Learn R for Data Analysis

Author

✍ Faik Erkam Minsin  

Resource

• A 1680 mins (28 hrs) lecture by Danny Arends
• The videos of the lecture are available on Danny's youtube page

Code

• Scripted on: Notepad++
• Executed on: R 4.1.1 and R Studio
• Dependent on: Standard R libraries

Topics and Documents

Introduction:

• R as a calculator
• Types of data
  • Logical, numeric, character, vector/list, matrix/data.frame
• Creating vectors and matrices
• Indexing data [1] and [[1]]
• Matrix and data.frame functions
• seq, rep, 1:20

Assignment 1

Solution 1

More Introduction:

• Variables
• Control structures
  • Statements and Expressions
• Functions
• Brackets
• Escaping the inevitable
• Some randomness

Addition

Solution_Addition

Assignment 2

Solution 2

Data:

• Reading data
  • Tables, Text files, Binary files, text files line by line
  • Compressed files
• Managing data
  • which, %in%, subset
• Saving data (in a long running analysis)

Assignment 3

Solution 3

Descriptive Statistics:

• Descriptive univariate statistics
  • Central Tendency
    • Mean, Median, Mode
  • Dispersion
    • Range, Quantiles, Variance, Standard deviation
    • Outliers
  • Shape
    • Skewness, Kurtosis
• Plots
  • Boxplot
  • Histogram
  • Image/Heatmap

Assignment 4

Solution 4

Plots:

• Create custom plots
  • Points
  • Scatter plots
  • Circles
  • Colors

Assignment 5

Solution 5

Statistical Testing:

• Experimental design
  • Questions
  • Microarrays
• Normalization
• Probability
  • Student's t-test
  • Normality assumption
  • Correlation
  • Multiple testing
• Free microarray data

Assignment 6

Solution 6

Algorithms and Functions:

• Algorithms as a concept
  • Design patterns
• Recursion
  • Higher order functions

Assignment 7

Solution 7