I have two data.frames similar to these:
#Dt1
Id Date Weight
1 2 10
2 3 20
3 4 30
4 4 30
5 6 40
and
#DT2
Id Date Weight late
1 2 10 3
2 3 20 4
3 4 30 5
8 5 10 6
I would like to merge these files considering only the different ID between them like this:
#Dt.final
Id Date Weight late
4 4 30 NA
5 6 40 NA
8 5 10 6
My originals files are bigger than these, thanks.
besides @yarnabrina answer, The anti_join
in dplyr
is also what you need, but we have to apply twice. anti_join(x, y)
drops all obs in x
that have a match in y
:
> full_join(anti_join(df1, df2, by = 'Id'), anti_join(df2, df1, by = 'Id'))
Joining, by = c("Id", "Date", "Weight")
Id Date Weight late
1 4 4 30 NA
2 5 6 40 NA
3 8 5 10 6
Are you looking for something like this?
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
df1 <- data.frame(Id = c(1, 2, 3, 4, 5),
Date = c(2, 3, 4, 4, 6),
Weight = c(10, 20, 30, 30, 40))
df2 <- data.frame(Id = c(1, 2, 3, 8),
Date = c(2, 3, 4, 5),
Weight = c(10, 20, 30, 10),
late = c(3, 4, 5, 6))
full_join(x = filter(.data = df1, Id %in% setdiff(x = df1$Id, y = df2$Id)),
y = filter(.data = df2, Id %in% setdiff(x = df2$Id, y = df1$Id)))
#> Joining, by = c("Id", "Date", "Weight")
#> Id Date Weight late
#> 1 4 4 30 NA
#> 2 5 6 40 NA
#> 3 8 5 10 6
Created on 2019-05-03 by the reprex package (v0.2.1)
maybe this can solve your problem, it is an hand made answer but I hope not too bad :
df_1 <- data.frame(ID = factor(1:5, levels=1:8),
Date = c(2, 3, 4, 4, 6),
Weight = c(10, 20, 20, 30, 40))
df_2 <- data.frame(ID = factor(4:8, levels=1:8),
Date = c(2, 3, 4, 4, 6),
Weight = c(10, 20, 20, 30, 40),
late = c(1, 2, 3, 4, 5))
# Temporary dataframe
df_temp <- data.frame(
df_1[!df_1$ID %in% df_2$ID, ],
late = NA)
df.final <- rbind(
df_temp,
df_2[!df_2$ID %in% df_1$ID, ])
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