R and RStudio are essential tools for biological data analysis. They offer powerful features for data manipulation, statistical analysis, and visualization. This intro covers the basics of installation, setup, and key functionalities.

Understanding R's syntax and data structures is crucial for effective analysis. We'll explore importing and exporting data, as well as techniques for data manipulation and cleaning. These skills form the foundation for advanced biological data analysis.

R and RStudio Setup for Biological Data

Installation Process

• R is a free, open-source programming language and software environment for statistical computing and graphics
• Installing R involves downloading the appropriate version for your operating system (Windows, macOS, Linux) from the official CRAN (Comprehensive R Archive Network) website
• RStudio is an integrated development environment (IDE) for R that provides a user-friendly interface and additional features to enhance productivity
• Installing RStudio requires downloading the appropriate version (Desktop or Server) from the official RStudio website
• RStudio installation is separate from R installation and should be performed after installing R

Configuration and Setup

• Setting up R and RStudio involves configuring preferences to customize the user experience and optimize workflow
• The working directory can be set to specify the default location for reading and writing files
• Appearance settings, such as font size, color scheme, and pane layout, can be adjusted to suit personal preferences
• Package management options, including default repositories and installation methods, can be configured to streamline package installation and updates
• Integrating version control systems (Git) and connecting to remote repositories (GitHub) can be set up within RStudio for collaborative projects

Basic R Syntax and Data Structures

Syntax and Operations

• R uses a command-line interface where users enter commands and receive output in the console
• Basic arithmetic operators in R include addition (+), subtraction (-), multiplication (``), division (/), and exponentiation (^)
• R is case-sensitive, meaning that uppercase and lowercase letters are treated as distinct (e.g., variable and Variable are different)
• Variables in R are assigned using the assignment operator (<- or =) and can store various data types, such as numeric, character, and logical values
• Comments in R code can be added using the # symbol to provide explanations or disable specific lines of code

Data Structures

• Vectors are one-dimensional arrays that can contain elements of the same data type, created using the c() function (e.g., c(1, 2, 3) creates a numeric vector)
  • Atomic vectors include logical (TRUE, FALSE), integer (1L, 2L), double (1.5, 2.7), character ("a", "hello"), complex (1+2i), and raw (as.raw(10)) types
• Matrices are two-dimensional arrays with elements of the same data type, created using the matrix() function (e.g., matrix(1:6, nrow = 2, ncol = 3))
• Data frames are two-dimensional data structures with columns that can contain different data types, similar to a spreadsheet or SQL table (e.g., data.frame(x = c(1, 2, 3), y = c("a", "b", "c")))
• Lists are ordered collections of objects that can contain elements of different data types and structures, created using the list() function (e.g., list(a = 1, b = "hello", c = TRUE))
• Factors are special vectors used to represent categorical data with predefined levels, created using the factor() function (e.g., factor(c("male", "female", "male")))

Data Import and Export in R

Importing Data

• read.table() and read.csv() functions are used to import tabular data from text files, such as CSV (comma-separated values) or TSV (tab-separated values) files
• read.xlsx() function from the openxlsx package allows importing data from Excel files (.xlsx or .xls)
• read.spss() function from the haven package enables importing data from SPSS (Statistical Package for the Social Sciences) files (.sav)
• read.dta() function from the haven package is used to import data from Stata files (.dta)
• read.sas() function from the haven package allows importing data from SAS (Statistical Analysis System) files (.sas7bdat)

Exporting Data

• write.table() and write.csv() functions are used to export data from R to text files, such as CSV or TSV files
• write.xlsx() function from the openxlsx package enables exporting data from R to Excel files (.xlsx)
• write.dta() function from the haven package allows exporting data from R to Stata files (.dta)
• write.sas() function from the haven package is used to export data from R to SAS files (.sas7bdat)
• Exporting data allows sharing analysis results, collaborating with others, or using the data in other software applications

Data Manipulation and Cleaning in R

Subsetting and Accessing Data

• Subsetting data using square brackets ([]) or the subset() function allows selecting specific rows, columns, or elements based on conditions
• The $ operator is used to access columns of a data frame by name (e.g., df$column_name)
• The head() and tail() functions display the first or last n rows of a data object, respectively, providing a quick preview of the data
• The str() function provides a concise summary of the structure of a data object, including data types and dimensions
• The summary() function generates descriptive statistics for a data object, such as minimum, maximum, mean, and quartiles for numeric variables

Data Cleaning and Transformation

• The is.na() function checks for missing values (NA) in a data object, while na.omit() removes rows with missing values
• The unique() function identifies unique values in a vector or data frame column, helpful for identifying distinct categories or levels
• The merge() function combines two data frames based on common columns, similar to a SQL join operation (e.g., merge(df1, df2, by = "common_column"))
• The reshape2 package provides functions like melt() and dcast() for reshaping data between wide and long formats, facilitating data manipulation for analysis and visualization
• The dplyr package offers a set of functions for data manipulation, such as filter() for subsetting rows, select() for selecting columns, mutate() for creating new variables, and summarise() for aggregating data