'How to collapse/sum-up a data-frame by not needed subpopulation variables in R? [duplicate]

Screenshot: raw data-frame organization of COVID-Cases in Germany

I downloaded the notified COVID-Cases in Germany from an official website. This raw data-frame is organized by the following columns (see also screenshot): "IdCounty", "NameCounty", "DateNotification", "AgeGroup", "Gender", "FreqCases".

What is a clever way in R to collapse/re-arrange/sum-up this raw data-frame by all categories in "AgeGroup" and "Gender", i.e. so this two subpopulation-breakdown variables will disappear, i.e. are collapsed ? Reason: I want to do analyses of the COVID-Cases by counties and time-points, but I don't want to differentiate further by age nor gender, i.e. just take all ages and all genders as sums together.

I struggled with various functions to achieve this, but I am pretty sure there is a smart & clever way to do this quite easily.

r dataframe

Solution 1:^[1]

library(tidyverse)

data <- read_csv("https://example.de/covid.csv")

data %>%
  # group only by county
  group_by(IdCounty, NameCounty) %>%
  summarise(FreqCases = sum(FreqCases))

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Solution 1	danlooo

'How to collapse/sum-up a data-frame by not needed subpopulation variables in R? [duplicate]

Solution 1:[1]

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Related Questions

Solution 1:^[1]