Objects in R
Lukáš Lehotský
Object: what is it?
• Object is container
• Element is anything in container – a peach
• To reuse elements, they must be stored as objects
• Any name defined by user
• Remain the same unless overwritten
• Must be removed by user as well
Object: creating/storing objects
Object
(container)
Element
(peach)
<-
Tips and tricks
• Keyboard hacks
• For Czech keyboard and Win machine users, Right Alt
(AltGr) allows you to type some special characters (AltGr
+ < for <)
• Script hacks
• # allows you to write comments in scripts
• When writing code, R will automatically add closing
bracket, as well as closing quote symbol
• Tab will finish the name of function or argument in R
Studio – try typing help( and press Tab
Object types
Object types: vector
Object types: vector
• Vector is the default object type
• Any object without more specific data structure is vector
– least fancy object type
• If contains more than 1 element, always created using
function c()
• Same data class within vector – otherwise converted to
character
c(2,3,5)
[1] 2 3 5
vec <- c("aa", "bb", "cc", "dd", "ee")
vec
[1] "aa" "bb" "cc" "dd" "ee“
Object types: matrix
Object types: matrix
• 2 dimensions
• Same data class within matrix – otherwise
converted to character
• Created using function matrix()
mat <- matrix(data = c(1,2,3,4,5,6,7,8,9,10,11,12),
nrow = 3,
ncol = 4)
mat
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12
Object types: data frame
Object types: data frame
• 2 dimensions
• Typical data set – observations (rows) and variables
(columns) – data class per column
• Function data.frame()
• In fact, it’s a set of vectors (columns)
• Always has to have same number of elements in
vectors, from which it is created
• Data classes may be different in each column, but same
within a column
Object types: data frame
cars <- c("BMW","Audi","VW")
type <- c("3","A4","Passat")
price <- c(1200000,1164000,950500)
consumption <- c(6.2,5.9,5.9)
mydf <- data.frame(cars,type,price,consumption)
mydf
cars type price consumption
1 BMW 3 1200000 6.2
2 Audi A4 1164000 5.9
3 VW Passat 950500 5.9
Object types: list
Object types: list
• Heterogeneous – objects nested within an object
• Function list() creates nested structure
num.vector <- c(2, 3, 5)
char.vector <- c("aa", "bb", "cc", "dd", "ee")
mylist <- list(num.vector, char.vector, 3)
mylist
[[1]]
[1] 2 3 5
[[2]]
[1] "aa" "bb" "cc" "dd" "ee"
[[3]]
[1] 3
Libraries/packages
• Libraries combine together many pre-defined
functions according to problem at hand
• Most libraries download and install automatically from
the integrated R repository (called CRAN) – only a proper
library name needs to be entered
• As of September 17, 2019, 14925 libraries available
(43 packages published on Sep. 17 alone)
Libraries/packages
Libraries/packages
• If libraries are not available, they have to be
installed
• Libraries must be loaded
install.packages("sna")
library("sna")
library(sna)
Libraries/packages
• Libraries don’t load/unload automatically
• Any time R is started, libraries have to be reloaded
• Packages may be unloaded when necessary
library("sna")
detach("package:sna", unload = TRUE)
Object analysis
• Data classes and object types
• Function class() returns data class/object type of
object
• Object structure (organization of object type)
• Function str()
• Object dimensions
• One dimension - length()
• Two and more dimensions – dim(), nrow(), ncol()
Object analysis
length(1:30)
[1] 30
dim(mydf)
[1] 3 4
length(mydf)
[1] 4
nrow(mydf)
[1] 3
ncol(mydf)
[1] 4
Object analysis
class(mydf)
[1] "data.frame"
str(mydf)
'data.frame': 3 obs. of 4 variables:
$ cars : chr "BMW" "Audi" "VW"
$ type : chr "3" "A4" "Passat"
$ price : num 1200000 1164000 950500
$ consumption: num 6.2 5.9 5.9
Object analysis
• More sophisticated function describe()
included in library “psych”
str(mydf)
'data.frame': 3 obs. of 4 variables:
$ cars : chr "BMW" "Audi" "VW"
$ type : chr "3" "A4" "Passat"
$ price : num 1200000 1164000 950500
$ consumption: num 6.2 5.9 5.9
describe(mydf)
vars n mean sd median
cars* 1 3 2 1.00 2.0
type* 2 3 2 1.00 2.0
price 3 3 1104833 134863.20 1164000.0
consumption 4 3 6 0.17 5.9
Object analysis
• Data frame or matrix preview
• Function head() and tail() returns first or last 5
rows of the data frame
• Object names
• Vector or list names may be accessed using names()
• Data frame or matrix row and column names may be
accessed using colnames() and rownames()
head(mydf)
tail(mydf)
colnames(mydf)
Object analysis
• Visual inspection of data is possible using function
View()
• Useful especially in visual inspection of matrices or data
frames
• There’s inconsistency in implementation – function
view() does not exist
• Manual edit of the data (Excel-like) is possible –
function fix() – not advised (replicability)
View(mydf)
fix(mydf)
Object analysis: useful functions
class() # explore elements’ data class
length() # explore number of first dim. of object
dim() # explore dimensions of two-dimensional obj.
nrow() # number of rows
ncol() # number of columns
head() # first few rows of data
tail() # last few rows of data
str() # explore structure of object
names() # names in the named vector - one dimension
rownames() # names of rows - two dimensions
colnames() # names of columns - two dimensions
Practice 1
• Create a data frame with the following structure
name age sex econ_scale soc_scale
Jose 17 male -3 -7.2
Sara 22 female 0.6 0.2
Maria 21 female 2 0
Frank 21 male -3 0.5
John 18 male 3.1 3
Practice 2
• Install a library called “psych”
• Enable the library
• Summarize and explore the data frame created in
the Practice 1
• Find and use all available functions (creative task)
Practice 3
• Type eurodist in your script
• Create an object from the eurodist dataset
• Visually inspect the data source
• Figure out the type of the data source
Practice 4
• Create a matrix with following parameters
• 6 by 6 layout
• Contains numbers 0 and 1 in a 0 1 0 1 0 1 0 1 pattern
(creative task)
• The pattern is organized by rows
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0 1 0 1 0 1
[2,] 0 1 0 1 0 1
[3,] 0 1 0 1 0 1
[4,] 0 1 0 1 0 1
[5,] 0 1 0 1 0 1
[6,] 0 1 0 1 0 1