查找列总和低于 R 中给定值的行

Question

I have a data frame (or data.table). I want to sort the rows in ascending order of a column and then select therows whose column value totals are just lower than a given value.

For example let's say I have the mtcars data frame. I've sorted the rows in increasing order of qsec column. Now I want to find rows whose sum of qsec values are lower than say 100. And if I add the next row the sum will exceed 100.

I wrote a while loop for this but I am looking for a better vectoral solution.

> head((mtcars[order(mtcars$qsec), ]))
                mpg cyl disp  hp drat   wt  qsec vs am gear carb
Ford Pantera L 15.8   8  351 264 4.22 3.17 14.50  0  1    5    4
Maserati Bora  15.0   8  301 335 3.54 3.57 14.60  0  1    5    8
Camaro Z28     13.3   8  350 245 3.73 3.84 15.41  0  0    3    4
Ferrari Dino   19.7   6  145 175 3.62 2.77 15.50  0  1    5    6
Duster 360     14.3   8  360 245 3.21 3.57 15.84  0  0    3    4
Mazda RX4      21.0   6  160 110 3.90 2.62 16.46  0  1    4    4

Answer 1

In data.table use order to arrange columns and the cumsum function to find the rows whose cumulative sum is less than your cutoff

library(data.table)
mtcars <- copy(mtcars)                            # because binding is locked
setDT(mtcars)                                     # convert to data.table   
setorder(mtcars, qsec)                            # reorder rows
out <- mtcars[cumsum(qsec) < 100]                 # filter rows
out

In the tidyverse use arrange to sort columns and filter to select rows by criteria

library(tidyverse)
mtcars %>% arrange(qsec) %>% filter(cumsum(qsec) < 100)

Answer 2

Here are data.table and dplyr solutions which preserve row names, ie, the names of the cars, in line with OP's expected result.

Note that data.table as well as tidyverse drop the row names atttribute from data.frames by default. To keep the row names as part of a data.table or tibble , resp., this has to be requested explicitely.

data.table

library(data.table)
as.data.table(mtcars, key = "qsec", keep.rownames = TRUE)[cumsum(qsec) < 100]

 rn mpg cyl disp hp drat wt qsec vs am gear carb 1: Ford Pantera L 15.8 8 351 264 4.22 3.17 14.50 0 1 5 4 2: Maserati Bora 15.0 8 301 335 3.54 3.57 14.60 0 1 5 8 3: Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 4: Ferrari Dino 19.7 6 145 175 3.62 2.77 15.50 0 1 5 6 5: Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 6: Mazda RX4 21.0 6 160 110 3.90 2.62 16.46 0 1 4 4

Here, as.data.table() replaces copy() , setDT() , and setorder() in one go. Setting the key on qsec orders the rows in ascending order of qsec as requested by the OP. In addition, data.table chaining is used.

dplyr

library(dplyr)
mtcars %>% 
  as_tibble(rownames = "rn") %>% 
  arrange(qsec) %>% 
  filter(cumsum(qsec) < 100)

 # A tibble: 6 x 12 rn mpg cyl disp hp drat wt qsec vs am gear carb <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 2 Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 3 Camaro Z28 13.3 8 350 245 3.73 3.84 15.4 0 0 3 4 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 5 Duster 360 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 6 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4