Printing more rows than default max has unexpected behavior

[szimmer]

If I have a summary which is long, I sometimes want to add print(n=bignumber) to the end to get all of the summary to print but this doesn't behave as I would expect. Below, I would expect Example 1 and Example 2 to print the same thing but they don't. This is more important when I have even more rows as is true in example 3 and 4. It seems n is being ignored altogether

Here's a reprex to show:

library(skimr)
#> Warning: package 'skimr' was built under R version 4.2.3
library(tidyverse)

sum_cut <- diamonds %>% group_by(cut) %>% skim(carat) %>% yank("numeric")

sum_cut_color <- diamonds %>% group_by(cut, color) %>% skim(carat) %>% yank("numeric")

sum_cut #example 1

Variable type: numeric

skim_variable	cut	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
carat	Fair	0	1	1.05	0.52	0.22	0.70	1.00	1.20	5.01	▇▂▁▁▁
carat	Good	0	1	0.85	0.45	0.23	0.50	0.82	1.01	3.01	▇▆▂▁▁
carat	Very Good	0	1	0.81	0.46	0.20	0.41	0.71	1.02	4.00	▇▃▁▁▁
carat	Premium	0	1	0.89	0.52	0.20	0.41	0.86	1.20	4.01	▇▆▁▁▁
carat	Ideal	0	1	0.70	0.43	0.20	0.35	0.54	1.01	3.50	▇▂▁▁▁

sum_cut %>% print(n=50) # example 2
#> 
#> ── Variable type: numeric ──────────────────────────────────────────────────────
#>   skim_variable cut    n_mis…¹ compl…²  mean    sd   p0  p25  p50  p75 p100 hist
#> 1 carat         Fair         0       1 1.05  0.516 0.22 0.7  1    1.2  5.01 ▇▂▁…
#> 2 carat         Good         0       1 0.849 0.454 0.23 0.5  0.82 1.01 3.01 ▇▆▂…
#> 3 carat         Very …       0       1 0.806 0.459 0.2  0.41 0.71 1.02 4    ▇▃▁…
#> 4 carat         Premi…       0       1 0.892 0.515 0.2  0.41 0.86 1.2  4.01 ▇▆▁…
#> 5 carat         Ideal        0       1 0.703 0.433 0.2  0.35 0.54 1.01 3.5  ▇▂▁…
#> # … with abbreviated variable names ¹​n_missing, ²​complete_rate

sum_cut_color # example 3

Variable type: numeric

skim_variable	cut	color	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
carat	Fair	D	0	1	0.92	0.41	0.25	0.70	0.90	1.01	3.40	▆▇▁▁▁
carat	Fair	E	0	1	0.86	0.36	0.22	0.55	0.90	1.01	2.04	▇▇▇▂▁
carat	Fair	F	0	1	0.90	0.42	0.25	0.60	0.90	1.01	2.58	▇▇▂▁▁
carat	Fair	G	0	1	1.02	0.49	0.23	0.70	0.98	1.07	2.60	▅▇▂▂▁
carat	Fair	H	0	1	1.22	0.55	0.33	0.90	1.01	1.51	4.13	▇▃▂▁▁
carat	Fair	I	0	1	1.20	0.52	0.41	0.88	1.01	1.50	3.02	▇▇▃▂▁
carat	Fair	J	0	1	1.34	0.73	0.30	0.90	1.03	1.69	5.01	▇▃▁▁▁
carat	Good	D	0	1	0.74	0.36	0.23	0.42	0.70	1.00	2.04	▇▅▅▁▁
carat	Good	E	0	1	0.75	0.38	0.23	0.41	0.70	1.00	3.00	▇▅▁▁▁
carat	Good	F	0	1	0.78	0.37	0.23	0.49	0.71	1.01	2.67	▇▆▁▁▁
carat	Good	G	0	1	0.85	0.43	0.23	0.50	0.90	1.01	2.80	▇▇▂▁▁
carat	Good	H	0	1	0.91	0.50	0.25	0.51	0.90	1.09	3.01	▇▇▂▁▁
carat	Good	I	0	1	1.06	0.58	0.30	0.70	1.00	1.50	3.01	▇▆▃▂▁
carat	Good	J	0	1	1.10	0.54	0.28	0.71	1.02	1.50	3.00	▇▇▅▂▁
carat	Very Good	D	0	1	0.70	0.37	0.23	0.40	0.61	1.00	2.58	▇▅▁▁▁
carat	Very Good	E	0	1	0.68	0.38	0.20	0.37	0.57	0.94	2.51	▇▆▁▁▁
carat	Very Good	F	0	1	0.74	0.39	0.23	0.40	0.70	1.01	2.48	▇▇▂▁▁
carat	Very Good	G	0	1	0.77	0.42	0.23	0.40	0.70	1.02	2.52	▇▆▂▁▁
carat	Very Good	H	0	1	0.92	0.50	0.23	0.47	0.90	1.20	3.00	▇▇▂▁▁
carat	Very Good	I	0	1	1.05	0.55	0.24	0.70	1.00	1.50	4.00	▇▆▂▁▁
carat	Very Good	J	0	1	1.13	0.56	0.24	0.71	1.06	1.51	2.74	▇▇▆▃▁
carat	Premium	D	0	1	0.72	0.40	0.20	0.40	0.58	1.01	2.57	▇▅▂▁▁
carat	Premium	E	0	1	0.72	0.41	0.20	0.38	0.58	1.00	3.05	▇▃▁▁▁
carat	Premium	F	0	1	0.83	0.42	0.20	0.43	0.76	1.04	3.01	▇▆▂▁▁
carat	Premium	G	0	1	0.84	0.48	0.23	0.40	0.76	1.12	3.01	▇▆▂▁▁
carat	Premium	H	0	1	1.02	0.54	0.23	0.51	1.01	1.30	3.24	▇▇▃▁▁
carat	Premium	I	0	1	1.14	0.61	0.23	0.59	1.14	1.54	4.01	▇▇▃▁▁
carat	Premium	J	0	1	1.29	0.61	0.30	0.81	1.25	1.70	4.01	▇▇▃▁▁
carat	Ideal	D	0	1	0.57	0.30	0.20	0.33	0.50	0.71	2.75	▇▂▁▁▁
carat	Ideal	E	0	1	0.58	0.31	0.20	0.33	0.50	0.72	2.28	▇▂▁▁▁
carat	Ideal	F	0	1	0.66	0.37	0.23	0.35	0.53	0.90	2.45	▇▃▁▁▁
carat	Ideal	G	0	1	0.70	0.41	0.23	0.34	0.54	1.03	2.54	▇▃▂▁▁
carat	Ideal	H	0	1	0.80	0.49	0.23	0.36	0.70	1.11	3.50	▇▅▁▁▁
carat	Ideal	I	0	1	0.91	0.55	0.23	0.41	0.74	1.22	3.22	▇▃▂▁▁
carat	Ideal	J	0	1	1.06	0.58	0.23	0.54	1.03	1.41	3.01	▇▆▃▂▁

sum_cut_color %>% print(n=50) # example 4
#> 
#> ── Variable type: numeric ──────────────────────────────────────────────────────
#>    skim_…¹ cut color n_mis…² compl…³  mean    sd   p0   p25   p50  p75 p100 hist
#>  1 carat   Fa… D           0       1 0.920 0.405 0.25 0.7   0.9   1.01 3.4  ▆▇▁…
#>  2 carat   Fa… E           0       1 0.857 0.365 0.22 0.552 0.9   1.01 2.04 ▇▇▇…
#>  3 carat   Fa… F           0       1 0.905 0.419 0.25 0.6   0.9   1.01 2.58 ▇▇▂…
#>  4 carat   Fa… G           0       1 1.02  0.493 0.23 0.7   0.98  1.07 2.6  ▅▇▂…
#>  5 carat   Fa… H           0       1 1.22  0.548 0.33 0.9   1.01  1.51 4.13 ▇▃▂…
#>  6 carat   Fa… I           0       1 1.20  0.522 0.41 0.885 1.01  1.50 3.02 ▇▇▃…
#>  7 carat   Fa… J           0       1 1.34  0.734 0.3  0.905 1.03  1.68 5.01 ▇▃▁…
#>  8 carat   Go… D           0       1 0.745 0.363 0.23 0.42  0.7   1    2.04 ▇▅▅…
#>  9 carat   Go… E           0       1 0.745 0.381 0.23 0.41  0.7   1    3    ▇▅▁…
#> 10 carat   Go… F           0       1 0.776 0.370 0.23 0.49  0.71  1.01 2.67 ▇▆▁…
#> 11 carat   Go… G           0       1 0.851 0.433 0.23 0.5   0.9   1.01 2.8  ▇▇▂…
#> 12 carat   Go… H           0       1 0.915 0.498 0.25 0.51  0.9   1.09 3.01 ▇▇▂…
#> 13 carat   Go… I           0       1 1.06  0.576 0.3  0.7   1     1.5  3.01 ▇▆▃…
#> 14 carat   Go… J           0       1 1.10  0.537 0.28 0.71  1.02  1.5  3    ▇▇▅…
#> 15 carat   Ve… D           0       1 0.696 0.369 0.23 0.4   0.61  1    2.58 ▇▅▁…
#> 16 carat   Ve… E           0       1 0.676 0.378 0.2  0.37  0.57  0.94 2.51 ▇▆▁…
#> 17 carat   Ve… F           0       1 0.741 0.389 0.23 0.4   0.7   1.01 2.48 ▇▇▂…
#> 18 carat   Ve… G           0       1 0.767 0.418 0.23 0.4   0.7   1.02 2.52 ▇▆▂…
#> 19 carat   Ve… H           0       1 0.916 0.503 0.23 0.467 0.9   1.2  3    ▇▇▂…
#> 20 carat   Ve… I           0       1 1.05  0.552 0.24 0.7   1.00  1.5  4    ▇▆▂…
#> 21 carat   Ve… J           0       1 1.13  0.556 0.24 0.71  1.06  1.51 2.74 ▇▇▆…
#> 22 carat   Pr… D           0       1 0.722 0.397 0.2  0.4   0.58  1.01 2.57 ▇▅▂…
#> 23 carat   Pr… E           0       1 0.718 0.410 0.2  0.38  0.58  1    3.05 ▇▃▁…
#> 24 carat   Pr… F           0       1 0.827 0.420 0.2  0.43  0.76  1.04 3.01 ▇▆▂…
#> 25 carat   Pr… G           0       1 0.841 0.480 0.23 0.4   0.755 1.12 3.01 ▇▆▂…
#> 26 carat   Pr… H           0       1 1.02  0.544 0.23 0.51  1.01  1.3  3.24 ▇▇▃…
#> 27 carat   Pr… I           0       1 1.14  0.614 0.23 0.59  1.14  1.54 4.01 ▇▇▃…
#> 28 carat   Pr… J           0       1 1.29  0.614 0.3  0.81  1.25  1.7  4.01 ▇▇▃…
#> 29 carat   Id… D           0       1 0.566 0.299 0.2  0.33  0.5   0.71 2.75 ▇▂▁…
#> 30 carat   Id… E           0       1 0.578 0.313 0.2  0.33  0.5   0.72 2.28 ▇▂▁…
#> 31 carat   Id… F           0       1 0.656 0.375 0.23 0.35  0.53  0.9  2.45 ▇▃▁…
#> 32 carat   Id… G           0       1 0.701 0.411 0.23 0.34  0.54  1.03 2.54 ▇▃▂…
#> 33 carat   Id… H           0       1 0.800 0.487 0.23 0.36  0.7   1.11 3.5  ▇▅▁…
#> 34 carat   Id… I           0       1 0.913 0.554 0.23 0.41  0.74  1.22 3.22 ▇▃▂…
#> 35 carat   Id… J           0       1 1.06  0.582 0.23 0.54  1.03  1.41 3.01 ▇▆▃…
#> # … with abbreviated variable names ¹​skim_variable, ²​n_missing, ³​complete_rate

^{Created on 2023-04-03 with reprex v2.0.2}

Session info

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.2 (2022-10-31 ucrt)
#>  os       Windows 10 x64 (build 19045)
#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language (EN)
#>  collate  English_United States.utf8
#>  ctype    English_United States.utf8
#>  tz       America/New_York
#>  date     2023-04-03
#>  pandoc   2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
#> 
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[elinw]

Thanks for this report. Can you clarify what you mean by printing "the same thing"?

sum_cut and sum_cut_color are both class

[1] "one_skim_df" "tbl_df" "tbl"
[4] "data.frame"

sum_cut has 5 rows while sum_cut_color is 35 rows.
sum_cut has has 12 columns, sum_cut_color has 13.

So you wouldn't expect them to be identical when they print because they are not identical objects.

Can you clarify what you mean by expecting them to print the same thing?

[szimmer]

One prints as a nice html table and one does not. The n parameter on print also seems to be ignored

[elinw]

I think it's the use of the print() function I don't see how they should be impacted by the value of n. When I use n=3 I get 3 rows so I think the parameter is not ignored. However it does seem like knit_print.skim_one_df() is not being used.

However, using knit_print() instead of print() seems to solve that.
See:

https://rpubs.com/elinw/1024699

@michaelquinn32 It seems like something is going wrong with the print dispatch? Or is calling print explicitly essentially an override?

[elinw]

I did notice that things seemed to go a bit wild with several hundred variables all of the same type. Maybe we need to be thinking about pagination.