Data Analysis and Management Using R-Software

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.

The Expedition

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.
• Subsetting 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.