7  Lösungen Plague

7.1 Lösung 1

#Aufgabe 1 

library(tidyverse)

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Deaths <- read.csv("https://raw.githubusercontent.com/SurgicalInformatics/healthyr_book/master/data/global_burden_disease_cause-year-sex-income.csv")

summary_data1 <- 
  Deaths%>% 
  group_by(year) %>% 
  summarise(total_per_year = sum(deaths_millions))

7.2 Lösung 2.1

# Aufgabe 2.1

summary_data2 <- 
  Deaths %>% 
  group_by(sex, year) %>% 
  summarise(sum(deaths_millions))

`summarise()` has grouped output by 'sex'. You can override using the `.groups`
argument.

7.3 Lösung 2.2

# Aufgabe 2.2
Deaths %>% 
  select(deaths_millions, income, sex) %>% 
  filter(sex == "Male") %>% 
  filter(income == "High")%>% 
  summarise(Tode = sum(deaths_millions))

Deaths %>% 
  select(deaths_millions, income, sex) %>% 
  filter(sex == "Female")%>% 
  filter(income == "Low") %>% 
  summarise(Tode = sum(deaths_millions))

7.4 Lösung 2.3

# Aufgabe 2.3

Deaths %>%
  group_by(cause) %>% 
  filter(year >= 2005,
         sex == "Male",
         income == "Low") %>% 
  summarise(Total = sum(deaths_millions), 
            durchschnittliche = mean(deaths_millions))

7.5 Lösung 3.1

# Aufgabe 3.1 

summary_data3 <- 
  Deaths %>% 
  group_by(year, cause) %>% 
  summarise(total_per_cause = sum(deaths_millions))

`summarise()` has grouped output by 'year'. You can override using the
`.groups` argument.

7.6 Lösung 3.2

# Aufgabe 3.2

Deaths %>%
  group_by(income) %>% 
  summarise(Total = sum(deaths_millions))

Deaths %>% 
  summarise(sum(deaths_millions))

7.7 Lösung 4

# Aufgabe 4

summary_data4 <- Deaths %>%
  summarise(Gesamt = sum(deaths_millions))


summary_data5 <- Deaths %>%
  group_by(cause)%>%
  summarise(total_per_cause = sum(deaths_millions)) %>% 
  mutate(InProzent = total_per_cause/359.85)