R for Researchers (corner stone course)

Professional course, CCHE-57357, department of basic research

Course Description

  • Introduction to R

    • Simple walk around R program and how to use it for beginners. The course is mainly designed to be familiar with the program before using it in a professional way for your research purpose.

    • Part 1: What is R, reason to use it, packages for R, Installation, loading data and play around.

    • Part 2:Mathematical operations, Expressions, Logical values, Variables, Functions, Basic data types, Dealing with NA, Finding appropriate functionality and exploring your mistakes.

  • Biostatistics with R

    • How to refine your data before analyzing it, choose you appropriate test and interpretate the data in a professional manner. This course is essential for all researchers for unbiased research analysis.

    • Part 1:Simple Mathematics, Descriptive statistics, dealing with outliers, Normality tests (Shapiro-Wilks, Kolmogrov-Smirnov, Anderson-Darling).

    • Part 2:Frequency and contingency tabulation (Cross tabulation), Test of independence (Chi- Square, Fisher exact test, Cochran Mantel Haenzel test), Measuring the strength of 2 way contingency tables.

    • Part 3:T test (dependent and independent), Pair wise T test, Mann-Whitneys U test, Wilcoxon signed rank test, ANOVA, Kruskal- Wallis, Friedman test, Post hoc test example (Tukey’s).

  • Basic graphics with R

    • This course enables you to learn how to draw the basic graphics for your research using the base built in graphics package in R.

    • Line chart, Bar chart, Histogram, Box and Whiskers, combining graphics, Scatter plot matrix.

  • Advanced graphics with R

    • A professional course enables you how to draw a professional graphics for international publications. Additionally, it lets you determine the appropriate graphics panel based on your data.

    • The course focuses on ggplot 2 package, the yet, most powerful graphics package recommended by top leading journals such as Nature and Cell.

    • Part 1:Whiskers and box plot, Whiskers and box plot overlaid with dot plot, Violin plot, Scatter plot.

    • Part 2: Introducing the power of faceting, line plot, error bars, Histograms, Histograms overlaid with density curve, density curve.

    • Part 3:Heat map analysis, bar plot, stacked bar plot, proportional stacked bar plot, scatter plot matrix.

  • Correlation and regression with R

    • Understand how to correlate variables and fit a regression model for your data in a professional manner.

    • The course focuses on ggplot 2 package, the yet, most powerful graphics package recommended by top leading journals such as Nature and Cell.

    • Part 1:Correlation (Pearson, Spearman, Kendall), Simple liner regression, Global validation of liner model assumption,

    • Part 2: Multiple liner regression, testing outliers and dropping values, non-liner regression, Quality check of fitted model.

  • Logistic regression with R

    • Understand how to perform a logistic regression and predict binomial (binary) variable. Additionally, the course gives you a hint on how to read and draw a logistic regression (S- shaped) curve.

  • Principle component analysis with R

    • A Multivariate analysis test used to predict strong correlation pattern within dataset variables. This course lets you know how to perform, read and draw a PCA

  • Receiver operating characteristics (ROC) curve with R

    • Understand how to perform and read a ROC curve. Choosing a cut point between poor observations and good ones.

  • Data manipulation with R

    • This course enables you dealing with large data input in a professional way. Unlike spreadsheet (excel) avoid errors and save time by using automated coding.

Selection criteria

  • Affiliated to research institute, university (governmental/private)


Tutor: Sameh Magdeldin DVM.,
2 PhD Head of proteomics research program unit
Basic research department .


Eman Abdelnabi : Senior Researcher, M.V.Sc, PhD,

Aya Osama : BSc

Mohamed Seoudi : BSc

Proposed modules

Module Name Course Abrv Duration Difficulty level Capacity
Introduction to R Intro 2 days Simple 20
Biostatistics with R Bio 6 days Moderate 20
Basic graphics with R Bgraph 3 days Simple 20
Advanced graphics with R Agraph 6 days Moderate 20
Correlation and regression with R Cor 2 days Moderate 20
Logistic regression with R Log 1 day Difficult 20
Principle component analysis (PCA) with R PCA 1 day Difficult 20
ROC curve with R ROC 1 days Moderate 20
Data manipulation with R Data 2 days Moderate 20



JCI Accredited

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