Reminder!

Project proposals are due TOMORROW 16 Oct (Wed), 23:59:59

• 1-2 paragraph description of project (datset, problem of interest, potential visualizations)
• Submit text file on Canvas (link to or sample of dataset preferred, not necessary)

Recap of week 3

• Transforming data with dplyr
  • select()
  • mutate()
  • arrange()
  • filter()
  • summarize()
  • group_by()
• Review of function syntax
• More data transformation with tidyr
  • gather()
  • separate()

Function syntax

The most important syntax in R is the function call. All R syntax has function calls underlying it.

A function call consists of:

• Function name
• Parentheses, and
• A list of arguments within the parentheses

function_name(<inputs to the function>,
              <arguments which change 
              how the function operates>)

Function example

function_name(<inputs to the function>,
              <arguments which change 
              how the function operates>)

x <- c(-5, -3, -1, 1, 3, NA)
mean(x)

## [1] NA

Function example

function_name(<inputs to the function>,
              <arguments which change 
              how the function operates>)

x <- c(-5, -3, -1, 1, 3, NA)
mean(x, na.rm = TRUE)

## [1] -1

%>% syntax with dplyr

Take the mtcars dataset, select just the wt and mpg columns, then select rows with mpg < 15

mtcars %>% 
    select(wt, mpg) %>% 
    filter(mpg < 15)

+ syntax with ggplot2

library(ggplot2)
ggplot(data = mtcars, mapping = aes(x = wt, y = hp)) +
    geom_point() +
    labs(title = "Horsepower vs. Weight", x = "Weight", 
         y = "Horsepower") +
    theme_classic()

Agenda for today

• Importing data with readr
  
• Factors

“Official” cheat sheet for readr available here.

Where does your data live?

• Data is stored in a “file”
  • e.g. .txt (text) or .csv (comma-separated values) file
• Files belong to a “folder”/“directory”
• Folders can be nested within other folders

Filepath example (Mac)

• Application: Finder
• Default “root” directory: /

(Source: iDB)

Filepath example (Windows)

• Application: File explorer
• Default “root” directory: C:/

(Source: Computer Problem and solution)

When I download a file, where does it go?

In Chrome: go to chrome://settings/downloads to find out

File paths

• A character string that tells you the location of a file
• Absolute path: starts from the “root” directory
  • e.g. /Users/kjytay/Downloads/datafile.csv

File paths

• A character string that tells you the location of a file
• Absolute path: starts from the “root” directory
  • e.g. /Users/kjytay/Downloads/datafile.csv
• Relative path: starts from the current directory (denoted by .)
  • e.g. If I am in the folder /Users/kjytay: ./Downloads/datafile.csv
  • e.g. If I am in the folder /Users/kjytay/Downloads: ./datafile.csv or simply datafile.csv

Working directories in R

• Directory where R looks for files that you ask it to load
• Also where R will put any files that you ask it to save
• You can see your current working directory at the top of the console or by typing getwd()

How can I change my working directory in RStudio?

1. You can issue the command setwd("<path of new directory>")
2. In the menu bar, click Session > Set Working Directory, then click one of the options in the sub-menu

Factors

• A concept unique to R
• Useful for working with categorical variables: variables that have a fixed and known set of possible values

Why use factor variables instead of character variables?

Reason 1: Character variables don’t protect you from typos

x <- c("Dec", "Apr", "Jam", "Mar")

Why use factor variables instead of character variables?

Reason 1: Character variables don’t protect you from typos

x <- c("Dec", "Apr", "Jam", "Mar")

Reason 2: Character variables don’t sort in a useful way

x <- c("Dec", "Apr", "Jan", "Mar")
sort(x)

## [1] "Apr" "Dec" "Jan" "Mar"

Why use factor variables instead of character variables?

Reason 1: Character variables don’t protect you from typos

x <- c("Dec", "Apr", "Jam", "Mar")

Reason 2: Character variables don’t sort in a useful way

x <- c("Dec", "Apr", "Jan", "Mar")
sort(x)

## [1] "Apr" "Dec" "Jan" "Mar"

Factor variables can fix both of these easily.

How to convert a character variable to a factor variable?

• Use factor() (in base R) or parse_factor() (from the readr package)
• Give the function the list of valid categories, or levels

month_levels <- c(
  "Jan", "Feb", "Mar", "Apr", "May", "Jun", 
  "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"
)
x <- c("Dec", "Apr", "Jam", "Mar")

How to convert a character variable to a factor variable?

• Use factor() (in base R) or parse_factor() (from the readr package)
• Give the function the list of valid categories, or levels

y1 <- factor(x, levels = month_levels)
y1

## [1] Dec  Apr  <NA> Mar 
## Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

How to convert a character variable to a factor variable?

• Use factor() (in base R) or parse_factor() (from the readr package)
• Give the function the list of valid categories, or levels

y1 <- factor(x, levels = month_levels)
y1

## [1] Dec  Apr  <NA> Mar 
## Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

y2 <- parse_factor(x, levels = month_levels)

## Warning: 1 parsing failure.
## row col           expected actual
##   3  -- value in level set    Jam

y2

## [1] Dec  Apr  <NA> Mar 
## attr(,"problems")
## # A tibble: 1 x 4
##     row   col expected           actual
##   <int> <int> <chr>              <chr> 
## 1     3    NA value in level set Jam   
## Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

How to convert a character variable to a factor variable?

• Use factor() (in base R) or parse_factor() (from the readr package)
• Give the function the list of valid categories, or levels

sort(y1)

## [1] Mar Apr Dec
## Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Today’s dataset: NBA data

(Source: Twitter)

Optional material

Different packages for working with different data formats

• .txt, .csv files (readr)
• .xls, .xlsx files (readxl)
• SPSS, Stata & SAS files (haven)
• Databases (DBI)
• JSON files (jsonlite)
• XML files (xml2)
• Web APIs (httr)
• HTML web scraping (rvest)

Factors under the hood

• Under the hood, the different levels of a factor are assigned numbers 1, 2, …
• The first level gets assigned the number 1, the next is 2, and so on

y1

## [1] Dec  Apr  <NA> Mar 
## Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

str(y1)

##  Factor w/ 12 levels "Jan","Feb","Mar",..: 12 4 NA 3

Ordered & unordered factors

• Factors can be ordered or unordered
• Example of ordered factor: T-shirt sizes (S < M < L)
• Example of unordered factor: ice-cream flavors
• R allows you to specify whether a factor should be ordered or not based on the ordered option in factor()
• Doesn’t matter which you use for the most part except when building models