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.