R Conference 2024, Kelantan

class: center, middle, inverse, title-slide

.title[
# <small>R Conference 2024, Kelantan</small>
]
.subtitle[
## <big>Publication-ready Statistical Results Using <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg></big>
]
.author[
### <span style="color:black">Dr. Wan Nor Arifin</span>
]
.institute[
### <span style="color:black"><small>Biostatistics & Research Methodology Unit</br>Universiti Sains Malaysia</br>wnarifin.github.io</small></span>
]
.date[
### <span style="color:black"><small>27 October 2024</small></span>
]

---

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## Outlines

- #### Preparing & Presenting Results
- #### Prepare Publication-ready Results Using <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:steelblue;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>
- #### Other Potentials Using <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:steelblue;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>

---
class: center, middle

# Preparing & Presenting Results

---
## Raw output _vs_ Expected output

.pull-left[
#### Raw analysis results &#x1F631;&#x1F631;&#x1F631;

```

Call:
glm(formula = cad ~ dbp10 + gender, family = "binomial", data = data)

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -6.1205     1.3167  -4.648 3.34e-06 ***
dbp10         0.4950     0.1463   3.383 0.000717 ***
genderman     0.8057     0.3908   2.062 0.039253 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 191.56  on 199  degrees of freedom
Residual deviance: 175.20  on 197  degrees of freedom
AIC: 181.2

Number of Fisher Scoring iterations: 4
```
]

.pull-right[
#### Expected presentation format &#x1F44D;&#x1F44D;&#x1F44D;
<span>
<table class="table lightable-classic" style="font-size: 12px; margin-left: auto; margin-right: auto; font-family: Georgia; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption style="font-size: initial !important;"><span style="font-size:small">
                   **Table 1**: Associated factors of coronary artery disease (_n_ = 200)
                   </span></caption>
 <thead>
  <tr>
   <th style="text-align:left;font-weight: bold;"> Factors </th>
   <th style="text-align:right;font-weight: bold;"> _b_ </th>
   <th style="text-align:right;font-weight: bold;"> SE </th>
   <th style="text-align:right;font-weight: bold;"> Adj. OR </th>
   <th style="text-align:left;font-weight: bold;"> 95% CI </th>
   <th style="text-align:right;font-weight: bold;"> _z_-stats </th>
   <th style="text-align:right;font-weight: bold;"> _P_-value </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> DBP (by 10mmHg) </td>
   <td style="text-align:right;"> 0.495 </td>
   <td style="text-align:right;"> 0.146 </td>
   <td style="text-align:right;"> 1.641 </td>
   <td style="text-align:left;"> 1.24, 2.21 </td>
   <td style="text-align:right;"> 3.383 </td>
   <td style="text-align:right;"> 0.001 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Gender (Male vs Female) </td>
   <td style="text-align:right;"> 0.806 </td>
   <td style="text-align:right;"> 0.391 </td>
   <td style="text-align:right;"> 2.238 </td>
   <td style="text-align:left;"> 1.055, 4.935 </td>
   <td style="text-align:right;"> 2.062 </td>
   <td style="text-align:right;"> 0.039 </td>
  </tr>
</tbody>
<tfoot><tr><td style="padding: 0; " colspan="100%">
<sup></sup> OR = odds ratio, SE = standard error.</td></tr></tfoot>
</table>
</span>
]

---
## Issues in Preparing & Presenting Statistical Results
- **Raw outputs &mdash; not suitable for publication / thesis / report / presentation**
- Different standards & guidelines for each
- Preparation &mdash; tedious, nightmare, error-prone!

---
## Issues in Preparing & Presenting Statistical Results
- **Raw outputs &mdash; not suitable for publication / thesis / report / presentation**
- **Different standards & guidelines for each**
- Preparation &mdash; tedious, nightmare, error-prone!

---
## Issues in Preparing & Presenting Statistical Results
- **Raw outputs &mdash; not suitable for publication / thesis / report / presentation**
- **Different standards & guidelines for each**
- **Preparation &mdash; tedious, nightmare, error-prone!**

<img src="img/img4.png" height="355px">
<img src="img/img2.png" height="355px">
<img src="img/img3.png" height="355px">
<br/><small><small>*Images generated with Copilot</small></small>

---
## Analyze & Prepare Statistical Results Using <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:steelblue;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>
- Raw outputs from <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:steelblue;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> can be unsightly
- But, <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:steelblue;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> outputs are easy to **reprocess**
- Packages and functions are abundant in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:steelblue;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> to refine raw outputs
- A breeze in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:steelblue;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> as compared to other statistical software
- Usually in combination with `R Markdown` / `Quarto Document`

.center[Table and plot straight from <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:steelblue;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>!]
.pull-left[
<span>
<table class="table lightable-classic" style="font-size: 12px; margin-left: auto; margin-right: auto; font-family: Georgia; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption style="font-size: initial !important;"><span style="font-size:small">
                   **Table 1**: Associated factors of coronary artery disease (_n_ = 200)
                   </span></caption>
 <thead>
  <tr>
   <th style="text-align:left;font-weight: bold;"> Factors </th>
   <th style="text-align:right;font-weight: bold;"> _b_ </th>
   <th style="text-align:right;font-weight: bold;"> SE </th>
   <th style="text-align:right;font-weight: bold;"> Adj. OR </th>
   <th style="text-align:left;font-weight: bold;"> 95% CI </th>
   <th style="text-align:right;font-weight: bold;"> _z_-stats </th>
   <th style="text-align:right;font-weight: bold;"> _P_-value </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> DBP (by 10mmHg) </td>
   <td style="text-align:right;"> 0.495 </td>
   <td style="text-align:right;"> 0.146 </td>
   <td style="text-align:right;"> 1.641 </td>
   <td style="text-align:left;"> 1.24, 2.21 </td>
   <td style="text-align:right;"> 3.383 </td>
   <td style="text-align:right;"> 0.001 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Gender (Male vs Female) </td>
   <td style="text-align:right;"> 0.806 </td>
   <td style="text-align:right;"> 0.391 </td>
   <td style="text-align:right;"> 2.238 </td>
   <td style="text-align:left;"> 1.055, 4.935 </td>
   <td style="text-align:right;"> 2.062 </td>
   <td style="text-align:right;"> 0.039 </td>
  </tr>
</tbody>
<tfoot><tr><td style="padding: 0; " colspan="100%">
<sup></sup> OR = odds ratio, SE = standard error.</td></tr></tfoot>
</table>
</span>
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-4-1.png" width="100%" />
.center[<span style='font-size:small;font-family:Georgia'>**Figure 1**: Associated factors of coronary artery disease (_n_ = 250)</span>]
]

---
class: center, middle

# Prepare Publication-ready Results Using R

---

## Formats of Statistical Results

.pull-left[
#### Table
<span>
<table class="table" style="font-size: 12px; font-family: Georgia; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;font-weight: bold;"> Factors </th>
   <th style="text-align:right;font-weight: bold;"> _b_ </th>
   <th style="text-align:right;font-weight: bold;"> SE </th>
   <th style="text-align:right;font-weight: bold;"> Adj. OR </th>
   <th style="text-align:left;font-weight: bold;"> 95% CI </th>
   <th style="text-align:right;font-weight: bold;"> _z_-stats </th>
   <th style="text-align:right;font-weight: bold;"> _P_-value </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> DBP (by 10mmHg) </td>
   <td style="text-align:right;"> 0.495 </td>
   <td style="text-align:right;"> 0.146 </td>
   <td style="text-align:right;"> 1.641 </td>
   <td style="text-align:left;"> 1.24, 2.21 </td>
   <td style="text-align:right;"> 3.383 </td>
   <td style="text-align:right;"> 0.001 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Gender (Male vs Female) </td>
   <td style="text-align:right;"> 0.806 </td>
   <td style="text-align:right;"> 0.391 </td>
   <td style="text-align:right;"> 2.238 </td>
   <td style="text-align:left;"> 1.055, 4.935 </td>
   <td style="text-align:right;"> 2.062 </td>
   <td style="text-align:right;"> 0.039 </td>
  </tr>
</tbody>
</table>
</span>

#### In-text statistics
<span style="font-family:Georgia;font-size:0.7em;">
Individuals with higher diastolic blood pressure (DBP) have higher odds of developing coronary artery disease (CAD), with an adjusted odds ratio (OR) of 1.641 (95% CI: 1.24, 2.21) for every 10mmHg increase in DBP. In addition, males have higher odds of developing CAD compared to females, with adjusted OR of 2.238 (95% CI: 1.055, 4.935).
</span>
]

.pull-right[
#### Plot
<span>
<img src="index_files/figure-html/unnamed-chunk-6-1.png" width="100%" />
</span>
]

---

## Approaches in R

- **Ready-made outputs** &mdash; basic outputs from packages
- **Custom outputs** &mdash; fine-tune outputs from packages
- **Custom functions** &mdash; custom outputs, avoid repetition from existing functions

--
<br/><br/><br/><br/>
.center[
###There are so many options available in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:steelblue;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>, amazing & exciting! ]

---

## Table

.pull-left[
#### Ready-made outputs

- So many packages for this purpose
  - `gtsummary` <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M47.6 300.4L228.3 469.1c7.5 7 17.4 10.9 27.7 10.9s20.2-3.9 27.7-10.9L464.4 300.4c30.4-28.3 47.6-68 47.6-109.5v-5.8c0-69.9-50.5-129.5-119.4-141C347 36.5 300.6 51.4 268 84L256 96 244 84c-32.6-32.6-79-47.5-124.6-39.9C50.5 55.6 0 115.2 0 185.1v5.8c0 41.5 17.2 81.2 47.6 109.5z"/></svg>
  - Summary tables for analysis: `epidisplay`, `finalfit`, `jtools`, `stargazer`, `jmv`, ...
  - Descriptive tables: `arsenal`, `table1`, ...
  - and many more...?

#### Custom outputs

- `knitr`: `kable()` <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M47.6 300.4L228.3 469.1c7.5 7 17.4 10.9 27.7 10.9s20.2-3.9 27.7-10.9L464.4 300.4c30.4-28.3 47.6-68 47.6-109.5v-5.8c0-69.9-50.5-129.5-119.4-141C347 36.5 300.6 51.4 268 84L256 96 244 84c-32.6-32.6-79-47.5-124.6-39.9C50.5 55.6 0 115.2 0 185.1v5.8c0 41.5 17.2 81.2 47.6 109.5z"/></svg>, `kableExtra` <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M47.6 300.4L228.3 469.1c7.5 7 17.4 10.9 27.7 10.9s20.2-3.9 27.7-10.9L464.4 300.4c30.4-28.3 47.6-68 47.6-109.5v-5.8c0-69.9-50.5-129.5-119.4-141C347 36.5 300.6 51.4 268 84L256 96 244 84c-32.6-32.6-79-47.5-124.6-39.9C50.5 55.6 0 115.2 0 185.1v5.8c0 41.5 17.2 81.2 47.6 109.5z"/></svg>
  - `broom`: `tidy()` <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M47.6 300.4L228.3 469.1c7.5 7 17.4 10.9 27.7 10.9s20.2-3.9 27.7-10.9L464.4 300.4c30.4-28.3 47.6-68 47.6-109.5v-5.8c0-69.9-50.5-129.5-119.4-141C347 36.5 300.6 51.4 268 84L256 96 244 84c-32.6-32.6-79-47.5-124.6-39.9C50.5 55.6 0 115.2 0 185.1v5.8c0 41.5 17.2 81.2 47.6 109.5z"/></svg>
  - `gt`, `flextable`
  - more...?
]

.pull-right[
#### Custom functions

- Mix & match preceding functions for repeated use, i.e. same format for same statistical analysis
  - Save your time, more readable code, reuse for your specific need
]

---

## Table

.pull-left[
#### Ready-made outputs

#### Custom outputs

.pull-right[
#### Custom functions

- Mix & match preceding functions for repeated use, i.e. same format for same statistical analysis
  - Save your time, more readable code, reuse for your specific need

These packages & options usually allow you to get output as plain text, HTML, LaTeX or Word formats, even as image or PDF. So, these are adaptable to any document.

]

---

##  Table: Ready-made outputs

.pull-left[
#### R code: Setup data & analyze

``` r
# load & prepare data
data = foreign::read.spss("slog.sav",T,T)
data$dbp10 = data$dbp/10
data$gender = factor(data$gender, labels = c("Female","Male"))
# analyze
mlog = glm(cad ~ dbp10 + gender, "binomial", data)
```

#### R code: Prepare & present

``` r
# present
gtsummary::theme_gtsummary_journal("jama")  # specify theme
*gtsummary::tbl_regression(mlog, exponentiate = TRUE,
                          label = list(
                            dbp10 = "DBP (by 10mmHg)",
                            gender = "Gender"))
gtsummary::reset_gtsummary_theme()
```
]

.pull-right[
#### Output

.center[
<img src="img/tbl_or.png" width="350px">
]
]

---

##  Table: Ready-made outputs

.pull-left[
#### R code: Prepare data

``` r
# load & prepare data
data = foreign::read.spss("slog.sav",T,T)
data$cad = factor(data$cad, labels = c("No CAD", "CAD"))
data$gender = factor(data$gender, labels=c("Female","Male"))
```

#### R code: Present

``` r
# present
library(gtsummary) # for all_x() settings
*gtsummary::tbl_summary(
  subset(data, select = c(cad, sbp:age, gender)),
  by = cad,
  label = list(cad = "CAD", sbp = "SBP",
               dbp = "DBP", chol = "Cholesterol",
               age = "Age", gender = "Gender"),
  statistic = list(
    all_continuous() ~ "{mean} ({sd})",
    all_categorical() ~ "{n} ({p}%)"),
  digits = list(all_continuous() ~ c(1,1),
                all_categorical() ~ c(0,1)))
```
]

.pull-right[
#### Output

.center[
<img src="img/tbl_des.png" width="350px">
]
]

---

## Table: Ready-made outputs

.pull-left[
#### R code: Prepare data

``` r
# load & prepare data
data = foreign::read.spss("slog.sav",T,T)
data$cad = factor(data$cad, 
                   labels = c("No CAD", "CAD"))
data$gender = factor(data$gender, 
                     labels=c("Female","Male"))
```

#### R code: Present

``` r
# present
gtsummary::theme_gtsummary_journal("jama") # combine
gtsummary::theme_gtsummary_mean_sd()       # two themes
*gtsummary::tbl_summary(
  subset(data, select = c(cad, sbp:age, gender)),
  by = cad,
  label = list(cad = "CAD", sbp = "SBP",
               dbp = "DBP", chol = "Cholesterol",
               age = "Age", gender = "Gender"),
  digits = list(all_continuous() ~ c(1,1),
                all_categorical() ~ c(0,1)))
gtsummary::reset_gtsummary_theme()
```
]

.pull-right[
#### Output
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</style>
<table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
  <thead>
    <tr class="gt_col_headings">
      <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>No CAD</strong>, N = 163"><strong>No CAD</strong>, N = 163</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>CAD</strong>, N = 37"><strong>CAD</strong>, N = 37</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td headers="label" class="gt_row gt_left">SBP, Mean (SD)</td>
<td headers="stat_1" class="gt_row gt_center">129.3 (22.3)</td>
<td headers="stat_2" class="gt_row gt_center">143.8 (25.6)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">DBP, Mean (SD)</td>
<td headers="stat_1" class="gt_row gt_center">80.8 (12.6)</td>
<td headers="stat_2" class="gt_row gt_center">89.0 (12.2)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">Cholesterol, Mean (SD)</td>
<td headers="stat_1" class="gt_row gt_center">6.1 (1.2)</td>
<td headers="stat_2" class="gt_row gt_center">6.6 (1.2)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">Age, Mean (SD)</td>
<td headers="stat_1" class="gt_row gt_center">45.2 (8.4)</td>
<td headers="stat_2" class="gt_row gt_center">47.4 (8.8)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">Gender, n (%)</td>
<td headers="stat_1" class="gt_row gt_center"><br /></td>
<td headers="stat_2" class="gt_row gt_center"><br /></td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Female</td>
<td headers="stat_1" class="gt_row gt_center">87 (53.4)</td>
<td headers="stat_2" class="gt_row gt_center">13 (35.1)</td></tr>
    <tr><td headers="label" class="gt_row gt_left">    Male</td>
<td headers="stat_1" class="gt_row gt_center">76 (46.6)</td>
<td headers="stat_2" class="gt_row gt_center">24 (64.9)</td></tr>
  </tbody>
  
  
</table>
</div>
]

---

##  Table: Custom outputs

.pull-left[
#### R code: Setup data & analyze

``` r
# load & prepare data
data = foreign::read.spss("slog.sav",T,T)
data$dbp10 = data$dbp/10
# analyze
mlog = glm(cad ~ dbp10 + gender, "binomial", data)
```

#### R code: Prepare with `broom::tidy()` + `tibble`

``` r
*out_tidy = broom::tidy(mlog)
*out_or_tidy = broom::tidy(mlog, exp = T, conf.int = T)
*out = tibble::tibble(
  Factors = c("DBP (by 10mmHg)", "Gender (Male vs Female)"),
  "_b_" = out_tidy$estimate[-1], SE = out_tidy$std.error[-1],
  "Adj. OR" = out_or_tidy$estimate[-1],
  "95% CI" = paste0(out_or_tidy$conf.low[-1]|>round(3), 
                    ", ",
                    out_or_tidy$conf.high[-1]|>round(3)),
  "_z_-stats" = out_tidy$statistic[-1],
  "_P_-value" = out_tidy$p.value[-1]
); out
```
]

.pull-right[
#### Output: Unformatted

```
# A tibble: 2 × 5
  Factors                 `_b_`    SE `Adj. OR` `95% CI`    
  <chr>                   <dbl> <dbl>     <dbl> <chr>       
1 DBP (by 10mmHg)         0.495 0.146      1.64 1.24, 2.21  
2 Gender (Male vs Female) 0.806 0.391      2.24 1.055, 4.935
```

```
# A tibble: 2 × 2
  `_z_-stats` `_P_-value`
        <dbl>       <dbl>
1        3.38    0.000717
2        2.06    0.0393  
```
]

---

## Table: Custom outputs

.pull-left[
#### R code: Present with `knitr::kable()` + `kableextra`

``` r
*knitr::kable(out, format = "html", digits = 3,
             caption = "<span style='font-size:small'>
                   **Table 1**: Associated factors of 
                   coronary artery disease (_n_ = 200)
                   </span>") |>
* kableExtra::kable_styling(font_size = 12) |>
  kableExtra::kable_classic(html_font = "Georgia") |>
  kableExtra::row_spec(row = 0, bold = T) |>
  kableExtra::footnote(general = "OR = odds ratio, 
                       SE = standard error.", 
                       general_title = "")
```
]

.pull-right[
#### Output
<span>
<table class="table lightable-classic" style="font-size: 12px; margin-left: auto; margin-right: auto; font-family: Georgia; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption style="font-size: initial !important;"><span style="font-size:small">
                   **Table 1**: Associated factors of 
                   coronary artery disease (_n_ = 200)
                   </span></caption>
 <thead>
  <tr>
   <th style="text-align:left;font-weight: bold;"> Factors </th>
   <th style="text-align:right;font-weight: bold;"> _b_ </th>
   <th style="text-align:right;font-weight: bold;"> SE </th>
   <th style="text-align:right;font-weight: bold;"> Adj. OR </th>
   <th style="text-align:left;font-weight: bold;"> 95% CI </th>
   <th style="text-align:right;font-weight: bold;"> _z_-stats </th>
   <th style="text-align:right;font-weight: bold;"> _P_-value </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> DBP (by 10mmHg) </td>
   <td style="text-align:right;"> 0.495 </td>
   <td style="text-align:right;"> 0.146 </td>
   <td style="text-align:right;"> 1.641 </td>
   <td style="text-align:left;"> 1.24, 2.21 </td>
   <td style="text-align:right;"> 3.383 </td>
   <td style="text-align:right;"> 0.001 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Gender (Male vs Female) </td>
   <td style="text-align:right;"> 0.806 </td>
   <td style="text-align:right;"> 0.391 </td>
   <td style="text-align:right;"> 2.238 </td>
   <td style="text-align:left;"> 1.055, 4.935 </td>
   <td style="text-align:right;"> 2.062 </td>
   <td style="text-align:right;"> 0.039 </td>
  </tr>
</tbody>
<tfoot><tr><td style="padding: 0; " colspan="100%">
<sup></sup> OR = odds ratio, SE = standard error.</td></tr></tfoot>
</table>
</span>
]

---

## Table: Custom functions

.pull-left[
#### R code: Function `mlg_tbl()`

``` r
*mlog_tbl = function(mlog_glm) {
  out_tidy = broom::tidy(mlog_glm)
  out_or_tidy = broom::tidy(mlog_glm, exp = T, conf.int = T)
  tibble::tibble(
    Factors = out_tidy$term[-1],
    "_b_" = out_tidy$estimate[-1],
    SE = out_tidy$std.error[-1],
    "Adj. OR" = out_or_tidy$estimate[-1],
    "95% CI" = paste0(out_or_tidy$conf.low[-1]|>round(3), 
                      ", ",
                      out_or_tidy$conf.high[-1]|>round(3)),
    "_z_-stats" = out_tidy$statistic[-1],
    "_P_-value" = out_tidy$p.value[-1])
}
```
]

.pull-right[
#### R code: Function `mlg_kbl()`

``` r
*mlog_kbl = function(mlog_tbl, caption = "Table",
                    font_size = 12, 
                    font_family = "Georgia") {
  knitr::kable(mlog_tbl, format = "html", digits = 3,
               caption = caption) |>
    kableExtra::kable_styling(font_size = font_size) |>
    kableExtra::kable_classic(html_font = font_family) |>
    kableExtra::row_spec(row = 0, bold = T) |>
    kableExtra::footnote(general = "OR = odds ratio, 
                       SE = standard error.", 
                       general_title = "")
}
```
]

---

## Table: Custom functions

.pull-left[
#### R code: `mlg_tbl()` + `mlg_kbl`

``` r
# present table
*tbl1 = mlog_tbl(mlog)
tbl1$Factors = c("DBP (by 10mmHg)", "Gender (Male vs Female)")
tbl1_caption = paste0(
  "**Table 1**: Associated factors of CAD (_n_ = ",
  nrow(mlog$data), ")")
*mlog_kbl(tbl1, caption = tbl1_caption,
*        font_size = 14, font_family = "Comic Sans MS")
```
Shorter, reusable code in lengthy document
]

.pull-right[
#### Output
<span>
<table class="table lightable-classic" style="font-size: 14px; margin-left: auto; margin-right: auto; font-family: Comic Sans MS; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption style="font-size: initial !important;">**Table 1**: Associated factors of CAD (_n_ = 200)</caption>
 <thead>
  <tr>
   <th style="text-align:left;font-weight: bold;"> Factors </th>
   <th style="text-align:right;font-weight: bold;"> _b_ </th>
   <th style="text-align:right;font-weight: bold;"> SE </th>
   <th style="text-align:right;font-weight: bold;"> Adj. OR </th>
   <th style="text-align:left;font-weight: bold;"> 95% CI </th>
   <th style="text-align:right;font-weight: bold;"> _z_-stats </th>
   <th style="text-align:right;font-weight: bold;"> _P_-value </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> DBP (by 10mmHg) </td>
   <td style="text-align:right;"> 0.495 </td>
   <td style="text-align:right;"> 0.146 </td>
   <td style="text-align:right;"> 1.641 </td>
   <td style="text-align:left;"> 1.24, 2.21 </td>
   <td style="text-align:right;"> 3.383 </td>
   <td style="text-align:right;"> 0.001 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Gender (Male vs Female) </td>
   <td style="text-align:right;"> 0.806 </td>
   <td style="text-align:right;"> 0.391 </td>
   <td style="text-align:right;"> 2.238 </td>
   <td style="text-align:left;"> 1.055, 4.935 </td>
   <td style="text-align:right;"> 2.062 </td>
   <td style="text-align:right;"> 0.039 </td>
  </tr>
</tbody>
<tfoot><tr><td style="padding: 0; " colspan="100%">
<sup></sup> OR = odds ratio, SE = standard error.</td></tr></tfoot>
</table>
</span>

_Comic Sans MS_ &#x1F60A;
]

---

## Plot

.pull-left[
####  Ready-made outputs

- Usually available in specific packages for specific analyses, must explore
  - Mostly rely on `ggplot2` <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M47.6 300.4L228.3 469.1c7.5 7 17.4 10.9 27.7 10.9s20.2-3.9 27.7-10.9L464.4 300.4c30.4-28.3 47.6-68 47.6-109.5v-5.8c0-69.9-50.5-129.5-119.4-141C347 36.5 300.6 51.4 268 84L256 96 244 84c-32.6-32.6-79-47.5-124.6-39.9C50.5 55.6 0 115.2 0 185.1v5.8c0 41.5 17.2 81.2 47.6 109.5z"/></svg> and base `graphics` <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M47.6 300.4L228.3 469.1c7.5 7 17.4 10.9 27.7 10.9s20.2-3.9 27.7-10.9L464.4 300.4c30.4-28.3 47.6-68 47.6-109.5v-5.8c0-69.9-50.5-129.5-119.4-141C347 36.5 300.6 51.4 268 84L256 96 244 84c-32.6-32.6-79-47.5-124.6-39.9C50.5 55.6 0 115.2 0 185.1v5.8c0 41.5 17.2 81.2 47.6 109.5z"/></svg>
  - Examples: `semPlot` (SEM path diagram) <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M47.6 300.4L228.3 469.1c7.5 7 17.4 10.9 27.7 10.9s20.2-3.9 27.7-10.9L464.4 300.4c30.4-28.3 47.6-68 47.6-109.5v-5.8c0-69.9-50.5-129.5-119.4-141C347 36.5 300.6 51.4 268 84L256 96 244 84c-32.6-32.6-79-47.5-124.6-39.9C50.5 55.6 0 115.2 0 185.1v5.8c0 41.5 17.2 81.2 47.6 109.5z"/></svg>, `OptimalCutpoints` (ROC curve), `finalfit` (OR plot), `survival` (survival analysis) &rarr; note they are usually tied to specific analyses
  - Again, explore!
]

.pull-right[
####  Custom outputs

- Customize options in above packages
  - `ggplot2` <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M47.6 300.4L228.3 469.1c7.5 7 17.4 10.9 27.7 10.9s20.2-3.9 27.7-10.9L464.4 300.4c30.4-28.3 47.6-68 47.6-109.5v-5.8c0-69.9-50.5-129.5-119.4-141C347 36.5 300.6 51.4 268 84L256 96 244 84c-32.6-32.6-79-47.5-124.6-39.9C50.5 55.6 0 115.2 0 185.1v5.8c0 41.5 17.2 81.2 47.6 109.5z"/></svg>, base `graphics` <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M47.6 300.4L228.3 469.1c7.5 7 17.4 10.9 27.7 10.9s20.2-3.9 27.7-10.9L464.4 300.4c30.4-28.3 47.6-68 47.6-109.5v-5.8c0-69.9-50.5-129.5-119.4-141C347 36.5 300.6 51.4 268 84L256 96 244 84c-32.6-32.6-79-47.5-124.6-39.9C50.5 55.6 0 115.2 0 185.1v5.8c0 41.5 17.2 81.2 47.6 109.5z"/></svg>, `lattice`

####  Custom functions

- Graphics options are often too many and tedious to repeatedly use without affecting readability of your code
  - Advisable to write functions for customizing the plots for your specific data and use-case
]

These packages & options usually allow you get output several image formats e.g. png, svg, pdf etc. So, this can be easily integrated in any document.

---

## Plot: Ready-made outputs

.pull-left[
#### R code

``` r
attr(data$cad, "label") = "Coronary artery disease"
attr(data$dbp10, "label") = "DBP (by 10mmHg)"
attr(data$gender, "label") = "Gender"
data$gender = factor(data$gender, labels=c("Female","Male"))
*finalfit::or_plot(data, dependent, explanatory)
```
]

.pull-right[
#### Output
<span>
<img src="index_files/figure-html/unnamed-chunk-27-1.png" width="100%" />
</span>
]

---

##  Plot: Ready-made outputs

.pull-left[
#### R code

``` r
attr(data$cad, "label") = "Coronary artery disease"
attr(data$dbp10, "label") = "DBP (by 10mmHg)"
data$gender = factor(data$gender, labels=c("Female","Male"))
attr(data$gender, "label") = "Gender (vs Female)"
explanatory = c("dbp10", "gender")
dependent = "cad"
finalfit::or_plot(data, dependent, explanatory,
                  dependent_label = "Odds ratio (OR) of
                  coronary artery disease by factor",
                  suffix = "",
                  remove_ref = T,
                  title_text_size = 16,
                  table_text_size = 5,
                  plot_opts = 
                    list(ggplot2::xlab("OR (95% CI)")))
```
]

.pull-right[
#### Output
<span>
<img src="index_files/figure-html/unnamed-chunk-29-1.png" width="100%" />
</span>
]

---

##  Plot: Ready-made outputs

.pull-left[
#### R code

``` r
# SEM model
model = "
F1 =~ Q1 + Q2 + Q3 + Q4 + Q5
F2 =~ Q6 + Q7 + Q8 + Q9 + Q10
F2 ~ F1
"
survey = lavaan::simulateData(model) # sim. data for practice
fit_model = lavaan::sem(model, data = survey)
# draw path diagram
*semPlot::semPaths(fit_model, what="path",
                  whatLabels="name", edge.color = "black",
                  layout = "tree2", rotation = 2)
```
]

.pull-right[
#### Output
<img src="index_files/figure-html/unnamed-chunk-31-1.png" width="100%" />
]

---

##  Plot: Ready-made outputs

.pull-left[
#### R code

``` r
data1 = data
data1$p_mlog = 
  predict(mlog, type = "response") # probability
OptimalCutpoints::optimal.cutpoints(X = "p_mlog", 
                                    status = "cad", 
                                    tag.healthy = "no cad",
                                    methods = "Youden", 
                                    data = data1,
                                    ci.fit = TRUE) |> 
  plot(which = 1)
```
]

.pull-right[
#### Output
<img src="index_files/figure-html/unnamed-chunk-33-1.png" width="100%" />
]

---

##  Plot: Custom outputs

Can use `ggplot2`, base `graphics` or `lattice`.

Typically, you have to customize the plot, this deserves a separate talk.

But, most often the settings are lengthy! &rarr; require custom functions for repeatedly used plots.

---

## Plot: Custom functions

.pull-left[
#### R code: Simulated data

``` r
set.seed(007)
data_hist = tibble::tibble(
  "Weight (kg)" = rnorm(1000, mean = 50, sd = 15),
  "Height (cm)" = rnorm(1000, mean = 150, sd = 10))
```

#### R code: Histogram by `ggplot2`

``` r
library(ggplot2)
ggplot(data = data_hist, aes(`Weight (kg)`)) +
  geom_histogram(bins = 10)
```
]

.pull-right[
#### Output: Without customization
<img src="index_files/figure-html/unnamed-chunk-36-1.png" width="100%" />
]

---

## Plot: Custom functions

.pull-left[
#### R code: Simulated data

``` r
set.seed(007)
data_hist = tibble::tibble(
  "Weight (kg)" = rnorm(1000, mean = 50, sd = 15),
  "Height (cm)" = rnorm(1000, mean = 150, sd = 10))
```

#### R code: Histogram by `ggplot2`

``` r
library(ggplot2)
ggplot(data = data_hist, aes(`Weight (kg)`)) +
  geom_histogram(bins = 10, color = "black", 
                 fill = "cornsilk", alpha = 0.5) +
  labs(title = "Histogram of Weight (kg)", y = "Frequency") +
  theme_classic() +
  theme(plot.title = element_text(color = "steelblue", 
                       size = 16, face = "bold", hjust = 0.5),
        axis.title = element_text(hjust = 1),
        axis.line = element_line(
          arrow = grid::arrow(length = unit(.2, "cm"))))
```
]

.pull-right[
#### Output: With customization + **lengthy options**
<img src="index_files/figure-html/unnamed-chunk-39-1.png" width="100%" />
]

---

## Plot: Custom functions

.pull-left[
#### R code: Function `my_hist`

``` r
*my_hist = function(data, variable, bins = 10) {
  title_x = paste0("Histogram of ", 
                   names(data[variable]))
  ggplot2::ggplot(data = data, aes(.data[[variable]])) +
    ggplot2::geom_histogram(bins = 10, color = "black", 
                            fill = "cornsilk", alpha = 0.5) +
    ggplot2::labs(title = title_x, y = "Frequency") +
    ggplot2::theme_classic() +
    ggplot2::theme(
      plot.title = 
        ggplot2::element_text(
          color = "steelblue", 
          size = 16, face = "bold",
          hjust = 0.5),
      axis.title = ggplot2::element_text(hjust = 1),
      axis.line = ggplot2::element_line(
        arrow = grid::arrow(length = unit(.2, "cm"))))
}
```
]

.pull-right[
]

---

## Plot: Custom functions

.pull-left[
#### R code: `my_hist` with **Weight (kg)**

``` r
set.seed(007)
data_hist = tibble::tibble(
  "Weight (kg)" = rnorm(1000, mean = 50, sd = 15),
  "Height (cm)" = rnorm(1000, mean = 150, sd = 10)
)
*my_hist(data_hist, "Weight (kg)")
```
]

.pull-right[
#### Output
<img src="index_files/figure-html/unnamed-chunk-42-1.png" width="100%" />
]

---

## Plot: Custom functions

.pull-left[
#### R code: `my_hist` with **Weight (kg)** and **Height (cm)**

``` r
set.seed(007)
data_hist = tibble::tibble(
  "Weight (kg)" = rnorm(1000, mean = 50, sd = 15),
  "Height (cm)" = rnorm(1000, mean = 150, sd = 10)
)
*my_hist(data_hist, "Weight (kg)")
*my_hist(data_hist, "Height (cm)")
```
]

.pull-right[
#### Output
<img src="index_files/figure-html/unnamed-chunk-44-1.png" width="100%" /><img src="index_files/figure-html/unnamed-chunk-44-2.png" width="100%" />
]

---

## In-text
For in-text ouput, <code>&#96;r code&#96;</code> is used with `R Markdown` to allow in-text integration.

.pull-left[
####  Ready-made outputs

- Specific text results directly from functions, or subset of output objects
  - <code>&#96;r mean(x)&#96;</code>, <code>&#96;r object$x&#96;</code> etc
  - `gtsummary`'s `inline_text()`
  
####  Custom functions

- Functions for repetitive `paste0()` use
  - mean (SD), _r_ coefficient (_p_-value = 0.xxx), OR (95% CI, LL OR, UL OR) etc
  - <code>&#96;r mean_sd(x)&#96;</code>, <code>&#96;r or_ci(object)&#96;</code> &rarr; simplify the code, easy to reuse
]

.pull-right[
####  Custom outputs

- Combine all these, `paste()` and `paste0()` <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M47.6 300.4L228.3 469.1c7.5 7 17.4 10.9 27.7 10.9s20.2-3.9 27.7-10.9L464.4 300.4c30.4-28.3 47.6-68 47.6-109.5v-5.8c0-69.9-50.5-129.5-119.4-141C347 36.5 300.6 51.4 268 84L256 96 244 84c-32.6-32.6-79-47.5-124.6-39.9C50.5 55.6 0 115.2 0 185.1v5.8c0 41.5 17.2 81.2 47.6 109.5z"/></svg> are commonly used
  - <code>&#96;r paste0(mean(x), " (", sd(x), ")&#96;</code>
  - <code>&#96;r paste0(object$estimate, " (95% CI: ", object$LL, ", ", object$UL, ")")&#96;</code>
  - Can also save `paste0()` resulting objects, so can use <code>&#96;r mean_sd&#96;</code>, <code>&#96;r or_ci&#96;</code>
]

---

##  Text: Ready-made outputs

.pull-left[
#### R code

``` r
# analysis part
fit = lm(sbp ~ saltadd, BP)
# using broom
*out = broom::tidy(fit, conf.int = T)
```

#### Markdown
<span style="font-family:Lucida Console;font-size:0.7em;">There were \_n\_ = <code>&#96;r nrow(BP)&#96;</code> participants, with <code>&#96;r table(BP$sex)[2]&#96;</code> of them are females. The mean SBP was <code>&#96;r mean(BP$sbp)|>round(1)&#96;</code> (SD = <code>&#96;r sd(BP$sbp)|>round(1)&#96;</code>). From the regression analysis, adding salt to the food increases SBP by <code>&#96;r out$estimate[2]|>round(1)&#96;</code> mmHg.</span>
]

.pull-right[
#### Output

<span style="font-family:Georgia;font-size:0.8em;">There were _n_ = 80 participants, with 39 of them are females. The mean SBP was 151.2 (SD = 37.3). From the regression analysis, adding salt to the food increases SBP by 25.6 mmHg.</span>
]

---

##  Text: Ready-made outputs

.pull-left[
#### R code

``` r
# analysis part
fit = lm(sbp ~ saltadd, BP)
# using gtsummary
*gtout = gtsummary::tbl_regression(fit, conf.int = T)
```

#### Markdown
<span style="font-family:Lucida Console;font-size:0.7em;">There were \_n\_ = <code>&#96;r nrow(BP)&#96;</code> participants, with <code>&#96;r table(BP$sex)[2]&#96;</code> of them are females. The mean SBP was <code>&#96;r mean(BP$sbp)|>round(1)&#96;</code> (SD = <code>&#96;r sd(BP$sbp)|>round(1)&#96;</code>). From the regression analysis, adding salt to the food increases SBP by <code>&#96;r gtsummary::inline_text(gtout, variable = saltadd, level = "yes")&#96;</code> mmHg.</span>
]

.pull-right[
#### Output

---

##  Text: Custom outputs

.pull-left[
#### R code

``` r
*tbl_sex = table(BP$sex)
# analysis part
fit = lm(sbp ~ saltadd, BP)
out = broom::tidy(fit, conf.int = T)
*coef_ci = paste0(out$estimate[2]|>round(1),
*   " (95% CI: ", out$conf.low[2]|>round(1),
*   ", ", out$conf.high[2]|>round(1), ")")
```

#### Markdown
<span style="font-family:Lucida Console;font-size:0.7em;">
There were \_n\_ = <code>&#96;r nrow(BP)&#96;</code> participants, with <code>&#96;r tbl_sex[2]&#96;</code> (<code>&#96;r prop.table(tbl_sex)[2]|>round(3)*100&#96;</code>%) of them are females. The mean SBP was <code>&#96;r paste0(mean(BP$sbp)|>round(1), " (SD = ", sd(BP$sbp)|>round(1), ")")&#96;</code>. From the regression analysis, adding salt to the food increases SBP by <code>&#96;r coef_ci&#96;</code> mmHg.</span>
]

.pull-right[
#### Output

<span style="font-family:Georgia;font-size:0.8em;">There were _n_ = 80 participants, with 39 (48.8%) of them are females. The mean SBP was 151.2 (SD = 37.3). From the regression analysis, adding salt to the food increases SBP by 25.6 (95% CI: 9.8, 41.3) mmHg.</span>

Using `paste0()` _makes it easier to add more complicated results
]

---

## Text: Custom functions

.pull-left[
#### R code

``` r
# custom functions
*mean_sd = function(x, digits = 1) {
  paste0(mean(x)|>round(digits), " (SD = ",
         sd(BP$sbp)|>round(digits), ")")}
*n_percent = function(x, cat_number = 1, digits = 1) {
  tbl = table(x)
  n = tbl[cat_number]
  p = prop.table(tbl)
  percent = p[cat_number] * 100
  paste0(n, " (", percent|>round(digits), "%)")
}
*coef_ci = function(tidy_out, estimate_number = 2,
*                  digits = 1) {
  paste0(tidy_out$estimate[estimate_number]|>round(digits),
    " (95% CI: ", 
    tidy_out$conf.low[estimate_number]|>round(digits),
    ", ", 
    tidy_out$conf.high[estimate_number]|>round(digits), ")")
}
# analysis part
*out = lm(sbp ~ saltadd, BP) |>
* broom::tidy(conf.int = T)
```
]

.pull-right[
#### Markdown
<span style="font-family:Lucida Console;font-size:0.7em;">
There were \_n\_ = <code>&#96;r nrow(BP)&#96;</code> participants, with <code>&#96;r n_percent(BP$sex, 2)&#96;</code> of them are females, and <code>&#96;r n_percent(BP$saltadd, 2)&#96;</code> participants added salt to their food. The mean SBP and DBP were <code>&#96;r mean_sd(BP$sbp)&#96;</code> and <code>&#96;r mean_sd(BP$dbp)&#96;</code> respectively. From the regression analysis, adding salt to the food increases SBP by <code>&#96;r coef_ci(out, 2)&#96;</code> mmHg.</span>

Now we use these customized functions in R markdown. Note we use `paste0()` in these functions
]

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## Text: Custom functions

.pull-left[
#### Markdown
<span style="font-family:Lucida Console;font-size:0.7em;">
There were \_n\_ = <code>&#96;r nrow(BP)&#96;</code> participants, with <code>&#96;r n_percent(BP$sex, 2)&#96;</code> of them are females, and <code>&#96;r n_percent(BP$saltadd, 2)&#96;</code> participants added salt to their food. The mean SBP and DBP were <code>&#96;r mean_sd(BP$sbp)&#96;</code> and <code>&#96;r mean_sd(BP$dbp)&#96;</code> respectively. From the regression analysis, adding salt to the food increases SBP by <code>&#96;r coef_ci(out, 2)&#96;</code> mmHg.</span>
]

.pull-right[
#### Output

<span style="font-family:Georgia;font-size:0.8em;">There were _n_ = 80 participants, with 39 (48.8%) of them are females, and 43 (53.8%) participants added salt to their food. The mean SBP and DBP were 151.2 (SD = 37.3) and 97.5 (SD = 37.3) respectively. From the regression analysis, adding salt to the food increases SBP by 25.6 (95% CI: 9.8, 41.3) mmHg.</span>

Customized functions make it easier to to obtain same statistical results for different variables
]

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# Other Potentials Using R

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## Custom Package for MJMS

- A new package for Malaysian Journal of Medical Sciences (MJMS)
- Functions to produce tables for common statistical analyses
- Utilize `broom` + `knitr::kable()` or `gtsummary`'s `theme_gtsummary_xxx()` 
- Encourage authors to use <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:steelblue;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> &mdash; can get presentation-ready results with minimal efforts
- Future, easy-to-specify journal specific templates that community can contribute &rarr; utilizing the same package

---
## Local LLM Using `rollama`

.pull-left[
- `rollama` links <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:steelblue;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> with local LLM tool `ollama`
- Eliminates the need to write templates
- Give it example tables/plots from the target journal &mdash; multi-shot prompts
- Let it format from raw <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:steelblue;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> outputs
]

.pull-right[
<img src="https://ollama.com/public/ollama.png" height="150px">&nbsp;&nbsp;`ollama`
</br></br>
<img src="https://jbgruber.github.io/rollama/logo.svg" height="150px">&nbsp;&nbsp;<svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:steelblue;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>`ollama`
]

---
## Local LLM Using `rollama`

.center[
But, running it locally needs GPU...
<img src="img/gpu.png" height="250px" alt="Image generated with Copilot"></br>
<small><small>*Image generated with Copilot</small></small>
]
]

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# PhD Position Vacancy

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![](img/eposter_GRA_DR_WAN_NOR_ARIFIN.jpg)

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# Thanks!