#Spss code not applicalbe as missing update

That allows these values to behave identical to NA in regular R operations, while still preserving the value of the tag. It does this by taking advantage of the internal structure of a floating point NA. To support StataвЂ™s extended and SASвЂ™s special missing value, haven implements a tagged NA. Haven provides labelled_spss() as a subclass of labelled() to model these additional user-defined missings. Each column can have either up to three distinct values that are considered as missing or a range. SPSSвЂ™s user-defined values work differently to SAS and Stata. Each column can declare up to three distinct values or a range of values (plus one distinct value) that should be treated as missing. SPSS has per-column вЂњuserвЂќ missing values.inf).Д®ach tool also provides a mechanism for recording multiple types of missingness: inf), and Stata treats it as the largest possible number (i.e. This is roughly equivalent to RвЂ™s NA, although neither Stata nor SAS propagate missingness in numeric comparisons: SAS treats the missing value as the smallest possible number (i.e. Missing ValuesДЄll three tools (SPSS, SAS, Stata) provide a global вЂњsystem missing valueвЂќ which is displayed as. SPSS and SAS can label numeric and character values, not just integer values. Factors differ from the labelled values provided by the other tools in important ways: This, however, is not the primary use of factors: they are instead designed to automatically generate useful contrasts for linear models. Value LabelsД«ase R has one data type that effectively maintains a mapping between integers and character labels: the factor. Metadata (information about the data) such as variable formats, missing values, and variable and value labels are stored with the dataset. IBM SPSS (The application) can import and export data in many formats and in non-rectangular configurations however, the data set is always translated to an SPSS rectangular data file, known as a system file (using the extension *.sav). If you wish to strip the labels and replace with R NA values you can use remove_labels(x, user_na_to_na = TRUE) Example: df df df Use Labelled package to remove labels and replace with R NA Now we can manipulate the data mean(df$s1, na.rm = TRUE) + set_value_labels(s1 = c(agree=1, disagree=2, dk=5, refused=6), Set_value_labels(s1 = c(agree=1, disagree=2, dk=5, refused=6),

#Spss code not applicalbe as missing update

See addendum for a more detailed example that uses a public data file to show an example that harnesses dplyr to update multiple columns, labels. Mark the labels, and the values as NA without removing the underlying imported data. Ok, I think I understand now what you trying to do.