R version 4.5.0 beta (2025-04-01 r88091 ucrt) -- "How About a Twenty-Six"
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Platform: x86_64-w64-mingw32/x64

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> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
> 
> library(testthat)
> library(butterfly)
> 
> test_check("butterfly")
The following rows are new in 'df_current': 
        time count
1 2024-03-01    23

i The following values have changes from the previous data.
old vs new
           count
  old[1, ]    17
  old[2, ]    22
  old[3, ]    55
- old[4, ]    18
+ new[4, ]    11

`old$count`: 17.0 22.0 55.0 18.0
`new$count`: 17.0 22.0 55.0 11.0

i Only these rows are returned.
i There are no new rows. Check 'butterflycount$january' is your most recent data, and 'butterflycount$january' is your previous data. If comparing directly, try waldo::compare().
i There are no new rows. Check 'butterflycount$january' is your most recent data, and 'butterflycount$february' is your previous data. If comparing directly, try waldo::compare().
The following rows are new in 'butterflycount$february': 
        time count
1 2024-02-01    17
v And there are no differences with previous data.
The following rows are new in 'butterflycount$march': 
        time count
1 2024-03-01    23

i The following values have changes from the previous data.
old vs new
           count
  old[1, ]    17
  old[2, ]    22
  old[3, ]    55
- old[4, ]    18
+ new[4, ]    11

`old$count`: 17.0 22.0 55.0 18.0
`new$count`: 17.0 22.0 55.0 11.0
The following rows are new in 'butterflycount$march': 
        time count
1 2024-03-01    23
v And there are no differences with previous data.
The following rows are new in 'butterflymess$march': 
         time count
1 2024-03-01     23

i The following values have changes from the previous data.
old vs new
           count
  old[1, ]    17
- old[2, ]    22
+ new[2, ]    NA
  old[3, ]    55
- old[4, ]    18
+ new[4, ]    NA

`old$count`: "17" "22" "55" "18"
`new$count`: "17" NA   "55" NA  
i There are no new rows. Check 'butterflymess$january' is your most recent data, and 'butterflymess$january' is your previous data. If comparing directly, try waldo::compare().
i There are no new rows. Check 'butterflymess$january' is your most recent data, and 'butterflymess$february' is your previous data. If comparing directly, try waldo::compare().
The following rows are new in 'df_current': 
        time count
1 2024-02-01    17
v And there are no differences with previous data.
The following rows are new in 'df_current': 
        time count
1 2024-03-01    23

i The following values have changes from the previous data.
old vs new
           count
  old[1, ]    17
  old[2, ]    22
  old[3, ]    55
- old[4, ]    18
+ new[4, ]    11

`old$count`: 17.0 22.0 55.0 18.0
`new$count`: 17.0 22.0 55.0 11.0
The following rows are new in 'df_current': 
        time count
1 2024-03-01    23

i The following values have changes from the previous data.
old vs new
           count
  old[1, ]    17
  old[2, ]    22
  old[3, ]    55
- old[4, ]    18
+ new[4, ]    11

`old$count`: 17.0 22.0 55.0 18.0
`new$count`: 17.0 22.0 55.0 11.0

i These will be dropped, but new rows are included.
v There are no time lags which are greater than the expected lag: 1 days. By this measure, the timeseries is continuous.
i There are time lags which are greater than the expected lag: 1 days. This indicates the timeseries is not continuous. There are 2 distinct continuous sequences. Use `timeline_group()` to extract.
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 35 ]
> 
> proc.time()
   user  system elapsed 
   2.64    0.34    2.93