@drsimonj here with five simple tricks I find myself sharing all the time with fellow R users to improve their code! This post was originally published on DataCamp’s community as one of their top 10 articles in 2017 1. More fun to sequence from 1 Next time you use the colon operator to create a sequence from 1 like 1:n, try seq(). # Sequence a vector x [1] 1 2 3 4 5 6 7 8 9 10 # Sequence an integer seq(nrow(mtcars)) #> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 #> [24] 24 25 26 27 28 29 30 31 32 The colon operator can produce unexpected results that can create all sorts of problems without you noticing! Take a look at what happens when you want to sequence the length of an empty vector: # Empty vector x [1] 1 0 seq(x) #> integer(0) You’ll also notice that this saves you from using functions like length(). When applied to an object of a certain length, seq() will automatically create a sequence from 1 to the length of the object. 2. vector() what you c() Next time you create an empty vector with c(), try to replace it with vector("type", length). # A numeric vector with 5 elements vector("numeric", 5) #> [1] 0 0 0 0 0 # A character vector with 3 elements vector("character", 3) #> [1] "" "" "" Doing this improves memory usage and increases speed! You often know upfront what type of values will go into a vector, and how long the vector will be. Using c() means R has to slowly work both of these things out. So help give it a boost with vector()! A good example of this value is in a for loop. People often write loops by declaring an empty vector and growing it with c() like this: x x at step 2 : 1, 2 #> x at step 3 : 1, 2, 3 #> x at step 4 : 1, 2, 3, 4 #> x at step 5 : 1, 2, 3, 4, 5 Instead, pre-define the type and length with vector(), and reference positions by index, like this: n x at step 3 : 1, 2, 3, 0, 0 #> x at step 4 : 1, 2, 3, 4, 0 #> x at step 5 : 1, 2, 3, 4, 5 Here’s a quick speed comparison: n [1] 6 7 # No which x[x > 5] #> [1] 6 7 Or counting number of values greater than 5: # Using which length(which(x > 5)) #> [1] 2 # Without which sum(x > 5) #> [1] 2 Why should you ditch which()? It’s often unnecessary and boolean vectors are all you need. For example, R lets you select elements flagged as TRUE in a boolean vector: condition 5 condition #> [1] FALSE FALSE FALSE TRUE TRUE x[condition] #> [1] 6 7 Also, when combined with sum() or mean(), boolean vectors can be used to get the count or proportion of values meeting a condition: sum(condition) #> [1] 2 mean(condition) #> [1] 0.4 which() tells you the indices of TRUE values: which(condition) #> [1] 4 5 And while the results are not wrong, it’s just not necessary. For example, I often see people combining which() and length() to test whether any or all values are TRUE. Instead, you just need any() or all(): x 10)) > 0) print("At least one value is greater than 10") #> [1] "At least one value is greater than 10" # Wrapping a boolean vector with `any()` if (any(x > 10)) print("At least one value is greater than 10") #> [1] "At least one value is greater than 10" # Using `which()` and `length()` to test if all values are positive if (length(which(x > 0)) == length(x)) print("All values are positive") #> [1] "All values are positive" # Wrapping a boolean vector with `all()` if (all(x > 0)) print("All values are positive") #> [1] "All values are positive" Oh, and it saves you a little time… x .5)]) #> user system elapsed #> 1.245 0.486 1.856 system.time(x[x > .5]) #> user system elapsed #> 1.085 0.395 1.541 4. factor that factor! Ever removed values from a factor and found you’re stuck with old levels that don’t exist anymore? I see all sorts of creative ways to deal with this. The simplest solution is often just to wrap it in factor() again. This example creates a factor with four levels ("a", "b", "c" and "d"): # A factor with four levels x [1] a b c d #> Levels: a b c d plot(x) If you drop all cases of one level ("d"), the level is still recorded in the factor: # Drop all values for one level x [1] a b c #> Levels: a b c d plot(x) A super simple method for removing it is to use factor() again: x [1] a b c #> Levels: a b c plot(x) This is typically a good solution to a problem that gets a lot of people mad. So save yourself a headache and factor that factor! Aside, thanks to Amy Szczepanski who contacted me after the original publication of this article and mentioned droplevels(). Check it out if this is a problem for you! 5. First you get the $, then you get the power Next time you want to extract values from a data.frame column where the rows meet a condition, specify the column with $ before the rows with [. Examples Say you want the horsepower (hp) for cars with 4 cylinders (cyl), using the mtcars data set. You can write either of these: # rows first, column second - not ideal mtcars[mtcars$cyl == 4, ]$hp #> [1] 93 62 95 66 52 65 97 66 91 113 109 # column first, rows second - much better mtcars$hp[mtcars$cyl == 4] #> [1] 93 62 95 66 52 65 97 66 91 113 109 The tip here is to use the second approach. But why is that? First reason: do away with that pesky comma! When you specify rows before the column, you need to remember the comma: mtcars[mtcars$cyl == 4,]$hp. When you specify column first, this means that you’re now referring to a vector, and don’t need the comma! Second reason: speed! Let’s test it out on a larger data frame: # Simulate a data frame... n user system elapsed #> 0.559 0.152 0.758 # column first, rows second - much better system.time(d$a[d$b > .5]) #> user system elapsed #> 0.093 0.013 0.107 Worth it, right? Still, if you want to hone your skills as an R data frame ninja, I suggest learning dplyr. You can get a good overview on the dplyr website or really learn the ropes with online courses like DataCamp’s Data Manipulation in R with dplyr. Sign off Thanks for reading and I hope this was useful for you. For updates of recent blog posts, follow @drsimonj on Twitter, or email me at [email protected] to get in touch. If you’d like the code that produced this blog, check out the blogR GitHub repository.
