Group two dfs based on dates that closely match - r

Question

These are subsets of two dataframes.
df1:
plot
mean_first_flower_date
gdd
1
2019-07-15
60
1
2019-07-21
50
1
2019-07-23
78
2
2019-05-13
100
2
2019-05-22
173
2
2019-05-25
245
(cont.)
df2:
plot
date
flowers
1
2019-07-12
2
1
2019-07-13
9
1
2019-07-14
3
1
2019-07-15
3
2
2019-05-12
10
2
2019-05-13
10
2
2019-05-14
14
2
2019-05-15
17
(cont.)
df2 has some matching dates with df1 but sometimes the dates are off for one or a couple days (highlighted in bold).
I would like to group both dfs based on both 'date' and 'plot', keeping df2, without losing 'gdd' data from df1.
This will happen if, for example, I inner_join both dfs because the dates will not match.
So if a date in df1 is one to three days earlier or later than what it's possible to match in df2, it's fine because the dates are relatively close. This is tricky because I want this data replacement only if there is not data available in df1 for that data range.
My goal is to have something like this:
plot
date
flowers
gdd
1
2019-07-12
2
60
1
2019-07-13
9
60
1
2019-07-14
3
60
1
2019-07-15
3
60
2
2019-05-12
10
100
2
2019-05-13
10
100
2
2019-05-14
14
100
2
2019-05-15
17
100
Is it possible to do?
I greatly appreciate any help!
Thanks!

Answer 1

I think a 'rolling join' from the data.table package can handle this:
library(data.table)
setDT(df1)
setDT(df2)
df1[, mean_first_flower_date := as.Date(mean_first_flower_date)]
df2[, date := as.Date(date)]
df1[df2, on=c("plot","mean_first_flower_date==date"), roll=3, rollends=TRUE]
# plot mean_first_flower_date gdd flowers
#1: 1 2019-07-12 60 2
#2: 1 2019-07-13 60 9
#3: 1 2019-07-14 60 3
#4: 1 2019-07-15 60 3
#5: 2 2019-05-12 100 10
#6: 2 2019-05-13 100 10
#7: 2 2019-05-14 100 14
#8: 2 2019-05-15 100 17
Using this data:
df1 <- read.table(text="plot mean_first_flower_date gdd
1 2019-07-15 60
1 2019-07-21 50
1 2019-07-23 78
2 2019-05-13 100
2 2019-05-22 173
2 2019-05-25 245", header=TRUE)
df2 <- read.table(text="plot date flowers
1 2019-07-12 2
1 2019-07-13 9
1 2019-07-14 3
1 2019-07-15 3
2 2019-05-12 10
2 2019-05-13 10
2 2019-05-14 14
2 2019-05-15 17", header=TRUE)

Answer 2

Try fill from dplyr. use this syntax
df2 %>% left_join(df1, by = c("plot" = "plot", "date" = "mean_first_flower_date")) %>%
fill(gdd, .direction = "up")
plot date flowers gdd
1 1 2019-07-12 2 60
2 1 2019-07-13 9 60
3 1 2019-07-14 3 60
4 1 2019-07-15 3 60
5 2 2019-05-12 10 100
6 2 2019-05-13 10 100
7 2 2019-05-14 14 NA
8 2 2019-05-15 17 NA
As you can notice there are two NAs in the last two rows which shouldn't be there if you'll join your actual df2 where these rows will be filled by 173 as there will be a match for 2019-05-22. Still if you want to fill the last NA rows, if any, you can use fill again with .direction = "down"
df2 %>% left_join(df1, by = c("plot" = "plot", "date" = "mean_first_flower_date")) %>%
fill(gdd, .direction = "up") %>% fill(gdd, .direction = "down")
plot date flowers gdd
1 1 2019-07-12 2 60
2 1 2019-07-13 9 60
3 1 2019-07-14 3 60
4 1 2019-07-15 3 60
5 2 2019-05-12 10 100
6 2 2019-05-13 10 100
7 2 2019-05-14 14 100
8 2 2019-05-15 17 100