Key Points

Analyzing Patient Data

  • Use variable <- value to assign a value to a variable in order to record it in memory.
  • Objects are created on demand whenever a value is assigned to them.
  • The function dim gives the dimensions of a data frame.
  • Use object[x, y] to select a single element from a data frame.
  • Use from:to to specify a sequence that includes the indices from from to to.
  • All the indexing and subsetting that works on data frames also works on vectors.
  • Use # to add comments to programs.
  • Use mean, max, min and sd to calculate simple statistics.
  • Use apply to calculate statistics across the rows or columns of a data frame.
  • Use plot to create simple visualizations.

Creating Functions

  • Define a function using name <- function(...args...) {...body...}.
  • Call a function using name(...values...).
  • R looks for variables in the current stack frame before looking for them at the top level.
  • Use help(thing) to view help for something.
  • Put comments at the beginning of functions to provide help for that function.
  • Annotate your code!
  • Specify default values for arguments when defining a function using name = value in the argument list.
  • Arguments can be passed by matching based on name, by position, or by omitting them (in which case the default value is used).

Analyzing Multiple Data Sets

  • Use for (variable in collection) to process the elements of a collection one at a time.
  • The body of a for loop is surrounded by curly braces ({}).
  • Use length(thing) to determine the length of something that contains other values.
  • Use list.files(path = "path", pattern = "pattern", full.names = TRUE) to create a list of files whose names match a pattern.

Making Choices

  • Save a plot in a pdf file using pdf("name.pdf") and stop writing to the pdf file with dev.off().
  • Use if (condition) to start a conditional statement, else if (condition) to provide additional tests, and else to provide a default.
  • The bodies of conditional statements must be surrounded by curly braces { }.
  • Use == to test for equality.
  • X && Y is only true if both X and Y are true.
  • X || Y is true if either X or Y, or both, are true.

Command-Line Programs

  • Use commandArgs(trailingOnly = TRUE) to obtain a vector of the command-line arguments that a program was run with.
  • Avoid silent failures.
  • Use file("stdin") to connect to a program’s standard input.
  • Use cat(vec, sep = "\n") to write the elements of vec to standard output, one per line.

Best Practices for Writing R Code

  • Start each program with a description of what it does.
  • Then load all required packages.
  • Consider what working directory you are in when sourcing a script.
  • Use comments to mark off sections of code.
  • Put function definitions at the top of your file, or in a separate file if there are many.
  • Name and style code consistently.
  • Break code into small, discrete pieces.
  • Factor out common operations rather than repeating them.
  • Keep all of the source files for a project in one directory and use relative paths to access them.
  • Keep track of the memory used by your program.
  • Always start with a clean environment instead of saving the workspace.
  • Keep track of session information in your project folder.
  • Have someone else review your code.
  • Use version control.

Dynamic Reports with knitr

  • Use knitr to generate reports that combine text, code, and results.
  • Use Markdown to format text.
  • Put code in blocks delimited by triple back quotes followed by {r}.

Making Packages in R

  • A package is the basic unit of reusability in R.
  • Every package must have a DESCRIPTION file and an R directory containing code. These are created by us.
  • A NAMESPACE file is needed as well, and a man directory containing documentation, but both can be autogenerated.

Introduction to RStudio

  • Using RStudio can make programming in R much more productive.

Addressing Data

  • Data in data frames can be addressed by index (subsetting), by logical vector, or by name (columns only).
  • Use the $ operator to address a column by name.

Reading and Writing CSV Files

  • Import data from a .csv file using the read.csv(...) function.
  • Understand some of the key arguments available for importing the data properly, including header, stringsAsFactors, as.is, and strip.white.
  • Write data to a new .csv file using the write.csv(...) function
  • Understand some of the key arguments available for exporting the data properly, such as row.names, col.names, and na.

Understanding Factors

  • Factors are used to represent categorical data.
  • Factors can be ordered or unordered.
  • Some R functions have special methods for handling factors.

Data Types and Structures

  • R’s basic data types are character, numeric, integer, complex, and logical.
  • R’s basic data structures include the vector, list, matrix, data frame, and factors. Some of these structures require that all members be of the same data type (e.g. vectors, matrices) while others permit multiple data types (e.g. lists, data frames).
  • Objects may have attributes, such as name, dimension, and class.

The Call Stack

  • R keeps track of active function calls using a call stack comprised of stack frames.
  • Only global variables and variables in the current stack frame can be accessed directly.

Loops in R

  • Where possible, use vectorized operations instead of for loops to make code faster and more concise.
  • Use functions such as apply instead of for loops to operate on the values in a data structure.