Covid-19 Deaths per Mio

[This article was first published on Blog – Michael's and Christian's Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Lost in Translation between R and Python 2

Hello again!

This is the next article in our series “Lost in Translation between R and Python”. The aim of this series is to provide high-quality R and Python 3 code to achieve some non-trivial tasks. If you are to learn R, check out the R tab below. Similarly, if you are to learn Python, the Python tab will be your friend.

Post 1:

In Post 2, we use a publicly available data of the European Centre for Disease Prevention and Control to calculate Covid-19 deaths per Mio persons over time and across countries . We will use slim Python and R codes to

  • fetch the data directly from the internet,
  • prepare and restructure it for plotting and
  • plot a curve per selected country.

Note that different countries use different definitions of whom to count as Covid-19 death and these definitions might also have changed over time. So be careful with comparisons!


# Source and countries
link <- ""
countries <- c("Switzerland", "United_States_of_America", 
               "Germany", "Sweden")

# Import
df0 <- read_csv(link)

# Data prep
df <- df0 %>%
  mutate(Date = lubridate::dmy(dateRep),
         Deaths = deaths_weekly / (popData2019 / 1e6))  %>%
  rename(Country = countriesAndTerritories) %>%
  filter(Date >= "2020-03-01",
         Country %in% countries)

# Plot
ggplot(df, aes(x = Date, y = Deaths, color = Country)) +
  geom_line(size = 1) +
  ylab("Weekly deaths per Mio") +
  theme(legend.position = c(0.2, 0.85))
import pandas as pd

# Source and countries
url = ""
countries = ["Switzerland", "United_States_of_America", 
             "Germany", "Sweden"]

# Fetch data
df0 = pd.read_csv(url)
# df0.head()

# Prepare data
df = df0.assign(
    Date=lambda x: pd.to_datetime(x["dateRep"], format="%d/%m/%Y"),
    Deaths=lambda x: x["deaths_weekly"] / x["popData2019"] * 1e6,
).rename(columns={"countriesAndTerritories": "Country"})
df = df.loc[
    (df["Country"].isin(countries)) & (df["Date"] >= "2020-03-01"),
    ["Country", "Date", "Deaths"],
df = df.pivot(index="Date", columns="Country")
df = df.droplevel(0, axis=1)

# Plot
ax = df.plot()
ax.set_ylabel('Weekly Covid-19 deaths per Mio');

Weekly Covid-19 deaths per Mio inhabitants as per January 26, 2021 (Python output).

The code can be found on with some other analyses regarding viruses.

To leave a comment for the author, please follow the link and comment on their blog: Blog – Michael's and Christian's Blog. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)