Source:
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 NULL , thedefault, *_join() will do a natural join, using all variables withcommon names across the two tables. A message lists the variables sothat you can check they're right (to suppress the message, simplyexplicitly list the variables that you want to join).To join by different variables on x and y use a named vector.For example, by = c('a' = 'b') will match x.a toy.b . |
copy | If x and y are not from the same data source,and copy is TRUE , then y will be copied into thesame src as x . This allows you to join tables across srcs, butit is a potentially expensive operation so you must opt into it. |
suffix | If there are non-joined duplicate variables in x andy , these suffixes will be added to the output to disambiguate them.Should be a character vector of length 2. |
.. | other parameters passed onto methods, for instance, na_matches to control how NA values are matched. See join.tbl_df for more. |
keep | If TRUE the by columns are kept in the nesting joins. |
name | the name of the list column nesting joins create. If NULL the name of y is used. |
Tidyverse Cheat Sheet For Beginners. This tidyverse cheat sheet will guide you through the basics of the tidyverse, and 2 of its core packages: dplyr and ggplot2! The tidyverse is a powerful collection of R packages that you can use for data science. They are designed. Dplyr provides a grammar for manipulating tables in R. This cheat sheet will guide you through the grammar, reminding you how to select, filter, arrange, mutate, summarise, group, and join. Data Transformation with dplyr:: CHEAT SHEET A B C A B C. Use a 'Mutating Join' to join one table to columns from another, matching values with the rows that they correspond to. Each join retains a different combination of values from the tables. Le!join(x, y, by = NULL.
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:
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
.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
Groups are ignored for the purpose of joining, but the result preservesthe grouping of
x
.Examples
8 min read2020/05/04Motivation
I use R to extract data held in Microsoft SQL Server databases on a daily basis.
When I first started I was confused by all the different ways to accomplish this task. I was a bit overwhelmed trying to choose the, “best,” option given the specific job at hand.
I want to share what approaches I’ve landed on to help others who may want a simple list of options to get started with.
Scope
This post is about reading data from a database, not writing to one.
I prefer to use packages in the tidyverse so I’ll focus on those packages.
While it’s possible to generalize many of the concepts I write about here to other DBMS systems I will focus exclusively on Microsoft SQL Server. I hope this will provide simple, prescriptive guidance for those working in a similar configuration.
The data for these examples is stored using Microsoft SQL Server Express. Free download available here.
One last thing - these are a few options I populated my toolbox with. They have served me well over the past two years as an analyst in an enterprise environment, but are definitely not the only options available. Scrabble word finder popular online.
Setup
Connect to the server
I use the keyring package to keep my credentials out of my R code. You can use the great documentation available from RStudio to learn how do the same.
Write some sample data
Note that I set the temporary argument to TRUE so that the data is written to the tempdb on SQL server, which will result in it being deleted on disconnection.
This results in dplyr prefixing the table name with, “##.”
SOURCE: https://db.rstudio.com/dplyr/#connecting-to-the-database
Option 1: Use dplyr syntax and let dbplyr handle the rest
When I use this option
This is my default option.
I do almost all of my analysis in R and this avoids fragmenting my work and thoughts across different tools.
Dplyr Cheat Sheet
Examples
Example 1: filter rows, and retrieve selected columns
Example 2: join across tables and retrieve selected columns
Example 3: Summarize and count
Quite a few tailnum values in flights, are not present in planes, interesting!
Option 2: Write SQL syntax and have dplyr and dbplyr run the query
When I use this option
I use this option when I am reusing a fairly short, existing SQL querywith minor modifications.
Example 1: Simple selection of records using SQL syntax
Turbotax premier 2020. Example 2: Use dplyr syntax to enhance a raw SQL query
Option 3: Store the SQL query in a text file and have dplyr and dbplyr run the query
When I use this option
I use this approach under the following conditions:
- I’m reusing existing SQL code or when collaborating with someone who will be writing new code in SQL
- The SQL code is longer than a line or two
I prefer to, “modularize,” my R code. Having an extremely long SQL statementin my R code doesn’t abstract away the complexity of the SQL query. Putting thequery into it’s own file helps achieve my desired level of abstraction.
In conjunction with source control it makes tracking changes to the definition of adata set simple.
More importantly, it’s a really useful way to collaborate with others whoare comfortable with SQL but don’t use R. For example, I recently used thisapproach on a project involving aggregation of multiple data sets.Another team member focused on building out the data collection logic forsome of the data sets in SQL. Once he had them built and validated he handed offthe query to me and I pasted it into a text file.
Dplyr Join Cheat Sheet Printable
Step 1: Put your SQL code into a text file
Here is some example SQL code that might be in a file
Dplyr Join Cheat Sheet
Let’s say that SQL code was stored in a text file called,
flights.sql
Dplyr Join Cheat Sheet Pdf
Step 2: Use the SQL code in the file to retrieve data and execute the query.