With the R command `sapply()`

we can easily apply a function many times. Here I simply want to highlight that `sapply()`

can be used within `sapply()`

: it can be nested.

First, a simple application: I have several countries in a dataset, and want to generate a table for each of them.

`sapply(c("AT", "DE", "CH"), function(x) round(prop.table(table(object[country == x]))*100, 1))`

Step by step: the `table()`

function counts how many cases there are in each category of the variable `object`

. The subscript `[country == x]`

means that R replaces the x with one of the items provided in each round: “AT”, then “DE”, and finally “CH”. The `prop.table()`

function turns the counts into proportions. Here I also multiply these by 100 to get percentages, and round them off to just one digit. The `function(x)`

part tells R that we define our own function, and that `x`

is the variable to use. The first argument is the countries I want to use. I end up with a table, with the countries across and with the categories of by variable `object`

down.

With just three countries, using `sapply()`

can be rather trivial, but how about running the code on all countries in the dataset? We can use `unique(country)`

. Using `sapply()`

rather than `for()`

loops has two important advantages. First, it is often faster. Second, we usually end up with (much) more compact code, reducing the risk of mistakes when copying and pasting code.

Let’s assume our dataset includes variation over time as well as across countries. We can simply nest two `sapply()`

commands. Here we have code to calculate the median salience by country and year.

`cy <- c("AT", "DE", "CH")`

yr <- 2000:2010

country.salience <- sapply(cy, function(x) sapply(yr, function(y) median(salience[country == x & year == y], na.rm=TRUE)))

rownames(country.salience) <- yr

colnames(country.salience) <- cy

At the top I define the countries of interest, and the years I want to examine. At the code, we take the median of the variable `salience`

for country x and year y. The first `sapply()`

runs this on the countries chosen, the second `sapply()`

runs this on the years chosen. The last two lines simply name the rows and columns to give an readily accessible table.

What if we now want the interpolated median instead of the median? We simply replace that part of the code. In contrast to copy & paste code, we make the change once, not for all the country/year combinations.

Do I get brownie points for using a quadruple sapply? This week I had fun calculating exponential moving averages on my data: a first sapply to get the average for the 7-year span, a second sapply to do this for all the 20 years in the dataset, a third sapply to do this for all the parties in the dataset, and fourth to this for all 8 countries in the data set. That’s a single line of code to produce a lot of calculations… Just to show that nesting sapply functions really can be useful!