Code Along 12

Using Atomic Vectors

sample(10) + 10

##  [1] 12 15 19 20 11 16 13 17 14 18

1:10 + 1:2

##  [1]  2  4  4  6  6  8  8 10 10 12

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]  5  4  7  6  9  2 10  3  8  1

x[c(5, 7)] 

## [1]  9 10

x[x>5]

## [1]  7  6  9 10  8

Recursive Vectors (Lists)

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]][[2]]

## [1] -5

For Loops

# example from the cheatsheet
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[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

# example 2: string operation - 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"

The Map Functions

# 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

#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

df <- tibble(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)


map_dbl(.x = df, .f = mean)

##          a          b          c          d 
##  0.3103117 -0.5927474  0.4715937  0.3746886

map_dbl(df, median)

##          a          b          c          d 
##  0.3353912 -0.8984302  0.6009846  0.3988495

map_dbl(df, sd)

##         a         b         c         d 
## 0.6972186 0.9947352 1.2610560 1.0500844

map_dbl(df, mean, trim = 0.5)

##          a          b          c          d 
##  0.3353912 -0.8984302  0.6009846  0.3988495