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https://ardata-fr.github.io/flextable-book/
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`fit_to_width()` replaces content with ---- within Quarto and its PDF outcomes #654

Open pedrohbraga opened 2 months ago

pedrohbraga commented 2 months ago

I have been trying to use fit_to_width() to produce tables with Quarto that fit a specific width within the page of the PDF. Unfortunately, autofit() has not helped, and fit_to_width() has not worked as intended. Whenever I use it, it replaces the table content with some placeholder text.

Please find the code and the issue below:

mpd.CFI.CHet.bS.gam.model_summaries <- structure(list(Scale = c("Global", "Global", "Global", "Global", 
"Global", "Global", "Global", "Global", "Hemispheric", "Hemispheric", 
"Hemispheric", "Hemispheric", "Hemispheric", "Hemispheric", "Hemispheric", 
"Hemispheric", "Realm", "Realm", "Realm", "Realm", "Realm", "Realm", 
"Realm", "Realm", "Biome", "Biome", "Biome", "Biome", "Biome", 
"Biome", "Biome", "Biome"), Taxon = c("Amphibians", "Amphibians", 
"Birds", "Birds", "Mammals", "Mammals", "Squamates", "Squamates", 
"Amphibians", "Amphibians", "Birds", "Birds", "Mammals", "Mammals", 
"Squamates", "Squamates", "Amphibians", "Amphibians", "Birds", 
"Birds", "Mammals", "Mammals", "Squamates", "Squamates", "Amphibians", 
"Amphibians", "Birds", "Birds", "Mammals", "Mammals", "Squamates", 
"Squamates"), Response = c("MPD", "MPD", "MPD", "MPD", "MPD", 
"MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", 
"MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", 
"MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD"
), Predictors = c("s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)", 
"s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)"
), Smooth.basis = c("Thin-plate splines", "Thin-plate splines", 
"Thin-plate splines", "Thin-plate splines", "Thin-plate splines", 
"Thin-plate splines", "Thin-plate splines", "Thin-plate splines", 
"Thin-plate splines", "Thin-plate splines", "Thin-plate splines", 
"Thin-plate splines", "Thin-plate splines", "Thin-plate splines", 
"Thin-plate splines", "Thin-plate splines", "Thin-plate splines", 
"Thin-plate splines", "Thin-plate splines", "Thin-plate splines", 
"Thin-plate splines", "Thin-plate splines", "Thin-plate splines", 
"Thin-plate splines", "Thin-plate splines", "Thin-plate splines", 
"Thin-plate splines", "Thin-plate splines", "Thin-plate splines", 
"Thin-plate splines", "Thin-plate splines", "Thin-plate splines"
), Adj..R.2 = c(0.175, 0.175, 0.077, 0.077, 0.053, 0.053, 0.295, 
0.295, 0.098, 0.098, 0.079, 0.079, 0.045, 0.045, 0.154, 0.154, 
0.037, 0.037, 0.076, 0.076, 0.065, 0.065, 0.148, 0.148, 0.132, 
0.132, 0.074, 0.074, 0.016, 0.016, 0.089, 0.089), dev.uniq.non.linear = c(0.039, 
0.039, 0.038, 0.038, 0.007, 0.007, 0.017, 0.017, 0.026, 0.026, 
0.031, 0.031, 0.007, 0.007, 0.036, 0.036, 0.025, 0.025, 0.018, 
0.018, 0.017, 0.017, 0.035, 0.035, 0.027, 0.027, 0.008, 0.008, 
0.015, 0.015, 0.031, 0.031), Predictor = c("s(CFI.bS)", "s(heterogeneity.meanEuclid)", 
"s(CFI.bS)", "s(heterogeneity.meanEuclid)", "s(CFI.bS)", "s(heterogeneity.meanEuclid)", 
"s(CFI.bS)", "s(heterogeneity.meanEuclid)", "s(CFI.bS)", "s(heterogeneity.meanEuclid)", 
"s(CFI.bS)", "s(heterogeneity.meanEuclid)", "s(CFI.bS)", "s(heterogeneity.meanEuclid)", 
"s(CFI.bS)", "s(heterogeneity.meanEuclid)", "s(CFI.bS)", "s(heterogeneity.meanEuclid)", 
"s(CFI.bS)", "s(heterogeneity.meanEuclid)", "s(CFI.bS)", "s(heterogeneity.meanEuclid)", 
"s(CFI.bS)", "s(heterogeneity.meanEuclid)", "s(CFI.bS)", "s(heterogeneity.meanEuclid)", 
"s(CFI.bS)", "s(heterogeneity.meanEuclid)", "s(CFI.bS)", "s(heterogeneity.meanEuclid)", 
"s(CFI.bS)", "s(heterogeneity.meanEuclid)"), edf = c(8.94725336461062, 
8.24474929940239, 8.94402808538318, 8.0433126490782, 8.5972718307536, 
6.90985590548356, 8.96129511999248, 8.5986067320282, 8.96563886692967, 
8.30961147988176, 8.98433079686555, 8.23076432847517, 8.90263880010934, 
6.86404424849931, 8.97398220224686, 8.69075105939943, 8.95949377680743, 
8.43700193247956, 8.97097220465126, 8.17551845665404, 8.94535035010068, 
6.58312089445243, 8.97836907118496, 8.72640907751301, 8.8820397256442, 
8.38989735222463, 8.92777933838305, 8.17965037889696, 8.44882219612362, 
6.67862792516023, 8.94350263716568, 8.72142016558614), Ref.df = c(8.99918106400064, 
8.84762420890626, 8.99907385783694, 8.76437213410865, 8.95428497695193, 
8.02973917476159, 8.99955633527575, 8.95532361379465, 8.99964476828757, 
8.87176481676359, 8.9999260146117, 8.84376427000312, 8.9971782037767, 
7.99158455565594, 8.99979564755562, 8.97315094545215, 8.99951059845159, 
8.91317490928138, 8.99974645533773, 8.82184062675737, 8.9991041994842, 
7.7535736056498, 8.99985929333262, 8.97888578568561, 8.99581098178945, 
8.89871654216935, 8.99841268199284, 8.8235135826169, 8.91354725246866, 
7.8363113129187, 8.99902711654054, 8.97812146878775), F = c(4036.31513633459, 
69.7911843367783, 1759.88065677894, 262.705849512569, 1443.42921525763, 
194.454421113521, 8955.41928264452, 92.0348721761402, 2002.68924477316, 
74.588383290832, 1780.02960506767, 322.540064864731, 1165.52780160569, 
200.429583280523, 3622.23334399431, 223.319132967035, 622.826313416701, 
123.844805888992, 1695.43875878549, 301.77693780504, 1757.59584121885, 
203.782496099787, 3445.81563957982, 222.04847289181, 2850.33857052798, 
108.262471571175, 1616.33541454028, 449.583131620976, 343.437567443098, 
98.3637516856449, 1751.09643636829, 340.923735307398), p.value = c(0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), class = "data.frame", row.names = c(NA, 
-32L))
#| label: tbl-mpd-cfi-clim-het-allscales
#| tbl-cap: "**Parameter estimates and model summaries from thin-plate regression smooth splines for the effects of climatic frequency and climatic heterogeneity on mean phylogenetic distances of tetrapod communities.** Climatic frequency was measured across four geographical scales: global, east-west hemisphere, biogeographical realm, and biome extents. Mean phylogenetic distances denote how closely-related co-occurring taxa are in a given community, and was estimate for each tetrapod group separately, being them: amphibians, birds, mammals, and squamate reptiles. Effective estimated degrees of freedom (edf) are taken as number of data minus model degrees of freedom (see Methods)."
#| echo: false
#| message: false
#| warning: false

mpd.CFI.CHet.bS.gam.model_summaries %>%
  mutate(p.value = ifelse(p.value <= 0.0001, 0.001, 0)) %>%
  group_by(Scale, Taxon, Predictor) %>%
  mutate("signif" = NA) %>%
  dplyr::select(
    -c("Response", 
       "Predictors",
       "Smooth.basis")
  ) %>%
  flextable() %>%
  merge_v(1:4) %>%
  valign(valign = 'top') %>%
  add_header_row(values = "Thin-plate regression splines with formula: \nMPD ~ s(Clim. Freq.) + s(Clim. Het.)",
                 colwidths = 10,
                 top = T) %>%
  bold(~ `p.value` <= 0.001, "p.value") %>%
  theme_vanilla() %>%
  mk_par(j = "signif", value = as_paragraph(pvalue_format(p.value)) ) %>% 
  colformat_double(j = c("edf",
                         "Ref.df",
                         "F" ,
                         "dev.uniq.non.linear"), digits = 3) %>%
  set_header_labels(Scale = "Geographical Scale",
                    Adj..R.2 = "Total Adj. R2", 
                    dev.uniq.non.linear = "Non-linear Adj. R2",
                    edf = "edf",
                    Ref.df = "df",
                    p.value = "p-value",
                    signif = "") %>%
  align(j = "signif", align = "left") %>% 
  padding(padding.right = 0, j = "p.value", part  = "all") %>% 
  bold(j = "signif", bold = TRUE) %>% 
  # padding(padding.left = 0, j = "signif", part  = "all") %>% 
  # set_table_properties(width = 1, layout = "autofit") %>%  
  add_footer_lines(values = c(
    "Signif. codes: 0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05 < '.' < 0.1 < '' < 1" ))  %>%
  # flextable::fontsize(size = 9, part = "all") %>%
  font(fontname = "Times New Roman", part = "all")  %>%
  autofit() %>%
  fit_to_width(max_width = 7)

Outcome:

image

> sessionInfo()
R version 4.3.3 RC (2024-02-22 r85999 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 26120)

Matrix products: default

locale:
[1] LC_COLLATE=English_Canada.utf8  LC_CTYPE=English_Canada.utf8    LC_MONETARY=English_Canada.utf8
[4] LC_NUMERIC=C                    LC_TIME=English_Canada.utf8    

time zone: America/Toronto
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] gt_0.11.0.9000      kableExtra_1.4.0    flextable_0.9.7.008 stringr_1.5.1      
[5] dplyr_1.1.4         tidyr_1.3.1         data.table_1.15.4  

loaded via a namespace (and not attached):
 [1] xfun_0.45               remotes_2.5.0           htmlwidgets_1.6.4       devtools_2.4.5         
 [5] processx_3.8.4          callr_3.7.6             ps_1.7.7                vctrs_0.6.5            
 [9] tools_4.3.3             generics_0.1.3          curl_5.2.1              parallel_4.3.3         
[13] tibble_3.2.1            fansi_1.0.6             pkgconfig_2.0.3         desc_1.4.3             
[17] RcppParallel_5.1.7      uuid_1.2-0              lifecycle_1.0.4         compiler_4.3.3         
[21] textshaping_0.4.0       munsell_0.5.1           httpuv_1.6.15           fontquiver_0.2.1       
[25] fontLiberation_0.1.0    sass_0.4.9              usethis_2.2.3           htmltools_0.5.8.1      
[29] urlchecker_1.0.1        later_1.3.2             pillar_1.9.0            crayon_1.5.3           
[33] gfonts_0.2.0            ellipsis_0.3.2          openssl_2.2.0           rsconnect_1.3.1        
[37] cachem_1.1.0            sessioninfo_1.2.2       mime_0.12               fontBitstreamVera_0.1.1
[41] commonmark_1.9.1        tidyselect_1.2.1        zip_2.3.1               digest_0.6.36          
[45] stringi_1.8.4           purrr_1.0.2             fastmap_1.2.0           grid_4.3.3             
[49] colorspace_2.1-0        cli_3.6.1               magrittr_2.0.3          Rfast_2.1.0            
[53] crul_1.4.2              pkgbuild_1.4.4          utf8_1.2.4              withr_3.0.0            
[57] scales_1.3.0            gdtools_0.3.7           promises_1.3.0          RcppZiggurat_0.1.6     
[61] rmarkdown_2.27          officer_0.6.6           askpass_1.2.0           ragg_1.3.2             
[65] memoise_2.0.1           shiny_1.8.1.1           evaluate_0.24.0         knitr_1.47             
[69] viridisLite_0.4.2       miniUI_0.1.1.1          markdown_1.13           profvis_0.3.8          
[73] rlang_1.1.4             Rcpp_1.0.12             xtable_1.8-4            glue_1.7.0             
[77] httpcode_0.3.0          xml2_1.3.6              pkgload_1.4.0           svglite_2.1.3          
[81] rstudioapi_0.16.0       jsonlite_1.8.8          R6_2.5.1                systemfonts_1.1.0      
[85] fs_1.6.4
davidgohel commented 2 months ago

hello

It does not replace content with ---- but defines a tiny font size that give you the feeling it is ----.

For PDF and given the content of your table, I would instead use a layout defined to "autofit" and not a fixed one. 

---
title: "Untitled"
format: pdf
---

```{r include=FALSE}
library(dplyr)
library(magrittr)
library(flextable)
library(scales)

mpd.CFI.CHet.bS.gam.model_summaries <- structure(list(Scale = c(
  "Global", "Global", "Global", "Global",
  "Global", "Global", "Global", "Global", "Hemispheric", "Hemispheric",
  "Hemispheric", "Hemispheric", "Hemispheric", "Hemispheric", "Hemispheric",
  "Hemispheric", "Realm", "Realm", "Realm", "Realm", "Realm", "Realm",
  "Realm", "Realm", "Biome", "Biome", "Biome", "Biome", "Biome",
  "Biome", "Biome", "Biome"
), Taxon = c(
  "Amphibians", "Amphibians",
  "Birds", "Birds", "Mammals", "Mammals", "Squamates", "Squamates",
  "Amphibians", "Amphibians", "Birds", "Birds", "Mammals", "Mammals",
  "Squamates", "Squamates", "Amphibians", "Amphibians", "Birds",
  "Birds", "Mammals", "Mammals", "Squamates", "Squamates", "Amphibians",
  "Amphibians", "Birds", "Birds", "Mammals", "Mammals", "Squamates",
  "Squamates"
), Response = c(
  "MPD", "MPD", "MPD", "MPD", "MPD",
  "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD",
  "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD",
  "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD", "MPD"
), Predictors = c(
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)",
  "s(Clim. Freq.) + s(Clim. Het.)", "s(Clim. Freq.) + s(Clim. Het.)"
), Smooth.basis = c(
  "Thin-plate splines", "Thin-plate splines",
  "Thin-plate splines", "Thin-plate splines", "Thin-plate splines",
  "Thin-plate splines", "Thin-plate splines", "Thin-plate splines",
  "Thin-plate splines", "Thin-plate splines", "Thin-plate splines",
  "Thin-plate splines", "Thin-plate splines", "Thin-plate splines",
  "Thin-plate splines", "Thin-plate splines", "Thin-plate splines",
  "Thin-plate splines", "Thin-plate splines", "Thin-plate splines",
  "Thin-plate splines", "Thin-plate splines", "Thin-plate splines",
  "Thin-plate splines", "Thin-plate splines", "Thin-plate splines",
  "Thin-plate splines", "Thin-plate splines", "Thin-plate splines",
  "Thin-plate splines", "Thin-plate splines", "Thin-plate splines"
), Adj..R.2 = c(
  0.175, 0.175, 0.077, 0.077, 0.053, 0.053, 0.295,
  0.295, 0.098, 0.098, 0.079, 0.079, 0.045, 0.045, 0.154, 0.154,
  0.037, 0.037, 0.076, 0.076, 0.065, 0.065, 0.148, 0.148, 0.132,
  0.132, 0.074, 0.074, 0.016, 0.016, 0.089, 0.089
), dev.uniq.non.linear = c(
  0.039,
  0.039, 0.038, 0.038, 0.007, 0.007, 0.017, 0.017, 0.026, 0.026,
  0.031, 0.031, 0.007, 0.007, 0.036, 0.036, 0.025, 0.025, 0.018,
  0.018, 0.017, 0.017, 0.035, 0.035, 0.027, 0.027, 0.008, 0.008,
  0.015, 0.015, 0.031, 0.031
), Predictor = c(
  "s(CFI.bS)", "s(heterogeneity.meanEuclid)",
  "s(CFI.bS)", "s(heterogeneity.meanEuclid)", "s(CFI.bS)", "s(heterogeneity.meanEuclid)",
  "s(CFI.bS)", "s(heterogeneity.meanEuclid)", "s(CFI.bS)", "s(heterogeneity.meanEuclid)",
  "s(CFI.bS)", "s(heterogeneity.meanEuclid)", "s(CFI.bS)", "s(heterogeneity.meanEuclid)",
  "s(CFI.bS)", "s(heterogeneity.meanEuclid)", "s(CFI.bS)", "s(heterogeneity.meanEuclid)",
  "s(CFI.bS)", "s(heterogeneity.meanEuclid)", "s(CFI.bS)", "s(heterogeneity.meanEuclid)",
  "s(CFI.bS)", "s(heterogeneity.meanEuclid)", "s(CFI.bS)", "s(heterogeneity.meanEuclid)",
  "s(CFI.bS)", "s(heterogeneity.meanEuclid)", "s(CFI.bS)", "s(heterogeneity.meanEuclid)",
  "s(CFI.bS)", "s(heterogeneity.meanEuclid)"
), edf = c(
  8.94725336461062,
  8.24474929940239, 8.94402808538318, 8.0433126490782, 8.5972718307536,
  6.90985590548356, 8.96129511999248, 8.5986067320282, 8.96563886692967,
  8.30961147988176, 8.98433079686555, 8.23076432847517, 8.90263880010934,
  6.86404424849931, 8.97398220224686, 8.69075105939943, 8.95949377680743,
  8.43700193247956, 8.97097220465126, 8.17551845665404, 8.94535035010068,
  6.58312089445243, 8.97836907118496, 8.72640907751301, 8.8820397256442,
  8.38989735222463, 8.92777933838305, 8.17965037889696, 8.44882219612362,
  6.67862792516023, 8.94350263716568, 8.72142016558614
), Ref.df = c(
  8.99918106400064,
  8.84762420890626, 8.99907385783694, 8.76437213410865, 8.95428497695193,
  8.02973917476159, 8.99955633527575, 8.95532361379465, 8.99964476828757,
  8.87176481676359, 8.9999260146117, 8.84376427000312, 8.9971782037767,
  7.99158455565594, 8.99979564755562, 8.97315094545215, 8.99951059845159,
  8.91317490928138, 8.99974645533773, 8.82184062675737, 8.9991041994842,
  7.7535736056498, 8.99985929333262, 8.97888578568561, 8.99581098178945,
  8.89871654216935, 8.99841268199284, 8.8235135826169, 8.91354725246866,
  7.8363113129187, 8.99902711654054, 8.97812146878775
), F = c(
  4036.31513633459,
  69.7911843367783, 1759.88065677894, 262.705849512569, 1443.42921525763,
  194.454421113521, 8955.41928264452, 92.0348721761402, 2002.68924477316,
  74.588383290832, 1780.02960506767, 322.540064864731, 1165.52780160569,
  200.429583280523, 3622.23334399431, 223.319132967035, 622.826313416701,
  123.844805888992, 1695.43875878549, 301.77693780504, 1757.59584121885,
  203.782496099787, 3445.81563957982, 222.04847289181, 2850.33857052798,
  108.262471571175, 1616.33541454028, 449.583131620976, 343.437567443098,
  98.3637516856449, 1751.09643636829, 340.923735307398
), p.value = c(
  0,
  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 0, 0, 0
)), class = "data.frame", row.names = c(
  NA,
  -32L
))

ft <- mpd.CFI.CHet.bS.gam.model_summaries %>%
  mutate(p.value = ifelse(p.value <= 0.0001, 0.001, 0)) %>%
  group_by(Scale, Taxon, Predictor) %>%
  mutate("signif" = NA) %>%
  dplyr::select(
    -c(
      "Response",
      "Predictors",
      "Smooth.basis"
    )
  ) %>%
  flextable() %>%
  merge_v(1:4) %>%
  valign(valign = "top") %>%
  add_header_lines(
    values = "Thin-plate regression splines with formula: \nMPD ~ s(Clim. Freq.) + s(Clim. Het.)") %>%
  bold(~ `p.value` <= 0.001, "p.value") %>%
  theme_vanilla() %>%
  colformat_double(j = c("edf", "Ref.df", "F", "dev.uniq.non.linear"), digits = 3) %>%
  set_header_labels(
    Scale = "Geographical Scale",
    Adj..R.2 = "Total Adj. R2",
    dev.uniq.non.linear = "Non-linear Adj. R2",
    edf = "edf", Ref.df = "df", p.value = "p-value", signif = "") %>%
  align(j = "signif", align = "left") %>%
  bold(j = "signif", bold = TRUE) %>%
  add_footer_lines(values = c(
    "Signif. codes: 0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05 < '.' < 0.1 < '' < 1"
  )) %>%
  flextable::fontsize(size = 8, part = "all") %>%
  font(fontname = "Times New Roman", part = "all") %>%
  set_table_properties(layout = "autofit")
ft


<img width="539" alt="Capture d’écran 2024-08-27 à 19 02 33" src="https://github.com/user-attachments/assets/2f50b75f-d864-4ad5-b415-9f0824d885e9">
pedrohbraga commented 2 months ago

Thank you for your response, @davidgohel! I understand your recommendation and will implement it!

I have an additional issue that I have been experiencing and observed in your output as well. The repeating header appears to overlap the content of tables split across pages, as in the example you pasted above. Would you have a recommendation on what I could do to fix this issue?

pedrohbraga commented 2 months ago

Here are a few examples of it happening. Note that it also messes up with the cases that are merged (using merge_v()).

image

Here, for instance, what should be "Global" in the first row, has disappeared, probably due to this issue.

image

This can be reproduced with the same code you sent me, but please let me know if you would like some logs or specific files.