R Markdown Tables

layout: true

<div class="footer-small"><span>https://github.com/mjfrigaard/ph-lacounty-r/</div>

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# R Markdown Tables
#.fancy[Creating Tables in R Markdown]

<br>

.large[by Martin Frigaard]

Written: October 03 2022

Updated: December 02 2022

.footer-large[.right[.fira[
<br><br><br><br><br>[Created using the "λέξις" theme](https://jhelvy.github.io/lexis/index.html#what-does-%CE%BB%CE%AD%CE%BE%CE%B9%CF%82-mean)
]]]

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# Materials

The slides are in the `slides.pdf` file

The materials for this training are in the `worksheets` folder:

```
worksheets
├── import.Rmd
├── export.Rmd
├── objects.Rmd
├── rmd-basic.Rmd
├── rmd-tables.Rmd
└── rmd-visualizations.Rmd
```

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# Outline

<br>

.leftcol[

#### 1. Importing data

#### 2. Common Data Objects

#### 3. R Markdown

]

.rightcol[

#### 4. R Markdown Data Visualizations

#### 5. .red[R Markdown Tables]

#### 6. Exporting Data

]

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# .large[R Markdown Tables]

.font90[.green[Open `rmd-tables.Rmd` to follow along]]

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# R Markdown Tables

Import the `nhanes_11_12` data from the `data/csv/` folder.

.code70[

```r
nhanes_11_12 <- data.table::fread(input = "../data/csv/nhanes_11_12.csv")
glimpse(nhanes_11_12)
```
]

.code60[

```
  Rows: 2,121
  Columns: 16
  $ id               <int> 62164, 62176, 62184, 62199, 62200, 62202, 62205, 6220…
  $ gender           <chr> "female", "female", "male", "male", "male", "male", "…
  $ age              <int> 44, 34, 26, 57, 42, 36, 28, 38, 77, 31, 29, 50, 31, 2…
  $ race3            <chr> "White", "White", "Black", "White", "Asian", "Mexican…
  $ education        <chr> "Some College", "College Grad", "High School", "Colle…
  $ hh_income        <chr> "45000-54999", "more 99999", "more 99999", "more 9999…
  $ poverty          <dbl> 1.67, 5.00, 3.85, 5.00, 4.07, 2.83, 5.00, 1.53, 0.97,…
  $ weight           <dbl> 67.2, 68.7, 68.9, 96.9, 73.7, 80.2, 84.8, 63.2, 69.8,…
  $ height           <dbl> 170.1, 171.6, 176.6, 186.0, 163.4, 180.1, 171.4, 168.…
  $ bp_sys_ave       <int> 119, 107, 121, 110, 127, 136, 122, 105, 131, 120, 99,…
  $ bp_dia_ave       <int> 62, 69, 68, 65, 88, 48, 87, 59, 56, 71, 61, 74, 84, 7…
  $ testosterone     <dbl> 44.35, 21.11, 746.95, 269.24, 256.92, 272.46, 466.11,…
  $ tot_chol         <dbl> 4.91, 4.42, 4.81, 4.42, 5.77, 7.29, 5.46, 5.25, 4.47,…
  $ diabetes         <chr> "No", "No", "No", "No", "No", "No", "No", "No", "Yes"…
  $ sleep_hrs_night  <int> 8, 7, 8, 8, 6, 8, 6, 5, 8, 6, 8, 7, 6, 5, 8, 8, 7, 8,…
  $ phys_active_days <int> 5, 7, 3, 2, 4, 2, 1, 1, 7, 4, 2, 5, 4, 7, 4, 3, 4, 6,…
```
]

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# R Markdown Tables

Check out the codebook in the `rmd-tables.Rmd` file to get a better understanding of the NHANES variables.

### Calculate Descriptives

Below we calculate the descriptive statistics with some help from  [`dplyr`](https://dplyr.tidyverse.org/)

```r
descriptives <- nhanes_11_12 |> 
    mutate(ht_meters = height*0.01,
           bmi = weight/ht_meters^2) |> 
    group_by(diabetes) |> 
    summarise(n = n(), 
              across(c(age, bmi, bp_sys_ave, tot_chol), 
              mean, na.rm = TRUE))
```

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# R Markdown Tables

.leftcol[
If we print `descriptives` to the console, we see a `tibble` output

<br>

.code70[

```r
descriptives
```
]
.code55[

```
  # A tibble: 2 × 6
    diabetes     n   age   bmi bp_sys_ave tot_chol
    <chr>    <int> <dbl> <dbl>      <dbl>    <dbl>
  1 No        1890  43.1  27.7       120.     5.00
  2 Yes        231  57.8  31.5       128.     4.83
```
]
]

.rightcol[
We can use `knitr::kable()` to get a basic formatted table.

<br>

.font70[

```r
knitr::kable(descriptives)
```

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> diabetes </th>
   <th style="text-align:right;"> n </th>
   <th style="text-align:right;"> age </th>
   <th style="text-align:right;"> bmi </th>
   <th style="text-align:right;"> bp_sys_ave </th>
   <th style="text-align:right;"> tot_chol </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> No </td>
   <td style="text-align:right;"> 1890 </td>
   <td style="text-align:right;"> 43.12857 </td>
   <td style="text-align:right;"> 27.68338 </td>
   <td style="text-align:right;"> 119.7624 </td>
   <td style="text-align:right;"> 5.001571 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Yes </td>
   <td style="text-align:right;"> 231 </td>
   <td style="text-align:right;"> 57.83117 </td>
   <td style="text-align:right;"> 31.47395 </td>
   <td style="text-align:right;"> 127.7749 </td>
   <td style="text-align:right;"> 4.829480 </td>
  </tr>
</tbody>
</table>
]
]

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# R Markdown Tables

`knitr::kable()` allows us to adust the contents of the table with arguments like `digits` and `col.names`

.code80[

```r
knitr::kable(descriptives, digits = 2, 
    col.names = c("Diabetic", "N", "Age", "BMI", 
        "Sys BP (Avg)", "Total Chol"))
```

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> Diabetic </th>
   <th style="text-align:right;"> N </th>
   <th style="text-align:right;"> Age </th>
   <th style="text-align:right;"> BMI </th>
   <th style="text-align:right;"> Sys BP (Avg) </th>
   <th style="text-align:right;"> Total Chol </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> No </td>
   <td style="text-align:right;"> 1890 </td>
   <td style="text-align:right;"> 43.13 </td>
   <td style="text-align:right;"> 27.68 </td>
   <td style="text-align:right;"> 119.76 </td>
   <td style="text-align:right;"> 5.00 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Yes </td>
   <td style="text-align:right;"> 231 </td>
   <td style="text-align:right;"> 57.83 </td>
   <td style="text-align:right;"> 31.47 </td>
   <td style="text-align:right;"> 127.77 </td>
   <td style="text-align:right;"> 4.83 </td>
  </tr>
</tbody>
</table>
]

<br>

Read more about `kable` table options [here](https://bookdown.org/yihui/rmarkdown-cookbook/kable.html)

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# R Markdown Tables

If we're looking at larger tables, we can use the `rmarkdown::paged_table()` function

<br>

.code80[

```r
big_descriptives <- nhanes_11_12 |>
    dplyr::select(-id) |> # remove id
    dplyr::mutate(ht_meters = height * 0.01, # calculate new vars
                  bmi = weight / ht_meters ^ 2) |>
    dplyr::group_by(diabetes) |> # calculate by diabetes
    dplyr::summarise(n = n(), # get total
        dplyr::across(.cols = where(is.numeric), # all numeric variables
                      .fns =  mean, na.rm = TRUE)) # calculate mean 
```
]

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# R Markdown Tables

If we're looking at larger tables, we can use the `rmarkdown::paged_table()` function

<br>

```r
rmarkdown::paged_table(big_descriptives)
```

<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
{"columns":[{"label":["diabetes"],"name":[1],"type":["chr"],"align":["left"]},{"label":["n"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["age"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["poverty"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["weight"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["height"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["bp_sys_ave"],"name":[7],"type":["dbl"],"align":["right"]},{"label":["bp_dia_ave"],"name":[8],"type":["dbl"],"align":["right"]},{"label":["testosterone"],"name":[9],"type":["dbl"],"align":["right"]},{"label":["tot_chol"],"name":[10],"type":["dbl"],"align":["right"]},{"label":["sleep_hrs_night"],"name":[11],"type":["dbl"],"align":["right"]},{"label":["phys_active_days"],"name":[12],"type":["dbl"],"align":["right"]},{"label":["ht_meters"],"name":[13],"type":["dbl"],"align":["right"]},{"label":["bmi"],"name":[14],"type":["dbl"],"align":["right"]}],"data":[{"1":"No","2":"1890","3":"43.12857","4":"2.879497","5":"79.54995","6":"169.2085","7":"119.7624","8":"71.18519","9":"238.5126","10":"5.001571","11":"6.852910","12":"3.597884","13":"1.692085","14":"27.68338"},{"1":"Yes","2":"231","3":"57.83117","4":"2.310563","5":"87.79481","6":"166.8013","7":"127.7749","8":"70.53247","9":"189.5030","10":"4.829481","11":"6.666667","12":"3.813853","13":"1.668013","14":"31.47395"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
  </script>
</div>

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# R Markdown Tables

The `gtsummary()` package has great functions for creating common tables.

.leftcol[

```r
library(gtsummary)
tbl_vars <- dplyr::select(nhanes_11_12, 
        diabetes, race3)
gtsummary::tbl_cross(
    data = tbl_vars,
    row = race3, col = diabetes)
```

]

.rightcol[

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</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="2" colspan="1" scope="col" id=""></th>
      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2" scope="colgroup" id="diabetes">
        <span class="gt_column_spanner">diabetes</span>
      </th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="2" colspan="1" scope="col" id="Total">Total</th>
    </tr>
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="No">No</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="Yes">Yes</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td headers="label" class="gt_row gt_left">race3</td>
<td headers="stat_1" class="gt_row gt_center"></td>
<td headers="stat_2" class="gt_row gt_center"></td>
<td headers="stat_0" class="gt_row gt_center"></td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Asian</td>
<td headers="stat_1" class="gt_row gt_center">308</td>
<td headers="stat_2" class="gt_row gt_center">32</td>
<td headers="stat_0" class="gt_row gt_center">340</td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Black</td>
<td headers="stat_1" class="gt_row gt_center">412</td>
<td headers="stat_2" class="gt_row gt_center">77</td>
<td headers="stat_0" class="gt_row gt_center">489</td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Hispanic</td>
<td headers="stat_1" class="gt_row gt_center">162</td>
<td headers="stat_2" class="gt_row gt_center">16</td>
<td headers="stat_0" class="gt_row gt_center">178</td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Mexican</td>
<td headers="stat_1" class="gt_row gt_center">154</td>
<td headers="stat_2" class="gt_row gt_center">27</td>
<td headers="stat_0" class="gt_row gt_center">181</td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Other</td>
<td headers="stat_1" class="gt_row gt_center">61</td>
<td headers="stat_2" class="gt_row gt_center">8</td>
<td headers="stat_0" class="gt_row gt_center">69</td></tr>
    <tr><td headers="label" class="gt_row gt_left">    White</td>
<td headers="stat_1" class="gt_row gt_center">793</td>
<td headers="stat_2" class="gt_row gt_center">71</td>
<td headers="stat_0" class="gt_row gt_center">864</td></tr>
    <tr><td headers="label" class="gt_row gt_left">Total</td>
<td headers="stat_1" class="gt_row gt_center">1,890</td>
<td headers="stat_2" class="gt_row gt_center">231</td>
<td headers="stat_0" class="gt_row gt_center">2,121</td></tr>
  </tbody>
  
  
</table>
</div>

]

---
background-image: url(www/pdg-hex.png)
background-position: 96% 4%
background-size: 6%

# R Markdown Tables

We can add lables with the [`labelled` package.](https://cran.r-project.org/web/packages/labelled/vignettes/intro_labelled.html)

.leftcol[

```r
library(labelled)
var_label(tbl_vars) <- list(
    race3 = "Race",
    diabetes = "Diabetes Status")
tbl_cross(data = tbl_vars, 
    row = race3, col = diabetes)
```

]

.rightcol[

<div id="wkjestdlgr" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
<style>html {
  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;
}

#wkjestdlgr .gt_table {
  display: table;
  border-collapse: collapse;
  margin-left: auto;
  margin-right: auto;
  color: #333333;
  font-size: 16px;
  font-weight: normal;
  font-style: normal;
  background-color: #FFFFFF;
  width: auto;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #A8A8A8;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #A8A8A8;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
}

#wkjestdlgr .gt_heading {
  background-color: #FFFFFF;
  text-align: center;
  border-bottom-color: #FFFFFF;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#wkjestdlgr .gt_caption {
  padding-top: 4px;
  padding-bottom: 4px;
}

#wkjestdlgr .gt_title {
  color: #333333;
  font-size: 125%;
  font-weight: initial;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
  border-bottom-color: #FFFFFF;
  border-bottom-width: 0;
}

#wkjestdlgr .gt_subtitle {
  color: #333333;
  font-size: 85%;
  font-weight: initial;
  padding-top: 0;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-color: #FFFFFF;
  border-top-width: 0;
}

#wkjestdlgr .gt_bottom_border {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#wkjestdlgr .gt_col_headings {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#wkjestdlgr .gt_col_heading {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  overflow-x: hidden;
}

#wkjestdlgr .gt_column_spanner_outer {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  padding-top: 0;
  padding-bottom: 0;
  padding-left: 4px;
  padding-right: 4px;
}

#wkjestdlgr .gt_column_spanner_outer:first-child {
  padding-left: 0;
}

#wkjestdlgr .gt_column_spanner_outer:last-child {
  padding-right: 0;
}

#wkjestdlgr .gt_column_spanner {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 5px;
  overflow-x: hidden;
  display: inline-block;
  width: 100%;
}

#wkjestdlgr .gt_group_heading {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  text-align: left;
}

#wkjestdlgr .gt_empty_group_heading {
  padding: 0.5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: middle;
}

#wkjestdlgr .gt_from_md > :first-child {
  margin-top: 0;
}

#wkjestdlgr .gt_from_md > :last-child {
  margin-bottom: 0;
}

#wkjestdlgr .gt_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  margin: 10px;
  border-top-style: solid;
  border-top-width: 1px;
  border-top-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  overflow-x: hidden;
}

#wkjestdlgr .gt_stub {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 5px;
  padding-right: 5px;
}

#wkjestdlgr .gt_stub_row_group {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 5px;
  padding-right: 5px;
  vertical-align: top;
}

#wkjestdlgr .gt_row_group_first td {
  border-top-width: 2px;
}

#wkjestdlgr .gt_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#wkjestdlgr .gt_first_summary_row {
  border-top-style: solid;
  border-top-color: #D3D3D3;
}

#wkjestdlgr .gt_first_summary_row.thick {
  border-top-width: 2px;
}

#wkjestdlgr .gt_last_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#wkjestdlgr .gt_grand_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#wkjestdlgr .gt_first_grand_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: double;
  border-top-width: 6px;
  border-top-color: #D3D3D3;
}

#wkjestdlgr .gt_striped {
  background-color: rgba(128, 128, 128, 0.05);
}

#wkjestdlgr .gt_table_body {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#wkjestdlgr .gt_footnotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#wkjestdlgr .gt_footnote {
  margin: 0px;
  font-size: 90%;
  padding-left: 4px;
  padding-right: 4px;
  padding-left: 5px;
  padding-right: 5px;
}

#wkjestdlgr .gt_sourcenotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#wkjestdlgr .gt_sourcenote {
  font-size: 90%;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
}

#wkjestdlgr .gt_left {
  text-align: left;
}

#wkjestdlgr .gt_center {
  text-align: center;
}

#wkjestdlgr .gt_right {
  text-align: right;
  font-variant-numeric: tabular-nums;
}

#wkjestdlgr .gt_font_normal {
  font-weight: normal;
}

#wkjestdlgr .gt_font_bold {
  font-weight: bold;
}

#wkjestdlgr .gt_font_italic {
  font-style: italic;
}

#wkjestdlgr .gt_super {
  font-size: 65%;
}

#wkjestdlgr .gt_footnote_marks {
  font-style: italic;
  font-weight: normal;
  font-size: 75%;
  vertical-align: 0.4em;
}

#wkjestdlgr .gt_asterisk {
  font-size: 100%;
  vertical-align: 0;
}

#wkjestdlgr .gt_indent_1 {
  text-indent: 5px;
}

#wkjestdlgr .gt_indent_2 {
  text-indent: 10px;
}

#wkjestdlgr .gt_indent_3 {
  text-indent: 15px;
}

#wkjestdlgr .gt_indent_4 {
  text-indent: 20px;
}

#wkjestdlgr .gt_indent_5 {
  text-indent: 25px;
}
</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="2" colspan="1" scope="col" id=""></th>
      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2" scope="colgroup" id="Diabetes Status">
        <span class="gt_column_spanner">Diabetes Status</span>
      </th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="2" colspan="1" scope="col" id="Total">Total</th>
    </tr>
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="No">No</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="Yes">Yes</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td headers="label" class="gt_row gt_left">Race</td>
<td headers="stat_1" class="gt_row gt_center"></td>
<td headers="stat_2" class="gt_row gt_center"></td>
<td headers="stat_0" class="gt_row gt_center"></td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Asian</td>
<td headers="stat_1" class="gt_row gt_center">308</td>
<td headers="stat_2" class="gt_row gt_center">32</td>
<td headers="stat_0" class="gt_row gt_center">340</td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Black</td>
<td headers="stat_1" class="gt_row gt_center">412</td>
<td headers="stat_2" class="gt_row gt_center">77</td>
<td headers="stat_0" class="gt_row gt_center">489</td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Hispanic</td>
<td headers="stat_1" class="gt_row gt_center">162</td>
<td headers="stat_2" class="gt_row gt_center">16</td>
<td headers="stat_0" class="gt_row gt_center">178</td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Mexican</td>
<td headers="stat_1" class="gt_row gt_center">154</td>
<td headers="stat_2" class="gt_row gt_center">27</td>
<td headers="stat_0" class="gt_row gt_center">181</td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Other</td>
<td headers="stat_1" class="gt_row gt_center">61</td>
<td headers="stat_2" class="gt_row gt_center">8</td>
<td headers="stat_0" class="gt_row gt_center">69</td></tr>
    <tr><td headers="label" class="gt_row gt_left">    White</td>
<td headers="stat_1" class="gt_row gt_center">793</td>
<td headers="stat_2" class="gt_row gt_center">71</td>
<td headers="stat_0" class="gt_row gt_center">864</td></tr>
    <tr><td headers="label" class="gt_row gt_left">Total</td>
<td headers="stat_1" class="gt_row gt_center">1,890</td>
<td headers="stat_2" class="gt_row gt_center">231</td>
<td headers="stat_0" class="gt_row gt_center">2,121</td></tr>
  </tbody>
  
  
</table>
</div>

]

---
background-image: url(www/pdg-hex.png)
background-position: 96% 4%
background-size: 6%

# R Markdown Tables

Options for adding percentages and p-values `add_p()`

.leftcol[

```r
library(labelled)
var_label(tbl_vars) <- list(
    race3 = "Race",
    diabetes = "Diabetes Status")
tbl_cross(data = tbl_vars, 
    row = race3, col = diabetes, 
    percent = "cell") |> 
    add_p() 
```

]

.rightcol[

<div id="xgieuimfey" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
<style>html {
  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;
}

#xgieuimfey .gt_table {
  display: table;
  border-collapse: collapse;
  margin-left: auto;
  margin-right: auto;
  color: #333333;
  font-size: 16px;
  font-weight: normal;
  font-style: normal;
  background-color: #FFFFFF;
  width: auto;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #A8A8A8;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #A8A8A8;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
}

#xgieuimfey .gt_heading {
  background-color: #FFFFFF;
  text-align: center;
  border-bottom-color: #FFFFFF;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#xgieuimfey .gt_caption {
  padding-top: 4px;
  padding-bottom: 4px;
}

#xgieuimfey .gt_title {
  color: #333333;
  font-size: 125%;
  font-weight: initial;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
  border-bottom-color: #FFFFFF;
  border-bottom-width: 0;
}

#xgieuimfey .gt_subtitle {
  color: #333333;
  font-size: 85%;
  font-weight: initial;
  padding-top: 0;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-color: #FFFFFF;
  border-top-width: 0;
}

#xgieuimfey .gt_bottom_border {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#xgieuimfey .gt_col_headings {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#xgieuimfey .gt_col_heading {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  overflow-x: hidden;
}

#xgieuimfey .gt_column_spanner_outer {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  padding-top: 0;
  padding-bottom: 0;
  padding-left: 4px;
  padding-right: 4px;
}

#xgieuimfey .gt_column_spanner_outer:first-child {
  padding-left: 0;
}

#xgieuimfey .gt_column_spanner_outer:last-child {
  padding-right: 0;
}

#xgieuimfey .gt_column_spanner {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 5px;
  overflow-x: hidden;
  display: inline-block;
  width: 100%;
}

#xgieuimfey .gt_group_heading {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  text-align: left;
}

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  font-weight: initial;
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  border-top-color: #D3D3D3;
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#xgieuimfey .gt_from_md > :last-child {
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#xgieuimfey .gt_first_summary_row.thick {
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#xgieuimfey .gt_last_summary_row {
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  border-bottom-style: solid;
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}

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</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="2" colspan="1" scope="col" id=""></th>
      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2" scope="colgroup" id="Diabetes Status">
        <span class="gt_column_spanner">Diabetes Status</span>
      </th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="2" colspan="1" scope="col" id="Total">Total</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="2" colspan="1" scope="col" id="p-value<sup class="gt_footnote_marks">1</sup>">p-value<sup class="gt_footnote_marks">1</sup></th>
    </tr>
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="No">No</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="Yes">Yes</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td headers="label" class="gt_row gt_left">Race</td>
<td headers="stat_1" class="gt_row gt_center"></td>
<td headers="stat_2" class="gt_row gt_center"></td>
<td headers="stat_0" class="gt_row gt_center"></td>
<td headers="p.value" class="gt_row gt_center"><0.001</td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Asian</td>
<td headers="stat_1" class="gt_row gt_center">308 (15%)</td>
<td headers="stat_2" class="gt_row gt_center">32 (1.5%)</td>
<td headers="stat_0" class="gt_row gt_center">340 (16%)</td>
<td headers="p.value" class="gt_row gt_center"></td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Black</td>
<td headers="stat_1" class="gt_row gt_center">412 (19%)</td>
<td headers="stat_2" class="gt_row gt_center">77 (3.6%)</td>
<td headers="stat_0" class="gt_row gt_center">489 (23%)</td>
<td headers="p.value" class="gt_row gt_center"></td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Hispanic</td>
<td headers="stat_1" class="gt_row gt_center">162 (7.6%)</td>
<td headers="stat_2" class="gt_row gt_center">16 (0.8%)</td>
<td headers="stat_0" class="gt_row gt_center">178 (8.4%)</td>
<td headers="p.value" class="gt_row gt_center"></td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Mexican</td>
<td headers="stat_1" class="gt_row gt_center">154 (7.3%)</td>
<td headers="stat_2" class="gt_row gt_center">27 (1.3%)</td>
<td headers="stat_0" class="gt_row gt_center">181 (8.5%)</td>
<td headers="p.value" class="gt_row gt_center"></td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Other</td>
<td headers="stat_1" class="gt_row gt_center">61 (2.9%)</td>
<td headers="stat_2" class="gt_row gt_center">8 (0.4%)</td>
<td headers="stat_0" class="gt_row gt_center">69 (3.3%)</td>
<td headers="p.value" class="gt_row gt_center"></td></tr>
    <tr><td headers="label" class="gt_row gt_left">    White</td>
<td headers="stat_1" class="gt_row gt_center">793 (37%)</td>
<td headers="stat_2" class="gt_row gt_center">71 (3.3%)</td>
<td headers="stat_0" class="gt_row gt_center">864 (41%)</td>
<td headers="p.value" class="gt_row gt_center"></td></tr>
    <tr><td headers="label" class="gt_row gt_left">Total</td>
<td headers="stat_1" class="gt_row gt_center">1,890 (89%)</td>
<td headers="stat_2" class="gt_row gt_center">231 (11%)</td>
<td headers="stat_0" class="gt_row gt_center">2,121 (100%)</td>
<td headers="p.value" class="gt_row gt_center"></td></tr>
  </tbody>
  
  <tfoot class="gt_footnotes">
    <tr>
      <td class="gt_footnote" colspan="5"><sup class="gt_footnote_marks">1</sup> Pearson's Chi-squared test</td>
    </tr>
  </tfoot>
</table>
</div>

]

---
background-image: url(www/pdg-hex.png)
background-position: 96% 4%
background-size: 6%

# R Markdown Tables

The `gt_summary::tbl_summary()` function creates publication-quality summary tables with multiple statistical summary options.

.leftcol[

.code70[

```r
tbl_vars <- nhanes_11_12 |> 
    dplyr::mutate(ht_meters = height*0.01,
           bmi = weight/ht_meters^2) |> 
    dplyr::select(age, gender, 
        bmi, bp_sys_ave, bp_dia_ave)
var_label(tbl_vars) <- list(
    age = "Age",
    gender = "Gender",
    bmi = "BMI",
    bp_sys_ave = "Sys Avg",
    bp_dia_ave = "Dia Avg")
```

]

.rightcol[

```r
gtsummary::tbl_summary(tbl_vars)
```

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  background-color: #FFFFFF;
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  padding-bottom: 8px;
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  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="<strong>Characteristic</strong>"><strong>Characteristic</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>N = 2,121</strong><sup class="gt_footnote_marks">1</sup>"><strong>N = 2,121</strong><sup class="gt_footnote_marks">1</sup></th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td headers="label" class="gt_row gt_left">Age</td>
<td headers="stat_0" class="gt_row gt_center">42 (30, 58)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">Gender</td>
<td headers="stat_0" class="gt_row gt_center"></td></tr>
    <tr><td headers="label" class="gt_row gt_left">    female</td>
<td headers="stat_0" class="gt_row gt_center">1,008 (48%)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">    male</td>
<td headers="stat_0" class="gt_row gt_center">1,113 (52%)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">BMI</td>
<td headers="stat_0" class="gt_row gt_center">27.0 (23.7, 31.3)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">Sys Avg</td>
<td headers="stat_0" class="gt_row gt_center">118 (110, 129)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">Dia Avg</td>
<td headers="stat_0" class="gt_row gt_center">71 (64, 78)</td></tr>
  </tbody>
  
  <tfoot class="gt_footnotes">
    <tr>
      <td class="gt_footnote" colspan="2"><sup class="gt_footnote_marks">1</sup> Median (IQR); n (%)</td>
    </tr>
  </tfoot>
</table>
</div>

]

---
background-image: url(www/pdg-hex.png)
background-position: 96% 4%
background-size: 6%

# R Markdown Tables

We can also create grouped summaries with `gtsummary::tbl_summary(by = )`:

.leftcol[

.code70[

```r
tbl_vars <- nhanes_11_12 |> 
    dplyr::mutate(
     ht_meters = height*0.01,
     bmi = weight/ht_meters^2,
     diabetes = if_else(diabetes == "Yes", 
                 "Diabetic", "Healthy")) |> 
    dplyr::select(age, diabetes, gender, 
        bmi, bp_sys_ave, bp_dia_ave)
var_label(tbl_vars) <- list(
    age = "Age",
    diabetes = "Diabetic",
    gender = "Gender",
    bmi = "BMI",
    bp_sys_ave = "Sys Avg",
    bp_dia_ave = "Dia Avg")
```

]

.rightcol[

```r
gtsummary::tbl_summary(tbl_vars, 
    by = diabetes)
```

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    <tr>
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      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>Diabetic</strong>, N = 231<sup class="gt_footnote_marks">1</sup>"><strong>Diabetic</strong>, N = 231<sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>Healthy</strong>, N = 1,890<sup class="gt_footnote_marks">1</sup>"><strong>Healthy</strong>, N = 1,890<sup class="gt_footnote_marks">1</sup></th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td headers="label" class="gt_row gt_left">Age</td>
<td headers="stat_1" class="gt_row gt_center">60 (50, 68)</td>
<td headers="stat_2" class="gt_row gt_center">40 (29, 56)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">Gender</td>
<td headers="stat_1" class="gt_row gt_center"></td>
<td headers="stat_2" class="gt_row gt_center"></td></tr>
    <tr><td headers="label" class="gt_row gt_left">    female</td>
<td headers="stat_1" class="gt_row gt_center">112 (48%)</td>
<td headers="stat_2" class="gt_row gt_center">896 (47%)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">    male</td>
<td headers="stat_1" class="gt_row gt_center">119 (52%)</td>
<td headers="stat_2" class="gt_row gt_center">994 (53%)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">BMI</td>
<td headers="stat_1" class="gt_row gt_center">30.2 (26.5, 35.4)</td>
<td headers="stat_2" class="gt_row gt_center">26.6 (23.4, 30.7)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">Sys Avg</td>
<td headers="stat_1" class="gt_row gt_center">127 (116, 136)</td>
<td headers="stat_2" class="gt_row gt_center">118 (109, 128)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">Dia Avg</td>
<td headers="stat_1" class="gt_row gt_center">70 (63, 79)</td>
<td headers="stat_2" class="gt_row gt_center">71 (64, 78)</td></tr>
  </tbody>
  
  <tfoot class="gt_footnotes">
    <tr>
      <td class="gt_footnote" colspan="3"><sup class="gt_footnote_marks">1</sup> Median (IQR); n (%)</td>
    </tr>
  </tfoot>
</table>
</div>

]

---
background-image: url(www/pdg-hex.png)
background-position: 96% 4%
background-size: 6%

# R Markdown Tables

### R Markdown also supports inline R code

---
background-image: url(www/pdg-hex.png)
background-position: 96% 4%
background-size: 6%

# R Markdown Tables

### Inline R code allows us to include summaries of our analysis in the report

---
background-image: url(www/pdg-hex.png)
background-position: 96% 4%
background-size: 6%

# R Markdown Tables

### We're going to add the summary statistics in `descriptives` to our `rmd-tables.Rmd` report.

.leftcol[

.font90[Include the following code under the `Summary` two-level header]

.code60[

```r
descriptives
```

```
  # A tibble: 2 × 6
    diabetes     n   age   bmi bp_sys_ave tot_chol
    <chr>    <int> <dbl> <dbl>      <dbl>    <dbl>
  1 No        1890  43.1  27.7       120.     5.00
  2 Yes        231  57.8  31.5       128.     4.83
```

]

.rightcol[

```
The average age in `nhanes_11_12` is 44.73. The correlation between age and average systolic blood pressure is 0.42
```

]