Data Structures
Data Structures
So we have all this lovely data to play with and in R we typically organize in a few ways:
Vectors
Vectors are collections of data, like a:
collection of numbers - c(1,2,3)
collection of characters - c("1","2","3")
collection of logical values - c(TRUE,FALSE,TRUE)
Note
It should be noted that a vector needs to be a collection of the same type of data. You will also note that each list is separated by commas and surrounded by c(). This is necessary to create vectors so make sure to remember the c()!
On the previous page we mentioned that values in vectors can be selected by their value (e.g. someVector[4] to grab the forth element) or their name if they are named (e.g. someVector["a"] to grab the element named a)
Strings
When dealing with character data there are a few functions that are helpful when manipulating them. Let's create a few character vectors to play with:
x=c("a","b","c")
y=c("d","e","f")
paste(x, y, sep = '_') # join two or more vectors together
output
[1] "a_d" "b_e" "c_f"
paste(x, collapse = '_') # collapes values in a vector together
output
[1] "a_b_c"
grep("a", x) # Find regular expression matches in x
output
[1] 1
Here we note that the pattern a was found 1 time. We can also replace patterns as well
gsub("a", "A", x) # Replace pattern matches in x
output
[1] "A" "b" "c"
toupper(x) # Convert to uppercase
output
[1] "A" "B" "C"
tolower(x) # Convert to lowercase.
output
[1] "a" "b" "c"
nchar(x) # Number of characters in a string
output
[1] 3
Matrices
A matrix can be created by combining vectors of the same length and same data type. They are used frequently when performing operations on numeric data but can include other data types. In R we can create a matrix with the matrix() function:
m <- matrix(data=1:9,nrow = 3,ncol=3)
m
output
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
Here we take a vector and specify how many columns and how many rows we'd like. To select elements in a matrix we can use the following:
m[2, ] # Select a row
output
[1] 2 5 8
m[ , 1] # Select a column
output
[1] 1 2 3
m[2, 3] # Select an element
output
[1] 8
nrow(m) # number of rows
output
[1] 3
ncol(m) # number of columns
output
[1] 3
dim(m) # number of rows then columns
output
[1] 3 3
Data Frames
Data frames are also collections of vectors of the same length. However, they do not need to be the same data type. Here we create a data.frame with the data.frame() function:
df <- data.frame(
characters=c("past","present","future"),
numbers=c(1,2,3),
logical=c(TRUE,FALSE,TRUE),
integer=c(1L,2L,3L)
)
output
characters numbers logical integer
1 past 1 TRUE 1
2 present 2 FALSE 2
3 future 3 TRUE 3
We can manipulate data frames the same way we manipulate matrices. However, we have a short hand option to grab a column:
df$characters # select the column with the title characters
output
[1] "past" "present" "future"
Lists
Lists are collections of data that do not need to be the same type or length. We can create lists with the list() function:
l <- list(
data.frame=data.frame(numbers=1:3,characters=c("past","present","future")),
numbers=1:5,
characters=c("past","present","future")
)
output
$data.frame
numbers characters
1 1 past
2 2 present
3 3 future
$numbers
[1] 1 2 3 4 5
$characters
[1] "past" "present" "future"
l[2] # list with just the second thing in the list
output
$numbers
[1] 1 2 3 4 5
l[[2]] # select the second thing in the list
output
[1] 1 2 3 4 5
l$characters # grab the element named characters
output
[1] "past" "present" "future"
l['numbers'] # list with just the item named numbers
output
$numbers
[1] 1 2 3 4 5