I would like to apply the below function (cut.at.n.tile) to a data frame (some_data_frame) whilst grouping by a chosen column (e.g. SomeGroupingColumn) and choosing the target column (e.g. ChosenColumn). I tried using sapply() without success - see code below. Any input very much appreciated. Apologies for this not being fully replicable/self contained ...
cut.at.n.tile <- function(X, n = 7) {
cut(X, breaks = quantile(X, probs = (0:n)/n, na.rm = TRUE),
labels = 1:n, include.lowest = TRUE)
}
some_data_frame$SeasonTypeNumber = sapply(split(some_data_frame['ChosenColumn'], SomeGroupingColumn), cut.at.n.tile)
CodePudding user response：
There are a few problems here.
some_data_frame['ChosenColumn']always returns a single-columndata.frame, not a vector which your function requires. I suggest switching tosome_data_frame[['ChosenColumn']].SomeGroupingColumnlooks like it should be a column (hence the name) in the data, but it is not referenced within a frame. Perhapssome_data_frame[['SomeGroupingColumn']].You need to ensure that the
breaks=used are unique. For example,cut.at.n.tile(subset(mtcars, cyl == 8)$disp) # Error in cut.default(X, breaks = quantile(X, probs = (0:n)/n, na.rm = TRUE), : # 'breaks' are not uniqueIf we debug that function, we see
X # [1] 360.0 360.0 275.8 275.8 275.8 472.0 460.0 440.0 318.0 304.0 350.0 400.0 351.0 301.0 quantile(X, probs = (0:n)/n, na.rm = TRUE) # 0% 14.28571% 28.57143% 42.85714% 57.14286% 71.42857% 85.71429% 100% # 275.8000 275.8000 303.1429 336.2857 354.8571 371.4286 442.8571 472.0000where
275.8is repeated. This can happen based on nuances in the raw data, and you can't really predict when it will occur.Since we'll likely have multiple groups, all of the subvectors'
levels=(sincecutreturns afactor) must be the same length, though admittedly1in one group is unlikely to be the same as1in another group.Since in this case we can never be certain which n-tile a number strictly applies (in
275.8in the first or second n-tile?), we can only adjust one of the dupes and accept the imperfection. I suggest acumsum(duplicated(.)*1e-9): the premise is that it adds an iota to each value that is a dupe, rendering it no-longer a dupe. It is possible that adding1e-9to one value will make it a dupe of the next ... so we can be a little OCD by repeatedly doing this until we have no duplicates.sapplyis unlikely to return a vector, much (almost "certainly") more likely to return alist(if the groups are not perfectly balanced) or amatrix(perfectly balanced). We cannot simplyunlist, since the order of the unlisted vectors will likely not be the order of the source data. We can use`split<-`, or we can use a few other techniques (dplyrand/ordata.table)
Updated function, and demonstration with mtcars:
cut.at.n.tile <- function(X, n = 7) {
brks <- quantile(X, probs = (0:n)/n, na.rm = TRUE)
while (any(dupes <- duplicated(brks))) brks <- brks cumsum(1e-9*dupes)
cut(X, breaks = brks, labels = 1:n, include.lowest = TRUE)
}
base R
ret <- lapply(split(mtcars[['disp']], mtcars[['cyl']]), cut.at.n.tile)
mtcars[["newcol"]] <- NA # create an empty column
split(mtcars[['newcol']], mtcars[['cyl']]) <- ret
mtcars
# mpg cyl disp hp drat wt qsec vs am gear carb newcol
# Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 2
# Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 2
# Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 4
# Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 7
# Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 5
# Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 6
# Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 5
# Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 7
# Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 7
# Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 4
# Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 4
# Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 1
# Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 1
# Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 1
# Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 7
# Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 7
# Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 6
# Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 2
# Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 1
# Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 1
# Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 5
# Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 3
# AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 3
# Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 4
# Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 6
# Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 3
# Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 5
# Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 3
# Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 4
# Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 1
# Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 2
# Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 6
Validation:
cut.at.n.tile(subset(mtcars, cyl == 8)$disp)
# [1] 5 5 1 1 1 7 7 6 3 3 4 6 4 2
# Levels: 1 2 3 4 5 6 7
subset(mtcars, cyl == 8)$newcol
# [1] 5 5 1 1 1 7 7 6 3 3 4 6 4 2
dplyr
library(dplyr)
mtcars %>%
group_by(cyl) %>%
mutate(newcol = cut.at.n.tile(disp)) %>%
ungroup()
# # A tibble: 32 × 12
# mpg cyl disp hp drat wt qsec vs am gear carb newcol
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
# 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 2
# 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 2
# 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 4
# 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 7
# 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 5
# 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 6
# 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 5
# 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 7
# 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 7
# 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 4
# # … with 22 more rows
# # ℹ Use `print(n = ...)` to see more rows
data.table
library(data.table)
as.data.table(mtcars)[, newcol := cut.at.n.tile(disp), by = .(cyl)][]
# mpg cyl disp hp drat wt qsec vs am gear carb newcol
# <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <fctr>
# 1: 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 2
# 2: 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 2
# 3: 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 4
# 4: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 7
# 5: 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 5
# 6: 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 6
# 7: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 5
# 8: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 7
# 9: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 7
# 10: 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 4
# ---
# 23: 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 3
# 24: 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 4
# 25: 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 6
# 26: 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 3
# 27: 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 5
# 28: 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 3
# 29: 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 4
# 30: 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 1
# 31: 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 2
# 32: 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 6