How to create multiple pivot_longer() columns in r?

Question

I am working on data set that has multiple cumulative field cols and multiple daily field cols and by using pivot_longer looking to convert them into:

1. single/long cumulative field col and
2. single/long daily field col

Snapshot of Data

"Updated.On","State","First.Dose.Administered","Second.Dose.Administered","Daily_First_dose","Daily_Second_dose"
2021-07-09,"India",297184419,69894633,2071121,1141754
2021-07-09,"Andaman and Nicobar Islands",164079,51646,795,6336
2021-07-09,"Andhra Pradesh",13372990,3379660,25571,11168
2021-07-09,"Arunachal Pradesh",583021,108393,8423,3430
2021-07-09,"Assam",6462249,1328646,52913,7681
2021-07-09,"Bihar",15741778,2638686,262299,55007
2021-07-09,"Chandigarh",483886,110610,4351,3491
2021-07-09,"Chhattisgarh",7438953,1819709,64193,42342
2021-07-09,"Dadra and Nagar Haveli and Daman and Diu",458057,57874,7122,1745
2021-07-09,"Delhi",6761634,2049318,95352,35848

Transformation of cols to:

1. club First.Dose.Administered, Second.Dose.Administered cols to names_to = "Dose_type", values_to = "Dose_Administered"
2. club Daily_First_dose, Daily_Second_dose cols to names_to = "Dose_type_daily", values_to = "Dose_Administered_daily"

Desired Data frame Example:

Updated.On State Dose_type                Dose_Administered Dose_type_daily   Dose_Administered_daily 
2021-07-09 India First.Dose.Administered  297184419         Daily_First_dose  2071121
2021-07-09 India Second.Dose.Administered  69894633         Daily_Second_dose 1141754

df can be downloaded from:

library(tidyverse)
library(lubridate)

file_url1 <- "https://raw.githubusercontent.com/johnsnow09/covid19-df_stack-code/main/df_vaccination_june.csv"

df_vaccination <- read.csv(url(file_url1))

df_vaccination <- df_vaccination %>%
  mutate(Updated.On = as.Date(Updated.On))

Attempt: I tried below code but not sure how to create two different values_to columns?

df_vaccination %>% 
  pivot_longer(cols = c(First.Dose.Administered:Second.Dose.Administered, 
                        Daily_First_dose:Daily_Second_dose),
               names_to = c("Dose_type","Dose_type_daily"), 
               values_to = c("Dose_Administered","Dose_Administered_daily"))

Answer 1

Update: 2 row solution Here is another way to get the two row solution:

1. Create a dataframe with lonng format of First.Dose.Administered and Second.Dose.Administered

2. mutate to long format of Daily_First_dose and Daily_Second_dose

library(dplyr)
# create a dataframe with First.Dose.Administered, Second.Dose.Administered

df_vaccination_1 <- df_vaccination %>% 
    select(First.Dose.Administered, Second.Dose.Administered) %>% 
    pivot_longer(
        cols = c(First.Dose.Administered, Second.Dose.Administered),
        names_to = "Dose_type", 
        values_to = "Dose_Administered"
    ) 


# resulting dataframe using columns of df_vaccination_1

df_vaccination_result <- df_vaccination %>% 
    select(-First.Dose.Administered, -Second.Dose.Administered) %>% 
    pivot_longer(
        cols = c(Daily_First_dose, Daily_Second_dose),
        names_to = "Dose_type_daily",
        values_to = "Dose_Administered_daily"
    ) %>% 
    mutate(First.Dose.Administered = df_vaccination_1$Dose_type,
           Second.Dose.Administered = df_vaccination_1$Dose_Administered)

Output:

 A tibble: 1,776 x 6
   Updated.On State     Dose_type_daily Dose_Administere~ First.Dose.Admin~ Second.Dose.Admi~
   <date>     <chr>     <chr>                       <int> <chr>                         <int>
 1 2021-07-09 India     Daily_First_do~           2071121 First.Dose.Admin~         297184419
 2 2021-07-09 India     Daily_Second_d~           1141754 Second.Dose.Admi~          69894633
 3 2021-07-09 Andaman ~ Daily_First_do~               795 First.Dose.Admin~            164079
 4 2021-07-09 Andaman ~ Daily_Second_d~              6336 Second.Dose.Admi~             51646
 5 2021-07-09 Andhra P~ Daily_First_do~             25571 First.Dose.Admin~          13372990
 6 2021-07-09 Andhra P~ Daily_Second_d~             11168 Second.Dose.Admi~           3379660
 7 2021-07-09 Arunacha~ Daily_First_do~              8423 First.Dose.Admin~            583021
 8 2021-07-09 Arunacha~ Daily_Second_d~              3430 Second.Dose.Admi~            108393
 9 2021-07-09 Assam     Daily_First_do~             52913 First.Dose.Admin~           6462249
10 2021-07-09 Assam     Daily_Second_d~              7681 Second.Dose.Admi~           1328646
# ... with 1,766 more rows

First answer:

Is this what you are looking for?

df_vaccination %>% 
    pivot_longer(
        cols = c(First.Dose.Administered, Second.Dose.Administered),
        names_to = "Dose_type", 
        values_to = "Dose_Administered"
    ) %>% 
    pivot_longer(
        cols = c(Daily_First_dose, Daily_Second_dose),
        names_to = "Dose_type_daily",
        values_to = "Dose_Administered_daily"
    )

Output:

Updated.On State        Dose_type      Dose_Administer~ Dose_type_daily Dose_Administered~
   <date>     <chr>        <chr>                     <int> <chr>                        <int>
 1 2021-07-09 India        First.Dose.Ad~        297184419 Daily_First_do~            2071121
 2 2021-07-09 India        First.Dose.Ad~        297184419 Daily_Second_d~            1141754
 3 2021-07-09 India        Second.Dose.A~         69894633 Daily_First_do~            2071121
 4 2021-07-09 India        Second.Dose.A~         69894633 Daily_Second_d~            1141754
 5 2021-07-09 Andaman and~ First.Dose.Ad~           164079 Daily_First_do~                795
 6 2021-07-09 Andaman and~ First.Dose.Ad~           164079 Daily_Second_d~               6336
 7 2021-07-09 Andaman and~ Second.Dose.A~            51646 Daily_First_do~                795
 8 2021-07-09 Andaman and~ Second.Dose.A~            51646 Daily_Second_d~               6336
 9 2021-07-09 Andhra Prad~ First.Dose.Ad~         13372990 Daily_First_do~              25571
10 2021-07-09 Andhra Prad~ First.Dose.Ad~         13372990 Daily_Second_d~              11168
# ... with 3,542 more rows

Answer 2

I think your final output should have nrow(df_vaccination) * 2 rows.

Here is one non-scalable solution but should work for this data.

df <- df_vaccination

res <- data.frame(Updated.on = rep(df$Updated.On, 2), 
            State = rep(df$State, 2), 
            Dose_type = rep(names(df)[3:4], each = nrow(df)), 
            Dose_Administered = unlist(df[3:4]), 
            Dose_type_daily = rep(names(df)[5:6],  each = nrow(df)),
            Dose_Administered_daily = unlist(df[5:6]), row.names = NULL)

res

#  Updated.on                       State               Dose_type Dose_Administered
#1 2021-07-09                       India First.Dose.Administered         297184419
#2 2021-07-09 Andaman and Nicobar Islands First.Dose.Administered            164079
#3 2021-07-09              Andhra Pradesh First.Dose.Administered          13372990
#4 2021-07-09           Arunachal Pradesh First.Dose.Administered            583021
#5 2021-07-09                       Assam First.Dose.Administered           6462249
#6 2021-07-09                       Bihar First.Dose.Administered          15741778

#   Dose_type_daily Dose_Administered_daily
#1 Daily_First_dose                 2071121
#2 Daily_First_dose                     795
#3 Daily_First_dose                   25571
#4 Daily_First_dose                    8423
#5 Daily_First_dose                   52913
#6 Daily_First_dose                  262299

Answer 3

I think you need pivot_longer twice with a proper names_pattern regex argument.

library(tidyverse)

df_vaccination %>%
  pivot_longer(cols = 3:4, names_to = c('dose_adminstered','.value'), 
               names_pattern = '(.*)\\.(Administered)') %>%
  pivot_longer(cols = 3:4, names_to = c('dose_type_daily', '.value'), 
               names_pattern = '(.*)_(.*)$')

#> # A tibble: 3,552 x 6
#>    Updated.On State         dose_adminstered Administered dose_type_daily   dose
#>    <date>     <chr>         <chr>                   <int> <chr>            <int>
#>  1 2021-07-09 India         First.Dose          297184419 Daily_First     2.07e6
#>  2 2021-07-09 India         First.Dose          297184419 Daily_Second    1.14e6
#>  3 2021-07-09 India         Second.Dose          69894633 Daily_First     2.07e6
#>  4 2021-07-09 India         Second.Dose          69894633 Daily_Second    1.14e6
#>  5 2021-07-09 Andaman and ~ First.Dose             164079 Daily_First     7.95e2
#>  6 2021-07-09 Andaman and ~ First.Dose             164079 Daily_Second    6.34e3
#>  7 2021-07-09 Andaman and ~ Second.Dose             51646 Daily_First     7.95e2
#>  8 2021-07-09 Andaman and ~ Second.Dose             51646 Daily_Second    6.34e3
#>  9 2021-07-09 Andhra Prade~ First.Dose           13372990 Daily_First     2.56e4
#> 10 2021-07-09 Andhra Prade~ First.Dose           13372990 Daily_Second    1.12e4
#> # ... with 3,542 more rows

---

To return only 2 rows per State you may do this workaround

df_vaccination[1:4] %>%
  pivot_longer(cols = 3:4, names_to = c('dose_adminstered','.value'), 
               names_pattern = '(.*)\\.(Administered)') %>%
  cbind(df_vaccination[5:6] %>% pivot_longer(everything(), names_to = c('dose_type_daily', '.value'), 
               names_pattern = '(.*)_(.*)$'))

    Updated.On                                    State dose_adminstered Administered dose_type_daily    dose
1   2021-07-09                                    India       First.Dose    297184419     Daily_First 2071121
2   2021-07-09                                    India      Second.Dose     69894633    Daily_Second 1141754
3   2021-07-09              Andaman and Nicobar Islands       First.Dose       164079     Daily_First     795
4   2021-07-09              Andaman and Nicobar Islands      Second.Dose        51646    Daily_Second    6336
5   2021-07-09                           Andhra Pradesh       First.Dose     13372990     Daily_First   25571
6   2021-07-09                           Andhra Pradesh      Second.Dose      3379660    Daily_Second   11168
7   2021-07-09                        Arunachal Pradesh       First.Dose       583021     Daily_First    8423
8   2021-07-09                        Arunachal Pradesh      Second.Dose       108393    Daily_Second    3430
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