How to perform a fuzzy join with fuzzyjoin::difference_* in R

Question

I'm working with two different datasets that I want to merge based on a threshold. Let's say the two dataframes look like this:

library(dplyr)
library(fuzzyjoin)
library(lubridate)

df1 = data_frame(Item=1:5, 
                 DateTime=c("2015-01-01 11:12:14", "2015-01-02 09:15:23", 
                            "2015-01-02 15:46:11", "2015-04-19 22:11:33", 
                            "2015-06-10 07:00:00"), 
                 Count=c(1, 6, 11, 15, 9), 
                 Name="Sterling", 
                 Friend=c("Pam", "Cyril", "Cheryl", "Mallory", "Lana"))
df1$DateTime = ymd_hms(df1$DateTime)

df2 = data_frame(Item=21:25, 
                 DateTime=c("2015-01-01 11:12:15", "2015-01-02 19:15:23", 
                            "2015-01-02 15:46:11", "2015-05-19 22:11:33", 
                            "2015-06-10 07:00:02"), 
                 Count=c(3, 7, 11, 15, 8), 
                 Name="Sterling", 
                 Friend=c("Pam", "Kreger", "Woodhouse", "Gillete", "Lana"))
df2$DateTime = ymd_hms(df2$DateTime)

What I would like now, is to be able to left join df2 with df1 based on a fuzzy match of DateTime and Count being within two seconds of their respective values, while all other values except Item are identical. I thought I could get there with the following:

df1 %>%
  difference_left_join(df2, by=c("DateTime", "Count"), max_dist=2)

But that gives me the following output:

 # A tibble: 8 × 10
  Item.x          DateTime.x Count.x   Name.x Friend.x Item.y          DateTime.y Count.y   Name.y  Friend.y
   <int>              <dttm>   <dbl>    <chr>    <chr>  <int>              <dttm>   <dbl>    <chr>     <chr>
1      1 2015-01-01 11:12:14       1 Sterling      Pam     21 2015-01-01 11:12:15       3 Sterling       Pam
2      1 2015-01-01 11:12:14       1 Sterling      Pam     21 2015-01-01 11:12:15       3 Sterling       Pam
3      2 2015-01-02 09:15:23       6 Sterling    Cyril     NA                <NA>      NA     <NA>      <NA>
4      3 2015-01-02 15:46:11      11 Sterling   Cheryl     23 2015-01-02 15:46:11      11 Sterling Woodhouse
5      3 2015-01-02 15:46:11      11 Sterling   Cheryl     23 2015-01-02 15:46:11      11 Sterling Woodhouse
6      4 2015-04-19 22:11:33      15 Sterling  Mallory     NA                <NA>      NA     <NA>      <NA>
7      5 2015-06-10 07:00:00       9 Sterling     Lana     25 2015-06-10 07:00:02       8 Sterling      Lana
8      5 2015-06-10 07:00:00       9 Sterling     Lana     25 2015-06-10 07:00:02       8 Sterling      Lana

This is close, except that row 3 should not have merged given that the names are different (and I would have expected row 2 to merge given the thresholds, even though I don't want it to).

How do I end up with the following dataframe? Note that the second row and third row from df2 was not merged despite DateTime and Count meeting the threshold limit. This is because the other columns (except Item) were not identical.

desired_output
#   Item            DateTime Count     Name  Friend
# 1    3 2015-01-02 15:46:11    11 Sterling  Cheryl
# 2    2 2015-01-02 09:15:23     6 Sterling   Cyril
# 3    5 2015-06-10 07:00:00     9 Sterling    Lana
# 4   25 2015-06-10 07:00:02     8 Sterling    Lana
# 5    4 2015-04-19 22:11:33    15 Sterling Mallory
# 6    1 2015-01-01 11:12:14     1 Sterling     Pam
# 7   21 2015-01-01 11:12:15     3 Sterling     Pam

Answer 1

OK, so, the message you got was because the fuzzy match can't be calculated on a non-numeric column.

The thing to do is to convert it to numeric. Since your caliper is in seconds I converted it to seconds and then made them numeric:

library(dplyr)
library(fuzzyjoin)
library(lubridate)

df1 = data_frame(Item=1:5, 
                 DateTime=c("2015-01-01 11:12:14", "2015-01-02 09:15:23", 
                            "2015-01-02 15:46:11", "2015-04-19 22:11:33", 
                            "2015-06-10 07:00:00"), 
                 Count=c(1, 6, 11, 15, 9), 
                 Name="Sterling", 
                 Friend=c("Pam", "Cyril", "Cheryl", "Mallory", "Lana"))
df1$DateTime1 = as.numeric(seconds(ymd_hms(df1$DateTime)))

df2 = data_frame(Item=21:25, 
                 DateTime=c("2015-01-01 11:12:15", "2015-01-02 19:25:56", 
                            "2015-01-02 15:46:11", "2015-05-19 22:11:33", 
                            "2015-06-10 07:00:02"), 
                 Count=c(3, 6, 11, 15, 8), 
                 Name="Sterling", 
                 Friend=c("Pam", "Kreger", "Woodhouse", "Gillete", "Lana"))
df2$DateTime1 = as.numeric(seconds(ymd_hms(df2$DateTime)))

df1 %>%
  difference_left_join(y=df2, by=c("DateTime1", "Count"), max_dist=2)

based on our discussion in the comments a simple tweak to subset this to the case of other character columns matching would be:

df1[df2$Friend == df1$Friend,] %>%
  difference_left_join(y=df2[df2$Friend == df1$Friend,], by=c("DateTime1", "Count"), max_dist=2)

That example is just for Friend but of course you could use & to do it with multiple columns.