Microdata or tabular frequency data is extended to contain all combinations of unique rows
of (hierarchical) groups of dimensional variables. Extra variables are extended by NA's or 0's.
Usage
Extend0(
data,
freqName = "freq",
hierarchical = TRUE,
varGroups = NULL,
dimVar = NULL,
extraVar = TRUE
)Arguments
- data
data frame
- freqName
Name of (existing) frequency variable
- hierarchical
Hierarchical variables treated atomatically when
TRUE- varGroups
List of variable groups, possibly with data (see details and examples).
- dimVar
The dimensional variables
- extraVar
Extra variables as variable names, TRUE (all remaining) or FALSE (none).
Details
With no frequency variable in input (microdata), the frequency variable in output consists of ones and zeros.
By default, all variables, except the frequencies, are considered as dimensional variables.
By default, the grouping of dimensional variables is based on hierarchical relationships (hierarchical = TRUE).
With varGroups = NULL and hierarchical = FALSE,
each dimensional variable forms a separate group (as as.list(dimVar)).
Parameter extraVar can be specified as variable names.
TRUE means all remaining variables and FALSE no variables.
When the contents of varGroups[[i]] is variable names, the data frame unique(data[varGroups[[i]]]) will be made as a
building block within the function. A possibility is to supply such a data frame instead of variable names.
Then, the building block will be unique(varGroups[[i]]). Names and data frames can be mixed.
See also
Advanced possibilities by varGroups-attribute. See Extend0rnd1.
Examples
z <- SSBtoolsData("sprt_emp_withEU")[c(1, 4:6, 8, 11:15), ]
z$age[z$age == "Y15-29"] <- "young"
z$age[z$age == "Y30-64"] <- "old"
Extend0(z[, -4])
#> age geo year eu freq
#> 1 young Spain 2014 EU 1
#> 2 old Spain 2014 EU 1
#> 3 old Iceland 2014 nonEU 1
#> 4 old Portugal 2014 EU 1
#> 5 young Iceland 2015 nonEU 1
#> 6 old Iceland 2015 nonEU 1
#> 7 old Portugal 2015 EU 1
#> 8 young Spain 2016 EU 1
#> 9 young Iceland 2016 nonEU 1
#> 10 young Portugal 2016 EU 1
#> 11 young Iceland 2014 nonEU 0
#> 12 young Portugal 2014 EU 0
#> 13 young Spain 2015 EU 0
#> 14 old Spain 2015 EU 0
#> 15 young Portugal 2015 EU 0
#> 16 old Spain 2016 EU 0
#> 17 old Iceland 2016 nonEU 0
#> 18 old Portugal 2016 EU 0
Extend0(z, hierarchical = FALSE, dimVar = c("age", "geo", "eu"))
#> age geo year ths_per eu freq
#> 1 young Spain 2014 66.9 EU 1
#> 2 old Spain 2014 120.3 EU 1
#> 3 old Iceland 2014 1.5 nonEU 1
#> 4 old Portugal 2014 20.2 EU 1
#> 5 young Iceland 2015 1.9 nonEU 1
#> 6 old Iceland 2015 1.6 nonEU 1
#> 7 old Portugal 2015 24.3 EU 1
#> 8 young Spain 2016 69.1 EU 1
#> 9 young Iceland 2016 1.9 nonEU 1
#> 10 young Portugal 2016 12.7 EU 1
#> 11 young Iceland <NA> 0.0 EU 0
#> 12 old Iceland <NA> 0.0 EU 0
#> 13 young Spain <NA> 0.0 nonEU 0
#> 14 old Spain <NA> 0.0 nonEU 0
#> 15 young Portugal <NA> 0.0 nonEU 0
#> 16 old Portugal <NA> 0.0 nonEU 0
Extend0(z, hierarchical = FALSE, dimVar = c("age", "geo", "eu"), extraVar = "year")
#> age geo year eu freq
#> 1 young Spain 2014 EU 1
#> 2 old Spain 2014 EU 1
#> 3 old Iceland 2014 nonEU 1
#> 4 old Portugal 2014 EU 1
#> 5 young Iceland 2015 nonEU 1
#> 6 old Iceland 2015 nonEU 1
#> 7 old Portugal 2015 EU 1
#> 8 young Spain 2016 EU 1
#> 9 young Iceland 2016 nonEU 1
#> 10 young Portugal 2016 EU 1
#> 11 young Iceland <NA> EU 0
#> 12 old Iceland <NA> EU 0
#> 13 young Spain <NA> nonEU 0
#> 14 old Spain <NA> nonEU 0
#> 15 young Portugal <NA> nonEU 0
#> 16 old Portugal <NA> nonEU 0
Extend0(z, hierarchical = FALSE, dimVar = c("age", "geo", "eu"), extraVar = FALSE)
#> age geo eu freq
#> 1 young Spain EU 1
#> 2 old Spain EU 1
#> 3 old Iceland nonEU 1
#> 4 old Portugal EU 1
#> 5 young Iceland nonEU 1
#> 6 old Iceland nonEU 1
#> 7 old Portugal EU 1
#> 8 young Spain EU 1
#> 9 young Iceland nonEU 1
#> 10 young Portugal EU 1
#> 11 young Iceland EU 0
#> 12 old Iceland EU 0
#> 13 young Spain nonEU 0
#> 14 old Spain nonEU 0
#> 15 young Portugal nonEU 0
#> 16 old Portugal nonEU 0
Extend0(z, varGroups = list(c("age", "geo", "year"), "eu"))
#> age geo year ths_per eu freq
#> 1 young Spain 2014 66.9 EU 1
#> 2 old Spain 2014 120.3 EU 1
#> 3 old Iceland 2014 1.5 nonEU 1
#> 4 old Portugal 2014 20.2 EU 1
#> 5 young Iceland 2015 1.9 nonEU 1
#> 6 old Iceland 2015 1.6 nonEU 1
#> 7 old Portugal 2015 24.3 EU 1
#> 8 young Spain 2016 69.1 EU 1
#> 9 young Iceland 2016 1.9 nonEU 1
#> 10 young Portugal 2016 12.7 EU 1
#> 11 old Iceland 2014 0.0 EU 0
#> 12 young Iceland 2015 0.0 EU 0
#> 13 old Iceland 2015 0.0 EU 0
#> 14 young Iceland 2016 0.0 EU 0
#> 15 young Spain 2014 0.0 nonEU 0
#> 16 old Spain 2014 0.0 nonEU 0
#> 17 old Portugal 2014 0.0 nonEU 0
#> 18 old Portugal 2015 0.0 nonEU 0
#> 19 young Spain 2016 0.0 nonEU 0
#> 20 young Portugal 2016 0.0 nonEU 0
Extend0(MakeFreq(z[c(1, 1, 1, 2, 2, 3:10), -4]))
#> age geo year eu freq
#> 1 old Iceland 2014 nonEU 1
#> 2 old Iceland 2015 nonEU 1
#> 3 old Portugal 2014 EU 1
#> 4 old Portugal 2015 EU 1
#> 5 old Spain 2014 EU 2
#> 6 young Iceland 2015 nonEU 1
#> 7 young Iceland 2016 nonEU 1
#> 8 young Portugal 2016 EU 1
#> 9 young Spain 2014 EU 3
#> 10 young Spain 2016 EU 1
#> 11 young Iceland 2014 nonEU 0
#> 12 young Portugal 2014 EU 0
#> 13 young Portugal 2015 EU 0
#> 14 old Spain 2015 EU 0
#> 15 young Spain 2015 EU 0
#> 16 old Iceland 2016 nonEU 0
#> 17 old Portugal 2016 EU 0
#> 18 old Spain 2016 EU 0
Extend0(z, "ths_per")
#> age geo year ths_per eu
#> 1 young Spain 2014 66.9 EU
#> 2 old Spain 2014 120.3 EU
#> 3 old Iceland 2014 1.5 nonEU
#> 4 old Portugal 2014 20.2 EU
#> 5 young Iceland 2015 1.9 nonEU
#> 6 old Iceland 2015 1.6 nonEU
#> 7 old Portugal 2015 24.3 EU
#> 8 young Spain 2016 69.1 EU
#> 9 young Iceland 2016 1.9 nonEU
#> 10 young Portugal 2016 12.7 EU
#> 11 young Iceland 2014 0.0 nonEU
#> 12 young Portugal 2014 0.0 EU
#> 13 young Spain 2015 0.0 EU
#> 14 old Spain 2015 0.0 EU
#> 15 young Portugal 2015 0.0 EU
#> 16 old Spain 2016 0.0 EU
#> 17 old Iceland 2016 0.0 nonEU
#> 18 old Portugal 2016 0.0 EU
# varGroups with data frames (same result as with names above)
Extend0(z, varGroups = list(z[c("age", "geo", "year")], z["eu"]))
#> age geo year ths_per eu freq
#> 1 young Spain 2014 66.9 EU 1
#> 2 old Spain 2014 120.3 EU 1
#> 3 old Iceland 2014 1.5 nonEU 1
#> 4 old Portugal 2014 20.2 EU 1
#> 5 young Iceland 2015 1.9 nonEU 1
#> 6 old Iceland 2015 1.6 nonEU 1
#> 7 old Portugal 2015 24.3 EU 1
#> 8 young Spain 2016 69.1 EU 1
#> 9 young Iceland 2016 1.9 nonEU 1
#> 10 young Portugal 2016 12.7 EU 1
#> 11 old Iceland 2014 0.0 EU 0
#> 12 young Iceland 2015 0.0 EU 0
#> 13 old Iceland 2015 0.0 EU 0
#> 14 young Iceland 2016 0.0 EU 0
#> 15 young Spain 2014 0.0 nonEU 0
#> 16 old Spain 2014 0.0 nonEU 0
#> 17 old Portugal 2014 0.0 nonEU 0
#> 18 old Portugal 2015 0.0 nonEU 0
#> 19 young Spain 2016 0.0 nonEU 0
#> 20 young Portugal 2016 0.0 nonEU 0
# varGroups with both names and data frame
Extend0(z, varGroups = list(c("year", "geo", "eu"), data.frame(age = c("middle", "old"))))
#> age geo year ths_per eu freq
#> 1 young Spain 2014 66.9 EU 1
#> 2 old Spain 2014 120.3 EU 1
#> 3 old Iceland 2014 1.5 nonEU 1
#> 4 old Portugal 2014 20.2 EU 1
#> 5 young Iceland 2015 1.9 nonEU 1
#> 6 old Iceland 2015 1.6 nonEU 1
#> 7 old Portugal 2015 24.3 EU 1
#> 8 young Spain 2016 69.1 EU 1
#> 9 young Iceland 2016 1.9 nonEU 1
#> 10 young Portugal 2016 12.7 EU 1
#> 11 middle Spain 2014 0.0 EU 0
#> 12 middle Iceland 2014 0.0 nonEU 0
#> 13 middle Portugal 2014 0.0 EU 0
#> 14 middle Iceland 2015 0.0 nonEU 0
#> 15 middle Portugal 2015 0.0 EU 0
#> 16 middle Spain 2016 0.0 EU 0
#> 17 middle Iceland 2016 0.0 nonEU 0
#> 18 middle Portugal 2016 0.0 EU 0
#> 19 old Spain 2016 0.0 EU 0
#> 20 old Iceland 2016 0.0 nonEU 0
#> 21 old Portugal 2016 0.0 EU 0