How to do range grouping on a column using dplyr?

by

We can use cut to do the grouping. We create the ‘gr’ column within the group_by, use summarise to create the number of elements in each group (n()), and order the output (arrange) based on ‘gr’.

library(dplyr)
 DT %>% 
     group_by(gr=cut(B, breaks= seq(0, 1, by = 0.05)) ) %>% 
     summarise(n= n()) %>%
     arrange(as.numeric(gr))

As the initial object is data.table, this can be done using data.table methods (included @Frank’s suggestion to use keyby)

library(data.table)
DT[,.N , keyby = .(gr=cut(B, breaks=seq(0, 1, by=0.05)))]

EDIT:

Based on the update in the OP’s post, we could substract a small number to the seq

lvls <- levels(cut(DT$B, seq(0, 1, by =0.05)))
DT %>%
   group_by(gr=cut(B, breaks= seq(0, 1, by = 0.05) -
                 .Machine$double.eps, right=FALSE, labels=lvls)) %>% 
   summarise(n=n()) %>% 
   arrange(as.numeric(gr))
#          gr n
#1   (0,0.05] 2
#2 (0.05,0.1] 2
#3 (0.1,0.15] 3
#4 (0.15,0.2] 2
#5 (0.7,0.75] 1

More Related Contents:

  1. Replace values greater than zero with 1 in r
  2. How does one access the piped data.frame in dplyr::funs?
  3. Extract row corresponding to minimum value of a variable by group
  4. How to select the rows with maximum values in each group with dplyr? [duplicate]
  5. Group by multiple columns and sum other multiple columns
  6. Filter multiple values on a string column in dplyr
  7. Concatenate strings by group with dplyr [duplicate]
  8. What does %>% function mean in R?
  9. Dplyr join on by=(a = b), where a and b are variables containing strings?
  10. R cumulative sum by condition with reset
  11. Proper idiom for adding zero count rows in tidyr/dplyr
  12. cut function in R- labeling without scientific notations for use in ggplot2
  13. Why is enquo + !! preferable to substitute + eval
  14. Add NAs to make all list elements equal length
  15. Assign intermediate output to temp variable as part of dplyr pipeline
  16. pass function arguments to both dplyr and ggplot
  17. Removing NA observations with dplyr::filter()
  18. Mutate multiple columns in a dataframe
  19. dplyr issues when using group_by(multiple variables)
  20. dplyr – using mutate() like rowmeans()
  21. How to deal with nonstandard column names (white space, punctuation, starts with numbers)
  22. dplyr if_else() vs base R ifelse()
  23. Using column names as function arguments
  24. How to change the now deprecated dplyr::funs() which includes an ifelse argument?
  25. ggplot multiple grouping bar
  26. Apply a ggplot-function per group with dplyr and set title per group
  27. Mutate multiple variable to create multiple new variables
  28. How to pass column name as argument to function for dplyr verbs?
  29. How to use data.table within functions and loops?
  30. Override column types when importing data using readr::read_csv() when there are many columns

Leave a Comment