In jjallaire/radix: 'R Markdown' Format for Scientific and Technical Writing

There are a number of options available for displaying tables within Distill articles, including:

• Using the knitr::kable() function to render a data frame as HTML.

• Using the rmarkdown::paged_table() function to create a page-able version of a data frame.

• Use the gtsummary and gt packages to create to create publication-ready analytical and summary tables.

• Using standard markdown table syntax to create a custom table.

Knitr kable

The knitr::kable() function will render an R data frame as an HTML table. For example:

```r`r ''`
library(knitr)
kable(head(mtcars))
```

library(knitr)
kable(head(mtcars))

Often times tables will require more width for their display than the standard Distill article text width. Here we use layout="l-body-outset" to cause the table to outset slightly from the main text column. All of the available figure layouts can also be used with tables.

Paged tables

If a data frame has a large number of rows it might not be practical to display it fully inline. In these situations you can use the rmarkdown::paged_table() function to create a table that allows pagination of rows and columns. For example:

```r`r ''`
library(rmarkdown)
paged_table(mtcars)
```

library(rmarkdown)
paged_table(mtcars)

There are a number of options available that control the display of paged tables. By default, 10 rows at a time are displayed but you can use the rows.print option to increase this. For example:

```r`r ''`
library(rmarkdown)
paged_table(mtcars, options = list(rows.print = 15))
```

library(rmarkdown)
paged_table(mtcars, options = list(rows.print = 15, cols.print = 5))

Here is a summary of the available paged table options:

| Option | Description | |------------------|------------------------------------------------| | rows.print | Maximum rows to print per page. | | max.print | Maximum rows in the table (defaults to 1000). | | cols.print | Maximum columns in the table (defaults to 10). | | rownames.print | Print row names as part of the table. |

gtsummary tables

The gtsummary package provides an elegant and flexible way to create publication-ready analytical and summary tables. The package summarizes data sets, regression models, and more, using sensible defaults with highly customizable capabilities. For example:

```r`r ''`
library(gtsummary)
library(survival)

# build glm table
mod1 <- glm(response ~ trt + age + grade, trial, family = binomial)
t1 <- tbl_regression(mod1, exponentiate = TRUE)

# build survival model table
t2 <-
  coxph(Surv(ttdeath, death) ~ trt + grade + age, trial) %>%
  tbl_regression(exponentiate = TRUE)

# merge tables 
tbl_merge(
  tbls = list(t1, t2),
  tab_spanner = c("**Tumor Response**", "**Time to Death**")
)
```

library(gtsummary)
library(survival)

# build glm table
mod1 <- glm(response ~ trt + age + grade, trial, family = binomial)
t1 <- tbl_regression(mod1, exponentiate = TRUE)

# build survival model table
t2 <-
  coxph(Surv(ttdeath, death) ~ trt + grade + age, trial) %>%
  tbl_regression(exponentiate = TRUE)

# merge tables 
tbl_merge(
  tbls = list(t1, t2),
  tab_spanner = c("**Tumor Response**", "**Time to Death**")
)

gt tables

The gtsummary package is built in top of the gt package, which provides a flexible foundation for creating just about any sort of table you can imagine. Here is a brief example of how to use gt to create a table from the included sp500 dataset:

```r`r ''`
library(gt)
library(tidyverse)
library(glue)

# Define the start and end dates for the data range
start_date <- "2010-06-07"
end_date <- "2010-06-14"

# Create a gt table based on preprocessed
# `sp500` table data
sp500 %>%
  dplyr::filter(date >= start_date & date <= end_date) %>%
  dplyr::select(-adj_close) %>%
  gt() %>%
  tab_header(
    title = "S&P 500",
    subtitle = glue::glue("{start_date} to {end_date}")
  ) %>%
  fmt_date(
    columns = vars(date),
    date_style = 3
  ) %>%
  fmt_currency(
    columns = vars(open, high, low, close),
    currency = "USD"
  ) %>%
  fmt_number(
    columns = vars(volume),
    suffixing = TRUE
  )
```

library(gt)
library(tidyverse)
library(glue)

# Define the start and end dates for the data range
start_date <- "2010-06-07"
end_date <- "2010-06-14"

# Create a gt table based on preprocessed
# `sp500` table data
sp500 %>%
  dplyr::filter(date >= start_date & date <= end_date) %>%
  dplyr::select(-adj_close) %>%
  gt() %>%
  tab_header(
    title = "S&P 500",
    subtitle = glue::glue("{start_date} to {end_date}")
  ) %>%
  fmt_date(
    columns = vars(date),
    date_style = 3
  ) %>%
  fmt_currency(
    columns = vars(open, high, low, close),
    currency = "USD"
  ) %>%
  fmt_number(
    columns = vars(volume),
    suffixing = TRUE
  )

Markdown tables

You can also define tables using markdown. For example, the table from the previous section that describes paged table options was defined with the following markdown:

| Option           | Description                                    |
|------------------|------------------------------------------------|
| `rows.print`     | Maximum rows to print per page.                |
| `max.print`      | Maximum rows in the table (defaults to 1000).  |
| `cols.print`     | Maximum columns in the table (defaults to 10). |
| `rownames.print` | Print row names as part of the table.          |

You can use Distill figure layout classes with markdown tables by enclosing them in markdown div (:::) named with the appropriate class, for example:

::: l-body-outset
| Option           | Description                                    |
|------------------|------------------------------------------------|
| `rows.print`     | Maximum rows to print per page.                |
| `max.print`      | Maximum rows in the table (defaults to 1000).  |
| `cols.print`     | Maximum columns in the table (defaults to 10). |
| `rownames.print` | Print row names as part of the table.          |
:::

jjallaire/radix documentation built on Nov. 28, 2023, 9:23 p.m.