package ¦ Learning about a package
1.5 Batch processing
1.6 Using output as input—reusing results
1.7 Working with large datasets
1.8 Working through an example
1.9 Summary
2 Creating a dataset :
2.1 Understanding datasets
2.2 Data structures
Vectors ¦ Matrices ¦ Arrays ¦ Data frames
Factors ¦ Lists
2.3 Data input
Entering data from the keyboard ¦ Importing data from a delimited text
file ¦ Importing data from Excel ¦ Importing data from XML
Webscraping ¦ Importing data from SPSS ¦ Importing data from SAS
Importing data from Stata ¦ Importing data from netCDF
Importing data from HDF5 ¦ Accessing database management systems
(DBMSs) ¦ Importing data via Stat/Transfer
2.4 Annotating datasets
Variable labels ¦ Value labels
2.5 Useful functions for working with data objects
2.6 Summary
3 Getting started with graphs :
3.1 Working with graphs
3.2 A simple example
3.3 Graphical parameters
Symbols and lines ¦ Colors ¦ Text characteristics
Graph and margin dimensions
3.4 Adding text, customized axes, and legends
Titles ¦ Axes ¦ Reference lines ¦ Legend
Text annotations
3.5 Combining graphs
Creating a figure arrangement with fine control
3.6 Summary
4 Basic data management :
4.1 A working example
4.2 Creating new variables
4.3 Recoding variables

4.4 Renaming variables
4.5 Missing values
Recoding values to missing ¦ Excluding missing values from analyses
4.6 Date values
Converting dates to character variables ¦ Going further
4.7 Type conversions
4.8 Sorting data
4.9 Merging datasets
Adding columns ¦ Adding rows
4.10 Subsetting datasets
Selecting (keeping) variables ¦ Excluding (dropping) variables
Selecting observations ¦ The subset() function ¦ Random samples
4.11 Using SQL statements to manipulate data frames
4.12 Summary
5 Advanced data management:
5.1 A data management challenge
5.2 Numerical and character functions
Mathematical functions ¦ Statistical functions ¦ Probability functions
Character functions ¦ Other useful functions ¦ Applying functions to
matrices and data frames
5.3 A solution for our data management challenge
5.4 Control flow
Repetition and looping ¦ Conditional execution
5.5 User-written functions
5.6 Aggregation and restructuring
Transpose ¦ Aggregating data ¦ The reshape package
5.7 Summary
Part II Basic methods:
6 Basic graphs:
6.1 Bar plots
Simple bar plots ¦ Stacked and grouped bar plots ¦ Mean bar plots
Tweaking bar plots ¦ Spinograms
6.2 Pie charts
6.3 Histograms
6.4 Kernel density plots
6.5 Box plots
Using parallel box plots to compare groups ¦ Violin plots
6.6 Dot plots
6.7 Summary
7 Basic statistics:
7.1 Descriptive statistics
A menagerie of methods ¦ Descriptive statistics by group
Visualizing results
7.2 Frequency and contingency tables
Generating frequency tables ¦ Tests of independence
Measures of association ¦ Visualizing results
Converting tables to flat files
7.3 Correlations
Types of correlations ¦ Testing correlations for significance
Visualizing correlations
7.4 t-tests
Independent t-test ¦ Dependent t-test ¦ When there are more than two
7.5 Nonparametric tests of group differences
Comparing two groups ¦ Comparing more than two groups
7.6 Visualizing group differences
7.7 Summary
Part III Intermediate methods:
8 Regression:
8.1 The many faces of regression
Scenarios for using OLS regression ¦ What you need to know
8.2 OLS regression
Fitting regression models with lm() ¦ Simple linear regression
Polynomial regression ¦ Multiple linear regression
Multiple linear regression with interactions
8.3 Regression diagnostics
A typical approach ¦ An enhanced approach ¦ Global validation of
linear model assumption ¦ Multicollinearity
8.4 Unusual observations
Outliers ¦ High leverage points ¦ Influential observations
8.5 Corrective measures
Deleting observations ¦ Transforming variables ¦ Adding or deleting
variables ¦ Trying a different approach
8.6 Selecting the “best” regression model
Comparing models ¦ Variable selection
8.7 Taking the analysis further
Cross-validation ¦ Relative importance
8.8 Summary
9 Analysis of variance:
9.1 A crash course on terminology
9.2 Fitting ANOVA models
The aov() function ¦ The order of formula terms
9.3 One-way ANOVA
Multiple comparisons ¦ Assessing test assumptions
9.4 One-way ANCOVA
Assessing test assumptions ¦ Visualizing the results
9.5 Two-way factorial ANOVA
9.6 Repeated measures ANOVA
9.7 Multivariate analysis of variance (MANOVA)
Assessing test assumptions ¦ Robust MANOVA
9.8 ANOVA as regression
9.9 Summary
10 Power analysis:
10.1 A quick review of hypothesis testing
10.2 Implementing power analysis with the pwr package
t-tests ¦ ANOVA ¦ Correlations ¦ Linear models
Tests of proportions ¦ Chi-square tests ¦ Choosing an appropriate effect
size in novel situations
10.3 Creating power analysis plots
10.4 Other packages
10.5 Summary
11 Intermediate graphs :
11.1 Scatter plots
Scatter plot matrices ¦ High-density scatter plots ¦ 3D scatter plots
Bubble plots
11.2 Line charts
11.3 Correlograms
11.4 Mosaic plots
11.5 Summary
12 Resampling statistics and bootstrapping
12.1 Permutation tests
12.2 Permutation test with the coin package
Independent two-sample and k-sample tests ¦ Independence in contingency
tables ¦ Independence between numeric variables
Dependent two-sample and k-sample tests ¦ Going further
12.3 Permutation tests with the lmPerm package
Simple and polynomial regression ¦ Multiple regression
One-way ANOVA and ANCOVA ¦ Two-way ANOVA
12.4 Additional comments on permutation tests
12.5 Bootstrapping
12.6 Bootstrapping with the boot package
Bootstrapping a single statistic ¦ Bootstrapping several statistics
12.7 Summary
Part IV Advanced methods :
13 Generalized linear models :
13.1 Generalized linear models and the glm() function
The glm() function ¦ Supporting functions ¦ Model fit and regression
13.2 Logistic regression
Interpreting the model parameters ¦ Assessing the impact of predictors on the
probability of an outcome ¦ Overdispersion ¦ Extensions
13.3 Poisson regression
Interpreting the model parameters ¦ Overdispersion ¦ Extensions
13.4 Summary
14 Principal components and factor analysis :
14.1 Principal components and factor analysis in R
14.2 Principal components 334
Selecting the number of cmponents to extract
Extracting principal components ¦ Rotating principal components
Obtaining principal components scores
14.3 Exploratory factor analysis
Deciding how many common factors to extract ¦ Extracting common
factors ¦ Rotating factors ¦ Factor scores ¦ Other EFA-related
14.4 Other latent variable models
14.5 Summary
15 Advanced methods for missing data :
15.1 Steps in dealing with missing data
15.2 Identifying missing values
15.3 Exploring missing values patterns
Tabulating missing values ¦ Exploring missing data visually ¦ Using
correlations to explore missing values
15.4 Understanding the sources and impact of missing data
15.5 Rational approaches for dealing with incomplete data
15.6 Complete-case analysis (listwise deletion)
15.7 Multiple imputation
15.8 Other approaches to missing data
Pairwise deletion ¦ Simple (nonstochastic) imputation
15.9 Summary
16 Advanced graphics:
16.1 The four graphic systems in R
16.2 The lattice package
Conditioning variables ¦ Panel functions ¦ Grouping variables
Graphic parameters ¦ Page arrangement
16.3 The ggplot2 package
16.4 Interactive graphs
Interacting with graphs: identifying points ¦ playwith
latticist ¦ Interactive graphics with the iplots package ¦ rggobi
16.5 Summary