如何计算两个日期之间的持续时间

Question

I'm working with a large data set in RStudio that includes multiple test scores for the same individuals. I've filtered my data set to display the same individual's scores in two consecutive rows with the test date for each test administration in one column. My data appears as follows:

id  test_date     score    baseline_number_1    baseline_number_2 
1   08/15/2017    21.18          Baseline             N/A
1   08/28/2019    28.55             N/A             Baseline
2   11/22/2017    33.38          Baseline             N/A
2   11/06/2019    35.3              N/A             Baseline
3   07/25/2018    30.77          Baseline             N/A
3   07/31/2019    33.42             N/A             Baseline

I would like to calculate the total duration of time between baseline 1 and baseline 2 administration and store that value in a new column. Therefore, my first question is what is the best way to calculate the duration of time between two dates? And two, what is the best way to condense each individual's data into one row to make calculating the difference between test scores easier and to be stored in a new column?

Thank you for any assistance!

Answer 1

This is a solution inside the tidyverse universe. The packages we are going to use are dplyr and tidyr .

First, we create the dataset (you read it from a file instead) and convert strings to date format:

library(dplyr)
library(tidyr)

dataset <- read.table(text = "id  test_date     score    baseline_number_1    baseline_number_2 
1   08/15/2017    21.18          Baseline             N/A
1   08/28/2019    28.55             N/A             Baseline
2   11/22/2017    33.38          Baseline             N/A
2   11/06/2019    35.3              N/A             Baseline
3   07/25/2018    30.77          Baseline             N/A
3   07/31/2019    33.42             N/A             Baseline", header = TRUE)
dataset$test_date <- as.Date(dataset$test_date, format = "%m/%d/%Y")

#   id  test_date score baseline_number_1 baseline_number_2
# 1  1 2017-08-15 21.18          Baseline              <NA>
# 2  1 2019-08-28 28.55              <NA>          Baseline
# 3  2 2017-11-22 33.38          Baseline              <NA>
# 4  2 2019-11-06 35.30              <NA>          Baseline
# 5  3 2018-07-25 30.77          Baseline              <NA>
# 6  3 2019-07-31 33.42              <NA>          Baseline

The best solution to condense each individual's data into one row and compute the difference between the two baselines can be achieved as follows:

dataset %>% 
  group_by(id) %>% 
  mutate(number = row_number()) %>% 
  ungroup() %>% 
  pivot_wider(
    id_cols = id,
    names_from = number, 
    values_from = c(test_date, score), 
    names_glue = "{.value}_{number}"
    ) %>% 
  mutate(
    time_between = test_date_2 - test_date_1
  )

Brief explanation: first we create the variable number which indicates the baseline number in each row; then we use pivot_wider to make the dataset "wider" indeed, ie we have one row for each id along with its features; finally we create the variable time_between which contains the difference in days between two baselines. In you are not familiar with some of these functions, I suggest you break the pipeline after each operation and analyse it step by step.

Final output

# A tibble: 3 x 6
#      id test_date_1 test_date_2 score_1 score_2 time_between
#   <int> <date>      <date>        <dbl>   <dbl> <drtn>      
# 1     1 2017-08-15  2019-08-28     21.2    28.6 743 days    
# 2     2 2017-11-22  2019-11-06     33.4    35.3 714 days    
# 3     3 2018-07-25  2019-07-31     30.8    33.4 371 days