R programming for Researchers

    Assuming unbiased experiment, the researcher’s dilemma is circled in their minimal capability to perform appropriate data manipulation, biostatistical interpretation, and professional graphical presentation for their data. As a result, their quality of research needs much improvement. This course focuses on building researchers’ skills in these 3 pillars for competitive international publication using a popular open source-code-based language called R.

1. 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.
2. 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)
3. 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.
4. 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.
5. Correlation and regression with R
   – Understand how to correlate variables and fit a regression model for your data in a professional manner.
   – 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.
6. 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.
7. Machine learning &SVM
   – A type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
8. 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.
9. Principle component analysis and PLS 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.
10. Survival analysis
    – To estimate and interpret survival and / or hazard functions from the survival data; to compare survival and / or hazard functions, and to assess the relationship of explanatory variables to survival time.
11. 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.

You have the option to attend all tracks or select one or more from the list below:

Tutor:
Sameh Magdeldin M.V.Sc, 2 Ph.D, MBA
Head of Proteomics and metabolomics unit, CCHE 57357
R programing and statistical inference certification, John Hopkins

Professional trainer:
Ahmed Karam
Senior bioinformatician Proteomics and Metabolomics Lab Research Program at CCHE 57357

Duration: 2 days/ week for 3 month

(Monday – Wednesday)

Researchers, Scientists, post graduate students and selective undergraduate students planning to pursue research in future.

Please Contact: [email protected]