examples/stackoverflow/61207599_reprex.md
In dewittpe/qwraps2: Quick Wraps 2
Adding a comparison row to each row group is not something that
qwraps2::summary_table will support directly. This is because
the issue is related to the limitations of markdown and the complexity of
supporting all the different ways to implement spanning multicol of a table
in LaTeX.
Using qwraps2::summary_table to generate the primary table is a good
starting point. Building the output table itself will require some other
packages.
With the release of qwraps2 version 0.5.0, the mtcars2 data is an exported
data set and does not need to be built explicitly.
library(qwraps2)
options(qwraps2_markup = "markdown")
summaries <- qsummary(mtcars2[, c("mpg", "wt", "gear_factor")])
by_cyl <-
summary_table(mtcars2, summaries = summaries, by = "cyl_factor")
Note that the output from summary_table is a character matrix.
str(by_cyl)
#> 'qwraps2_summary_table' chr [1:11, 1:3] "17.80" "19.70 (18.65, 21.00)" ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : chr [1:11] "minimum" "median (IQR)" "mean (sd)" "maximum" ...
#> ..$ : chr [1:3] "6 cylinders (N = 7)" "4 cylinders (N = 11)" "8 cylinders (N = 14)"
#> - attr(*, "rgroups")= Named int [1:3] 4 4 3
#> ..- attr(*, "names")= chr [1:3] "mpg" "wt" "gear_factor"
Instead of Choen’s D I’ll report and F-statistic and p-value form an
analysis of variance.
mpg_comp <-
paste(extract_fstat(lm(mpg ~ cyl_factor, data = mtcars2)),
extract_fpvalue(lm(mpg ~ cyl_factor, data = mtcars2)),
collapse = ", ")
wt_comp <-
paste(extract_fstat(lm(wt ~ cyl_factor, data = mtcars2)),
extract_fpvalue(lm(wt ~ cyl_factor, data = mtcars2)),
collapse = ", ")
mpg_comp
#> [1] "$F_{2, 29} = 39.70$ *P* < 0.0001"
wt_comp
#> [1] "$F_{2, 29} = 22.91$ *P* < 0.0001"
For building the table, there are many options. Spanning multiple columns in
markdown is not trivial. Different flavors of markdown will render the
tables differently. Some will support multicolumn spanning, other flavors
will not.
For a markdown table, I recommend having a new column with the comparison
reported. For the by_cyl table I would put the F stat and p-value on the
rows where the mean is reported. This puts the statistical test and results
on the line related to the summary statistic.
by_cyl2 <- cbind(by_cyl, "comparison" = " ")
by_cyl2[grepl("mean", rownames(by_cyl2)), "comparison"] <- c(mpg_comp, wt_comp)
by_cyl2
#>
#>
#> | |6 cylinders (N = 7) |4 cylinders (N = 11) |8 cylinders (N = 14) |comparison |
#> |:----------------------------|:--------------------|:--------------------|:--------------------|:--------------------------------|
#> |**mpg** | | | | |
#> | minimum |17.80 |21.40 |10.40 | |
#> | median (IQR) |19.70 (18.65, 21.00) |26.00 (22.80, 30.40) |15.20 (14.40, 16.25) | |
#> | mean (sd) |19.74 ± 1.45 |26.66 ± 4.51 |15.10 ± 2.56 |$F_{2, 29} = 39.70$ *P* < 0.0001 |
#> | maximum |21.40 |33.90 |19.20 | |
#> |**wt** | | | | |
#> | minimum |2.62 |1.51 |3.17 | |
#> | median (IQR) |3.21 (2.82, 3.44) |2.20 (1.89, 2.62) |3.75 (3.53, 4.01) | |
#> | mean (sd) |3.12 ± 0.36 |2.29 ± 0.57 |4.00 ± 0.76 |$F_{2, 29} = 22.91$ *P* < 0.0001 |
#> | maximum |3.46 |3.19 |5.42 | |
#> |**gear_factor** | | | | |
#> | 3 forward gears |2 (29) |1 (9) |12 (86) | |
#> | 4 forward gears |4 (57) |8 (73) |0 (0) | |
#> | 5 forward gears |1 (14) |2 (18) |2 (14) | |
If, instead, the comparison is a new row, I like the idea of using the
summary to add a blank row then add the comparison to the blank row.
summaries[[1]] <- c(summaries[[1]], "comparison" = ~ qwraps2::frmt(""))
summaries[[2]] <- c(summaries[[2]], "comparison" = ~ qwraps2::frmt(""))
by_cyl3 <- summary_table(mtcars2, summaries, by = "cyl_factor")
by_cyl3[grepl("comparison", rownames(by_cyl3)), 1] <- c(mpg_comp, wt_comp)
by_cyl3
#>
#>
#> | |6 cylinders (N = 7) |4 cylinders (N = 11) |8 cylinders (N = 14) |
#> |:----------------------------|:--------------------------------|:--------------------|:--------------------|
#> |**mpg** | | | |
#> | minimum |17.80 |21.40 |10.40 |
#> | median (IQR) |19.70 (18.65, 21.00) |26.00 (22.80, 30.40) |15.20 (14.40, 16.25) |
#> | mean (sd) |19.74 ± 1.45 |26.66 ± 4.51 |15.10 ± 2.56 |
#> | maximum |21.40 |33.90 |19.20 |
#> | comparison |$F_{2, 29} = 39.70$ *P* < 0.0001 | | |
#> |**wt** | | | |
#> | minimum |2.62 |1.51 |3.17 |
#> | median (IQR) |3.21 (2.82, 3.44) |2.20 (1.89, 2.62) |3.75 (3.53, 4.01) |
#> | mean (sd) |3.12 ± 0.36 |2.29 ± 0.57 |4.00 ± 0.76 |
#> | maximum |3.46 |3.19 |5.42 |
#> | comparison |$F_{2, 29} = 22.91$ *P* < 0.0001 | | |
#> |**gear_factor** | | | |
#> | 3 forward gears |2 (29) |1 (9) |12 (86) |
#> | 4 forward gears |4 (57) |8 (73) |0 (0) |
#> | 5 forward gears |1 (14) |2 (18) |2 (14) |
This does not address spanning the multiple columns. That issue is, at least
to my knowledge, non-trivial. I would use different tools and methods
depending on the target file format. If I’m going to build a .pdf I would be
working in LaTeX, not markdown, and use \multicolumn{}{}{} explicitly. If
the target output was html I would build a html table explicitly.
htmlTable is a great package
for that. There are compatibility options with might help when building
.docx or other Office style outputs.
Created on 2020-09-15 by the reprex package (v0.3.0)
devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
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#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz America/Denver
#> date 2020-09-15
#>
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#> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.0.0)
#> testthat 2.3.2 2020-03-02 [1] CRAN (R 4.0.0)
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#> withr 2.2.0 2020-04-20 [1] CRAN (R 4.0.0)
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#> yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.0)
#>
#> [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
dewittpe/qwraps2 documentation built on Jan. 4, 2024, 1:59 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Adding a comparison row to each row group is not something that
qwraps2::summary_table will support directly. This is because
the issue is related to the limitations of markdown and the complexity of
supporting all the different ways to implement spanning multicol of a table
in LaTeX.
Using qwraps2::summary_table to generate the primary table is a good
starting point. Building the output table itself will require some other
packages.
With the release of qwraps2 version 0.5.0, the mtcars2 data is an exported data set and does not need to be built explicitly.
library(qwraps2)
options(qwraps2_markup = "markdown")
summaries <- qsummary(mtcars2[, c("mpg", "wt", "gear_factor")])
by_cyl <-
summary_table(mtcars2, summaries = summaries, by = "cyl_factor")
Note that the output from summary_table is a character matrix.
str(by_cyl)
#> 'qwraps2_summary_table' chr [1:11, 1:3] "17.80" "19.70 (18.65, 21.00)" ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : chr [1:11] "minimum" "median (IQR)" "mean (sd)" "maximum" ...
#> ..$ : chr [1:3] "6 cylinders (N = 7)" "4 cylinders (N = 11)" "8 cylinders (N = 14)"
#> - attr(*, "rgroups")= Named int [1:3] 4 4 3
#> ..- attr(*, "names")= chr [1:3] "mpg" "wt" "gear_factor"
Instead of Choen’s D I’ll report and F-statistic and p-value form an analysis of variance.
mpg_comp <-
paste(extract_fstat(lm(mpg ~ cyl_factor, data = mtcars2)),
extract_fpvalue(lm(mpg ~ cyl_factor, data = mtcars2)),
collapse = ", ")
wt_comp <-
paste(extract_fstat(lm(wt ~ cyl_factor, data = mtcars2)),
extract_fpvalue(lm(wt ~ cyl_factor, data = mtcars2)),
collapse = ", ")
mpg_comp
#> [1] "$F_{2, 29} = 39.70$ *P* < 0.0001"
wt_comp
#> [1] "$F_{2, 29} = 22.91$ *P* < 0.0001"
For building the table, there are many options. Spanning multiple columns in markdown is not trivial. Different flavors of markdown will render the tables differently. Some will support multicolumn spanning, other flavors will not.
For a markdown table, I recommend having a new column with the comparison
reported. For the by_cyl table I would put the F stat and p-value on the
rows where the mean is reported. This puts the statistical test and results
on the line related to the summary statistic.
by_cyl2 <- cbind(by_cyl, "comparison" = " ")
by_cyl2[grepl("mean", rownames(by_cyl2)), "comparison"] <- c(mpg_comp, wt_comp)
by_cyl2
#>
#>
#> | |6 cylinders (N = 7) |4 cylinders (N = 11) |8 cylinders (N = 14) |comparison |
#> |:----------------------------|:--------------------|:--------------------|:--------------------|:--------------------------------|
#> |**mpg** | | | | |
#> | minimum |17.80 |21.40 |10.40 | |
#> | median (IQR) |19.70 (18.65, 21.00) |26.00 (22.80, 30.40) |15.20 (14.40, 16.25) | |
#> | mean (sd) |19.74 ± 1.45 |26.66 ± 4.51 |15.10 ± 2.56 |$F_{2, 29} = 39.70$ *P* < 0.0001 |
#> | maximum |21.40 |33.90 |19.20 | |
#> |**wt** | | | | |
#> | minimum |2.62 |1.51 |3.17 | |
#> | median (IQR) |3.21 (2.82, 3.44) |2.20 (1.89, 2.62) |3.75 (3.53, 4.01) | |
#> | mean (sd) |3.12 ± 0.36 |2.29 ± 0.57 |4.00 ± 0.76 |$F_{2, 29} = 22.91$ *P* < 0.0001 |
#> | maximum |3.46 |3.19 |5.42 | |
#> |**gear_factor** | | | | |
#> | 3 forward gears |2 (29) |1 (9) |12 (86) | |
#> | 4 forward gears |4 (57) |8 (73) |0 (0) | |
#> | 5 forward gears |1 (14) |2 (18) |2 (14) | |
If, instead, the comparison is a new row, I like the idea of using the summary to add a blank row then add the comparison to the blank row.
summaries[[1]] <- c(summaries[[1]], "comparison" = ~ qwraps2::frmt(""))
summaries[[2]] <- c(summaries[[2]], "comparison" = ~ qwraps2::frmt(""))
by_cyl3 <- summary_table(mtcars2, summaries, by = "cyl_factor")
by_cyl3[grepl("comparison", rownames(by_cyl3)), 1] <- c(mpg_comp, wt_comp)
by_cyl3
#>
#>
#> | |6 cylinders (N = 7) |4 cylinders (N = 11) |8 cylinders (N = 14) |
#> |:----------------------------|:--------------------------------|:--------------------|:--------------------|
#> |**mpg** | | | |
#> | minimum |17.80 |21.40 |10.40 |
#> | median (IQR) |19.70 (18.65, 21.00) |26.00 (22.80, 30.40) |15.20 (14.40, 16.25) |
#> | mean (sd) |19.74 ± 1.45 |26.66 ± 4.51 |15.10 ± 2.56 |
#> | maximum |21.40 |33.90 |19.20 |
#> | comparison |$F_{2, 29} = 39.70$ *P* < 0.0001 | | |
#> |**wt** | | | |
#> | minimum |2.62 |1.51 |3.17 |
#> | median (IQR) |3.21 (2.82, 3.44) |2.20 (1.89, 2.62) |3.75 (3.53, 4.01) |
#> | mean (sd) |3.12 ± 0.36 |2.29 ± 0.57 |4.00 ± 0.76 |
#> | maximum |3.46 |3.19 |5.42 |
#> | comparison |$F_{2, 29} = 22.91$ *P* < 0.0001 | | |
#> |**gear_factor** | | | |
#> | 3 forward gears |2 (29) |1 (9) |12 (86) |
#> | 4 forward gears |4 (57) |8 (73) |0 (0) |
#> | 5 forward gears |1 (14) |2 (18) |2 (14) |
This does not address spanning the multiple columns. That issue is, at least
to my knowledge, non-trivial. I would use different tools and methods
depending on the target file format. If I’m going to build a .pdf I would be
working in LaTeX, not markdown, and use \multicolumn{}{}{} explicitly. If
the target output was html I would build a html table explicitly.
htmlTable is a great package
for that. There are compatibility options with might help when building
.docx or other Office style outputs.
Created on 2020-09-15 by the reprex package (v0.3.0)
devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.0.2 (2020-06-22)
#> os macOS Catalina 10.15.6
#> system x86_64, darwin17.0
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz America/Denver
#> date 2020-09-15
#>
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#> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.0)
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#> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.0.0)
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#>
#> [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
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