Solving Biological Problems with R

Solving Biological Problems with R, Summarizing Data.

1: Getting started with R

2: Setting up your R environment, data types and structures, loading and installing packages

3:Data exploration:

Reading and writing data files, looking into  data, basic graphs and basic statistics

4:Introduction to common packages (tidyr,dplyr, ggplot2,reshape2,ggthemes,ggpubr, RColorBrewer, psych,corrplot, Hmisc)

5:Statistical tests in R:

Statistical tests are applied according to the data and your questions.

1. ANNOVA test is used to test the means of the groups.

   One-way ANOVA

   Two-way ANOVA

2. Two-Sample t-Test
3. Chi-squared test
4. Wilcoxon test
5. Kruskal-Wallis test
6. Pearson Correlation Test
7. Spearman Correlation Test
8. Kendall Correlation Test
9. Friedman Test
10. Mann-Whitney U Test

6:Graphics with R:

• hist() function used to create Histograms.
• boxplot() function for creating Boxplots.
• Pie charts can be created by using a simple function pie()
• stripchart() function can be used for Strip charts.
• barplot() function used for Bar plots in R.

7:Creating reproducible reports in R

This is very important for R code integration and reports. We want to share our reports with Classfellows, collaborators or instructors.

Then, the R Markdown file can help us to recognise and compile the basic components of reports.

Create the R Markdown file to submit your results in PDF, Word, or HTML using Knit.