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: https://lorentzen.ch/index.php/2021/01/07/illustrating-the-central-limit-theorem/

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!

library(tidyverse)

# Source and countries
link <- "https://opendata.ecdc.europa.eu/covid19/casedistribution/csv"
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))

library(tidyverse)

​

# Source and countries

link <- "https://opendata.ecdc.europa.eu/covid19/casedistribution/csv"

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 = "https://opendata.ecdc.europa.eu/covid19/casedistribution/csv"
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');

import pandas as pd

​

# Source and countries

url = "https://opendata.ecdc.europa.eu/covid19/casedistribution/csv"

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');

The code can be found on https://github.com/mayer79/covid with some other analyses regarding viruses.