We have already covered character and numeric types.

class(c("tree", "cloud", "stars_&_sky"))

## [1] "character"

class(c(1, 4, 7))

## [1] "numeric"

Character predominates if there are mixed classes.

class(c(1, 2, "tree"))

## [1] "character"

class(c("1", "4", "7"))

## [1] "character"

logical is a type that only has two possible elements: TRUE and FALSE

x <- c(TRUE, FALSE, TRUE, TRUE, FALSE)
class(x)

## [1] "logical"

logical elements are NOT in quotes.

The class of the data tells R how to process the data.

For example, it determines whether you can make summary statistics (numbers) or if you can sort alphabetically (characters).

There is one useful functions associated with practically all R classes:

as.CLASS_NAME(x) coerces between classes. It turns x into a certain class.

Examples:

• as.numeric()
• as.character()
• as.logical()

Sometimes coercing works great!

as.character(4)

## [1] "4"

as.numeric(c("1", "4", "7"))

## [1] 1 4 7

as.logical(c("TRUE", "FALSE", "FALSE"))

## [1]  TRUE FALSE FALSE

as.logical(0)

## [1] FALSE

When interpretation is ambiguous, R will return NA (an R constant representing “Not Available” i.e. missing value)

as.numeric(c("1", "4", "7a"))

## Warning: NAs introduced by coercion

## [1]  1  4 NA

as.logical(c("TRUE", "FALSE", "UNKNOWN"))

## [1]  TRUE FALSE    NA

What is one reason we might want to convert data to numeric?

A. So we can take the mean

B. So the data looks better

C. So our data is correct

There are two major number subclasses or types

1. Double (1.003)
2. Integer (1)

Double is equivalent to numeric. It is a number that contains fractional values . Can be any amount of places after the decimal.

Double stands for double-precision

For most purposes, the difference between integers and doubles doesn’t matter.

The num function of the tibble package can be used to change format. See here for more: https://tibble.tidyverse.org/articles/numbers.html

A factor is a special character vector where the elements have pre-defined groups or ‘levels’. You can think of these as qualitative or categorical variables. Order is often important.

Examples:

• red, orange, yellow, green, blue, purple
• breakfast, lunch, dinner
• baby, toddler, child, teen, adult
• Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree
• beginner, novice, intermediate, expert

** We will learn more about factors in a later module. **

There are two most popular R classes used when working with dates and times:

• Date class representing a calendar date
• POSIXct class representing a calendar date with hours, minutes, seconds

We convert data from character to Date/POSIXct to use functions to manipulate date/date and time

lubridate is a powerful, widely used R package from “tidyverse” family to work with Date / POSIXct class objects

class("2021-06-15")

## [1] "character"

library(lubridate)

x <- ymd("2021-06-15") # lubridate package Year Month Day
class(x)

## [1] "Date"

Note for function ymd: year month day

a <- ymd("2021-06-15")
b <- ymd("2021-06-18")
a - b

## Time difference of -3 days

mdy("06/15/2021")

## [1] "2021-06-15"

dmy("15-June-2021")

## [1] "2021-06-15"

ymd("2021-06-15")

## [1] "2021-06-15"

Here’s a dataset on the SARS-CoV-2 viral load measured in wastewater between 2022 and 2024, collected by the collected by the National Wastewater Surveillance System.

Let’s look at the date_start variable, the first date of the sampling window.

sars_ww <- 
  read_csv("https://daseh.org/data/SARS-CoV-2_Wastewater_Data.csv") 
 
# Selecting a few columns for easy viewing
sars_ww <- sars_ww %>% select(town_name, date_start)

Notice that date_start is chr class, not date.

sars_ww

## # A tibble: 2,813 × 2
##    town_name date_start
##    <chr>     <chr>     
##  1 Barry     6/21/2020 
##  2 Barry     6/22/2020 
##  3 Barry     6/23/2020 
##  4 Barry     6/24/2020 
##  5 Barry     6/25/2020 
##  6 Barry     6/26/2020 
##  7 Barry     6/27/2020 
##  8 Barry     6/28/2020 
##  9 Barry     6/29/2020 
## 10 Barry     6/30/2020 
## # ℹ 2,803 more rows

We would need to use mutate() to help us modify that column.

sars_ww %>% 
  mutate(date_start_fixed = mdy(date_start))

## # A tibble: 2,813 × 3
##    town_name date_start date_start_fixed
##    <chr>     <chr>      <date>          
##  1 Barry     6/21/2020  2020-06-21      
##  2 Barry     6/22/2020  2020-06-22      
##  3 Barry     6/23/2020  2020-06-23      
##  4 Barry     6/24/2020  2020-06-24      
##  5 Barry     6/25/2020  2020-06-25      
##  6 Barry     6/26/2020  2020-06-26      
##  7 Barry     6/27/2020  2020-06-27      
##  8 Barry     6/28/2020  2020-06-28      
##  9 Barry     6/29/2020  2020-06-29      
## 10 Barry     6/30/2020  2020-06-30      
## # ℹ 2,803 more rows

Two-dimensional classes are those we would often use to store data read from a file

• a data frame (data.frame or tibble class)

• a matrix (matrix class)

  • also composed of rows and columns
  • unlike data.frame or tibble, the entire matrix is composed of one R class
  • for example: all entries are numeric, or all entries are character

• One other data type that is the most generic are lists.
• Can hold vectors, strings, matrices, models, list of other list!
• Lists are used when you need to do something repeatedly across lots of data - for example wrangling several similar files at once
• Lists are a bit more advanced but you may encounter them when you work with others or look up solutions

• Can be created using list()

mylist <- list(c("A", "b", "c"), c(1, 2, 3))
mylist

## [[1]]
## [1] "A" "b" "c"
## 
## [[2]]
## [1] 1 2 3

class(mylist)

## [1] "list"

• coerce between classes using as.numeric() or as.character()
• data frames, tibbles, matrices, and lists are all classes of objects
• lists can contain multiples of any other class of data including lists!
• calendar dates can be represented with the Date class using ymd(), mdy() functions from lubridate package
• can then easily subtract Date or POSIXct class variables or pull out aspects like year

🏠 Class Website

💻 Lab

📃Day 4 Cheatsheet

For more advanced learning: see the extra slides in this file!

Image by Gerd Altmann from Pixabay

as.matrix() creates a matrix from a data frame or tibble (where all values are the same class).

matrix() creates a matrix from scratch.

matrix(1:6, ncol = 2)

##      [,1] [,2]
## [1,]    1    4
## [2,]    2    5
## [3,]    3    6

List elements can be named

mylist_named <- list(
  letters = c("A", "b", "c"),
  numbers = c(1, 2, 3),
  one_matrix = matrix(1:4, ncol = 2)
)
mylist_named

## $letters
## [1] "A" "b" "c"
## 
## $numbers
## [1] 1 2 3
## 
## $one_matrix
##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4

x <- ymd(c("2021-06-15", "2021-07-15"))
x

## [1] "2021-06-15" "2021-07-15"

day(x) # see also: month(x) , year(x)

## [1] 15 15

x + days(10)

## [1] "2021-06-25" "2021-07-25"

x + months(1) + days(10)

## [1] "2021-07-25" "2021-08-25"

wday(x, label = TRUE)

## [1] Tue Thu
## Levels: Sun < Mon < Tue < Wed < Thu < Fri < Sat

x <- ymd_hms("2013-01-24 19:39:07")
x

## [1] "2013-01-24 19:39:07 UTC"

date(x)

## [1] "2013-01-24"

x + hours(3)

## [1] "2013-01-24 22:39:07 UTC"

floor_date(x, "1 hour") # see also: ceiling_date()

## [1] "2013-01-24 19:00:00 UTC"

x1 <- ymd(c("2021-06-15"))
x2 <- ymd(c("2021-07-15"))

difftime(x2, x1, units = "weeks")

## Time difference of 4.285714 weeks

as.numeric(difftime(x2, x1, units = "weeks"))

## [1] 4.285714

Similar can be done with time (e.g. difference in hours).

n <- 1:9
n

## [1] 1 2 3 4 5 6 7 8 9

mat <- matrix(n, nrow = 3)
mat

##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9

To get element(s) of a vector (one-dimensional object):

• Type the name of the variable and open the rectangular brackets [ ]
• In the rectangular brackets, type index (/vector of indexes) of element (/elements) you want to pull. In R, indexes start from 1 (not: 0)

x <- c("a", "b", "c", "d", "e", "f", "g", "h")
x

## [1] "a" "b" "c" "d" "e" "f" "g" "h"

x[2]

## [1] "b"

x[c(1, 2, 100)]

## [1] "a" "b" NA

Note you cannot use dplyr functions (like select) on matrices. To subset matrix rows and/or columns, use matrix[row_index, column_index].

mat

##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9

mat[1, 1] # individual entry: row 1, column 1

## [1] 1

mat[1, 2] # individual entry: row 1, column 2

## [1] 4

mat[1, ] # first row

## [1] 1 4 7

mat[, 1] # first column

## [1] 1 2 3

mat[c(1, 2), c(2, 3)] # subset of original matrix: two rows and two columns

##      [,1] [,2]
## [1,]    4    7
## [2,]    5    8

You can reference data from list using $ (if elements are named) or using [[ ]]

mylist_named[[1]]

## [1] "A" "b" "c"

mylist_named[["letters"]] # works only for a list with elements' names

## [1] "A" "b" "c"

mylist_named$letters # works only for a list with elements' names

## [1] "A" "b" "c"