Plots three different color schemes on the top-left and bottom-right triangles and the diagonal of a symmetric matrix
Home Page: https://jhrcook.github.io/ggasym/index.html
License: GNU General Public License v3.0
Change the example so that a warning is not printed. The warning is good, but not really good to show in the showcase. Also show how the function observes groups from group_by – talk it up in the @description, too.
Use complete() from 'tidyr' in asymmetrise().
Make a vignette out of the README as a normal user manual.
Build integration with ‘corrr’ package for tidy correlations.
Let the user change the diagonal between bottom-left to top-right and top-left to bottom-right.
Need to work on formatting the Stats Plotting output of images. They don't look like they do in the Red in RStudio, but they do look the same as the Markdown output
Hi there! The broom dev team just ran reverse dependency checks on the upcoming broom 0.7.0 release and found new errors/test failures for the CRAN version of this package. I've pasted the results below.
ERROR
Running the tests in ‘tests/testthat.R’ failed.
Last 13 lines of output:
>
> test_check("ggasym")
Error : No tidy method for objects of class character
[31m──[39m [31m1. Error: stats asymmetrization works (@test-asymmetrise_stats.R#13) [39m [31m───────[39m
Could not handle input data; try turning into a tibble using the broom package
[1mBacktrace:[22m
[90m 1. [39mtestthat::expect_warning(prepare_data(grps))
[90m 6. [39mggasym::prepare_data(grps)
══ testthat results ═══════════════════════════════════════════════════════════
[ OK: 260 | SKIPPED: 0 | WARNINGS: 1 | FAILED: 1 ]
1. Error: stats asymmetrization works (@test-asymmetrise_stats.R#13)
Error: testthat unit tests failed
Execution halted
I believe these result from the following line in test-asymmetrise_stats:
expect_warning(prepare_data(grps))
broom now errors when given input data for which there are no tidiers.
We hope to submit this new version of the package to CRAN in the coming weeks. If you encounter any problems fixing these issues, please feel free to reach out!🙂
I have gone through the vignettes and adapted my dataframe accordingly but still can't seem to get this to work.
# setup
set.seed(123)
library(tidyverse)
library(ggasym)
library(parameters)
library(correlation)
# dataframe with cprrelations
(df <- as.data.frame(correlation(iris)))
#> Parameter1 Parameter2 r CI_low CI_high t df
#> 1 Sepal.Length Sepal.Width -0.1175698 -0.2726932 0.04351158 -1.440287 148
#> 2 Sepal.Length Petal.Length 0.8717538 0.8270363 0.90550805 21.646019 148
#> 3 Sepal.Length Petal.Width 0.8179411 0.7568971 0.86483606 17.296454 148
#> 4 Sepal.Width Petal.Length -0.4284401 -0.5508771 -0.28794993 -5.768449 148
#> 5 Sepal.Width Petal.Width -0.3661259 -0.4972130 -0.21869663 -4.786461 148
#> 6 Petal.Length Petal.Width 0.9628654 0.9490525 0.97298532 43.387237 148
#> p Method n_Obs
#> 1 1.518983e-01 Pearson 150
#> 2 5.193337e-47 Pearson 150
#> 3 9.301992e-37 Pearson 150
#> 4 1.353994e-07 Pearson 150
#> 5 8.146457e-06 Pearson 150
#> 6 2.805002e-85 Pearson 150
# modifying it to the format expected by ggasym
(asymmat_tib <-
tidyr::unite(
df,
"contrast",
Parameter1:Parameter2,
remove = FALSE
) %>%
asymmetrise_stats(.) %>%
parameters::standardize_names(., "broom") %>%
tibble::as_tibble())
#> # A tibble: 61 x 13
#> contrast parameter1 parameter2 estimate conf.low conf.high statistic df
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 Sepal.L… Sepal.Len… Sepal.Wid… -0.118 -0.273 0.0435 -1.44 148
#> 2 Sepal.L… Sepal.Len… Petal.Len… 0.872 0.827 0.906 21.6 148
#> 3 Sepal.L… Sepal.Len… Petal.Wid… 0.818 0.757 0.865 17.3 148
#> 4 Sepal.W… Sepal.Wid… Petal.Len… -0.428 -0.551 -0.288 -5.77 148
#> 5 Sepal.W… Sepal.Wid… Petal.Wid… -0.366 -0.497 -0.219 -4.79 148
#> 6 Petal.L… Petal.Len… Petal.Wid… 0.963 0.949 0.973 43.4 148
#> 7 Sepal.L… Sepal.Len… Sepal.Wid… -0.118 -0.273 0.0435 -1.44 148
#> 8 Sepal.L… Sepal.Len… Petal.Len… 0.872 0.827 0.906 21.6 148
#> 9 Sepal.L… Sepal.Len… Petal.Wid… 0.818 0.757 0.865 17.3 148
#> 10 Sepal.W… Sepal.Wid… Petal.Len… -0.428 -0.551 -0.288 -5.77 148
#> # … with 51 more rows, and 5 more variables: p.value <dbl>, method <chr>,
#> # n.obs <int>, x <chr>, y <chr>
# plot
ggplot(asymmat_tib, aes(x = x, y = y)) +
geom_asymmat(aes(fill_tl = estimate, fill_br = -log(p.value)))Created on 2020-12-22 by the reprex package (v0.3.0)
devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.0.3 (2020-10-10)
#> os macOS Mojave 10.14.6
#> system x86_64, darwin17.0
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz Europe/Berlin
#> date 2020-12-22
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date lib source
#> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.2)
#> backports 1.2.1 2020-12-09 [1] CRAN (R 4.0.3)
#> bayestestR 0.8.0 2020-12-05 [1] CRAN (R 4.0.3)
#> broom 0.7.3 2020-12-16 [1] CRAN (R 4.0.3)
#> callr 3.5.1 2020-10-13 [1] CRAN (R 4.0.2)
#> cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.0.2)
#> cli 2.2.0 2020-11-20 [1] CRAN (R 4.0.3)
#> colorspace 2.0-0 2020-11-11 [1] CRAN (R 4.0.2)
#> correlation * 0.5.0 2020-12-02 [1] CRAN (R 4.0.3)
#> crayon 1.3.4 2017-09-16 [1] CRAN (R 4.0.2)
#> curl 4.3 2019-12-02 [1] CRAN (R 4.0.1)
#> DBI 1.1.0 2019-12-15 [1] CRAN (R 4.0.2)
#> dbplyr 2.0.0 2020-11-03 [1] CRAN (R 4.0.2)
#> desc 1.2.0 2018-05-01 [1] CRAN (R 4.0.2)
#> devtools 2.3.2 2020-09-18 [1] CRAN (R 4.0.2)
#> digest 0.6.27 2020-10-24 [1] CRAN (R 4.0.2)
#> dplyr * 1.0.2 2020-08-18 [1] CRAN (R 4.0.2)
#> effectsize 0.4.1 2020-12-07 [1] CRAN (R 4.0.3)
#> ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.2)
#> evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.1)
#> fansi 0.4.1 2020-01-08 [1] CRAN (R 4.0.2)
#> farver 2.0.3 2020-01-16 [1] CRAN (R 4.0.2)
#> forcats * 0.5.0 2020-03-01 [1] CRAN (R 4.0.2)
#> fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2)
#> generics 0.1.0 2020-10-31 [1] CRAN (R 4.0.2)
#> ggasym * 0.1.5 2020-07-15 [1] CRAN (R 4.0.2)
#> ggplot2 * 3.3.2 2020-06-19 [1] CRAN (R 4.0.2)
#> glue 1.4.2 2020-08-27 [1] CRAN (R 4.0.2)
#> gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.2)
#> haven 2.3.1 2020-06-01 [1] CRAN (R 4.0.2)
#> highr 0.8 2019-03-20 [1] CRAN (R 4.0.2)
#> hms 0.5.3 2020-01-08 [1] CRAN (R 4.0.2)
#> htmltools 0.5.0 2020-06-16 [1] CRAN (R 4.0.2)
#> httr 1.4.2 2020-07-20 [1] CRAN (R 4.0.2)
#> insight 0.11.1 2020-12-08 [1] CRAN (R 4.0.3)
#> jsonlite 1.7.2 2020-12-09 [1] CRAN (R 4.0.3)
#> knitr 1.30 2020-09-22 [1] CRAN (R 4.0.2)
#> lifecycle 0.2.0 2020-03-06 [1] CRAN (R 4.0.2)
#> lubridate 1.7.9.2 2020-11-13 [1] CRAN (R 4.0.3)
#> magrittr 2.0.1 2020-11-17 [1] CRAN (R 4.0.3)
#> memoise 1.1.0 2017-04-21 [1] CRAN (R 4.0.2)
#> mime 0.9 2020-02-04 [1] CRAN (R 4.0.2)
#> modelr 0.1.8 2020-05-19 [1] CRAN (R 4.0.2)
#> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.2)
#> parameters * 0.10.1 2020-12-08 [1] CRAN (R 4.0.3)
#> pillar 1.4.7 2020-11-20 [1] CRAN (R 4.0.3)
#> pkgbuild 1.2.0 2020-12-15 [1] CRAN (R 4.0.3)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.2)
#> pkgload 1.1.0 2020-05-29 [1] CRAN (R 4.0.2)
#> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.2)
#> processx 3.4.5 2020-11-30 [1] CRAN (R 4.0.3)
#> ps 1.5.0 2020-12-05 [1] CRAN (R 4.0.3)
#> purrr * 0.3.4 2020-04-17 [1] CRAN (R 4.0.2)
#> R6 2.5.0 2020-10-28 [1] CRAN (R 4.0.2)
#> Rcpp 1.0.5 2020-07-06 [1] CRAN (R 4.0.2)
#> readr * 1.4.0 2020-10-05 [1] CRAN (R 4.0.2)
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#> reprex 0.3.0 2019-05-16 [1] CRAN (R 4.0.2)
#> rlang 0.4.9 2020-11-26 [1] CRAN (R 4.0.3)
#> rmarkdown 2.6 2020-12-14 [1] CRAN (R 4.0.3)
#> rprojroot 2.0.2 2020-11-15 [1] CRAN (R 4.0.3)
#> rvest 0.3.6 2020-07-25 [1] CRAN (R 4.0.2)
#> scales 1.1.1 2020-05-11 [1] CRAN (R 4.0.2)
#> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.2)
#> stringi 1.5.3 2020-09-09 [1] CRAN (R 4.0.2)
#> stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.0.2)
#> testthat 3.0.1 2020-12-17 [1] CRAN (R 4.0.3)
#> tibble * 3.0.4 2020-10-12 [1] CRAN (R 4.0.2)
#> tidyr * 1.1.2 2020-08-27 [1] CRAN (R 4.0.2)
#> tidyselect 1.1.0 2020-05-11 [1] CRAN (R 4.0.2)
#> tidyverse * 1.3.0 2019-11-21 [1] CRAN (R 4.0.2)
#> usethis 2.0.0 2020-12-10 [1] CRAN (R 4.0.3)
#> utf8 1.1.4 2018-05-24 [1] CRAN (R 4.0.2)
#> vctrs 0.3.6 2020-12-17 [1] CRAN (R 4.0.3)
#> withr 2.3.0 2020-09-22 [1] CRAN (R 4.0.2)
#> xfun 0.19 2020-10-30 [1] CRAN (R 4.0.2)
#> xml2 1.3.2 2020-04-23 [1] CRAN (R 4.0.2)
#> yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.2)
#>
#> [1] /Users/patil/Library/R/4.0/library
#> [2] /Library/Frameworks/R.framework/Versions/4.0/Resources/libraryThe development version of scales now uses farver to perform colour interpolation. This has result in a few non-perceptual changes to changes, and it looks like you're testing for exact equality:
Failure: scale_fill_tl/br/diag_gradient2 values populate properly (@test-scale_continuous_asym.R#185)
g3_build$data[[3]]$fill_diag not equal to `diag_cols`.
2/6 mismatches
x[1]: "#E40081"
y[1]: "#E30081"
x[3]: "#E40081"
y[3]: "#E30081"
Would you mind relaxing the test so I can get scales to CRAN in the next week or so?
ggasym/vignettes/ggasym-stats.Rmd
Line 7 in e2471b0
Line 47 in 5b8fe40
Add installation from CRAN.
Line 231 in e2471b0
To follow summarise pattern in dplyr
ggasym/R/scale_fill_gradient.R
Line 1 in 597939f
No @return in documentation of scale_fill_tl/br_gradient* functions.
Follow this example to make ggasym key glyphs.
We have detected the following issue with the next ggplot2 release
Run cloud_details(, "ggasym") for more info
Running ‘spelling.R’
Running ‘testthat.R’
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(ggasym)
>
> test_check("ggasym")
── 1. Failure: geom_asymmat works (@test-geom_asymmat.R#48) ───────────────────
`ggplot_build(g_asymmat)` produced warnings.
══ testthat results ═══════════════════════════════════════════════════════════
[ OK: 266 | SKIPPED: 0 | WARNINGS: 36 | FAILED: 1 ]
1. Failure: geom_asymmat works (@test-geom_asymmat.R#48)
Error: testthat unit tests failed
Execution halted
Looking into it, the issue is in this line
Line 48 in 94e6c51
where you check for factor inheritance by comparing the class attribute with "factor". As the class attribute may be longer than one (and in the new release is for discrete mappings), you'll get the warning the condition has length > 1 and only the first element will be used. To fix this, change the class check to use inherit():
if (inherit(.x_data, "factor") | inherit(.y_data, "inherit")) {
...which is the correct way to check for class inheritance.
We will submit ggplot2 v3.3.1 in two weeks, so please submit a fix before then — you are welcome to reach out with any questions
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