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WEEK 1: Fundamentals

Introduction to RStudio

Orientation

Building programs that support your research workflow.

  1. R was created by statisticians for statisticians (and other researchers)
  2. R contains multitudes; this can be good and bad

RStudio configuration

Configuration menu

  1. PC/Linux: Tools > Global Options
  2. MacOS: RStudio > Preferences or Tools > Global Options

Helpful configuration settings

  1. General > Basic
    • Don't save or restore .RData
  2. Code > Editing
    • Use native pipe operator
    • Ctrl+Enter executes single line (or Multi-line R statement)
  3. Code > Display
    • Rainbow parentheses
  4. Appearance: Adjust font and syntax colors
  5. Pane Layout: Move IDE panes

(Optional) Workstation configuration

By default, your view of your file system will be opaque. We want to make it transparent (e.g. you may have a local Desktop and a cloud Desktop folder).

Mac OS Finder > Preferences

Your local Desktop folder is in your Home directory.

  1. General
    1. New finder window shows: /Users/<home>
  2. Sidebar
    1. Favorites: /Users/<home>
    2. iCloud: iCloud Drive
    3. Locations: <computer name>, Cloud Storage
  3. Advanced
    1. Show all filename extensions
    2. Keep folders on top (all)

Windows System > File Explorer

Your local Desktop folder is in your Home directory or Computer directory.

  1. File > Change folder and search options > View
    1. Files and Folders
      1. Show hidden files, folders, and drives
      2. Hide protected operating system files
      3. Uncheck Hide extensions for known file types
    2. Navigation Pane
      1. Show all folders
  2. View
    1. File name extensions

Workflow in RStudio

  1. Set working directory

  2. Test code snippets in the R console [REPL]

    print("hello")

  3. Create an .R script in the working directory

    print("hello")

  4. Run the script

    1. Keyboard shortcut
      • Windows/Linux: Control-Enter
      • MacOS: Command-Enter
    2. Run button
    3. Highlight and run lines

  5. Source the script to reduce console clutter and make contents available to other scripts

  6. Insert assignment arrow <-

    • MacOS: Option -
    • Windows/Linux: Alt -
    • Good customization: Control -

  7. Break execution if console hangs

    1. Windows: ESC
    2. MacOS/Linux: Control-c

  8. Clear console

    1. RStudio: C-l
    2. Emacs: C-c M-o / M-x comint-clear-buffer

  9. Comment/Uncomment code

    • MacOS: Command-/

Introduction to R

A whirlwind tour of R fundamentals

Mathematical expressions

1 + 100
(3 + 5) * 2  # operator precedence
5 * (3 ^ 2)  # powers
2/10000      # outputs 2e-04
2 * 10^(-4)  # 2e-04 explicated

Built-in functions

  1. Some functions need inputs ("arguments")

    getwd()      # no argument required
sin(1)       # requires arg
log(1)       # natural log

  2. RStudio has auto-completion

    log...

  3. Use help() to find out more about a function

    help(exp)
exp(0.5)    # e^(1/2)

Comparing things

  1. Basic comparisons

    1 == 1
1 != 2
1 < 2
1 <= 1

  2. Use all.equal() for floating point numbers

    all.equal(3.0, 3.0)        # TRUE
all.equal(2.99, 3.0)       # 7 places: Gives difference
all.equal(2.99999999, 3.0) # 8 places: TRUE
2.99999999 == 3.0          # 8 places: FALSE

Variables and assignment

  1. R uses the assignment arrow (C-c C-= in ESS)

    # Assign a value to the variable name
x <- 0.025

  2. You can inspect a variable's value in the Environment tab or by evaluating it in the console

    # Evaluate the variable and echo its value to the console
x

  3. Variables can be re-used and re-assigned

    log(x)
x <- 100
x <- x + 1
y <- x * 2

  4. Use a standard naming scheme for your variables

    r.style.variable <- 10
python_style_variable <- 11
javaStyleVariable <- 12

Vectorization

Vectorize all the things! This makes idiomatic R very different from most programming languages, which use iteration ("for" loops) by default.

# Create a sequence 1 - 5
1:5

# Raise 2 to the Nth power for each element of the sequence
2^(1:5)

# Assign the resulting vector to a variable
v <- 1:5
2^v

Managing your environment

ls()             # List the objects in the environment
ls               # Echo the contents of ls(), i.e. the code
rm(x)            # Remove the x object
rm(list = ls())  # Remove all objects in environment

Note that parameter passing (=) is not the same as assignment (<-) in R!

Built-in data sets

data()

R Packages

"Package" and "library" are roughly interchangeable.

  1. Install additional packages

    install.packages("tidyverse")
## install.packages("rmarkdown")

  2. Activate a package for use

    library("tidyverse")

Project management with RStudio

General file management

See /scripts/curriculum.Rmd

project_name
├── project_name.Rproj
├── README.md
├── script_1.R
├── script_2.R
├── data
│   ├── processed
│   └── raw
├── results
└── temp

Create projects with Rstudio

  1. File > New Project
  2. Create in existing Folder
  3. If you close RStudio and double-click Rproj, RStudio will open to the project location and set the working directory.

Seeking help

Basic help syntax

help(write.csv)
?write.csv

Help file format

  1. Description
  2. Usage
  3. Arguments
  4. Details
  5. Examples (highlight and run with C-Enter)

Special operators

help("<-")

Library examples

vignette("dplyr")

What if you don't know where to start?

  1. RStudio autocomplete

  2. Fuzzy search

    ??set

  3. Browse by topic: https://cran.r-project.org/web/views/

Data structures

R stores "atomic" data as vectors

There are no scalars in R; everything is a vector, even if it's a vector of length 1.

v <- 1:5

length(v)
length(3.14)

Every vector has a type

There are 5 basic (vector) data types: double, integer, complex, logical and character.

typeof(v)
typeof(3.14)
typeof(1L)
typeof(1+1i)
typeof(TRUE)
typeof("banana")

Vectors and type coercion

  1. A vector must be all one type. If you mix types, R will perform type coercion. See coercion rules in scripts/curriculum.Rmd

    c(2, 6, '3')
c(0, TRUE)

  2. You can change vector types

    # Create a character vector
chr_vector <- c('0', '2', '4')
str(chr_vector)

# Use it to create a numeric vector
num_vector <- as.numeric(chr_vector)

# Show the structure of the collection
str(num_vector)

  3. There are multiple ways to generate vectors

    # Two options for generating sequences
1:10
seq(10)

# The seq() function is more flexible
series <- seq(1, 10, by=0.1)
series

  4. Get information about a collection

    # Don't print everything to the screen
length(series)
head(series)
tail(series, n=2)
    # You can add informative labels to most things in R
names(v) <- c("a", "b", "c", "d", "e")
v
str(v)

  5. Get an item by its position or label

    v[1]
v["a"]

  6. Set an item by its position or label

    v[1] = 4
v

  7. (Optional) New vectors are empty by default

    # Vectors are logical by default
vector1 <- vector(length = 3)
vector1

# You can specify the type of an empty vector
vector2 <- vector(mode="character", length = 3)
vector2
str(vector2)

Challenge 1: Generate and label a vector

See /scripts/curriculum.Rmd

Matrices

  1. A matrix is 2-dimensional vector

    # Create a matrix of zeros
mat1 <- matrix(0, ncol = 6, nrow = 3)

# Inspect it
class(mat1)
typeof(mat1)
str(mat1)

  2. Some operations act as if the matrix is a 1-D wrapped vector

    mat2 <- matrix(1:25, nrow = 5, byrow = TRUE)
str(mat2)
length(mat2)

(Optional) Factors

  1. Factors represent unique levels (e.g., experimental conditions)

    coats <- c("tabby", "tortoise", "tortoise", "black", "tabby")
str(coats)

# The reprentation has 3 levels, some of which have multiple instances
categories <- factor(coats)
str(categories)

  2. R assumes that the first factor represents the baseline level, so you may need to change your factor ordering so that it makes sense for your variables

    ## "control" should be the baseline, regardless of trial order
trials <- c("manipulation", "control", "control", "manipulation")

trial_factors <- factor(trials, levels = c("control", "manipulation"))
str(trial_factors)

Data Frames are central to working with tabular data

  1. Create a data frame

    coat = c("calico", "black", "tabby")
weight = c(2.1, 5.0, 3.2)
chases_bugs = c(1, 0, 1)

cats <- data.frame(coat, weight, chases_bugs)

cats         # show contents of data frame
str(cats)    # inspect structure of data frame

# Convert chases_bugs to logical vector
cats$chases_bugs <- as.logical(cats$chases_bugs)
str(cats)

  2. Write the data frame to a CSV and re-import it. You can use read.delim() for tab-delimited files, or read.table() for flexible, general-purpose input.

    write.csv(x = cats, file = "../data/feline_data.csv", row.names = FALSE)
cats <- read.csv(file = "../data/feline_data.csv", stringsAsFactors = TRUE)

str(cats) # the chr column is now a factor column

  3. Access the column (vectors) of the data frame

    cats$weight
cats$coat

  4. A vector can only hold one type. Therefore, in a data frame each data column (vector) has to be a single type.

    typeof(cats$weight)

  5. Use data frame vectors in operations

    cats$weight + 2
paste("My cat is", cats$coat)

# Operations have to be legal for the data type
cats$coat + 2

# Operations are ephemeral unless their outputs are reassigned to the variable
cats <- cats$weight + 1

  6. Data frames have column names names() gets or sets a name

    names(cats)
names(cats)[2] <- "weight_kg"
cats

Lists

  1. Lists can contain anything

    list1 <- list(1, "a", TRUE, 1+4i)

# Inspect each element of the list
list1[[1]]
list1[[2]]
list1[[3]]
list1[[4]]

    If you use a single bracket [], you get back a shorter section of the list, which is also a list. Use double brackets [[]] to drill down to the actual value.

  2. (Optional) This includes complex data structures

    list2 <- list(title = "Numbers", numbers = 1:10, data = TRUE)

# Single brackets retrieve a slice of the list, containing the name:value pair
list2[2]

# Double brackets retrieve the value, i.e. the contents of the list item
list2[[2]]

  3. Data frames are lists of vectors and factors

    typeof(cats)

  4. Some operations return lists, others return vectors (basically, are you getting the column with its label, or are you drilling down to the data?)

    1. Get list slices

      # List slices
cats[1]      # list slice by index
cats["coat"] # list slice by name
cats[1, ]    # get data frame row by row number

    2. Get list contents (in this case, vectors)

      # List contents (in this case, vectors)
cats[[1]]      # content by index
cats[["coat"]] # content by name
cats$coat      # content by name; shorthand for `cats[["coat"]]`
cats[, 1]      # content by index, across all rows
cats[1, 1]     # content by index, single row

    3. You can inspect all of these with typeof()

    4. Note that you can address data frames by row and columns

(Optional) Challenge 2: Creating matrices

See /scripts/curriculum.Rmd

Exploring data frames

Adding columns

age <- c(2, 3, 5)
cbind(cats, age)
cats                     # cats is unchanged
cats <- cbind(cats, age) # overwrite old cats
# Data frames enforce consistency
age <- c(2, 5)
cats <- cbind(cats, age)

Appending rows (remember, rows are lists!)

newRow <- list("tortoiseshell", 3.3, TRUE, 9)
cats <- rbind(cats, newRow)

# Legal values added, illegal values are NA
cats

# Update the factor set so that "tortoiseshell" is a legal value
levels(cats$coat) <- c(levels(cats$coat), "tortoiseshell")
cats <- rbind(cats, list("tortoiseshell", 3.3, TRUE, 9))

Removing missing data

cats is now polluted with missing data

na.omit(cats)
cats
cats <- na.omit(cats)

Working with realistic data

gapminder <- read.csv("../data/gapminder_data.csv", stringsAsFactors = TRUE)

# Get an overview of the data frame
str(gapminder)
dim(gapminder)

# It's a list
length(gapminder)
colnames(gapminder)

# Look at the data
summary(gapminder$gdpPercap)  # summary varies by data type
head(gapminder)

Challenge 3: New gapminder data frame

See /scripts/curriculum.Rmd

Subsetting data

Subset by index

v <- 1:5

  1. Index selection

    v[1]
v[1:3]     # index range
v[c(1, 3)] # selected indices

  2. (Optional) Index exclusion

    v[-1]
v[-c(1, 3)]

Subset by name

letters[1:5]
names(v) <- letters[1:5]

  1. Character selection

    v["a"]
v[names(v) %in% c("a", "c")]

  2. (Optional) Character exclusion

    v[! names(v) %in% c("a", "c")]

Subsetting matrices

m <- matrix(1:28, nrow = 7, byrow = TRUE)

# Matrices are just 2D vectors
m[2:4, 1:3]
m[c(1, 3, 5), c(2, 4)]

(Optional) Extracting list elements

Single brackets get you subsets of the same type (list -> list, vector -> vector, etc.). Double brackets extract the underlying vector from a list or data frame.

# Create a new list and give it names
l <- replicate(5, sample(15), simplify = FALSE)
names(l) <- letters[1:5]

# You can extract one element
l[[1]]
l[["a"]]

# You can't extract multiple elements
l[[1:3]]
l[[names(l) %in% c("a", "c")]]

Subsetting by logical operations

  1. Explicitly mask each item using TRUE or FALSE. This returns the reduced vector.

    v[c(FALSE, TRUE, TRUE, FALSE, FALSE)]

  2. Evaluate the truth of each item, then produce the TRUE ones

    # Use a criterion to generate a truth vector
v > 4

# Filter the original vector by the criterion
v[v > 4]

  3. Combining logical operations

    v[v < 3 | v > 4]

(Optional) Subset by factor

# First three items
gapminder$country[1:3]

# All items in factor set
north_america <- c("Canada", "Mexico", "United States")
gapminder$country[gapminder$country %in% north_america]

Subsetting Data Frames

Data frames have characteristics of both lists and matrices.

  1. Get first three rows

    gapminder <- read.csv("../data/gapminder_data.csv", stringsAsFactors = TRUE)

# Get first three rows
gapminder[1:3,]

  2. Rows and columns

    gapminder[1:6, 1:3]
gapminder[1:6, c("country", "pop")]

  3. Data frames are lists, so one index gets you the columns

    gapminder[1:3]

  4. Filter by contents

    gapminder[gapminder$country == "Mexico",]
north_america <- c("Canada", "Mexico", "United States")
gapminder[gapminder$country %in% north_america,]
gapminder[gapminder$country %in% north_america & gapminder$year > 1999,]
gapminder[gapminder$country %in% north_america & gapminder$year > 1999, c("country", "pop")]

Challenge 4: Extract data by region

See /scripts/curriculum.Rmd

WEEK 2: Building Programs in R

Control flow

Conditionals

  1. Look at Conditional template in curriculum.Rmd

  2. If

    x <- 8

if (x >= 10) {
  print("x is greater than or equal to 10")
}

  3. Else

    if (x >= 10) {
  print("x is greater than or equal to 10")
} else {
  print("x is less than 10")
}

  4. Else If

    if (x >= 10) {
  print("x is greater than or equal to 10")
} else if (x > 5) {
  print("x is greater than 5, but less than 10")
} else {
  print("x is less than 5")
}

  5. Vectorize your tests

    x <- 1:4

if (any(x < 2)) {
  print("Some x less than 2")
}

if (all(x < 2)){
  print("All x less than 2")
}

Review Subsetting section

Subsetting is frequently an alternative to if-else statements in R

Iteration

  1. Look at Iteration template in curriculum.Rmd

  2. Basic For loop

    for (i in 1:10) {
  print(i)
}

  3. Nested For loop

    for (i in 1:5) {
  for (j in letters[1:4]) {
    print(paste(i,j))
  }
}

  4. This is where we skip the example where we append things to the end of a data frame. For loops are slow, vectorize operations are fast (and idiomatic). Use for loops where they're the appropriate tool (e.g., loading files, cycling through whole data sets, etc). We will see more of this in the section on reading and writing data.

Vectorization

Vector operations are element-wise by default

x <- 1:4
y <- 6:9
x + y
log(x)

# A more realistic example
gapminder$pop_millions <- gapminder$pop / 1e6
head(gapminder)

Vectors of unequal length are recycled

z <- 1:2
x + z

Logical comparisons

  1. Do the elements match a criterion?

    x > 2
a <- (x > 2) # you can assign the output to a variable

# Evaluate a boolean vector
any(a)
all(a)

  2. Can you detect missing data?

    nan_vec <- c(1, 3, NaN)

## Which elements are NaN?
is.nan(nan_vec)

## Which elements are not NaN?
!is.nan(nan_vec)

## Are any elements NaN?
any(is.nan(nan_vec))

## Are all elements NaN?
all(is.nan(nan_vec))

Matrix operations are also element-wise by default

m <- matrix(1:12, nrow=3, ncol=4)

# Multiply each item by -1
m * -1

Linear algebra uses matrix multiplication

# Multiply two vectors
1:4 %*% 1:4

# Matrix-wise multiplication
m2 <- matrix(1, nrow = 4, ncol = 1)
m2
m %*% m2

# Most functions operate on the whole vector or matrix
mean(m)
sum(m)

Challenge 5: Sum of squares

See /scripts/curriculum.Rmd

Higher-order functions

apply() lets you apply an arbitrary function over a collection. This is an example of a higher-order function (map, apply, filter, reduce, fold, etc.) that can (and should) replace loops for most purposes. They are an intermediate case between vectorized operations (very fast) and for loops (very slow). Use them when you need to build a new collection and vectorized operations aren't available.

apply(): Apply a function over the margins of an array

m <- matrix(1:28, nrow = 7, byrow = TRUE)

apply(m, 1, mean)
apply(m, 2, mean)
apply(m, 1, sum)
apply(m, 2, sum)

lapply(): Apply a function over a list, returning a list

lst <- list(title = "Numbers", numbers = 1:10, data = TRUE)

## length() returns the length of the whole list
length(lst)

## Use lapply() to get the length of the individual elements
lapply(lst, length)

sapply(): Apply a function polymorphically over list, returning vector, matrix, or array as appropriate

## Simplify and return a vector by default
sapply(lst, length)

## Optionally, eturn the original data type
sapply(lst, length, simplify = FALSE)

Use apply and friends to extract nested data from a list

  1. Read a file JSON into a nested list

    ## Read JSON file into nested list
library("jsonlite")
books <- fromJSON("../data/books.json")

## View list structure
str(books)

  2. Extract all of the authors with lapply(). This requires us to define an anonymous function.

    ## Extract a single author
books[["bk110"]]$author

## Use lapply to extract all the authors
authors <- lapply(books, function(x) x$author)

## Returns list
str(authors)

  3. Extract all of the authors with sapply()

    authors <- sapply(books, function(x) x$author)

# Returns vector
str(authors)

(Optional) Convert nested list into data frame

  1. Method 1: Create a list of data frames, then bind them together into a single data frame

    ## This approach omits the top-level book id
df <- do.call(rbind, lapply(books, data.frame))
    • lapply() applies a given function for each element in a list, so there will be several function calls.
    • do.call() applies a given function to the list as a whole, so there is only one function call.

  2. Method 2: Use the rbindlist() function from data.table

    ## This approach includes the top-level book id
df <- data.table::rbindlist(books, idcol = TRUE)

Functions explained

Functions let you encapsulate and re-use chunks of code. This has several benefits:

  1. Eliminates repetition in your code. This saves labor, but more importantly it reduces errors, and makes it easier for you to find and correct errors.
  2. Allows you to write more generic (i.e. flexible) code.
  3. Reduces cognitive overhead.

Defining a function

  1. Look at Function template in data/curriculum.Rmd

  2. Define a simple function

    # Convert Fahrenheit to Celcius
f_to_celcius <- function(temp) {
  celcius <- (temp - 32) * (5/9)
  return(celcius)
}

  3. Call the function

    f_to_celcius(32)

boiling <- f_to_celcius(212)

Combining functions

Define a second function and call the first function within the second.

f_to_kelvin <- function(temp) {
  celcius <- f_to_celcius(temp)
  kelvin <- celcius + 273.15
  return(kelvin)
}

f_to_kelvin(212)

Most functions work with collections

## Create a vector of temperatures
temps <- seq(from = 1, to = 101, by = 10)

# Vectorized calculation (fast)
f_to_kelvin(temps)

# Apply
sapply(temps, f_to_kelvin)

Defensive programming

  1. Check whether input meets criteria before proceeding (this is `assert` in other languages).

    f_to_celcius <- function(temp) {
  ## Check inputs
  stopifnot(is.numeric(temp), temp > -460)
  celcius <- (temp - 32) * (5/9)
  return(celcius)
}

f_to_celcius("a")
f_to_celcius(-470)

  2. Fail with a custom error if criterion not met

    f_to_celcius <- function(temp) {
  if(!is.numeric(temp)) {
    stop("temp must be a numeric vector")
  }
  celcius <- (temp - 32) * (5/9)

  return(celcius)
}

Working with rich data

## Prerequisites
gapminder <- read.csv("../data/gapminder_data.csv", stringsAsFactors = TRUE)
north_america <- c("Canada", "Mexico", "United States")

  1. Calculate the total GDP for each entry in the data set

    gapminder <- read.csv("../data/gapminder_data.csv", stringsAsFactors = TRUE)

gdp <- gapminder$pop * gapminder$gdpPercap

  2. Write a function to perform a total GDP calculation on a filtered subset of your data.

    calcGDP <- function(df, year=NULL, country=NULL) {
  if(!is.null(year)) {
    df <- df[df$year %in% year, ]
  }
  if (!is.null(country)) {
    df <- df[df$country %in% country,]
  }
  gdp <- df$pop * df$gdpPercap

  new_df <- cbind(df, gdp=gdp)
  return(new_df)
}

  3. Mutating df inside the function doesn't affect the global gapminder data frame (because of pass-by-value and scope).

Challenge 6: Testing and debugging your function

See data/curriculum.Rmd

Reading and writing data

Create sample data sets and write them to the `processed` directory

  1. Preliminaries

    if (!dir.exists("../processed")) {
  dir.create("../processed")
}

north_america <- c("Canada", "Mexico", "United States")

  2. Version 1: Use calcGDP function

    for (year in unique(gapminder$year)) {
  df <- calcGDP(gapminder, year = year, country = north_america)

  ## Generate a file name. This will fail if "processed" doesn't exist
  fname <- paste("../processed/north_america_", as.character(year), ".csv", sep = "")

  ## Write the file
  write.csv(x = df, file = fname, row.names = FALSE)
}

  3. Version 2: Bypass calcGDP function

    for (year in unique(gapminder$year)) {
  df <- gapminder[gapminder$year == year, ]
  df <- df[df$country %in% north_america, ]
  fname <- paste("processed/north_america_", as.character(year), ".csv", sep="")
  write.csv(x = df, file = fname, row.names = FALSE)
}

How to find files

## Get matching files from the `processed` subdirectory
dir(path = "../processed", pattern = "north_america_[1-9]*.csv")

Read files using a for loop

  1. Read each file into a data frame and add it to a list

    ## Create an empty list
df_list <- list()

## Get the locations of the matching files
file_names <- dir(path = "../processed", pattern = "north_america_[1-9]*.csv")
file_paths <- file.path("../processed", file_names)

for (f in file_paths){
  df_list[[f]] <- read.csv(f, stringsAsFactors = TRUE)
}

  2. Access the list items to view the individual data frames

    length(df_list)
names(df_list)
lapply(df_list, length)
df_list[["north_america_1952.csv"]]

Read files using apply

  1. Instead of a for loop that handles each file individually, use a single vectorized function.

    df_list <- lapply(file_paths, read.csv, stringsAsFactors = TRUE)

## The resulting list does not have names set by default
names(df_list)

## You can still access by index position
df_list[[2]]

  2. Add names manually

    names(df_list) <- file_names
df_list$north_america_1952.csv

  3. (Optional) Automatically set names for the output list This example sets each name to the complete path name (e.g., "../processed/north_america_1952.csv").

    df_list <- sapply(file_paths, read.csv, simplify = FALSE, USE.NAMES = TRUE)

Concatenate list of data frames into a single data frame

  1. Method 1: Create a list of data frames, then bind them together into a single data frame

    df <- do.call(rbind, df_list)
    • lapply() applies a given function for each element in a list, so there will be several function calls.
    • do.call() applies a given function to the list as a whole, so there is only one function call.

  2. (Optional) Method 2: Use the rbindlist() function from data.table. This can be faster for large data sets. It also give you the option of preserving the list names (in this case, the source file names) as a new column in the new data frame.

    df_list <- sapply(file.path("../processed", file_names), read.csv, simplify = FALSE, USE.NAMES = TRUE)
df <- data.table::rbindlist(df_list, idcol = TRUE)

WEEK 3: Tidyverse

Data frame manipulation with dplyr

Orientation

library("dplyr")
  1. Explain Tidyverse briefly: https://www.tidyverse.org/packages/
  2. (Optional) Demo unix pipes with history | grep
  3. Explain tibbles briefly
  4. dplyr allows you to treat data frames like relational database tables; i.e. as sets

Select data frame variables

  1. select() provides a mini-language for selecting data frame variables

    df <- select(gapminder, year, country, gdpPercap)
str(df)

  2. select() understands negation (and many other intuitive operators)

    df2 <- select(gapminder, -continent)
str(df2)

  3. You can link multiple operations using pipes. This will be more intuitive once we see this combined with filter()

    df <- gapminder %>% select(year, country, gdpPercap)

## You can use the native pipe. This has a few limitations:
## df <- gapminder |> select(year, country, gdpPercap)

Filter data frames by content

  1. Filter by continent

    df_europe <- gapminder %>%
  filter(continent == "Europe") %>%
  select(year, country, gdpPercap)

str(df_europe)

  2. Filter by continent and year

    europe_2007 <- gapminder %>%
  filter(continent == "Europe", year == 2007) %>%
  select(country, lifeExp)

str(europe_2007)

(Optional) Challenge 7: Filter

See data/curriculum.Rmd

Group rows

  1. Group data by a data frame variable

    grouped_df <- gapminder %>% group_by(continent)

## This produces a tibble
str(grouped_df)

  2. The grouped data frame contains metadata (i.e. bookkeeping) that tracks the group membership of each row. You can inspect this metadata:

    grouped_df %>% tally ()
grouped_df %>% group_keys ()
grouped_df %>% group_vars ()

## These produce a lot of output:
grouped_df %>% group_indices ()
grouped_df %>% group_rows ()

Summarize grouped data

  1. Calculate mean gdp per capita by continent

    grouped_df %>% summarise(mean_gdpPercap = mean(gdpPercap))

  2. (Optional) Using pipes allows you to do ad hoc reporting with creating intermediate variables

    gapminder %>%
  group_by(continent) %>%
  summarise(mean_gdpPercap = mean(gdpPercap))

  3. Group data by multiple variables

    df <- gapminder %>%
  group_by(continent, year) %>%
  summarise(mean_gdpPercap = mean(gdpPercap))

  4. Create multiple data summaries

    df <- gapminder %>%
  group_by(continent, year) %>%
  summarise(mean_gdp = mean(gdpPercap),
            sd_gdp = sd(gdpPercap),
            mean_pop = mean(pop),
            sd_pop = sd(pop))

Use group counts

  1. count() lets you get an ad hoc count of any variable

    gapminder %>%
  filter(year == 2002) %>%
  count(continent, sort = TRUE)

  2. n() gives the number of observations in a group

    ## Get the standard error of life expectancy by continent
gapminder %>%
  group_by(continent) %>%
  summarise(se_le = sd(lifeExp)/sqrt(n()))

Mutate the data to create new variables

Mutate creates a new variable within your pipeline

## Total GDP and population by continent and year
df <- gapminder %>%
  mutate(gdp_billion = gdpPercap * pop / 10^9) %>%
  group_by(continent, year) %>%
  summarise(mean_gdp = mean(gdp_billion),
            sd_gdp = sd(gdp_billion),
            mean_pop = mean(pop),
            sd_pop = sd(pop))

Add conditional filtering to a pipeline with ifelse

  1. Perform previous calculation, but only in cases in which the life expectancy is over 25

    df <- gapminder %>%
  mutate(gdp_billion = ifelse(lifeExp > 25, gdpPercap * pop / 10^9, NA)) %>%
  group_by(continent, year) %>%
  summarise(mean_gdp = mean(gdp_billion),
            sd_gdp = sd(gdp_billion),
            mean_pop = mean(pop),
            sd_pop = sd(pop))

  2. (Optional) Predict future GDP per capita for countries with higher life expectancies

    df <- gapminder %>%
  mutate(gdp_expected = ifelse(lifeExp > 40, gdpPercap * 1.5, gdpPercap)) %>%
  group_by(continent, year) %>%
  summarize(mean_gdpPercap = mean(gdpPercap),
            mean_gdpPercap_expected = mean(gdp_expected))

Challenge 8: Life expectancy in random countries

gapminder %>%
  filter(year == 2002) %>%
  group_by(continent) %>%
  sample_n(2) %>%
  summarize(mean_lifeExp = mean(lifeExp), country = country) %>%
  arrange(desc(mean_lifeExp))

Data frame manipulation with tidyr

  1. Long format: All rows are unique observations (ideally)
    1. each column is a variable
    2. each row is an observation
  2. Wide format: Rows contain multiple observations
    1. Repeated measures
    2. Multiple variables

Gapminder data

library("tidyr")
library("dplyr")

str(gapminder)
  • 3 ID variables: continent, country, year
  • 3 Observation variables: pop, lifeExp, gdpPercap

Wide to long with pivot_longer()

  1. Load wide gapminder data

    gap_wide <- read.csv("../data/gapminder_wide.csv", stringsAsFactors = FALSE)
str(gap_wide)

  2. Group comparable columns into a single variable. Here we group all of the "pop" columns, all of the "lifeExp" columns, and all of the "gdpPercap" columns.

    gap_long <- gap_wide %>%
  pivot_longer(
    cols = c(starts_with('pop'), starts_with('lifeExp'), starts_with('gdpPercap')),
    names_to = "obstype_year", values_to = "obs_values"
  )

str(gap_long)
head(gap_long, n=20)
    1. Original column headers become keys
    2. Original column values become values
    3. This pushes all values into a single column, which is unintuitive. We will generate the intermediate format later.

  3. (Optional) Same pivot operation as (2), specifying the columns to be omitted rather than included.

    gap_long <- gap_wide %>%
  pivot_longer(
    cols = c(-continent, -country),
    names_to = "obstype_year", values_to = "obs_values"
  )

str(gap_long)

  4. Split compound variables into individual variables

    gap_long <- gap_long %>% separate(obstype_year, into = c('obs_type', 'year'), sep = "_")
gap_long$year <- as.integer(gap_long$year)

Long to intermediate with pivot_wider()

  1. Recreate the original gapminder data frame (as a tibble)

    ## Read in the original data without factors for comparison purposes
gapminder <- read.csv("../data/gapminder_data.csv", stringsAsFactors = FALSE)

gap_normal <- gap_long %>%
  pivot_wider(names_from = obstype, values_from = obs_values)

str(gap_normal)
str(gapminder)

  2. Rearrange the column order of gap_normal so that it matches gapminder

    gap_normal <- gap_normal[, names(gapminder)]

  3. Check whether the data frames are equivalent (they aren't yet)

    all.equal(gap_normal, gapminder)

head(gap_normal)
head(gapminder)

  4. Change the sort order of gap_normal so that it matches

    gap_normal <- gap_normal %>% arrange(country, year)
all.equal(gap_normal, gapminder)

Long to wide with pivot_wider()



    

  1. 

    Create variable labels for wide columns. In this case, the new
variables are all combinations of metric (pop, lifeExp, or
gdpPercap) and year. Effectively we are squishing many columns
together.
    

    help(unite)

df_temp <- gap_long %>%
  ## unite(ID_var, continent, country, sep = "_") %>%
  unite(var_names, obs_type, year, sep = "_")

str(df_temp)
head(df_temp, n=20)
    

    2. 

  2. 

    Pivot to wide format, distributing data into columns for each unique
label
    

    gap_wide_new <- gap_long %>%
  ## unite(ID_var, continent, country, sep = "_") %>%
  unite(var_names, obs_type, year, sep = "_") %>%
  pivot_wider(names_from = var_names, values_from = obs_values)

str(gap_wide_new)
    

    3. 

  3. 

    Sort columns alphabetically by variable name, then check for
equality. You can move a single column to a different positions with
relocate()
    

    gap_wide_new <- gap_wide_new[,order(colnames(gap_wide_new))]
all.equal(gap_wide, gap_wide_new)
    

    4. 



Additional tidyverse libraries


Reading data with readr


Fast, user-friendly file imports.


String processing with stringr


Real string processing for R.


Functional programming with purrr


Functional programming for the Tidyverse. The map family of functions
replaces the apply family for most use cases. Map functions are
strongly typed. For example, you can use purrr:::map_chr() to extract
nested data from a list:


## View the relevant map function
library("purrr")
library("jsonlite")

help(map_chr)

books <- fromJSON("books.json")

## Returns vector
authors <- map_chr(books, ~.x$author)


    

  1. The ~ operation in Purrr creates an anonymous function that
applies to all the elements in the .x collection.

      

    1. Best overview in as_mapper() documentation:
https://purrr.tidyverse.org/reference/as_mapper.html
      2. 

    2. https://stackoverflow.com/a/53160041
      3. 

    3. https://stackoverflow.com/a/62488532
      4. 

    4. https://stackoverflow.com/a/44834671
      5. 

    

    2. 

  2. Additional references

      

    1. https://purrr.tidyverse.org/reference/map.html
      2. 

    2. https://jtr13.github.io/spring19/ss5593&fq2150.html
      3. 

    

    3. 



(Optional) Database interfaces


Data frame joins with dplyr


    

  1. https://jozef.io/r006-merge/#alternatives-to-base-r
    2. 

  2. https://dplyr.tidyverse.org/reference/mutate-joins.html
    3. 



Access databases using dplyr


    

  1. https://dbplyr.tidyverse.org
    2. 



Endnotes


Credits



References



Data Sources


    

  1. Gapminder data:


    2. 

  2. JSON derived from Microsoft sample XML file:
https://learn.microsoft.com/en-us/previous-versions/windows/desktop/ms762271(v=vs.85)
    3. 



   



        


  
      

      

        

          

            

  
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