R- Training Course for Data Management, Analysis and Graphics Using

Overview

The training objective of this one week Training in Data Management, Analysis and Graphics with R will impact skills that are of very high demand in data management and analysis. R is an integrated suite of software facilities for data manipulation, calculation and graphical display.

 R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. 

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering,) and graphical techniques, and is highly extensible. 

The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. 

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. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

WHO SHOULD ATTEND

• Statisticians & Researchers
• Planners & Monitors and Evaluators
• NGOS & Government Ministries
• Project Managers

LEARNING OBJECTIVES

• The participants will learn ways of effectively handling data and how to use R as a storage facility.
• The participants will learn how to use R as a suite of operators for calculations on arrays, in particular matrices.
• The participants will learn how to use R for large, coherent, integrated collection of intermediate tools for data analysis
• The participants will learn how to use R graphical facilities for data analysis and display either on-screen or on hardcopy
• The participants will learn how to use R for well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
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COURSE CONTENT

Introduction to R

• Why use R?
• Obtaining and installing R
• The R environment
• Working with R
• Packages
• The available help
• Batch processing
• Using output as input—reusing results
• Working with large datasets
• The R workspace, managing objects
• R Packages
• Conflicting objects
• Editors for R scripts

Data Objects (Data types and Data structures) Data types

• Double
• Integer
• Complex
• Logical
• Character
• Factor
• Dates and Times
• Missing data and Infinite values.

Data structures

• Vectors
• Matrices
• Arrays
• Data frames
• Time-series objects
• Lists
• The string function

Importing data

• Text files
• Excel files
• Databases
• From other statistical software

Data Entry, management and Manipulation with R

• Creating a dataset
• Understanding datasets
• Data structures
• Data input
• Annotating datasets
• Useful functions for working with data objects
• Creating new variables
• Recoding variables
• Renaming variables
• Missing values
• Date values
• Type conversions
• Sorting data
• Merging datasets
• Sub setting datasets
• Using SQL statements to manipulate data frames

Introduction to R Graphics

• Introduction
• High-level plotting commands
• Low-level plotting commands
• Interacting with graphics
• Modifying a graph

Working with Graphics in R

• Graphs and charts for dichotomous and categorical variables
• Graphs and charts for ordinal variables
• Tabulations for summary statistics for continuous variables
• Graphs and charts for continuous variables

Summarising data using R

• Numerical summaries for discrete variables
• Tables for dichotomous variables
• Tables for categorical variables
• Tables for ordinal variables

Quantitative data Analysis using R

• Planning for qualitative data analysis
• Basics for statistical analysis
• Testing for normality of data
• Choosing the correct statistical test
• Hypothesis testing
• Confidence intervals
• Tests of statistical significance (Parametric and non-parametric tests)
• Hypothesis testing versus confidence intervals

For further inquiries, please contact us on

Tel: +254 20 2000 957.

Email: [email protected]

REQUIREMENTS AND TRAINING VENUE

The training is residential and will be held at AJT Training Centre. Delegates should be reasonably proficient in English. Applicants must live up to Alexander James Training (AJT) admission criteria.

ACCREDITATION

Upon successful completion of this training, participants will be issued with an Alexander James Training (AJT) certificate.

CUSTOMISED/TAILOR- MADE

We will customise this training to fit your professional and institution expected outcome. You can have it delivered in our AJT Training Centre or at a convenient location.

Kindly Note: The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and delivery methodology.