bonus_lesson_for_loops.Rmd

---
title: "Automation with for loops"
---

> ## Learning objectives
>
> - Understand the anatomy of a for loop
> - Understand the iterative looping variable
> - Be able to build a for loop to automate a block of code
>

# 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. 

```{r vectorization}
x <- c(1, 4, 98, 2)
log(x)
```

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.

```{r anatomy}
for(i in 1:10) {
  print(i)
}
```

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 `i`th letter of the alphabet by using the `letters` vector

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

# Example with gapminder

```{r loadLibraries}
library(tidyverse)
library(gapminder)
```

```{r createFunction}
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))
}
```

```{r demoTheFunction}
# plotPopGrowth("Afghanistans") # gives error
plotPopGrowth("Afghanistan") 
plotPopGrowth("United States")
```

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

```{r buildALoop, eval = FALSE}
### 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

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

```{r usingMap}
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)
```

This lesson was contributed by [Michael Koontz](https://orcid.org/0000-0002-8276-210X).