joyn empowers you to assess the results of joining data frames, making
it easier and more efficient to combine your tables. Similar in
philosophy to the merge command in Stata, joyn offers matching key
variables and detailed join reports to ensure accurate and insightful
results.
Merging tables in R can be tricky. Ensuring accuracy and understanding
the joined data fully can be tedious tasks. That’s where joyn comes
in. Inspired by Stata’s informative approach to merging, joyn makes
the process smoother and more insightful.
While standard R merge functions are powerful, they often lack features
like assessing join accuracy, detecting potential issues, and providing
detailed reports. joyn fills this gap by offering:
- Intuitive join handling: Whether you’re dealing with one-to-one,
one-to-many, or many-to-many relationships,
joynhelps you navigate them confidently. - Informative reports: Get clear insights into the join process with helpful reports that identify duplicate observations, missing values, and potential inconsistencies.
While standard R merge functions offer basic functionality, joyn goes
above and beyond by providing comprehensive tools and features tailored
to your data joining needs:
1. Flexibility in join types: Choose your ideal join type (“left”,
“right”, or “inner”) with the keep argument. Unlike R’s default,
joyn performs a full join by default, ensuring all observations are
included, but you have full control to tailor the results.
2. Seamless variable handling: No more wrestling with duplicate
variable names! joyn offers multiple options:
-
Update values: Use
update_valuesorupdate_NAto automatically update conflicting variables in the left table with values from the right table. -
Keep both (with different names): Enable
keep_common_vars = TRUEto retain both variables, each with a unique suffix. -
Selective inclusion: Choose specific variables from the right table with
y_vars_to_keep, ensuring you get only the data you need.
3. Relationship awareness: joyn recognizes one-to-one,
one-to-many, many-to-one, and many-to-many relationships between tables.
While it defaults to many-to-many for compatibility, remember this is
often not ideal. Always specify the correct relationship using by
arguments for accurate and meaningful results.
4. Join success at a glance: Get instant feedback on your join with the automatically generated reporting variable. Identify potential issues like unmatched observations or missing values to ensure data integrity and informed decision-making.
By addressing these common pain points and offering enhanced
flexibility, joyn empowers you to confidently and effectively join
your data frames, paving the way for deeper insights and data-driven
success.
While raw speed is essential, understanding your joins every step of the
way is equally crucial. joyn prioritizes providing insightful
information and preventing errors over solely focusing on speed.
Unlike other functions, it adds:
- Meticulous checks:
joynperforms comprehensive checks to ensure your join is accurate and avoids potential missteps, like unmatched observations or missing values. - Detailed reporting: Get a clear picture of your join with a dedicated report, highlighting any issues you should be aware of.
- User-friendly summary: Quickly grasp the join’s outcome with a concise overview presented in a clear table.
These valuable features contribute to a slightly slower performance
compared to functions like data.table::merge.data.table() or
collapse::join(). However, the benefits of preventing errors and
gaining invaluable insights far outweigh the minor speed difference.
- Speed is your top priority for massive datasets? Consider using
data.tableorcollapsedirectly. - Seek clear understanding and error prevention for your joins?
joynis your trusted guide.
joyn intentionally restricts certain actions and provides clear
messages when encountering unexpected data configurations. This might
seem opinionated, but it’s designed to protect you from
accidentally creating inaccurate or misleading joins. This “safety
net” empowers you to confidently merge your data, knowing joyn has
your back.
Currently, joyn focuses on the most common and valuable join types.
Future development might explore expanding its flexibility based on user
needs and feedback.
While joyn::join() offers the core functionality and Stata-inspired
arguments, you might prefer a syntax more aligned with your existing
workflow. joyn has you covered!
Embrace base R and data.table:
joyn::merge(): Leverage familiar base R anddata.tablesyntax for seamless integration with your existing code.
Join with flair using dplyr:
joyn::{dplyr verbs}(): Enjoy the intuitive verb-based syntax ofdplyrfor a powerful and expressive way to perform joins.
Dive deeper: Explore the corresponding vignettes to unlock the full potential of these alternative interfaces and find the perfect fit for your data manipulation style.
You can install the stable version of joyn from
CRAN with:
install.packages("joyn")The development version from GitHub with:
# install.packages("devtools")
devtools::install_github("randrescastaneda/joyn")library(joyn)
#>
#> Attaching package: 'joyn'
#> The following object is masked from 'package:base':
#>
#> merge
library(data.table)
#> Warning: package 'data.table' was built under R version 4.4.2
x1 = data.table(id = c(1L, 1L, 2L, 3L, NA_integer_),
t = c(1L, 2L, 1L, 2L, NA_integer_),
x = 11:15)
y1 = data.table(id = c(1,2, 4),
y = c(11L, 15L, 16))
x2 = data.table(id = c(1, 4, 2, 3, NA),
t = c(1L, 2L, 1L, 2L, NA_integer_),
x = c(16, 12, NA, NA, 15))
y2 = data.table(id = c(1, 2, 5, 6, 3),
yd = c(1, 2, 5, 6, 3),
y = c(11L, 15L, 20L, 13L, 10L),
x = c(16:20))
# using common variable `id` as key.
joyn(x = x1,
y = y1,
match_type = "m:1")
#>
#> ── JOYn Report ──
#>
#> .joyn n percent
#> 1 x 2 33.3%
#> 2 y 1 16.7%
#> 3 x & y 3 50%
#> 4 total 6 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable .joyn
#> ℹ Note: Removing key variables id from id and y
#> id t x y .joyn
#> <num> <int> <int> <num> <fctr>
#> 1: 1 1 11 11 x & y
#> 2: 1 2 12 11 x & y
#> 3: 2 1 13 15 x & y
#> 4: 3 2 14 NA x
#> 5: NA NA 15 NA x
#> 6: 4 NA NA 16 y
# keep just those observations that match
joyn(x = x1,
y = y1,
match_type = "m:1",
keep = "inner")
#>
#> ── JOYn Report ──
#>
#> .joyn n percent
#> 1 x 2 66.7%
#> 2 y 1 33.3%
#> 3 total 3 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable .joyn
#> ℹ Note: Removing key variables id from id and y
#> id t x y .joyn
#> <num> <int> <int> <num> <fctr>
#> 1: 1 1 11 11 x & y
#> 2: 1 2 12 11 x & y
#> 3: 2 1 13 15 x & y
# Bad merge for not specifying by argument
joyn(x = x2,
y = y2,
match_type = "1:1")
#>
#> ── JOYn Report ──
#>
#> .joyn n percent
#> 1 x 4 44.4%
#> 2 y 4 44.4%
#> 3 x & y 1 11.1%
#> 4 total 9 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable .joyn
#> ℹ Note: Removing key variables id and x from id, yd, y, and x
#> id t x yd y .joyn
#> <num> <int> <num> <num> <int> <fctr>
#> 1: 1 1 16 1 11 x & y
#> 2: 4 2 12 NA NA x
#> 3: 2 1 NA NA NA x
#> 4: 3 2 NA NA NA x
#> 5: NA NA 15 NA NA x
#> 6: 2 NA 17 2 15 y
#> 7: 5 NA 18 5 20 y
#> 8: 6 NA 19 6 13 y
#> 9: 3 NA 20 3 10 y
# good merge, ignoring variable x from y
joyn(x = x2,
y = y2,
by = "id",
match_type = "1:1")
#>
#> ── JOYn Report ──
#>
#> .joyn n percent
#> 1 x 2 28.6%
#> 2 y 2 28.6%
#> 3 x & y 3 42.9%
#> 4 total 7 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable .joyn
#> ℹ Note: Removing key variables id from id, yd, y, and x
#> id t x yd y .joyn
#> <num> <int> <num> <num> <int> <fctr>
#> 1: 1 1 16 1 11 x & y
#> 2: 4 2 12 NA NA x
#> 3: 2 1 NA 2 15 x & y
#> 4: 3 2 NA 3 10 x & y
#> 5: NA NA 15 NA NA x
#> 6: 5 NA NA 5 20 y
#> 7: 6 NA NA 6 13 y
# update NAs in var x in table x from var x in y
joyn(x = x2,
y = y2,
by = "id",
update_NAs = TRUE)
#>
#> ── JOYn Report ──
#>
#> .joyn n percent
#> <char> <int> <char>
#> 1: x 2 28.6%
#> 2: x & y 1 14.3%
#> 3: NA updated 4 57.1%
#> 4: total 7 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable .joyn
#> ℹ Note: Removing key variables id from id, yd, y, and x
#> id t x yd y .joyn
#> <num> <int> <num> <num> <int> <fctr>
#> 1: 1 1 16 1 11 x & y
#> 2: 4 2 12 NA NA x
#> 3: 2 1 17 2 15 NA updated
#> 4: 3 2 20 3 10 NA updated
#> 5: NA NA 15 NA NA x
#> 6: 5 NA 18 5 20 NA updated
#> 7: 6 NA 19 6 13 NA updated
# update values in var x in table x from var x in y
joyn(x = x2,
y = y2,
by = "id",
update_values = TRUE)
#>
#> ── JOYn Report ──
#>
#> .joyn n percent
#> <char> <int> <char>
#> 1: NA updated 4 57.1%
#> 2: value updated 1 14.3%
#> 3: not updated 2 28.6%
#> 4: total 7 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable .joyn
#> ℹ Note: Removing key variables id from id, yd, y, and x
#> id t x yd y .joyn
#> <num> <int> <num> <num> <int> <fctr>
#> 1: 1 1 16 1 11 value updated
#> 2: 4 2 12 NA NA not updated
#> 3: 2 1 17 2 15 NA updated
#> 4: 3 2 20 3 10 NA updated
#> 5: NA NA 15 NA NA not updated
#> 6: 5 NA 18 5 20 NA updated
#> 7: 6 NA 19 6 13 NA updated
# do not bring any variable from y into x, just the report
joyn(x = x2,
y = y2,
by = "id",
y_vars_to_keep = NULL)
#>
#> ── JOYn Report ──
#>
#> .joyn n percent
#> 1 x 2 28.6%
#> 2 y 2 28.6%
#> 3 x & y 3 42.9%
#> 4 total 7 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable .joyn
#> id t x .joyn
#> <num> <int> <num> <fctr>
#> 1: 1 1 16 x & y
#> 2: 4 2 12 x
#> 3: 2 1 NA x & y
#> 4: 3 2 NA x & y
#> 5: NA NA 15 x
#> 6: 5 NA NA y
#> 7: 6 NA NA y