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@Roon
Created August 23, 2019 16:57
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# This function takes a vector x and returns a factor representation of the same vector.
# The key advantage of factorize is that you can assign levels for infrequent categories,
# as well as empty and NA values. This makes it much easier to perform
# multidimensional/thematic analysis on your largest population subsets.
factorize <- function(
x, # vector to be transformed
min_freq = .01, # all levels < this % of records will be bucketed
min_n = 1, # all levels < this # of records will be bucketed
NA_level = '(missing)', # level created for NA values
blank_level = '(blank)', # level created for "" values
infrequent_level = 'Other', # level created for bucketing rare values
infrequent_can_include_blank_and_NA = F, # default NA and blank are not bucketed
order = T, # default to ordered
reverse_order = F # default to increasing order
) {
if (class(x) != 'factor'){
x <- as.factor(x)
}
# suspect this is faster than reassigning new factor object
levels(x) <- c(levels(x), NA_level, infrequent_level, blank_level)
# Swap out the NA and blank categories
x[is.na(x)] <- NA_level
x[x == ''] <- blank_level
# Going to use this table to reorder
f_tb <- table(x, useNA = 'always')
# Which levels will be bucketed?
infreq_set <- c(
names(f_tb[f_tb < min_n]),
names(f_tb[(f_tb/sum(f_tb)) < min_freq])
)
# If NA and/or blank were infrequent levels above, this prevents bucketing
if(!infrequent_can_include_blank_and_NA){
infreq_set <- infreq_set[!infreq_set %in% c(NA_level, blank_level)]
}
# Relabel all the infrequent choices
x[x %in% infreq_set] <- infrequent_level
# Return the reordered factor
reorder(droplevels(x), rep(1-(2*reverse_order),length(x)), FUN = sum, order = order)
}
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