The objective of this module is to introduce additional data structures in R (arrays, matrices, lists, and data frames) and to learn how to extract, filter, and subset data from them.
GO TO: https://github.com/fuzzyatelin/fuzzyatelin.github.io/tree/master/AN588_Fall23/,
select the random-people.csv file, then press the
RAW button, highlight, and copy the text to a text editor
and save it to your Desktop.
Install this package in R: {stringr}
So far, we have seen how to create vectors, which is the most fundamental data structure in R. Today, we will explore and learn how to manipulate other data structures, including matrices, arrays, lists, and data frames.
A matrix is like a collection of vectors stored
together, and like vectors, matrices can only store data of one class
(e.g., numerical or character). Matrices are created
using the matrix() function,
m <- matrix(data = c(1, 2, 3, 4), nrow = 2, ncol = 2)
m## [,1] [,2]
## [1,] 1 3
## [2,] 2 4Matrices are typically filled column-wise, with the argument,
byrow, set to FALSE by default, but this can be changed by
specifying this argument as TRUE.
m <- matrix(data = c(1, 2, 3, 4, 5, 6), nrow = 2, ncol = 3, byrow = FALSE)
m## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6m <- matrix(data = c(1, 2, 3, 4, 5, 6), nrow = 2, ncol = 3, byrow = TRUE)
m## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 4 5 6You can also create matrices in R by
binding vectors of the same length together either
row-wise (with the function rbind()) or column-wise (with
the function cbind()).
v1 <- c(1, 2, 3, 4)
v2 <- c(6, 7, 8, 9)
m1 <- rbind(v1, v2)
m1## [,1] [,2] [,3] [,4]
## v1 1 2 3 4
## v2 6 7 8 9m2 <- cbind(v1, v2)
m2## v1 v2
## [1,] 1 6
## [2,] 2 7
## [3,] 3 8
## [4,] 4 9Metadata about a matrix can be extracted using the
class(), dim(), names(),
rownames(), colnames() and other commands. The
dim() command returns a vector containing the number of
rows at index position 1 and the number of columns at index position
2.
class(m1)## [1] "matrix" "array"dim(m1)## [1] 2 4class(m2)## [1] "matrix" "array"dim(m2)## [1] 4 2colnames(m2)## [1] "v1" "v2"rownames(m2)## NULLNOTE: in this example, rownames are not defined,
since cbind() was used to create the matrix
The structure (str()) command can be applied to
any data structure to provide details about that object. This is an
incredibly useful function that you will find yourself using over and
over again.
str(m2)## num [1:4, 1:2] 1 2 3 4 6 7 8 9
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:2] "v1" "v2"An array is a more general data structure, of which
a vector (with 1 implicit dimension) and a matrix (with 2 defined
dimensions) are but examples. Arrays can include additional dimensions,
but (like vectors and matrices) they can only include elements that are
all of the same atomic data class (e.g., numeric,
character). The example below shows the construction of a 3
dimensional array with 5 rows, 6 columns, and 3 “levels”). Visualizing
higher and higher dimension arrays, obviously, becomes challenging!
a <- array(data = 1:90, dim = c(5, 6, 3))
a## , , 1
##
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1 6 11 16 21 26
## [2,] 2 7 12 17 22 27
## [3,] 3 8 13 18 23 28
## [4,] 4 9 14 19 24 29
## [5,] 5 10 15 20 25 30
##
## , , 2
##
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 31 36 41 46 51 56
## [2,] 32 37 42 47 52 57
## [3,] 33 38 43 48 53 58
## [4,] 34 39 44 49 54 59
## [5,] 35 40 45 50 55 60
##
## , , 3
##
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 61 66 71 76 81 86
## [2,] 62 67 72 77 82 87
## [3,] 63 68 73 78 83 88
## [4,] 64 69 74 79 84 89
## [5,] 65 70 75 80 85 90You can select elements from vectors, matrices, and arrays by subsetting them using their index position(s) in bracket notation. For vectors, you would specify an index value in one dimension. For matrices, you would give the index values in two dimensions. For arrays generally, you would give index values for each dimension in the array.
For example, suppose you have the following vector:
v <- 1:100 #This creates a vector containing all integers from 1 to 100
v## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100You can select the first 15 elements as follows:
v[1:15]## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15You can also give a vector of index values to use to subset:
v[c(2, 4, 6, 8, 10)]## [1] 2 4 6 8 10You can also use a function or a calculation to subset a vector. What does the following return?
v <- 101:200
v[seq(from = 1, to = 100, by = 2)]## [1] 101 103 105 107 109 111 113 115 117 119 121 123 125 127 129 131 133 135 137
## [20] 139 141 143 145 147 149 151 153 155 157 159 161 163 165 167 169 171 173 175
## [39] 177 179 181 183 185 187 189 191 193 195 197 199First, create a vector of 1, word character strings comprising the first line of the novel Ulysses, by James Joyce: “Stately, plump Buck Mulligan came from the stairhead, bearing a bowl of lather on which a mirror and a razor lay crossed.”
Then, extract every third element of this vector.
HINT 1: Take a look at the {stringr} package and
the function str_split() to see if you can easily divide a
single string into a vector of substrings.
HINT 2: If you’re ambitious, the following
function, gsub("[[:punct:]]","",*string*), applied to
*string*, will remove punctuation and special characters
from that string.
As an example for a matrix, suppose you have the following:
m <- matrix(data = 1:80, nrow = 8, ncol = 10, byrow = FALSE)
m## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 9 17 25 33 41 49 57 65 73
## [2,] 2 10 18 26 34 42 50 58 66 74
## [3,] 3 11 19 27 35 43 51 59 67 75
## [4,] 4 12 20 28 36 44 52 60 68 76
## [5,] 5 13 21 29 37 45 53 61 69 77
## [6,] 6 14 22 30 38 46 54 62 70 78
## [7,] 7 15 23 31 39 47 55 63 71 79
## [8,] 8 16 24 32 40 48 56 64 72 80You can extract the element in row 4, column 5 and assign it to a new variable, x, as follows:
x <- m[4, 5]
x## [1] 36You can also extract an entire row or an entire column (or set of rows or set of columns) from a matrix by specifying the desired row or column number(s) and leaving the other value blank.
Given the matrix, m, above, extract the 2nd, 3rd, and 6th columns and assign them to the variable x
Given the matrix, m, above, extract the 6th to 8th row and assign them to the variable x
Given the matrix, m, above, extract the elements from row 2, column 2 to row 6, column 9 and assign them to the variable x
Construct a 4-dimensional, 400 element array (5 x 5 x 4 x 4) named a consisting of the numbers 400 to 1 (i.e., a descending series)
Given this matrix, what would the following return?
a[1, 1, 1, 2]
a[2, 3, 2, ]
a[1:5, 1:5, 3, 3]
You can replace elements in a vector or matrix, or even entire rows or columns, by identifying the elements to be replaced and then assigning them new values.
Starting with the matrix, m, defined above, what will be the effects of operations below? Pay careful attention to row and column index values, vector recycling, and automated conversion/recasting among data classes.
m[7, 1] <- 564
m[, 8] <- 2
m[2:5, 4:8] <- 1
m[2:5, 4:8] <- c(20, 19, 18, 17)
m[2:5, 4:8] <- matrix(data = c(20:1), nrow = 4, ncol = 5, byrow = TRUE)
m[, 8] <- c("a", "b")Lists and data frames, unlike vectors, matrices, and arrays, can be used to group together a mix of R structures and objects. A single list could contain a matrix, vector of character strings, vector of factors, an array, even another list.
Lists are created using the list() function where the
elements to add to the list are given as arguments to the function,
separated by commas. Type in the following example:
s <- c("this", "is", "a", "vector", "of", "strings") # here we have a vector
m <- matrix(data = 1:40, nrow = 5, ncol = 8) # this is a matrix
b <- FALSE #and this is a logical statement
l <- list(s, m, b) # this puts them all together as a list
l## [[1]]
## [1] "this" "is" "a" "vector" "of" "strings"
##
## [[2]]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 6 11 16 21 26 31 36
## [2,] 2 7 12 17 22 27 32 37
## [3,] 3 8 13 18 23 28 33 38
## [4,] 4 9 14 19 24 29 34 39
## [5,] 5 10 15 20 25 30 35 40
##
## [[3]]
## [1] FALSEYou can extract elements from a list similarly to how you would from other data structures, except that you use double brackets to reference a single element in the list.
l[[2]] #let's take another look at our matrix## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 6 11 16 21 26 31 36
## [2,] 2 7 12 17 22 27 32 37
## [3,] 3 8 13 18 23 28 33 38
## [4,] 4 9 14 19 24 29 34 39
## [5,] 5 10 15 20 25 30 35 40An extension of this notation can be used to access elements contained within an element in the list. For example:
l[[2]][2, 6] #this will show us the entry at row 2, column 6 of the matrix in the second position of the list## [1] 27To reference/extract multiple elements from a list, you would use single bracket notation, which would itself return a list. This is called “list slicing”.
l[1:2] # here we're slicing our list so it just has the first and second elements## [[1]]
## [1] "this" "is" "a" "vector" "of" "strings"
##
## [[2]]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 6 11 16 21 26 31 36
## [2,] 2 7 12 17 22 27 32 37
## [3,] 3 8 13 18 23 28 33 38
## [4,] 4 9 14 19 24 29 34 39
## [5,] 5 10 15 20 25 30 35 40l[c(1, 3)] # and here we're only taking the first and third elements## [[1]]
## [1] "this" "is" "a" "vector" "of" "strings"
##
## [[2]]
## [1] FALSEUsing str() provides details about the three elements in
our list:
str(l)## List of 3
## $ : chr [1:6] "this" "is" "a" "vector" ...
## $ : int [1:5, 1:8] 1 2 3 4 5 6 7 8 9 10 ...
## $ : logi FALSEYou can name the elements in a list using the
names() function, which adds a name attribute to each list
item.
names(l) <- c("string", "matrix", "logical")
names(l)## [1] "string" "matrix" "logical"Now, you can use the name of an item in the list to refer to it:
l$string # this is calling the element named 'string' in list 'l'## [1] "this" "is" "a" "vector" "of" "strings"l$matrix[3, 5] # this is calling the entry in row 3, column 5 of the element named 'matrix' in list 'l'## [1] 23Create a list representing this (simplified) primate taxonomy:
HINT: you can use lists as elements in a list, which would nest the names like in a phylogeny.
I’ll get you started with the Strepsirhini:
Lemuroidea <- c("Cheirogaleidae", "Lepilemuridae", "Indriidae", "Lemuridae",
"Daubentoniidae")
Lorisoidea <- c("Lorisidae", "Galagidae")
Strepsirhini <- list(Lemuroidea, Lorisoidea)
names(Strepsirhini) <- list("Lemuroidea", "Lorisoidea")
Strepsirhini## $Lemuroidea
## [1] "Cheirogaleidae" "Lepilemuridae" "Indriidae" "Lemuridae"
## [5] "Daubentoniidae"
##
## $Lorisoidea
## [1] "Lorisidae" "Galagidae"A data frame is perhaps the most useful (and most familiar) data structure that we can operate with in R as it most closely aligns with how we tend to represent tabular data, with rows as cases or observations and columns as variables describing those observations (e.g., a measurement of a particular type). Variables tend to be measured using the same units and thus fall into the same data class and can be thought of as analogous to vectors.
The data.frame() command can be used to create
dataframes from scratch, like this dataframe representing data on some
(fictional) BU undergrads:
df <- data.frame(firstName = c("Nitin", "Silvy", "Ingrid", "Claire"), major = c("Biology (CM)",
"Anthropology (Bio)", "Anthropology (Medical)", "Biology (EEB)"), sex = c("M",
"F", "F", "F"), yearInProgram = c(1, 2, 2, 3))
df## firstName major sex yearInProgram
## 1 Nitin Biology (CM) M 1
## 2 Silvy Anthropology (Bio) F 2
## 3 Ingrid Anthropology (Medical) F 2
## 4 Claire Biology (EEB) F 3Rather than creating one from scratch, we more commonly read tabular data into R from a pre-existing data file (i.e., a spreadsheet), which typically results in the table being represented as a dataframe. The following code will read from the file “random-people.csv”, currently stored on a user’s Desktop:
df <- read.csv("~/Desktop/random-people.csv", sep = ",", header = TRUE, stringsAsFactors = FALSE)
# only print select columns
df[, c(1, 3, 4, 11, 12)]## gender name.first name.last login.password dob
## 1 male ted wright rolex 11/8/73 1:33
## 2 male quentin schmitt norton 5/24/51 3:16
## 3 female laura johansen stevens 5/22/77 21:03
## 4 male ismael herrero 303030 8/1/58 9:13
## 5 female susana blanco aloha 12/18/55 3:21
## 6 male mason wilson topdog 6/23/60 9:19
## 7 male lutz strauio close-up 7/20/80 3:51
## 8 female kaylee gordon atlantis 3/24/48 12:22
## 9 male baraek limoncuocu tobias 5/8/92 22:01
## 10 male basile perrin ellie 2/28/65 0:37
## 11 male ruben lopez beth 5/27/76 3:30
## 12 male valtteri waisanen nocturne 12/24/80 10:40
## 13 female vanessa brewer gladiato 1/15/68 17:39
## 14 female kimberly brown nebraska 1/9/86 8:54
## 15 female loreen baettner rovers 9/3/49 4:56
## 16 female becky wallace bambi 3/30/59 5:03
## 17 male hector gonzalez calgary 12/10/53 6:48
## 18 female ella neva f00tball 7/18/91 14:30
## 19 male simon barnaby buddie 6/18/89 5:06
## 20 male max moser penetrating 8/12/61 12:13str(df)## 'data.frame': 20 obs. of 17 variables:
## $ gender : chr "male" "male" "female" "male" ...
## $ name.title : chr "mr" "mr" "ms" "mr" ...
## $ name.first : chr "ted" "quentin" "laura" "ismael" ...
## $ name.last : chr "wright" "schmitt" "johansen" "herrero" ...
## $ location.street : chr "2020 royal ln" "2433 rue dubois" "2142 elmelunden" "3897 calle del barquillo" ...
## $ location.city : chr "coffs harbour" "vitry-sur-seine" "silkeboeg" "gandia" ...
## $ location.state : chr "tasmania" "indre-et-loire" "hovedstaden" "ceuta" ...
## $ location.postcode: chr "4126" "99856" "16264" "61349" ...
## $ email : chr "ted.wright@example.com" "quentin.schmitt@example.com" "laura.johansen@example.com" "ismael.herrero@example.com" ...
## $ login.username : chr "organicleopard402" "bluegoose191" "orangebird528" "heavyswan518" ...
## $ login.password : chr "rolex" "norton" "stevens" "303030" ...
## $ dob : chr "11/8/73 1:33" "5/24/51 3:16" "5/22/77 21:03" "8/1/58 9:13" ...
## $ date.registered : chr "5/5/07 20:26" "4/11/11 7:05" "5/16/14 15:53" "2/17/06 16:53" ...
## $ phone : chr "01-0349-5128" "05-72-65-32-21" "81616775" "974-117-403" ...
## $ cell : chr "0449-989-455" "06-83-24-92-41" "697-993-20" "665-791-673" ...
## $ picture.large : chr "https://randomuser.me/api/portraits/men/48.jpg" "https://randomuser.me/api/portraits/men/53.jpg" "https://randomuser.me/api/portraits/women/70.jpg" "https://randomuser.me/api/portraits/men/79.jpg" ...
## $ nat : chr "AU" "FR" "DK" "ES" ...As for other data structures, you can select and subset data frames using bracket notation. You can also select named columns from a data frame the $ operator.
df[, 3]## [1] "ted" "quentin" "laura" "ismael" "susana" "mason"
## [7] "lutz" "kaylee" "baraek" "basile" "ruben" "valtteri"
## [13] "vanessa" "kimberly" "loreen" "becky" "hector" "ella"
## [19] "simon" "max"str(df[, 3])## chr [1:20] "ted" "quentin" "laura" "ismael" "susana" "mason" "lutz" ...# returns a vector of data in column 3df$name.last## [1] "wright" "schmitt" "johansen" "herrero" "blanco"
## [6] "wilson" "strauio" "gordon" "limoncuocu" "perrin"
## [11] "lopez" "waisanen" "brewer" "brown" "baettner"
## [16] "wallace" "gonzalez" "neva" "barnaby" "moser"str(df$name.last)## chr [1:20] "wright" "schmitt" "johansen" "herrero" "blanco" "wilson" ...# returns a vector of data for column *name.last*df[3]## name.first
## 1 ted
## 2 quentin
## 3 laura
## 4 ismael
## 5 susana
## 6 mason
## 7 lutz
## 8 kaylee
## 9 baraek
## 10 basile
## 11 ruben
## 12 valtteri
## 13 vanessa
## 14 kimberly
## 15 loreen
## 16 becky
## 17 hector
## 18 ella
## 19 simon
## 20 maxstr(df[3])## 'data.frame': 20 obs. of 1 variable:
## $ name.first: chr "ted" "quentin" "laura" "ismael" ...# returns a data frame of data from column 3df["name.last"]## name.last
## 1 wright
## 2 schmitt
## 3 johansen
## 4 herrero
## 5 blanco
## 6 wilson
## 7 strauio
## 8 gordon
## 9 limoncuocu
## 10 perrin
## 11 lopez
## 12 waisanen
## 13 brewer
## 14 brown
## 15 baettner
## 16 wallace
## 17 gonzalez
## 18 neva
## 19 barnaby
## 20 moserstr(df["name.last"])## 'data.frame': 20 obs. of 1 variable:
## $ name.last: chr "wright" "schmitt" "johansen" "herrero" ...# returns a data frame of data for column *name.last*You can add rows (additional cases) or columns (additional variables)
to a data frame using rbind() and cbind().
df <- cbind(df, id = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20))
df <- cbind(df, school = c("BU", "BU", "Harvard", "Harvard", "BU", "Boston College",
"MIT", "BU", "BU", "UMASS Boston", "Harvard", "BU", "Boston College", "BU",
"Harvard", "MIT", "Harvard", "BU", "UMASS Boston", "Harvard"))
df## gender name.title name.first name.last location.street
## 1 male mr ted wright 2020 royal ln
## 2 male mr quentin schmitt 2433 rue dubois
## 3 female ms laura johansen 2142 elmelunden
## 4 male mr ismael herrero 3897 calle del barquillo
## 5 female ms susana blanco 2208 avenida de america
## 6 male mr mason wilson 4576 wilson road
## 7 male mr lutz strauio 5707 eichenweg
## 8 female miss kaylee gordon 5475 camden ave
## 9 male mr baraek limoncuocu 2664 baedat cd
## 10 male mr basile perrin 3683 avenue jean-jaurias
## 11 male monsieur ruben lopez 4204 rue principale
## 12 male mr valtteri waisanen 9850 hemeentie
## 13 female mrs vanessa brewer 3436 henry street
## 14 female miss kimberly brown 8654 manor road
## 15 female miss loreen baettner 2234 erlenweg
## 16 female miss becky wallace 5965 the crescent
## 17 male monsieur hector gonzalez 5104 rue pasteur
## 18 female ms ella neva 4620 visiokatu
## 19 male mr simon barnaby 2206 simcoe st
## 20 male mr max moser 3045 koenigsberger strasse
## location.city location.state location.postcode
## 1 coffs harbour tasmania 4126
## 2 vitry-sur-seine indre-et-loire 99856
## 3 silkeboeg hovedstaden 16264
## 4 gandia ceuta 61349
## 5 mastoles extremadura 29445
## 6 dunedin taranaki 91479
## 7 emden brandenburg 13341
## 8 flint oregon 84509
## 9 siirt tokat 86146
## 10 versailles dordogne 25177
## 11 montricher aargau 7993
## 12 halsua south karelia 58124
## 13 celbridge fingal 30030
## 14 bangor borders HI92 8RY
## 15 chemnitzer land niedersachsen 27167
## 16 ely west yorkshire GR8 0UP
## 17 bercher st. gallen 1745
## 18 kerava finland proper 26385
## 19 odessa nova scotia 90889
## 20 weimar mecklenburg-vorpommern 18553
## email login.username login.password
## 1 ted.wright@example.com organicleopard402 rolex
## 2 quentin.schmitt@example.com bluegoose191 norton
## 3 laura.johansen@example.com orangebird528 stevens
## 4 ismael.herrero@example.com heavyswan518 303030
## 5 susana.blanco@example.com silverkoala701 aloha
## 6 mason.wilson@example.com organicduck470 topdog
## 7 lutz.strauio@example.com purplemouse467 close-up
## 8 kaylee.gordon@example.com beautifulgoose794 atlantis
## 9 baraek.limoncuoculu@example.com whitebutterfly599 tobias
## 10 basile.perrin@example.com organicmeercat418 ellie
## 11 ruben.lopez@example.com crazybear348 beth
## 12 valtteri.waisanen@example.com redswan919 nocturne
## 13 vanessa.brewer@example.com purpleleopard113 gladiato
## 14 kimberly.brown@example.com crazyelephant996 nebraska
## 15 loreen.baettner@example.com blacktiger499 rovers
## 16 becky.wallace@example.com crazypeacock937 bambi
## 17 hector.gonzalez@example.com organiccat637 calgary
## 18 ella.neva@example.com orangegorilla786 f00tball
## 19 simon.barnaby@example.com redmeercat724 buddie
## 20 max.moser@example.com bigladybug459 penetrating
## dob date.registered phone cell
## 1 11/8/73 1:33 5/5/07 20:26 01-0349-5128 0449-989-455
## 2 5/24/51 3:16 4/11/11 7:05 05-72-65-32-21 06-83-24-92-41
## 3 5/22/77 21:03 5/16/14 15:53 81616775 697-993-20
## 4 8/1/58 9:13 2/17/06 16:53 974-117-403 665-791-673
## 5 12/18/55 3:21 10/3/02 17:55 917-199-202 612-612-929
## 6 6/23/60 9:19 12/1/08 8:31 (137)-326-5772 (700)-060-1523
## 7 7/20/80 3:51 4/10/11 20:44 0802-1871274 0170-4221269
## 8 3/24/48 12:22 5/5/13 8:14 (817)-962-1275 (831)-325-1142
## 9 5/8/92 22:01 9/12/04 0:56 (023)-879-4331 (837)-014-1113
## 10 2/28/65 0:37 4/12/07 4:50 04-16-53-97-17 06-63-08-15-52
## 11 5/27/76 3:30 4/17/06 17:40 (931)-692-1073 (747)-833-3781
## 12 12/24/80 10:40 9/22/03 20:47 02-227-661 042-153-83-79
## 13 1/15/68 17:39 2/5/05 21:02 041-167-2755 081-154-0245
## 14 1/9/86 8:54 12/3/11 0:41 017684 80873 0799-553-944
## 15 9/3/49 4:56 3/24/16 17:27 0693-0473309 0176-2769900
## 16 3/30/59 5:03 7/15/08 2:42 015395 04615 0740-849-325
## 17 12/10/53 6:48 3/19/04 20:51 (569)-609-0669 (032)-958-9790
## 18 7/18/91 14:30 3/17/14 7:13 02-351-279 043-436-42-30
## 19 6/18/89 5:06 12/24/12 12:08 618-983-5566 088-025-2948
## 20 8/12/61 12:13 7/19/03 1:08 0503-3338884 0175-7158431
## picture.large nat id school
## 1 https://randomuser.me/api/portraits/men/48.jpg AU 1 BU
## 2 https://randomuser.me/api/portraits/men/53.jpg FR 2 BU
## 3 https://randomuser.me/api/portraits/women/70.jpg DK 3 Harvard
## 4 https://randomuser.me/api/portraits/men/79.jpg ES 4 Harvard
## 5 https://randomuser.me/api/portraits/women/18.jpg ES 5 BU
## 6 https://randomuser.me/api/portraits/men/60.jpg NZ 6 Boston College
## 7 https://randomuser.me/api/portraits/men/31.jpg DE 7 MIT
## 8 https://randomuser.me/api/portraits/women/65.jpg US 8 BU
## 9 https://randomuser.me/api/portraits/men/94.jpg TR 9 BU
## 10 https://randomuser.me/api/portraits/men/82.jpg FR 10 UMASS Boston
## 11 https://randomuser.me/api/portraits/men/84.jpg CH 11 Harvard
## 12 https://randomuser.me/api/portraits/men/80.jpg FI 12 BU
## 13 https://randomuser.me/api/portraits/women/15.jpg IE 13 Boston College
## 14 https://randomuser.me/api/portraits/women/49.jpg GB 14 BU
## 15 https://randomuser.me/api/portraits/women/50.jpg DE 15 Harvard
## 16 https://randomuser.me/api/portraits/women/67.jpg GB 16 MIT
## 17 https://randomuser.me/api/portraits/men/9.jpg CH 17 Harvard
## 18 https://randomuser.me/api/portraits/women/68.jpg FI 18 BU
## 19 https://randomuser.me/api/portraits/men/61.jpg CA 19 UMASS Boston
## 20 https://randomuser.me/api/portraits/men/49.jpg DE 20 HarvardAlternatively, you can extend a data frame by adding a new variable directly using the $ operator, like this:
df$school <- c("BU", "BU", "Harvard", "Harvard", "BU", "Boston College", "MIT",
"BU", "BU", "UMASS Boston", "Harvard", "BU", "Boston College", "BU", "Harvard",
"MIT", "Harvard", "BU", "UMASS Boston", "Harvard")NOTE: cbind() results in school being
added as a factor while using the $ operator
results in school being added as a character
vector. You can see this by using the str() command.
A factor is another atomic data class for R for dealing efficiently with nominal variables, usually character strings. Internally, R assigns integer values to each unique string (e.g., 1 for “female”, 2 for “male”, etc.).
Logical vectors may also be used to subset data frames. Here, we subset the data frame for only those rows where the variable school is “BU”.
new_df <- df[df$school == "BU", ]
new_df## gender name.title name.first name.last location.street
## 1 male mr ted wright 2020 royal ln
## 2 male mr quentin schmitt 2433 rue dubois
## 5 female ms susana blanco 2208 avenida de america
## 8 female miss kaylee gordon 5475 camden ave
## 9 male mr baraek limoncuocu 2664 baedat cd
## 12 male mr valtteri waisanen 9850 hemeentie
## 14 female miss kimberly brown 8654 manor road
## 18 female ms ella neva 4620 visiokatu
## location.city location.state location.postcode
## 1 coffs harbour tasmania 4126
## 2 vitry-sur-seine indre-et-loire 99856
## 5 mastoles extremadura 29445
## 8 flint oregon 84509
## 9 siirt tokat 86146
## 12 halsua south karelia 58124
## 14 bangor borders HI92 8RY
## 18 kerava finland proper 26385
## email login.username login.password
## 1 ted.wright@example.com organicleopard402 rolex
## 2 quentin.schmitt@example.com bluegoose191 norton
## 5 susana.blanco@example.com silverkoala701 aloha
## 8 kaylee.gordon@example.com beautifulgoose794 atlantis
## 9 baraek.limoncuoculu@example.com whitebutterfly599 tobias
## 12 valtteri.waisanen@example.com redswan919 nocturne
## 14 kimberly.brown@example.com crazyelephant996 nebraska
## 18 ella.neva@example.com orangegorilla786 f00tball
## dob date.registered phone cell
## 1 11/8/73 1:33 5/5/07 20:26 01-0349-5128 0449-989-455
## 2 5/24/51 3:16 4/11/11 7:05 05-72-65-32-21 06-83-24-92-41
## 5 12/18/55 3:21 10/3/02 17:55 917-199-202 612-612-929
## 8 3/24/48 12:22 5/5/13 8:14 (817)-962-1275 (831)-325-1142
## 9 5/8/92 22:01 9/12/04 0:56 (023)-879-4331 (837)-014-1113
## 12 12/24/80 10:40 9/22/03 20:47 02-227-661 042-153-83-79
## 14 1/9/86 8:54 12/3/11 0:41 017684 80873 0799-553-944
## 18 7/18/91 14:30 3/17/14 7:13 02-351-279 043-436-42-30
## picture.large nat id school
## 1 https://randomuser.me/api/portraits/men/48.jpg AU 1 BU
## 2 https://randomuser.me/api/portraits/men/53.jpg FR 2 BU
## 5 https://randomuser.me/api/portraits/women/18.jpg ES 5 BU
## 8 https://randomuser.me/api/portraits/women/65.jpg US 8 BU
## 9 https://randomuser.me/api/portraits/men/94.jpg TR 9 BU
## 12 https://randomuser.me/api/portraits/men/80.jpg FI 12 BU
## 14 https://randomuser.me/api/portraits/women/49.jpg GB 14 BU
## 18 https://randomuser.me/api/portraits/women/68.jpg FI 18 BUIn this case, R evaluates the expression `df$school == “BU” and returns a logical vector equal in length to the number of rows in df. It then subsets df based on that vector, returning only rows that evaluate to TRUE.
We can also choose to only return particular columns when we subset.
new_df <- df[df$school == "BU", c("name.last", "name.first", "school")]
new_df## name.last name.first school
## 1 wright ted BU
## 2 schmitt quentin BU
## 5 blanco susana BU
## 8 gordon kaylee BU
## 9 limoncuocu baraek BU
## 12 waisanen valtteri BU
## 14 brown kimberly BU
## 18 neva ella BUWe were introduced to the factor data class above. Again, factors are numeric codes that R can use internally that correspond to character value “levels”.
When we load in data from an external source (as we will do in our next module), R tends to import character string data as factors, assigning to each unique string an integer numeric code, and assigning the string as a “label” for that code. Using factors can make some code run much more quickly (e.g., in ANOVA, ANCOVA, and other forms of regression using categorical variables).
You can convert factor to character data (and vice
versa) using the as.character() or as.factor()
commands. You can also convert/coerce any vector to a different class
using similar constructs (e.g., as.numeric()), although not
all such conversions are really meaningful. Converting factor
data to numeric results in the the converted data having the
value of R’s internal numeric code for the
factor level, while converting character data to
numeric results in the data being coerced into the special data
value of NA (see below) for missing data.
Finally, R has three special data values that it uses in a variety of situations.
NA (for not available) is used for missing
data. Many statistical functions offer the possibility to include as an
argument na.rm=TRUE (“remove NAs”) so that NAs
are excluded from a calculation.Inf (and -Inf) is used when the result of
a numerical calculation is too extreme for R
to expressNaN (for not a number) is used when
R cannot express the results of a calculation
, e.g., when you try to take the square root of a negative numberStore the following numbers as a 5 x 3 matrix, called “m3”: 3, 0, 1 ,23, 1, 2, 33, 1, 1, 42, 0, 1, 41, 0, 2. Be sure to fill the matrix ROW-WISE.
Then, do the following:
Coerce the matrix to a data frame.
As a data frame, coerce the second column to be logical-valued
As a data frame, coerce the third column to be factor-valued
When you are done, use the str() command to show the
data type for each variable in your dataframe.