In AshirBorah/cp_bootcamp_r_tutorials:

knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
library(learnr)
library(tidyverse)
library(useful)
tutorial_options(exercise.timelimit = 60, exercise.blanks = "___+")

Outline

• Working in Rstudio with notebooks

• Loading and inspecting data

• Working with vectors

• Working with matrices

• Working with dataframes (the old fashioned way)

R and Rstudio

Different ways to work with R

• Enter commands directly into the console

• Write 'scripts': set of R code to execute as a unit (.R files)

• Write 'packages': sets of functions that others can use

• Write analysis 'notebooks' (R markdown: .Rmd files)

• Easily share results and methods in different formats

• Encourages good code and analysis practices

Rstudio: Live demo

• Different components of Rstudio interface

• Running code lines

• Running code chunks

• Creating new chunks

• Creating, inspecting and clearing variables

• There are many good Rstudio video tutorials, e.g.: (https://youtu.be/kfcX5DEMAp4)

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Make your code readable!

• Why?

• Others can understand what you've done (including future you)

• Reproducibility and open science

• Clarify your logical thought process while writing code

• How?

• Write descriptive text before each code chunk in R notebooks:

  • Describe data used, analysis approach

  • What are you trying to test/show with a given analysis?

Comments

Also good to include comments in your code for specifics/lightweight explanation

• R ignores stuff after #

#add a fixed offset to avoid negative values
new_data <- old_data + offset 

#now normalize so max value is 1
new_data <- new_data/max(new_data)

new_data <- my_pipeline_function(new_data, outliers = 'ignore') #drop 6-sigma outliers

Create project directory

• Organize your work as 'Projects' in Rstudio

• Each project has a separate folder, with data, code, results.

• You will practice setting up a new project for today's assignment.

Loading data

Basics of data formats

• Small-scale data are typically stored as text files

• Different 'rows' of data appear on different lines

• Values for a given 'row' are typically separated by either a comma, or a tab

Comma-separated values (.csv)

name,age,member_since
bob,20,2015
frank,40,2015
tammy,15,2016

Tab-separated values (.tsv or .txt)

name  age  member_since
bob  20  2015
frank  40  2015
tammy  15  2016

A zoo of data loading functions

| Data type | Extension | Function | Package | |:------------------------|:----------|:------------------|:-------------------| | Comma separated values | csv | read.csv() | utils (default) | | | | read_csv() | readr (tidyverse) | | Tab separated values | tsv | read_tsv() | readr | | Other delimited formats | txt | read.table() | utils | | | | read_table() | readr | | | | read_delim() | readr | | Excel | xlsx, xls | read_excel() | readxl (tidyverse) |

Practical advice on data loading

• For loading data tables, we suggest:

• Use read_delim (part of the tidyverse megapackage) for text files (.csv, .tsv, .txt)

• Use read_excel for loading .xls, .xlsx files

• We'll discuss more about loading data matrices later.

• For example:

my_table <- read_delim('my_data_file.csv')

Specifying where a file 'is'

File locations ('paths') can be specified in absolute or relative terms

• An 'absolute' path specifies where exactly in your computer's 'directory structure' a given file lives

• For example:

imported_data <- read_csv("/Users/jmmcfarl/BootCamp/cp_r_bootcamp/
                          data/data_file.csv")

• data_file.csv lives inside data folder, inside cp_r_boocamp, etc.

• But these will vary from computer to computer, so makes code sharing harder..

Relative paths

This will look for the file inside your 'working directory'

imported_data <- read_csv('data_file.csv')

What's the working directory and how does it get set?

To avoid having to worry about this

• Organize project-specific data in a 'data' subfolder and use Rstudio projects

• Use the here function from the here package to find the right relative path within the project.

library(here)
here('data', 'my_data_file.csv')

You can use it like this:

imported_csv <- read_delim(here('data', 'data_file.csv'))
imported_tsv <- read_delim(here('data', 'tab_separated_table.tsv'))

Loading from Excel sheets

Load the second sheet from an xlsx file

my_table <- read_excel(here('data', 'my_metadata_file.xlsx'), sheet = 2)

Uses the readxl package part of tidyverse

Loading matrices

For matrices:

• The size is often too large for read_delim to work well.

• The first column is special: 'row names'

Loading matrices

• Let's use fread from the data.table package

• fread creates a slightly different type of table than we want. as_tibble converts to the right kind.

library(data.table)
counts_matrix <-  fread(here('data', 'counts_rpkm.csv'))
counts_matrix <- as_tibble(counts_matrix) #convert to a 'tibble'
corner(counts_matrix)

---

Then we want to make one of the column rownames and mint our new data matrix

counts_matrix <- column_to_rownames(counts_matrix, var = 'Gene') #set Gene column to rownames
counts_matrix <- as.matrix(counts_matrix) #formally make it a matrix, not really necessary
corner(counts_matrix)

Inspecting data {.smaller}

• Always start with some quick inspection of data after reading it into R

• Check for:

• Column (always) and row (matrix only) names are correct

• Make sure data types of columns are correct

• Useful inspection functions:

• View(df): Open spreadsheet viewer

• head(df): Show top K (10) rows
• glimpse(df): quick overview of a table
• useful::corner(mat): Show upper left corner of matrix
• dim(mat): Number of rows and columns
• rownames(mat)/colnames(mat): extract row/column names

Backup plan

• If you're having trouble with the above data loading strategies

• Or you want to load a data file that's not part of your local project directory (and don't want to bother with file paths)

• You can import files using the RStudio Import Dataset tool (see here for more info).

Working with data in R

3 ways to access data

• By position

• By logical conditions

• By name

Selecting by position

age <- c(15, 22, 45, 52, 73, 81)

age[5]

idx <- c(3,5,6) # create vector of the elements of interest
age[idx]

Selecting elements with logical statements {.smaller}

| Operator | Description | | :-----------:|:-------------| | > | greater than | | >= | greater than or equal to| | < | less than | | <= | less than or equal to | | == | equal to | | != | not equal to | | & | and | | \| |or |

---

age > 50

log_idx <- age > 50
age[log_idx]
#same as age[age > 50]

---

age == 52

age[age == 52]

age[age != 52]

Subsetting using names

Can assign names to each element in a vector

age <- c(Allice = 15, Bob = 22, Charlie = 45, Dan = 52)
age

names(age)

Can also set names on a given vector

age <- c(15, 22, 45, 52)
names(age) <- c('Allice', 'Bob', 'Charlie', 'Dan')

Subsetting using names

age

Selecting elements by name is generally unambiguous

age[c('Bob', 'Charlie')]

• Vectors often are unnamed.

• Most useful when working with lists (more to come)

Brief asside on NULL

R has another special value NULL which represents 'the absence of a value'.

For example

my_vec <- c(1,2,3)
names(my_vec)

Note: Subtly different from NA, but don't worry about that now.

Reordering data

x <- c(4, 2, 3, 5, 1)
sort(x)

• Or in decreasing order

sort(x, decreasing = TRUE)

Reordering using indexing

Indexing can also be used to reorder data

teaching_team <- c("Mary", "Meeta", "Radhika")

reorder_teach <- teaching_team[c(3, 1, 2)] # Saving the results to a variable
reorder_teach

Subsetting matrices

• Same as subsetting vectors, but can be applied to both rows and columns!

• General formula:

matrix[row_set, column_set]

• row_set and column_set can be individual elements or vectors

• Leave either blank if you want all rows/columns, e.g.:

matrix[row_set, ] #keeps all columns

matrix[, column_set] #keeps all rows

Matrix subsetting examples

counts_mat <- fread(here('data', 'counts_rpkm.csv'))
counts_mat <- as_tibble(counts_mat)
counts_mat <- column_to_rownames(counts_mat, var = 'Gene')
counts_mat <- as.matrix(counts_mat)

useful::corner(counts_mat)

---

• 'Slice' out the third row

counts_mat[3, ]

---

• Or slice out the third column

counts_mat[, 3]

---

• Slice a set of rows and set of columns

counts_mat[1:4, 2:3]

Using rownames and column names

• Use colnames(mat) and rownames(mat) to get/set the column and row names of a matrix.

• Data for a specified set of rows (genes)

counts_mat[c('ENSMUSG00000000028', 'ENSMUSG00000000037'), ]

---

• Specifying columns and rows by name

counts_mat['ENSMUSG00000000001', c('sample7', 'sample5')]

The %in% operator

• Check whether each element of vector1 is contained in the set vector2

A <- c(1, 2, 3, 4)
B <- c(3, 4, 5, 6)

A %in% B

Applications of %in%

Useful for restricting to the intersection of elements in two lists

A <- c(1, 2, 3, 4)
B <- c(3, 4, 5, 6)

A[A %in% B]

---

Or checking whether any/all elements of A are contained in B

any(A %in% B)
all(A %in% B)

Other 'set' functions

• intersect(A, B): return the elements in both A and B

• setdiff(A, B): return the elements in A that are not in B

• union(A, B): return the elements in either A or B

Inspecting data visually

x <- counts_mat['ENSMUSG00000081010',]
hist(x)

---

x <- counts_mat['ENSMUSG00000081010',]
y <- counts_mat['ENSMUSG00000000037',]
plot(x,y)

Subsetting lists {.smaller}

A list of lists

people <- list(
  Allice = list(age = 20, height = 50, school = 'MIT'),
  Bob = list(age = 10, height = 30, school = 'Harvard'),
  Charlie = list(age = 40, height = 60, school = 'BU'),
  Frank = c(age = 10, height = 2)
  )

• To select a specific element from the list use 'double brackets'

people[[2]]

Subsetting lists {.smaller}

With lists it's especially useful to access elements by name

people[['Bob']]

Another (equivalent) way is to use the $ symbol. This is nice because it works with 'tab-complete'

people$Bob

Nested indexing of lists

• You can use multiple indices to pull out specific elements nested within a list

people[['Allice']][['school']]

• Using $ makes it easier to read

people$Allice$height

Creating dataframes

species <- c("ecoli", "human", "corn")
glengths <- c(4.6, 3000, 50000)

• Create a tibble (dataframe, table) using tibble() function.

• Input as many columns as you want (must have same length)

df <- tibble(species, glengths)
df

---

You can name the columns

df <- tibble(animal_species = species, genome_lengths = glengths)
df

Quick note on new/old ways

• data.frame() is the old way, gives a similar table

df <- data.frame(species = species, glengths = glengths)
df

• But it makes strings into 'factors', and has some other subtle differences.

• Use tibble() to avoid some rare but confusing issues

• Sometimes we'll use the terms 'dataframe', 'tibble', and 'table' interchangeably though.

Extracting a single column

df <- tibble(species, glengths)

You can access a column from a tibble as if it were a list of vectors (it is):

df$species
df[['species']]

Subsetting dataframes

You can access rows and columns of dataframes like matrix indexing

df[2,]

df[2:3, 'glengths']

Saving data to a file

• Just like the inverse of reading data

• write_csv to save tables

• write.csv to save matrices (keeps row names)

• Use here function to specify the path to save the file

write_csv(df, here('results', 'my_dataframe.csv'))

Key concepts recap

• Reading data in R is kinda a mess, but stick with read_delim for tables, read_excel for Excel files, and data.table::fread for matrices

• You can extract data from a vector by numeric index, name, or using logical conditions

• Matrices and data frames are the same, you just specify which rows and columns

• Lists are a bit weird, but use $ to pull out elements by name

AshirBorah/cp_bootcamp_r_tutorials documentation built on May 16, 2024, 3:24 p.m.