5.5.5.1 Logical Based Indexing One very useful method that R provides is to access elements of a vector using a different, logical vector of the same length. In R, true values are designated with TRUE, and false values with FALSE. Table 7.1 contains some that I frequently use: Logical vectors aren’t just good for indexing, you can also use them to figure out which values in a vector satisfy some criteria. You get the same answer: # Which boats had prices greater than 200 OR less than 100? Take a step back, and look at the result of x > 2: If you have a missing value in your vector, any comparison returns NA for that value. You can use these logical vectors very efficiently to select some values from a vector. If you give NA as a value for the index, R puts NA in that place as well. Figure 7.3: Logical comparison operators in R. However, creating logical vectors using c() is tedious. Thankfully, the %in% operation allows you to combine multiple OR comparisons much faster. Browse other questions tagged r vector or ask your own question. Here, I’ll use logical vectors to get the prices of boats whose ages were greater than 100: Here’s how logical indexing works step-by-step: In addition to using single comparison operators, you can combine multiple logical vectors using the OR (which looks like | and AND & commands. This is a really powerful tool. Subsetting variables To manipulate data frames in R we can use the bracket notation to If that was confusing, think about it this way: a logical vector, combined with the brackets [ ], acts as a filter for the vector it is indexing. When used with the indexing notation the items within a vector that are NA can be easily removed: > a <- c ( 1 , 2 , 3 , 4 , NA ) > is.na ( a ) [1] FALSE FALSE FALSE FALSE TRUE > ! Good for R aliens and R pirates. # Show me all of the boat prices where the logical vector is TRUE: # Doing it all in one step! Now, let’s say you want to create a logical vector indicating which values are either a or b or c or d. You could create this logical vector with multiple | (OR) commands: However, this takes a long time to write. We can create a logical vector to see which values are greater than 0: Now, we’ll use sum() and mean() on that logical vector to see how many of the values in x are positive, and what percent are positive. There are different ways to do this, but it is generally easiest to use two numbers in a double index, the first for the row number (s) and the second for the column number (s). Many (if not all) R functions will interpret TRUE values as 1 and FALSE values as 0. For example, consider the following vector s of length 5. # one value m [2,2] ## b ## 5 # another one m [1,3] ## c ## 3. We’ll start with a vector x of length 10, containing 3 positive numbers and 5 negative numbers. One of the nice things about logical indexing is that it is very easy and natural to combine the results of different conditions to select items based on multiple criteria. If you want to keep only the values larger than 2 in the vector x, you could do that with the following code: Wait — what is that NA value doing there? They are as follows : [ ] = always returns a list with a single element. # Writing the logical index by hand (you'd never do this!). When you index a vector with a logical vector, R will return values of the vector for which the indexing vector is TRUE. We will learn how to create them and how to name their components. For example, let’s create a logical vector indicating which boats had a price greater than 200 OR less than 100, and then use that vector to see what the names of these boats were: You can combine as many logical vectors as you want (as long as they all have the same length! To do this, use the function which(). is.na ( a )] > b [1] 1 2 3 4 Logical indexing allows us to extract elements that meet specified criteria, as specified by an R logical expression. 5.5.5.1 Logical Based Indexing One very useful method that R provides is to access elements of a vector using a different, logical vector of the same length. When you do this, R will compare values in the same position (e.g. Figure 7.2: FALSE values in a logical vector are like lots of mini-Gandolfs. The index function in R doesn’t take only numerical vectors as arguments; it also works with logical vectors. logInd = X < target. R‘s array indexing notation is really powerful, so we will use it for our examples. Let's talk about the basic rules of logical indexing, and then we'll reexamine the expression B(isnan(B)). If I didn’t use parentheses above, I would get a different answer. You can use these logical vectors very efficiently to select some values from a vector. To use the %in% function, just put it in between the original vector, and a new vector of possible values. The OR | operation will return TRUE if any of the logical vectors is TRUE, while the AND & operation will only return TRUE if all of the values in the logical vectors is TRUE. R’s default < # Boat names of boats with a color of black OR with a price > 100, # Names of blue boats with a price greater than 200. In R, true values are designated with TRUE, and false values with FALSE. It only lets values of the vector pass through for which the logical vector is TRUE. # What were the prices of boats older than 100? logInd = Columns 1 through 13 1 0 1 0 0 0 0 0 0 0 0 0 0 Columns 14 through 20 1 0 0 0 0 0 1. In this example, I am indexing a vector x with a logical vector y (y for example could be x > 0, so all positive values of x are TRUE and all negative values are FALSE). For example, we can compare the boat.cost and boat.price vectors to see which boats sold for a higher price than their cost: Once you’ve created a logical vector using a comparison operator, you can use it to index any vector with the same length. R list can contain a string, a numeric variable, a vector, a matrix, an array, a function, and even another list. This is going to be long (because I am trying to slow the exposition down enough to see all the steps and relations) and hard to follow without working examples (say with R ), and working through the logic with pencil and a printout (math is not a spectator sport). is.na ( a ) [1] TRUE TRUE TRUE TRUE FALSE > a [ ! It may seem that this NA is translated into TRUE, but that isn’t the case. A logical vector is a vector that only contains TRUE and FALSE values. Pretty much any time you want to answer a question like “How many of X are Y” or “What percent of X are Y”, you use sum() or mean() function with a logical vector as an argument. For example, you can use the is.na() function to test which values of a vector are missing. If you use a logical vector to index, R returns a vector with only the values for which the logical vector is TRUE. Version info: Code for this page was tested in R version 3.0.2 (2013-09-25) On: 2013-11-19 With: lattice 0.20-24; foreign 0.8-57; knitr 1.5 1. symbol. When you index a vector with a logical vector, R will return values of the vector for which the indexing vector is TRUE. is.na ( a )] [1] 1 2 3 4 > b <- a [ ! R has lots of special functions that take vectors as arguments, and return logical vectors based on multiple criteria. Logical Index Vector A new vector can be sliced from a given vector with a logical index vector, which has the same length as the original vector. With over 20 years of experience, he provides consulting and training services in the use of R. Joris Meys is a statistician, R programmer and R lecturer with the faculty of Bio-Engineering at the University of Ghent. You could create logical vectors directly using c(). The %in% function goes through every value in the vector x, and returns TRUE if it finds it in the vector of possible values – otherwise it returns FALSE. The %in% operation helps you to easily create multiple OR arguments.Imagine you have a vector of categorical data that can take on many different values. If you use a logical vector to index, R returns a vector with only the values for which the logical vector is TRUE. For example, you could have a vector x indicating people’s favorite letters. I've talked about logical indexing before in some of the linked posts, but recent work makes me want to show it off again. The index function in R doesn’t take only numerical vectors as arguments; it also works with logical vectors. Figure 7.1: Logical indexing. [[ ]] = returns a object of the class of item contained in the list. # Which boats had a lower price than cost? Gandolf stopped all the values of x for which y was FALSE. Andrie de Vries is a leading R expert and Business Services Director for Revolution Analytics. For example, let’s create some logical vectors from our boat.ages vector: You can also create logical vectors by comparing a vector to another vector of the same length. R has main 3 indexing operators. We should find that there are 5 TRUE values, and that 50% of the values (5 / 10) are TRUE. The Overflow Blog Podcast 287: How do you make software reliable enough for space travel? The second way to index vectors is with logical vectors. # Which boats were eithe black or brown, AND had a price less than 100. If you apply the function which() to a logical vector, R will tell you which values of the index are TRUE. Logical indexing is a compact and expressive notation that's very useful for many image processing operations. For example, the following logical vector returns TRUE for cases where the boat colors are black OR brown, AND where the price was less than 100: When using multiple criteria, make sure to use parentheses when appropriate.

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