Quarto with R

Abstrave quarto theme

This document showcases the use of R.
Author

Roy Francis

Published

27-Sep-2026

1 Code execution

R code can be executed inline: `r Sys.Date()` renders as 2026-09-27.

R code can also be executed in code chunks.

```{r}
Sys.Date()
```

This shows the source code and output.

Sys.Date()
[1] "2026-09-27"

Here is another example with R source code and output.

data(iris)
head(iris[,1:2])
Sepal.Length Sepal.Width
5.1 3.5
4.9 3.0
4.7 3.2
4.6 3.1
5.0 3.6
5.4 3.9

2 Chunk options

Code chunk behavior is controlled with chunk options. In R chunks, each option starts with #| and uses YAML key: value syntax, for example, #| eval: false.

In this example, the R source code and results are hidden but the code is evaluated.

```{r}
#| eval: true
#| echo: false
#| results: hide
Sys.Date()
```

Source code blocks can be folded.

```{r}
#| code-fold: true
Sys.Date()
```
Code 
Sys.Date()
[1] "2026-09-27"

Quarto does not provide built-in output folding, so this template uses the quarto-collapse-output extension.

```{r}
#| output-fold: true
Sys.Date()
```
Sys.Date()
Code Output 
[1] "2026-09-27"

Use filename to display a filename above a code chunk.

```{r}
#| filename: R code
Sys.Date()
```
R code
Sys.Date()

Enable line numbers using code-line-numbers: true.

```{r}
#| code-line-numbers: true
Sys.Date()
sessionInfo()
```
Sys.Date()
sessionInfo()

An example showing Bash code generated from R:

```{r}
#| attr-output: "filename='bash'"
#| class-output: bash
#| echo: false
d <- "custom"
cat(paste("mkdir", d))
```
bash
mkdir custom

See Quarto’s execution options and HTML code options.

3 Tables

3.1 Manual

For simple cases, tables can be manually created in Markdown.

|speed|dist|
|-----|----|
|4    |   2|
|4    |  10|
|7    |   4|
speed dist
4 2
4 10
7 4

3.2 kable

A simple table using knitr::kable().

library(knitr)
kable(head(iris))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5.0 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa

3.2.1 Layout

```{r}
#| column: body-outset
#| label: tbl-dual-table
#| tbl-cap: "Example"
#| tbl-subcap: 
#|   - "Cars"
#|   - "Pressure"
#| layout-ncol: 2

kable(head(cars))
kable(head(pressure))
```
Table 1: Example
(a) Cars
speed dist
4 2
4 10
7 4
7 22
8 16
9 10
(b) Pressure
temperature pressure
0 0.0002
20 0.0012
40 0.0060
60 0.0300
80 0.0900
100 0.2700

3.2.2 Cross-referencing

Figures and tables can be numbered and cross-referenced by assigning a label that starts with fig- or tbl-, respectively. For example, @tbl-dual-table references the table above and renders as Table 1.

3.2.3 Margin table

```{r}
#| tbl-cap: This table is in the margin.
#| column: margin

head(cars)
```
This table is in the margin.
speed dist
4 2
4 10
7 4
7 22
8 16
9 10

3.3 paged

An interactive table using rmarkdown::paged_table().

library(rmarkdown)
rmarkdown::paged_table(head(iris))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5.0 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa

3.4 gt

The gt package provides a grammar of tables with extensive customization options.

library(gt)

iris |>
    group_by(Species) |>
    slice(1:4) |>
    gt() |>
    cols_label(
      Sepal.Length = "Sepal Length", Sepal.Width = "Sepal Width",
      Petal.Length = "Petal Length", Petal.Width = "Petal Width"
    ) |>
    tab_source_note(
        source_note = md("Source: Iris data. Anderson, 1936; Fisher, 1936)")
    )
Sepal Length Sepal Width Petal Length Petal Width
setosa
5.1 3.5 1.4 0.2
4.9 3.0 1.4 0.2
4.7 3.2 1.3 0.2
4.6 3.1 1.5 0.2
versicolor
7.0 3.2 4.7 1.4
6.4 3.2 4.5 1.5
6.9 3.1 4.9 1.5
5.5 2.3 4.0 1.3
virginica
6.3 3.3 6.0 2.5
5.8 2.7 5.1 1.9
7.1 3.0 5.9 2.1
6.3 2.9 5.6 1.8
Source: Iris data. Anderson, 1936; Fisher, 1936)

3.5 htmlTable

The htmlTable package creates styled HTML tables from R objects.

library(htmlTable)

iris1 <- iris[c(1:4,51:53,105:108),]
htmlTable(iris1, rgroup=unique(iris1$Species), n.rgroup=rle(as.character(iris1$Species))$lengths)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
setosa
  1 5.1 3.5 1.4 0.2 setosa
  2 4.9 3 1.4 0.2 setosa
  3 4.7 3.2 1.3 0.2 setosa
  4 4.6 3.1 1.5 0.2 setosa
versicolor
  51 7 3.2 4.7 1.4 versicolor
  52 6.4 3.2 4.5 1.5 versicolor
  53 6.9 3.1 4.9 1.5 versicolor
virginica
  105 6.5 3 5.8 2.2 virginica
  106 7.6 3 6.6 2.1 virginica
  107 4.9 2.5 4.5 1.7 virginica
  108 7.3 2.9 6.3 1.8 virginica

3.6 kableExtra

The kableExtra and formattable packages provide advanced table styling.

library(kableExtra)

iris[c(1:4,51:53,105:108),] |>
  mutate(Sepal.Length=color_bar("lightsteelblue")(Sepal.Length)) |>
  mutate(Sepal.Width=color_tile("white","orange")(Sepal.Width)) |>
  mutate(Species=cell_spec(Species,"html",color="white",bold=T,
    background=c("#8dd3c7","#fb8072","#bebada")[factor(Species)])) |>
  kable("html",escape=F) |>
  kable_styling(bootstrap_options=c("striped","hover","responsive"),
                full_width=F,position="left") |>
  column_spec(5,width="3cm")
Table using kableextra.
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
51 7.0 3.2 4.7 1.4 versicolor
52 6.4 3.2 4.5 1.5 versicolor
53 6.9 3.1 4.9 1.5 versicolor
105 6.5 3.0 5.8 2.2 virginica
106 7.6 3.0 6.6 2.1 virginica
107 4.9 2.5 4.5 1.7 virginica
108 7.3 2.9 6.3 1.8 virginica

3.7 DT

The DT package creates interactive data tables.

library(DT)

iris |>
  slice(1:15) |>
  datatable(options = list(pageLength = 7))

3.8 reactable

The reactable package creates advanced interactive tables.

library(reactable)

reactable(iris[sample(1:150,10),],
  columns = list(
    Sepal.Length = colDef(name = "Sepal Length"),
    Sepal.Width = colDef(name = "Sepal Width"),
    Petal.Length = colDef(name = "Petal Length"),
    Petal.Width = colDef(name = "Petal Width")
  ),
  striped = TRUE,
  highlight = TRUE,
  filterable = TRUE
)

The reactablefmtr package can simplify and extend reactable tables.

4 Static images using R

Use the out-width chunk option to control an image’s displayed width.

The image below is displayed at 300 pixels wide.

```{r}
#| out-width: 300px
knitr::include_graphics("assets/image.webp")
```

The image below is displayed at 75 pixels wide and has a caption.

```{r}
#| out-width: 75px
#| fig-cap: This is a caption
knitr::include_graphics("assets/image.webp")
```

This is a caption

This is a caption

See Quarto’s figure documentation for more information. R-generated plots are demonstrated below.

5 Static plots

5.1 Base plot

  • Plots using base R are widely used and may be good enough for most situations.
  • But they lack a consistent coding framework.
{
  plot(x=iris$Sepal.Length,y=iris$Sepal.Width,
      col=c("coral","steelblue","forestgreen")[iris$Species],
      xlab="Sepal Length",ylab="Sepal Width",pch=19)

  legend(x=7,y=4.47,legend=c("setosa","versicolor","virginica"),
        col=c("coral","steelblue","forestgreen"),pch=19)
}
Figure 1: Static plot using base plot.

5.1.1 Multiple plots

```{r}
#| column: screen-inset-shaded
#| layout-nrow: 1
#| fig-cap:
#|   - "Scatterplot of speed vs distance"
#|   - "Pairwise scatterplot of all variables"
#|   - "Scatterplot of temperature vs pressure"

plot(cars)
plot(iris)
plot(pressure)
```

Scatterplot of speed vs distance

Scatterplot of speed vs distance

Pairwise scatterplot of all variables

Pairwise scatterplot of all variables

Scatterplot of temperature vs pressure

Scatterplot of temperature vs pressure

5.1.2 Margin plot

```{r}
#| column: margin

plot(cars)
```

5.2 ggplot2

The ggplot2 package provides a grammar of graphics for creating static plots.

library(ggplot2)

iris |>
  ggplot(aes(x=Sepal.Length,y=Sepal.Width,col=Species))+
  geom_point(size=2)+
  labs(x="Sepal Length",y="Sepal Width")+
  theme_report()
Figure 2: Static plot using ggplot2.

6 Interactive plots

6.1 highcharter

The highcharter package provides an R interface to the Highcharts JavaScript library.

library(highcharter)

h <- hchart(iris,"scatter",hcaes(x="Sepal.Length",y="Sepal.Width",group="Species")) |>
  hc_xAxis(title=list(text="Sepal Length"),crosshair=TRUE) |>
  hc_yAxis(title=list(text="Sepal Width"),crosshair=TRUE) |>
  hc_chart(zoomType="xy",inverted=FALSE) |>
  hc_legend(verticalAlign="top",align="right") |>
  hc_size(height=400)

htmltools::tagList(list(h))
Figure 3: Interactive scatterplot using highcharter.

6.2 plotly

The plotly package provides an R interface to the Plotly.js JavaScript library.

library(plotly)

p <- iris |>
  plot_ly(x=~Sepal.Length,y=~Sepal.Width,color=~Species,width=500,height=400) |>
  add_markers()
p
Figure 4: Interactive scatterplot using plotly.

6.3 ggplotly

plotly::ggplotly() converts a ggplot2 plot into an interactive Plotly graphic.

library(plotly)

p <- iris |>
  ggplot(aes(x=Sepal.Length,y=Sepal.Width,col=Species))+
  geom_point()+
  labs(x="Sepal Length",y="Sepal Width")+
  theme_bw(base_size=12)

ggplotly(p,width=500,height=400)
Figure 5: Interactive scatterplot using ggplotly.

6.4 ggiraph

The ggiraph package adds tooltips, hover effects, and other interactions to ggplot2 graphics.

library(ggiraph)

p <- ggplot(iris,aes(x=Sepal.Length,y=Petal.Length,colour=Species))+
      geom_point_interactive(aes(tooltip=paste0("<b>Petal Length:</b> ",Petal.Length,"\n<b>Sepal Length: </b>",Sepal.Length,"\n<b>Species: </b>",Species)),size=2)+
  theme_bw()

tooltip_css <- "background-color:#e7eef3;font-family:Roboto;padding:10px;border-style:solid;border-width:2px;border-color:#125687;border-radius:5px;"

girafe(code=print(p),
  options=list(
    opts_hover(css="cursor:pointer;stroke:black;fill-opacity:0.3"),
    opts_zoom(max=5),
    opts_tooltip(css=tooltip_css,opacity=0.9)
  )
)
Figure 6: Interactive scatterplot using ggiraph.

6.5 dygraphs

The dygraphs package provides R bindings to the dygraphs JavaScript library for interactive time-series charts.

library(dygraphs)

lungDeaths <- cbind(ldeaths, mdeaths, fdeaths)
dygraph(lungDeaths,main="Deaths from Lung Disease (UK)") |>
  dyOptions(colors=c("#66C2A5","#FC8D62","#8DA0CB"))
Figure 7: Interactive time series plot using dygraph.

6.6 Network graph

The networkD3 package creates interactive network graphs using D3.js.

library(networkD3)

data(MisLinks,MisNodes)
forceNetwork(Links=MisLinks,Nodes=MisNodes,Source="source",
             Target="target",Value="value",NodeID="name",
             Group="group",opacity=0.4)
Figure 8: Interactive network plot.

6.7 leaflet

The leaflet package provides R bindings to the Leaflet JavaScript library.

library(leaflet)

leaflet(height=500,width=700) |>
  addTiles(urlTemplate='https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png') |>
  #addProviderTiles(providers$Esri.NatGeoWorldMap) |>
  addMarkers(lat=57.639327,lng=18.288534,popup="RaukR") |>
  setView(lat=57.639327,lng=18.288534,zoom=15)
Figure 9: Interactive map using leaflet.

6.8 crosstalk

The crosstalk package links selections and filters across compatible HTML widgets that share a SharedData object.

library(crosstalk)

shared_quakes <- SharedData$new(quakes[sample(nrow(quakes), 100),])
lf <- leaflet(shared_quakes,height=300) |>
        addTiles(urlTemplate='https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png') |>
        addMarkers()
py <- plot_ly(shared_quakes,x=~depth,y=~mag,size=~stations,height=300) |>
        add_markers()

htmltools::div(lf,py)
Figure 10: Linking independent plots using crosstalk.

7 Observable JS

Quarto supports Observable JS for interactive visualizations in the browser.

Pass data from R to OJS:

irism <- iris
colnames(irism) <- gsub("[.]","_",tolower(colnames(irism)))
ojs_define(ojsd = irism)
ojsdata = transpose(ojsd)

Display the data as a table:

viewof filtered_table = Inputs.table(ojsdata)

Define inputs:

viewof x = Inputs.select(Object.keys(ojsdata[0]), {value: "sepal_length", multiple: false, label: "X axis"})
viewof y = Inputs.select(Object.keys(ojsdata[0]), {value: "sepal_width", multiple: false, label: "Y axis"})

Display the plot:

Plot.plot({
  marks: [
    Plot.dot(ojsdata, {
      x: x,
      y: y,
      fill: "species",
      title: (d) =>
        `${d.species} \n Petal length: ${d.petal_length} \n Sepal length: ${d.sepal_length}`
    })
  ],
  grid: true
})

See Quarto’s Observable JS documentation.

8 Icons

Icons can be generated from R with the fontawesome package.

`r fontawesome::fa('lightbulb')`

height and fill are optional arguments.

`r fontawesome::fa('lightbulb',height='30px',fill='steelblue')`

See Font Awesome for a full list of icons.

9 Session information

R version 4.6.1 (2026-06-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

time zone: UTC
tzcode source: system (glibc)

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

other attached packages:
 [1] rmarkdown_2.32    fontawesome_0.5.3 crosstalk_1.2.2   leaflet_2.2.3    
 [5] networkD3_0.4.1   dygraphs_1.1.1.6  ggiraph_0.9.6     plotly_4.12.1    
 [9] highcharter_0.9.5 ggplot2_4.0.3     reactable_0.4.5   DT_0.34.0        
[13] gt_1.3.0          formattable_0.2.1 kableExtra_1.4.1  htmlTable_2.5.0  
[17] knitr_1.52        stringr_1.6.0     tidyr_1.3.2       dplyr_1.2.1      

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1        viridisLite_0.4.3       farver_2.1.2           
 [4] S7_0.2.2                fastmap_1.2.0           fontquiver_0.2.1       
 [7] digest_0.6.39           timechange_0.4.0        lifecycle_1.0.5        
[10] magrittr_2.0.5          compiler_4.6.1          rlang_1.3.0            
[13] sass_0.4.10             tools_4.6.1             igraph_2.3.3           
[16] yaml_2.3.12             data.table_1.18.6.1     labeling_0.4.3         
[19] htmlwidgets_1.6.4       curl_8.0.0              xml2_1.6.0             
[22] TTR_0.24.4              RColorBrewer_1.1-3      withr_3.0.3            
[25] purrr_1.2.2             grid_4.6.1              gdtools_0.5.1          
[28] xts_0.14.3              data.tree_1.2.0         scales_1.4.0           
[31] MASS_7.3-65             cli_3.6.6               generics_0.1.4         
[34] otel_0.2.0              rlist_0.4.6.2           rstudioapi_0.19.0      
[37] httr_1.4.9              commonmark_2.0.0        cachem_1.1.0           
[40] assertthat_0.2.1        base64enc_0.1-6         vctrs_0.7.3            
[43] jsonlite_2.0.0          fontBitstreamVera_0.1.1 litedown_0.11          
[46] systemfonts_1.3.2       jquerylib_0.1.4         quantmod_0.4.29        
[49] glue_1.8.1              reactR_0.6.1            lubridate_1.9.5        
[52] stringi_1.8.9           gtable_0.3.6            tibble_3.3.1           
[55] pillar_1.11.1           htmltools_0.5.9         R6_2.6.1               
[58] textshaping_1.0.5       evaluate_1.0.5          lattice_0.22-9         
[61] markdown_2.0            backports_1.5.1         broom_1.0.13           
[64] bslib_0.12.0            fontLiberation_0.1.0    Rcpp_1.1.2             
[67] svglite_2.2.2           checkmate_2.3.4         xfun_0.61              
[70] fs_2.1.0                zoo_1.9-1               pkgconfig_2.0.3