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IndrajeetPatil opened this issue Nov 28, 2018 · 17 comments
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feature request: layer_data returns a tibble #3018

IndrajeetPatil opened this issue Nov 28, 2018 · 17 comments

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@IndrajeetPatil
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I often use the layer_ functions from ggplot2 for units tests. I was wondering if the layer_data function can return a tibble, which would make it much easier to peruse the data. Currently, this just overwhelms the console.

library(ggplot2)
#> Registered S3 methods overwritten by 'ggplot2':
#>   method         from 
#>   [.quosures     rlang
#>   c.quosures     rlang
#>   print.quosures rlang

# plot
p <- ggplot(iris, aes(Species, Sepal.Length)) + geom_point()

# checking data used
ggplot2::layer_data(p)
#>     x   y PANEL group shape colour size fill alpha stroke
#> 1   1 5.1     1     1    19  black  1.5   NA    NA    0.5
#> 2   1 4.9     1     1    19  black  1.5   NA    NA    0.5
#> 3   1 4.7     1     1    19  black  1.5   NA    NA    0.5
#> 4   1 4.6     1     1    19  black  1.5   NA    NA    0.5
#> 5   1 5.0     1     1    19  black  1.5   NA    NA    0.5
#> 6   1 5.4     1     1    19  black  1.5   NA    NA    0.5
#> 7   1 4.6     1     1    19  black  1.5   NA    NA    0.5
#> 8   1 5.0     1     1    19  black  1.5   NA    NA    0.5
#> 9   1 4.4     1     1    19  black  1.5   NA    NA    0.5
#> 10  1 4.9     1     1    19  black  1.5   NA    NA    0.5
#> 11  1 5.4     1     1    19  black  1.5   NA    NA    0.5
#> 12  1 4.8     1     1    19  black  1.5   NA    NA    0.5
#> 13  1 4.8     1     1    19  black  1.5   NA    NA    0.5
#> 14  1 4.3     1     1    19  black  1.5   NA    NA    0.5
#> 15  1 5.8     1     1    19  black  1.5   NA    NA    0.5
#> 16  1 5.7     1     1    19  black  1.5   NA    NA    0.5
#> 17  1 5.4     1     1    19  black  1.5   NA    NA    0.5
#> 18  1 5.1     1     1    19  black  1.5   NA    NA    0.5
#> 19  1 5.7     1     1    19  black  1.5   NA    NA    0.5
#> 20  1 5.1     1     1    19  black  1.5   NA    NA    0.5
#> 21  1 5.4     1     1    19  black  1.5   NA    NA    0.5
#> 22  1 5.1     1     1    19  black  1.5   NA    NA    0.5
#> 23  1 4.6     1     1    19  black  1.5   NA    NA    0.5
#> 24  1 5.1     1     1    19  black  1.5   NA    NA    0.5
#> 25  1 4.8     1     1    19  black  1.5   NA    NA    0.5
#> 26  1 5.0     1     1    19  black  1.5   NA    NA    0.5
#> 27  1 5.0     1     1    19  black  1.5   NA    NA    0.5
#> 28  1 5.2     1     1    19  black  1.5   NA    NA    0.5
#> 29  1 5.2     1     1    19  black  1.5   NA    NA    0.5
#> 30  1 4.7     1     1    19  black  1.5   NA    NA    0.5
#> 31  1 4.8     1     1    19  black  1.5   NA    NA    0.5
#> 32  1 5.4     1     1    19  black  1.5   NA    NA    0.5
#> 33  1 5.2     1     1    19  black  1.5   NA    NA    0.5
#> 34  1 5.5     1     1    19  black  1.5   NA    NA    0.5
#> 35  1 4.9     1     1    19  black  1.5   NA    NA    0.5
#> 36  1 5.0     1     1    19  black  1.5   NA    NA    0.5
#> 37  1 5.5     1     1    19  black  1.5   NA    NA    0.5
#> 38  1 4.9     1     1    19  black  1.5   NA    NA    0.5
#> 39  1 4.4     1     1    19  black  1.5   NA    NA    0.5
#> 40  1 5.1     1     1    19  black  1.5   NA    NA    0.5
#> 41  1 5.0     1     1    19  black  1.5   NA    NA    0.5
#> 42  1 4.5     1     1    19  black  1.5   NA    NA    0.5
#> 43  1 4.4     1     1    19  black  1.5   NA    NA    0.5
#> 44  1 5.0     1     1    19  black  1.5   NA    NA    0.5
#> 45  1 5.1     1     1    19  black  1.5   NA    NA    0.5
#> 46  1 4.8     1     1    19  black  1.5   NA    NA    0.5
#> 47  1 5.1     1     1    19  black  1.5   NA    NA    0.5
#> 48  1 4.6     1     1    19  black  1.5   NA    NA    0.5
#> 49  1 5.3     1     1    19  black  1.5   NA    NA    0.5
#> 50  1 5.0     1     1    19  black  1.5   NA    NA    0.5
#> 51  2 7.0     1     2    19  black  1.5   NA    NA    0.5
#> 52  2 6.4     1     2    19  black  1.5   NA    NA    0.5
#> 53  2 6.9     1     2    19  black  1.5   NA    NA    0.5
#> 54  2 5.5     1     2    19  black  1.5   NA    NA    0.5
#> 55  2 6.5     1     2    19  black  1.5   NA    NA    0.5
#> 56  2 5.7     1     2    19  black  1.5   NA    NA    0.5
#> 57  2 6.3     1     2    19  black  1.5   NA    NA    0.5
#> 58  2 4.9     1     2    19  black  1.5   NA    NA    0.5
#> 59  2 6.6     1     2    19  black  1.5   NA    NA    0.5
#> 60  2 5.2     1     2    19  black  1.5   NA    NA    0.5
#> 61  2 5.0     1     2    19  black  1.5   NA    NA    0.5
#> 62  2 5.9     1     2    19  black  1.5   NA    NA    0.5
#> 63  2 6.0     1     2    19  black  1.5   NA    NA    0.5
#> 64  2 6.1     1     2    19  black  1.5   NA    NA    0.5
#> 65  2 5.6     1     2    19  black  1.5   NA    NA    0.5
#> 66  2 6.7     1     2    19  black  1.5   NA    NA    0.5
#> 67  2 5.6     1     2    19  black  1.5   NA    NA    0.5
#> 68  2 5.8     1     2    19  black  1.5   NA    NA    0.5
#> 69  2 6.2     1     2    19  black  1.5   NA    NA    0.5
#> 70  2 5.6     1     2    19  black  1.5   NA    NA    0.5
#> 71  2 5.9     1     2    19  black  1.5   NA    NA    0.5
#> 72  2 6.1     1     2    19  black  1.5   NA    NA    0.5
#> 73  2 6.3     1     2    19  black  1.5   NA    NA    0.5
#> 74  2 6.1     1     2    19  black  1.5   NA    NA    0.5
#> 75  2 6.4     1     2    19  black  1.5   NA    NA    0.5
#> 76  2 6.6     1     2    19  black  1.5   NA    NA    0.5
#> 77  2 6.8     1     2    19  black  1.5   NA    NA    0.5
#> 78  2 6.7     1     2    19  black  1.5   NA    NA    0.5
#> 79  2 6.0     1     2    19  black  1.5   NA    NA    0.5
#> 80  2 5.7     1     2    19  black  1.5   NA    NA    0.5
#> 81  2 5.5     1     2    19  black  1.5   NA    NA    0.5
#> 82  2 5.5     1     2    19  black  1.5   NA    NA    0.5
#> 83  2 5.8     1     2    19  black  1.5   NA    NA    0.5
#> 84  2 6.0     1     2    19  black  1.5   NA    NA    0.5
#> 85  2 5.4     1     2    19  black  1.5   NA    NA    0.5
#> 86  2 6.0     1     2    19  black  1.5   NA    NA    0.5
#> 87  2 6.7     1     2    19  black  1.5   NA    NA    0.5
#> 88  2 6.3     1     2    19  black  1.5   NA    NA    0.5
#> 89  2 5.6     1     2    19  black  1.5   NA    NA    0.5
#> 90  2 5.5     1     2    19  black  1.5   NA    NA    0.5
#> 91  2 5.5     1     2    19  black  1.5   NA    NA    0.5
#> 92  2 6.1     1     2    19  black  1.5   NA    NA    0.5
#> 93  2 5.8     1     2    19  black  1.5   NA    NA    0.5
#> 94  2 5.0     1     2    19  black  1.5   NA    NA    0.5
#> 95  2 5.6     1     2    19  black  1.5   NA    NA    0.5
#> 96  2 5.7     1     2    19  black  1.5   NA    NA    0.5
#> 97  2 5.7     1     2    19  black  1.5   NA    NA    0.5
#> 98  2 6.2     1     2    19  black  1.5   NA    NA    0.5
#> 99  2 5.1     1     2    19  black  1.5   NA    NA    0.5
#> 100 2 5.7     1     2    19  black  1.5   NA    NA    0.5
#> 101 3 6.3     1     3    19  black  1.5   NA    NA    0.5
#> 102 3 5.8     1     3    19  black  1.5   NA    NA    0.5
#> 103 3 7.1     1     3    19  black  1.5   NA    NA    0.5
#> 104 3 6.3     1     3    19  black  1.5   NA    NA    0.5
#> 105 3 6.5     1     3    19  black  1.5   NA    NA    0.5
#> 106 3 7.6     1     3    19  black  1.5   NA    NA    0.5
#> 107 3 4.9     1     3    19  black  1.5   NA    NA    0.5
#> 108 3 7.3     1     3    19  black  1.5   NA    NA    0.5
#> 109 3 6.7     1     3    19  black  1.5   NA    NA    0.5
#> 110 3 7.2     1     3    19  black  1.5   NA    NA    0.5
#> 111 3 6.5     1     3    19  black  1.5   NA    NA    0.5
#> 112 3 6.4     1     3    19  black  1.5   NA    NA    0.5
#> 113 3 6.8     1     3    19  black  1.5   NA    NA    0.5
#> 114 3 5.7     1     3    19  black  1.5   NA    NA    0.5
#> 115 3 5.8     1     3    19  black  1.5   NA    NA    0.5
#> 116 3 6.4     1     3    19  black  1.5   NA    NA    0.5
#> 117 3 6.5     1     3    19  black  1.5   NA    NA    0.5
#> 118 3 7.7     1     3    19  black  1.5   NA    NA    0.5
#> 119 3 7.7     1     3    19  black  1.5   NA    NA    0.5
#> 120 3 6.0     1     3    19  black  1.5   NA    NA    0.5
#> 121 3 6.9     1     3    19  black  1.5   NA    NA    0.5
#> 122 3 5.6     1     3    19  black  1.5   NA    NA    0.5
#> 123 3 7.7     1     3    19  black  1.5   NA    NA    0.5
#> 124 3 6.3     1     3    19  black  1.5   NA    NA    0.5
#> 125 3 6.7     1     3    19  black  1.5   NA    NA    0.5
#> 126 3 7.2     1     3    19  black  1.5   NA    NA    0.5
#> 127 3 6.2     1     3    19  black  1.5   NA    NA    0.5
#> 128 3 6.1     1     3    19  black  1.5   NA    NA    0.5
#> 129 3 6.4     1     3    19  black  1.5   NA    NA    0.5
#> 130 3 7.2     1     3    19  black  1.5   NA    NA    0.5
#> 131 3 7.4     1     3    19  black  1.5   NA    NA    0.5
#> 132 3 7.9     1     3    19  black  1.5   NA    NA    0.5
#> 133 3 6.4     1     3    19  black  1.5   NA    NA    0.5
#> 134 3 6.3     1     3    19  black  1.5   NA    NA    0.5
#> 135 3 6.1     1     3    19  black  1.5   NA    NA    0.5
#> 136 3 7.7     1     3    19  black  1.5   NA    NA    0.5
#> 137 3 6.3     1     3    19  black  1.5   NA    NA    0.5
#> 138 3 6.4     1     3    19  black  1.5   NA    NA    0.5
#> 139 3 6.0     1     3    19  black  1.5   NA    NA    0.5
#> 140 3 6.9     1     3    19  black  1.5   NA    NA    0.5
#> 141 3 6.7     1     3    19  black  1.5   NA    NA    0.5
#> 142 3 6.9     1     3    19  black  1.5   NA    NA    0.5
#> 143 3 5.8     1     3    19  black  1.5   NA    NA    0.5
#> 144 3 6.8     1     3    19  black  1.5   NA    NA    0.5
#> 145 3 6.7     1     3    19  black  1.5   NA    NA    0.5
#> 146 3 6.7     1     3    19  black  1.5   NA    NA    0.5
#> 147 3 6.3     1     3    19  black  1.5   NA    NA    0.5
#> 148 3 6.5     1     3    19  black  1.5   NA    NA    0.5
#> 149 3 6.2     1     3    19  black  1.5   NA    NA    0.5
#> 150 3 5.9     1     3    19  black  1.5   NA    NA    0.5

Created on 2018-11-28 by the reprex package (v0.2.1)
@batpigandme
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So basically default to the wrapped output below, or am I missing something?

library(tidyverse)
p <- ggplot(iris, aes(Species, Sepal.Length)) + geom_point()
layer_data <- as_tibble(ggplot2::layer_data(p))
layer_data
#> # A tibble: 150 x 10
#>        x     y PANEL group shape colour  size fill  alpha stroke
#>    <int> <dbl> <fct> <int> <dbl> <chr>  <dbl> <lgl> <lgl>  <dbl>
#>  1     1   5.1 1         1    19 black    1.5 NA    NA       0.5
#>  2     1   4.9 1         1    19 black    1.5 NA    NA       0.5
#>  3     1   4.7 1         1    19 black    1.5 NA    NA       0.5
#>  4     1   4.6 1         1    19 black    1.5 NA    NA       0.5
#>  5     1   5   1         1    19 black    1.5 NA    NA       0.5
#>  6     1   5.4 1         1    19 black    1.5 NA    NA       0.5
#>  7     1   4.6 1         1    19 black    1.5 NA    NA       0.5
#>  8     1   5   1         1    19 black    1.5 NA    NA       0.5
#>  9     1   4.4 1         1    19 black    1.5 NA    NA       0.5
#> 10     1   4.9 1         1    19 black    1.5 NA    NA       0.5
#> # ... with 140 more rows

Created on 2018-11-28 by the reprex package (v0.2.1.9000)

@IndrajeetPatil
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Oh, yeah, I can convert the output to a tibble afterward. But I just thought it would be consistent behavior across tidyverse packages if ggplot2 also returned a tibble by default (e.g., broom, readr, purrr all return tibbles).

I usually expect a tibble as an output from tidyverse functions and so found it a bit surprising that this particular function returned a data.frame and raised this issue. You can close this issue if you feel this won't be worth it, but just thought I'd point this out! :)

@batpigandme
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No, it's not necessarily a bad idea, I just genuinely wanted to make sure I wasn't missing something!

@clauswilke
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I think it is critical that layer_data() return exactly the data structure that ggplot2 uses internally, since we use it for debugging and testing.

@thomasp85 Would it be possible for your new data_frame() constructor to also set class tbl_df? Would this have performance implications? Any other reasons why this might be a bad idea?

@yutannihilation
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I'm not sure how this affect the overall performance of ggplot2, but tibble seems slower than data.frame for subsetting by [, especially if the index is character. For $ and [[, tibble is faster.

library(tibble)

d <- data.frame(a = 1, b = 2, c = 3)
t <- as_tibble(d)

m <- bench::mark(
  "`[`,  int, data.frame" = d[, 3, drop = FALSE],
  "`[`,  chr, data.frame" = d[, "c", drop = FALSE],
  "`$`,       data.frame" = d$c,
  "`[[`, int, data.frame" = d[[3]],
  "`[[`, chr, data.frame" = d[["c"]],
  "`[`,  int, tibble" = t[, 3],
  "`[`,  chr, tibble" = t[, "c"],
  "`$`,       tibble" = t$c,
  "`[[`, int, tibble" = t[[3]],
  "`[[`, chr, tibble" = t[["c"]],
  check = FALSE
)

library(ggplot2)

autoplot(m) +
  theme(axis.text.y = element_text(hjust = 0))

Created on 2018-11-30 by the reprex package (v0.2.1)

@clauswilke
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Wow, [ is up to a thousand times slower for tibble than for data.frame. Do the tibble developers know? @krlmlr

@krlmlr krlmlr mentioned this issue Nov 30, 2018
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@krlmlr
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krlmlr commented Nov 30, 2018

Thanks for letting me know. We have a very expensive check and S3 dispatch which both slow down the code, but this is fairly easy to fix. Will do for the 2.0.0 release.

@clauswilke
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Can subsetting for ints be made as fast as data.frame or will it remain ~10x slower?

@yutannihilation
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Can subsetting for ints be made as fast as data.frame or will it remain ~10x slower?

Let's discuss this on tidyverse/tibble#544 (or on a new issue on tibble's repo).

@krlmlr
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krlmlr commented Dec 19, 2018

Subsetting tibbles is now a tad faster on my machine for the use case shown here.

@clauswilke
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Thanks, Kirill!

@thomasp85 Can we now use tibbles internally in ggplot2?

@thomasp85
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I’m unsure about any reason to doing this? Is the impediment simply so that layer_data returns a tibble?

tibbles and data frames have very different sub setting behaviour so the change will likely require changes throughout the codebase

@clauswilke
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clauswilke commented Dec 19, 2018 •
edited
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I think the argument would be that all of tidyverse is moving/has moved to tibbles, so using them internally for ggplot2 would keep things consistent with the rest of the ecosystem. Also, apparently, now they're consistently faster (as the graphic above shows, almost by a factor of 10 for the most common use case, $).

@yutannihilation
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One more argument to support using tibble is that we are already using tibble's spec at least. NEWS says:

Internally, ggplot2 now uses as.data.frame(tibble::as_tibble(x)) to convert a list into a data frame.

@yutannihilation yutannihilation mentioned this issue Dec 27, 2018
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@yutannihilation
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yutannihilation commented Jan 20, 2019 •
edited
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After a short discussion with @thomasp85 and @hadley, and since #3048 actually seems difficult, I'd concude that we cannot use tibble for internal uses; it's technically possible, but it requires a lot of work while the gain is relatively small. So, if we agree with this argument by @clauswilke, I will close this issue.

I think it is critical that layer_data() return exactly the data structure that ggplot2 uses internally, since we use it for debugging and testing.

For a side note, I think it's good if we describe a concrete spec for internal data.frame. It's coincidently similar to tibble's spec for now, but it might diverge someday.

@clauswilke
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I agree.

@yutannihilation yutannihilation mentioned this issue Apr 24, 2019
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This old issue has been automatically locked. If you believe you have found a related problem, please file a new issue (with reprex) and link to this issue. https://reprex.tidyverse.org/

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6 participants
@batpigandme @krlmlr @thomasp85 @yutannihilation @clauswilke @IndrajeetPatil