delete_dashboard.qmd

---
title: "Palmer Penguins"
format: dashboard
---


```{python}
#| include: false
from palmerpenguins import load_penguins
penguins = load_penguins()
ojs_define(penguins_OJS = penguins)
```



# Page 1

## Row


```{python}
#| content: valuebox
#| title: "Penguin Count"
#| color: primary
len(penguins)
```

```{python}
#| content: valuebox
#| title: "Penguin Count"
#| color: green
len(penguins)
```

::: {.card title="My Title"}
This text will be displayed within a card
:::



## Row {height=70%}

```{python}
penguins.head()
```

```{python}
#| title: Option 2
from IPython.display import Markdown
from tabulate import tabulate
Markdown( 
  tabulate(penguins, showindex=False, 
           headers=penguins.columns)
)
```

## Row {height=30%}

### Column {.tabset}

```{python}
#| title: Chart 2
print("1")
```

```{python}
#| title: Chart 3
print("1")
```

### Column

```{python}
print("1")
```

::: {.card title="My Title"}
This text will be displayed within a card
:::

# Page 2


I'm thinking that if you use ggplot2 and you want interactivity, the solution is shiny. 

If you think shiny is a little silly and would prefer a more professional approach and still be in Quarto, use observable. It has plotting capabilities and interactive user input capabilities. The R/Python to ObservableJS connection is `ojs_define()`. I would not wrangle in observable though. 
At the same time, if you're in the R space, use R things!


```{python}
# import pandas as pd
# penguins = pd.read_csv("palmer-penguins.csv")
ojs_define(data2 = penguins)
```


```{ojs}
filtered = transpose(data2).filter(function(penguin) {
  return penguin.bill_length_mm;
})
```


```{ojs}
Plot.dot(
    penguins, 
      {x: "bill_length_mm", y: "bill_depth_mm"}
    ).plot()
```