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Mastering Data Analysis with R

Video Introducing this tutorial

Chapter 1: Hello, Data! :
Loading text files of a reasonable size
Benchmarking text file parsers
Loading a subset of text files
Loading data from databases
Importing data from other statistical systems
Loading Excel spreadsheets

Chapter 2: Getting Data from the Web :
Loading datasets from the Internet
Other popular online data formats
Reading data from HTML tables
Scraping data from other online sources
R packages to interact with data source APIs

Chapter 3: Filtering and Summarizing Data :
Drop needless data
Running benchmarks
Summary functions

Chapter 4: Restructuring Data :
Transposing matrices
Filtering data by string matching
Rearranging data
dplyr versus data.table
Computing new variables
Merging datasets
Reshaping data in a flexible way
The evolution of the reshape packages

Chapter 5: Building Models (authored by Renata Nemeth and Gergely Toth) :
The motivation behind multivariate models
Linear regression with continuous predictors
Model assumptions
How well does the line fit in the data?
Discrete predictors

Chapter 6: Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth) :
The modeling workflow
Logistic regression
Models for count data

Chapter 7: Unstructured Data :
Importing the corpus
Cleaning the corpus
Visualizing the most frequent words in the corpus
Further cleanup
Analyzing the associations among terms
Some other metrics
The segmentation of documents

Chapter 8: Polishing Data :
The types and origins of missing data
Identifying missing data
By-passing missing values
Getting rid of missing data
Filtering missing data before or during the actual analysis
Data imputation
Extreme values and outliers
Using robust methods

Chapter 9: From Big to Small Data :
Adequacy tests
Principal Component Analysis
Factor analysis
Principal Component Analysis versus Factor Analysis
Multidimensional Scaling

Chapter 10: Classification and Clustering :
Cluster analysis
Latent class models
Discriminant analysis
Logistic regression
Machine learning algorithms

Chapter 11: Social Network Analysis of the R Ecosystem :
Loading network data
Centrality measures of networks
Visualizing network data
Further network analysis resources

Chapter 12: Analyzing Time-series :
Creating time-series objects
Visualizing time-series
Seasonal decomposition
Holt-Winters filtering
Autoregressive Integrated Moving Average models
Outlier detection
More complex time-series objects
Advanced time-series analysis

Chapter 13: Data Around Us :
Visualizing point data in space
Finding polygon overlays of point data
Plotting thematic maps
Rendering polygons around points
Satellite maps
Interactive maps
Alternative map designs
Spatial statistics

Chapter 14: Analyzing the R Community :
R Foundation members
R package maintainers
The R-help mailing list
Analyzing overlaps between our lists of R users
The number of R users in social media
R-related posts in social media