Basic structure

1. for all of these values of something
2. do this thing (usually with one of those values)
3. save that thing somewhere
4. start over as long as there are more values to deal with

Example

⊕This has no scientific meaning, it’s just an example! We’ll use the gapminder dataset, which we’ve looked at before. Let’s say we just want to compute some means. The dataset includes life expectancy (lifeExp), population size (pop), and per capita GDP (gdpPercap), so we’ll just look at the averages across the dataset for each of the variables.

library(tidyverse)
library(gapminder)
data(gapminder)

# variables we're interested in
variables <- c("lifeExp", "pop", "gdpPercap")

We have 3 things (here, variables) to loop over. So we could create a for loop structure like this:

# for each of the 3 values (we'll call the one we want i)
for (i in 1:3) {
    # do this thing (usually with one of those values)
    # save that thing somewhere
} # start over as long as there are more values to deal with

but that requires us know a priori that there are only three things we want to do. What if you decide to add on or take away another variable? A safer way is to take the value 3 directly from the length of the set of variables. You could write this 1:length(variables) but an even safer way to do this is with the seq_along() function, which does the same thing (just has better behavior if there’s nothing in variables):

for (i in seq_along(variables)) {
  print(i) # to see that we're really getting 1:3
}

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

OK, now we have to replace the middle part of our loop with what we actually want to happen with the values 1, 2, and 3.

for (i in seq_along(variables)) {
  var_of_interest <- variables[i] # choose the i'th variable from our list
  col_of_interest <- gapminder[[var_of_interest]] # extract that column
  print(mean(col_of_interest, na.rm = TRUE)) # print the mean
}

## [1] 59.47444
## [1] 29601212
## [1] 7215.327

We often use the letter i to denote the value we’re iterating over, though you certainly don’t have to. And it often refers to the iteration number itself, because we often want to reuse that number. ⊕It is important for speed in R to create sufficient space for whatever you’re outputting before you begin. For example, we printed the results above, but usually we’d want to save them somewhere. Before we run the for loop, we want to create an empty vector or matrix to store these numbers. This vector will be indexed by i too, to go along with the variables.

You could use something like this, where you create an empty vector of the type of output you expect (here, numbers):

means <- vector("numeric", length(variables))
means

## [1] 0 0 0

This creates a vector of 0s. I don’t especially like this as the default for numeric vectors because it makes it hard to tell if you make a mistake (you may expect some 0s in your outpit). I like to start off with NA:

means <- rep(NA, length(variables))
means

## [1] NA NA NA

Now we just have to replace those missing values with those that we calculate.

for (i in seq_along(variables)) {
  var_of_interest <- variables[i]
  col_of_interest <- gapminder[[var_of_interest]] 
  # now store each of the values in its correct position
  means[i] <- mean(col_of_interest, na.rm = TRUE)
}
means

## [1] 5.947444e+01 2.960121e+07 7.215327e+03

Notice that when we create a for loop, if we want anything to come out of it directly (i.e., be printed to the console), we have to use the print() function. But we can access objects that we create/fill during the loop to see output too.

We don’t have to use i and what we’re iterating over doesn’t need to be a number, however. For example, we could iterate directly over the variable names:

for (var in variables) {
  print(var) # to show what we get
}

## [1] "lifeExp"
## [1] "pop"
## [1] "gdpPercap"

for (var in variables) {
  col_of_interest <- gapminder[[var]]
  print(mean(col_of_interest, na.rm = TRUE))
}

## [1] 59.47444
## [1] 29601212
## [1] 7215.327

We get the same answer and skip a line of code! However, since we haven’t used numbers, it just means we need to think a little harder about how to store (as opposed to print) our output.

Challenge

Find the mean GDP per capita within each year and store the results. (There are easier ways to do this that we’ve already seen, so if you finish the for loop, try to recall another way!)

years <- unique(gapminder$year)
means <- rep(NA, length(years))
for (i in seq_along(years)) {
  year_dat <- filter(gapminder, year == years[i])
  gdp_dat <- year_dat[["gdpPercap"]]
  means[i] <- mean(gdp_dat, na.rm = TRUE)
}
means

##  [1]  3725.276  4299.408  4725.812  5483.653  6770.083  7313.166  7518.902
##  [8]  7900.920  8158.609  9090.175  9917.848 11680.072

(Easier solution without for loop)

gapminder %>%
  group_by(year) %>%
  summarise(mean(gdpPercap))

## # A tibble: 12 x 2
##     year `mean(gdpPercap)`
##    <int>             <dbl>
##  1  1952             3725.
##  2  1957             4299.
##  3  1962             4726.
##  4  1967             5484.
##  5  1972             6770.
##  6  1977             7313.
##  7  1982             7519.
##  8  1987             7901.
##  9  1992             8159.
## 10  1997             9090.
## 11  2002             9918.
## 12  2007            11680.