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| Name | Last modified | Size | Description |
|---|---|---|---|---|
| Parent Directory | - | ||
| README.html | 2020-09-17 11:30 | 19K | |
Record linkage and deduplication of individual-level data, such as repeated spells in hospital, or recurrent cases of infection is a common task in epidemiological analysis and other fields of research.
The diyar package aims to provide a simple and flexible implementation of multistage deterministic record linkage and episode grouping.
# Install the latest CRAN release
install.packages("diyar")
# Or, install the development version from GitHub
install.packages("devtools")
devtools::install_github("OlisaNsonwu/diyar")
There are two main aspects of the diyar package; multistage data linkage grouping (links()) and episode tracking (episodes()). number_line objects are used in both.
number_line() - creates a range of real numbers on a number line.
reverse_number_line, shift_number_line, expand_number_line, number_line_sequence, invert_number_line, compress_number_line, union_number_lines, subtract_number_lines, intersect_number_lines.overlaps, overlap, exact, reverse, chain, across, aligns_start, aligns_end, inbetween.library(diyar)
l <- as.Date("01/04/2019", "%d/%m/%Y"); r <- as.Date("30/04/2019", "%d/%m/%Y")
nl <- number_line(l, r)
nl
#> [1] "2019-04-01 -> 2019-04-30"
reverse_number_line(nl)
#> [1] "2019-04-30 <- 2019-04-01"
expand_number_line(nl, -2)
#> [1] "2019-04-03 -> 2019-04-28"episodes() - tracks temporal episodes.data(infections);
db <- infections[c("date")]
# Dates
db$date
#> [1] "2018-04-01" "2018-04-07" "2018-04-13" "2018-04-19" "2018-04-25"
#> [6] "2018-05-01" "2018-05-07" "2018-05-13" "2018-05-19" "2018-05-25"
#> [11] "2018-05-31"
# Fixed episodes
episodes(date = db$date,
case_length = 15,
display = "none",
group_stats = TRUE)
#> Episode tracking completed in < 0.01 secs!
#> [1] "E.01 2018-04-01 -> 2018-04-13 (C)" "E.01 2018-04-01 -> 2018-04-13 (D)"
#> [3] "E.01 2018-04-01 -> 2018-04-13 (D)" "E.04 2018-04-19 -> 2018-05-01 (C)"
#> [5] "E.04 2018-04-19 -> 2018-05-01 (D)" "E.04 2018-04-19 -> 2018-05-01 (D)"
#> [7] "E.07 2018-05-07 -> 2018-05-19 (C)" "E.07 2018-05-07 -> 2018-05-19 (D)"
#> [9] "E.07 2018-05-07 -> 2018-05-19 (D)" "E.10 2018-05-25 -> 2018-05-31 (C)"
#> [11] "E.10 2018-05-25 -> 2018-05-31 (D)"
# Rolling episodes
episodes(date = db$date,
case_length = 15,
recurrence_length = 40,
display = "none",
episode_type = "rolling",
group_stats = TRUE)
#> Episode tracking completed in < 0.01 secs!
#> [1] "E.1 2018-04-01 -> 2018-05-31 (C)" "E.1 2018-04-01 -> 2018-05-31 (D)"
#> [3] "E.1 2018-04-01 -> 2018-05-31 (D)" "E.1 2018-04-01 -> 2018-05-31 (R)"
#> [5] "E.1 2018-04-01 -> 2018-05-31 (D)" "E.1 2018-04-01 -> 2018-05-31 (D)"
#> [7] "E.1 2018-04-01 -> 2018-05-31 (D)" "E.1 2018-04-01 -> 2018-05-31 (D)"
#> [9] "E.1 2018-04-01 -> 2018-05-31 (D)" "E.1 2018-04-01 -> 2018-05-31 (R)"
#> [11] "E.1 2018-04-01 -> 2018-05-31 (D)"links() - multistage deterministic linkage with missing data.# Two-stage exact matches
data(staff_records);
staff_records$pids_a <- links(criteria = list(staff_records$forename,
staff_records$surname),
data_source = staff_records$sex,
display = "none")
#> Data linkage completed in < 0.01 secs!
staff_records
#> r_id forename surname sex dataset pids_a
#> 1 1 James Green M Staff list P.1 (CRI 002)
#> 2 2 <NA> Anderson M Staff list P.2 (CRI 002)
#> 3 3 Jamey Green M Pay slips P.1 (CRI 002)
#> 4 4 <NA> F Pay slips P.4 (No Hits)
#> 5 5 Derrick Anderson M Staff list P.2 (CRI 002)
#> 6 6 Darrack Anderson M Pay slips P.2 (CRI 002)
#> 7 7 Christie Green F Staff list P.1 (CRI 002)
# Single-stage user-defined logical tests
# Matching `sex` and + 20-year age gaps
age <- c(30, 28, 40, 25, 25, 29, 27)
sex <- c("M", "M", "M", "F", "M", "M", "F")
f1 <- function(x, y) (y - x) %in% 0:20
links(criteria = sex,
sub_criteria = list(cr1 = sub_criteria(age, funcs = f1)),
display = "none")
#> Data linkage completed in < 0.01 secs!
#> [1] "P.3 (CRI 001)" "P.3 (CRI 001)" "P.3 (CRI 001)" "P.7 (CRI 001)"
#> [5] "P.3 (CRI 001)" "P.3 (CRI 001)" "P.7 (CRI 001)"
# + 20-year age gaps only
links(criteria = "place_holder",
sub_criteria = list(cr1 = sub_criteria(age, funcs = f1)),
display = "none")
#> Data linkage completed in < 0.01 secs!
#> [1] "P.3 (CRI 001)" "P.3 (CRI 001)" "P.3 (CRI 001)" "P.3 (CRI 001)"
#> [5] "P.3 (CRI 001)" "P.3 (CRI 001)" "P.3 (CRI 001)"Find out more here!
Please report any bug or issues with using this package here.