R Language - The fundamentals of R programming for Data Science
Discovering R
• Presentation of R
• Installation and resources
• The package system
• RStudio IDE and creating a first script
The basics of the R language
• Variables and types
• Formats: vectors
• Formats: matrices
• Formats: DataFrame
• Formats: lists
• Conditions (if ... else)
• The loops
• The functions
• The apply functions
R and the data
• Retrieving data from a CSV file
• Retrieving other data formats: Excel, SQL, API
Statistical analysis and first graphs
• Analysis of numerical variables
• Analysis of categorical variables
• Other statistical analysis methods
Rendering results: overview of possibilities
• Graphs
R language - Access all your data types
Some reminders about R and its environment
• Why R ?
• Installation of the environment
• Resources related to R
Data in CSV format
• Accessing data in CSV format
• Putting it into practice - Data Visualization: bar chart
Data in Excel format
• Accessing data in Excel format
• Practical Guide - Data Visualization: Scatterplot
Data from a SQL database
• Accessing data from an SQL database
• Practical Guide - Data Visualization: Word Cloud
Data from an API
• Accessing data from an API
• Data Visualization Practice: Dynamic Bar Chart for the Web
Data from a NoSQL MongoDB database
• Accessing data from a NoSQL database
• Putting it into practice - Data Visualization: dynamic bar chart for the web
R language - Manipulate your data with the dplyr and tidyr packages
Reminders on how to access different data formats with R
• Access to data in CSV format
• Access to other data formats (Excel, SQL databases, API...)
Manipulation using the dplyr package
• Introduction to the "dplyr" package: syntax and pipe
• Manipulation of observations (rows)
• Manipulation of variables (columns)
• Creation of summary data tables
• Data joins
Introduction to data cleaning with the tidyr package
• Reshaping data
• Completing missing values