17 Code Chunks

While being able to write documents with all of these built-in features from the previous chapter is all well and good, one of the biggest draws of Quarto is being able to put all of your code to process and visualise your data inside the document, removing the need for any copying-and-pasting.

Tip

As your Quarto documents get bigger and more complex, it can be a good idea to move some of the code out into separate files to make it neater and easier to debug - but for now, we’ll just assume that we’re putting the end-to-end code inside the documents.

17.1 Your first code block

Code blocks take the following format:

```{language}
your_code
```

For example, a Python block might look like this:

```{python}
import pandas as pd

address = 'https://raw.githubusercontent.com/MichaelAllen1966/' + \
            '1804_python_healthcare/master/titanic/data/train.csv'

data = pd.read_csv(address)

data.head(2)
```

And the output would be this.

import pandas as pd

address = 'https://raw.githubusercontent.com/MichaelAllen1966/' + \
            '1804_python_healthcare/master/titanic/data/train.csv'

data = pd.read_csv(address)

data.head(2)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C

17.2 (Duck’s Quack’s Don’t) Echo

You may have noticed that both the code and the output appeared after running the code block above.

This is because, by default, the ‘echo’ parameter is set to true, which means that the code used to create an output will be included.

We can override this parameter by making the first line of our code block be

#| echo: false

```{python}
#| echo: false
import pandas as pd

address = 'https://raw.githubusercontent.com/MichaelAllen1966/' + \
            '1804_python_healthcare/master/titanic/data/train.csv'

data = pd.read_csv(address)

data.head(2)
```

Now you can see that we just get the output:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C

Tip

In the next chapter, we’ll look at how to set these parameters on a per-document level - though you may find yourself wanting to overwrite the default you set for certain cells, so it’s useful to know both ways of doing it.

For multipage documents like books, you may even want to set these parameters at a project level.

17.3 Making the most of the persistent environment

As we move through the document, any variables we have created will persist.

Therefore, in the later steps, we can refer to the same variables and reuse the same libraries without having to recreate them.

```{python}
data.value_counts('Pclass')
```

Pclass
3    491
1    216
2    184
Name: count, dtype: int64

17.4 Charts

Most outputs can be displayed in Quarto documents - static and interactive charts and static and interactive maps are supported by default.

```{python}
import plotly.express as px

px.bar(data.value_counts('Sex'),
       title="Number of Passengers by Recorded Sex")
```

```{python}
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10,5))
pclass_counts = pd.DataFrame(data.value_counts('Pclass')).reset_index()
plt.bar(x=pclass_counts['Pclass'], height=pclass_counts['count'])
fig.show()
```

C:\Users\Sammi\AppData\Local\Temp\ipykernel_30328\3960798882.py:6: UserWarning:

FigureCanvasAgg is non-interactive, and thus cannot be shown

17.5 Maps

17.5.1 Static Maps

Tip

For more details about this code, head to the HSMA Geographic Modelling and Visualisation Book - Static Maps

As static maps created using the geopandas plot method are just matplotlib plots, we don’t need to do anything special with them.

```{python}
import geopandas

lsoa_2011_crime_figures_df = geopandas.read_file("https://github.com/hsma-programme/h6_3b_advanced_qgis_mapping_python/raw/main/h6_3b_advanced_qgis_and_mapping_in_python/example_code/lsoa_2011_sw5forces_crime_figures.gpkg")

lsoa_2011_crime_figures_df.plot(column="sw_5forces_street_by_lsoa_Other crime")
```

17.5.2 Interactive Maps

Tip

For more details about this code, head to the HSMA Geographic Modelling and Visualisation Book - Interactive Maps

```{python}
import folium

gp_list = pd.read_csv("https://github.com/hsma-programme/h6_3b_advanced_qgis_mapping_python/raw/main/h6_3b_advanced_qgis_and_mapping_in_python/example_code/gp_surgery_locations_plus_patient_list_size.csv")

gp_list_gdf = geopandas.GeoDataFrame(
    gp_list, # Our pandas dataframe
    geometry = geopandas.points_from_xy(
        gp_list['result_eastings'], # Our 'x' column (horizontal position of points)
        gp_list['result_northings'] # Our 'y' column (vertical position of points)
        ),
    crs = 'EPSG:27700'
    )

# Convert to 4326 (lat/long) for working with Folium
gp_list_gdf = gp_list_gdf.to_crs('EPSG:4326')

# Filter out instances with no 'list' (e.g. things like specialist clinics)
gp_list = gp_list[~gp_list['Total List Size'].isna()]

# reduce to the southwest to not overload Folium
xmin, xmax = -6.449974,-2.717735
ymin, ymax =  49.814737,51.246969
gp_list_gdf_sw = gp_list_gdf.cx[xmin:xmax, ymin:ymax]

# Filter out instances with no geometry
gp_list_gdf_sw = gp_list_gdf_sw[~gp_list_gdf_sw['geometry'].is_empty]

# Create a geometry list from the GeoDataFrame
geo_df_list = [[point.xy[1][0], point.xy[0][0]] for point in gp_list_gdf_sw.geometry]

gp_map_tooltip = folium.Map(
    location=[50.7, -4.2],
    zoom_start=8,
    tiles='openstreetmap',
    )

for i, coordinates in enumerate(geo_df_list):

    gp_map_tooltip = gp_map_tooltip.add_child(
        folium.Marker(
            location=coordinates,
            tooltip=gp_list_gdf_sw['name'].values[i],
            icon=folium.Icon(icon="user-md", prefix='fa', color="black")
            )
     )

gp_map_tooltip
```

Make this Notebook Trusted to load map: File -> Trust Notebook

17.6 Additional Code Chunk Options

Like #| echo: false, there are a range of different code chunk options that can be set.

Each value needs to be set as #| parameter: value on its own line at the start of the code block.

The key ones are

eval
- this determines whether the cell will actually run or not
- true or false
echo
- this determines whether the code is included
- true or false
code-fold
- this allows you to include the code as a collapsible element
- true (put it in a collapsible element and collapse it), false (don’t collapse - default), show (put it in a collapsible element but leave it expanded by default)
label
- this gives the code cell an internal label that will appear in the terminal printout when the code is being rendered, which can make it a lot easier to work out which code cell is the culprit if you receive errors
- a-continuous-string-describing-the-cell
warning
- this determines whether warning messages outputted by any of the actions in the cell will be included in the output
- true/false
output
- this determines whether the output of the code is actually shown. Can be useful for preprocessing steps you want to execute
- true/false/asis
  - asis is a special parameter we’ll cover more later - it can be helpful for producing, for example, automated tabsets in a loop
fig-cap
- a caption to display below an outputted figure
- “A string, surrounded by quotation marks”

You can find a full list of the different options in the Quarto documentation

Here’s an example with multiple options set

```{python}
#| code-fold: true
#| label: multiple-code-chunk-options-example
#| fig-cap: "A bar plot"
#| warning: false
fig, ax = plt.subplots()
pclass_counts = pd.DataFrame(data.value_counts('Pclass')).reset_index()
plt.bar(x=pclass_counts['Pclass'], height=pclass_counts['count'])
fig.show()
```

Code

fig, ax = plt.subplots()
pclass_counts = pd.DataFrame(data.value_counts('Pclass')).reset_index()
plt.bar(x=pclass_counts['Pclass'], height=pclass_counts['count'])
fig.show()