**Marcelo S. Perlin**, and kindly contributed to R-bloggers)

An Introduction to Loops in R –

# Loops in R

First, if you are new to programming, you should know that loops are a

way to tell the computer that you want to repeat some operation for a

number of times. This is a very common task that can be found in many

programming languages. For example, let’s say you invited five friends

for dinner at your home and the whole cost of four pizzas will be split

evenly. Assume now that you **must** give instructions to a computer on

calculating how much each one will pay at the end of dinner. For that,

you need to sum up the individual tabs and divide by the number of

people. Your instructions to the computer could be: *start with a value
of x=zero, take each individual pizza cost and sum it to x until all
costs are processed, dividing the result by the number of friends at the
end*.

The great thing about *loops* is that the length of it is dynamically

set. Using the previous example, if we had 500 friends (and a large

dinner table!), we could use the same instructions for calculating the

individual tabs. That means we can encapsulate a generic procedure for

processing any given number of friends at dinner. With it, you have at

your reach a tool for the execution of any sequential process. In other

words, you are the boss of your computer and, as long as you can write

it down clearly, you can set it to do any kind of repeated task for you.

Now, about the code, we could write the solution to the *pizza problem*

in R as:

```
pizza.costs <- c(50, 80, 30, 60) # each cost of pizza
n.friends <- 5 # number of friends
x <- 0 # set first cost to zero
for (i.cost in pizza.costs) {
x <- x + i.cost # sum it up
}
x <- x/n.friends # divide for average per friend
print(x)
## [1] 44
```

Don’t worry if you didn’t understand the code. We’ll get to the

structure of a loop soon.

Back to our case, each friend would pay 44 for the meal. We can check

the result against function `sum`

:

```
x == sum(pizza.costs)/n.friends
## [1] TRUE
```

The output `TRUE`

shows that the results are equal.

## The Structure of a Loop

Knowing how to use loops can be a powerful ally in a complex data

related problem. Let’s talk more about how *loops* are defined in R. The

structure of a *loop* in R follows:

```
for (i in i.vec){
...
}
```

In the previous code, command `for`

indicates the beginning of a *loop*.

Object `i`

in `(i in i.vec)`

is the iterator of the *loop*. This

iterator will change its value in each iteration, taking each individual

value contained in `i.vec`

. Note the *loop* is encapsulated by curly

braces (`{}`

). These are important, as they define where the *loop*

starts and where it ends. The indentation (use of bigger margins) is

also important for visual cues, but not necessary. Consider the

following practical example:

```
# set seq
my.seq <- seq(-5,5)
# do loop
for (i in my.seq){
cat(paste('\nThe value of i is',i))
}
##
## The value of i is -5
## The value of i is -4
## The value of i is -3
## The value of i is -2
## The value of i is -1
## The value of i is 0
## The value of i is 1
## The value of i is 2
## The value of i is 3
## The value of i is 4
## The value of i is 5
```

In the code, we created a sequence from -5 to 5 and presented a text for

each element with the `cat`

function. Notice how we also broke the

prompt line with `'\n'`

. The *loop* starts with `i=-5`

, execute command

`cat(paste('\nThe value of i is', -5))`

, proceed to the next iteration

by setting `i=-4`

, rerun the `cat`

command, and so on. At its final

iteration, the value of `i`

is `5`

.

The iterated sequence in the *loop* is not exclusive to numerical

vectors. Any type of vector or list may be used. See next:

```
# set char vec
my.char.vec <- letters[1:5]
# loop it!
for (i.char in my.char.vec){
cat(paste('\nThe value of i.char is', i.char))
}
##
## The value of i.char is a
## The value of i.char is b
## The value of i.char is c
## The value of i.char is d
## The value of i.char is e
```

The same goes for `lists`

:

```
# set list
my.l <- list(x = 1:5,
y = c('abc','dfg'),
z = factor('A','B','C','D'))
# loop list
for (i.l in my.l){
cat(paste0('\nThe class of i.l is ', class(i.l), '. '))
cat(paste0('The number of elements is ', length(i.l), '.'))
}
##
## The class of i.l is integer. The number of elements is 5.
## The class of i.l is character. The number of elements is 2.
## The class of i.l is factor. The number of elements is 1.
```

In the definition of *loops*, the iterator does not have to be the only

object incremented in each iteration. We can create other objects and

increment them using a simple sum operation. See next:

```
# set vec and iterators
my.vec <- seq(1:5)
my.x <- 5
my.z <- 10
for (i in my.vec){
# iterate "manually"
my.x <- my.x + 1
my.z <- my.z + 2
cat('\nValue of i = ', i,
' | Value of my.x = ', my.x,
' | Value of my.z = ', my.z)
}
##
## Value of i = 1 | Value of my.x = 6 | Value of my.z = 12
## Value of i = 2 | Value of my.x = 7 | Value of my.z = 14
## Value of i = 3 | Value of my.x = 8 | Value of my.z = 16
## Value of i = 4 | Value of my.x = 9 | Value of my.z = 18
## Value of i = 5 | Value of my.x = 10 | Value of my.z = 20
```

Using nested *loops*, that is, a *loop* inside of another *loop* is also

possible. See the following example, where we present all the elements

of a matrix:

```
# set matrix
my.mat <- matrix(1:9, nrow = 3)
# loop all values of matrix
for (i in seq(1,nrow(my.mat))){
for (j in seq(1,ncol(my.mat))){
cat(paste0('\nElement [', i, ', ', j, '] = ', my.mat[i,j]))
}
}
##
## Element [1, 1] = 1
## Element [1, 2] = 4
## Element [1, 3] = 7
## Element [2, 1] = 2
## Element [2, 2] = 5
## Element [2, 3] = 8
## Element [3, 1] = 3
## Element [3, 2] = 6
## Element [3, 3] = 9
```

## A Real World Example

Now, the computational needs of the real world is far more complex than

dividing a dinner expense. A practical example of using *loops* is

processing data according to groups. Using an example from Finance, if

we have a return dataset for several stocks and we want to calculate the

average return of each stock, we can use a *loop* for that. In this

example, we will use *Yahoo Finance* data from three stocks: FB, GE and

AA. The first step is downloading it with package `BatchGetSymbols`

.

```
library(BatchGetSymbols)
## Loading required package: rvest
## Loading required package: xml2
## Loading required package: dplyr
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
##
my.tickers <- c('FB', 'GE', 'AA')
df.stocks <- BatchGetSymbols(tickers = my.tickers,
first.date = '2012-01-01',
freq.data = 'yearly')[[2]]
##
## Running BatchGetSymbols for:
## tickers = FB, GE, AA
## Downloading data for benchmark ticker | Found cache file
## FB | yahoo (1|3) | Found cache file - Good job!
## GE | yahoo (2|3) | Found cache file - Nice!
## AA | yahoo (3|3) | Found cache file - You got it!
```

It worked fine. Let’s check the contents of the dataframe:

```
dplyr::glimpse(df.stocks)
## Observations: 21
## Variables: 10
## $ ticker
``` "AA", "AA", "AA", "AA", "AA", "AA", "AA", ...
## $ ref.date 2012-01-03, 2013-01-02, 2014-01-02, 2015-...
## $ volume 2217410500, 2149575500, 2146821400, 268355...
## $ price.open 21.48282, 21.33864, 25.30359, 38.13561, 22...
## $ price.high 25.85628, 25.68807, 42.29280, 41.01921, 32...
## $ price.low 19.27206, 18.50310, 24.27030, 18.79146, 16...
## $ price.close 22.17969, 21.60297, 25.30359, 38.15964, 23...
## $ price.adjusted 20.89342, 20.62187, 24.48568, 37.24207, 23...
## $ ret.adjusted.prices NA, -0.01299715, 0.18736494, 0.52097326, -...
## $ ret.closing.prices NA, -0.02600212, 0.17130149, 0.50807215, -...

All financial data is there. Notice that the return series is available

at column ret.adjusted.prices.

Now we will use a loop to build a table with the mean return of each

stock:

```
# find unique tickers in column ticker
unique.tickers <- unique(df.stocks$ticker)
# create empty df
tab.out <- data.frame()
# loop tickers
for (i.ticker in unique.tickers){
# create temp df with ticker i.ticker
temp <- df.stocks[df.stocks$ticker==i.ticker, ]
# row bind i.ticker and mean.ret
tab.out <- rbind(tab.out,
data.frame(ticker = i.ticker,
mean.ret = mean(temp$ret.adjusted.prices, na.rm = TRUE)))
}
# print result
print(tab.out)
## ticker mean.ret
## 1 AA 0.24663684
## 2 FB 0.35315566
## 3 GE 0.06784693
```

In the code, we used function `unique`

to find out the names of all the

tickers in the dataset. Soon after, we create an empty *dataframe* to

save the results and a loop to filter the data of each stock

sequentially and average its returns. At the end of the *loop*, we use

function `rbind`

to paste the results of each stock with the results of

the main table. As you can see, we can use the data to perform group

calculations with *loop*.

By now, I must be forward in saying that the previous loop is by no

means the best way of performing the data operation. What we just did by

loops is called a *split-apply-combine* procedure. There are base

function in R such as `tapply`

, `split`

and `lapply`

/`sapply`

that can

do the same job but with a more intuitive and functional approach. Going

further, functions from package `tidyverse`

can do the same procuedure

with an even more intuitive approach. In a future post I shall discuss

this possibilities further.

I hope you guys liked the post. Got a question? Just drop it at the

comment section.

**leave a comment**for the author, please follow the link and comment on their blog:

**Marcelo S. Perlin**.

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