clprep

Presets: a published dataset’s columns onto a generic function’s arguments

clprep(data = NULL, preset = NULL)

A preset says how the columns of one published dataset (an Ontario segregation table, the Special Investigations Unit’s cases, a use-of-force file) feed the arguments of one of crimlab’s generic functions, so that generic(clprep(data, preset)) gives the published analysis while the same generic runs on anyone’s data given as plain vectors. A preset describes a layout and ships no data: the user supplies the table.

Parameters

data: The dataset: a data frame with the columns the preset names. NULL (with preset NULL) lists the presets.
preset: The preset’s name, as clprep() lists them; NULL for the list.

Returns

With a preset, an object of class clpre: a list of the generic’s arguments, which the generic takes as its first argument, with attributes preset, generic, source, units, note and drop. Without one, a data frame of the presets: preset, generic, args and source.

Notes

The presets are rows of presets.tsv (in the installed package’s presets folder), one per preset and argument: preset, generic, arg, column (the dataset’s column), recode, filter, units, note, source. Python reads the same file. A recode is one of: empty (the column as it is); fac (a factor, one effect per value); num (numbers); one (a 1 for every row); sub:a,b (characters a to b, as a year from an ISO date); const:v (the argument is v, no column); or a mapping x|y=1;has:abc=1;*=0 from labels, compared without case or surrounding space (has: a label containing the text), to values, * covering every other label and *=NA dropping those rows. A filter col=value keeps the rows whose column has that value. An arg of recipe derives columns first: long stacks the two count columns named in recode as count with with 1 for the first; acvm and pyr reduce one row per placement to one per person and year (see Details of the returned attributes).

Rows with a missing value in any column the preset reads are dropped, as the source analyses drop them; their number is the attribute drop.

Examples

head(clprep())
# a table laid out as the published OTIS c01 table (numbers made up)
d <- data.frame(EndFiscalYear = rep(2020:2022, each = 2),
                Gender = rep(c("Female", "Male"), 3),
                NumberIndividuals_RestrictiveConfinement =
                  c(40, 310, 52, 335, 47, 360))
p <- clprep(d, "otis-c01-cnt")
cntreg(p)
# the same model on plain vectors
cntreg(d$NumberIndividuals_RestrictiveConfinement, d$Gender == "Female",
       adj = factor(d$EndFiscalYear))

Generated from the R help page: clprep in the R reference.