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---
title: "How to Create Publication-Ready Tables in R"
subtitle: "UseR! 2022"
author: "Raymond Balise with Anna Calderon, Francisco Cardozo, Lauren Nahodyl"
date: "`r format(Sys.time(), '%b %d, %Y')`"
output:
xaringan::moon_reader:
css: ["default", "assets/sydney.css", "assets/cssninja-scaffold.css", "assets/ninjutsu.css"]
lib_dir: libs
nature:
highlightLines: true
highlightSpans: false
countIncrementalSlides: false
beforeInit: "assets/macros.js"
ratio: '16:9' # alternatives '16:9' or '4:3' or others e.g. 13:9
navigation:
scroll: false # disable slide transitions by scrolling
editor_options:
chunk_output_type: console
---
```{r setup, echo = FALSE}
knitr::opts_chunk$set(include = TRUE)
knitr::opts_chunk$set(comment = NA)
xaringanExtra::use_tile_view()
```
```{r jquery, echo=FALSE}
# This is to deal with issues when making slides.
# See: https://github.com/jhelvy/xaringanBuilder/issues/46
htmltools::tagList(rmarkdown::html_dependency_jquery())
```
```{r slides, eval=FALSE, echo=FALSE}
# make pdf of slides
xaringanBuilder::build_pdf("./slides.Rmd")
```
```{r packages, echo=FALSE}
# Helper packages - need dplyr and ggplot
library(conflicted)
conflict_prefer("select", "dplyr", quiet = TRUE)
conflict_prefer("filter", "dplyr", quiet = TRUE)
library(dplyr)
library(tidyr)
library(knitr)
conflict_prefer("continuous_summary", "gtsummary", quiet = TRUE)
conflict_prefer("as_flextable", "gtsummary", quiet = TRUE)
conflict_prefer("void", "reactablefmtr", quiet = TRUE)
```
# Who are we?
+ Francisco - A Ph.D. student at The University of Miami's Miller School of Medicine, quantifying policies to prevent/delay childhood alcohol use.
The rest of us work at UM:
+ Ray - An Associate Professor of biostatistics (really a data scientist) studying the intersection of HIV, mental health and drug addiction.
+ Lauren - A statistician/data scientist at UM's Sylvester Comprehensive Cancer Center
+ Anna - A data scientist working with Ray on HIV, mental health and addiction
---
# Disclosures
We have no financial disclosures to report.
# License
.small[
This work is licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
]
---
# General Comments
.small[
+ I use and teach with .blue[`tidyverse`] and .blue[`tidymodels`]. Because not everybody has been able to upgrade to R 4.2, we left .blue[`%>%`] pipes in this presentation but we strongly suggest that people upgrade and use the most excellent .blue[`|>`] pipe.
+ The slides are done with the .blue[`xaringan`] package. There is bug somewhere that causes the slides to render quickly but then there is a delay (about a minute) before the details are shown on two of my Macs. Sorry! If you have the same issue add a comment:
+ https://community.rstudio.com/t/rstudio-miserably-slow-on-os-monterey-when-knitting-xaringan-slides/140183
]
---
## Where did the idea for this talk come from? $_1$
.center[
https://vimeo.com/585821837
]
---
## Where did the idea for this talk come from? $_2$
+ When I used to primarily work in SAS, I would get my tables close to correct with *proc whatever* and then export to HTML, tweak things in Excel, and wait for the call saying:
> We are really sorry but there is "this guy"...
+ Of course, every number in the paper, including the tables, would change and I would mutter about needing to learn R.
+ I learned R Markdown and finally stopped crying whenever my phone would ring.
---
# When I was in school...
+ Journal tables (and figures) were monochrome and simple.
+ Now we have tables that are artfully formatted and have art that is ready for Twitter.
+ The distinction between tables and figures has completely blurred.
.pull-left[
.center[]
]
.pull-right[
.center[]
]
---
# When should you use a table?
+ Tables are useful to show the exact values of your data or estimates.
+ They are not the best solution to show a lot of data or if you want to show the data in a compact space.
+ They are not usually intended to give a quick, visual representation of data.
+ However, the core psychological principles leading to fast and accurate judgment in graphics apply to both charts and tables.
+ Read/Study/Live *Visualizing Data* and *The Elements of Graphing Data* by William S. Cleveland.
---
class: segue
# Best Practices in Plots (And Graphics)
---
# Tufte on Visualizations
.small[
+ One of the visualization thought leaders in the 1980s, Edward Tufte, stressed the importance of erasing all unnecessary ink from the page. The same holds true for tables.
+ When in doubt, erase.
.center[]
]
.tiny[https://playfairdata.com/data-ink-ratio-animation-and-how-to-apply-it-in-tableau/]
---
# Gestalt Principles
.pull-left[
Gestalt Law of Proximity - Things close together are automatically grouped.]
.center[
]
.pull-left[Gestalt Law of Similarity - Things of the same color will be grouped. If one thing is a different color, it will pop off of the page.]
.center[
]
.pull-left[
Gestalt Law of Closure - You can draw part of a bounding box and the brain will fill in the rest.]
.center[
<br>
]
.tiny[https://blog.xlcubed.com/2008/05/gestalt-laws-charts-and-tables-the-way-your-brain-wants-them-to-be/]
---
# Schwabish's Rules for Better Tables
.center[
.pull-left[
https://doi.org/10.1017/bca.2020.11
]
.pull-right[
https://cup.columbia.edu/book/better-data-visualizations/9780231193115
]]
---
### Schwabish's Ten Rules for Better Tables + Balise Three
.small[
1. Offset the .red[Heads] from the Body
2. Use .red[Subtle Dividers] Rather Than Heavy Gridlines
3. .red[Right-Align Numbers] and Heads
4. .red[Left-Align Text] and Heads
5. Select the Appropriate Level of .red[Precision]
6. Guide Your Reader with .red[Space] between Rows and Columns
7. .red[Remove] Unit .red[Repetition]
8. Highlight .red[Outliers]
9. .red[Group Similar] Data and Increase White Space
10. Add .red[Visualizations] When Appropriate
11. Draw Attention to the .red[Key Point(s)]
12. Use .red[Annotations] to Explain the Statistics
13. Make .red[Captions/Titles] Self-Contained
+ Explain Sample Size, Who and When
]
.tiny[Schwabish, Jonathan A., Journal of Benefit-Cost Analysis; Berlin Vol. 11, Iss. 2, (Summer 2020): 151-178. DOI:10.1017/bca.2020.11]
---
# What is the Key Take-Away Point in These Data?
.pull-left[
]
--
.pull-left[.center[
]]
---
# Critique PReP Table 1 - Excel Default
.pull-left[
]
.pull-right[.small[
1. .red[Heads] `r emo::ji("check")`
2. .red[Subtle Dividers] `r emo::ji("x")`
3. .red[Right-Align Numbers] `r emo::ji("x")`
4. .red[Left-Align Text] `r emo::ji("x")`
5. .red[Precision] `r emo::ji("check")`
6. .red[Space] `r emo::ji("check")`
7. .red[Remove Repetition] `r emo::ji("x")`
8. .red[Outliers]
9. .red[Group Similar] `r emo::ji("check")`
10. .red[Visualizations]
11. .red[Key Point(s)] `r emo::ji("x")`
12. .red[Annotations]
13. .red[Caption/Title] `r emo::ji("x")`
]]
---
# Critique PReP Table 1 - Also Excel
.pull-left[
]
.pull-right[.small[
1. .red[Heads] `r emo::ji("check")`
2. .red[Subtle Dividers] `r emo::ji("check")`
3. .red[Right-Align Numbers] `r emo::ji("x")`
4. .red[Left-Align Text] `r emo::ji("check")`
5. .red[Precision] `r emo::ji("check")`
6. .red[Space] `r emo::ji("check")`
7. .red[Remove Repetition] `r emo::ji("x")`
8. .red[Outliers]
9. .red[Group Similar] `r emo::ji("check")`
10. .red[Visualizations]
11. .red[Key Point(s)] `r emo::ji("check")`
12. .red[Annotations]
13. .red[Caption/Title] `r emo::ji("check")`
]]
---
# Critique PReP Table 1 - (Version 1.1)
.pull-left[
]
.pull-right[.small[
1. .red[Heads] `r emo::ji("check")`
2. .red[Subtle Dividers] `r emo::ji("check")`
3. .red[Right-Align Numbers] `r emo::ji("x")`
4. .red[Left-Align Text] `r emo::ji("check")`
5. .red[Precision] `r emo::ji("check")`
6. .red[Space] `r emo::ji("check")`
7. .red[Remove Repetition] `r emo::ji("check")`
8. .red[Outliers]
9. .red[Group Similar] `r emo::ji("check")`
10. .red[Visualizations]
11. .red[Key Point(s)] `r emo::ji("check")`
12. .red[Annotations]
13. .red[Caption/Title] `r emo::ji("check")`
]]
---
class: segue
# Where We are Going
A Religious Experience
Playing with Babies
Fun with Cameras in the Surgical Theater
What is going on in Florida?
---
# Categorical Data Analysis
.pull-left[
+ One of the best-written statistics books:
*An Introduction to Categorical Data Analysis* by Alan Agresti
]
.pull-right[
]
https://raymondbalise.github.io/Agresti_IntroToCategorical/
https://github.com/RaymondBalise/Agresti_IntroToCategorical
---
# Good Tables vs. Really???
.pull-left[
The physical book has lovely tables printed on horribly thin paper:
<br><br>
.center[]
]
.pull-right[
The online version through the UM library is not beautiful:
<br><br>
.center[]
]
---
# Critique - Agresti Book
.pull-left[
The physical book has lovely tables printed on horribly thin paper:
<br><br>
.center[]
]
.pull-right[.small[
1. .red[Heads] `r emo::ji("check")`
2. .red[Subtle Dividers] `r emo::ji("check")`
3. .red[Right-Align Numbers] `r emo::ji("check")`
4. .red[Left-Align Text] `r emo::ji("check")`
5. .red[Precision] `r emo::ji("check")`
6. .red[Space] `r emo::ji("check")`
7. .red[Remove Repetition] `r emo::ji("check")`
8. .red[Outliers]
9. .red[Group Similar] `r emo::ji("check")`
10. .red[Visualizations]
11. .red[Key Point(s)] `r emo::ji("x")`
12. .red[Annotations]
13. .red[Caption/Title] `r emo::ji("check")`
]]
---
# Critique - Agresti Online
.pull-left[
The physical book has lovely tables printed on horribly thin paper:
<br><br>
.center[]
]
.pull-right[.small[
1. .red[Heads] `r emo::ji("check")`
2. .red[Subtle Dividers] `r emo::ji("x")`
3. .red[Right-Align Numbers] `r emo::ji("check")`
4. .red[Left-Align Text] `r emo::ji("check")`
5. .red[Precision] `r emo::ji("check")`
6. .red[Space] `r emo::ji("x")`
7. .red[Remove Repetition] `r emo::ji("check")`
8. .red[Outliers]
9. .red[Group Similar] `r emo::ji("check")`
10. .red[Visualizations]
11. .red[Key Point(s)] `r emo::ji("x")`
12. .red[Annotations]
13. .red[Caption/Title] `r emo::ji("check")`
]]
---
# The .blue[`medicaldata`] Package
There is a dataset called .blue[`opt`] that has information from a controlled trial looking to see if treatment for periodontal disease can reduce the risk of preterm and low birth weight babies.
.center[]
---
## NEJM Style Table 1
.pull-left-60[]
.pull-left-36[.small[
1. .red[Heads] `r emo::ji("check")`
2. .red[Subtle Dividers] `r emo::ji("check")`
3. .red[Right-Align Numbers] `r emo::ji("check")`
4. .red[Left-Align Text] `r emo::ji("check")`
5. .red[Precision] `r emo::ji("check")`
6. .red[Space] `r emo::ji("check")`
7. .red[Remove Repetition] `r emo::ji("check")`
8. .red[Outliers]
9. .red[Group Similar] `r emo::ji("check")`
10. .red[Visualizations]
11. .red[Key Point(s)] `r emo::ji("x")`
12. .red[Annotations] `r emo::ji("check")`
13. .red[Caption/Title] `r emo::ji("check")`
]]
---
### Device Clinical Trial - Laryngoscope Camera
.pull-left-60[
.center[
]
* How hard is it to replicate these and/or make an interactive summary?
]
.pull-right-36[.small[
1. .red[Heads] `r emo::ji("check")`
2. .red[Subtle Dividers] `r emo::ji("check")`
3. .red[Right-Align Numbers] `r emo::ji("x")`
4. .red[Left-Align Text] `r emo::ji("check")`
5. .red[Precision] `r emo::ji("check")`
6. .red[Space] `r emo::ji("shrug")`
7. .red[Remove Repetition] `r emo::ji("check")`
8. .red[Outliers]
9. .red[Group Similar] `r emo::ji("check")`
10. .red[Visualizations]
11. .red[Key Point(s)] `r emo::ji("check")`
12. .red[Annotations] `r emo::ji("check")`
13. .red[Caption/Title] `r emo::ji("check")`
]]
---
# What is going on in Florida?
.pull-left[.center[]]
.pull-right[.center[]]
.tiny[
https://www.floridahealth.gov/provider-and-partner-resources/community-health-workers/health-professional-shortage-designations/Rural%20Counties%20Map%202016.pdf
https://www.flhealthcharts.gov/FLQUERY_New/Population/Count#
]
---
class: segue
# So Many Tools ... So Little Time
---
# I wish there was a "best" tool.
+ You have a lot of package options for making static (i.e., Word, PDF or HTML) and dynamic (i.e., HTML) publication-ready tables.
+ All packages make web-friendly graphics.
+ Others make static graphics that are beautifully formatted for the the web: .blue[`gt`], .blue[`table1`], .blue[`gtsummary`], .blue[`kableExtra`].
+ Yet others make static tables that look great in Word: .blue[`flextable`].
+ Some are ideal for interactive web content: .blue[`dt`], .blue[`reactable`]
---
# Output for Packages that Make Tables
Not all packages support all R Markdown output formats.
.pull-left-60[
```{r supported, echo=FALSE}
library(gt)
path_figure <- list(
"img/icons8-smiling-100.png",
"img/icons8-neutral-100.png",
"img/icons8-disappointed-100.png",
"img/icons8-no-entry-100.png",
"img/icons8-under-construction-100.png",
"img/icons8-camera-100.png"
)
list(
printer = c(
"tibble", "kable", "kableExtra", "gt", "flextable", "huxtable", "dt", "reactable"
),
output = c("HTML", "PDF", "RTF", "Word")
) %>%
purrr::cross_df() %>%
dplyr::mutate(
rating = dplyr::case_when(
printer == "gt" & output == "HTML" ~ 1, # good output
printer == "gt" & output %in% c("PDF", "RTF") ~ 5, # under construction
printer == "gt" & output == "Word" ~ 4, # not supported
printer == "kable" ~ 2, # okay output
printer == "flextable" & output != "RTF" ~ 1, # good output
printer == "flextable" & output == "RTF" ~ 4, # not supported
printer == "kableExtra" & output %in% c("PDF", "HTML") ~ 1, # good output
printer == "kableExtra" & output %in% c("RTF", "Word") ~ 4, # not supported
printer == "huxtable" ~ 1, # good output
printer == "tibble" ~ 3, # not great
printer == "reactable" & output == "HTML" ~ 1, # good output
printer == "reactable" & output %in% c("RTF", "Word") ~ 4, # not supported
printer == "reactable" & output == "PDF" ~ 6, # picture
printer == "dt" & output == "HTML" ~ 1, # good output
printer == "dt" & output == "PDF" ~ 6, # picture
printer == "dt" & output %in% c("RTF", "Word") ~ 4, # not supported
) %>%
factor()
) %>%
tidyr::pivot_wider(id_cols = printer, names_from = output, values_from = rating) %>%
dplyr::mutate(
link = dplyr::case_when(
printer == "gt" ~
"[gt](https://gt.rstudio.com/index.html)",
printer == "kable" ~
"[kable](https://bookdown.org/yihui/rmarkdown-cookbook/kable.html)",
printer == "flextable" ~
"[flextable](https://davidgohel.github.io/flextable/articles/overview.html)",
printer == "kableExtra" ~
"[kableExtra](http://haozhu233.github.io/kableExtra/)",
printer == "huxtable" ~
"[huxtable](https://hughjonesd.github.io/huxtable/)",
printer == "tibble" ~
"[tibble](https://tibble.tidyverse.org/)",
printer == "reactable" ~
"[reactable](https://glin.github.io/reactable/index.html)",
printer == "dt" ~
"[DT](https://rstudio.github.io/DT/)"
),
fns = dplyr::case_when(
printer == "gt" ~ "`gt()`",
printer == "kable" ~ "`kable()`",
printer == "flextable" ~ "`flextable()`",
printer == "kableExtra" ~ "`kbl() + stuff`",
printer == "huxtable" ~ "`huxtable()`",
printer == "tibble" ~ "`tibble()`",
printer == "reactable" ~ "`reactable()`",
printer == "dt" ~ "`datatable()`",
)
) %>%
gt() %>%
cols_move_to_start(columns = c(link, fns)) %>%
cols_hide(columns = c(printer)) %>%
cols_label(
link = md("**Print Engine**"),
fns = md("**Function**"),
HTML = md("**HTML**"), PDF = md("**PDF**"),
RTF = md("**RTF**"), Word = md("**Word**")
) %>%
fmt_markdown(columns = c(fns, link)) %>%
data_color(
columns = c(HTML, PDF, RTF, Word),
colors = scales::col_factor(
palette = c(
"#D1B3F9", "#bae1ff", "#ffb3ba", "#ffdfba", "#ffffba", "#baffc9"
),
domain = NULL,
reverse = TRUE
),
alpha = 0.8
) %>%
text_transform(
locations = cells_body(columns = c(HTML, PDF, RTF, Word)),
fn = function(x) {
dplyr::case_when(
x == 1 ~ local_image(filename = path_figure[[1]]),
x == 2 ~ local_image(filename = path_figure[[2]]),
x == 3 ~ local_image(filename = path_figure[[3]]),
x == 4 ~ local_image(filename = path_figure[[4]]),
x == 5 ~ local_image(filename = path_figure[[5]]),
x == 6 ~ local_image(filename = path_figure[[6]])
)
}
) %>%
cols_width(
c(HTML, PDF, RTF, Word) ~ px(60),
c(link) ~ px(110),
c(link, fns) ~ px(200)
)
```
]
.pull-right-36[
```{r supported-key, echo=FALSE}
tibble::tibble(
figure = 1:6,
desc = c(
"Output fully supported",
"Formatted output, but missing indentation, footnotes, spanning headers",
"No formatted output",
"Output not supported",
"Under development",
"Image of first page with controls"
)
) %>%
gt() %>%
cols_label(figure = md("**Key**"), desc = "") %>%
data_color(
columns = c(figure),
colors = scales::col_factor(
palette = c(
"#D1B3F9", "#bae1ff", "#ffb3ba", "#ffdfba", "#ffffba", "#baffc9"
),
domain = NULL,
reverse = TRUE
),
alpha = 0.8
) %>%
text_transform(
locations = cells_body(columns = c(figure)),
fn = function(x) {
dplyr::case_when(
x == 1 ~ local_image(filename = path_figure[[1]], height = 20),
x == 2 ~ local_image(filename = path_figure[[2]], height = 20),
x == 3 ~ local_image(filename = path_figure[[3]], height = 20),
x == 4 ~ local_image(filename = path_figure[[4]], height = 20),
x == 5 ~ local_image(filename = path_figure[[5]], height = 20),
x == 6 ~ local_image(filename = path_figure[[6]], height = 20),
)
}
) %>%
tab_options(table.font.size = "x-small", data_row.padding = px(3))
```
.tiny[.center[Thanks to Daniel D. Sjoberg for the table code.]]
.pull-down[.tinyRight[
Icons from [icons8](https://icons8.com/)]]
]
---
class: segue
# Make a Table
When You Already Have Preprocessed the Data
A religious experience...
---
# Categorical Data Analysis
.pull-left[
+ *An Introduction to Categorical Data Analysis* by Alan Agresti has solid SAS code.
+ A couple years ago, I decided to try to replicate everything in R.
.center[]
]
.pull-right[
]
---
# Agresti in R
+ This is quick code for exploratory data analysis but we can do a lot better.
```{r old-table, comment=NA, echo=TRUE, eval=FALSE}
table2_1 <- data.frame(
Gender = c("Female", "Female", "Male", "Male"),
Belief = c(" Yes", "No", " Yes", "No"),
Count = c(1230, 357, 859, 413)
)
addmargins(xtabs(Count ~ Gender + Belief, table2_1))
```
.pull-left-60[.center[
```{r old-table-output, echo=FALSE}
table2_1 <- data.frame(
Gender = c("Female", "Female", "Male", "Male"),
Belief = c(" Yes", "No", " Yes", "No"),
Count = c(1230, 357, 859, 413)
)
addmargins(xtabs(Count ~ Gender + Belief, table2_1))
```
]]
Notice that I used a space before "Y" in "Yes" to set the order.
---
# .blue[`tibble`] - Not Exactly Publication Ready....
```{r new-table-2-1, eval=FALSE}
`Table 2.1` <-
xtabs(Count ~ Gender + Belief, table2_1) %>%
addmargins() %>%
as_tibble()
`Table 2.1`
```
.center[
```{r new-table-2-1-output, echo=FALSE}
`Table 2.1` <-
xtabs(Count ~ Gender + Belief, table2_1) %>%
addmargins() %>%
as_tibble()
`Table 2.1`
```
]
---
# Pivot to Make a Table Wide - .blue[`tibble`] `r gt::local_image(filename = path_figure[[3]])`
The `Table 2.1` data set was saved with .blue[`as_tibble()`].
```{r make-wide}
wide <-
`Table 2.1` %>%
pivot_wider(id_cols = Gender, names_from = Belief, values_from = n) %>%
mutate(
Gender = if_else(Gender == "Sum", "Total", Gender)
) %>%
rename(
Yes = ` Yes`,
# I could also do: `No or Undecided` = No,
Total = Sum
)
wide
```
---
# .blue[`knitr::kable()`] `r gt::local_image(filename = path_figure[[2]])`
+ Drop that tibble into .blue[`kable()`] and add on pretty column names with the .blue[col.names=] argument.
```{r kable-show, eval=FALSE}
wide %>%
kable(
col.names = c("Gender", "Yes", "No or Undecided", "Total")
)
```
.center[]
Not bad...
---
# .blue[`kableExtra::kbl()`] + stuff
```{r kable-code, echo=FALSE}
if ("flextable" %in% (.packages())) {
detach("package:flextable", unload = TRUE)
}
```
```{r kableExtra, eval=FALSE}
library(kableExtra)
wide %>%
kbl(
col.names = c("Gender", "Yes", "No or Undecided", "Total"),
align = c("l", "r", "c", "r")
) %>%
add_header_above(c(" ", "Belief in Afterlife" = 2, " ")) %>%
column_spec(3, width = "10em") %>% # make the 3rd column extra wide
footnote(
general = "Data from 2016 General Social Survey.",
general_title = "Source: ",
footnote_as_chunk = T, title_format = c("italic")
)
```
.center[]
Closer, but not good...
---
# Add Some CSS
+ I am using the `xaringan` package to render slides, so I need a small tweak.
+ Web page formatting is done with CSS. Here I am adding instructions for things tagged as being in a table. I need to set them to have a white background.
---
# Adding CSS
.center[]
```{css, echo=FALSE, eval=TRUE}
.remark-slide thead, .remark-slide tr {
background-color: white;
}
.remark-slide thead, .remark-slide tfoot {
background-color: white;
}
```
```{r kableExtra-show, eval=FALSE}
library(kableExtra)
wide %>%
kbl(
col.names = c("Gender", "Yes", "No or Undecided", "Total"),
align = c("l", "r", "c", "r")
) %>%
add_header_above(c(" ", "Belief in Afterlife" = 2, " ")) %>%
column_spec(3, width = "10em") %>%
footnote(
general = "Data from 2016 General Social Survey.",
general_title = "Source: ",
footnote_as_chunk = T, title_format = c("italic")
)
```
---
# Close to Perfect - .blue[`kableExtra`] `r gt::local_image(filename = path_figure[[1]])`
.small[
```{css, echo=F}
.remark-slide thead, .remark-slide tr:nth-child(2n) {
background-color: white;
}
.remark-slide thead, .remark-slide tfoot {
background-color: white;
}
```
```{r kableExtra-run, echo=FALSE}
suppressPackageStartupMessages(library(kableExtra))
wide %>%
kbl(
col.names = c("Gender", "Yes", "No or Undecided", "Total"),
align = c("l", "r", "c", "r")
) %>%
add_header_above(c(" ", "Belief in Afterlife" = 2, " ")) %>%
column_spec(3, width = "10em") %>%
footnote(
general = "Data from 2016 General Social Survey.",
general_title = "Source: ",
footnote_as_chunk = T, title_format = c("italic")
)
detach("package:kableExtra", unload = TRUE)
```
]
---
# How did I figure that out?
+ The .blue[`kableExtra`] documentation does not show the striping behavior...
+ So I tried to simplify.
+ I copied the code into a vanilla HTML R Markdown file.
+ It didn't include the stripes.
+ I did a Google search for .blue[`xaringan kableExtra`] formatting
+ It led me to this post which included the CSS:
.small[.center[https://stackoverflow.com/questions/55319141/xaringan-kableextrakable-styling-and-wider-tables]]
+ StackOverflow is your friend.
+ To learn how to find CSS elements, watch this:
.small[
.center[https://medium.com/@HadrienD/how-to-customize-font-size-in-r-markdown-documents-f5adff36e2cc]]
---
# .blue[`gt::gt()`]
+ RStudio has been working on GT for years...
```{r gt-basic}
library(gt)
wide %>%
gt()
```
---
# .blue[`gt::gt()`] Code Duplicates the Book $_1$ `r gt::local_image(filename = path_figure[[1]])`
```{r gt-show, eval=FALSE}
library(gt)
wide %>%
gt() %>%
tab_header(
title = "Table 2.1 Cross-classification of belief in afterlife by gender."
) %>%
tab_spanner(
label = "Belief in the Afterlife",
columns = 2:3
) %>%
tab_source_note(
source_note = "<em>Source:</em> Data from 2016 General Social Survey."
) %>%
tab_options(
table_body.hlines.color = "white",
table_body.border.bottom.color = "#D3D3D3",
table.border.bottom.color = "white"
)
```
---
# .blue[`gt::gt()`] Code Duplicates the Book $_2$ `r gt::local_image(filename = path_figure[[1]])`
.center[]
```{r gt-code, eval=TRUE, echo=FALSE}
library(gt)
wide %>%
gt() %>%
tab_header(
title = "Table 2.1 Cross-classification of belief in afterlife by gender."
) %>%
tab_spanner(
label = "Belief in the Afterlife",
columns = 2:3
) %>%
cols_align(
align = "center",
columns = c(Yes, No)
) %>%
cols_label(
No = md("No or Undecided")
) %>%
tab_source_note(
source_note = "<em>Source:</em> Data from 2016 General Social Survey."
) %>%
tab_options(
table_body.hlines.color = "white",
table_body.border.bottom.color = "#D3D3D3",
table.border.bottom.color = "white",
table.border.top.color = "white"
)
```
---
# GT and Word
.pull-left-60[
+ People have been asking for a direct export into Word for *years*.
+ Currently, you get nothing when you knit. `r emo::ji("angry_face")`
+ You can copy and paste GT tables from HTML output into a Word file. `r emo::ji("nauseated_face")`
+ I hope something in Quarto is going to fix it. `r emo::ji("pray")`
]
.pull-right-36[
.center[]
]
---
# Clean Exports into Word
I know of two options:
+ The .blue[`flextable`] package is what I have used for hardcore tables.
+ I have been using it for years and I still think it is work.
+ Search my Agresti notes on GitHub for examples: https://github.com/RaymondBalise/Agresti_IntroToCategorical
+ https://ardata-fr.github.io/flextable-book/index.html
+ I discovered the .blue[`huxtable`] package in 2022 and I am exploring it.
---
# .blue[`flextable::flextable()`] $_1$
+ The weird thing is that you will want to specify the header first.
+ Add a value for every variable/column in every row of the header.
+ If you put the same words into multiple rows (like "Gender" and "Total" in the example below), the values can be merged later with .blue[`merge_*()`] commands.
```{r flex-head}
suppressPackageStartupMessages(library(flextable))
my_header <- data.frame(
col_keys = colnames(wide),
line1 = c("Gender", rep("Belief in the Afterlife", 2), "Total"),
# line2 is Gender, Yes, No, Total
line2 = colnames(wide)
)
```
---
# .blue[`flextable::flextable()`] $_2$
.small[
+ You specify the columns using the `col_keys` that you previously defined in the data frame and then tell it to use the data frame to make the columns.
+ Notice the merging of the cells with identical content in the header.
+ Column 1 (Gender) will be left-aligned. The rest of the columns are centered. Sadly, .blue[`flextable`] can not decimal-align.
]
```{r flex-show, eval=FALSE}
wide %>%
flextable(col_keys = my_header$col_keys) %>%
set_header_df(
mapping = my_header,
key = "col_keys"
) %>%
theme_booktabs() %>%
autofit(part = "all") %>%
align_nottext_col(align = "center") %>%
merge_h(part = "header") %>%
merge_v(part = "header") %>%
set_caption(caption = "Table 2.1 Cross-classification of belief in afterlife by gender.") %>%
add_footer_lines(values = "Source: Data from 2016 General Social Survey.")
```
---
# .blue[`flextable::flextable()`] $_3$
```{r flex-code, echo=FALSE}
wide %>%
flextable(col_keys = my_header$col_keys) %>%
set_header_df(
mapping = my_header,
key = "col_keys"
) %>%
theme_booktabs() %>%
autofit(part = "all") %>%
align_nottext_col(align = "center") %>%
merge_h(part = "header") %>%
merge_v(part = "header") %>%
set_caption(caption = "Table 2.1 Cross-classification of belief in afterlife by gender.") %>%
add_footer_lines(values = "Source: Data from 2016 General Social Survey.")
```