nfl-injuries.qmd

---
title: "NFL Injuries --- 10 Weeks"
author: "Max Bade"
format: 
  dashboard:
    theme: united
---

```{python}
# prepare data
import pandas as pd
injuries_df = pd.read_csv("nfl-injuries.csv")
group_df = injuries_df.groupby(['data','year'])['row_number'].agg('count').reset_index()
group_df.columns = ['status','year','count_players']

status_avg = group_df.groupby(['status']).mean(1)['count_players'].astype('int')
[out_avg, questionable_avg] = status_avg
injuries_avg = out_avg + questionable_avg
```

# Stats

## Row

```{python}
#| component: valuebox
#| title: Average Injured
dict(
    icon = "exclamation-triangle",
    color = "primary",
    value = injuries_avg
)
```


```{python}
#| component: valuebox
#| title: Average Out
dict(
    icon = "x-square",
    color = "warning",
    value = out_avg
)
```


```{python}
#| component: valuebox
#| title: Average Questionable
dict(
    icon = "question-square",
    value = questionable_avg
)
```


## Row

```{python}
# injuries by status
import plotly.express as px
px.line(group_df,
    x='year', y='count_players',
    color='status', text='count_players' 
)
```

## Row

```{python}
# total injuries
group_df1 = group_df.groupby('year')['count_players'].agg('sum').reset_index()
group_df1.columns = ['year','count_players']
px.bar(group_df1
    ,x='year'
    ,y='count_players'
    ,text='count_players'
)
```

# Data

This dashboard is based on Max Bade's [NFL Injury Data with BS4 & Plotly in Python](https://maxbade.medium.com/nfl-injury-data-with-bs4-plotly-in-python-f0f89309f5da) article on Medium.

If you want to conduct your own analysis, see the following resources:

| Resource             | Link               |
|----------------------|--------------------|
| Injuries Dataset     | [nfl-injuries.csv](nfl-injuries.csv) |
| Data Scraping Script | [collect.py](collect.py)       |