Ch20 vectors

Introduction

Vector Basics

Important types of automic vector

Using automic vectors

sample(10) + 10
##  [1] 19 18 14 12 13 17 11 20 16 15
1:10 + 1:2
##  [1]  2  4  4  6  6  8  8 10 10 12
1:10 + 1:3
## Warning in 1:10 + 1:3: longer object length is not a multiple of shorter object
## length
##  [1]  2  4  6  5  7  9  8 10 12 11
data.frame(a = 1:10, b = 1:2)
##     a b
## 1   1 1
## 2   2 2
## 3   3 1
## 4   4 2
## 5   5 1
## 6   6 2
## 7   7 1
## 8   8 2
## 9   9 1
## 10 10 2
#data.frame(a = 1:10, b = 1:3)
x <- sample(10)
x
##  [1]  8  4 10  1  6  5  7  3  9  2
x[c(5, 7)]
## [1] 6 7
x[x>5]
## [1]  8 10  6  7  9

Recursive vectors

a <- list(a = 1:3, b = "a string", c = pi, d = list(-1, -5))
a
## $a
## [1] 1 2 3
## 
## $b
## [1] "a string"
## 
## $c
## [1] 3.141593
## 
## $d
## $d[[1]]
## [1] -1
## 
## $d[[2]]
## [1] -5
a[1:2]
## $a
## [1] 1 2 3
## 
## $b
## [1] "a string"
a[[4]]
## [[1]]
## [1] -1
## 
## [[2]]
## [1] -5
a[[4]][2]
## [[1]]
## [1] -5
a[[4]]
## [[1]]
## [1] -1
## 
## [[2]]
## [1] -5

Attributes

x <- 1:10
attr(x, "greeting")
## NULL
#> NULL
attr(x, "greeting") <- "Hi!"
attr(x, "farewell") <- "Bye!"
attributes(x)
## $greeting
## [1] "Hi!"
## 
## $farewell
## [1] "Bye!"
#> $greeting
#> [1] "Hi!"
#> 
#> $farewell
#> [1] "Bye!"

Augmented vectors

x <- factor(c("ab", "cd", "ab"), levels = c("ab", "cd", "ef"))
typeof(x)
## [1] "integer"
#> [1] "integer"
attributes(x)
## $levels
## [1] "ab" "cd" "ef"
## 
## $class
## [1] "factor"
#> $levels
#> [1] "ab" "cd" "ef"
#> 
#> $class
#> [1] "factor"

Ch21 Iteration

Introduction

For loops

# example from the cheatsheat 
for (i in 1:4){
    j <- i + 10
    print(j)
}
## [1] 11
## [1] 12
## [1] 13
## [1] 14
# example 1:numeric calculation- add 10
x <- 11:15

for (i in seq_along(x)){
    j <- x[i] + 10
    print(j)
}
## [1] 21
## [1] 22
## [1] 23
## [1] 24
## [1] 25
# save output 
y <- vector("integer", length(x)) 
for (i in seq_along(x)){
    y <- x[i] + 10
    print(j[i])
}
## [1] 25
## [1] NA
## [1] NA
## [1] NA
## [1] NA
# output 
y
## [1] 25
# example 2: string operatioin- extract first letter 
x <- c("abc", "xyz")

y <- vector("character", length(x)) 

for (i in seq_along(x)){
    y[i] <- x[i] %>% str_extract("[a-z]")
    print(y[i])
}
## [1] "a"
## [1] "x"
# output 
y
## [1] "a" "x"

For loop variations

df <- tibble(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)
rescale01 <- function(x) {
  rng <- range(x, na.rm = TRUE)
  (x - rng[1]) / (rng[2] - rng[1])
}

df$a <- rescale01(df$a)
df$b <- rescale01(df$b)
df$c <- rescale01(df$c)
df$d <- rescale01(df$d)

For loops vs functionals

output <- vector("double", length(df))
for (i in seq_along(df)) {
  output[[i]] <- mean(df[[i]])
}
output
## [1] 0.6248896 0.3410264 0.3677357 0.5279960
#> [1] -0.3260369  0.1356639  0.4291403 -0.2498034

The map functions

# example 1:numeric calculation- add 10
x <- 11:15

y <- vector("integer", length(x)) 

for (i in seq_along(x)){
    y[i] <- x[i] + 10
    print(y[i])
}
## [1] 21
## [1] 22
## [1] 23
## [1] 24
## [1] 25
# output 
y 
## [1] 21 22 23 24 25
# using map function 
x
## [1] 11 12 13 14 15
map(.x = x, .f = ~.x +10)
## [[1]]
## [1] 21
## 
## [[2]]
## [1] 22
## 
## [[3]]
## [1] 23
## 
## [[4]]
## [1] 24
## 
## [[5]]
## [1] 25
map_dbl(.x = x, .f = ~.x +10)
## [1] 21 22 23 24 25
add_10 <- function(x) {x + 10}
11 %>% add_10()
## [1] 21
map_dbl(.x = x, .f = add_10)
## [1] 21 22 23 24 25