{"id":192511,"date":"2023-06-27T10:36:26","date_gmt":"2023-06-27T14:36:26","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=192511"},"modified":"2023-06-27T10:36:26","modified_gmt":"2023-06-27T14:36:26","slug":"those-other-apply-functions","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/those-other-apply-functions\/","title":{"rendered":"Those “Other” Apply Functions…"},"content":{"rendered":"\n

So you know lapply, sapply, and apply<\/a>\u2026but\u2026what about rapply<\/em>, vapply<\/em>, or eapply<\/em>? These are generally a little less known as far as the apply family of functions in R go, so this post will explore how they work.<\/p>\n\n\n\n

rapply<\/strong><\/h2>\n\n\n\n

Let\u2019s start with rapply<\/em>. This function has a couple of different purposes. One is to recursively apply a function to a list. We\u2019ll get to that in a moment. The other use of rapply<\/em> is to a apply a function to only those elements in a list (or columns in a data frame) that belong to a specified class. For example, let\u2019s say we have a data frame with a mix of categorical and numeric variables, but we want to evaluate a function only<\/em> on the numeric variables.<\/p>\n\n\n\n

Use rapply to apply a function to elements of a given class<\/strong><\/h4>\n\n\n\n

Using the traditional iris dataset, we can run the one-liner below to get the mean of each numeric column. This works almost exactly like sapply<\/em>, except we add an extra parameter, class<\/em>, to specify we only want to apply our function to the numeric columns in iris.<\/p>\n\n\n\n

# apply to only numeric variables\nrapply(iris, mean, class = \"numeric\")\n \nrapply(iris, max, class = \"numeric\")\n \nrapply(iris, min, class = \"numeric\")\n \n# or apply to only factor columns\nrapply(iris, summary, class = \"factor\")<\/pre>\n\n\n\n
If you\u2019re unsure of the class of a particular column in a data frame, df<\/em>, just run the following to get the clases of each variable.<\/p>\n\n\n\n
sapply(df, class)<\/pre>\n\n\n\n
The other purpose of rapply<\/em> is to apply a function recursively to a list.<\/p>\n\n\n\n
temp <- list(a = c(1, 2, 3), \n             b = list(a = c(4, 5, 6), b = c(7, 8, 9)),\n             c = list(a = c(10, 11, 12), b = c(13, 14, 15)))\n \n \nrapply(temp, sum)<\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Running rapply<\/em> here will recursively sum the elements of each vector in each list and sub-list of temp<\/em>.<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
rapply<\/em>, similar to sapply<\/em>, has an optional parameter how<\/em>, which specifies how the output should be returned. So in the above example, if we wanted the output to be returned as a list instead of a vector, we could do this:<\/p>\n\n\n\n
rapply(temp, sum, how = \"list\")<\/pre>\n\n\n\n
Also, if our nested list contained mixed data types, we could specify applying a function to only a specific type:<\/p>\n\n\n\n
vapply<\/strong><\/h2>\n\n\n\n
vapply<\/em> works similarly to sapply<\/em>, except that it requires an extra parameter specifying the type of the expected return value. This extra parameter is useful in coding because it can help ensure silent errors don\u2019t cause issues for you.<\/p>\n\n\n\n
For example, suppose we have the following list of mixed data types:<\/p>\n\n\n\n
sample_list <- list(10, 20, 30, \"some_string\")<\/pre>\n\n\n\n
Now if we run the below code with sapply<\/em>, we get a vector of characters, which is not exactly what we want. For instance, if we didn\u2019t know our list had mixed data types, and we ran our function to the get the max of each element in the list, then R doesn\u2019t return an error here. Instead, it silently converts the output to a character vector.<\/p>\n\n\n\n
sapply(sample_list, max)<\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
However, we can catch this error using vapply<\/em>.<\/p>\n\n\n\n
vapply(sample_list, max, numeric(1))<\/pre>\n\n\n\n
This third parameter, numeric(1)<\/em> specifies that we want the output returned by vapply<\/em> to be numeric. This means if an issue occurs in returning a numeric result, vapply<\/em> will result in an error, rather than trying to coerce the result to a different data type. This could allow you to investigate why a character is in the list, for example.<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
vapply\u2019s<\/em> ability to catch errors based off data types makes it useful for running R code in production or as a scheduled task<\/a> as an extra measure to guard against type issues.<\/p>\n\n\n\n
eapply<\/strong><\/h2>\n\n\n\n
eapply<\/em> applies a function to every named element in an environment. This requires a little knowledge about how environments work in R<\/a>. It works pretty similarly to lapply<\/em> in that it also returns a list as output. The input to eapply<\/em>, however, must be an environment, whereas lapply<\/em> can take a variety of objects as inputs.<\/p>\n\n\n\n
Here\u2019s an example:<\/p>\n\n\n\n
# create a new environment\ne <- new.env()\n \ne$a <- 10\ne$b <- 20\ne$c <- 30\n \n# multiply every element in the environment by 2\nnew_e <- eapply(e, function(x) x * 2)<\/pre>\n\n\n\n
In the above example, we create a list from the initiated environment, e<\/em>, and then double the value of each element.<\/p>\n\n\n\n
Here\u2019s another example, using the environment within a function:<\/p>\n\n\n\n
sample_func <- function()\n{\n \n   df1 <- data.frame(a = c(1, 2, 3, 4), b = c(5, 6, 7, 8))\n   df2 <- data.frame(a = c(1, 2, 3, 4, 5), b = c(5, 6, 7, 8, 9))\n   df3 <- data.frame(a = c(1, 2, 3, 4, 5, 6), b = c(5, 6, 7, 8, 9, 10)) \n \n   eapply(environment(), nrow)\n \n}\n \nsample_func()<\/pre>\n\n\n\n
Above, calling sample_func<\/em> will return a list of the number of rows for each respective data frame defined within the function\u2019s environment:<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
eapply<\/em> can also be called with the parameter USE.NAMES = FALSE<\/strong>, which will return an unnamed list.<\/p>\n\n\n\n
eapply(environment(), nrow, USE.NAMES = FALSE)<\/pre>\n\n\n\n
One other difference versus lapply<\/em> is that eapply<\/em> can take an optional Boolean parameter called all.names<\/em> that specifies if the function input should be applied to just the visible elements of the environment, or to all objects. Here\u2019s an example to illustrate:<\/p>\n\n\n\n
e <- new.env()\ne$.test <- 100\ne$other_test <- 64<\/pre>\n\n\n\n
Here, we defined an environment with one object called .test<\/em> and one called other_test<\/em>. Next, if we run this:<\/p>\n\n\n\n
eapply(e, sqrt)<\/pre>\n\n\n\n
we\u2019ll get back a list with one element \u2013 8. As in, eapply<\/em> is only applying the sqrt<\/em> function to the object named other_test<\/em> in e<\/em> because it is not a hidden object. Names that begin with a dot (hidden objects) are excluded from the function\u2019s application. To apply the function to every object in the environment, we need to set the parameter all.names = TRUE<\/strong>.<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Originally posted on TheAutomatic.net<\/a> blog.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"
Let’s start with rapply. 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