diyar

Installation

# Install the latest CRAN release 
install.packages("diyar")

# Or, install the development version from GitHub
install.packages("devtools")
devtools::install_github("OlisaNsonwu/diyar")

Overview

diyar is an R package for linking records with shared characteristics. The linked records represent an entity, which depending on the context of the analysis can be unique patients, infection episodes, overlapping periods of care, clusters or other occurrences as defined by a case definition. This makes it useful in ordinarily complex analyses such as record linkage,
contact or network analyses e.t.c.

The main functions are links(), episodes() and partitions(). They are flexible in regards to how they compare records, as well as what are considered matches. Their functionality can sometimes overlap however, each is better suited to particular use cases:

• links() - link records with no relevance to an index record. For example, deterministic record linkage
• episodes() - link records in relation to an index record. For example, contact and network analysis.
• partitions() - link records in relation to a fixed interval.

links()

Key features;

• multi-stage record linkage. Here, multiple linkage criteria are assessed in a specified order of priority.

library(diyar)
data(missing_staff_id)
dfr_stages <- missing_staff_id[c("age", "hair_colour", "branch_office")]
priority_order_1 <- c("hair_colour", "branch_office")
priority_order_2 <- c("branch_office", "hair_colour")

dfr_stages$id.1 <- links(criteria = as.list(dfr_stages[priority_order_1]))
dfr_stages$id.2 <- links(criteria = as.list(dfr_stages[priority_order_2]))

• create and use complex rules for record matching. This is done with a sub_criteria().

sub.cri.1 <- sub_criteria(
  hair.color = dfr_stages$hair_colour,
  age = dfr_stages$age,
  match_funcs = c(
    "exact" = exact_match,
    "age.range" = range_match)
)
last_word_wf <- function(x) tolower(gsub("^.* ", "", x))
last_word_cmp <- function(x, y) last_word_wf(x) == last_word_wf(y)
not_equal <- function(x, y) x != y
sub.cri.2 <- sub_criteria(
  dfr_stages$branch_office, 
  dfr_stages$age,
  match_funcs = c(
    "last.word" = last_word_cmp,
    "not.equal" = not_equal)
)
sub.cri.3 <- sub_criteria(sub.cri.1, sub.cri.2, operator = "and")
sub.cri.1
#> {
#> exact(hair.color) OR age.range(age)
#> }
sub.cri.2
#> {
#> last.word(Republic of Ghana,France ...) OR not.equal(30,30 ...)
#> }
sub.cri.3
#> {
#>   {
#>   exact(hair.color) OR age.range(age)
#>   } AND 
#>   {
#>   last.word(Republic of Ghana,France ...) OR not.equal(30,30 ...)
#>   }
#> }
dfr_stages$id.3 <- links(
  criteria = "place_holder",
  sub_criteria = list("cr1" = sub.cri.3)
)
dfr_stages
#>   age hair_colour     branch_office          id.1          id.2          id.3
#> 1  30       Brown Republic of Ghana P.1 (CRI 001) P.1 (CRI 001) P.1 (CRI 001)
#> 2  30        Teal            France P.4 (CRI 002) P.2 (CRI 001) P.2 (CRI 001)
#> 3  30        <NA>              <NA> P.3 (No hits) P.3 (No hits) P.3 (No hits)
#> 4  30       Green              <NA> P.4 (CRI 001) P.2 (CRI 002) P.4 (No hits)
#> 5  30       Green            France P.4 (CRI 001) P.2 (CRI 001) P.2 (CRI 001)
#> 6  30  Dark brown             Ghana P.6 (No hits) P.6 (No hits) P.1 (CRI 001)
#> 7  30       Brown Republic of Ghana P.1 (CRI 001) P.1 (CRI 001) P.1 (CRI 001)

There are variations of links() like links_wf_probabilistic() and links_sv_probabilistic() for specific use cases such as probabilistic record linkage.

episodes()

Key features;

• link records within a specified period from an index record.

dfr_2 <- data.frame(date = as.Date("2020-01-01") + c(1:5, 10:15, 20:25))
dfr_2$id.1 <- episodes(
  date = dfr_2$date, case_length = 2,
  episodes_max = 1)

• change the index record.

dfr_2$pref <- c(rep(2, 8), 1, rep(2, 8))
dfr_2$id.2 <- episodes(
  date = dfr_2$date, case_length = number_line(-2, 2),
  episodes_max = 1, 
  custom_sort = dfr_2$pref)

• add a recurrence period

dfr_2$id.3 <- episodes(
  date = dfr_2$date, case_length = number_line(-2, 2), 
  episode_type = "rolling", recurrence_length = 1,
  episodes_max = 1, rolls_max = 1)

• link overlapping periods

dfr_2$period <- number_line(dfr_2$date, dfr_2$date + 5)
dfr_2$id.4 <- episodes(
  date = dfr_2$period, case_length = index_window(dfr_2$period),
  episodes_max = 1)
dfr_2
#>          date     id.1 pref     id.2     id.3                   period     id.4
#> 1  2020-01-02 E.01 (C)    2 E.01 (S) E.01 (C) 2020-01-02 -> 2020-01-07 E.01 (C)
#> 2  2020-01-03 E.01 (D)    2 E.02 (S) E.01 (D) 2020-01-03 -> 2020-01-08 E.01 (D)
#> 3  2020-01-04 E.01 (D)    2 E.03 (S) E.01 (D) 2020-01-04 -> 2020-01-09 E.01 (D)
#> 4  2020-01-05 E.04 (S)    2 E.04 (S) E.01 (R) 2020-01-05 -> 2020-01-10 E.01 (D)
#> 5  2020-01-06 E.05 (S)    2 E.05 (S) E.05 (S) 2020-01-06 -> 2020-01-11 E.01 (D)
#> 6  2020-01-11 E.06 (S)    2 E.06 (S) E.06 (S) 2020-01-11 -> 2020-01-16 E.06 (S)
#> 7  2020-01-12 E.07 (S)    2 E.09 (D) E.07 (S) 2020-01-12 -> 2020-01-17 E.07 (S)
#> 8  2020-01-13 E.08 (S)    2 E.09 (D) E.08 (S) 2020-01-13 -> 2020-01-18 E.08 (S)
#> 9  2020-01-14 E.09 (S)    1 E.09 (C) E.09 (S) 2020-01-14 -> 2020-01-19 E.09 (S)
#> 10 2020-01-15 E.10 (S)    2 E.09 (D) E.10 (S) 2020-01-15 -> 2020-01-20 E.10 (S)
#> 11 2020-01-16 E.11 (S)    2 E.09 (D) E.11 (S) 2020-01-16 -> 2020-01-21 E.11 (S)
#> 12 2020-01-21 E.12 (S)    2 E.12 (S) E.12 (S) 2020-01-21 -> 2020-01-26 E.12 (S)
#> 13 2020-01-22 E.13 (S)    2 E.13 (S) E.13 (S) 2020-01-22 -> 2020-01-27 E.13 (S)
#> 14 2020-01-23 E.14 (S)    2 E.14 (S) E.14 (S) 2020-01-23 -> 2020-01-28 E.14 (S)
#> 15 2020-01-24 E.15 (S)    2 E.15 (S) E.15 (S) 2020-01-24 -> 2020-01-29 E.15 (S)
#> 16 2020-01-25 E.16 (S)    2 E.16 (S) E.16 (S) 2020-01-25 -> 2020-01-30 E.16 (S)
#> 17 2020-01-26 E.17 (S)    2 E.17 (S) E.17 (S) 2020-01-26 -> 2020-01-31 E.17 (S)

There are variations of episodes() like episodes_wf_splits() for specific use cases such as more efficient handling of duplicate records.

partitions()

Key features;

• link all records within a specific periods in time

dfr_3 <- dfr_2["date"]
dfr_3$id.1 <- partitions(
  date = dfr_3$date, 
  window = number_line(as.Date(c("2020-01-10", "2020-01-17")), 
                       as.Date(c("2020-01-12", "2020-01-24")))
  )

• link all records within a splits of an interval

dfr_3$id.2 <- partitions(date = dfr_3$date, by = 3, separate = TRUE) 
dfr_3$id.3 <- partitions(date = dfr_3$date, length.out = 3, separate = TRUE)
dfr_3
#>          date      id.1      id.2      id.3
#> 1  2020-01-02 PN.01 (S) PN.01 (I) PN.01 (I)
#> 2  2020-01-03 PN.02 (S) PN.01 (D) PN.01 (D)
#> 3  2020-01-04 PN.03 (S) PN.01 (D) PN.01 (D)
#> 4  2020-01-05 PN.04 (S) PN.04 (I) PN.01 (D)
#> 5  2020-01-06 PN.05 (S) PN.04 (D) PN.01 (D)
#> 6  2020-01-11 PN.06 (I) PN.06 (I) PN.06 (I)
#> 7  2020-01-12 PN.06 (D) PN.06 (D) PN.06 (D)
#> 8  2020-01-13 PN.08 (S) PN.06 (D) PN.06 (D)
#> 9  2020-01-14 PN.09 (S) PN.09 (I) PN.06 (D)
#> 10 2020-01-15 PN.10 (S) PN.09 (D) PN.06 (D)
#> 11 2020-01-16 PN.11 (S) PN.09 (D) PN.06 (D)
#> 12 2020-01-21 PN.06 (D) PN.12 (I) PN.12 (I)
#> 13 2020-01-22 PN.06 (D) PN.12 (D) PN.12 (D)
#> 14 2020-01-23 PN.06 (D) PN.14 (I) PN.12 (D)
#> 15 2020-01-24 PN.06 (D) PN.14 (D) PN.12 (D)
#> 16 2020-01-25 PN.16 (S) PN.14 (D) PN.12 (D)
#> 17 2020-01-26 PN.17 (S) PN.14 (D) PN.12 (D)

Other useful functions include in the diyar package are combi() and sets().

Find out more!

• number_line and overlaps - vignette("number_line")
• Introduction to epidemiological case definitions with diyar - vignette("episodes")
• Introduction to record linkage with diyar - vignette("links")
• Divvy up events with partitions - vignette("panes")

Bugs and issues

Please report any bug or issues with using this package here.