{"id":646,"date":"2014-06-20T11:53:50","date_gmt":"2014-06-20T18:53:50","guid":{"rendered":"http:\/\/homepages.uc.edu\/~yaozo\/wordpress\/?p=646"},"modified":"2014-06-20T11:53:50","modified_gmt":"2014-06-20T18:53:50","slug":"for-loops-and-how-to-avoid-them","status":"publish","type":"post","link":"https:\/\/zhuoyao.net\/index.php\/2014\/06\/20\/for-loops-and-how-to-avoid-them\/","title":{"rendered":"For loops (and how to avoid them)"},"content":{"rendered":"

My experience when starting out in R was trying to clean and recode data using<\/span>for()<\/i>\u00a0loops,\u00a0usually with a few\u00a0<\/span>if()<\/i>\u00a0statements in the loop as well, and finding the whole thing complicated and frustrating.<\/span>

In this post, I’ll go over how you can avoid\u00a0<\/span>for()<\/i>\u00a0loops for both improving the quality and speed of your programming, as well as your sanity.<\/span>

So here we have our classic dataset called mydata.Rdata<\/span>\u00a0<\/i>(you can download this if you want, link at the right):<\/span>

<\/p>\n

\"\"<\/a><\/div>\n



And if I were in Stata and wanted to create an age group variable, I could just do:<\/span>

gen Agegroup=1<\/span>
replace Agegroup=2 if Age>10 & Age<20<\/span>
replace Agegroup=3 if Age>=20<\/span>

But when I try this in R, it fails:<\/span>

\"\"<\/a>





Why does it fail? It fails because Age is a\u00a0<\/span>vector<\/i>\u00a0so the condition<\/span>if(mydata$Age<10)<\/span>\u00a0is asking “is the vector Age less than 10”, which is not what we want to know. \u00a0We want to ask, row by row is each element of Age<10, so we need to specify\u00a0the element of the vector we’re referring to. We don’t specify the element and thus we get the warning (really, error), “only the first element will be used.” \u00a0So when this fails, the first way people try to solve this problem is with a crazy\u00a0<\/span>for()<\/i>\u00a0loop like this:<\/span>

###########Unnecessarily\u00a0long and ugly code below#######<\/span>
mydata$Agegroup1<-0<\/span>

\n<\/span>for (i in \u00a01:10){<\/span>
\u00a0 if(mydata$Age[i]>10 & mydata$Age[i]<20){<\/span>
\u00a0 \u00a0 mydata$Agegroup1[i]<-1<\/span>
\u00a0 }<\/span>
\u00a0 if(mydata$Age[i]>=20){<\/span>
\u00a0 \u00a0 mydata$Agegroup1[i]<-2<\/span>
\u00a0 }<\/span>
}<\/span>

Here we tell R to go down the rows from i=1 to i=10, and for each of those rows indexed by i, check to see what value of Age it is, and then assign Agegroup a value of 1 or 2. \u00a0<\/span>This works, but at a high cost<\/b>\u00a0– you can easily make a mistake with all those indexed vectors, and also\u00a0<\/span>for()<\/i>\u00a0loops take a lot of computing time<\/b>, which would be a big deal if this dataset were 10000 observations instead of 10.<\/span>

So how can we avoid doing this?<\/span>

One of the most useful functions I have found is one that I have referred to a number of times in my blog so far – the\u00a0<\/span>ifelse()<\/a><\/span>\u00a0function. \u00a0The\u00a0<\/span>ifelse()<\/span>function evaluates a condition, and then assigns a value if it’s true and a value if it’s false. \u00a0The great part about it is that it can read in a vector and check each element of the vector one by one so\u00a0<\/span>you don’t need indices or a loop<\/b>. You don’t even need to initialize some new variable before you run the statement. \u00a0Like this:<\/span>

mydata$newvariable<-ifelse(Condition of some variable<\/span>,<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Value of\u00a0<\/span><\/span>new variable if condition is true<\/span>,\u00a0<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Value of new variable if condition is false<\/span>)<\/span>

\n<\/span>so for example:<\/span>

mydata$Old<-ifelse(mydata$Age>40<\/span>,1<\/span>,0<\/span>)<\/span>

This says, check to see if the elements of the vector mydata$Age are greater than 40: if an element is greater than 40, it assigns the value of 1 to mydata$Old, and if it’s not greater than 40, it assigns the value of 0 to mydata$Old.<\/span>

But we wanted to assign values 0, 1, and 2 to an Agegroup variable. \u00a0To do this, we can use\u00a0<\/span>nested\u00a0ifelse()\u00a0statements<\/b>:<\/span>

mydata$Agegroup2<-ifelse(mydata$Age>10 & mydata$Age<20,1, \u00a0 \u00a0\u00a0<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ifelse(mydata$Age>20, 2,0))<\/span><\/p>\n

<\/div>\n
Now this says, first check whether each element of the Age vector is >10 and <20. \u00a0If it is, assign 1 to Agegroup2. \u00a0If it’s not, then evaluate the next\u00a0ifelse()<\/i>statement, whether Age>20. \u00a0If it is, assign Agegroup2 a value of 2. \u00a0If it’s not any of those, then assign it 0. \u00a0We can see that both the loop and the\u00a0ifelse()<\/i>statements give us the same result:<\/div>\n
<\/div>\n
\"\"<\/a><\/div>\n
<\/div>\n

You can nest\u00a0<\/span>ifelse()<\/i>\u00a0statement as much as you like.\u00a0<\/span>Just be careful about your final category<\/b>\u00a0– it assigns the last value to whatever values are left over that didn’t meet any condition (including if a value is NA!) so make sure you want that to happen.<\/span>


Other examples of ways to use the\u00a0ifelse()<\/i>\u00a0function:<\/span><\/p>\n