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Tables and Quarto reports

Source: vignettes/reporting.qmd

Formatting numbers 

fmt_p(c(0.21, 0.031, 0.00042, 1e-20))
#> [1] "0.21"       "0.031"      "4.2×10⁻⁴"   "<2.2×10⁻¹⁶"
fmt_num(c(1.2345, 0.00002, 2.5e7), digits = 2)
#> [1] "1.23"     "2.0×10⁻⁵" "2.5×10⁷"
fmt_stars(c(0.2, 0.07, 0.03, 0.004))
#> [1] "ns" "."  "*"  "**"

Publication tables

gt_pub() rounds numbers, detects and formats p-value columns, and puts p < 0.05 in bold. gt_signif() adds shades of green for nominal and FDR-significant results.

cm <- cor_matrix_test(cohort_sim, c(NFL_V0, IL6_V0, CRP_V0, MOCA_V0, UPDRS_V0))
gt_pub(cm$long, title = "Spearman correlations", note = "Pairwise complete observations") |>
  gt_signif(p_cols = "p", fdr_cols = "p_adj")
Spearman correlations
Var1 Var2 r p n p_adj
NFL_V0 IL6_V0 0.06 0.48 120 0.65
NFL_V0 CRP_V0 -0.06 0.54 115 0.65
IL6_V0 CRP_V0 0.05 0.6 115 0.65
NFL_V0 MOCA_V0 -0.55 7.0×10⁻¹¹ 120 7.0×10⁻¹⁰
IL6_V0 MOCA_V0 -0.05 0.6 120 0.65
CRP_V0 MOCA_V0 -0.07 0.43 115 0.65
NFL_V0 UPDRS_V0 0.40 5.3×10⁻⁶ 120 2.7×10⁻⁵
IL6_V0 UPDRS_V0 0.04 0.65 120 0.65
CRP_V0 UPDRS_V0 -0.11 0.23 115 0.57
MOCA_V0 UPDRS_V0 -0.20 0.027 120 0.088
Pairwise complete observations
p < 0.05significant after FDR (< 0.05)

Interactive tables with export buttons:

dt_export(na_summary(cohort_sim, list(All = names(cohort_sim))))

Quarto tabsets and callouts

In a Quarto report, a chunk with #| results: asis can print a named list of figures and tables as tabs, and raw console output in a collapsed callout:


```{.r .cell-code}
qmd_tabset(list(
  Distribution = plot_distribution(cohort_sim, NFL_V0, group = GROUP),
  Normality    = describe_normality(cohort_sim, c(NFL_V0, CRP_V0))
))
```



::: panel-tabset



### Distribution 

![](reporting_files/figure-html/unnamed-chunk-2-1.png){width=100%}



### Normality 

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  <thead>
    <tr class="gt_col_headings">
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Variable">Variable</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Label">Label</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="N">N</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="W">W</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="p_Shapiro">p_Shapiro</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="p_AD">p_AD</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Skewness">Skewness</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Kurtosis">Kurtosis</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Verdict">Verdict</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td headers="Variable" class="gt_row gt_left">NFL_V0</td>
<td headers="Label" class="gt_row gt_left">NFL_V0</td>
<td headers="N" class="gt_row gt_right">120</td>
<td headers="W" class="gt_row gt_right">0.808</td>
<td headers="p_Shapiro" class="gt_row gt_right" style="font-weight: bold;">3.1×10⁻¹¹</td>
<td headers="p_AD" class="gt_row gt_right" style="font-weight: bold;">1.5×10⁻¹⁰</td>
<td headers="Skewness" class="gt_row gt_right">2.476</td>
<td headers="Kurtosis" class="gt_row gt_right">10.476</td>
<td headers="Verdict" class="gt_row gt_left">Strong skewness: transform / non-parametric</td></tr>
    <tr><td headers="Variable" class="gt_row gt_left">CRP_V0</td>
<td headers="Label" class="gt_row gt_left">CRP_V0</td>
<td headers="N" class="gt_row gt_right">115</td>
<td headers="W" class="gt_row gt_right">0.589</td>
<td headers="p_Shapiro" class="gt_row gt_right" style="font-weight: bold;"><2.2×10⁻¹⁶</td>
<td headers="p_AD" class="gt_row gt_right" style="font-weight: bold;"><2.2×10⁻¹⁶</td>
<td headers="Skewness" class="gt_row gt_right">3.935</td>
<td headers="Kurtosis" class="gt_row gt_right">20.215</td>
<td headers="Verdict" class="gt_row gt_left">Strong skewness: transform / non-parametric</td></tr>
  </tbody>
  
</table>
</div> 


:::

```{.r .cell-code}
qmd_callout_raw(`Linear model` = summary(lm(MOCA_V0 ~ AGE, data = cohort_sim)))
```



::: {.callout-note collapse="true"}
## Raw model output

**Linear model**

```

Call:
lm(formula = MOCA_V0 ~ AGE, data = cohort_sim)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.3772 -1.2669  0.3106  1.4749  3.7590 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 33.78094    1.65615  20.397  < 2e-16 ***
AGE         -0.10329    0.02485  -4.156 6.15e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.054 on 118 degrees of freedom
Multiple R-squared:  0.1277,    Adjusted R-squared:  0.1203 
F-statistic: 17.28 on 1 and 118 DF,  p-value: 6.154e-05

```

:::

The Markdown they write:

qmd_tabset(list(First = "Some text", Second = "Other text"))
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#> Other text
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