Return first matched row using date lookup to a date range lookup table-CodePudding

I have 2 dataframes, one with list of dates (df1) and another one with date ranges by ID (df2). I would like to add a column from df2 if a date in df1 sits within the date range in df2.

## This is my base data
df1 <- 
structure(list(Date = structure(c(18848, 18848, 18849, 18890, 
18892, 18901, 18904, 18904, 18906, 18906, 18907, 18911, 18911, 
18922, 18923, 18925, 18926, 18927, 18929), class = "Date"), Id = c(4, 
6, 4, 6, 4, 4, 4, 6, 4, 6, 4, 4, 6, 4, 4, 4, 4, 6, 6)), row.names = c(NA, 
19L), class = "data.frame")

## This is my date range table
df2 <- structure(list(Date.Start = structure(c(18898, 18897, 18848, 
18898, 18897), class = "Date"), Date.End = structure(c(18924, 
18924, 18903, 18924, 18924), class = "Date"), Id = c(6, 6, 4, 
4, 4), Return.Value = c(1, 2, 3, 4, 5)), row.names = c(NA, 5L
), class = "data.frame")

So for the first row of df1, Date = '2021-08-09' sits within date range '2021-08-09' to '2021-10-03' (row 3 of df2) so I want to add a Return Value column to df1 with a value of 3. There is no match for row 2 in df1 so this will return nothing.

My expected output is:

I tried this link Check if a date is in range of lookup table but I want to add a column from df2 instead of returning logical vector.

CodePudding user response：

data.table version using the mult="first" option on a non-equi join allowing >= and <= comparisons:

library(data.table)
setDT(df1)
setDT(df2)
df1[, Return.Value := df2[
    df1, on=c("Id", "Date.Start<=Date", "Date.End>=Date"), Return.Value, mult="first"]]
df1
#          Date Id Return.Value
# 1: 2021-08-09  4            3
# 2: 2021-08-09  6           NA
# 3: 2021-08-10  4            3
# 4: 2021-09-20  6           NA
# 5: 2021-09-22  4            3
# 6: 2021-10-01  4            3
# 7: 2021-10-04  4            4
# 8: 2021-10-04  6            1
# 9: 2021-10-06  4            4
#10: 2021-10-06  6            1
#11: 2021-10-07  4            4
#12: 2021-10-11  4            4
#13: 2021-10-11  6            1
#14: 2021-10-22  4            4
#15: 2021-10-23  4            4
#16: 2021-10-25  4           NA
#17: 2021-10-26  4           NA
#18: 2021-10-27  6           NA
#19: 2021-10-29  6           NA

CodePudding user response：

It's pretty messy...

library(dplyr)

df1 %>%
  left_join(df2, by = 'Id') %>%
  rowwise %>%
  mutate(Return.Value = ifelse(between(Date, Date.Start, Date.End), Return.Value, NA)) %>%
  select(-Date.Start, -Date.End) %>%
  group_by(Date, Id) %>%
  filter({if (sum(!is.na(Return.Value) >0)) !is.na(Return.Value) else row_number() == 1}) %>%
  filter(row_number() == 1) 

   Date          Id Return.Value
   <date>     <dbl>        <dbl>
 1 2021-08-09     4            3
 2 2021-08-09     6           NA
 3 2021-08-10     4            3
 4 2021-09-20     6           NA
 5 2021-09-22     4            3
 6 2021-10-01     4            3
 7 2021-10-04     4            4
 8 2021-10-04     6            1
 9 2021-10-06     4            4
10 2021-10-06     6            1
11 2021-10-07     4            4
12 2021-10-11     4            4
13 2021-10-11     6            1
14 2021-10-22     4            4
15 2021-10-23     4            4
16 2021-10-25     4           NA
17 2021-10-26     4           NA
18 2021-10-27     6           NA
19 2021-10-29     6           NA

CodePudding user response：

We could do this also with match_fun() function from fuzzyjoin package:

library(dplyr)
library(fuzzyjoin)

fuzzy_left_join(
  df1, df2,
  by = c(
    "Id" = "Id",
    "Date" = "Date.Start",
    "Date" = "Date.End"
    ),
  match_fun = list(`==`, `>=`, `<=`)) %>% 
  group_by(Date, Id.x) %>% 
  slice(1) %>% 
  dplyr::select(Date, Id = Id.x, Return.Value)

   Date          Id Return.Value
   <date>     <dbl>        <dbl>
 1 2021-08-09     4            3
 2 2021-08-09     6           NA
 3 2021-08-10     4            3
 4 2021-09-20     6           NA
 5 2021-09-22     4            3
 6 2021-10-01     4            3
 7 2021-10-04     4            4
 8 2021-10-04     6            1
 9 2021-10-06     4            4
10 2021-10-06     6            1
11 2021-10-07     4            4
12 2021-10-11     4            4
13 2021-10-11     6            1
14 2021-10-22     4            4
15 2021-10-23     4            4
16 2021-10-25     4           NA
17 2021-10-26     4           NA
18 2021-10-27     6           NA
19 2021-10-29     6           NA