DATA MANAGEMENT USING R-SOFTWARE TRAINING

Data Management using R-Software Training

About the Program

Statistical Data Management and Analysis using R course provides an insight into quantitative data management and analysis (exploring, summarizing, statistical analyzing, visualizing). R is an open source software with many features for quantitative data management and analysis.

Program Objectives

• To introduce new users into using R statistical software.
• To empower participants on data management and data analysis.
• To broaden the knowledge of participants on understanding data types and making correct choices for data analysis.
• To facilitate participants’ understanding of the types of analysis to conduct on their datasets for results.
• Understand and appropriately use statistical terms and concepts.
• Design computer aided data capture screens using CSPRO.
• Use mobile phone data collection tool Open Data Kit (ODK) to collect survey data.
• Convert data into various formats using appropriate software.
• Perform basic data analysis tasks with R.
• Perform simple to complex data management tasks using R.
• Correctly identify appropriate statistical test for basic analysis s and perform them using R.
• Perform Advanced Statistical Analysis such as GLM, PCA and Power Analysis.

Target Audience

• This training program is ideal for Advanced Microsoft Excel professionals who need to automate Excel spreadsheet tasks using Visual Basic for Applications (VBA).
• Big Data enthusiast

Training Period

• Classroom: 5 Days
• Online: 7 Days

Module 1: Basic statistical terms and concepts.

• Basic data quality checks.
• Basic exploratory data analysis procedures.
• Basic Descriptive Statistics.
• The core functions of inferential statistics.
• Common inferential statistics.
• Concepts and Software for Data Processing.
• Data Processing using Census and Surveys Processing Software (CsPro).
• Use of Mobile Phones for Data Collection and Processing.

Module 2: Introduction to R.

• Why use R?
• Obtaining and installing R.
• Working with R.
• Packages.
• Batch processing.
• Using output as input—reusing results.
• Working with large datasets.

Module 3: 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.

Module 4: Tabulations and Graphics with R.

• Graphing Qualitative data.
• Graphing Quantitative data.
• Getting Started R Graphics.
• Working with graphs.
• A simple example.
• Graphical parameters.
• Adding text, customized axes, and legends.
• Combining graphs.
• Basic Graphs (Bar plots Pie charts, Histograms, Kernel density plots, Box plots, Dot plots).
• Intermediate graphs (Scatter plots, Line charts, Correlograms, Mosaic plots).
• Frequency and contingency tables.

Module 5: Quantitative Analysis using R.

• Descriptive statistics.
• Correlations.
• t-tests.
• Nonparametric tests of group differences.
• Visualizing group differences.
• Regression.
• Analysis of Variance.
• Power Analysis.

Delivery Method

This program is taught through a mix of practical activities, theory, group work and case studies. Training manuals and additional reference materials are provided to the participants.

CERTIFICATION

Upon successful completion of the training, participants will be awarded a certificate of course completion