library(joyn)
#>
#> Attaching package: 'joyn'
#> The following object is masked from 'package:base':
#>
#> merge
library(data.table)This vignette will give you a brief overview of how you can use some
auxiliary functions that joyn makes available to the
user.
Verifying if dt is uniquely identified
One of the advantages of joyn is that you can perform
one-to-one (1:1), one-to-many (1:m), many-to-one (m:1), and many-to-many
(m:m) joins. is_id() is a function that might come in handy
when you want to check whether your data table is uniquely identified by
the variables you want to merge by. In fact this is what
is_id() checks by default, returning either TRUE or FALSE
depending on whether the data table is uniquely identified or not.
Alternatively, you can set return_report = FALSE to get a
summary of the duplicates.
x1 <- data.table(id = c(1L, 1L, 2L, 3L, NA_integer_),
t = c(1L, 2L, 1L, 2L, NA_integer_),
x = 11:15,
c = c("a", "b", "a", "t", "d"),
c1 = c("h", "j", "k", "l", "y"))
y1 <- data.table(id = c(1,2, 4),
y = c(11L, 15L, 16))
# Checking if x1 is uniquely identified by "id" with return_report = TRUE
is_id(dt = x1,
by = "id")
#> ! Duplicates found by: `id`
#> [1] FALSE
# Checking duplicates in x1 with return_report = FALSE
is_id(dt = x1,
by = "id",
return_report = FALSE)
#> ! Duplicates found by: `id`
#> [1] FALSEPossible unique identifiers
In joyn, you can also search for variables which
possibly uniquely identify your data table x using the
possible_ids() function. For example,
# Identify possible unique identifier excluding variable t
possible_ids(dt = x1,
exclude = "t")
#> ℹ Variables to test: id, x, c, and c1
#> ℹ Found unique identifiers: `x` and `c1`
#> [[1]]
#> [1] "x"
#>
#> [[2]]
#> [1] "c1"
#>
#> attr(,"checked_ids")
#> [1] "id" "c" "x" "c1"
# Identify possible unique identifier excluding character variables
possible_ids(dt = x1,
exclude = "_character")
#> ! var `_character` not found in dataframe
#> ℹ Variables to test: id, t, x, c, and c1
#> ℹ Found unique identifiers: `x` and `c1`
#> [[1]]
#> [1] "x"
#>
#> [[2]]
#> [1] "c1"
#>
#> attr(,"checked_ids")
#> [1] "t" "id" "c" "x" "c1"
# Identify possible unique identifiers, excluding character variables but considering variable c1
possible_ids(dt = x1,
exclude_classes = "character",
include = "c1")
#> ℹ Variables to test: id, t, x, and c1
#> ℹ Found unique identifiers: `x` and `c1`
#> [[1]]
#> [1] "x"
#>
#> [[2]]
#> [1] "c1"
#>
#> attr(,"checked_ids")
#> [1] "t" "id" "x" "c1"Verifying if data table is balanced
Additionally, joyn makes available to the user the
is_balanced() function. This is instrumental in assessing
the completeness of the data table within a specified group, i.e., if
the table contains all the combinations of observations in the group. By
default, is_balanced() will tell you if/if not the table is
balanced. However, if you set return = "table", you will
get a summary of the unbalanced observations. In other words, those
combinations of elements between the specified variables that is not
contained in the input table.
# Example with return = "logic", the default
is_balanced(df = x1,
by = c("id", "t"))
#> [1] FALSE
# Example with return = "table"
is_balanced(df = x1,
by = c("id", "t"),
return = "table")
#> id t
#> 1 3 1
#> 2 2 2Tabulating simple frequencies
Furthermore, joyn provides a function that generates
simple frequency tables, so that you can easily have an overview of the
distribution of values within your data tables.
# Tabulating frequencies of var `id`
freq_table(x = x1,
byvar = "id")[]
#> id n percent
#> <char> <int> <char>
#> 1: 1 2 40%
#> 2: 2 1 20%
#> 3: 3 1 20%
#> 4: <NA> 1 20%
#> 5: total 5 100%
# Removing NAs from the calculation
freq_table(x = x1,
byvar = "id",
na.rm = TRUE)[]
#> id n percent
#> <char> <int> <char>
#> 1: 1 2 50%
#> 2: 2 1 25%
#> 3: 3 1 25%
#> 4: total 4 100%