R语言中的fulljoin、innerjoin、leftjoin和rightjoin连接 两个dataframe数据之间的连接关系. 经常会碰到需要把两个数据进行合并,大的方向有按'列'和按'行'合并两种方式,这里主要讲下按'列'进行合并,如下图,假设有a、b两个数据,注意共有的列是'chr'和'bin';值为1,2,3,4,5的bin是a、b共有的; 值为. I have two files having different number of rows and column. I am trying to apply merge function because i was applying vlookup in excel. Still I am not getting the desired result in merge function which I was getting in vlookup function in excel.
R's data.table package provides fast methods for handling large tables of data with simplistic syntax. The following is an introduction to basic join operations using data.table.
Suppose you have two data.tables – a table of insurance policies
and a table of insurance claims.
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If you want to see the policy data for each claim, you need to do a join on the policy number. In SQL terms, this is a right/left outer join. That is, you want the result to include every row from the claims table, and only rows from the policy table that are associated with a claim in the claims table. Right outer joins are the default behavior of data.table's join method.
First we need to set the key of each table based on the column we want to use to match the rows of the tables.
Note: Technically we only need to specify the key of the policies table for this join to work, but the join runs quicker when you key both tables.
Now do the join.
Since claim 126's policy number, 4, was not in the policies table its effective and expiration dates are set as NA.
The important thing to remember when doing a basic X[Y] join using data.table is that the table inside of the brackets will have all of its rows in the resultant table. So, doing claims[policies]
will return all policies and any matching claims.
If you want to return only claims that have a matching policy (i.e. rows where the key is in both tables), set the nomatch argument of data.table to 0.
(This is equivalent to claims[policies, nomatch = 0]
and is referred to as an inner join.)
If you want to return rows in the claims table which are not in the policies table, you can do
Or, for policies with no claims…
Now suppose we add a field, Company, to each table and set all the values to 'ABC'.
What would the result be if we try to join policies and claims based on the new Company field?
data.table throws an error in this situation because our resultant table has more rows than the combined number of rows in each of the tables being joined. This is a common sign of a mistake, but in our case it's desired. In this situation we need to tell data.table that this isn't a mistake by specifying allow.cartesian = TRUE
.
![Left Outer Join In R Left Outer Join In R](https://2.bp.blogspot.com/-oBPhcEuXFA0/VwpQHERiVPI/AAAAAAAAFsg/r4yUWXmXeQ0ec4YsAGp-UTBeGpvS3mUDg/s1600/LEFT%2Bvs%2BRight%2BOuter%2BJoin%2Bin%2BSQL.png)
Next to come – rolling joins.
R/join.r
These are generic functions that dispatch to individual tbl methods - see themethod documentation for details of individual data sources. x
andy
should usually be from the same data source, but if copy
isTRUE
, y
will automatically be copied to the same source as x
.
Arguments
x, y | tbls to join |
---|---|
by | a character vector of variables to join by. If To join by different variables on x and y use a named vector.For example, |
copy | If |
suffix | If there are non-joined duplicate variables in |
.. | other parameters passed onto methods, for instance, |
keep | If |
name | the name of the list column nesting joins create. If |
Join types
![Left outer join in r Left outer join in r](https://blogs.sap.com/wp-content/uploads/2018/08/outerJoin.jpg)
What would the result be if we try to join policies and claims based on the new Company field?
data.table throws an error in this situation because our resultant table has more rows than the combined number of rows in each of the tables being joined. This is a common sign of a mistake, but in our case it's desired. In this situation we need to tell data.table that this isn't a mistake by specifying allow.cartesian = TRUE
.
Next to come – rolling joins.
R/join.r
These are generic functions that dispatch to individual tbl methods - see themethod documentation for details of individual data sources. x
andy
should usually be from the same data source, but if copy
isTRUE
, y
will automatically be copied to the same source as x
.
Arguments
x, y | tbls to join |
---|---|
by | a character vector of variables to join by. If To join by different variables on x and y use a named vector.For example, |
copy | If |
suffix | If there are non-joined duplicate variables in |
.. | other parameters passed onto methods, for instance, |
keep | If |
name | the name of the list column nesting joins create. If |
Join types
Currently dplyr supports four types of mutating joins, two types of filtering joins, anda nesting join.
Mutating joins combine variables from the two data.frames:
inner_join()
return all rows from x
where there are matchingvalues in y
, and all columns from x
and y
. If there are multiple matchesbetween x
and y
, all combination of the matches are returned.
left_join()
return all rows from x
, and all columns from x
and y
. Rows in x
with no match in y
will have NA
values in the newcolumns. If there are multiple matches between x
and y
, all combinationsof the matches are returned.
right_join()
return all rows from y
, and all columns from x
and y. Rows in y
with no match in x
will have NA
values in the newcolumns. If there are multiple matches between x
and y
, all combinationsof the matches are returned.
full_join()
return all rows and all columns from both x
and y
.Where there are not matching values, returns NA
for the one missing.
Filtering joins keep cases from the left-hand data.frame: Amazon prime smart tv.
semi_join()
return all rows from x
where there are matchingvalues in y
, keeping just columns from x
. A semi join differs from an inner join because an inner join will returnone row of x
for each matching row of y
, where a semijoin will never duplicate rows of x
.
Left Join Vs Left Outer Join
anti_join()
return all rows from x
where there are notmatching values in y
, keeping just columns from x
.
Nesting joins create a list column of data.frames:
nest_join()
return all rows and all columns from x
. Adds alist column of tibbles. Each tibble contains all the rows from y
that match that row of x
. When there is no match, the list column isa 0-row tibble with the same column names and types as y
. nest_join()
is the most fundamental join since you can recreate the other joins from it.An inner_join()
is a nest_join()
plus an tidyr::unnest()
, and left_join()
is anest_join()
plus an unnest(.drop = FALSE)
.A semi_join()
is a nest_join()
plus a filter()
where you check that every element of data hasat least one row, and an anti_join()
is a nest_join()
plus a filter()
where you check every element has zero rows.
Grouping
R Join By
Groups are ignored for the purpose of joining, but the result preservesthe grouping of x
.