问题
I have two data frames.
The first one has two columns: x is water depth, y is temperature at each depth.
The second one has two columns too, x is also water depth, but at different depth compared to that in the first table. The second column z is salinity.
I want to join the two tables by x, by adding z to the first table. I have learned how to join tables using 'key' in tidyr , but that only works if the keys are identical. The x in these two tables are not the same.
What I want to do is to match the depth x in table 2 to that within 10% of that in table 1 (i.e. match 1.1 in table 2 x to 1.0 in table 1 x).
How can I do this?
Table 1
| x | y |
|---|----|
| 1 | 25 |
| 2 | 26 |
| 3 | 27 |
Table 2
| x | z |
|------|----|
| 1.1 | 30 |
| 2.05 | 35 |
| 3.8 | 34 |
I want
Table 1
| x | y | z |
|---|----|----|
| 1 | 25 | 30 |
| 2 | 26 | 35 |
| 3 | 27 | NA |
The first two 'x' values have matches (within 10% absolute difference) in Table 2. The third one doesn't.
回答1:
Edit after data was provided:
Taking data definition from @MKR's post:
library(fuzzyjoin)
fuzzy_left_join(Table_1, Table_2,match_fun = function(x,y) y> x & y<=1.1*x )
# Joining by: "x"
# x.x y x.y z
# 1 1 25 1.10 30
# 2 2 26 2.05 35
# 3 3 27 NA NA
general explanations with fake data (first answer)
fake data
iris1 <- head(iris[1:2])
iris1
# Sepal.Length Sepal.Width
# 1 5.1 3.5
# 2 4.9 3.0
# 3 4.7 3.2
# 4 4.6 3.1
# 5 5.0 3.6
# 6 5.4 3.9
iris2 <- head(iris[c(1,3)])
set.seed(1)
# add noise
iris2$Sepal.Length <- iris2$Sepal.Length + rnorm(6,sd=0.05)
# shuffle rows
iris2 <- iris2[sample(seq(nrow(iris2))),]
iris2
# Sepal.Length Petal.Length
# 5 5.016475 1.4
# 2 4.909182 1.4
# 4 4.679764 1.5
# 6 5.358977 1.7
# 3 4.658219 1.3
# 1 5.068677 1.4
code
library(fuzzyjoin)
fuzzy_left_join(iris1,iris2,match_fun= function(x,y) y>0.99*x & y<1.01*x )
# Joining by: "Sepal.Length"
# Sepal.Length.x Sepal.Width Sepal.Length.y Petal.Length
# 1 5.1 3.5 5.068677 1.4
# 2 4.9 3.0 4.909182 1.4
# 3 4.7 3.2 4.679764 1.5
# 4 4.7 3.2 4.658219 1.3
# 5 4.6 3.1 NA NA
# 6 5.0 3.6 5.016475 1.4
# 7 5.4 3.9 5.358977 1.7
We see that some rows matched really well, let's take a look at the exceptions. The row number 4 had too much noise added in iris2, so it got paired with row 3, which has 2 matches. As I chose a left join, row 4 is still shown, but with NAs for iris2's columns.
As I understand it:
- The joining columns will be expanded
- The function takes these long columns (
6*6==36elements here) as arguments - We an apply vectorized functions (such as
<or&in this case) to return a vector of logical that will filter these long columns in order to build the outputdata.frame.
distance_left_join is more straightforward to use, but then it's absolute distance, not relative.
回答2:
An option using sqldf can be achieved as:
library(sqldf)
sqldf("select T1.x, T1.y, A.z from Table_1 T1
left join (select Table_1.x, Table_1.y, Table_2.z from Table_1
left join Table_2 where round((100*abs(Table_1.x - Table_2.x)/Table_1.x),0) <= 10) A
on T1.x = A.x")
# x y z
# 1 1 25 30
# 2 2 26 35
# 3 3 27 NA
Data:
Table_1 <- read.table(text =
"x y
1 25
2 26
3 27",
header = TRUE)
Table_2 <- read.table(text =
"x z
1.1 30
2.05 35
3.8 34",
header = TRUE)
来源：https://stackoverflow.com/questions/50708000/joining-matching-data-frames-in-r