Control Flow
Last updated on 2024-10-03 | Edit this page
Overview
Questions
- How can I make data-dependent choices in R?
- How can I repeat operations in R?
Objectives
- Write conditional statements with
if...else
statements andifelse()
. - Write and understand
for()
loops.
Often when we’re coding we want to control the flow of our actions. This can be done by setting actions to occur only if a condition or a set of conditions are met. Alternatively, we can also set an action to occur a particular number of times.
There are several ways you can control flow in R. For conditional statements, the most commonly used approaches are the constructs:
R
# if
if (condition is true) {
perform action
}
# if ... else
if (condition is true) {
perform action
} else { # that is, if the condition is false,
perform alternative action
}
Say, for example, that we want R to print a message if a variable
x
has a particular value:
R
x <- 8
if (x >= 10) {
print("x is greater than or equal to 10")
}
x
OUTPUT
[1] 8
The print statement does not appear in the console because x is not
greater than 10. To print a different message for numbers less than 10,
we can add an else
statement.
R
x <- 8
if (x >= 10) {
print("x is greater than or equal to 10")
} else {
print("x is less than 10")
}
OUTPUT
[1] "x is less than 10"
You can also test multiple conditions by using
else if
.
R
x <- 8
if (x >= 10) {
print("x is greater than or equal to 10")
} else if (x > 5) {
print("x is greater than 5, but less than 10")
} else {
print("x is less than 5")
}
OUTPUT
[1] "x is greater than 5, but less than 10"
Important: when R evaluates the condition inside
if()
statements, it is looking for a logical element, i.e.,
TRUE
or FALSE
. This can cause some headaches
for beginners. For example:
R
x <- 4 == 3
if (x) {
"4 equals 3"
} else {
"4 does not equal 3"
}
OUTPUT
[1] "4 does not equal 3"
As we can see, the not equal message was printed because the vector x
is FALSE
R
x <- 4 == 3
x
OUTPUT
[1] FALSE
Challenge 1
Use an if()
statement to print a suitable message
reporting whether there are any records from 2002 in the
gapminder
dataset. Now do the same for 2012.
We will first see a solution to Challenge 1 which does not use the
any()
function. We first obtain a logical vector describing
which element of gapminder$year
is equal to
2002
:
R
gapminder[(gapminder$year == 2002),]
Then, we count the number of rows of the data.frame
gapminder
that correspond to the 2002:
R
rows2002_number <- nrow(gapminder[(gapminder$year == 2002),])
The presence of any record for the year 2002 is equivalent to the
request that rows2002_number
is one or more:
R
rows2002_number >= 1
Putting all together, we obtain:
R
if(nrow(gapminder[(gapminder$year == 2002),]) >= 1){
print("Record(s) for the year 2002 found.")
}
All this can be done more quickly with any()
. The
logical condition can be expressed as:
R
if(any(gapminder$year == 2002)){
print("Record(s) for the year 2002 found.")
}
Did anyone get a warning message like this?
ERROR
Error in if (gapminder$year == 2012) {: the condition has length > 1
The if()
function only accepts singular (of length 1)
inputs, and therefore returns an error when you use it with a vector.
The if()
function will still run, but will only evaluate
the condition in the first element of the vector. Therefore, to use the
if()
function, you need to make sure your input is singular
(of length 1).
Tip: Built in ifelse()
function
R
accepts both if()
and
else if()
statements structured as outlined above, but also
statements using R
’s built-in ifelse()
function. This function accepts both singular and vector inputs and is
structured as follows:
where the first argument is the condition or a set of conditions to
be met, the second argument is the statement that is evaluated when the
condition is TRUE
, and the third statement is the statement
that is evaluated when the condition is FALSE
.
R
y <- -3
ifelse(y < 0, "y is a negative number", "y is either positive or zero")
OUTPUT
[1] "y is a negative number"
Tip: any()
and
all()
The any()
function will return TRUE
if at
least one TRUE
value is found within a vector, otherwise it
will return FALSE
. This can be used in a similar way to the
%in%
operator. The function all()
, as the name
suggests, will only return TRUE
if all values in the vector
are TRUE
.
Repeating operations
If you want to iterate over a set of values, when the order of
iteration is important, and perform the same operation on each, a
for()
loop will do the job. We saw for()
loops
in the shell
lessons earlier. This is the most flexible of looping operations,
but therefore also the hardest to use correctly. In general, the advice
of many R
users would be to learn about for()
loops, but to avoid using for()
loops unless the order of
iteration is important: i.e. the calculation at each iteration depends
on the results of previous iterations. If the order of iteration is not
important, then you should learn about vectorized alternatives, such as
the purrr
package, as they pay off in computational
efficiency.
The basic structure of a for()
loop is:
For example:
R
for (i in 1:10) {
print(i)
}
OUTPUT
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10
The 1:10
bit creates a vector on the fly; you can
iterate over any other vector as well.
We can use a for()
loop nested within another
for()
loop to iterate over two things at once.
R
for (i in 1:5) {
for (j in c('a', 'b', 'c', 'd', 'e')) {
print(paste(i,j))
}
}
OUTPUT
[1] "1 a"
[1] "1 b"
[1] "1 c"
[1] "1 d"
[1] "1 e"
[1] "2 a"
[1] "2 b"
[1] "2 c"
[1] "2 d"
[1] "2 e"
[1] "3 a"
[1] "3 b"
[1] "3 c"
[1] "3 d"
[1] "3 e"
[1] "4 a"
[1] "4 b"
[1] "4 c"
[1] "4 d"
[1] "4 e"
[1] "5 a"
[1] "5 b"
[1] "5 c"
[1] "5 d"
[1] "5 e"
We notice in the output that when the first index (i
) is
set to 1, the second index (j
) iterates through its full
set of indices. Once the indices of j
have been iterated
through, then i
is incremented. This process continues
until the last index has been used for each for()
loop.
Rather than printing the results, we could write the loop output to a new object.
R
output_vector <- c()
for (i in 1:5) {
for (j in c('a', 'b', 'c', 'd', 'e')) {
temp_output <- paste(i, j)
output_vector <- c(output_vector, temp_output)
}
}
output_vector
OUTPUT
[1] "1 a" "1 b" "1 c" "1 d" "1 e" "2 a" "2 b" "2 c" "2 d" "2 e" "3 a" "3 b"
[13] "3 c" "3 d" "3 e" "4 a" "4 b" "4 c" "4 d" "4 e" "5 a" "5 b" "5 c" "5 d"
[25] "5 e"
This approach can be useful, but ‘growing your results’ (building the result object incrementally) is computationally inefficient, so avoid it when you are iterating through a lot of values.
Tip: don’t grow your results
One of the biggest things that trips up novices and experienced R users alike, is building a results object (vector, list, matrix, data frame) as your for loop progresses. Computers are very bad at handling this, so your calculations can very quickly slow to a crawl. It’s much better to define an empty results object before hand of appropriate dimensions, rather than initializing an empty object without dimensions. So if you know the end result will be stored in a matrix like above, create an empty matrix with 5 row and 5 columns, then at each iteration store the results in the appropriate location.
A better way is to define your (empty) output object before filling in the values. For this example, it looks more involved, but is still more efficient.
R
output_matrix <- matrix(nrow = 5, ncol = 5)
j_vector <- c('a', 'b', 'c', 'd', 'e')
for (i in 1:5) {
for (j in 1:5) {
temp_j_value <- j_vector[j]
temp_output <- paste(i, temp_j_value)
output_matrix[i, j] <- temp_output
}
}
output_vector2 <- as.vector(output_matrix)
output_vector2
OUTPUT
[1] "1 a" "2 a" "3 a" "4 a" "5 a" "1 b" "2 b" "3 b" "4 b" "5 b" "1 c" "2 c"
[13] "3 c" "4 c" "5 c" "1 d" "2 d" "3 d" "4 d" "5 d" "1 e" "2 e" "3 e" "4 e"
[25] "5 e"
Tip: While loops
Sometimes you will find yourself needing to repeat an operation as
long as a certain condition is met. You can do this with a
while()
loop.
R will interpret a condition being met as “TRUE”.
As an example, here’s a while loop that generates random numbers from
a uniform distribution (the runif()
function) between 0 and
1 until it gets one that’s less than 0.1.
R
z <- 1
while(z > 0.1){
z <- runif(1)
cat(z, "\n")
}
while()
loops will not always be appropriate. You have
to be particularly careful that you don’t end up stuck in an infinite
loop because your condition is always met and hence the while statement
never terminates.
Challenge 2
Compare the objects output_vector
and
output_vector2
. Are they the same? If not, why not? How
would you change the last block of code to make
output_vector2
the same as output_vector
?
We can check whether the two vectors are identical using the
all()
function:
R
all(output_vector == output_vector2)
However, all the elements of output_vector
can be found
in output_vector2
:
R
all(output_vector %in% output_vector2)
and vice versa:
R
all(output_vector2 %in% output_vector)
therefore, the element in output_vector
and
output_vector2
are just sorted in a different order. This
is because as.vector()
outputs the elements of an input
matrix going over its column. Taking a look at
output_matrix
, we can notice that we want its elements by
rows. The solution is to transpose the output_matrix
. We
can do it either by calling the transpose function t()
or
by inputting the elements in the right order. The first solution
requires to change the original
R
output_vector2 <- as.vector(output_matrix)
into
R
output_vector2 <- as.vector(t(output_matrix))
The second solution requires to change
R
output_matrix[i, j] <- temp_output
into
R
output_matrix[j, i] <- temp_output
Challenge 3
Write a script that loops through the gapminder
data by
continent and prints out whether the mean life expectancy is smaller or
larger than 50 years.
Step 1: We want to make sure we can extract all the unique values of the continent vector
R
gapminder <- read.csv("data/gapminder_data.csv")
unique(gapminder$continent)
Step 2: We also need to loop over each of these
continents and calculate the average life expectancy for each
subset
of data. We can do that as follows:
- Loop over each of the unique values of ‘continent’
- For each value of continent, create a temporary variable storing that subset
- Return the calculated life expectancy to the user by printing the output:
R
for (iContinent in unique(gapminder$continent)) {
tmp <- gapminder[gapminder$continent == iContinent, ]
cat(iContinent, mean(tmp$lifeExp, na.rm = TRUE), "\n")
rm(tmp)
}
Step 3: The exercise only wants the output printed
if the average life expectancy is less than 50 or greater than 50. So we
need to add an if()
condition before printing, which
evaluates whether the calculated average life expectancy is above or
below a threshold, and prints an output conditional on the result. We
need to amend (3) from above:
3a. If the calculated life expectancy is less than some threshold (50 years), return the continent and a statement that life expectancy is less than threshold, otherwise return the continent and a statement that life expectancy is greater than threshold:
R
thresholdValue <- 50
for (iContinent in unique(gapminder$continent)) {
tmp <- mean(gapminder[gapminder$continent == iContinent, "lifeExp"])
if (tmp < thresholdValue){
cat("Average Life Expectancy in", iContinent, "is less than", thresholdValue, "\n")
} else {
cat("Average Life Expectancy in", iContinent, "is greater than", thresholdValue, "\n")
} # end if else condition
rm(tmp)
} # end for loop
Challenge 4
Modify the script from Challenge 3 to loop over each country. This time print out whether the life expectancy is smaller than 50, between 50 and 70, or greater than 70.
We modify our solution to Challenge 3 by now adding two thresholds,
lowerThreshold
and upperThreshold
and
extending our if-else statements:
R
lowerThreshold <- 50
upperThreshold <- 70
for (iCountry in unique(gapminder$country)) {
tmp <- mean(gapminder[gapminder$country == iCountry, "lifeExp"])
if(tmp < lowerThreshold) {
cat("Average Life Expectancy in", iCountry, "is less than", lowerThreshold, "\n")
} else if(tmp > lowerThreshold && tmp < upperThreshold) {
cat("Average Life Expectancy in", iCountry, "is between", lowerThreshold, "and", upperThreshold, "\n")
} else {
cat("Average Life Expectancy in", iCountry, "is greater than", upperThreshold, "\n")
}
rm(tmp)
}
Challenge 5 - Advanced
Write a script that loops over each country in the
gapminder
dataset, tests whether the country starts with a
‘B’, and graphs life expectancy against time as a line graph if the mean
life expectancy is under 50 years.
We will use the grep()
command that was introduced in
the Unix
Shell lesson to find countries that start with “B.” Lets understand
how to do this first. Following from the Unix shell section we may be
tempted to try the following
R
grep("^B", unique(gapminder$country))
But when we evaluate this command it returns the indices of the
factor variable country
that start with “B.” To get the
values, we must add the value=TRUE
option to the
grep()
command:
R
grep("^B", unique(gapminder$country), value = TRUE)
We will now store these countries in a variable called
candidateCountries, and then loop over each entry in the variable.
Inside the loop, we evaluate the average life expectancy for each
country, and if the average life expectancy is less than 50 we use
base-plot to plot the evolution of average life expectancy using
with()
and subset()
:
R
thresholdValue <- 50
candidateCountries <- grep("^B", unique(gapminder$country), value = TRUE)
for (iCountry in candidateCountries) {
tmp <- mean(gapminder[gapminder$country == iCountry, "lifeExp"])
if (tmp < thresholdValue) {
cat("Average Life Expectancy in", iCountry, "is less than", thresholdValue, "plotting life expectancy graph... \n")
with(subset(gapminder, country == iCountry),
plot(year, lifeExp,
type = "o",
main = paste("Life Expectancy in", iCountry, "over time"),
ylab = "Life Expectancy",
xlab = "Year"
) # end plot
) # end with
} # end if
rm(tmp)
} # end for loop
Key Points
- Use
if
andelse
to make choices. - Use
for
to repeat operations.