(Builds on: Manipulation basics)
(Leads to: Date/time basics, Function basics, Lists, Parsing basics, Iteration basics, Atomic vectors)
It’s helpful to know a little bit about how data structures are organised in R. The simplest data structure in R is the vector, which can be broken up in to atomic vectors and augmented vectors. Vectors of related data are organised together in tibbles and data.frames.
library(tidyverse)
The atomic vectors are the “atoms” of R, the simple building blocks upon which all else is built. There are four types of atomic vector that are important for data analysis:
<lgl> contain TRUE or FALSE.<int> contain integers.<dbl> contain real numbers.<chr> contain strings made with "".All vectors can also contain the missing value NA. You’ll learn more
about missing values later on. Collectively integer and double vectors
are known as numeric vectors. The difference is rarely important in
R.
You create atomic vectors by hand with the c() function:
logical <- c(TRUE, FALSE, FALSE)
# The difference between the real number 1 and the integer 1 is not
# usually important, but it sometimes comes up. R uses the suffix
# "L" to indicate that a number is an integer.
integer <- c(1L, 2L, 3L)
double <- c(1.5, 2.8, pi)
character <- c("this", "is", "a character", "vector")
Use [[ extract a single value out of a vector:
x <- c(5.1, 4.2, 5.3, 1.4)
x[[2]]
#> [1] 4.2
Use [ to extract multiple values:
# Keep selected locations
x[c(1, 3)]
#> [1] 5.1 5.3
# Drop selected locations
x[-1]
#> [1] 4.2 5.3 1.4
# Select locations where the condition is true
x[x > 5]
#> [1] 5.1 5.3
The names of these functions are [ and [[ but are used like x[y]
(pronounced “x square-bracket y”) and x[[y]] (pronounced “x
double-square-bracket y”). You can get help on them with ?`[` and
?`[[`.
Augmented vectors are atomic vectors with additional metadata. There are four important augmented vectors:
factors <fct>, which are used to represent categorical
variables can take one of a fixed and known set of possible values
(called the levels).
ordered factors <ord>, which are like factors but where the
levels have an intrinsic ordering (i.e. it’s reasonable to say that
one level is “less than” or “greater than” another variable).
dates <dt>, record a date.
date-times <dttm>, which are also known as POSIXct, record a
date and a time.
For now, you just need to recognise these when you encounter them. You’ll learn how to create each type of augmented vector later in the course.
Related vectors (both atomic and augmented) are collected together into data frames or tibbles. You can think of them as a list of vectors, where every vector has the same length. Later you’ll learn the precise different between data.frames and tibbles, but don’t worry about it for now. There are two ways to create tibbles by hand:
From individual vectors, each representing a column:
my_tibble <- tibble(
x = c(1, 9, 5),
y = c(TRUE, FALSE, FALSE),
z = c("apple", "pear", "banana")
)
my_tibble
#> # A tibble: 3 x 3
#> x y z
#> <dbl> <lgl> <chr>
#> 1 1.00 T apple
#> 2 9.00 F pear
#> 3 5.00 F banana
From individual values, organised in rows:
my_tibble <- tribble(
~x, ~y, ~z,
1, TRUE, "apple",
9, FALSE, "pear",
5, FALSE, "banana"
)
my_tibble
#> # A tibble: 3 x 3
#> x y z
#> <dbl> <lgl> <chr>
#> 1 1.00 T apple
#> 2 9.00 F pear
#> 3 5.00 F banana
Typically it will be obvious whether you need to use tibble() or
tribble(). One representation will either be much shorter or much
clearer than the other.
When you print a tibble it tell you its column names and the overall dimensions:
diamonds
#> # A tibble: 53,940 x 10
#> carat cut color clarity depth table price x y z
#> <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 0.230 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43
#> 2 0.210 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31
#> 3 0.230 Good E VS1 56.9 65.0 327 4.05 4.07 2.31
#> 4 0.290 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63
#> 5 0.310 Good J SI2 63.3 58.0 335 4.34 4.35 2.75
#> 6 0.240 Very Good J VVS2 62.8 57.0 336 3.94 3.96 2.48
#> 7 0.240 Very Good I VVS1 62.3 57.0 336 3.95 3.98 2.47
#> 8 0.260 Very Good H SI1 61.9 55.0 337 4.07 4.11 2.53
#> 9 0.220 Fair E VS2 65.1 61.0 337 3.87 3.78 2.49
#> 10 0.230 Very Good H VS1 59.4 61.0 338 4.00 4.05 2.39
#> # ... with 53,930 more rows
If you want to get access dimensions directly, you have three options:
dim(diamonds)
#> [1] 53940 10
nrow(diamonds)
#> [1] 53940
ncol(diamonds)
#> [1] 10
To get the variable names, use names():
names(diamonds)
#> [1] "carat" "cut" "color" "clarity" "depth" "table" "price"
#> [8] "x" "y" "z"
There isn’t currently a convenient way to get the variable types, but
you can use purrr::map_chr() to apply type_sum() (short for type
summary) to each variable.
type_sum(diamonds)
#> [1] "tibble"
map_chr(diamonds, type_sum)
#> carat cut color clarity depth table price x y
#> "dbl" "ord" "ord" "ord" "dbl" "dbl" "int" "dbl" "dbl"
#> z
#> "dbl"
You can extract a variable out of a tibble by using [[ or $:
mtcars[["mpg"]]
#> [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2
#> [15] 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4
#> [29] 15.8 19.7 15.0 21.4
mtcars$mpg
#> [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2
#> [15] 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4
#> [29] 15.8 19.7 15.0 21.4
For this reason, when we want to be precise about which tibble a
variable comes from, we use the syntax dataset$variablename.
The dplyr equivalent, which can more easily be used in a pipe, is
pull():
mtcars %>% pull(mpg)
#> [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2
#> [15] 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4
#> [29] 15.8 19.7 15.0 21.4