Intro to Base R iterationsAnd Lab 1Daniel AndersonWeek 21 / 85

Agenda

For loops
Apply family of loops
- lapply()
- sapply()
- vapply()
- apply() (briefly)

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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.

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Basic overview: `for` loops

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Basic overview: `for` loops

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"

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Basic overview: `for` loops

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"

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

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

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Basic overview: `for` loops

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"

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

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

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

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Creating indexes

You will commonly see code like

for(i in 1:nrow(df))

Instead, preference seq_along() or seq_len().

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Creating indexes

You will commonly see code like

for(i in 1:nrow(df))

Instead, preference seq_along() or seq_len().

Examples

x <- c(3, 2.6, 8)
seq_along(x)

## [1] 1 2 3

seq_len(length(x))

## [1] 1 2 3

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`seq_*`

seq_along() will create an index from 1 to the length of the vector
seq_len() takes a single positive integer argument, and creates an index from 1 to that integer

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`seq_*`

seq_along() will create an index from 1 to the length of the vector
seq_len() takes a single positive integer argument, and creates an index from 1 to that integer

Why are these preferable?

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Avoid the `1 0` problem

means <- c()
out <- rep(NA, length(means))
for (i in 1:length(means)) {
  out[i] <- rnorm(10, means[i])
}

## Error in rnorm(10, means[i]): invalid arguments

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Avoid the `1 0` problem

means <- c()
out <- rep(NA, length(means))
for (i in 1:length(means)) {
  out[i] <- rnorm(10, means[i])
}

## Error in rnorm(10, means[i]): invalid arguments

`seq_*` version

means <- c()
out <- rep(NA, length(means))
for (i in seq_along(means)) {
  out[i] <- rnorm(10, means[i])
}

Basically, the loop is just not executed.

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Another basic example

Simulate tossing a coin, record results

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Another basic example

Simulate tossing a coin, record results

For a single toss

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

## [1] "Heads"

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Another basic example

Simulate tossing a coin, record results

For a single toss

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

## [1] "Heads"

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

result <- rep(NA, 10)
result

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

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

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

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

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Growing vectors

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

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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

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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 not growing a vector

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Example

100,000 coin flips by growing a vector

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()

## 50.637 sec elapsed

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same exact thing with pre-allocated vector

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

## 0.935 sec elapsed

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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)

identical(not_allocated, allocated)

## [1] TRUE

Speed is obviously not identical

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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

03:00

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Answer

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"
## [17] "Qq" "Rr" "Ss" "Tt" "Uu" "Vv" "Ww" "Xx" "Yy" "Zz"

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Another example

Say we wanted to simulate 100 cases from random normal distribution, where we varied the standard deviation in increments of 0.2, ranging from 1 to 5

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Another example

Say we wanted to simulate 100 cases from random normal distribution, where we varied the standard deviation in increments of 0.2, ranging from 1 to 5
Hopefully this is a relatable example, but if not, that's okay - focus on the process.

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Another example

Say we wanted to simulate 100 cases from random normal distribution, where we varied the standard deviation in increments of 0.2, ranging from 1 to 5
Hopefully this is a relatable example, but if not, that's okay - focus on the process.
First, specify a vector standard deviations

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

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Another example

Say we wanted to simulate 100 cases from random normal distribution, where we varied the standard deviation in increments of 0.2, ranging from 1 to 5
Hopefully this is a relatable example, but if not, that's okay - focus on the process.
First, specify a vector standard deviations

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.

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

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Look at our vector

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

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Write `for` loop

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

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Write `for` loop

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

Now look at our vector

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 ...

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List/data frame

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

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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

names(simulated) <- paste0("sd_", increments)
str(simulated)

## List of 21
##  $ sd_1  : num [1:100] -2.387 0.405 -1.599 -0.285 0.288 ...
##  $ sd_1.2: num [1:100] 0.298 0.433 -1.021 1.384 -0.323 ...
##  $ sd_1.4: num [1:100] 0.893 -1.799 -0.819 -1.11 -2.198 ...
##  $ sd_1.6: num [1:100] -0.332 1.067 -0.823 2.899 1.863 ...
##  $ sd_1.8: num [1:100] -2.568 -0.672 -0.244 -1.645 2.221 ...
##  $ sd_2  : num [1:100] 2.4 -1.95 1.13 3.05 3.56 ...
##  $ sd_2.2: num [1:100] -2.978 0.798 2.212 2.15 -2.197 ...
##  $ sd_2.4: num [1:100] -0.211 -1.768 3.35 2.06 0.213 ...
##  $ sd_2.6: num [1:100] 0.718 -4.029 -1.093 0.417 -3.952 ...
##  $ sd_2.8: num [1:100] 0.632 3.084 -2.62 -1.282 -2.965 ...
##  $ sd_3  : num [1:100] 2.1759 -0.4681 1.6349 -0.0809 -0.7611 ...
##  $ sd_3.2: num [1:100] 1.236 3.055 -2.575 -0.868 4.369 ...
##  $ sd_3.4: num [1:100] 0.7795 -1.0125 -6.465 0.0926 1.8629 ...
##  $ sd_3.6: num [1:100] 3.466 -1.245 0.496 3.67 -2.207 ...
##  $ sd_3.8: num [1:100] -2.712 -4.21 -3.686 -0.728 -0.142 ...
##  $ sd_4  : num [1:100] 2.83 6.08 -3 4.29 4.18 ...
##  $ sd_4.2: num [1:100] 0.335 0.574 4.106 4.414 0.897 ...
##  $ sd_4.4: num [1:100] -2.123 3.165 1.104 -4.065 0.578 ...
##  $ sd_4.6: num [1:100] 2.448 -1.472 4.411 2.34 -0.346 ...
##  $ sd_4.8: num [1:100] 0.672 -4.724 3.378 -1.811 10.33 ...
##  $ sd_5  : num [1:100] -3.46 2.83 -11.49 -1.86 7.54 ...

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Convert to df

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

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tidyverse

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

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")

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

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Base R Method

Calculate all the densities

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"

23 / 85

Next, plot the first density

plot(densities[[1]])

24 / 85

Finally, loop through all the other densities

plot(densities[[1]], xlim = c(-20, 20))
for(i in seq(2, length(densities))) {
    lines(x = densities[[i]]$x, 
          y = densities[[i]]$y)    
}

25 / 85

Skipping iterations

On the prior slide, I set the index to skip over the first by using seq(2, length(densities))

26 / 85

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

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)    
}

26 / 85

Breaking loops

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

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

27 / 85

*apply

28 / 85

`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)

29 / 85

`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)
lapply will always return a list

29 / 85

Revisiting our simulation with $n = 10$

Our for loop version

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]) 
}
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

30 / 85

The `lapply` version

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

31 / 85

Some more examples

Loop through a data frame

Remember - a data frame is a list. We can loop through it easily

32 / 85

Some more examples

Loop through a data frame

Remember - a data frame is a list. We can loop through it easily

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

32 / 85

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

33 / 85

Add a condition

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

34 / 85

Add a second condition

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

35 / 85

Passing arguments

There's missing data, so this won't work

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

lapply(airquality, mean)

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

36 / 85

But this will.

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

37 / 85

Alternative notation

The prior code could also be written like this

lapply(airquality, function(x) mean(x, 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

38 / 85

Simulation again

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

39 / 85

Operations by group

40 / 85

Mimic `dplyr::group_by`

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 ...

41 / 85

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

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

42 / 85

Your turn

Try splitting the penguins dataset by species and calculating the average bill_length_mm

04:00

43 / 85

[demo]

44 / 85

Produce separate plots

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

45 / 85

## $`4`

## 
## $`6`

## 
## $`8`

46 / 85

Your turn

Produce separate plots of the relation between bill_length_mm and body_mass_g

04:00

47 / 85

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.

48 / 85

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)

48 / 85

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

48 / 85

Saving w/`for` loop

Save plots to an object (list)

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

49 / 85

Saving w/`for` loop

Save plots to an object (list)

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

Specify file names/directory

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

## [1] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/cyl4.png"
## [2] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/cyl6.png"
## [3] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/cyl8.png"

49 / 85

Saving

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

50 / 85

You try!

04:00

51 / 85

More than one groups

Let's say we wanted to create a plot of bill length vs bill depth by for each combination of species/island.

Just split by both! Pass both variables as a list().

splt2 <- split(
  penguins, 
  list(penguins$species, penguins$island)
)

52 / 85

Inspect it

splt2

## $Adelie.Biscoe
## # A tibble: 44 × 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  Biscoe           37.8          18.3               174        3400 female
##  2 Adelie  Biscoe           37.7          18.7               180        3600 male  
##  3 Adelie  Biscoe           35.9          19.2               189        3800 female
##  4 Adelie  Biscoe           38.2          18.1               185        3950 male  
##  5 Adelie  Biscoe           38.8          17.2               180        3800 male  
##  6 Adelie  Biscoe           35.3          18.9               187        3800 female
##  7 Adelie  Biscoe           40.6          18.6               183        3550 male  
##  8 Adelie  Biscoe           40.5          17.9               187        3200 female
##  9 Adelie  Biscoe           37.9          18.6               172        3150 female
## 10 Adelie  Biscoe           40.5          18.9               180        3950 male  
## # … with 34 more rows, and 1 more variable: year <int>
## 
## $Chinstrap.Biscoe
## # A tibble: 0 × 8
## # … with 8 variables: species <fct>, island <fct>, bill_length_mm <dbl>,
## #   bill_depth_mm <dbl>, flipper_length_mm <int>, body_mass_g <int>, sex <fct>,
## #   year <int>
## 
## $Gentoo.Biscoe
## # A tibble: 124 × 8
##    species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex   
##    <fct>   <fct>           <dbl>         <dbl>             <int>       <int> <fct> 
##  1 Gentoo  Biscoe           46.1          13.2               211        4500 female
##  2 Gentoo  Biscoe           50            16.3               230        5700 male  
##  3 Gentoo  Biscoe           48.7          14.1               210        4450 female
##  4 Gentoo  Biscoe           50            15.2               218        5700 male  
##  5 Gentoo  Biscoe           47.6          14.5               215        5400 male  
##  6 Gentoo  Biscoe           46.5          13.5               210        4550 female
##  7 Gentoo  Biscoe           45.4          14.6               211        4800 female
##  8 Gentoo  Biscoe           46.7          15.3               219        5200 male  
##  9 Gentoo  Biscoe           43.3          13.4               209        4400 female
## 10 Gentoo  Biscoe           46.8          15.4               215        5150 male  
## # … with 114 more rows, and 1 more variable: year <int>
## 
## $Adelie.Dream
## # A tibble: 56 × 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  Dream            39.5          16.7               178        3250 female
##  2 Adelie  Dream            37.2          18.1               178        3900 male  
##  3 Adelie  Dream            39.5          17.8               188        3300 female
##  4 Adelie  Dream            40.9          18.9               184        3900 male  
##  5 Adelie  Dream            36.4          17                 195        3325 female
##  6 Adelie  Dream            39.2          21.1               196        4150 male  
##  7 Adelie  Dream            38.8          20                 190        3950 male  
##  8 Adelie  Dream            42.2          18.5               180        3550 female
##  9 Adelie  Dream            37.6          19.3               181        3300 female
## 10 Adelie  Dream            39.8          19.1               184        4650 male  
## # … with 46 more rows, and 1 more variable: year <int>
## 
## $Chinstrap.Dream
## # A tibble: 68 × 8
##    species   island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex   
##    <fct>     <fct>           <dbl>         <dbl>             <int>       <int> <fct> 
##  1 Chinstrap Dream            46.5          17.9               192        3500 female
##  2 Chinstrap Dream            50            19.5               196        3900 male  
##  3 Chinstrap Dream            51.3          19.2               193        3650 male  
##  4 Chinstrap Dream            45.4          18.7               188        3525 female
##  5 Chinstrap Dream            52.7          19.8               197        3725 male  
##  6 Chinstrap Dream            45.2          17.8               198        3950 female
##  7 Chinstrap Dream            46.1          18.2               178        3250 female
##  8 Chinstrap Dream            51.3          18.2               197        3750 male  
##  9 Chinstrap Dream            46            18.9               195        4150 female
## 10 Chinstrap Dream            51.3          19.9               198        3700 male  
## # … with 58 more rows, and 1 more variable: year <int>
## 
## $Gentoo.Dream
## # A tibble: 0 × 8
## # … with 8 variables: species <fct>, island <fct>, bill_length_mm <dbl>,
## #   bill_depth_mm <dbl>, flipper_length_mm <int>, body_mass_g <int>, sex <fct>,
## #   year <int>
## 
## $Adelie.Torgersen
## # A tibble: 52 × 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          17.4               186        3800 female
##  3 Adelie  Torgersen           40.3          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           39.3          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 42 more rows, and 1 more variable: year <int>
## 
## $Chinstrap.Torgersen
## # A tibble: 0 × 8
## # … with 8 variables: species <fct>, island <fct>, bill_length_mm <dbl>,
## #   bill_depth_mm <dbl>, flipper_length_mm <int>, body_mass_g <int>, sex <fct>,
## #   year <int>
## 
## $Gentoo.Torgersen
## # A tibble: 0 × 8
## # … with 8 variables: species <fct>, island <fct>, bill_length_mm <dbl>,
## #   bill_depth_mm <dbl>, flipper_length_mm <int>, body_mass_g <int>, sex <fct>,
## #   year <int>

53 / 85

Create plots

plots2 <- lapply(splt2, function(x) {
  ggplot(x, aes(bill_length_mm, bill_depth_mm)) +
    geom_point()
})

54 / 85

First few

plots2[[1]]

55 / 85

First few

plots2[[2]]

56 / 85

First few

plots2[[3]]

## Warning: Removed 1 rows containing missing values (geom_point).

57 / 85

Uh oh

Let's get rid of our empty plots - how? Ideas?

58 / 85

Uh oh

Let's get rid of our empty plots - how? Ideas?

Create a logical vector that checks the number of rows.

58 / 85

Uh oh

Let's get rid of our empty plots - how? Ideas?

Create a logical vector that checks the number of rows.

We'll do this in a moment.

58 / 85

Variants of `lapply`

sapply
- Will try to simplify 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)

59 / 85

Variants of `lapply`

sapply
- Will try to simplify 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)

59 / 85

Examples

Our simulation

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

## [1] "matrix" "array"

dim(sim_s)

## [1] 10 21

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

60 / 85

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

61 / 85

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

61 / 85

head(penguins)

## # A tibble: 6 × 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          17.4               186        3800 female
## 3 Adelie  Torgersen           40.3          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           39.3          20.6               190        3650 male  
## # … with 1 more variable: year <int>

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

## # A tibble: 6 × 2
##   bill_length_mm bill_depth_mm
##            <dbl>         <dbl>
## 1           39.1          18.7
## 2           39.5          17.4
## 3           40.3          18  
## 4           NA            NA  
## 5           36.7          19.3
## 6           39.3          20.6

62 / 85

Challenge

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

02:00

63 / 85

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

## # A tibble: 6 × 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

64 / 85

Clean up our plots

Can you recreate the plots while omitting the empty ones now?

Try!

03:00

65 / 85

Remove zero-row dfs

# check if n rows > 0
keep <- sapply(splt2, function(x) nrow(x) > 0)
keep

##       Adelie.Biscoe    Chinstrap.Biscoe       Gentoo.Biscoe        Adelie.Dream 
##                TRUE               FALSE                TRUE                TRUE 
##     Chinstrap.Dream        Gentoo.Dream    Adelie.Torgersen Chinstrap.Torgersen 
##                TRUE               FALSE                TRUE               FALSE 
##    Gentoo.Torgersen 
##               FALSE

# Use this to subset the list
splt3 <- splt2[keep]

66 / 85

Recreate plots

plots3 <- lapply(splt3, function(x) {
  ggplot(x, aes(bill_length_mm, bill_depth_mm)) +
    geom_point()
})

67 / 85

First few

plots3[[1]]

68 / 85

First few

plots3[[2]]

## Warning: Removed 1 rows containing missing values (geom_point).

69 / 85

First few

plots3[[3]]

70 / 85

`vapply`

As you can probably see, simplifying can be really helpful for interactive work.

71 / 85

`vapply`

As you can probably see, simplifying can be really helpful for interactive work.

BUT

71 / 85

`vapply`

As you can probably see, simplifying can be really helpful for interactive work.

BUT

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

71 / 85

`vapply`

As you can probably see, simplifying can be really helpful for interactive work.

BUT

Not ideal for programmatic work - need to be able to reliably predict the output
vapply solves this issue.

71 / 85

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

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'

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

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

72 / 85

Coercion with `vapply`

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

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

73 / 85

Count missing data

vapply(airquality, function(col) {
  sum(is.na(col))
  },
  FUN.VALUE = double(1)
)

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

74 / 85

`sapply` alternative

For interactive work, the code on the previous slide is maybe too much. Could be reduced to

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

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

75 / 85

Summary

for loops are incredibly flexible and there's nothing inherently "wrong" about them

76 / 85

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

76 / 85

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

76 / 85

Summary

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

77 / 85

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

77 / 85

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

77 / 85

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

77 / 85

apply

Quickly

78 / 85

`apply(x, dimension, function, function_args)`

x The thing to loop over (usually a matrix or data frame)
dimension
- 1 = apply the function to each row
- 2 = apply the function to each column
- n = apply to $n$ th dimension of an array (rare for the work I do)

79 / 85

Rows example

people <- data.frame(
  first = c("Frederick", "Anna", "Julia"),
  last = c("Douglass", "Murray", "Griffiths")
)
people

##       first      last
## 1 Frederick  Douglass
## 2      Anna    Murray
## 3     Julia Griffiths

Suppose we wanted a new column that was the first and last name.

80 / 85

We might try this

people %>% 
  mutate(full_name = paste(first, last, collapse = " "))

##       first      last                                      full_name
## 1 Frederick  Douglass Frederick Douglass Anna Murray Julia Griffiths
## 2      Anna    Murray Frederick Douglass Anna Murray Julia Griffiths
## 3     Julia Griffiths Frederick Douglass Anna Murray Julia Griffiths

... but it pastes together the full columns

81 / 85

Instead, do it by row

apply(people, 1, paste, collapse = " ")

## [1] "Frederick Douglass" "Anna Murray"        "Julia Griffiths"

82 / 85

Instead, do it by row

apply(people, 1, paste, collapse = " ")

## [1] "Frederick Douglass" "Anna Murray"        "Julia Griffiths"

Or within a mutate() call

people %>% 
  mutate(full_name = apply(people, 1, paste, collapse = " "))

##       first      last          full_name
## 1 Frederick  Douglass Frederick Douglass
## 2      Anna    Murray        Anna Murray
## 3     Julia Griffiths    Julia Griffiths

82 / 85

Column example

Back to the airquality example - standardize all columns.

Notice it returns a matrix though, which is less than ideal.

apply(airquality, 2, function(x) {
  as.numeric(scale(x))
})

##              Ozone       Solar.R        Wind        Temp       Month         Day
##   [1,] -0.03423409  0.0451761540 -0.72594816 -1.14971398 -1.40729432 -1.67001947
##   [2,] -0.18580489 -0.7543048742 -0.55563883 -0.62146702 -1.40729432 -1.55721020
##   [3,] -0.91334473 -0.4100838759  0.75006604 -0.41016823 -1.40729432 -1.44440094
##   [4,] -0.73145977  1.4109562438  0.43783226 -1.67796094 -1.40729432 -1.33159168
##   [5,]          NA            NA  1.23260914 -2.31185730 -1.40729432 -1.21878242
##   [6,] -0.42831817            NA  1.40291847 -1.25536337 -1.40729432 -1.10597316
##   [7,] -0.57988897  1.2555015994 -0.38532950 -1.36101276 -1.40729432 -0.99316389
##   [8,] -0.70114561 -0.9652790344  1.09068470 -1.99490912 -1.40729432 -0.88035463
##   [9,] -1.03460136 -1.8535912880  2.87893266 -1.78361034 -1.40729432 -0.76754537
##  [10,]          NA  0.0895917667 -0.38532950 -0.93841519 -1.40729432 -0.65473611
##  [11,] -1.06491552            NA -0.86787260 -0.41016823 -1.40729432 -0.54192685
##  [12,] -0.79208809  0.7780337632 -0.07309573 -0.93841519 -1.40729432 -0.42911758
##  [13,] -0.94365889  1.1555664709 -0.21502017 -1.25536337 -1.40729432 -0.31630832
##  [14,] -0.85271641  0.9779040202  0.26752293 -1.04406459 -1.40729432 -0.20349906
##  [15,] -0.73145977 -1.3428117422  0.92037537 -2.10055851 -1.40729432 -0.09068980
##  [16,] -0.85271641  1.6441382104  0.43783226 -1.46666216 -1.40729432  0.02211946
##  [17,] -0.24643321  1.3443328248  0.57975671 -1.25536337 -1.40729432  0.13492873
##  [18,] -1.09522968 -1.1984610010  2.39638956 -2.20620791 -1.40729432  0.24773799
##  [19,] -0.36768985  1.5108913723  0.43783226 -1.04406459 -1.40729432  0.36054725
##  [20,] -0.94365889 -1.5759937087 -0.07309573 -1.67796094 -1.40729432  0.47335651
##  [21,] -1.24680048 -1.9757342228 -0.07309573 -1.99490912 -1.40729432  0.58616577
##  [22,] -0.94365889  1.4886835660  1.88546157 -0.51581762 -1.40729432  0.69897503
##  [23,] -1.15585800 -1.7869678689 -0.07309573 -1.78361034 -1.40729432  0.81178430
##  [24,] -0.30706153 -1.0430063566  0.57975671 -1.78361034 -1.40729432  0.92459356
##  [25,]          NA -1.3317078390  1.88546157 -2.20620791 -1.40729432  1.03740282
##  [26,]          NA  0.8890727949  1.40291847 -2.10055851 -1.40729432  1.15021208
##  [27,]          NA            NA -0.55563883 -2.20620791 -1.40729432  1.26302134
##  [28,] -0.57988897 -1.9202147070  0.57975671 -1.14971398 -1.40729432  1.37583061
##  [29,]  0.08702254  0.7336181505  1.40291847  0.32937752 -1.40729432  1.48863987
##  [30,]  2.20901373  0.4116049586 -1.20849126  0.11807873 -1.40729432  1.60144913
##  [31,] -0.15549073  1.0334235361 -0.72594816 -0.19886945 -1.40729432  1.71425839
##  [32,]          NA  1.1111508582 -0.38532950  0.01242934 -0.70134012 -1.67001947
##  [33,]          NA  1.1222547614 -0.07309573 -0.41016823 -0.70134012 -1.55721020
##  [34,]          NA  0.6225791188  1.74353713 -1.14971398 -0.70134012 -1.44440094
##  [35,]          NA  0.0007605413 -0.21502017  0.64632570 -0.70134012 -1.33159168
##  [36,]          NA  0.3782932491 -0.38532950  0.75197509 -0.70134012 -1.21878242
##  [37,]          NA  0.8668649885  1.23260914  0.11807873 -0.70134012 -1.10597316
##  [38,] -0.39800401 -0.6543697457 -0.07309573  0.43502691 -0.70134012 -0.99316389
##  [39,]          NA  0.9668001170 -0.86787260  0.96327387 -0.70134012 -0.88035463
##  [40,]  0.87519070  1.1666703741  1.09068470  1.28022205 -0.70134012 -0.76754537
##  [41,] -0.09486241  1.5219952755  0.43783226  0.96327387 -0.70134012 -0.65473611
##  [42,]          NA  0.8113454727  0.26752293  1.59717023 -0.70134012 -0.54192685
##  [43,]          NA  0.7114103441 -0.21502017  1.49152084 -0.70134012 -0.42911758
##  [44,] -0.57988897 -0.4211877791 -0.55563883  0.43502691 -0.70134012 -0.31630832
##  [45,]          NA  1.6219304040  1.09068470  0.22372813 -0.70134012 -0.20349906
##  [46,]          NA  1.5108913723  0.43783226  0.11807873 -0.70134012 -0.09068980
##  [47,] -0.64051729  0.0562800572  1.40291847 -0.09322005 -0.70134012  0.02211946
##  [48,] -0.15549073  1.0889430519  3.04924199 -0.62146702 -0.70134012  0.13492873
##  [49,] -0.67083145 -1.6537210309 -0.21502017 -1.36101276 -0.70134012  0.24773799
##  [50,] -0.91334473 -0.7320970679  0.43783226 -0.51581762 -0.70134012  0.36054725
##  [51,] -0.88303057 -0.5433307140  0.09721360 -0.19886945 -0.70134012  0.47335651
##  [52,]          NA -0.3989799728 -1.03818193 -0.09322005 -0.70134012  0.58616577
##  [53,]          NA -1.4094351612 -2.34388679 -0.19886945 -0.70134012  0.69897503
##  [54,]          NA -1.0541102598 -1.52072503 -0.19886945 -0.70134012  0.81178430
##  [55,]          NA  0.7114103441 -1.03818193 -0.19886945 -0.70134012  0.92459356
##  [56,]          NA -0.5655385203 -0.55563883 -0.30451884 -0.70134012  1.03740282
##  [57,]          NA -0.6543697457 -0.55563883  0.01242934 -0.70134012  1.15021208
##  [58,]          NA -1.5426819992  0.09721360 -0.51581762 -0.70134012  1.26302134
##  [59,]          NA -0.9763829376  0.43783226  0.22372813 -0.70134012  1.37583061
##  [60,]          NA -1.7203444499  1.40291847 -0.09322005 -0.70134012  1.48863987
##  [61,]          NA -0.5322268108 -0.55563883  0.54067630 -0.70134012  1.60144913
##  [62,]  2.81529692  0.9223845044 -1.66264947  0.64632570  0.00461408 -1.67001947
##  [63,]  0.20827918  0.6892025378 -0.21502017  0.75197509  0.00461408 -1.55721020
##  [64,] -0.30706153  0.5559556998 -0.21502017  0.32937752  0.00461408 -1.44440094
##  [65,]          NA -0.9430712281  0.26752293  0.64632570  0.00461408 -1.33159168
##  [66,]  0.66299158 -0.1213823935 -1.52072503  0.54067630  0.00461408 -1.21878242
##  [67,] -0.06454825  1.4220601470  0.26752293  0.54067630  0.00461408 -1.10597316
##  [68,]  1.05707566  1.0001118265 -1.37880059  1.06892327  0.00461408 -0.99316389
##  [69,]  1.66335885  0.9001766980 -1.03818193  1.49152084  0.00461408 -0.88035463
##  [70,]  1.66335885  0.9556962139 -1.20849126  1.49152084  0.00461408 -0.76754537
##  [71,]  1.29958893 -0.1213823935 -0.72594816  1.17457266  0.00461408 -0.65473611
##  [72,]          NA -0.5211229076 -0.38532950  0.43502691  0.00461408 -0.54192685
##  [73,] -0.97397305  0.8668649885  1.23260914 -0.51581762  0.00461408 -0.42911758
##  [74,] -0.45863233 -0.1213823935  1.40291847  0.32937752  0.00461408 -0.31630832
##  [75,]          NA  1.1666703741  1.40291847  1.38587145  0.00461408 -0.20349906
##  [76,] -1.06491552 -1.5315780960  1.23260914  0.22372813  0.00461408 -0.09068980
##  [77,]  0.17796502  0.8224493758 -0.86787260  0.32937752  0.00461408  0.02211946
##  [78,] -0.21611905  0.9779040202  0.09721360  0.43502691  0.00461408  0.13492873
##  [79,]  0.57204910  1.1000469551 -1.03818193  0.64632570  0.00461408  0.24773799
##  [80,]  1.11770398  0.0118644445 -1.37880059  0.96327387  0.00461408  0.36054725
##  [81,]  0.63267742  0.3782932491  0.43783226  0.75197509  0.00461408  0.47335651
##  [82,] -0.79208809 -1.9868381260 -0.86787260 -0.41016823  0.00461408  0.58616577
##  [83,]          NA  0.8002415695 -0.07309573  0.32937752  0.00461408  0.69897503
##  [84,]          NA  1.2110859868  0.43783226  0.43502691  0.00461408  0.81178430
##  [85,]  1.14801813  1.1999820836 -0.38532950  0.85762448  0.00461408  0.92459356
##  [86,]  1.99681461  0.4116049586 -0.55563883  0.75197509  0.00461408  1.03740282
##  [87,] -0.67083145 -1.1651492915 -0.38532950  0.43502691  0.00461408  1.15021208
##  [88,]  0.29922166 -1.1540453883  0.57975671  0.85762448  0.00461408  1.26302134
##  [89,]  1.20864645  0.3005659269 -0.72594816  1.06892327  0.00461408  1.37583061
##  [90,]  0.23859334  0.9890079234 -0.72594816  0.85762448  0.00461408  1.48863987
##  [91,]  0.66299158  0.7447220537 -0.72594816  0.54067630  0.00461408  1.60144913
##  [92,]  0.51142078  0.7558259568 -0.21502017  0.32937752  0.00461408  1.71425839
##  [93,] -0.09486241 -1.1429414851 -0.86787260  0.32937752  0.71056828 -1.67001947
##  [94,] -1.00428721 -1.7980717721  1.09068470  0.32937752  0.71056828 -1.55721020
##  [95,] -0.79208809 -1.2095649041 -0.72594816  0.43502691  0.71056828 -1.44440094
##  [96,]  1.08738982            NA -0.86787260  0.85762448  0.71056828 -1.33159168
##  [97,] -0.21611905            NA -0.72594816  0.75197509  0.71056828 -1.21878242
##  [98,]  0.72361990            NA -1.52072503  0.96327387  0.71056828 -1.10597316
##  [99,]  2.42121284  0.7669298600 -1.69103436  1.17457266  0.71056828 -0.99316389
## [100,]  1.42084557  0.4782283776  0.09721360  1.28022205  0.71056828 -0.88035463
## [101,]  2.05744293  0.2339425079 -0.55563883  1.28022205  0.71056828 -0.76754537
## [102,]          NA  0.4005010554 -0.38532950  1.49152084  0.71056828 -0.65473611
## [103,]          NA -0.5433307140  0.43783226  0.85762448  0.71056828 -0.54192685
## [104,]  0.05670838  0.0673839603  0.43783226  0.85762448  0.71056828 -0.42911758
## [105,] -0.42831817  0.9668001170  0.43783226  0.43502691  0.71056828 -0.31630832
## [106,]  0.69330574 -0.3212526506 -0.07309573  0.22372813  0.71056828 -0.20349906
## [107,]          NA -1.3539156453  0.43783226  0.11807873  0.71056828 -0.09068980
## [108,] -0.61020313 -1.2761883232  0.09721360 -0.09322005  0.71056828  0.02211946
## [109,]  0.51142078 -1.4982663865 -1.03818193  0.11807873  0.71056828  0.13492873
## [110,] -0.57988897 -0.7876165837 -0.72594816 -0.19886945  0.71056828  0.24773799
## [111,] -0.33737569  0.6447869251  0.26752293  0.01242934  0.71056828  0.36054725
## [112,]  0.05670838  0.0451761540  0.09721360  0.01242934  0.71056828  0.47335651
## [113,] -0.64051729  0.8113454727  1.57322780 -0.09322005  0.71056828  0.58616577
## [114,] -1.00428721 -1.6648249341  1.23260914 -0.62146702  0.71056828  0.69897503
## [115,]          NA  0.7669298600  0.75006604 -0.30451884  0.71056828  0.81178430
## [116,]  0.08702254  0.2894620237 -0.07309573  0.11807873  0.71056828  0.92459356
## [117,]  3.81566419  0.5781635061 -1.86134369  0.32937752  0.71056828  1.03740282
## [118,]  0.93581902  0.3227737332 -0.55563883  0.85762448  0.71056828  1.15021208
## [119,]          NA -0.3656682633 -1.20849126  1.06892327  0.71056828  1.26302134
## [120,]  1.02676150  0.1895268952 -0.07309573  2.01976780  0.71056828  1.37583061
## [121,]  2.29995620  0.4338127649 -2.17357746  1.70281962  0.71056828  1.48863987
## [122,]  1.26927477  0.5670596029 -1.03818193  1.91411841  0.71056828  1.60144913
## [123,]  1.29958893  0.0229683477 -1.03818193  1.70281962  0.71056828  1.71425839
## [124,]  1.63304469 -0.2102136189 -0.86787260  1.38587145  1.41652248 -1.67001947
## [125,]  1.08738982  0.1229034762 -1.37880059  1.49152084  1.41652248 -1.55721020
## [126,]  0.93581902 -0.0325511682 -2.03165302  1.59717023  1.41652248 -1.44440094
## [127,]  1.48147389  0.0340722508 -1.52072503  1.59717023  1.41652248 -1.33159168
## [128,]  0.14765086 -1.0096946471 -0.72594816  0.96327387  1.41652248 -1.21878242
## [129,] -0.30706153 -1.0430063566  1.57322780  0.64632570  1.41652248 -1.10597316
## [130,] -0.67083145  0.7336181505  0.26752293  0.22372813  1.41652248 -0.99316389
## [131,] -0.57988897  0.3782932491  0.09721360  0.01242934  1.41652248 -0.88035463
## [132,] -0.64051729  0.4893322808  0.26752293 -0.30451884  1.41652248 -0.76754537
## [133,] -0.54957481  0.8113454727 -0.07309573 -0.51581762  1.41652248 -0.65473611
## [134,]  0.05670838  0.5559556998  1.40291847  0.32937752  1.41652248 -0.54192685
## [135,] -0.64051729  0.8113454727  1.57322780 -0.19886945  1.41652248 -0.42911758
## [136,] -0.42831817  0.5781635061 -1.03818193 -0.09322005  1.41652248 -0.31630832
## [137,] -1.00428721 -1.7980717721  0.26752293 -0.72711641  1.41652248 -0.20349906
## [138,] -0.88303057 -0.8209282932  0.43783226 -0.72711641  1.41652248 -0.09068980
## [139,]  0.11733670  0.5670596029 -0.86787260  0.01242934  1.41652248  0.02211946
## [140,] -0.73145977  0.4227088617  1.09068470 -1.14971398  1.41652248  0.13492873
## [141,] -0.88303057 -1.7647600626  0.09721360 -0.19886945  1.41652248  0.24773799
## [142,] -0.54957481  0.5781635061  0.09721360 -1.04406459  1.41652248  0.36054725
## [143,] -0.79208809  0.1673190889 -0.55563883  0.43502691  1.41652248  0.47335651
## [144,] -0.88303057  0.5781635061  0.75006604 -1.46666216  1.41652248  0.58616577
## [145,] -0.57988897 -1.9091108038 -0.21502017 -0.72711641  1.41652248  0.69897503
## [146,] -0.18580489 -0.5211229076  0.09721360  0.32937752  1.41652248  0.81178430
## [147,] -1.06491552 -1.5204741929  0.09721360 -0.93841519  1.41652248  0.92459356
## [148,] -0.85271641 -1.8424873848  1.88546157 -1.57231155  1.41652248  1.03740282
## [149,] -0.36768985  0.0784878635 -0.86787260 -0.83276580  1.41652248  1.15021208
## [150,]          NA -0.4544994886  0.92037537 -0.09322005  1.41652248  1.26302134
## [151,] -0.85271641  0.0562800572  1.23260914 -0.30451884  1.41652248  1.37583061
## [152,] -0.73145977 -0.6099541330 -0.55563883 -0.19886945  1.41652248  1.48863987
## [153,] -0.67083145  0.4116049586  0.43783226 -1.04406459  1.41652248  1.60144913

83 / 85

Last bit

There are other loops, like tapply(), but I tend to not use them much (instead just use lapply(split(x)).

All of this stuff takes practice, both to understand how it works and to start to see use cases

Careful not to get into too deep of nested loops - if you're nesting beyond two levels (and I honestly never go beyond one anymore) there's probably better ways to approach it.

84 / 85

Lab 1

85 / 85

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Intro to Base R iterations

And Lab 1

Daniel Anderson

Week 2

Agenda

Learning objectives

Basic overview: for loops

Basic overview: for loops

Basic overview: for loops

Basic overview: for loops

Creating indexes

Creating indexes

Examples

seq_*

seq_*

Avoid the 1 0 problem

Avoid the 1 0 problem

seq_* version

Another basic example

Simulate tossing a coin, record results

Another basic example

Simulate tossing a coin, record results

Another basic example

Simulate tossing a coin, record results

Growing vectors

Growing vectors

Growing vectors

Example

Result

You try

Answer

Another example

Another example

Another example

Another example

Look at our vector

Write for loop

Write for loop

List/data frame

List/data frame

Convert to df

tidyverse

Base R Method

Skipping iterations

Skipping iterations

Breaking loops

*apply

lapply

lapply

Revisiting our simulation with n=10n=10

The lapply version

Some more examples

Loop through a data frame

Some more examples

Loop through a data frame

Add a condition

Add a second condition

Passing arguments

Alternative notation

Simulation again

Operations by group

Mimic dplyr::group_by

Your turn

[demo]

Produce separate plots

Your turn

Saving

Saving

Saving

Saving w/for loop

Save plots to an object (list)

Saving w/for loop

Save plots to an object (list)

Specify file names/directory

Saving

You try!

More than one groups

Inspect it

Create plots

First few

Basic overview: `for` loops

Basic overview: `for` loops

Basic overview: `for` loops

Basic overview: `for` loops

`seq_*`

`seq_*`

Avoid the `1 0` problem

Avoid the `1 0` problem

`seq_*` version

Write `for` loop

Write `for` loop

`lapply`

`lapply`

Revisiting our simulation with $n = 10$

The `lapply` version

Mimic `dplyr::group_by`

Saving w/`for` loop

Saving w/`for` loop

Variants of `lapply`

Variants of `lapply`

`vapply`

`vapply`

`vapply`

`vapply`

Coercion with `vapply`

`sapply` alternative

`apply(x, dimension, function, function_args)`