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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Data structures and subsetting</title>
<meta charset="utf-8" />
<meta name="author" content="Shawn Santo" />
<link rel="stylesheet" href="slides.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# Data structures and subsetting
## Programming for Statistical Science
### Shawn Santo
---
## Supplementary materials
Full video lecture available in Zoom Cloud Recordings
Companion videos
- [Git from the command line](https://warpwire.duke.edu/w/V04EAA/)
Additional resources
- [Sections 3.3 - 3.4](https://adv-r.hadley.nz/vectors-chap.html#attributes) Advanced R
- [Chapter 4](https://adv-r.hadley.nz/subsetting.html) Advanced R
---
class: inverse, center, middle
# Recall
---
## Atomic vector creation
We can use functions such as `c()`, `vector()`, and `:` to create atomic
vectors.
```r
c(5, 10, pi, 0, -sqrt(3))
```
```
#> [1] 5.000000 10.000000 3.141593 0.000000 -1.732051
```
```r
vector(mode = "character", length = 4)
```
```
#> [1] "" "" "" ""
```
```r
vector(mode = "integer", length = 3)
```
```
#> [1] 0 0 0
```
```r
-10:-3
```
```
#> [1] -10 -9 -8 -7 -6 -5 -4 -3
```
---
## Generic vector creation
Function `list()` allows us to create a generic vector.
```r
x <- list(
a = -100:100,
b = list(lower = letters, upper = LETTERS),
cars_data = cars
)
str(x)
```
```
#> List of 3
#> $ a : int [1:201] -100 -99 -98 -97 -96 -95 -94 -93 -92 -91 ...
#> $ b :List of 2
#> ..$ lower: chr [1:26] "a" "b" "c" "d" ...
#> ..$ upper: chr [1:26] "A" "B" "C" "D" ...
#> $ cars_data:'data.frame': 50 obs. of 2 variables:
#> ..$ speed: num [1:50] 4 4 7 7 8 9 10 10 10 11 ...
#> ..$ dist : num [1:50] 2 10 4 22 16 10 18 26 34 17 ...
```
---
class: inverse, center, middle
# Attributes
---
## Data structures
You may have heard of factors, matrices, arrays, and date-times. These are
just atomic vectors with special attributes.
- Attributes attach metadata to an object.
--
- Function `attr()` can retrieve and modify a single attribute.
```r
attr(x, which) # get attribute
attr(x, which) <- value # set / modify attribute
```
--
- Function `attributes()` can retrieve and set attributes en masse.
```r
attributes(x) # get attributes
attributes(x) <- value # set / modify attributes
```
---
## Attribute: `names`
Get or set the names of an object.
**One option:**
```r
x <- 1:4
attributes(x)
```
```
#> NULL
```
```r
attr(x = x, which = "names") <- c("a", "b", "c", "d")
attributes(x)
```
```
#> $names
#> [1] "a" "b" "c" "d"
```
```r
x
```
```
#> a b c d
#> 1 2 3 4
```
---
**Another option:**
```r
a <- 1:4
names(a) <- c("a", "b", "c", "d")
attributes(a)
```
```
#> $names
#> [1] "a" "b" "c" "d"
```
```r
a
```
```
#> a b c d
#> 1 2 3 4
```
<br/>
Either method is okay to use, but stick with using the replacement function.
---
## Attribute: `dim`
Get or set the dimension of an object.
```r
z <- 1:9
z
```
```
#> [1] 1 2 3 4 5 6 7 8 9
```
```r
attr(x = z, which = "dim") <- c(3, 3)
attributes(z)
```
```
#> $dim
#> [1] 3 3
```
```r
z
```
```
#> [,1] [,2] [,3]
#> [1,] 1 4 7
#> [2,] 2 5 8
#> [3,] 3 6 9
```
--
We have a 3 x 3 matrix.
---
```r
y <- matrix(z, nrow = 3, ncol = 3)
attributes(y)
```
```
#> $dim
#> [1] 3 3
```
```r
y
```
```
#> [,1] [,2] [,3]
#> [1,] 1 4 7
#> [2,] 2 5 8
#> [3,] 3 6 9
```
---
## Exercise
Create a 3 x 3 x 2 array using the `dim` attribute with the vector below.
```r
x <- c(5, 1, 5, 5, 1, 1, 5, 3, 2, 3, 2, 6, 4, 4, 1, 2, 1, 3)
```
<br/>
Try to create the same array using function `array()`. What do you notice about
how the array object is populated?
???
## Solution
.tiny[
```r
x <- c(5, 1, 5, 5, 1, 1, 5, 3, 2,
3, 2, 6, 4, 4, 1, 2, 1, 3)
attr(x = x, which = "dim") <- c(3, 3, 2)
x
```
```
#> , , 1
#>
#> [,1] [,2] [,3]
#> [1,] 5 5 5
#> [2,] 1 1 3
#> [3,] 5 1 2
#>
#> , , 2
#>
#> [,1] [,2] [,3]
#> [1,] 3 4 2
#> [2,] 2 4 1
#> [3,] 6 1 3
```
```r
attributes(x)
```
```
#> $dim
#> [1] 3 3 2
```
```r
array(x, dim = c(3, 3, 2))
```
```
#> , , 1
#>
#> [,1] [,2] [,3]
#> [1,] 5 5 5
#> [2,] 1 1 3
#> [3,] 5 1 2
#>
#> , , 2
#>
#> [,1] [,2] [,3]
#> [1,] 3 4 2
#> [2,] 2 4 1
#> [3,] 6 1 3
```
]
---
## Factors
Factors are built on top of integer vectors with two attributes: `class` and
`levels`. Factors are how R stores and represents categorical data.
A quick way to create a categorical variable as a factor is with function
`factor()`.
```r
x <- factor(c("walk", "single", "double", "triple", "home run"))
x
```
```
#> [1] walk single double triple home run
#> Levels: double home run single triple walk
```
--
```r
typeof(x)
```
```
#> [1] "integer"
```
```r
attributes(x)
```
```
#> $levels
#> [1] "double" "home run" "single" "triple" "walk"
#>
#> $class
#> [1] "factor"
```
---
## Ordered factors
To induce an ordering we can use function `ordered()` as opposed to `factor()`.
```r
y <- ordered(c("walk", "single", "double", "triple", "home run"),
levels = c("walk", "single", "double", "triple", "home run"))
y
```
```
#> [1] walk single double triple home run
#> Levels: walk < single < double < triple < home run
```
--
```r
attributes(y)
```
```
#> $levels
#> [1] "walk" "single" "double" "triple" "home run"
#>
#> $class
#> [1] "ordered" "factor"
```
```r
str(y)
```
```
#> Ord.factor w/ 5 levels "walk"<"single"<..: 1 2 3 4 5
```
---
## Exercise
Create a factor vector based on the vector of airport codes below. Try to do
it without using function `factor()`.
```r
airports <- c("RDU", "ABE", "DTW", "GRR", "RDU", "GRR", "GNV",
"JFK", "JFK", "SFO", "DTW")
```
Assume all the possible levels are
```r
c("RDU", "ABE", "DTW", "GRR", "GNV", "JFK", "SFO")
```
*Hint*: Think about what type of object factors are built on.
<br/>
What if the possible levels are
```r
c("RDU", "ABE", "DTW", "GRR", "GNV", "JFK", "SFO", "GSO", "ORD", "PHL")
```
???
## Solution
.tiny[
```r
z <- as.integer(c(1,2,3,4,1,4,5,6,6,7,3))
attr(x = z, which = "levels") <- c("RDU", "ABE", "DTW",
"GRR", "GNV", "JFK", "SFO")
attr(x = z, which = "class") <- "factor"
z
```
```
#> [1] RDU ABE DTW GRR RDU GRR GNV JFK JFK SFO DTW
#> Levels: RDU ABE DTW GRR GNV JFK SFO
```
```r
attributes(z)
```
```
#> $levels
#> [1] "RDU" "ABE" "DTW" "GRR" "GNV" "JFK" "SFO"
#>
#> $class
#> [1] "factor"
```
]
---
## Matrices and arrays
- Homogeneous in their type.
- Matrices are populated based on column major ordering (use `byrow` argument
to change this).
- Arrays can have one, two or more dimensions.
---
## Data frames
Data frames are built on top of lists with attributes: `names`, `row.names`,
and `class`. Here the class is `data.frame`.
```r
typeof(longley)
```
```
#> [1] "list"
```
```r
attributes(longley)
```
```
#> $names
#> [1] "GNP.deflator" "GNP" "Unemployed" "Armed.Forces" "Population"
#> [6] "Year" "Employed"
#>
#> $class
#> [1] "data.frame"
#>
#> $row.names
#> [1] 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961
#> [16] 1962
```
--
Here `names` refers to variable names.
---
## Data frame characteristics
- Data frames can be heterogeneous across columns.
- Data frames are rectangular in structure (not always tidy).
- They have column names and row names.
- Data frames can be subset by name or position.
---
## Data frame creation by setting attributes
Start with a list
```r
x <- list(c("48501", "48507", "48505"),
c(3, 4, 21),
c(2, 1, 2))
str(x)
```
```
#> List of 3
#> $ : chr [1:3] "48501" "48507" "48505"
#> $ : num [1:3] 3 4 21
#> $ : num [1:3] 2 1 2
```
--
Add attributes
```r
attributes(x) <- list(class = "data.frame",
names = c("zip", "lead_value", "time"),
row.names = 1:3)
```
---
Then we have a data frame
```r
x
```
```
#> zip lead_value time
#> 1 48501 3 2
#> 2 48507 4 1
#> 3 48505 21 2
```
```r
str(x)
```
```
#> 'data.frame': 3 obs. of 3 variables:
#> $ zip : chr "48501" "48507" "48505"
#> $ lead_value: num 3 4 21
#> $ time : num 2 1 2
```
Of course, we could have used function `data.frame()` to create our data
frame object. There is also function `tidyverse::tibble()` - it creates a
tibble object. Similar to a data frame but with two addition class components.
---
## Length coercion
Coercion is slightly different for data frames.
.pull-left[
```r
data.frame(x = 1:3, y = c("a"))
```
```
#> x y
#> 1 1 a
#> 2 2 a
#> 3 3 a
```
]
.pull-right[
```r
data.frame(x = 1:3,
y = c("a","b"))
```
```
#> Error in
#> data.frame(x = 1:3,
#> y = c("a", "b")) :
#> arguments imply differing number of
#> rows: 3, 2
```
]
If a shorter vector is not a multiple of the longest vector an error will
occur.
--
<br/>
What do you think will happen here?
```r
data.frame(num = 1:6,
treatment = c(0, 10, 20),
type = c("a", "b"))
```
---
## Summary
.small-text[
| Data Structure | Built On | Attribute(s) | Quick creation |
|----------------|-----------------------|-------------------------------|--------------------------------|
| Matrix, Array | Atomic vector | `dim` | `matrix()`, `array()` |
| Factor | Atomic integer vector | `class`, `levels` | `factor()`, `ordered()` |
| Date | Atomic double vector | `class` | `as.Date()` |
| Date-times | Atomic double vector | `class` | `as.POSIXct()`, `as.POSIXlt()` |
| Data frame | List | `class`, `names`, `row.names` | `data.frame()` |
]
---
class: inverse, center, middle
# Subsetting
---
## Subsetting techniques
R has three operators (functions) for subsetting:
1. `[`
2. `[[`
3. `$`
Which one you use will depend on the object you are working with, its
attributes, and what you want as a result.
We can subset with
- integers
- logicals
- `NULL`, `NA`
- character values
---
## Numeric (positive) subsetting
**Indexing begins at 1, not 0.**
.tiny[
```r
x <- c("NC", "SC", "VA", "TN")
y <- list(states = x, rank = 1:4, message = "")
```
]
--
.tiny.pull-left[
**Atomic vector**
```r
x[1]
```
```
#> [1] "NC"
```
```r
x[c(1, 3)]
```
```
#> [1] "NC" "VA"
```
```r
x[c(1:5)]
```
```
#> [1] "NC" "SC" "VA" "TN" NA
```
```r
x[c(2.2, 3.9)]
```
```
#> [1] "SC" "VA"
```
]
.tiny.pull-right[
**List**
```r
str(y[1])
```
```
#> List of 1
#> $ states: chr [1:4] "NC" "SC" "VA" "TN"
```
```r
str(y[c(1, 3)])
```
```
#> List of 2
#> $ states : chr [1:4] "NC" "SC" "VA" "TN"
#> $ message: chr ""
```
```r
str(y[c(1:4)])
```
```
#> List of 4
#> $ states : chr [1:4] "NC" "SC" "VA" "TN"
#> $ rank : int [1:4] 1 2 3 4
#> $ message: chr ""
#> $ NA : NULL
```
]
---
## Numeric (negative) subsetting
.tiny[
```r
x <- c("NC", "SC", "VA", "TN")
y <- list(states = x, rank = 1:4, message = "")
```
]
.tiny.pull-left[
**Atomic vector**
```r
x[-1]
```
```
#> [1] "SC" "VA" "TN"
```
```r
x[-c(1, 3)]
```
```
#> [1] "SC" "TN"
```
```r
x[c(-1, 3)]
```
```
#> Error in x[c(-1, 3)]: only 0's may be mixed with negative subscripts
```
```r
*x[-c(2.2, 3.9)]
```
```
#> [1] "NC" "TN"
```
]
.tiny.pull-right[
**List**
```r
str(y[-1])
```
```
#> List of 2
#> $ rank : int [1:4] 1 2 3 4
#> $ message: chr ""
```
```r
str(y[-c(1, 3)])
```
```
#> List of 1
#> $ rank: int [1:4] 1 2 3 4
```
```r
str(y[c(-1, 3)])
```
```
#> Error in y[c(-1, 3)]: only 0's may be mixed with negative subscripts
```
```r
*str(y[-c(2.2, 3.9)])
```
```
#> List of 2
#> $ states : chr [1:4] "NC" "SC" "VA" "TN"
#> $ message: chr ""
```
]
---
## Logical subsetting
It returns elements that correspond to `TRUE` in the logical vector. The length
of the logical vector is expected to be of the same length as the vector
being subset.
.tiny.pull-left[
**Atomic vector**
```r
x <- c(1, 4, 7, 12)
x[c(TRUE, TRUE, FALSE, TRUE)]
```
```
#> [1] 1 4 12
```
```r
x[c(TRUE, FALSE)]
```
```
#> [1] 1 7
```
```r
x[x %% 2 == 0]
```
```
#> [1] 4 12
```
]
.tiny.pull-right[
**List**
```r
y <- list(1, 4, 7, 12)
str(y[c(TRUE, TRUE, FALSE, TRUE)])
```
```
#> List of 3
#> $ : num 1
#> $ : num 4
#> $ : num 12
```
```r
str(y[c(TRUE, FALSE)])
```
```
#> List of 2
#> $ : num 1
#> $ : num 7
```
```r
str(y[y %% 2 == 0])
```
```
#> Error in y%%2: non-numeric
#> argument to binary operator
```
]
---
## Empty subsetting
It returns the original vector.
```r
x <- c(1,4,7)
x[]
```
```
#> [1] 1 4 7
```
```r
y <- list(1,4,7)
str(y[])
```
```
#> List of 3
#> $ : num 1
#> $ : num 4
#> $ : num 7
```
---
## Zero subsetting
Returns an empty vector of the same type as the vector being subset.