Intro to iteration with Base R

class: center, middle, inverse, title-slide

# Intro to iteration with Base R
### Daniel Anderson
### Week 2, Class 1

---

layout: true

---
# Agenda
* For loops 
* Apply family of loops
    + `lapply()`
    + `sapply()`
    + `vapply()`

--
Note - we won't get to `apply` or `tapply`, but the former in particular is probably worth investigating.

---
# Learning objectives
* Understand the basics of what it means to loop through a vector

* Begin to recognize use cases

* Be able to apply basic `for` loops and write their equivalents with `lapply`.

---
# Basic overview: `for` loops

![](img/for-loops.png)

```r
a <- letters[1:26]
a
```

```
##  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u"
## [22] "v" "w" "x" "y" "z"
```

.pull-left[

```r
for(i in 1:5){
	print(a[i])
}
```

```
## [1] "a"
## [1] "b"
## [1] "c"
## [1] "d"
## [1] "e"
```
]

--
.pull-right[
Note these are five different character scalars (atomic vectors of length one).
It is NOT a single vector.
]

---
# Another basic example
### Simulate tossing a coin, record results

--
* For a single toss

```r
sample(c("Heads", "Tails"), 1)
```

```
## [1] "Tails"
```

--
* For multiple tosses, first allocate a vector with `length` equal to the number of iterations

```r
result <- rep(NA, 10)
result
```

```
##  [1] NA NA NA NA NA NA NA NA NA NA
```

---
* Next, run the trial `$n$` times, storing the result in your pre-allocated vector.

```r
for(i in seq_along(result)) {
	result[i] <- sample(c("Heads", "Tails"), 1)
}
result
```

```
##  [1] "Tails" "Heads" "Tails" "Tails" "Heads" "Heads" "Heads" "Heads" "Heads" "Tails"
```

---
# Growing vectors

* **Always** pre-allocate a vector for storage before running a `for` loop.

--
* Contrary to some opinions you may see out there, `for` loops are not actually slower than `lapply`, etc., provided the `for` loop is written well

--
* This primarily means .b[not] growing a vector

---
# Example
100,000 coin flips by growing a vector

```r
library(tictoc)

set.seed(1)
tic()
not_allocated <- sample(c("Heads", "Tails"), 1)
for(i in seq_len(1e5 - 1)) {
	not_allocated <- c(
	  not_allocated, 
	  sample(c("Heads", "Tails"), 1)
	 )
  
}
toc()
```

```
## 37.572 sec elapsed
```

---
same *exact* thing with pre-allocated vector

```r
set.seed(1)
tic()
allocated <- rep(NA, 1e5)
for(i in seq_len(1e5)) {
	allocated[i] <- sample(c("Heads", "Tails"), 1)
}
toc()
```

```
## 0.698 sec elapsed
```

---
# Result
* The result is the same, regardless of the approach (notice I forced the random number generator to start at the same place in both samples)

```r
identical(not_allocated, allocated)
```

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

* Speed is obviously not identical

---
# You try

Base R comes with `letters` and `LETTERS`
* Make an alphabet of upper/lower case. For example, create "Aa" with `paste0(LETTERS[1], letters[1])`

* Write a `for` loop for all letters

---
# Answer

```r
alphabet <- rep(NA, length(letters))

for(i in seq_along(alphabet)) {
	alphabet[i] <- paste0(LETTERS[i], letters[i])
}
alphabet
```

```
##  [1] "Aa" "Bb" "Cc" "Dd" "Ee" "Ff" "Gg" "Hh" "Ii" "Jj" "Kk" "Ll" "Mm" "Nn" "Oo" "Pp" "Qq"
## [18] "Rr" "Ss" "Tt" "Uu" "Vv" "Ww" "Xx" "Yy" "Zz"
```

---
# Quick style note
* Why am I always using `seq_along`?

--
* When writing functions, it's safer to use `seq_*` because you can't always be guaranteed of the input

```r
x <- data.frame()
1:length(x)
```

```
## [1] 1 0
```

```r
seq_along(x)
```

```
## integer(0)
```

---
# Running the loop

```r
for(i in 1:length(x)) {
	print(letters[i])
}
```

```
## [1] "a"
## character(0)
```

```r
for(i in seq_along(x)) {
	print(letters[i])
}
```

* The first  may return unhelpful error messages or unexpected output, while the latter simply won't run, which is generally easier to diagnose.

--
* Even better, if you're using a loop in a function, you should probably have a condition that checks the input before running it

---
# Another example
* Say we wanted to simulate 100 cases from random normal data, where we varied the standard deviation in increments of 0.2, ranging from 1 to 5

--
* First, specify a vector standard deviations

```r
increments <- seq(1, 5, by = 0.2)
```

--
* Next, allocate a vector. There are many ways I could store this result (data frame, matrix, list). I'll do it in a list.

```r
simulated <- vector("list", length(increments))
str(simulated)
```

```
## List of 21
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
##  $ : NULL
```

---
# Write `for` loop

```r
for(i in seq_along(simulated)) {
	simulated[[i]] <- rnorm(100, 0, increments[i]) 
	# note use of `[[` above
}
str(simulated)
```

```
## List of 21
##  $ : num [1:100] -2.387 0.405 -1.599 -0.285 0.288 ...
##  $ : num [1:100] 0.298 0.433 -1.021 1.384 -0.323 ...
##  $ : num [1:100] 0.893 -1.799 -0.819 -1.11 -2.198 ...
##  $ : num [1:100] -0.332 1.067 -0.823 2.899 1.863 ...
##  $ : num [1:100] -2.568 -0.672 -0.244 -1.645 2.221 ...
##  $ : num [1:100] 2.4 -1.95 1.13 3.05 3.56 ...
##  $ : num [1:100] -2.978 0.798 2.212 2.15 -2.197 ...
##  $ : num [1:100] -0.211 -1.768 3.35 2.06 0.213 ...
##  $ : num [1:100] 0.718 -4.029 -1.093 0.417 -3.952 ...
##  $ : num [1:100] 0.632 3.084 -2.62 -1.282 -2.965 ...
##  $ : num [1:100] 2.1759 -0.4681 1.6349 -0.0809 -0.7611 ...
##  $ : num [1:100] 1.236 3.055 -2.575 -0.868 4.369 ...
##  $ : num [1:100] 0.7795 -1.0125 -6.465 0.0926 1.8629 ...
##  $ : num [1:100] 3.466 -1.245 0.496 3.67 -2.207 ...
##  $ : num [1:100] -2.712 -4.21 -3.686 -0.728 -0.142 ...
##  $ : num [1:100] 2.83 6.08 -3 4.29 4.18 ...
##  $ : num [1:100] 0.335 0.574 4.106 4.414 0.897 ...
##  $ : num [1:100] -2.123 3.165 1.104 -4.065 0.578 ...
##  $ : num [1:100] 2.448 -1.472 4.411 2.34 -0.346 ...
##  $ : num [1:100] 0.672 -4.724 3.378 -1.811 10.33 ...
##  $ : num [1:100] -3.46 2.83 -11.49 -1.86 7.54 ...
```

---
# List/data frame
* Remember, if all the vectors of our list are the same length, it can be transformed into a data frame.

* First, let's provide meaningful names

```r
names(simulated) <- paste0("sd_", increments)
sim_d <- data.frame(simulated)
head(sim_d)
```

```
##         sd_1     sd_1.2     sd_1.4     sd_1.6     sd_1.8       sd_2     sd_2.2
## 1 -2.3872613  0.2979273  0.8930471 -0.3319310 -2.5676229  2.3954045 -2.9775420
## 2  0.4051212  0.4329239 -1.7989656  1.0673114 -0.6722755 -1.9542566  0.7980123
## 3 -1.5992856 -1.0209222 -0.8192303 -0.8232251 -0.2435614  1.1309855  2.2118028
## 4 -0.2847246  1.3838266 -1.1097486  2.8991565 -1.6445420  3.0545722  2.1497809
## 5  0.2881735 -0.3233308 -2.1982580  1.8633398  2.2213515  3.5578607 -2.1969056
## 6  0.1175257 -0.4150724  0.6353818  0.7142314 -0.5219616 -0.8029945  0.1464460
##       sd_2.4     sd_2.6     sd_2.8        sd_3     sd_3.2      sd_3.4     sd_3.6
## 1 -0.2105980  0.7175135  0.6321448  2.17589282  1.2362364  0.77947966  3.4658329
## 2 -1.7675364 -4.0290962  3.0843027 -0.46812673  3.0554725 -1.01245886 -1.2451870
## 3  3.3501949 -1.0927053 -2.6196216  1.63492841 -2.5751022 -6.46499466  0.4960868
## 4  2.0601974  0.4174713 -1.2824915 -0.08085208 -0.8678742  0.09259855  3.6701383
## 5  0.2125117 -3.9521276 -2.9646399 -0.76111234  4.3687915  1.86290325 -2.2067855
## 6  1.7822910 -0.1081454  4.5420524  3.53122922 -2.4194781 -1.14660593 -1.7557261
##       sd_3.8      sd_4     sd_4.2    sd_4.4     sd_4.6     sd_4.8       sd_5
## 1 -2.7119876  2.834307  0.3347596 -2.122876  2.4479578  0.6723229  -3.464762
## 2 -4.2104097  6.083823  0.5735697  3.164909 -1.4721622 -4.7240885   2.825094
## 3 -3.6861183 -3.000288  4.1063037  1.104068  4.4111688  3.3779921 -11.486532
## 4 -0.7277885  4.293730  4.4141833 -4.064636  2.3400425 -1.8110032  -1.857547
## 5 -0.1416683  4.178344  0.8965279  0.578414 -0.3461786 10.3301558   7.544994
## 6 -2.2110984  4.644032 -5.7370545 -5.072939  4.7260451  0.4489877   2.956989
```

---
# tidyverse
* One of the *best* things about the tidyverse is that it often does the looping for you

.pull-left[

```r
library(tidyverse)
pd <- sim_d %>%
	pivot_longer(
	  everything(),
	  names_to = "sd", 
	  values_to = "sim",
	  names_prefix = "sd_",
	  names_ptypes = list(
	    sd = factor()
	  )
	)

ggplot(pd, aes(sim)) +
 geom_density(
   aes(color = sd)
 ) +
 guides(color = "none") 
```
]

.pull-right[
![](w2p1_files/figure-html/plot-eval-1.png)
]

---
class: inverse-red middle

> Of course, someone has to write loops. It doesn't have to be you.
> --- Jenny Bryan

---
# Base R Method

* Calculate all the densities

```r
densities <- vector("list", length(sim_d))
for(i in seq_along(densities)) {
	densities[[i]] <- density(sim_d[ ,i])
}
str(densities)
```

```
## List of 21
##  $ :List of 7
##   ..$ x        : num [1:512] -3.45 -3.44 -3.42 -3.41 -3.4 ...
##   ..$ y        : num [1:512] 0.000173 0.000195 0.000219 0.000245 0.000274 ...
##   ..$ bw       : num 0.355
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -3.39 -3.38 -3.36 -3.35 -3.33 ...
##   ..$ y        : num [1:512] 0.000261 0.000296 0.000334 0.000377 0.000425 ...
##   ..$ bw       : num 0.405
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -4.9 -4.88 -4.87 -4.85 -4.83 ...
##   ..$ y        : num [1:512] 9.72e-05 1.09e-04 1.22e-04 1.37e-04 1.53e-04 ...
##   ..$ bw       : num 0.495
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -5.64 -5.62 -5.59 -5.57 -5.54 ...
##   ..$ y        : num [1:512] 0.000132 0.00015 0.000171 0.000195 0.000221 ...
##   ..$ bw       : num 0.572
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -5.97 -5.95 -5.92 -5.9 -5.88 ...
##   ..$ y        : num [1:512] 0.00012 0.000137 0.000155 0.000175 0.000198 ...
##   ..$ bw       : num 0.559
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -5.57 -5.55 -5.53 -5.5 -5.48 ...
##   ..$ y        : num [1:512] 0.000137 0.000153 0.000169 0.000187 0.000207 ...
##   ..$ bw       : num 0.698
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -7.7 -7.67 -7.64 -7.61 -7.58 ...
##   ..$ y        : num [1:512] 5.93e-05 6.65e-05 7.46e-05 8.37e-05 9.35e-05 ...
##   ..$ bw       : num 0.768
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -9.48 -9.44 -9.4 -9.37 -9.33 ...
##   ..$ y        : num [1:512] 5.38e-05 6.18e-05 7.07e-05 8.06e-05 9.17e-05 ...
##   ..$ bw       : num 0.834
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -9.05 -9.02 -8.98 -8.95 -8.91 ...
##   ..$ y        : num [1:512] 5.98e-05 6.80e-05 7.71e-05 8.74e-05 9.91e-05 ...
##   ..$ bw       : num 0.813
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -9.05 -9.02 -8.99 -8.95 -8.92 ...
##   ..$ y        : num [1:512] 6.33e-05 7.07e-05 7.87e-05 8.79e-05 9.79e-05 ...
##   ..$ bw       : num 0.954
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -9.69 -9.65 -9.61 -9.57 -9.54 ...
##   ..$ y        : num [1:512] 0.000107 0.00012 0.000135 0.000151 0.000169 ...
##   ..$ bw       : num 1.08
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -9.73 -9.69 -9.64 -9.6 -9.56 ...
##   ..$ y        : num [1:512] 9.15e-05 1.03e-04 1.15e-04 1.29e-04 1.44e-04 ...
##   ..$ bw       : num 1.12
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -11.3 -11.2 -11.2 -11.2 -11.1 ...
##   ..$ y        : num [1:512] 4.55e-05 5.14e-05 5.79e-05 6.53e-05 7.34e-05 ...
##   ..$ bw       : num 0.989
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -12 -11.9 -11.9 -11.8 -11.8 ...
##   ..$ y        : num [1:512] 4.34e-05 4.86e-05 5.45e-05 6.09e-05 6.78e-05 ...
##   ..$ bw       : num 1.04
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -11 -10.9 -10.9 -10.9 -10.8 ...
##   ..$ y        : num [1:512] 7.10e-05 7.95e-05 8.87e-05 9.88e-05 1.10e-04 ...
##   ..$ bw       : num 1.18
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -11.6 -11.5 -11.5 -11.4 -11.4 ...
##   ..$ y        : num [1:512] 6.28e-05 7.04e-05 7.89e-05 8.84e-05 9.87e-05 ...
##   ..$ bw       : num 1.3
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -15 -15 -14.9 -14.9 -14.8 ...
##   ..$ y        : num [1:512] 3.18e-05 3.64e-05 4.14e-05 4.70e-05 5.32e-05 ...
##   ..$ bw       : num 1.41
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -13.9 -13.9 -13.8 -13.8 -13.7 ...
##   ..$ y        : num [1:512] 6.35e-05 7.18e-05 8.15e-05 9.22e-05 1.04e-04 ...
##   ..$ bw       : num 1.4
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -15.6 -15.5 -15.5 -15.4 -15.4 ...
##   ..$ y        : num [1:512] 3.11e-05 3.48e-05 3.88e-05 4.33e-05 4.83e-05 ...
##   ..$ bw       : num 1.48
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -14 -13.9 -13.9 -13.8 -13.7 ...
##   ..$ y        : num [1:512] 4.97e-05 5.57e-05 6.26e-05 7.01e-05 7.83e-05 ...
##   ..$ bw       : num 1.78
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
##  $ :List of 7
##   ..$ x        : num [1:512] -17.4 -17.3 -17.2 -17.2 -17.1 ...
##   ..$ y        : num [1:512] 3.78e-05 4.25e-05 4.76e-05 5.34e-05 5.98e-05 ...
##   ..$ bw       : num 1.71
##   ..$ n        : int 100
##   ..$ call     : language density.default(x = sim_d[, i])
##   ..$ data.name: chr "sim_d[, i]"
##   ..$ has.na   : logi FALSE
##   ..- attr(*, "class")= chr "density"
```

---
* Next, plot the first density

```r
plot(densities[[1]])
```

![](w2p1_files/figure-html/first-density-1.png)

---
* Finally, loop through all the other densities

```r
plot(densities[[1]], xlim = c(-20, 20))

for(i in seq(2, length(densities))) {
	lines(x = densities[[i]]$x, 
	      y = densities[[i]]$y)	
}
```

![](w2p1_files/figure-html/all-densities-1.png)

---
# Skipping iterations
* On the prior slide, I set the index to skip over the first by using `seq(2, length(densities))`

--
* Alternatively, the loop could have been written like this

```r
plot(densities[[1]], xlim = c(-20, 20))

for(i in seq_along(densities)) {
	if(i == 1) next
	lines(x = densities[[i]]$x, 
	      y = densities[[i]]$y)	
}
```

![](w2p1_files/figure-html/loop-skip-1.png)

---
# Breaking loops

* Similarly, if a condition is met, you may want to break out of the loop

```r
set.seed(1)

rand_unif <- vector("double", 10)

for(i in seq_along(rand_unif)) {
	rand_unif[i] <- runif(1, 0, 10)
	if(any(rand_unif > 5)) {
		break
	}
}

rand_unif
```

```
##  [1] 2.655087 3.721239 5.728534 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [10] 0.000000
```

---
class: inverse-blue center middle
# *apply

---
# `lapply`
* One of numerous *functionals* in R

* A functional "takes a function as an input and returns a vector as output" ([adv-r, Chpt 9](https://adv-r.hadley.nz/functionals.html))

* `lapply` will **always** return a list

---
## Revisiting our simulation with `$n = 10$`

Our `for` loop version

```r
increments <- seq(1, 5, by = 0.2)

simulated <- vector("list", length(increments))

for(i in seq_along(simulated)) {
	simulated[[i]] <- rnorm(10, 0, increments[i]) 
	# note use of `[[`
}

simulated
```

```
## [[1]]
##  [1]  1.329799263  1.272429321  0.414641434 -1.539950042 -0.928567035 -0.294720447
##  [7] -0.005767173  2.404653389  0.763593461 -0.799009249
## 
## [[2]]
##  [1] -1.3771884 -0.3473539 -0.3590581 -0.4938130  0.3026681 -1.0703054  0.5228200
##  [8] -1.4850461 -0.2691215  0.4528748
## 
## [[3]]
##  [1]  0.18667091  1.12586531 -0.07994948  0.70505116  1.52007711 -0.96733538 -1.79843910
##  [8]  0.06541664 -0.32998918 -0.76004356
## 
## [[4]]
##  [1] -0.6932965 -1.0391546  1.1628012  1.8430588  1.5874566 -0.6872210  1.9812866
##  [8] -0.4469541  2.8126449  0.8971937
## 
## [[5]]
##  [1] -0.8150112 -1.4976779 -2.0998270 -1.9180630 -2.8148077  2.0817666  1.4976848
##  [8] -0.4091916  0.4790473 -0.6780649
## 
## [[6]]
##  [1]  4.88272926 -1.59067823 -0.10975495  0.50028265  1.23648659 -0.34524701 -4.44780055
##  [8] -2.52722877  0.71745779 -0.02209096
## 
## [[7]]
##  [1] -2.06942816 -0.25481571 -1.79293116  0.53297966 -3.13521647  0.80507047  0.54650783
##  [8]  0.14363400  0.04214406  0.56614443
## 
## [[8]]
##  [1] -1.5576242 -0.2860050  1.5939257  2.6423258  0.3450516 -0.2826086 -2.1889641
##  [8] -3.4502070 -1.9130149  3.0097995
## 
## [[9]]
##  [1]  2.0075697 -0.5707406 -1.1045067 -1.0893483  2.5921658 -0.7170229  3.2656489
##  [8]  1.6813534  3.3782120 -2.2704815
## 
## [[10]]
##  [1]  0.02343869 -2.46644083  1.66952525  0.33520940 -0.79008686  4.07676752  0.64125485
##  [8]  2.79032300  2.18920572 -2.17497454
## 
## [[11]]
##  [1] -1.8479697  0.1397409 -3.3911573  1.7301563 -3.8422483  4.8763419 -1.5020898
##  [8]  5.0348916 -1.2375597 -2.9168605
## 
## [[12]]
##  [1]  0.08122518  0.08792107 -5.37658471  3.37200276 -3.58271713  1.07397507  1.58334645
##  [8]  0.44176867 -0.38013448  0.63258964
## 
## [[13]]
##  [1] -3.63355522 -2.73092494 -3.78680146  5.37231172  5.09258379  0.89299456 -4.19186408
##  [8] -0.01266001  5.13968576 -1.61737417
## 
## [[14]]
##  [1]  2.8724992 -3.5064092  2.4817417 -3.4410208 -4.4341454 -3.4448108 -3.1312183
##  [8] -3.2784505  2.6685947  0.2466415
## 
## [[15]]
##  [1] -1.230253 -4.128712 -3.860530 -2.917603 -4.254936 -1.703062  1.792598 -4.485865
##  [9]  5.586977 -4.983398
## 
## [[16]]
##  [1] -0.38609969  9.47887963  3.56250591 -1.00873265 -3.46305502  2.33034400 -0.05011739
##  [8] -1.49941905  1.27154294 -1.95522254
## 
## [[17]]
##  [1] 11.166364  7.057168  3.274253  2.995610 -2.280104  3.720269 -1.464098 -4.233829
##  [9]  7.909367 -3.901679
## 
## [[18]]
##  [1] -1.2944644 -2.7057812 -4.1671335  2.6354907 -6.7039055 -0.9072316 -2.5268998
##  [8] -6.1167306 -0.3098365 -1.8958699
## 
## [[19]]
##  [1] -2.72423672  4.51313434  2.44908304 -0.41609817  0.71985626 -3.39163378 -0.92616955
##  [8]  5.07001234 -0.07704198  0.74422771
## 
## [[20]]
##  [1]  9.71885467 -3.37773242  4.61180344  8.59432826 -5.10799278  0.08465542 -1.87156142
##  [8] -2.35599721 -5.01944473 -4.30181407
## 
## [[21]]
##  [1]  6.3469358  2.9692047  3.8781716  7.7868519 -1.8270090  4.0827822 -0.3031739
##  [8] -2.5068916  4.6303136  0.1846885
```

---
# The `lapply` version

```r
increments <- seq(1, 5, by = 0.2)
sim_l <- lapply(increments, function(sd) rnorm(10, 0, sd))
sim_l
```

```
## [[1]]
##  [1] -1.06620017 -0.23845635  1.49522344  1.17215855 -1.45770721  0.09505623  0.84766496
##  [8] -1.62436453  1.40856336 -0.54176036
## 
## [[2]]
##  [1]  0.33439767 -0.23276729  1.89138982 -1.77065716 -0.17352985 -1.14384377  0.48785128
##  [8]  2.67511464 -1.81739641 -0.07404891
## 
## [[3]]
##  [1] -0.2061791  2.1582303 -1.3745979  0.6952094  2.3757270 -0.3650308 -0.9883000
##  [8] -0.2256499  0.7018506 -1.4189555
## 
## [[4]]
##  [1]  2.583603577  0.009027176 -4.647838497 -1.771463710  2.476107092 -1.562928561
##  [7] -0.162405516  0.068240400 -2.554748823  0.785547796
## 
## [[5]]
##  [1]  0.7588861  3.3730270  1.8621258  0.1472586 -0.1485428  1.0909322 -1.5973563
##  [8]  0.1897585  0.6351741  0.9907080
## 
## [[6]]
##  [1] -2.2686619  2.9247031  1.4042334  5.0142223 -3.7800543 -1.1796256 -3.4290046
##  [8] -0.8419958  0.6202828  3.4051412
## 
## [[7]]
##  [1] -0.9754466 -2.6369136 -0.6762380  1.3663192  0.4001848  2.9004820 -0.6576005
##  [8] -3.6260878  2.0932966 -2.4488705
## 
## [[8]]
##  [1]  1.4807195  1.2323849  0.8867018  4.1373459 -0.4947470 -3.1540683  0.1523378
##  [8] -0.5567459  1.5241448  3.9231463
## 
## [[9]]
##  [1] -4.7016517 -0.5561035  0.1829520  1.4292399 -1.8117412  1.0154715  0.9916693
##  [8] -0.0321692 -0.3235310  3.8135359
## 
## [[10]]
##  [1]  1.8870003  5.4779907 -0.7533148 -3.4847443 -1.1079682  0.2727106 -0.6674835
##  [8] -1.1531183 -4.4162105 -2.2323731
## 
## [[11]]
##  [1] -3.2887103  0.9249262  1.0343854  4.6189443 -0.9885426  2.8451681 -1.4377668
##  [8] -4.5446604  1.3036100 -1.5586100
## 
## [[12]]
##  [1] -2.6705889 -2.4212723  3.4864112  5.0317853  3.2235660 -0.8741056 -4.1909745
##  [8]  0.7122363  3.5629862  2.6796621
## 
## [[13]]
##  [1]  1.0693570  0.7555414 -2.8682923  1.5089381  0.1897102  0.2311061 -0.6866699
##  [8] -3.9372519 -2.0153271  2.6046223
## 
## [[14]]
##  [1]  0.14014122  0.05272852 -0.67074108  5.04212698  0.06654805  0.89710564  0.53719465
##  [8] -3.46763945 -0.23928039  4.63291935
## 
## [[15]]
##  [1]  1.7408760 -5.5177075  0.2939007  2.1276020 -0.2847970  2.9742513 -0.6561405
##  [8] -3.9949164  2.7719149  0.9981279
## 
## [[16]]
##  [1]  2.1746314  4.1642412  0.7900246 -6.5183131  0.4841609 -6.5496878 -2.1241724
##  [8]  3.8147192 -6.8826026  0.4252825
## 
## [[17]]
##  [1] -2.5564077 -1.2650273  4.1000506  1.9152364  5.4365139 -4.7594493 -3.6517335
##  [8] -3.1708752 -0.5444685 -4.2075656
## 
## [[18]]
##  [1] -3.6074204 -4.2880309  2.6589614  2.4146654  4.0323037 11.7108920 -0.7931311
##  [8]  3.0140650 14.3722239  2.4666420
## 
## [[19]]
##  [1] -0.3174796 -4.4732375 -2.5142983 -7.7679847 -7.2329144 -1.8629409  1.4687175
##  [8]  0.1859673 -1.7940440 -8.3684223
## 
## [[20]]
##  [1]  3.1640674  2.2061840  7.7598064 -8.9097144 -1.3767546  8.4015451  0.5587853
##  [8]  6.6444152  2.7562604  0.6551559
## 
## [[21]]
##  [1]  4.57107994 -9.00413159 -1.69940320  3.03132286  6.70565155  3.83643644  0.96862833
##  [8]  5.70283345  0.06932402 -5.52652955
```

---
# Some more examples
### Loop through a data frame
* Remember - a data frame is a list. We can loop through it easily

```r
library(palmerpenguins)
lapply(penguins, is.double)
```

```
## $species
## [1] FALSE
## 
## $island
## [1] FALSE
## 
## $bill_length_mm
## [1] TRUE
## 
## $bill_depth_mm
## [1] TRUE
## 
## $flipper_length_mm
## [1] FALSE
## 
## $body_mass_g
## [1] FALSE
## 
## $sex
## [1] FALSE
## 
## $year
## [1] FALSE
```

---

```r
lapply(mtcars, mean)
```

```
## $mpg
## [1] 20.09062
## 
## $cyl
## [1] 6.1875
## 
## $disp
## [1] 230.7219
## 
## $hp
## [1] 146.6875
## 
## $drat
## [1] 3.596563
## 
## $wt
## [1] 3.21725
## 
## $qsec
## [1] 17.84875
## 
## $vs
## [1] 0.4375
## 
## $am
## [1] 0.40625
## 
## $gear
## [1] 3.6875
## 
## $carb
## [1] 2.8125
```

---
# Add a condition

```r
lapply(penguins, function(x) {
	if(is.numeric(x)) {
		mean(x, na.rm = TRUE)
	}
})
```

```
## $species
## NULL
## 
## $island
## NULL
## 
## $bill_length_mm
## [1] 43.92193
## 
## $bill_depth_mm
## [1] 17.15117
## 
## $flipper_length_mm
## [1] 200.9152
## 
## $body_mass_g
## [1] 4201.754
## 
## $sex
## NULL
## 
## $year
## [1] 2008.029
```

---
# Add a second condition

```r
lapply(penguins, function(x) {
	if(is.numeric(x)) {
		return(mean(x, na.rm = TRUE))
	}
	else if(is.character(x) | 
		        is.factor(x)) {
    return(table(x))
  }
})
```

```
## $species
## x
##    Adelie Chinstrap    Gentoo 
##       152        68       124 
## 
## $island
## x
##    Biscoe     Dream Torgersen 
##       168       124        52 
## 
## $bill_length_mm
## [1] 43.92193
## 
## $bill_depth_mm
## [1] 17.15117
## 
## $flipper_length_mm
## [1] 200.9152
## 
## $body_mass_g
## [1] 4201.754
## 
## $sex
## x
## female   male 
##    165    168 
## 
## $year
## [1] 2008.029
```

---
# Passing arguments

```r
head(airquality)
```

```
##   Ozone Solar.R Wind Temp Month Day
## 1    41     190  7.4   67     5   1
## 2    36     118  8.0   72     5   2
## 3    12     149 12.6   74     5   3
## 4    18     313 11.5   62     5   4
## 5    NA      NA 14.3   56     5   5
## 6    28      NA 14.9   66     5   6
```

```r
lapply(airquality, mean, na.rm = TRUE)
```

```
## $Ozone
## [1] 42.12931
## 
## $Solar.R
## [1] 185.9315
## 
## $Wind
## [1] 9.957516
## 
## $Temp
## [1] 77.88235
## 
## $Month
## [1] 6.993464
## 
## $Day
## [1] 15.80392
```

---
# Simulation again

```r
lapply(seq(1, 5, 0.2), rnorm, n = 10, mean = 0)
```

```
## [[1]]
##  [1] -0.02516264 -0.16367334  0.37005975 -0.38082454  0.65295237  2.06134181 -1.79664494
##  [8]  0.58407712 -0.72275312 -0.62916466
## 
## [[2]]
##  [1] -2.1794473 -0.3111469  0.4015587 -1.7126011  2.3263539 -0.9114363 -2.7345314
##  [8] -0.1368609  2.8222280  1.9155850
## 
## [[3]]
##  [1]  1.7884237  1.1045592  0.6460515 -0.6132968 -2.1109298 -3.1121246 -1.6501414
##  [8] -2.4958643 -1.3830868  1.0198842
## 
## [[4]]
##  [1] -1.4154959 -2.4615063 -1.6710007 -2.7490179  1.2860121 -2.4028595 -0.2327985
##  [8]  0.9271338  1.9224409  3.0302573
## 
## [[5]]
##  [1] -3.1684074  1.6641842 -1.0017759 -0.3250514  2.6053925 -1.0928366  1.2228524
##  [8] -0.1684038 -0.8821553  2.5391869
## 
## [[6]]
##  [1] -0.4491476 -0.4249910  1.3927569  1.8303650 -1.8467486  2.2937465 -1.2717301
##  [8] -1.7728866 -4.6662734 -0.2909816
## 
## [[7]]
##  [1]  0.6973209 -1.5564340  2.7327422  1.3643348  0.2197867  3.9769477 -3.3053460
##  [8]  0.6284090  1.8605553 -2.1897538
## 
## [[8]]
##  [1] -0.6164499 -0.1340545 -1.0680126  0.1672682 -0.3712121 -1.9950323  1.8277064
##  [8] -1.3836166 -1.5032837  1.1552048
## 
## [[9]]
##  [1]  4.40770482 -4.57918836  0.51483384  1.03310766  0.07598629  6.65671081  3.26853205
##  [8] -1.38979798 -1.62559131  2.37600659
## 
## [[10]]
##  [1]  2.82015870  2.01401711 -1.69319265  1.50935234 -0.21512648  5.17977477 -2.39374114
##  [8]  0.09138443 -2.87016655 -2.75029741
## 
## [[11]]
##  [1]  0.01230587 -0.70028153 -1.49666466  4.64913889  0.26249075  3.95610339 -2.94367236
##  [8] -0.73686776 -4.21180151  4.32267944
## 
## [[12]]
##  [1] -3.14035197  4.71758369 -3.17183118 -0.30239150 -9.20045339 -0.78997152  0.04718235
##  [8] -6.14108065 -0.92100398 -1.10923983
## 
## [[13]]
##  [1] -6.2546012  3.0552024 -4.1237070 -0.7444784  1.9185117 -1.7864769  2.5308724
##  [8]  0.4385380  5.0601325 -2.2531186
## 
## [[14]]
##  [1] -4.1783580  1.2915872 -0.7014470 -1.0630152  1.7879055  1.7456860  0.0676242
##  [8]  2.2851884  2.7159987  3.0009205
## 
## [[15]]
##  [1]  3.6698929  4.9167439 -0.5188939 -1.6725270 -4.6636789 -0.9030817 -3.5220610
##  [8]  1.5626944 -0.7556854 -2.1183086
## 
## [[16]]
##  [1] -3.908628  0.242941 -2.388918 -5.035795 -5.640189  4.405216 -2.708968 -3.048694
##  [9] -1.166994 -2.300754
## 
## [[17]]
##  [1] -1.8645558 -1.3127959 -2.5326186 -4.5945258  3.0017660 -0.4570115 -6.0639513
##  [8]  3.3857177 -7.3073073 -1.6855453
## 
## [[18]]
##  [1] -1.265363 -4.128993  1.265735 -6.623765  6.684907  1.616601  7.479395  2.834467
##  [9] -7.426325  2.849642
## 
## [[19]]
##  [1]  2.064453  4.720990  4.944900  2.108224  2.905299 -2.670145  7.287284 -8.114368
##  [9] -8.650861 -5.941908
## 
## [[20]]
##  [1]   4.36641814  -5.31722727  -1.84379460   0.39712720  -2.32263588 -10.00675740
##  [7]   5.60441743  -0.36876370   2.54602270   0.02356222
## 
## [[21]]
##  [1] -2.6511571  7.5743333  4.0772982 -7.5326093 -5.7859009  6.5077747 -4.5282039
##  [8]  0.0778174 -7.1400260  3.4398985
```

---
# Mimic `dplyr::group_by`

```r
by_cyl <- split(mtcars, mtcars$cyl)
str(by_cyl)
```

```
## List of 3
##  $ 4:'data.frame':	11 obs. of  11 variables:
##   ..$ mpg : num [1:11] 22.8 24.4 22.8 32.4 30.4 33.9 21.5 27.3 26 30.4 ...
##   ..$ cyl : num [1:11] 4 4 4 4 4 4 4 4 4 4 ...
##   ..$ disp: num [1:11] 108 146.7 140.8 78.7 75.7 ...
##   ..$ hp  : num [1:11] 93 62 95 66 52 65 97 66 91 113 ...
##   ..$ drat: num [1:11] 3.85 3.69 3.92 4.08 4.93 4.22 3.7 4.08 4.43 3.77 ...
##   ..$ wt  : num [1:11] 2.32 3.19 3.15 2.2 1.61 ...
##   ..$ qsec: num [1:11] 18.6 20 22.9 19.5 18.5 ...
##   ..$ vs  : num [1:11] 1 1 1 1 1 1 1 1 0 1 ...
##   ..$ am  : num [1:11] 1 0 0 1 1 1 0 1 1 1 ...
##   ..$ gear: num [1:11] 4 4 4 4 4 4 3 4 5 5 ...
##   ..$ carb: num [1:11] 1 2 2 1 2 1 1 1 2 2 ...
##  $ 6:'data.frame':	7 obs. of  11 variables:
##   ..$ mpg : num [1:7] 21 21 21.4 18.1 19.2 17.8 19.7
##   ..$ cyl : num [1:7] 6 6 6 6 6 6 6
##   ..$ disp: num [1:7] 160 160 258 225 168 ...
##   ..$ hp  : num [1:7] 110 110 110 105 123 123 175
##   ..$ drat: num [1:7] 3.9 3.9 3.08 2.76 3.92 3.92 3.62
##   ..$ wt  : num [1:7] 2.62 2.88 3.21 3.46 3.44 ...
##   ..$ qsec: num [1:7] 16.5 17 19.4 20.2 18.3 ...
##   ..$ vs  : num [1:7] 0 0 1 1 1 1 0
##   ..$ am  : num [1:7] 1 1 0 0 0 0 1
##   ..$ gear: num [1:7] 4 4 3 3 4 4 5
##   ..$ carb: num [1:7] 4 4 1 1 4 4 6
##  $ 8:'data.frame':	14 obs. of  11 variables:
##   ..$ mpg : num [1:14] 18.7 14.3 16.4 17.3 15.2 10.4 10.4 14.7 15.5 15.2 ...
##   ..$ cyl : num [1:14] 8 8 8 8 8 8 8 8 8 8 ...
##   ..$ disp: num [1:14] 360 360 276 276 276 ...
##   ..$ hp  : num [1:14] 175 245 180 180 180 205 215 230 150 150 ...
##   ..$ drat: num [1:14] 3.15 3.21 3.07 3.07 3.07 2.93 3 3.23 2.76 3.15 ...
##   ..$ wt  : num [1:14] 3.44 3.57 4.07 3.73 3.78 ...
##   ..$ qsec: num [1:14] 17 15.8 17.4 17.6 18 ...
##   ..$ vs  : num [1:14] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ am  : num [1:14] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ gear: num [1:14] 3 3 3 3 3 3 3 3 3 3 ...
##   ..$ carb: num [1:14] 2 4 3 3 3 4 4 4 2 2 ...
```

---

```r
lapply(by_cyl, function(x) mean(x$mpg))
```

```
## $`4`
## [1] 26.66364
## 
## $`6`
## [1] 19.74286
## 
## $`8`
## [1] 15.1
```

---
# Your turn
Try splitting the penguins dataset by species and calculating the average `bill_length_mm`

---
# Produce separate plots

```r
lapply(by_cyl, function(x) {
	ggplot(x, aes(disp, mpg)) +
		geom_point() +
		geom_smooth()
})
```

---

```
## $`4`
```

![](w2p1_files/figure-html/plots_by_cyl-eval-1.png)

```
## 
## $`6`
```

![](w2p1_files/figure-html/plots_by_cyl-eval-2.png)

```
## 
## $`8`
```

![](w2p1_files/figure-html/plots_by_cyl-eval-3.png)

```
## # A tibble: 344 x 8
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex   
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int> <fct> 
##  1 Adelie  Torgersen         39.1          18.7                 181        3750 male  
##  2 Adelie  Torgersen         39.5          17.400               186        3800 female
##  3 Adelie  Torgersen         40.300        18                   195        3250 female
##  4 Adelie  Torgersen         NA            NA                    NA          NA <NA>  
##  5 Adelie  Torgersen         36.7          19.3                 193        3450 female
##  6 Adelie  Torgersen         39.300        20.6                 190        3650 male  
##  7 Adelie  Torgersen         38.9          17.8                 181        3625 female
##  8 Adelie  Torgersen         39.2          19.6                 195        4675 male  
##  9 Adelie  Torgersen         34.1          18.1                 193        3475 <NA>  
## 10 Adelie  Torgersen         42            20.2                 190        4250 <NA>  
## # … with 334 more rows, and 1 more variable: year <int>
```

---
# Your turn
Produce separate plots of the relation between `bill_length_mm` and `body_mass_g`

---
# Saving
* You can extend this example further by saving the plot outputs to an object, then looping through that object to save the plots to disk.

--
* Using functionals, this would require parallel iterations, which we'll cover later (need to loop through plots and a file name)

--
* Could extend it fairly easily with a `for` loop

---
# Saving w/`for` loop

### Save plots to an object (list)

```r
plots <- lapply(by_cyl, function(x) {
	ggplot(x, aes(disp, mpg)) +
		geom_point() +
		geom_smooth()
})
```

--
### Specify file names/directory

```r
#dir.create(here::here("plots")) 
filenames <- here::here("plots", 
                        paste0("cyl", names(by_cyl), ".png"))
filenames
```

```
## [1] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/cyl4.png"
## [2] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/cyl6.png"
## [3] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/cyl8.png"
```

---
# Saving

```r
for(i in seq_along(plots)) {
	ggsave(filenames[i], # single bracket
	       plots[[i]], # double bracket
	       device = "png",
	       width = 6.5, 
	       height = 8)
}
```

---
class: inverse-orange middle

# You try!

---
# Variants of `lapply`

* `sapply`

+ Will try to .blue[**s**]implify the output, if possible. Otherwise it will return a list.

+ Fine for interactive work, but I strongly recommend against it if writing a function (difficult to predict the output)

--
* `vapply`

+ Strict - you specify the output

+ Use if writing functions (or just always stick with `lapply`), or consider jumping to `{purrr}` (next week)

---
# Examples
### Our simulation

```r
sim_s <- sapply(seq(1, 5, by = 0.2), function(x) {
  rnorm(10, 0, x)
})
class(sim_s)
```

```
## [1] "matrix" "array"
```

```r
dim(sim_s)
```

```
## [1] 10 21
```

```r
sim_s
```

```
##               [,1]        [,2]       [,3]       [,4]        [,5]       [,6]       [,7]
##  [1,] -2.939773695 -0.38696441 -2.0067288 -0.6857975  0.26119194 -2.6882602 -5.3073906
##  [2,]  0.002415809 -1.60656089 -1.4772590 -1.0498872 -4.39616038 -3.0463115 -0.6903443
##  [3,]  0.509665571  0.82578723 -1.0263566  1.5350309  1.04457363 -0.8439364  3.6517332
##  [4,] -1.084720001  0.08553678  0.2952702  2.4896842  1.17909360  2.7218489  0.2880968
##  [5,]  0.704832977  2.62770283 -1.3984890 -1.6652743 -0.54811591  3.5075897  2.4109551
##  [6,]  0.330976350 -1.38924912  1.5089905  1.4889159 -1.27362282  3.1367295  1.0765501
##  [7,]  0.976327473  1.41802568 -1.6785641 -0.1207135  3.54882963  2.5935111 -1.7136026
##  [8,] -0.843339880 -0.63284203  0.3032918 -3.1475126 -0.16199763 -0.4751925  3.8358306
##  [9,] -0.970579905 -1.74795361  0.2003218 -1.2094458 -0.02523105 -2.4483003 -0.1724520
## [10,] -1.771531349  0.68756084 -1.4920501  0.7378387 -2.02222249 -0.6556254 -2.1462183
##             [,8]       [,9]     [,10]       [,11]      [,12]      [,13]      [,14]
##  [1,]  0.1695836 -1.1734150 -3.768222 -6.10501062  5.4168761 -3.3350679 -4.8069314
##  [2,] -3.6446389  2.4052205  2.894263  6.06404098  2.0548247  3.6963109  1.4332445
##  [3,]  2.0730697 -0.5164141 -2.272974  3.01792046  4.1031457  0.4737119 -0.4017125
##  [4,]  1.2037641  3.1066126  5.044831  2.45137080  0.4497503 -1.3133251  2.4326781
##  [5,] -0.8514752  1.2884162  4.960317 -1.99196485 -3.5600086  3.8201904 -2.8389526
##  [6,] -1.1722293 -5.8373967 -4.073136 -0.03384369 -1.0869663 -2.5834752 -0.3131508
##  [7,]  2.2471055 -3.4719655 -2.367832  1.85903177 -5.3272468  3.9064610  4.9762224
##  [8,] -2.5497801  3.3352155 -3.501343 -3.84371623  2.9723256 -2.8644193  0.6065646
##  [9,] -2.3611701  1.7960694  1.868407 -0.37278398  4.5338459  1.3308054  2.9634874
## [10,]  1.0181949 -2.5143629 -3.614155  0.52722496 -0.2007065  3.0306826 -0.7952206
##             [,15]      [,16]      [,17]       [,18]     [,19]       [,20]     [,21]
##  [1,] -3.91168829 -0.7117919  8.3144599  0.73152216  5.361125   0.7562907 -4.721494
##  [2,] -0.04151763 -1.7039254  3.3465371 -5.58623589 10.287085   4.4802659 -3.829450
##  [3,] -4.65496639  3.9866351 -7.1789600 10.33777061  1.390419   1.4535757 -4.768896
##  [4,] -9.86522328  2.9106428 -6.9874086 -6.21282379 -4.795530  -9.3895211 -1.990022
##  [5,]  4.44266585 -6.9065224  2.0626601 -0.07463057 -4.524295   1.6969762 -1.556085
##  [6,] -4.13025350  1.4135940 -0.7310330 -2.39500515  9.226305   2.1620377  3.980464
##  [7,] -6.93911545  2.9072547  4.0374204  7.92049427 -9.524629   3.1658442  4.932142
##  [8,]  3.78207087  2.6730439  1.2340720  4.45033678 14.056415  -4.9508195 -3.972658
##  [9,] -0.04507477 -9.6972692  0.3401973 -2.48035285 -1.202213 -11.3809098 -1.544090
## [10,] -2.27858790 -0.9414297  0.7713797  0.90385150 -2.090209  -1.5579663  1.807224
```

---

```r
sapply(penguins, is.double)
```

```
##           species            island    bill_length_mm     bill_depth_mm 
##             FALSE             FALSE              TRUE              TRUE 
## flipper_length_mm       body_mass_g               sex              year 
##             FALSE             FALSE             FALSE             FALSE
```

--
* Now that it's a vector we can easily use it for subsetting

---

```r
head(penguins)
```

```
## # A tibble: 6 x 8
##   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex   
##   <fct>   <fct>              <dbl>         <dbl>             <int>       <int> <fct> 
## 1 Adelie  Torgersen         39.1          18.7                 181        3750 male  
## 2 Adelie  Torgersen         39.5          17.400               186        3800 female
## 3 Adelie  Torgersen         40.300        18                   195        3250 female
## 4 Adelie  Torgersen         NA            NA                    NA          NA <NA>  
## 5 Adelie  Torgersen         36.7          19.3                 193        3450 female
## 6 Adelie  Torgersen         39.300        20.6                 190        3650 male  
## # … with 1 more variable: year <int>
```

```r
head( penguins[ ,sapply(penguins, is.double)] )
```

```
## # A tibble: 6 x 2
##   bill_length_mm bill_depth_mm
##            <dbl>         <dbl>
## 1         39.1          18.7  
## 2         39.5          17.400
## 3         40.300        18    
## 4         NA            NA    
## 5         36.7          19.3  
## 6         39.300        20.6
```

---
# Challenge
Can you make return the opposite? In other words - all those that are *not* double?

---

```r
head( penguins[ ,!sapply(penguins, is.double)] )
```

```
## # A tibble: 6 x 6
##   species island    flipper_length_mm body_mass_g sex     year
##   <fct>   <fct>                 <int>       <int> <fct>  <int>
## 1 Adelie  Torgersen               181        3750 male    2007
## 2 Adelie  Torgersen               186        3800 female  2007
## 3 Adelie  Torgersen               195        3250 female  2007
## 4 Adelie  Torgersen                NA          NA <NA>    2007
## 5 Adelie  Torgersen               193        3450 female  2007
## 6 Adelie  Torgersen               190        3650 male    2007
```

---
# `vapply`
* As you can probably see, simplifying can be *really* helpful for interactive work.

--
.center[.blue[.realbig[BUT]]]

--
* Not ideal for programmatic work - need to be able to reliably predict the output

--
* `vapply` solves this issue.

---

```r
vapply(mtcars, mean, FUN.VALUE = double(1))
```

```
##        mpg        cyl       disp         hp       drat         wt       qsec         vs 
##  20.090625   6.187500 230.721875 146.687500   3.596563   3.217250  17.848750   0.437500 
##         am       gear       carb 
##   0.406250   3.687500   2.812500
```

```r
vapply(penguins, is.double, FUN.VALUE = character(1))
```

```
## Error in vapply(penguins, is.double, FUN.VALUE = character(1)): values must be type 'character',
##  but FUN(X[[1]]) result is type 'logical'
```

```r
vapply(penguins, is.double, FUN.VALUE = logical(1))
```

---
# Coercion with `vapply`

* If it can coerce the vector without loss of information, it will

```r
vapply(penguins, is.double, FUN.VALUE = double(1))
```

```
##           species            island    bill_length_mm     bill_depth_mm 
##                 0                 0                 1                 1 
## flipper_length_mm       body_mass_g               sex              year 
##                 0                 0                 0                 0
```

---
# Count missing data

```r
vapply(airquality, function(col) {
  sum(is.na(col))
  },
  double(1)
)
```

```
##   Ozone Solar.R    Wind    Temp   Month     Day 
##      37       7       0       0       0       0
```

---
# `sapply` alternative
For interactive work, the code on the previous slide is maybe too much. Could be reduced to

```r
sapply(airquality, function(col) sum(is.na(col)))
```

```
##   Ozone Solar.R    Wind    Temp   Month     Day 
##      37       7       0       0       0       0
```

---
# Summary
* `for` loops are incredibly flexible and there's nothing inherently "wrong" about them

--
	+ Do require more text, and often repetitive text, which can lead to errors/bugs

--
	+ The flexibility can actually be more of a curse than a blessing

---
# Summary

* The `lapply` family of functions help put the focus on a given function, and what values are being looped through the function

--
	+ `lapply` will always return a list

--
	+ `sapply` will try to simplify, which is problematic for programming, but fine for interactive work

--
	+ `vapply` is strict, and will only return the type specified

---
class: inverse-green middle
# Next time
### Lab 1