How can I create a function in R that calculations across rows?-CodePudding

I have a dataset that looks similar to this:

Patient ID	Unit Type	Status
1234	ABC	Returned
1234	ABC	Returned
1234	ABC	Returned
1234	ABC	Returned
1234	ABC	Transfused
1234	DEF	Transfused
1234	DEF	Transfused
1234	DEF	Transfused
1234	GHI	Transfused

Table Code:

> dput(df)
structure(list(Patient_ID = c("1234", "1234", "1234", "1234", 
"1234", "1234", "1234", "1234", "1234"), Unit_Type = c("ABC", 
"ABC", "ABC", "ABC", "ABC", "DEF", "DEF", "DEF", "GHI"), Status = c("Returned", 
"Returned", "Returned", "Returned", "Transfused", "Transfused", 
"Transfused", "Transfused", "Transfused")), class = "data.frame", row.names = c(NA, 
-9L))

I want my output to look like this (preferably in a new dataframe):

Patient ID	ABC_Returned	ABC_Transfused	DEF_Tranfused	GHI_Transfused
1234	4	1	3	1

How can I create a function that would automate this process?

CodePudding user response：

library(tidyverse)
df %>%
  count(Patient_ID, Unit_Type, Status) %>%
  pivot_wider(names_from = c(Unit_Type, Status), values_from = n)

result

# A tibble: 1 × 5
  Patient_ID ABC_Returned ABC_Transfused DEF_Transfused GHI_Transfused
  <chr>             <int>          <int>          <int>          <int>
1 1234                  4              1              3              1

CodePudding user response：

library(dplyr)


df <- structure(list(Patient_ID = c("1234", "1234", "1234", "1234", 
                              "1234", "1234", "1234", "1234", "1234"), Unit_Type = c("ABC", 
                                                                                     "ABC", "ABC", "ABC", "ABC", "DEF", "DEF", "DEF", "GHI"), Status = c("Returned", 
                                                                                                                                                         "Returned", "Returned", "Returned", "Transfused", "Transfused", 
                                                                                                                                                         "Transfused", "Transfused", "Transfused")), class = "data.frame", row.names = c(NA, 
                                                                                                                                                                                                                                         -9L))  
df2 <- df %>% 
      mutate(unit_stat = paste(Unit_Type, Status, sep = "_")) %>% 
      count(Patient_ID, unit_stat) %>% 
      tidyr::pivot_wider(id_cols = "Patient_ID", names_from = unit_stat, values_from = n)