# Install the latest CRAN releaseinstall.packages("diyar")# Or, install the development version from GitHubinstall.packages("devtools")devtools::install_github("OlisaNsonwu/diyar")diyar is anR package for linking recordswith 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 otheroccurrences as defined by a case definition. This makes it useful inordinarily complex analyses such as record linkage,
contact or network analyses e.t.c.
The main functions arelinks(),episodes()andpartitions(). They are flexible in regards to how theycompare records, as well as what are considered matches. Theirfunctionality can sometimes overlap however, each is better suited toparticular use cases:
links() - link records with no relevance to an indexrecord. For example, deterministic record linkageepisodes() - link records in relation to an indexrecord. For example, contact and network analysis.partitions() - link records in relation to a fixedinterval.Key features;
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]))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!= ysub.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,NA ...) OR not.equal(30,30,30 ...)#> }sub.cri.3#> {#> {#> exact(hair.color) OR age.range(age)#> } AND#> {#> last.word(Republic of Ghana,France,NA ...) OR not.equal(30,30,30 ...)#> }#> }dfr_stages$id.3<-links(criteria ="place_holder",sub_criteria =list("cr1"= sub.cri.3))dfr_stages#> age hair_colour branch_office id.1 id.2 id.3#> 1 30 Brown Republic of Ghana P.1 (CRI 001) P.1 (CRI 001) P.1 (CRI 001)#> 2 30 Teal France P.4 (CRI 003) P.2 (CRI 001) P.2 (CRI 001)#> 3 30 <NA> <NA> P.3 (No hits) P.3 (No hits) P.3 (No hits)#> 4 30 Green <NA> P.4 (CRI 001) P.2 (CRI 003) P.4 (No hits)#> 5 30 Green France P.4 (CRI 001) P.2 (CRI 001) P.2 (CRI 001)#> 6 30 Dark brown Ghana P.6 (No hits) P.6 (No hits) P.1 (CRI 001)#> 7 30 Brown Republic of Ghana P.1 (CRI 001) P.1 (CRI 001) P.1 (CRI 001)There are variations oflinks() likelinks_wf_probabilistic() andlinks_af_probabilistic() for specific use cases such asprobabilistic record linkage.
Key features;
dfr_2<-data.frame(date =as.Date("2020-01-01")+c(1:5,10:15,20:25))dfr_2$id.1<-episodes(date = dfr_2$date,case_length =2,episodes_max =1)dfr_2$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$pref)dfr_2$id.3<-episodes(date = dfr_2$date,case_length =number_line(-2,2),episode_type ="rolling",recurrence_length =1,episodes_max =1,rolls_max =1)dfr_2$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#> 1 2020-01-02 E.01 2020-01-02 -> 2020-01-04 (C) 2#> 2 2020-01-03 E.01 2020-01-02 -> 2020-01-04 (D) 2#> 3 2020-01-04 E.01 2020-01-02 -> 2020-01-04 (D) 2#> 4 2020-01-05 E.04 2020-01-05 == 2020-01-05 (S) 2#> 5 2020-01-06 E.05 2020-01-06 == 2020-01-06 (S) 2#> 6 2020-01-11 E.06 2020-01-11 == 2020-01-11 (S) 2#> 7 2020-01-12 E.07 2020-01-12 == 2020-01-12 (S) 2#> 8 2020-01-13 E.08 2020-01-13 == 2020-01-13 (S) 2#> 9 2020-01-14 E.09 2020-01-14 == 2020-01-14 (S) 1#> 10 2020-01-15 E.10 2020-01-15 == 2020-01-15 (S) 2#> 11 2020-01-16 E.11 2020-01-16 == 2020-01-16 (S) 2#> 12 2020-01-21 E.12 2020-01-21 == 2020-01-21 (S) 2#> 13 2020-01-22 E.13 2020-01-22 == 2020-01-22 (S) 2#> 14 2020-01-23 E.14 2020-01-23 == 2020-01-23 (S) 2#> 15 2020-01-24 E.15 2020-01-24 == 2020-01-24 (S) 2#> 16 2020-01-25 E.16 2020-01-25 == 2020-01-25 (S) 2#> 17 2020-01-26 E.17 2020-01-26 == 2020-01-26 (S) 2#> id.2 id.3#> 1 E.01 2020-01-02 == 2020-01-02 (S) E.01 2020-01-02 -> 2020-01-05 (C)#> 2 E.02 2020-01-03 == 2020-01-03 (S) E.01 2020-01-02 -> 2020-01-05 (D)#> 3 E.03 2020-01-04 == 2020-01-04 (S) E.01 2020-01-02 -> 2020-01-05 (D)#> 4 E.04 2020-01-05 == 2020-01-05 (S) E.01 2020-01-02 -> 2020-01-05 (R)#> 5 E.05 2020-01-06 == 2020-01-06 (S) E.05 2020-01-06 == 2020-01-06 (S)#> 6 E.06 2020-01-11 == 2020-01-11 (S) E.06 2020-01-11 == 2020-01-11 (S)#> 7 E.09 2020-01-12 -> 2020-01-16 (D) E.07 2020-01-12 == 2020-01-12 (S)#> 8 E.09 2020-01-12 -> 2020-01-16 (D) E.08 2020-01-13 == 2020-01-13 (S)#> 9 E.09 2020-01-12 -> 2020-01-16 (C) E.09 2020-01-14 == 2020-01-14 (S)#> 10 E.09 2020-01-12 -> 2020-01-16 (D) E.10 2020-01-15 == 2020-01-15 (S)#> 11 E.09 2020-01-12 -> 2020-01-16 (D) E.11 2020-01-16 == 2020-01-16 (S)#> 12 E.12 2020-01-21 == 2020-01-21 (S) E.12 2020-01-21 == 2020-01-21 (S)#> 13 E.13 2020-01-22 == 2020-01-22 (S) E.13 2020-01-22 == 2020-01-22 (S)#> 14 E.14 2020-01-23 == 2020-01-23 (S) E.14 2020-01-23 == 2020-01-23 (S)#> 15 E.15 2020-01-24 == 2020-01-24 (S) E.15 2020-01-24 == 2020-01-24 (S)#> 16 E.16 2020-01-25 == 2020-01-25 (S) E.16 2020-01-25 == 2020-01-25 (S)#> 17 E.17 2020-01-26 == 2020-01-26 (S) E.17 2020-01-26 == 2020-01-26 (S)#> period id.4#> 1 2020-01-02 -> 2020-01-07 E.01 2020-01-02 -> 2020-01-11 (C)#> 2 2020-01-03 -> 2020-01-08 E.01 2020-01-02 -> 2020-01-11 (D)#> 3 2020-01-04 -> 2020-01-09 E.01 2020-01-02 -> 2020-01-11 (D)#> 4 2020-01-05 -> 2020-01-10 E.01 2020-01-02 -> 2020-01-11 (D)#> 5 2020-01-06 -> 2020-01-11 E.01 2020-01-02 -> 2020-01-11 (D)#> 6 2020-01-11 -> 2020-01-16 E.06 2020-01-11 -> 2020-01-16 (S)#> 7 2020-01-12 -> 2020-01-17 E.07 2020-01-12 -> 2020-01-17 (S)#> 8 2020-01-13 -> 2020-01-18 E.08 2020-01-13 -> 2020-01-18 (S)#> 9 2020-01-14 -> 2020-01-19 E.09 2020-01-14 -> 2020-01-19 (S)#> 10 2020-01-15 -> 2020-01-20 E.10 2020-01-15 -> 2020-01-20 (S)#> 11 2020-01-16 -> 2020-01-21 E.11 2020-01-16 -> 2020-01-21 (S)#> 12 2020-01-21 -> 2020-01-26 E.12 2020-01-21 -> 2020-01-26 (S)#> 13 2020-01-22 -> 2020-01-27 E.13 2020-01-22 -> 2020-01-27 (S)#> 14 2020-01-23 -> 2020-01-28 E.14 2020-01-23 -> 2020-01-28 (S)#> 15 2020-01-24 -> 2020-01-29 E.15 2020-01-24 -> 2020-01-29 (S)#> 16 2020-01-25 -> 2020-01-30 E.16 2020-01-25 -> 2020-01-30 (S)#> 17 2020-01-26 -> 2020-01-31 E.17 2020-01-26 -> 2020-01-31 (S)There are variations ofepisodes() likeepisodes_wf_splits() for specific use cases such as moreefficient handling of duplicate records.
Key features;
dfr_3<- dfr_2["date"]dfr_3$id.1<-partitions(date = dfr_3$date,window =number_line(as.Date(c("2020-01-10","2020-01-17")),as.Date(c("2020-01-12","2020-01-24"))) )dfr_3$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 (I) PN.18 (I) PN.18 (I)#> 2 2020-01-03 PN.02 (I) PN.18 (D) PN.18 (D)#> 3 2020-01-04 PN.03 (I) PN.18 (D) PN.18 (D)#> 4 2020-01-05 PN.04 (I) PN.19 (I) PN.18 (D)#> 5 2020-01-06 PN.05 (I) PN.19 (D) PN.18 (D)#> 6 2020-01-11 PN.06 (I) PN.21 (I) PN.18 (D)#> 7 2020-01-12 PN.07 (I) PN.21 (D) PN.18 (D)#> 8 2020-01-13 PN.08 (I) PN.21 (D) PN.18 (D)#> 9 2020-01-14 PN.09 (I) PN.22 (I) PN.19 (I)#> 10 2020-01-15 PN.10 (I) PN.22 (D) PN.19 (D)#> 11 2020-01-16 PN.11 (I) PN.22 (D) PN.19 (D)#> 12 2020-01-21 PN.12 (I) PN.24 (I) PN.19 (D)#> 13 2020-01-22 PN.13 (I) PN.24 (D) PN.19 (D)#> 14 2020-01-23 PN.14 (I) PN.25 (I) PN.19 (D)#> 15 2020-01-24 PN.15 (I) PN.25 (D) PN.19 (D)#> 16 2020-01-25 PN.16 (I) PN.25 (D) PN.19 (D)#> 17 2020-01-26 PN.17 (I) PN.25 (D) PN.19 (D)Find out more!
vignette("number_line")vignette("episodes")vignette("links")vignette("panes")Please report any bug or issues with using this packagehere.