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)
INSTRUCTORS
Tutor:
Sameh Magdeldin DVM.,
2 PhD
Head of proteomics research program unit
Basic research department
.
Co-tutors:
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 |
Registration
Closed