Python-ggplot: Adding moving average to plot

Question

p = ggplot(cases, aes(x="Specimen date", y="Daily lab-confirmed cases", group = 1)) + geom_point() + geom_line() + labs(title = "Daily COVID-19 Cases")
p.save(filename = date_today, height=5, width=15, units = 'in', dpi=1000)

This is my current code to plot a graph from a DataFrame containing COVID-19 cases in England, which is then saved. I'm trying to add a trend line that is similar to the Worldometer graphs (as shown below).

I cannot post images yet, so I will provide the example here.

This is what my graph currently looks like.

I am trying to achieve the '3-day moving average' and the '7-day moving average'.

has2k1 · Accepted Answer · 2020-07-23 18:50:46Z

1

See stat_smooth, you can smooth using a moving average.

For example you may end up adding code like

+ stat_smooth(method='mavg', method_args={'window': 3}, color='cyan')
+ stat_smooth(method='mavg', method_args={'window': 7}, color='blue')

But this will not give you a legend, because the moving average is not a variable (with a corresponding value) in the dataframe, i.e. given what you want to plot the data is not tidy. So if you want a legend you will have to compute the moving average, create a tidy dataframe and plot the computed averages that are in tidy form.

How? Use pandas.melt e.g.

# Compute moving averages
cases['mavg_3'] = cases['Daily lab-confirmed cases'].rolling(window=3).mean()
cases['mavg_7'] = cases['Daily lab-confirmed cases'].rolling(window=7).mean()

# Convert data Long/tidy format
cases_long = cases.melt(
    id_vars=['Specimen date', 'Daily lab-confirmed cases'],
    value_vars=['mavg_3', 'mavg_7'],
    var_name='mavg_type',
    value_name='mavg_value'
)

# Plot tidy data
(ggplot(cases_long, aes(x="Specimen date", y="Daily lab-confirmed cases"))
 + geom_point()
 + geom_line(aes(y='mavg_value', color='mavg_type'), na_rm=True)
)

answered Jul 23, 2020 at 18:50

has2k1

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1 Comment

SpiritedByte Over a year ago

I'll have to do some research into this as I'm still fairly new to dataframes and such. However, you've pointed me in the right direction so thank you very much!

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