Background

We can automate chunks of code using a for loop. We’ve seen how many tasks are automatically automated using vectorization. Remember how we can often pass a vector of values as an argument to a function, and the function will happily operate on each element in turn.

x <- c(1, 4, 98, 2)
log(x)

## [1] 0.0000000 1.3862944 4.5849675 0.6931472

R understands that we want to call the log() function on the first element in the x vector, then call the log() function on the second element in the x vector, and so on.

Sometimes vectorization can’t help us automate a set of commands. This can happen when commands are particularly complex, when the commands are using multiple objects at once, or when the next iteration of the block of code relies on the result of the previous iteration of the block of code. In these cases, we can tell R exactly what commands we want repeated inside of a loop.

Here is what a basic for loop looks like.

for(i in 1:10) {
  print(i)
}

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

We use the for() function to indicate we are starting a loop. The { and } mark the start and end of the block of code that we will repeat over and over. i in 1:10 means that i will take on the first value in the 1:10 vector, the block of code will execute, then i will take on the second value in the 1:10 vector, the block of code will execute a second time, and so on until i takes on the last value in the 1:10 vector, the block of code executes one final time, and then the loop is finished. R will now proceed to the next line of code after the }. We can call this iterative looping variable whatever we’d like. Often, it is called i (or j or k if i is already used), but it could be called monkey and R would be fine with that.

It’s key to notice that the value of i changes each time the code block in the loop executes. We say that i is iterating through the values of the vector. We can use i (or whatever you’ve called it) inside the for loop code chunk. Above, we printed out the value of i (see how i changed?) Below, we print out the ith letter of the alphabet by using the letters vector

letters # all the letters in the alphabet

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

for(i in 1:10) {
  # use square bracket notation to access the ith element in the letters vector
  print(letters[i]) 
}

## [1] "a"
## [1] "b"
## [1] "c"
## [1] "d"
## [1] "e"
## [1] "f"
## [1] "g"
## [1] "h"
## [1] "i"
## [1] "j"

Example with gapminder

library(tidyverse)
library(gapminder)

plotPopGrowth <- function(countryToPlot, theData = gapminder) {
  # this filters data to a specific country
  oneCountry <- theData %>%
    filter(country == countryToPlot)
  
  # can add some error checking if you want
  if(!nrow(oneCountry)>0) { # checks if there are values for country
    stop("No Data for this Country, please try again")
  }

  # this runs the plot
  ggplot(data=oneCountry) + 
           geom_line(aes(x=year, y=pop, color=country))
}

# plotPopGrowth("Afghanistans") # gives error
plotPopGrowth("Afghanistan") 

plotPopGrowth("United States")

What if we want to make a plot and then save it for every country?

### LOOPING
countries <- unique(gapminder$country)
for(i in seq_along(countries)){
  plotPopGrowth(countries[i])
  ggsave(filename = paste0("Figures/plot_",countries[i], ".png"))
}

Example with file imports

The tidyverse has its own way to do loops

xlist <- list.files(path = "data/2001_mauna_loa_met_data/", 
                    pattern = "*.txt", 
                    full.names = TRUE)
xlist

##  [1] "data/2001_mauna_loa_met_data//met_mlo_insitu_1_obop_minute_2001_01.txt"
##  [2] "data/2001_mauna_loa_met_data//met_mlo_insitu_1_obop_minute_2001_02.txt"
##  [3] "data/2001_mauna_loa_met_data//met_mlo_insitu_1_obop_minute_2001_03.txt"
##  [4] "data/2001_mauna_loa_met_data//met_mlo_insitu_1_obop_minute_2001_04.txt"
##  [5] "data/2001_mauna_loa_met_data//met_mlo_insitu_1_obop_minute_2001_05.txt"
##  [6] "data/2001_mauna_loa_met_data//met_mlo_insitu_1_obop_minute_2001_06.txt"
##  [7] "data/2001_mauna_loa_met_data//met_mlo_insitu_1_obop_minute_2001_07.txt"
##  [8] "data/2001_mauna_loa_met_data//met_mlo_insitu_1_obop_minute_2001_08.txt"
##  [9] "data/2001_mauna_loa_met_data//met_mlo_insitu_1_obop_minute_2001_09.txt"
## [10] "data/2001_mauna_loa_met_data//met_mlo_insitu_1_obop_minute_2001_10.txt"
## [11] "data/2001_mauna_loa_met_data//met_mlo_insitu_1_obop_minute_2001_11.txt"
## [12] "data/2001_mauna_loa_met_data//met_mlo_insitu_1_obop_minute_2001_12.txt"

column_headers <- 
  c("siteID", "year", "month", 
    "day", "hour24", "min",
    "windDir", "windSpeed_m_s", "windSteady", 
    "baro_hPa", "temp_C_2m", "temp_C_10m", 
    "temp_C_towertop", "rel_humid", "precip_intens_mm_hr")

ml <-
  xlist %>%
  map_df( ~ read.table(., 
                       sep = "",
                       header = FALSE, 
                       quote = "", 
                       col.names = column_headers))
         
glimpse(ml)

## Observations: 459,769
## Variables: 15
## $ siteID              <fctr> MLO, MLO, MLO, MLO, MLO, MLO, MLO, MLO, M...
## $ year                <int> 2001, 2001, 2001, 2001, 2001, 2001, 2001, ...
## $ month               <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ day                 <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ hour24              <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ min                 <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, ...
## $ windDir             <int> 340, 334, 314, 323, 349, 345, 339, 333, 32...
## $ windSpeed_m_s       <dbl> 3.4, 2.8, 3.3, 2.4, 0.4, 1.8, 2.5, 2.2, 1....
## $ windSteady          <int> -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9...
## $ baro_hPa            <dbl> -999.9, -999.9, -999.9, -999.9, -999.9, -9...
## $ temp_C_2m           <dbl> 12.7, 12.5, 12.5, 11.6, 11.2, 12.0, 11.2, ...
## $ temp_C_10m          <dbl> 11.2, 11.2, 11.2, 10.6, 10.8, 11.1, 10.6, ...
## $ temp_C_towertop     <dbl> 10.2, 10.5, 10.5, 10.3, 10.4, 10.6, 10.1, ...
## $ rel_humid           <int> 53, 54, 54, 57, 57, 54, 57, 55, 52, 54, 54...
## $ precip_intens_mm_hr <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...