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.

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

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"

library(dasehr)
covidww <- covid_wastewater %>% select(contains("date"))

head(covidww)

## # A tibble: 6 × 3
##   date_start date_end  first_sample_date
##   <chr>      <chr>     <chr>            
## 1 6/21/2020  7/5/2020  7/5/2020         
## 2 6/22/2020  7/6/2020  7/5/2020         
## 3 6/23/2020  7/7/2020  7/5/2020         
## 4 6/24/2020  7/8/2020  7/5/2020         
## 5 6/25/2020  7/9/2020  7/5/2020         
## 6 6/26/2020  7/10/2020 7/5/2020

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

covidww %>% 
  mutate(date_start = mdy(date_start))

## # A tibble: 776,059 × 3
##    date_start date_end  first_sample_date
##    <date>     <chr>     <chr>            
##  1 2020-06-21 7/5/2020  7/5/2020         
##  2 2020-06-22 7/6/2020  7/5/2020         
##  3 2020-06-23 7/7/2020  7/5/2020         
##  4 2020-06-24 7/8/2020  7/5/2020         
##  5 2020-06-25 7/9/2020  7/5/2020         
##  6 2020-06-26 7/10/2020 7/5/2020         
##  7 2020-06-27 7/11/2020 7/5/2020         
##  8 2020-06-28 7/12/2020 7/5/2020         
##  9 2020-06-29 7/13/2020 7/5/2020         
## 10 2020-06-30 7/14/2020 7/5/2020         
## # ℹ 776,049 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"

• two dimensional object classes include: data frames, tibbles, matrices, and lists
• matrix has columns and rows but is all one data class
• 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
• Make sure you choose the right function for the way the date is formatted!
• POSIXct class representing a calendar date with hours, minutes, seconds. Can use ymd_hms() or ymd_hm() or ymd_h()functions from the lubridate package
• can then easily subtract Date or POSIXct class variables or pull out aspects like year

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

covidww_mat <- select(covidww, contains("Missouri")) %>%
  head(n = 3)
covidww_mat

## # A tibble: 3 × 0

as.matrix(covidww_mat)

##     
## [1,]
## [2,]
## [3,]

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"