Will use the na.action specified in the R options. You can see that na.action is one of the listed arguments. For example, if you look at the help for the lm command, In some R functions, one of the arguments the user can provide is the # Error in na.fault(g) : missing values in object We can create a data frame with missing values and see how it is treated withĮach of the above. To see the na.action currently in in options, use getOption(“na.action”). na.fail: returns the object only if it contains no missing values.Removed if they contain any missing values differences between omitting andĮxcluding NAs can be seen in some prediction and residual functions
na.omit and na.exclude: returns the object with observations.That describes how missing values should be treated. YouĬan view these current settings with options(). The other functions for NA are options for na.action.ĭefault settings for functions, there are similar underlying defaults for R as a software. It is one of several functions built around NA. We have introduced is.na as a tool for both finding and creating In R, you would need to explicitly change these values Non-NA values cannot be interpreted as missing: Other packages allow you toĭesignate values as “system missing” so that these values will be interpreted in.“numeric” object and x2 is a “character” object. We saw a small example of this in x1 and x2. In R, NA represents all types of missing data. Strings and missing numbers might be represented differently–empty quotationsįor strings, periods for numbers. NA is used for all kinds of missing data: In other packages, missing.Our missing value cannot be compared to 0 and none of our values can be compared to NA because NA is not assigned a value–it
Symbol and see that both comparisons are valid (and that the missing symbol is
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In the example SAS code below, we compare the values in y to 0 and to the missing Result, values coded as missing can 1) be compared to other values and 2) other Value is assigned an extreme numeric value–either very high or very low. NA cannot be used in comparisons: In other packages, a “missing”.We can see that R distinguishes between the NA and “NA” in x2–NA is Vector with as any elements as the vector we provide. To see which values in each of these vectors R recognizesĪs missing, we can use the is.na function. In x2, the third value is missing while the fourth value is theĬharacter string “NA”. NA is the one of the few non-numbers that we could include in x1 without generatingĪn error (and the other exceptions are letters representing numbers or numeric We can create vectors with missing values. NA is not a string or a numeric value, butĪn indicator of missingness. Finally, we will introduce some of the tools for working with missing values in R, both in data management and analysis.
Next, for those coming from SAS, SPSS, and/or Stata, we will outline some of the differences between missing values in R and missing values elsewhere. On this page, we will present first the basics of how missing values are represented in R. However, to those accustomed to working with missing values in other packages, the way in which R handles missing values may require a shift in thinking.
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Like other statistical software packages, R is capable of handling missing values. Version info: Code for this page was tested in R Under development (unstable) ( r58461)
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