R Software for Data Analysis
Traditional training – (5 days, R13,000.00 excl. VAT)
Online training – (4 weeks, R9,000.00 excl. VAT)
About This Course
R is a programming language and free software environment for statistical computing and graphics. It is the language for data science, built for data analysts. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques. Being a full programming language, what R offers are highly extensible.
R is now considered by many as the best software for statistical analysis and data science. It is a fast, powerful statistical package designed by statisticians for researchers of all disciplines. With Base R and a library of packages, the analyst has everything for data management and basic and advanced analysis. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. In this course, participants will experience the desired qualities and functionalities that make R widely preferred.
R is absolutely free. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.
In this course participants will learn how to program in R and how to use R for data analysis. The course covers practical issues in statistical computing which include introduction to R programming, accessing R packages, reading data into R, using R functions, using R script files, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Participants will learn to import other types of data files, like Excel and CSV files, into R. They will learn to use R for data cleaning, management and statistical analysis. They will achieve the understanding of descriptive statistics, graphics, confidence intervals, p-values and bi-variate inferential statistics (hypothesis testing). The theory behind the statistics will be explained and participants will be able to interpret statistical outputs. Practical exercises will provide hand-on experience and problem-based learning.
The course uses a fine blend of interactive discussions, group exercises for every session, self-paced learning and two project reports that simulate real-life scenarios. At the end of the course, participants will be able to produce and interpret basic and intermediate descriptive and bi-variate inferential statistics using R. As a result, they will be able to take raw data, clean them, summarise them, analyse them and take appropriate action. They will also improve their skills to critically analyse research reports.
Week 1 - Learn to Use R
- Research questions and data structure
- Introduction to R, explore the capabilities of R, comparison with Stata and Python
- Downloading R and R Studio
- Working in R – data structures and functions
- Accessing R packages, writing R functions
- Introduction to R programming
Week 2 – Programming in R
- R script files – organizing and running R codes
- Reading and importing data into R
- Basic data management
Week 3 – Data Analysis: Descriptive statistics and Subgroup analysis
- Making sense of data (concepts in descriptive statistics)
- Using R for descriptive analysis
- Using R for subgroup analysis
Week 4 – Inferential Statistics (Confidence Interval and Hypothesis Testing)
- Calculating confidence intervals
- Different types of statistical tests
- Chi-square test
- Parametric and non-parametric tests
The course will teach participants the following:
CESAR statistical computing courses well-paced providing room for sharing rich and relevant skills and experiences by the course facilitators and participants. Participants are encouraged to ask and answer questions throughout the course. After the training, participants will be able to take raw data collected in their settings, clean them, summarise them, analyse them and take appropriate action. They will achieve the understanding of descriptive statistics and bi-variate inferential statistics. They will also be able to use R in their practical professional work to produce neat and reproducible analysis outputs. In addition, they will improve their ability able to critically review research reports and papers.
- Research analysts
- Data analysts
- Programme managers
- Postgraduate students
- Market researchers
- Clinical and medical researchers
- Government practitioners