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Import R data frame with explicit meta data sheets

Source: R/import_raw.R
import_raw.Rd

Function to import a data.frame object for use in eatGADS while adding explicit variable and value meta information through separate data.frames.

Usage

import_raw(df, varLabels, valLabels = NULL, checkVarNames = TRUE)

Arguments

df

A data.frame.

varLabels

A data.frame containing the variable labels. All variables in the data have to have exactly one column in this data.frame.

valLabels

A data.frame containing the value labels. All referenced variables have to appear in the data, but not all variables in the data have to receive value labels. Can be omitted.

checkVarNames

Should variable names be checked for violations of SQLite and R naming rules?

Value

Returns a list with the actual data dat and with all meta information in long format labels.

Details

The argument varLables has to contain exactly two variables, namely varName and varLabel. valLables has to contain exactly four variables, namely varName, value, valLabel and missings. The column value can only contain numerical values. The column missings can only contain the values "valid" and "miss". Variables of type factor are not supported in any of the data.frames.

Examples

dat <- data.frame(ID = 1:5, grade = c(1, 1, 2, 3, 1))
varLabels <- data.frame(varName = c("ID", "grade"),
                       varLabel = c("Person Identifier", "School grade Math"),
                       stringsAsFactors = FALSE)
valLabels <- data.frame(varName = c("grade", "grade", "grade"),
                       value = c(1, 2, 3),
                       valLabel = c("very good", "good", "sufficient"),
                       missings = c("valid", "valid", "valid"),
                       stringsAsFactors = FALSE)

gads <- import_raw(df = dat, varLabels = varLabels, valLabels = valLabels, checkVarNames = FALSE)

# Inspect Meta data
extractMeta(gads)
#>   varName          varLabel format display_width labeled value   valLabel
#> 1      ID Person Identifier   <NA>            NA      no    NA       <NA>
#> 2   grade School grade Math   <NA>            NA     yes     1  very good
#> 3   grade School grade Math   <NA>            NA     yes     2       good
#> 4   grade School grade Math   <NA>            NA     yes     3 sufficient
#>   missings
#> 1     <NA>
#> 2    valid
#> 3    valid
#> 4    valid

# Extract Data
dat <- extractData(gads, convertLabels = "character")