FA-0807Data & AnalyticsSoftware Development
R Programming for Data Analysis and Reports
Introduction
Why this course
Build introductory R skills for statistics and data analysis, from objects and functions to data frames, charts and a simple regression example. The two-day sequence also introduces generating Markdown-based reports to communicate code, results and narrative.
Learning outcomes
Learning outcomes
- Use R data types, vectors, matrices, lists and indexing.
- Write conditions, loops and user-defined functions.
- Import, clean, transform, sample and export data frames.
- Create introductory ggplot2 charts.
- Fit and interpret a simple linear model, distinguishing correlation from regression.
- Generate a basic Markdown-based analysis report.
Prerequisites
Prerequisites
No prior R experience is required. Basic computer/file skills are useful. Use a supported R and RStudio Desktop environment on a compatible Windows, macOS or Linux system, selected packages and prepared datasets.
Training outline
7 modules
·
01Environment and data sources3 topics
- Environment
- Workspace
- Data Source
02Basic R programming4 topics
- Data types
- Operations
- Coercion
- Functions
03Composite objects and indexing9 topics
- Vectors
- Vector operations
- Recycling
- Dimensions
- Matrices
- Indexing
- Slicing
- Arithmetics and operations
- Lists
04Control flow and functions4 topics
- Operators
- Conditions
- Loops
- User-defined functions (UDFs)
05Data frames and transformation8 topics
- Data Frames
- Import from data source
- Export to data destination
- Functions and operations with Data Frames
- Data cleaning
- Transformations
- Sampling
- Tidying
06Visualisation with ggplot21 topics
- Histograms, box-and-whisker plots, bar charts and scatter plots.
07Regression, reporting and assessment2 topics
- The linear regression model
- Correlation vs regression
- Geometrical representation
- Practical regression in R
- How to interpret the regression table
- Generating basic R Markdown analysis reports
- Assessment
Work through a small prepared dataset; interpret model output without implying causation from correlation.
A programme built around your team.
Share your training goals and requirements.