Introduction to R

Extra bits we probably won't have time to cover

Overview

Teaching: 0 min
Exercises: 0 min
Questions

  • What is the fastest way to read in my data?

Objectives

  • Learn difference between read.table and fread

##9 Other useful things I probably won’t have time to cover I’m guessing we will have run out of town by this point, but I wanted to mention the extremely useful tidyr and dplyr libraries. Together these two libraries provide really great functions for manipulating data frames. Here’s a cheatsheet and you can read more about it here. A couple of quick examples are in the following. Typically I use it to convert between wide and long data formats.

# install.packages("dplyr")
# install.packages("tidyr")
# install.packages("Lahman")
library(dplyr)
library(tidyr)
library(Lahman)  #I'm using a data set from this library

# Use this Batting data (this is in the Lahman library)
dim(Batting)
names(Batting)

players = group_by(Batting, playerID)
#players looks the same, but has more info, as the grouping has been defined
dim(players)
dim(Batting)
head(players)
head(Batting)
# Now I can create easy summaries, over players...
games = summarise(players, total = sum(G))

# dplyr adds the %>% function, which serves as a "pipe", piping the output from one
#  command into the input of the next.  Really cleans up code.  This does what the above code did
games.using.dplyr = Batting %>%
  group_by(playerID) %>%
  summarise(total = sum(G))

There are more and more libraries for dealing with fMRI data, or simulating fMRI data, but I typically use the fmri library to read in NIfTI data. The only snag is the nifti file needs to be unzipped first.

library(fmri)
# First load in the structure
# This file is already unzipped
dat.struct = read.NIFTI("~/Dropbox/NeuroHackWeek/bold.nii")
dat = extract.data(dat.struct)
dim(dat)

Summary of functions

Function Name | What it does ————————- | ————————- tidy (library) | really useful library of functions dplry (library) | Another really useful library groupby | Function that adds grouping to data frame summarise | Once data are grouped (using groupby) allows summaries within group %>% | The piping function from dplyr. Once you get the hang of this, it is a fantastic tool read.NIFTI | In fmri library. Reads in header info extract.data | in fmri library, extracts data from NIfTI file.

Key Points