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From a dataframe I get a character vector of a column by: arrange by date, time and group_by date

fall_hc %>% arrange(a_dat,AZeit) %>% group_by(a_dat) %>%
        mutate(time_vec = str_c(snzeit,collapse= ",")) %>% 
        ungroup() %>% 
        filter(!is.na(time_vec))

This results in character vectors like:

x <- c("5, 31, 16, 64, 9, 10, 31")

I need a numeric vector from this x within a dataframe: 5 31 16 64 9 10 31.
because I want to calculate diff(x): 26 -15 48 -55 1 21.
and further process this to calculate the number of negativ differences.

Here are some data:

tibble::tribble(
              ~a_dat,  ~AZeit, ~snzeit,
        "2019-01-02", "24180",      31,
        "2019-01-02", "24360",      27,
        "2019-01-02", "24480",      16,
        "2019-01-02", "24780",      64,
        "2019-01-02", "30420",       9,
        "2019-01-02", "30840",      10,
        "2019-01-02", "35280",      31,
        "2019-01-03", "24120",      40,
        "2019-01-03", "24120",      27,
        "2019-01-03", "24480",       6,
        "2019-01-03", "24480",       4,
        "2019-01-03", "24780",       9,
        "2019-01-03", "25380",      25,
        "2019-01-03", "26460",      33,
        "2019-01-04", "24000",       5,
        "2019-01-04", "24360",       2,
        "2019-01-04", "24900",       1,
        "2019-01-04", "27180",      29,
        "2019-01-04", "30600",       8,
        "2019-01-07", "24780",      25,
        "2019-01-07", "24840",       4,
        "2019-01-07", "28920",       3,
        "2019-01-07", "31620",      11,
        "2019-01-08", "24060",      46,
        "2019-01-08", "24480",       7,
        "2019-01-08", "25260",       4,
        "2019-01-08", "27900",       5,
        "2019-01-08", "29820",       5,
        "2019-01-08", "30060",      74,
        "2019-01-08", "33360",       5,
        "2019-01-08", "33600",      28,
        "2019-01-08", "34200",      15,
        "2019-01-08", "35520",      13,
        "2019-01-08", "36000",      19,
        "2019-01-08", "44100",      24
        )

They are already sorted, so you can leave out %>% arrange(a_dat,AZeit).

To clarify the purpose: I need to know how the operations are sorted with respect to snzeit, thats the time from cut to suture. Want to identify those days where they are sorted from short to long.

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4
  • You can do as.numeric before diff Commented Jul 6, 2022 at 12:28
  • So for each date group you want to calculate the number of negative differences in snzeit column ? Commented Jul 7, 2022 at 9:08
  • at the moment I want to identify these groups with increasing snzeit. Theory: with increasing snzeit the waiting time of the patients is reduced. Want to prove that with my data. Commented Jul 7, 2022 at 9:23
  • I am quite lost about what you want to do, but I suppose you should be able to solve it now from the solutions given to this question. Thanks. Commented Jul 7, 2022 at 10:05

2 Answers 2

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2

If I have understood your problem correctly, you donot need to create that character vector in the 1st place, you just need list-column and then applying diff on each list and calculate the number of negative values for each a_dat


library(dplyr)
library(tibble)


fall_hc <- tibble::tribble(
  ~a_dat,  ~AZeit, ~snzeit,
  "2019-01-02", "24180",      31,
  "2019-01-02", "24360",      27,
  "2019-01-02", "24480",      16,
  "2019-01-02", "24780",      64,
  "2019-01-02", "30420",       9,
  "2019-01-02", "30840",      10,
  "2019-01-02", "35280",      31,
  "2019-01-03", "24120",      40,
  "2019-01-03", "24120",      27,
  "2019-01-03", "24480",       6,
  "2019-01-03", "24480",       4,
  "2019-01-03", "24780",       9,
  "2019-01-03", "25380",      25,
  "2019-01-03", "26460",      33,
  "2019-01-04", "24000",       5,
  "2019-01-04", "24360",       2,
  "2019-01-04", "24900",       1,
  "2019-01-04", "27180",      29,
  "2019-01-04", "30600",       8,
  "2019-01-07", "24780",      25,
  "2019-01-07", "24840",       4,
  "2019-01-07", "28920",       3,
  "2019-01-07", "31620",      11,
  "2019-01-08", "24060",      46,
  "2019-01-08", "24480",       7,
  "2019-01-08", "25260",       4,
  "2019-01-08", "27900",       5,
  "2019-01-08", "29820",       5,
  "2019-01-08", "30060",      74,
  "2019-01-08", "33360",       5,
  "2019-01-08", "33600",      28,
  "2019-01-08", "34200",      15,
  "2019-01-08", "35520",      13,
  "2019-01-08", "36000",      19,
  "2019-01-08", "44100",      24
)


fall_hc %>%
    arrange(a_dat, AZeit) %>%
    group_by(a_dat) %>%
    summarise(
        time_vec = list(snzeit)
    ) %>%
    group_by(a_dat) %>%
    summarise(
        time_vec = diff(unlist(time_vec)) # caluculate diffs for each list
    ) %>%
    group_by(a_dat) %>%
    summarise(
        time_vec_neg = sum(time_vec < 0) # count number of negative values
    )

#> `summarise()` has grouped output by 'a_dat'. You can override using the
#> `.groups` argument.
#> # A tibble: 5 × 2
#>   a_dat      time_vec_neg
#>   <chr>             <int>
#> 1 2019-01-02            3
#> 2 2019-01-03            3
#> 3 2019-01-04            3
#> 4 2019-01-07            2
#> 5 2019-01-08            5

Created on 2022-07-07 by the reprex package (v2.0.1)

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5 Comments

Thanks I tried that already. Works only on single vectors. Within a dataframe I get an error: must be size 8350 or 1, not 87908.
@PeterHahn, can you then share a sample of your fall_hc data, so that i can give a try?!?
Will prepare it, take some time, must remove data by which someone can identify patients.
Here are the data:
@PeterHahn, I have updated the code...does this work for you?!
0

Keep numbers as numeric in a list:

mtcars %>% 
  group_by(cyl) %>% 
  summarise(x = list(mpg))
# # A tibble: 3 x 2
#         cyl x         
#      <dbl> <list>    
#    1     4 <dbl [11]>
#    2     6 <dbl [7]> 
#    3     8 <dbl [14]>

Then we can do below on the list of numbers:

mtcars %>% 
  group_by(cyl) %>% 
  summarise(x = list(mpg)) %>% 
  group_by(cyl) %>% 
  summarise(xDiffSum = sum(diff(unlist(x))))
# # A tibble: 3 x 2
#      cyl xDiffSum
#   <dbl>    <dbl>
# 1     4    -1.40
# 2     6    -1.3 
# 3     8    -3.7
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