Transform quantitative variables. Aggregate or interpolate time series data.
getTransformTS(data, col_date = "date", col_series = setdiff(colnames(data),
col_date), ts = "10 min", tz = "UTC", fun_aggr = "mean",
treat_missing = FALSE, control_date = TRUE, maxgap = Inf,
keep_last = TRUE, type_aggr = "first", showwarn = FALSE)
: data.frame to transform
: Date column name, default to "date". Must be "POSIXct"
: Column name of quantitative variable(s) to be transformed. Default to setdiff(colnames(data), "date")
: Increment of the sequence. Default to "10 min". Can be a number, in seconds, or a character string containing one of "min", "hour", "day". This can optionally be preceded by a positive integer and a space
: Timezone of result. Defaut to "UTC".
: Aggregation function to use ("min", "max", "sum", "mean", "first", "last"). Default to "mean".
: Boolean. Default to FALSE
Whether or not to interpolate missing values ?
see na.approx
: Boolean. Control full data sequence ? Defaut to TRUE and set to TRUE if treat_missing
: When interpolate missing values with na.approx
.
Maximum number of consecutive NAs to fill. Defaut to Inf.
: Boolean. Keep last date/time value after interpolation ?
: Character. Type of aggregation
"first" : Date/Time result is equal to minimum of sequence, and this minimum is included in aggregation
"last" : Date/Time result is equal to maximum of sequence, and this maximum is included in aggregation
: Boolean. Show warnings ?
a data.frame