Using Hmisc cut2 arguments - how does the max argument work?

Question

I have uneven lengths in my huge data set. Ie, 700 observations for 2016, 400 observations from 2017. I have a lot of years of data, so manually clipping the datasets is not feasible.

I want to cut them both into quantiles for observations, but only the first 400 for each group.=

There is a tantalizing "minmax" argument in the Hmisc documentation . Is it possible to use the minmax an argument so Hmisc to only cut quantiles from observations 1-400?

Answer 1

Using dplyr , you can select the first 400 records for each value of year using group_by and slice . Then create quantiles, either within each year or overall.

set.seed(911) # Simulate some uneven data
df <- data.frame(year=rep(2016:2018, times=c(400,500,600)),
                 val=rnorm(1500,50,5))

library(dplyr); library(tidyr)

This creates quantiles within each year

df %>% group_by(year) %>%
  slice(1:400) %>%
  mutate(q4 = cut(val, 
                  breaks=quantile(val, 
                                  probs = seq(0,1,1/4)), 
                  include=TRUE, labels=FALSE)) %>%
# You can stop here and save the output, here I continue to check the counts
  count(q4) %>%
  pivot_wider(names_from=q4, values_from=n)
# A tibble: 3 x 5
# Groups:   year [3]
#   year   `1`   `2`   `3`   `4`
#  <int> <int> <int> <int> <int>
#1  2016   100   100   100   100
#2  2017   100   100   100   100
#3  2018   100   100   100   100

Or you can ungroup to create overall quantiles (counts will differ per year).

df %>% group_by(year) %>%
  slice(1:400) %>%
  ungroup() %>%
  mutate(q4 = cut(val, 
                  breaks=quantile(val, 
                                  probs = seq(0,1,1/4)), 
                  include=TRUE, labels=FALSE)) %>% 
# Stop here to save, or continue to check the counts
  group_by(year) %>%
  count(q4) %>%
  pivot_wider(names_from=q4, values_from=n)

# A tibble: 3 x 5
# Groups:   year [3]
#   year   `1`   `2`   `3`   `4`
#  <int> <int> <int> <int> <int>
#1  2016   116    88   102    94
#2  2017    86   114    85   115
#3  2018    98    98   113    91