Vectors and iteration

---

---

# Atomic vectors

---

## Logical vectors

```r
parse_logical(c("TRUE", "TRUE", "FALSE", "TRUE", "NA"))
## [1]  TRUE  TRUE FALSE  TRUE    NA
```

## Numeric vectors

```r
parse_integer(c("1", "5", "3", "4", "12423"))
## [1]     1     5     3     4 12423
parse_double(c("4.2", "4", "6", "53.2"))
## [1]  4.2  4.0  6.0 53.2
```

## Character vectors

```r
parse_character(c("Goodnight Moon", "Runaway Bunny", "Big Red Barn"))
## [1] "Goodnight Moon" "Runaway Bunny"  "Big Red Barn"
```

---

## Scalars

```r
(x <- sample(10))
```

```
##  [1] 10  6  5  4  1  8  2  7  9  3
```

```r
x + c(100, 100, 100, 100, 100, 100, 100, 100, 100, 100)
```

```
##  [1] 110 106 105 104 101 108 102 107 109 103
```

```r
x + 100
```

```
##  [1] 110 106 105 104 101 108 102 107 109 103
```

---

## Vector recycling

```r
# create a sequence of numbers between 1 and 10
(x1 <- seq(from = 1, to = 2))
```

```
## [1] 1 2
```

```r
(x2 <- seq(from = 1, to = 10))
```

```
##  [1]  1  2  3  4  5  6  7  8  9 10
```

```r
# add together two sequences of numbers
x1 + x2
```

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

---

## Subsetting vectors

```r
x <- c("one", "two", "three", "four", "five")
```

* With positive integers

```r
x[c(3, 2, 5)]
## [1] "three" "two"   "five"
```

* With negative integers

```r
x[c(-1, -3, -5)]
## [1] "two"  "four"
```

* Don't mix positive and negative

```r
x[c(-1, 1)]
## Error in x[c(-1, 1)]: only 0's may be mixed with negative subscripts
```

---

## Subset with a logical vector

```r
(x <- c(10, 3, NA, 5, 8, 1, NA))
```

```
## [1] 10  3 NA  5  8  1 NA
```

```r
# All non-missing values of x
!is.na(x)
```

```
## [1]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
```

```r
x[!is.na(x)]
```

```
## [1] 10  3  5  8  1
```

```r
# All even (or missing!) values of x
x[x %% 2 == 0]
```

```
## [1] 10 NA  8 NA
```

---

# Lists

---

## Lists

```r
x <- list(1, 2, 3)
x
```

```
## [[1]]
## [1] 1
## 
## [[2]]
## [1] 2
## 
## [[3]]
## [1] 3
```

---

## Lists: `str()`

```r
str(x)
```

```
## List of 3
##  $ : num 1
##  $ : num 2
##  $ : num 3
```

```r
x_named <- list(a = 1, b = 2, c = 3)
str(x_named)
```

```
## List of 3
##  $ a: num 1
##  $ b: num 2
##  $ c: num 3
```

---

## Store a mix of objects

```r
y <- list("a", 1L, 1.5, TRUE)
str(y)
```

```
## List of 4
##  $ : chr "a"
##  $ : int 1
##  $ : num 1.5
##  $ : logi TRUE
```
   
---

---

## Nested lists

```r
z <- list(list(1, 2), list(3, 4))
str(z)
```

```
## List of 2
##  $ :List of 2
##   ..$ : num 1
##   ..$ : num 2
##  $ :List of 2
##   ..$ : num 3
##   ..$ : num 4
```

---

## Secret lists

```r
str(gun_deaths)
```

```
## spec_tbl_df [100,798 × 10] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ id       : num [1:100798] 1 2 3 4 5 6 7 8 9 10 ...
##  $ year     : num [1:100798] 2012 2012 2012 2012 2012 ...
##  $ month    : chr [1:100798] "Jan" "Jan" "Jan" "Feb" ...
##  $ intent   : chr [1:100798] "Suicide" "Suicide" "Suicide" "Suicide" ...
##  $ police   : num [1:100798] 0 0 0 0 0 0 0 0 0 0 ...
##  $ sex      : chr [1:100798] "M" "F" "M" "M" ...
##  $ age      : num [1:100798] 34 21 60 64 31 17 48 41 50 NA ...
##  $ race     : chr [1:100798] "Asian/Pacific Islander" "White" "White" "White" ...
##  $ place    : chr [1:100798] "Home" "Street" "Other specified" "Home" ...
##  $ education: Factor w/ 4 levels "Less than HS",..: 4 3 4 4 2 1 2 2 3 NA ...
```

---

---

## Exercise on subsetting vectors

---

# Iteration

---

## Iteration

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

```r
median(df$a)
## [1] 0.1642894
median(df$b)
## [1] 0.01641118
median(df$c)
## [1] 0.2734794
median(df$d)
## [1] -0.639297
```

---

## Iteration with `for` loop

```r
output <- vector(mode = "double", length = ncol(df))
for (i in seq_along(df)) {
  output[[i]] <- median(df[[i]])
}
output
```

```
## [1]  0.16428940  0.01641118  0.27347942 -0.63929695
```

---

## Output

```r
output <- vector(mode = "double", length = ncol(df))
```

```r
vector(mode = "double", length = ncol(df))
## [1] 0 0 0 0
vector(mode = "logical", length = ncol(df))
## [1] FALSE FALSE FALSE FALSE
vector(mode = "character", length = ncol(df))
## [1] "" "" "" ""
vector(mode = "list", length = ncol(df))
## [[1]]
## NULL
## 
## [[2]]
## NULL
## 
## [[3]]
## NULL
## 
## [[4]]
## NULL
```

---

## Sequence

```r
i in seq_along(df)
```

```r
seq_along(df)
```

```
## [1] 1 2 3 4
```

---

## Body

```r
output[[i]] <- median(df[[i]])
```

---

## Preallocation

for(i in 1:100){
  mpg_no_preall <- bind_rows(mpg_no_preall, mpg)
}

# with preallocation using a list
mpg_preall <- vector(mode = "list", length = 100)

for(i in 1:100){
  mpg_preall[[i]] <- mpg
}

mpg_preall <- bind_rows(mpg_preall)
```
]

.panel[.panel-name[Plot]
<img src="index_files/figure-html/unnamed-chunk-6-1.png" width="70%" style="display: block; margin: auto;" />
]
]

---

## Map functions

* Why `for` loops are good
* Why `map()` functions may be better
* Types of `map()` functions
    * `map()` makes a list
    * `map_lgl()` makes a logical vector
    * `map_int()` makes an integer vector
    * `map_dbl()` makes a double vector
    * `map_chr()` makes a character vector

---

## Map functions

```r
map_dbl(df, mean)
```

```
##          a          b          c          d 
##  0.1694536 -0.1974360  0.3113976 -0.5095255
```

```r
map_dbl(df, median)
```

```
##           a           b           c           d 
##  0.16428940  0.01641118  0.27347942 -0.63929695
```

```r
map_dbl(df, sd)
```

```
##         a         b         c         d 
## 0.5311992 1.0300788 0.8834578 1.0414939
```

---

## Map functions

```r
map_dbl(df, mean, na.rm = TRUE)
```

```
##          a          b          c          d 
##  0.1694536 -0.1974360  0.3113976 -0.5095255
```

```r
df %>%
  map_dbl(mean, na.rm = TRUE)
```

```
##          a          b          c          d 
##  0.1694536 -0.1974360  0.3113976 -0.5095255
```

---

## Exercise on writing iterative operations