country continent  year  lifeExp       pop   gdpPercap iso_alpha  \
0  Afghanistan      Asia  1952   28.801   8425333  779.445314       AFG   
1  Afghanistan      Asia  1957   30.332   9240934  820.853030       AFG   
2  Afghanistan      Asia  1962   31.997  10267083  853.100710       AFG   

   iso_num  pop_log10  
0        4   6.925587  
1        4   6.965716  
2        4   7.011447  

      country continent year lifeExp      pop gdpPercap iso_alpha iso_num
1 Afghanistan      Asia 1952  28.801  8425333  779.4453       AFG       4
2 Afghanistan      Asia 1957  30.332  9240934  820.8530       AFG       4
3 Afghanistan      Asia 1962  31.997 10267083  853.1007       AFG       4
4 Afghanistan      Asia 1967  34.020 11537966  836.1971       AFG       4
  pop_log10
1  6.925587
2  6.965716
3  7.011447
4  7.062129

---
title: "Example #2"
format: dashboard
---

```{r}
# use of reticulate allows variables created in python
# to be accessed by R
library(reticulate)
```

```{python}
# comments
import pandas as pd
import math
import plotly.express as px
df = px.data.gapminder()

# this works but probably a better way 
# it would be much easier in R for example
df['pop_log10'] = df['pop'] 
df['pop_log10'] = df['pop_log10'].map(lambda pop_log10: math.log10(pop_log10) )
# could be used to render size differently 

```

# GDP and Life Expectancy

## Row {height="20%"}

Like Example #1, but modified for the following reasons: (1) exploration of layouts (2) testing the facility and functionality to
mashup Python and R code and to access and mutate the same variables and data structures (3) main plot of GDP and Life Expectancy
is more easily interpreted

## Row {height="80%"}

```{python}
#| title: GDP and Life Expectancy 
px.scatter(  
  df, x="gdpPercap", y="lifeExp", 
  animation_frame="year", animation_group="country", 
  size="pop", color="continent", hover_name="country",
  log_x=False, size_max=35, 
  range_x=[0,50000], range_y=[25,85] 
)  
```

# Population and Life Expectancy

## Row {.tabset}

```{python}
#| title: Population
px.area(
  df, x="year", y="pop", 
  color="continent", line_group="country"
)
```

```{python}
#| title: Life Expectancy
px.line(
  df, x="year", y="lifeExp", 
  color="continent", line_group="country"
)
```

# Data

This is a demonstration of the facility to access variables and data structures created in Python with R code. The head of a data
frame is printed using both languages whilst the number of rows is set by a variable in Python and read in R. Note that the row
indexes differ by 1 (0 and 1 based) and that the precision of printing is set differently. Note also, that R prints an additional
line of data; the reason is that the code increments a python variable in R i.e. the code performs a mutability test and it works.

## Row

```{python}
#| title: Head of python pandas data frame 
n_head = 3
with pd.option_context(
                       'display.max_columns', 10,
                       'display.precision', 6,
                       ):
     print(df.head(n_head))
    
```

## Row

```{r}
#| title: The head of the same data frame but printed with R
# we get access to python variables in R 
py$n_head <- py$n_head + 1
head(py$df,py$n_head)

```

# Code

## Row

```{python}
from pathlib import Path
from textwrap import wrap

# wrap just the long lines over a specified number of characters
def wrap_long_lines(input_text, max_line_chars):
    lines = input_text.split('\n')
    wrapped_lines = []
    for line in lines:
        if len(line) > max_line_chars:
            wrapped_lines.extend(wrap(line, width=max_line_chars))
        else:
            wrapped_lines.append(line)
    wrapped_text = '\n'.join(wrapped_lines)
    return wrapped_text

txt = Path('example_2.qmd').read_text()
# its a wrap!
# those long comments are now readable the Code tab
wrapped_txt = wrap_long_lines(txt, 130)
print(wrapped_txt)

```