02-25-25 Data Tidying

[hayoung98]

R4DS exercise

[hayoung98]

#2 Sketch out the process you’d use to calculate the rate for table2 and table3. You will need to perform four operations:

view(table2)

table2 |> 
  pivot_wider(names_from = type, values_from = count) |> 
  mutate(rate = cases/population * 10000)
  

[kychoe9547]

4.6
Restyle the following pipelines following the guidelines above.

flights|>
  filter(dest == "IAH")|>
  group_by(year, month, day)|>
  summarize(n = n(), delay = mean(arr_delay, na.rm = TRUE))|>
  filter(n > 10)

flights|>
  filter(carrier == "UA", dest %in% c("IAH","HOU"), sched_dep_time > 0900, sched_arr_time < 2000)|>
  group_by(flight)|>
  summarize(delay=mean(arr_delay, na.rm = TRUE), cancelled = sum(is.na(arr_delay)),n = n())|>
  filter(n > 10)

5.2.1 Exercises
1.For each of the sample tables, describe what each observation and each column represents.
table1: country year (cases 발병횟수) (population 전체 인구)
table2: country year (type :case인지 population인지 구분) (count 횟수)
table3: country year (rate 인구1명당 발병자 수 비율)

2.Sketch out the process you’d use to calculate the rate for table2 and table3. You will need to perform four operations:

a.Extract the number of TB cases per country per year.
b.Extract the matching population per country per year.
c.Divide cases by population, and multiply by 10000.
d.Store back in the appropriate place.

You haven’t yet learned all the functions you’d need to actually perform these operations, but you should still be able to think through the transformations you’d need.

table2_wide <- table2 %>%
  pivot_wider(names_from = type, values_from = count) %>% 
  mutate( rate = (cases / population)*10000)

print(table2_wide)

table3 %>% 
  separate(rate, into = c("cases","population"), sep = "/") %>% 
  mutate(cases = as.numeric(cases),
         population = as.numeric(population),
         proportion = (cases / population)*10000)

[hayoung98]

4.6
Restyle the following pipelines following the guidelines above.

flights|>filter(dest=="IAH")|>
  group_by(year,month,day)|>
  summarize(
    n=n(),
    delay=mean(arr_delay,na.rm=TRUE))|>
  filter(n>10)

flights|>
  filter(
    carrier=="UA",
    dest%in%c("IAH","HOU"),
    sched_dep_time>0900,sched_arr_time<2000)|>
  group_by(flight)|>
  summarize(
    delay=mean(arr_delay,na.rm=TRUE),
    cancelled=sum(is.na(arr_delay)),
    n=n())|>
  filter(n>10)

[hayoung98]

5.2.1

Sketch out the process you’d use to calculate the rate for table2 and table3. You will need to perform four operations:

table2 |> 
  pivot_wider(
    names_from = type,
    values_from = count) |> 
  mutate(rate = cases/population*10000)

table3 |> 
  separate(rate,
    into = c("cases", "population"),
    sep = "/") |>
  mutate(
    cases = as.numeric(cases), 
    population = as.numeric(population),
    rate = cases/population * 10000)