Import a Viva Insights Query from a .csv file, with variable classifications optimised for other functions in the package.
Usage
import_query(
x,
pid = NULL,
dateid = NULL,
date_format = "%m/%d/%Y",
convert_date = TRUE,
encoding = "UTF-8"
)
Arguments
- x
String containing the path to the Viva Insights query to be imported. The input file must be a .csv file, and the file extension must be explicitly entered, e.g.
"/files/standard query.csv"
- pid
String specifying the unique person or individual identifier variable.
import_query
renames this toPersonId
so that this is compatible with other functions in the package. Defaults toNULL
, where no action is taken.- dateid
String specifying the date variable.
import_query
renames this toMetricDate
so that this is compatible with other functions in the package. Defaults toNULL
, where no action is taken.- date_format
String specifying the date format for converting any variable that may be a date to a Date variable. Defaults to
"%m/%d/%Y"
.- convert_date
Logical. Defaults to
TRUE
. When set toTRUE
, any variable that matches true withis_date_format()
gets converted to a Date variable. When set toFALSE
, this step is skipped.- encoding
String to specify encoding to be used within
data.table::fread()
. Seedata.table::fread()
documentation for more information. Defaults to'UTF-8'
.
Details
import_query()
uses data.table::fread()
to import .csv files for
speed, and by default stringsAsFactors
is set to FALSE. A data frame is
returned by the function (not a data.table
). Column names are automatically
cleaned, replacing spaces and special characters with underscores.