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RhysPeploeRhysPeploe
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Merge pull request Infectious-Diseases-Data-Observatory#30 from Infectious-Diseases-Data-Observatory/values_fn_msg
Move value function message into if statements for more accurate value
2 parents 9191ac7 + 61957ef commit defacb2

29 files changed

Lines changed: 255 additions & 69 deletions

‎DESCRIPTION‎

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Package: iddoverse
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Title: Converting IDDO-SDTM Data To Analysis Datasets
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Version: 0.9.0
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Version: 0.9.1
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Authors@R:
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person("Rhys", "Peploe", , "rhyspeploe1998@gmail.com", role = c("aut", "cre"),
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comment = c(ORCID = "0009-0001-1669-3716"))

‎NAMESPACE‎

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@@ -28,6 +28,7 @@ importFrom(dplyr,group_by)
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importFrom(dplyr,last)
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importFrom(dplyr,left_join)
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importFrom(dplyr,mutate)
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importFrom(dplyr,n)
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importFrom(dplyr,relocate)
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importFrom(dplyr,rename)
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importFrom(dplyr,right_join)

‎NEWS.md‎

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# iddoverse 0.9.1
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* prepare_domain: message about the number of rows using value_fn now more accurately reflects the number of rows, as it is now groups by LOC and METHOD if appropiate
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* Additional tests to cover change to prepare_domain
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* print_messages added to table functions, with equivalent documentation changes
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* Logo consistency changes with worlddatr
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# iddoverse 0.9.0
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* create_participant_table: baseline timing EPOCH == BASELINE then VISITDY == 1, change from any of VISITDY == 1, DY == 1 and EPOCH == BASELINE
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* prepare_domain: data first, domain second in parameter order to align with tidy principles, changes to downstream functions, examples, documentation, tests and vignette

‎R/create_clinical_hist_table.R‎

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#' first(), i.e. if there is two rows from the same day and time, the first
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#' record will be taken, the second will be dropped. Choice of
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#' timing_variables will impact the number of rows affected.
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#' @param print_messages Boolean. Should messages from the function be generated
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#' and shown in the user's console. Default is TRUE.
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#'
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#' @returns An analysis dataset, one row per person, per timepoint.
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#'
@@ -36,15 +38,17 @@ create_clinical_hist_table <- function(sa_domain,
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timing_variables = c(
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"SAHR", "SADY", "SASTDY", "SACDSTDY",
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"VISITDY", "EPOCH"),
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values_funct = first){
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values_funct = first,
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print_messages = TRUE){
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assert_data_frame(sa_domain, required_vars = exprs(SACAT))
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data_sa <- sa_domain %>%
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filter(SACAT == "MEDICAL HISTORY")
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data <- prepare_domain(data_sa, "SA",
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variables_include = c("fever", "anemia", "malaria", "hiv"),
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timing_variables = timing_variables, values_fn = values_funct) %>%
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timing_variables = timing_variables, values_fn = values_funct,
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print_messages = print_messages) %>%
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remove_empty("cols")
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return(data)

‎R/create_clinical_table.R‎

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@@ -15,6 +15,8 @@
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#' first(), i.e. if there is two rows from the same day and time, the first
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#' record will be taken, the second will be dropped. Choice of
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#' timing_variables will impact the number of rows affected.
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#' @param print_messages Boolean. Should messages from the function be generated
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#' and shown in the user's console. Default is TRUE.
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#'
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#' @returns An analysis dataset, one row per person, per timepoint.
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#'
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#'
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create_clinical_table <- function(dm_domain, mb_domain = NULL, mp_domain = NULL,
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sa_domain = NULL, vs_domain = NULL,
31-
values_funct = first){
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values_funct = first,
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print_messages = TRUE){
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data <- prepare_domain(dm_domain, "dm", variables_include = "")
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if(!is.null(mb_domain)){
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data <- data %>%
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full_join(prepare_domain(mb_domain, "mb", variables_include = c("MTB", "HIV"),
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values_fn = values_funct))
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values_fn = values_funct,
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print_messages = print_messages))
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}
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4044
if(!is.null(mp_domain)){
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data <- data %>%
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full_join(prepare_domain(mp_domain, "mp", include_LOC = TRUE,
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values_fn = values_funct))
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values_fn = values_funct,
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print_messages = print_messages))
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}
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if(!is.null(vs_domain)){
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data <- data %>%
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full_join(prepare_domain(vs_domain, "vs",
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values_fn = values_funct))
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values_fn = values_funct,
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print_messages = print_messages))
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}
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if(!is.null(sa_domain)){
@@ -57,7 +63,8 @@ create_clinical_table <- function(dm_domain, mb_domain = NULL, mp_domain = NULL,
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"vomiting", "nausea", "fever", "diarrhea",
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"abdominal pain", "jaundice", "bleeding",
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"anemia", "anorexia", "blood transfusion"),
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values_fn = values_funct))
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values_fn = values_funct,
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print_messages = print_messages))
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}
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data <- data %>%

‎R/create_labs_table.R‎

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#' Default is first(), i.e. if there is two rows from the same day and time,
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#' the first record will be taken, the second will be dropped. Choice of
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#' timing_variables will impact the number of rows affected.
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#' @param print_messages Boolean. Should messages from the function be generated
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#' and shown in the user's console. Default is TRUE.
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#'
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#' @returns An analysis dataset, one row per person, per timepoint.
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#'
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timing_variables = c(
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"LBHR", "LBDY", "LBSTDY", "LBCDSTDY", "VISITDY",
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"EPOCH"),
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values_funct = first){
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values_funct = first,
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print_messages = TRUE){
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prepare_domain(lb_domain, "LB",
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include_METHOD = include_method,
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variables_include = variables,
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timing_variables = timing_variables,
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values_fn = values_funct)
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values_fn = values_funct,
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print_messages = print_messages)
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}
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‎R/create_malaria_parasitemia_table.R‎

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#' Default is first(), i.e. if there is two rows from the same day and time,
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#' the first record will be taken, the second will be dropped. Choice of
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#' timing_variables will impact the number of rows affected.
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#' @param print_messages Boolean. Should messages from the function be generated
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#' and shown in the user's console. Default is TRUE.
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#'
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#' @returns An analysis dataset, one row per person, per timepoint.
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#'
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timing_variables = c(
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"MBHR", "MBDY", "MBSTDY", "MBCDSTDY",
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"VISITDY", "EPOCH"),
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values_funct = first){
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values_funct = first,
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print_messages = TRUE){
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prepare_domain(mb_domain, "MB",
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include_METHOD = include_method,
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include_LOC = include_location,
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variables_include = variables,
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timing_variables = timing_variables,
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values_fn = values_funct)
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values_fn = values_funct,
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print_messages = print_messages)
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}

‎R/create_malaria_pcr_table.R‎

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#' first(), i.e. if there is two rows from the same day and time, the first
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#' record will be taken, the second will be dropped. Choice of
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#' timing_variables will impact the number of rows affected.
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#' @param print_messages Boolean. Should messages from the function be generated
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#' and shown in the user's console. Default is TRUE.
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#'
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#' @returns An analysis dataset, one row per person, per timepoint.
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#'
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#' create_malaria_pcr_table(PF_RPTESTB, RS_RPTESTB)
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#'
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create_malaria_pcr_table <- function(pf_domain, rs_domain, ds_domain = NULL,
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values_funct = first){
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values_funct = first,
28+
print_messages = TRUE){
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2730

2831
data_pf <- prepare_domain(pf_domain, "PF", variables_include = "INTP",
29-
values_fn = values_funct)
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values_fn = values_funct,
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print_messages = print_messages)
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data_rs <- prepare_domain(rs_domain, "RS", variables_include = "WHOMAL01",
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values_fn = values_funct)
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values_fn = values_funct,
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print_messages = print_messages)
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3540
data <- full_join(data_pf, data_rs)
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3742
if(!is.null(ds_domain)){
38-
data_ds <- prepare_domain(ds_domain, "DS", values_fn = values_funct)
43+
data_ds <- prepare_domain(ds_domain, "DS", values_fn = values_funct,
44+
print_messages = print_messages)
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data <- full_join(data, data_ds)
4147
}

‎R/create_participant_table.R‎

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#' @param rp_domain A reproductive system findings/RP domain data frame.
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#' @param sc_domain A subject characteristics/SC domain data frame.
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#' @param vs_domain A vital signs/VS domain data frame.
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#' @param print_messages Boolean. Should messages from the function be generated
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#' and shown in the user's console. Default is TRUE.
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#'
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#' @returns An analysis dataset, one row per participant.
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#'
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mb_domain = NULL,
2830
rp_domain = NULL,
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sc_domain = NULL,
30-
vs_domain = NULL){
32+
vs_domain = NULL,
33+
print_messages = TRUE){
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data <- prepare_domain(dm_domain, "dm",
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variables_include = c("STUDYID", "USUBJID", "AGEU",
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"AGE", "SEX","RFSTDTC",
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left_join(
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prepare_domain(sc_domain, "sc",
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variables_include = c("EDULEVEL", "MARISTAT"),
44-
timing_variables = c("VISITDY", "EPOCH"))%>%
47+
timing_variables = c("VISITDY", "EPOCH"),
48+
print_messages = print_messages)%>%
4549
filter(
4650
(TIME == 1 & TIME_SOURCE == "VISITDY") |
4751
(TIME == "BASELINE" & TIME_SOURCE == "EPOCH")
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6165
left_join(
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prepare_domain(vs_domain, "vs",
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variables_include = c("HEIGHT", "WEIGHT", "BMI", "MUARMCIR"),
64-
timing_variables = c("VISITDY", "EPOCH")) %>%
68+
timing_variables = c("VISITDY", "EPOCH"),
69+
print_messages = print_messages) %>%
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filter(
6671
(TIME == 1 & TIME_SOURCE == "VISITDY") |
6772
(TIME == "BASELINE" & TIME_SOURCE == "EPOCH")
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left_join(
8287
prepare_domain(lb_domain, "lb",
8388
variables_include = c("G6PD"),
84-
timing_variables = c("VISITDY", "EPOCH")) %>%
89+
timing_variables = c("VISITDY", "EPOCH"),
90+
print_messages = print_messages) %>%
8591
filter(
8692
(TIME == 1 & TIME_SOURCE == "VISITDY") |
8793
(TIME == "BASELINE" & TIME_SOURCE == "EPOCH")
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101107
left_join(
102108
prepare_domain(mb_domain, "mb",
103109
variables_include = c("HIV"),
104-
timing_variables = c("VISITDY", "EPOCH")) %>%
110+
timing_variables = c("VISITDY", "EPOCH"),
111+
print_messages = print_messages) %>%
105112
filter(
106113
(TIME == 1 & TIME_SOURCE == "VISITDY") |
107114
(TIME == "BASELINE" & TIME_SOURCE == "EPOCH")
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121128
left_join(
122129
prepare_domain(rp_domain, "rp",
123130
variables_include = c("PREGIND", "EGESTAGE"),
124-
timing_variables = c("VISITDY", "EPOCH")) %>%
131+
timing_variables = c("VISITDY", "EPOCH"),
132+
print_messages = print_messages) %>%
125133
filter(
126134
(TIME == 1 & TIME_SOURCE == "VISITDY") |
127135
(TIME == "BASELINE" & TIME_SOURCE == "EPOCH")

‎R/iddoverse-package.R‎

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#' @importFrom dplyr last
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#' @importFrom dplyr left_join
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#' @importFrom dplyr mutate
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#' @importFrom dplyr n
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#' @importFrom dplyr relocate
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#' @importFrom dplyr rename
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#' @importFrom dplyr right_join

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