Selecting values from specific columns and skipping NA values in R

Question

I am working with the cancer registry data. In the following data example (ex_data), variables id and diagnosis_yr stand for ID and year at cancer diagnosis receptively. Columns x_2005 to x_2010 and y_2005 to y_2010 respectively stand for x and y status for each year (2005 to 2010).In my actual working data, I have many columns for many years (2005-2020). I would like to extract x and y values from the earliest available year, latest available year, and at the diagnosis year (ie. x_earliest, y_latest,x_at_diagnosis,y_at_diagnosis variables in "wanted" ) by excluding NAs . For id 1, for example , I would like to extract x values from the earliest year and y values from the latest year by skipping NAs. For x and y values at the diagnosis year, if there is NAs at the diagnosis year, I would like to skip NAs and extract the available data from the preceding year. How can I implement to get wanted variables in R?

library(tidyverse)

#example data
ex_data <- tribble(
~id,~diagnosis_yr,~x_2005,~x_2006,~x_2007,~x_2008,~x_2009,~x_2010,~y_2005,~y_2006,~y_2007,~y_2008,~y_2009,~y_2010,
1,  2007,   NA, NA, 1,  2,  2,  3,  "a",    "b",    "c",    "d",    "e",    NA, 
2,  2008,   1,  3,  1,  NA, 1,  2,   NA,    "b",    "b",    "e",    "d", "d",
3,  2010,   NA, 2,  2,  2,  3,  NA, "a",    "b",    "c",     NA,     NA,    NA,
4,  2009, 1,    3,  1,  NA, 1,  2,   NA,     NA,     NA,     NA,     NA,    NA,
5,  2005, NA,   1,  1,  2,  2,  3,  "a",    "b",    "c",    "d",    "e",    "e"
)

#wanted variables
wanted <- tribble(
  ~id,~diagnosis_yr,~x_earliest,~y_latest,~x_at_diagnosis,~y_at_diagnosis,
  1,    2007,   1,  "e",    1,  "c",
  2,    2008,   1,  "d",    1,  "e",
  3,    2010,   2,  "c",    3,  "c",
  4,  2009, 1,   NA,  1,  NA,
  5,  2005, 1,  "e", NA,  "a"
)

Answer 1

I'm not completely sure, if this is correct:

library(dplyr)
library(tidyr)

ex_data %>% 
  pivot_longer(-c(id, diagnosis_yr), 
               names_to = c(".value", "year"),
               names_pattern = "(.*)_(\\d+)") %>% 
  group_by(id) %>% 
  mutate(x_earliest     = first(na.omit(x)),
         x_at_diagnosis = last(na.omit(x[diagnosis_yr >= year])),
         y_latest       = last(na.omit(y)),
         y_at_diagnosis = last(na.omit(y[diagnosis_yr >= year]))) %>% 
  select(id, diagnosis_yr, x_earliest, y_latest, x_at_diagnosis, y_at_diagnosis) %>% 
  distinct() %>% 
  ungroup()

This returns

# A tibble: 3 x 6
     id diagnosis_yr x_earliest y_latest x_at_diagnosis y_at_diagnosis
  <dbl>        <dbl>      <dbl> <chr>             <dbl> <chr>         
1     1         2007          1 e                     1 c             
2     2         2008          1 d                     1 e             
3     3         2010          2 c                     3 c