In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, and organizing and commenting R code. Topics in statistical data analysis and optimization will provide working examples.
This course is intended for Data Analysts, Data Scientists, Business Intelligence Analysts and any other professional who want to explore the vast range of analytical and graphical capabilities of R.
1. Understand quantitative analysis techniques
2. Master R and R Studio user interface
3. Import data from various sources
4. R Scripts
5. Carry out quantitative data analysis using R
6. Write a quantitative report
- open Source
- R provides exemplary support for data wrangling. The packages like dplyr , readr are capable of transforming messy data into a structured form.
- With packages like Shiny and Markdown, reporting the results of an analysis is extremely easy with R. You can make reports with the data, plots and R scripts embedded in them. You can even make interactive web apps that allow the user to play with the results and the data.
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
Background in Statistics is an added advantage but any one with passion to be a top quantitative researcher is encouraged to apply.
This course aims to build the capacity of quantitative research in quantitative data management and analysis using R software. This software has many features that make it the most endearing quantitative data analysis software available, given it’s open source nature.
We shall be looking at the basic steps of the research process
1. Explain the basic steps of the research process .
2. Explain differences between populations and samples .
3. Explain differences between experimental and non-experimental research designs.
4. Explain differences between independent and dependent variables .
5. Overview of the R Studio IDE.
6. Installing, loading and updating R packages.<
7. Creating objects in R.
8. Data types
9. Data structures
10. Sorting vectors and data frames
11. Directory management commands
12. Direct data entry in R (for small data sets)
13. Importing data from other software
14. Decision structures (if, if-else, if-else if-else)
17. The user will be required to learn to print conditional tables/filtering, sort data, append, merge and reshape datasets, and concatenate and order (reposition) variables.
18. The user will be taught to declare string date variables as to be of type date, among other type conversions.
R provides exemplary support for data wrangling. Will look into the packages like dplyr, readr are capable of transforming messy data into a structured form..
1. Working with variables
2. Transform continuous variables to categorical variables
3. Add new variables to data frames
4. Handling missing values
5. Sub-setting data frames
6. Appending and merging data frames
7. Spit data frames
8. Stack and unstack data frames
6. Creating tables of frequencies and proportions
7. Cross tabulations of categorical variables
8. Descriptive statistics for continuous variables
9. measures of dispersion (variance, standard deviation, interquartile range, range, coefficient of variation)
Descriptive statistics, Inferential statistics
1. The learner will be taken through R’s powerful commands for calculation of measures of central tendency (mean, mode and median) and their confidence intervals
2. Skewness and kurtosis, proportions and frequencies and interpretations of the same.
3. Introduction to parametric and non-parametric methods of data analysis and the concepts underlying each. This will be accompanied by a rigorous hands-on insight into the various forms of regressions (linear, logistic, and probit) and their related concepts including model goodness of fit
4. R square, marginal effects and sensitivity and specificity, odds ratios for the case of binary logistic regression
5. Correlations (Pearson’s r, Spearman’s rho, Kendall’s tau, Cramer’s v and phi)
6. Tests of associations (Chi-square, Fisher’s exact, McNemar)
7. Tests of hypothesis (ANOVA and its post hoc tests, t-tests, Kruskal-Wallis, Mann-Whitney U, and Cochrane’s Q)
learn to analyze longitudinal data using survival analysis approaches and be able to visualize the analysed data using various charts
The participants will learn to analyze longitudinal data using survival analysis approaches that include non-parametric (Kaplan-Meier, Nelson-Aalen); semi-parametric (cox-proportional hazards) and its assumptions; parametric (lognormal, exponential, Weibull, gaussian) and interpretations of Hazard ratios and risk ratios obtained from generalized estimating equations (GEE).
The participant will be able to visualize – continuous variables in histograms, box plots, scatter plots, density plots and line plots; categorical variables in the form of bar graphs and pie-charts; sensitivity and specificity in the form of Receiver Operating Characteristic (ROC) curves.
This course is delivered by our seasoned trainers who have vast experience as expert professionals in the respective fields of practice. The course is taught through a mix of practical activities, theory, group works and case studies. Training manuals and additional reference materials are provided to the participants.
Accommodation will be arranged for our participants upon request.
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