Creating a new data frame in R based on unique values and time stamp-CodePudding

I'm new to R, hence this elementary question.

I have a data frame with ~700 rows and 25 columns. Each row is a single appointment with the information about that appointment (time, priority, gender). The rows have a unique identifier in the form of a 7 digit number and there are multiple rows for the same identifier (when the same person came in for more than one appointment).

ID	PRIORITY	TIME
234	Reading	10/29
546	Writing	10/30
678	Communication	10/29
546	Communication	11/1
234	Writing	11/1

What I would like to do is create a new dataframe that has each unique ID along with the priority of their first visit, second visit, etc.

ID	PRIORITY 1	PRIORITY 2
234	Reading	Writing
546	Writing	Communication
678	Communication

So far I have the list of all unique identifiers:

uniqueID <- unique(data$ID)

Now I would like to pull the data from PRIORITY based on these unique identifiers.

CodePudding user response：

You can do:

df <- data.frame(ID = c(234, 546, 678, 546, 234),
                 PRIORITY = c("Reading", "Writing", "Communication", "Communication", "Writing"),
                 TIME = c("10/29", "10/30", "10/29", "11/1", "11/1"))

library(tidyverse)

df %>%
  group_by(ID) %>%
  mutate(ID_count = 1:n()) %>%
  ungroup() %>%
  pivot_wider(id_cols = ID,
              values_from = c(PRIORITY, TIME),
              names_from = ID_count)

which gives:

# A tibble: 3 x 5
     ID PRIORITY_1    PRIORITY_2    TIME_1 TIME_2
  <dbl> <chr>         <chr>         <chr>  <chr> 
1   234 Reading       Writing       10/29  11/1  
2   546 Writing       Communication 10/30  11/1  
3   678 Communication <NA>          10/29  <NA>

CodePudding user response：

This would be another option also:

library(dplyr)
library(tidyr)

dummy_data <- data.frame(
"ID" = c(234,546,678,546,234),
"PRIORITY" = c("Reading","Writing","Communication","Communication","Writing"),
"TIME" = c("10/29","10/30","10/29","11/1","11/1"))

income_data_drop <- dummy_data %>% pivot_wider(names_from = "TIME", values_from = "PRIORITY")
income_data_drop

     ID    `10/29`      `10/30`   `11/1`       
    <dbl>   <chr>        <chr>     <chr>        
1   234    Reading       <NA>     Writing      
2   546     <NA>        Writing Communication
3   678  Communication   <NA>      <NA>