From dcc81c4ad57ce729f2d830b499c97fbf99d5215d Mon Sep 17 00:00:00 2001 From: cecicampanile Date: Wed, 18 Dec 2024 16:24:31 +0000 Subject: [PATCH 1/6] modified description, readme, and mockOmopSketch updated of versions of packages removed warning in readme now mockdata have all the columns --- DESCRIPTION | 6 +++--- R/mockOmopSketch.R | 26 +++++++++++++++++++++++++- README.Rmd | 29 +++++++++++++++++++---------- 3 files changed, 47 insertions(+), 14 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index c951252a..6d4f55cf 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -54,7 +54,7 @@ Suggests: omock (>= 0.3.0), covr, ggplot2, - visOmopResults (>= 0.4.0) + visOmopResults (>= 0.5.0) Config/testthat/edition: 3 Config/testthat/parallel: true Imports: @@ -65,7 +65,7 @@ Imports: dplyr, glue, lifecycle, - omopgenerics (>= 0.3.1), + omopgenerics (>= 0.4.1), PatientProfiles (>= 1.2.1), purrr, rlang, @@ -78,4 +78,4 @@ URL: https://OHDSI.github.io/OmopSketch/ BugReports: https://github.com/OHDSI/OmopSketch/issues VignetteBuilder: knitr Remotes: - darwin-eu/omopgenerics + diff --git a/R/mockOmopSketch.R b/R/mockOmopSketch.R index d746c8bc..6c776f20 100644 --- a/R/mockOmopSketch.R +++ b/R/mockOmopSketch.R @@ -48,10 +48,34 @@ mockOmopSketch <- function(con = NULL, omock::mockProcedureOccurrence(seed = seed) |> omock::mockVisitOccurrence(seed = seed) |> # Create device exposure table - empty (Eunomia also has it empty) - omopgenerics::emptyOmopTable("device_exposure") + omopgenerics::emptyOmopTable("device_exposure")|> + checkColumns() + # WHEN WE SUPORT LOCAL CDMs WE WILL HAVE TO ACCOUNT FOR THAT HERE cdm <- CDMConnector::copy_cdm_to(con = con, cdm = cdm, schema = writeSchema) return(cdm) } + +checkColumns <- function(cdm_local){ + info <- omopgenerics::omopTableFields() + for (table in names(cdm_local)){ + cols <- info |> + dplyr::filter(.data$cdm_table_name == table)|> + dplyr::distinct(.data$cdm_field_name)|> + dplyr::pull() + + missing_cols <- setdiff(cols, colnames(cdm_local[[table]])) + + + if (length(missing_cols) > 0) { + + missing_tbl <- tibble::tibble(!!!rlang::set_names(rep(list(NA_character_), length(missing_cols)), missing_cols)) + + cdm_local[[table]] <- dplyr::bind_cols(cdm_local[[table]], missing_tbl) + + } + } + return(cdm_local) +} diff --git a/README.Rmd b/README.Rmd index 807452a7..00ef6bf5 100644 --- a/README.Rmd +++ b/README.Rmd @@ -16,13 +16,10 @@ knitr::opts_chunk$set( # OmopSketch OmopSketch website -[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental) -[![R-CMD-check](https://github.com/OHDSI/OmopSketch/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/OHDSI/OmopSketch/actions/workflows/R-CMD-check.yaml) -[![CRAN status](https://www.r-pkg.org/badges/version/OmopSketch)](https://CRAN.R-project.org/package=OmopSketch) -[![Codecov test coverage](https://codecov.io/gh/OHDSI/OmopSketch/branch/main/graph/badge.svg)](https://app.codecov.io/gh/OHDSI/OmopSketch?branch=main) - -### WARNING: this package is under-development and has only been tested using mock data +[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental) [![R-CMD-check](https://github.com/OHDSI/OmopSketch/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/OHDSI/OmopSketch/actions/workflows/R-CMD-check.yaml) [![CRAN status](https://www.r-pkg.org/badges/version/OmopSketch)](https://CRAN.R-project.org/package=OmopSketch) [![Codecov test coverage](https://codecov.io/gh/OHDSI/OmopSketch/branch/main/graph/badge.svg)](https://app.codecov.io/gh/OHDSI/OmopSketch?branch=main) + + The goal of OmopSketch is to characterise and visualise an OMOP CDM instance to asses if it meets the necessary criteria to answer a specific clinical question and conduct a certain study. @@ -48,16 +45,19 @@ con <- dbConnect(duckdb(), eunomia_dir()) cdm <- cdmFromCon(con = con, cdmSchema = "main", writeSchema = "main") cdm ``` + ### Snapshot + We first create a snapshot of our database. This will allow us to track when the analysis has been conducted and capture details about the CDM version or the data release. + ```{r} summariseOmopSnapshot(cdm) |> tableOmopSnapshot(type = "flextable") ``` - ### Characterise the clinical tables -Once we have collected the snapshot information, we can start characterising the clinical tables of the CDM. By using `summariseClinicalRecords()` and `tableClinicalRecords()`, we can easily visualise the main characteristics of specific clinical tables. + +Once we have collected the snapshot information, we can start characterising the clinical tables of the CDM. By using `summariseClinicalRecords()` and `tableClinicalRecords()`, we can easily visualise the main characteristics of specific clinical tables. ```{r} summariseClinicalRecords(cdm, c("condition_occurrence", "drug_exposure")) |> @@ -70,7 +70,9 @@ We can also explore trends in the clinical table records over time. summariseRecordCount(cdm, c("condition_occurrence", "drug_exposure")) |> plotRecordCount(facet = "omop_table") ``` + ### Characterise the observation period + After visualising the main characteristics of our clinical tables, we can explore the observation period details. OmopSketch provides several functions to have an overview the dataset study period. Using `summariseInObservation()` and `plotInObservation()`, we can gather information on the number of records per year. @@ -79,20 +81,25 @@ Using `summariseInObservation()` and `plotInObservation()`, we can gather inform summariseInObservation(cdm$observation_period, output = "records") |> plotInObservation() ``` -You can also visualise and explore the characteristics of the observation period per each individual in the database using `summariseObservationPeriod()`. + +You can also visualise and explore the characteristics of the observation period per each individual in the database using `summariseObservationPeriod()`. + ```{r} summariseObservationPeriod(cdm$observation_period) |> tableObservationPeriod(type = "flextable") ``` Or if visualisation is preferred, you can easily build a histogram to explore how many participants have more than one observation period. + ```{r} summariseObservationPeriod(cdm$observation_period) |> plotObservationPeriod() ``` ### Characterise the concepts + OmopSketch also provides functions to explore some of (or all) the concepts in the dataset. + ```{r} acetaminophen <- c(1125315, 1127433, 1127078) @@ -102,10 +109,12 @@ summariseConceptSetCounts(cdm, conceptSet = list("acetaminophen" = acetaminophen ``` ### Characterise the population + Finally, OmopSketch can also help us to characterise the population at the start and end of the observation period. + ```{r} summarisePopulationCharacteristics(cdm) |> tablePopulationCharacteristics(type = "flextable") ``` -As seen, OmopSketch offers multiple functionalities to provide a general overview of a database. Additionally, it includes more tools and arguments that allow for deeper exploration, helping to assess the database's suitability for specific research studies. For further information, please refer to the vignettes. +As seen, OmopSketch offers multiple functionalities to provide a general overview of a database. Additionally, it includes more tools and arguments that allow for deeper exploration, helping to assess the database's suitability for specific research studies. For further information, please refer to the vignettes. From 2a155179dfce64561db56b42ba2bf7989685c257 Mon Sep 17 00:00:00 2001 From: cecicampanile Date: Wed, 18 Dec 2024 17:46:18 +0000 Subject: [PATCH 2/6] Update plotConceptSetCounts.R --- R/plotConceptSetCounts.R | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/R/plotConceptSetCounts.R b/R/plotConceptSetCounts.R index d9082935..f59e172d 100644 --- a/R/plotConceptSetCounts.R +++ b/R/plotConceptSetCounts.R @@ -54,13 +54,13 @@ plotConceptSetCounts <- function(result, )) } - result1 <- result |> omopgenerics::splitAdditional() + result1 <- result |> omopgenerics::splitAll() # Detect if there are several time intervals if("time_interval" %in% colnames(result1)){ # Line plot where each concept is a different line p <- result1 |> dplyr::filter(.data$time_interval != "overall") |> - omopgenerics::uniteAdditional(cols = c("time_interval", "standard_concept_name", "standard_concept_id", "source_concept_name", "source_concept_id", "domain_id")) |> + omopgenerics::pivotEstimates() |> visOmopResults::scatterPlot(x = "time_interval", y = "count", line = TRUE, @@ -71,17 +71,28 @@ plotConceptSetCounts <- function(result, colour = colour) }else{ if("standard_concept_name" %in% colnames(result1)){ - p <- result |> + p <- result1 |> + omopgenerics::pivotEstimates() |> visOmopResults::barPlot(x = c("standard_concept_name", "standard_concept_id"), y = "count", facet = facet, colour = colour) + p$data <- p$data |> + dplyr::mutate( + standard_concept_name_standard_concept_id = factor( + .data$standard_concept_name_standard_concept_id, + levels = c("overall - overall", sort(setdiff(.data$standard_concept_name_standard_concept_id, "overall - overall"))) + ) + ) + }else{ - p <- result |> + p <- result1 |> visOmopResults::barPlot(x = "codelist_name", y = "count", facet = facet, colour = colour) + p$data <- p$data |> + dplyr::arrange(.data$codelist_name) } p <- p + ggplot2::labs( From 0097dd28696c63ae1be22f9d37dbc38d217761aa Mon Sep 17 00:00:00 2001 From: cecicampanile Date: Thu, 19 Dec 2024 19:47:34 +0000 Subject: [PATCH 3/6] updated some plots functions for the new versions of the functions --- DESCRIPTION | 1 - R/plotInObservation.R | 20 +- R/plotRecordCount.R | 23 + README.Rmd | 23 +- README.md | 635 ++--------------------- man/figures/README-unnamed-chunk-3-1.png | Bin 100547 -> 102394 bytes man/figures/README-unnamed-chunk-4-1.png | Bin 202402 -> 238185 bytes man/figures/README-unnamed-chunk-5-1.png | Bin 8263 -> 53391 bytes man/figures/README-unnamed-chunk-6-1.png | Bin 6184 -> 37643 bytes man/figures/README-unnamed-chunk-7-1.png | Bin 111504 -> 84230 bytes man/figures/README-unnamed-chunk-8-1.png | Bin 5321 -> 21935 bytes man/figures/README-unnamed-chunk-9-1.png | Bin 5894 -> 28527 bytes 12 files changed, 81 insertions(+), 621 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 6d4f55cf..09b95694 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -77,5 +77,4 @@ Depends: URL: https://OHDSI.github.io/OmopSketch/ BugReports: https://github.com/OHDSI/OmopSketch/issues VignetteBuilder: knitr -Remotes: diff --git a/R/plotInObservation.R b/R/plotInObservation.R index 750297e7..9fc6227e 100644 --- a/R/plotInObservation.R +++ b/R/plotInObservation.R @@ -59,8 +59,8 @@ plotInObservation <- function(result, # plot if(length(unique(result$additional_level)) > 1 ){ - result |> - dplyr::mutate(additional_level = as.character(gsub("-01$","",as.Date(gsub(" to.*","",.data$additional_level))))) |> + p <- result |> + dplyr::mutate(additional_level = as.character(gsub("-01-01$","",as.Date(gsub(" to.*","",.data$additional_level))))) |> dplyr::filter(.data$estimate_name == "count") |> visOmopResults::scatterPlot( x = "time_interval", @@ -78,6 +78,22 @@ plotInObservation <- function(result, y = variable, x = "Date" ) + p$data <- p$data |> + dplyr::arrange(time_interval) |> + dplyr::mutate( + show_label = seq_along(time_interval) %% + (10 * nrow(p$data|>dplyr::distinct(.data$omop_table))) == 0 # Show every 10th label + ) + + p <- p + + ggplot2::scale_x_discrete( + breaks = p$data$time_interval[p$data$show_label] + ) + + ggplot2::theme( + axis.text.x = ggplot2::element_text(angle = 90, hjust = 1, size = 8), + plot.margin = ggplot2::margin(t = 5, r = 5, b = 30, l = 5) + ) + p }else{ result |> dplyr::filter(.data$estimate_name == "count") |> diff --git a/R/plotRecordCount.R b/R/plotRecordCount.R index 818122c3..0af399e1 100644 --- a/R/plotRecordCount.R +++ b/R/plotRecordCount.R @@ -59,6 +59,27 @@ plotRecordCount <- function(result, y = "Number records", x = "Date" ) + + p$data <- p$data |> + dplyr::mutate( + time_interval = as.character( + gsub("-01-01$", "", as.Date(gsub(" to.*", "", time_interval))) + ) + ) |> + dplyr::arrange(time_interval) |> + dplyr::mutate( + show_label = seq_along(time_interval) %% + (10 * nrow(p$data|>dplyr::distinct(.data$omop_table))) == 0 # Show every 10th label + ) + + p <- p + + ggplot2::scale_x_discrete( + breaks = p$data$time_interval[p$data$show_label] + ) + + ggplot2::theme( + axis.text.x = ggplot2::element_text(angle = 90, hjust = 1, size = 8), + plot.margin = ggplot2::margin(t = 5, r = 5, b = 30, l = 5) + ) }else{ p <- result |> visOmopResults::barPlot(x = "variable_name", @@ -70,5 +91,7 @@ plotRecordCount <- function(result, x = "" ) } + p } + diff --git a/README.Rmd b/README.Rmd index 00ef6bf5..02c46760 100644 --- a/README.Rmd +++ b/README.Rmd @@ -67,8 +67,8 @@ summariseClinicalRecords(cdm, c("condition_occurrence", "drug_exposure")) |> We can also explore trends in the clinical table records over time. ```{r} -summariseRecordCount(cdm, c("condition_occurrence", "drug_exposure")) |> - plotRecordCount(facet = "omop_table") +summariseRecordCount(cdm, c("condition_occurrence", "drug_exposure"), interval = "years") |> + plotRecordCount(facet = "omop_table", colour = "cdm_name") ``` ### Characterise the observation period @@ -78,8 +78,8 @@ After visualising the main characteristics of our clinical tables, we can explor Using `summariseInObservation()` and `plotInObservation()`, we can gather information on the number of records per year. ```{r} -summariseInObservation(cdm$observation_period, output = "records") |> - plotInObservation() +summariseInObservation(cdm$observation_period, output = "records", interval = "years") |> + plotInObservation(colour = "cdm_name") ``` You can also visualise and explore the characteristics of the observation period per each individual in the database using `summariseObservationPeriod()`. @@ -93,7 +93,7 @@ Or if visualisation is preferred, you can easily build a histogram to explore ho ```{r} summariseObservationPeriod(cdm$observation_period) |> - plotObservationPeriod() + plotObservationPeriod(colour = "observation_period_ordinal") ``` ### Characterise the concepts @@ -104,17 +104,8 @@ OmopSketch also provides functions to explore some of (or all) the concepts in t acetaminophen <- c(1125315, 1127433, 1127078) summariseConceptSetCounts(cdm, conceptSet = list("acetaminophen" = acetaminophen)) |> - filter(estimate_name == "record_count") |> - plotConceptSetCounts() -``` - -### Characterise the population - -Finally, OmopSketch can also help us to characterise the population at the start and end of the observation period. - -```{r} -summarisePopulationCharacteristics(cdm) |> - tablePopulationCharacteristics(type = "flextable") + filter(variable_name == "Number records") |> + plotConceptSetCounts(colour = "codelist_name") ``` As seen, OmopSketch offers multiple functionalities to provide a general overview of a database. Additionally, it includes more tools and arguments that allow for deeper exploration, helping to assess the database's suitability for specific research studies. For further information, please refer to the vignettes. diff --git a/README.md b/README.md index e939a3f1..ab384931 100644 --- a/README.md +++ b/README.md @@ -12,9 +12,8 @@ experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](h status](https://www.r-pkg.org/badges/version/OmopSketch)](https://CRAN.R-project.org/package=OmopSketch) [![Codecov test coverage](https://codecov.io/gh/OHDSI/OmopSketch/branch/main/graph/badge.svg)](https://app.codecov.io/gh/OHDSI/OmopSketch?branch=main) - -### WARNING: this package is under-development and has only been tested using mock data + The goal of OmopSketch is to characterise and visualise an OMOP CDM instance to asses if it meets the necessary criteria to answer a @@ -69,7 +68,6 @@ version or the data release. ``` r summariseOmopSnapshot(cdm) |> tableOmopSnapshot(type = "flextable") -#> ! Results have not been suppressed. ``` @@ -84,35 +82,14 @@ visualise the main characteristics of specific clinical tables. ``` r summariseClinicalRecords(cdm, c("condition_occurrence", "drug_exposure")) |> tableClinicalRecords(type = "flextable") -#> ℹ Summarising table counts -#> ℹ The following estimates will be computed: -#> → Start summary of data, at 2024-09-25 12:14:06.676817 -#> -#> ✔ Summary finished, at 2024-09-25 12:14:06.815944 -#> ℹ Summarising records per person -#> ℹ The following estimates will be computed: -#> • records_per_person: mean, sd, median, q25, q75, min, max -#> ! Table is collected to memory as not all requested estimates are supported on -#> the database side -#> → Start summary of data, at 2024-09-25 12:14:07.908258 -#> -#> ✔ Summary finished, at 2024-09-25 12:14:07.955041 -#> ℹ Summarising in_observation, standard, domain_id, and type information -#> ℹ Summarising table counts -#> ℹ The following estimates will be computed: -#> → Start summary of data, at 2024-09-25 12:14:11.725276 -#> -#> ✔ Summary finished, at 2024-09-25 12:14:11.877293 -#> ℹ Summarising records per person -#> ℹ The following estimates will be computed: -#> • records_per_person: mean, sd, median, q25, q75, min, max -#> ! Table is collected to memory as not all requested estimates are supported on -#> the database side -#> → Start summary of data, at 2024-09-25 12:14:12.808874 -#> -#> ✔ Summary finished, at 2024-09-25 12:14:12.850686 -#> ℹ Summarising in_observation, standard, domain_id, and type information -#> ! Results have not been suppressed. +#> ℹ Adding variables of interest to condition_occurrence. +#> ℹ Summarising records per person in condition_occurrence. +#> ℹ Summarising condition_occurrence: `in_observation`, `standard_concept`, +#> `source_vocabulary`, `domain_id`, and `type_concept`. +#> ℹ Adding variables of interest to drug_exposure. +#> ℹ Summarising records per person in drug_exposure. +#> ℹ Summarising drug_exposure: `in_observation`, `standard_concept`, +#> `source_vocabulary`, `domain_id`, and `type_concept`. ``` @@ -120,47 +97,47 @@ summariseClinicalRecords(cdm, c("condition_occurrence", "drug_exposure")) |> We can also explore trends in the clinical table records over time. ``` r -summariseRecordCount(cdm, c("condition_occurrence", "drug_exposure")) |> - plotRecordCount(facet = "omop_table") -#> ! The following column type were changed: -#> • variable_level: from double to character +summariseRecordCount(cdm, c("condition_occurrence", "drug_exposure"), interval = "years") |> + plotRecordCount(facet = "omop_table", colour = "cdm_name") ``` - \### -Characterise the observation period After visualising the main -characteristics of our clinical tables, we can explore the observation -period details. OmopSketch provides several functions to have an -overview of the dataset study period. + + +### Characterise the observation period + +After visualising the main characteristics of our clinical tables, we +can explore the observation period details. OmopSketch provides several +functions to have an overview the dataset study period. Using `summariseInObservation()` and `plotInObservation()`, we can gather information on the number of records per year. ``` r -summariseInObservation(cdm$observation_period, output = "records") |> - plotInObservation() -#> ! The following column type were changed: -#> • variable_level: from double to character +summariseInObservation(cdm$observation_period, output = "records", interval = "years") |> + plotInObservation(colour = "cdm_name") +#> `result_id` is not present in result. +#> `result_id` is not present in result. ``` - You -can also visualise and explore the characteristics of the observation -period per each individual in the database using + + +You can also visualise and explore the characteristics of the +observation period per each individual in the database using `summariseObservationPeriod()`. ``` r summariseObservationPeriod(cdm$observation_period) |> tableObservationPeriod(type = "flextable") -#> ! Results have not been suppressed. ``` -Or if visualisation is prefered, you can easily build a histogram to +Or if visualisation is preferred, you can easily build a histogram to explore how many participants have more than one observation period. ``` r summariseObservationPeriod(cdm$observation_period) |> - plotObservationPeriod() + plotObservationPeriod(colour = "observation_period_ordinal") ``` @@ -174,561 +151,15 @@ concepts in the dataset. acetaminophen <- c(1125315, 1127433, 1127078) summariseConceptSetCounts(cdm, conceptSet = list("acetaminophen" = acetaminophen)) |> - filter(estimate_name == "record_count") |> - plotConceptCounts() -#> ℹ Getting use of codes from acetaminophen -#> ! The following column type were changed: -#> • variable_name: from integer to character + filter(variable_name == "Number records") |> + plotConceptSetCounts(colour = "codelist_name") +#> Warning: ! `codelist` contains numeric values, they are casted to integers. +#> ℹ Searching concepts from domain drug in drug_exposure. +#> ℹ Counting concepts ``` -### Characterise the population - -Finally, OmopSketch can also help us to characterise the population at -the start and end of the observation period. - -``` r -summarisePopulationCharacteristics(cdm) |> - tablePopulationCharacteristics(type = "flextable") -#> Warning: ! 1 casted column in og_015_1727262876 (cohort_set) as do not match expected -#> column type: -#> • `cohort_definition_id` from numeric to integer -#> Warning: ! 1 column in og_015_1727262876 do not match expected column type: -#> • `cohort_definition_id` is numeric but expected integer -#> ! cohort columns will be reordered to match the expected order: -#> cohort_definition_id, subject_id, cohort_start_date, and cohort_end_date. -#> ℹ Building new trimmed cohort -#> Warning: ! 1 column in tmp_011_og_017_1727262877 do not match expected column type: -#> • `cohort_definition_id` is numeric but expected integer -#> Creating initial cohort -#> ! cohort columns will be reordered to match the expected order: -#> cohort_definition_id, subject_id, cohort_start_date, and cohort_end_date. -#> ! cohort columns will be reordered to match the expected order: -#> cohort_definition_id, subject_id, cohort_start_date, and cohort_end_date. -#> ✔ Cohort trimmed -#> ℹ adding demographics columns -#> -#> ℹ summarising data -#> -#> ✔ summariseCharacteristics finished! -#> -#> ! The following column type were changed: -#> • variable_name: from integer to character -#> ! Results have not been suppressed. -``` - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Variable nameVariable levelEstimate name - Database name -
Synthea synthetic health database
Number records-N2,694
Number subjects-N2,694
Cohort start date-Median [Q25 - Q75]1961-03-18 [1950-07-13 - 1970-08-29]
Range1908-09-22 to 1986-11-03
Cohort end date-Median [Q25 - Q75]2018-12-14 [2018-08-02 - 2019-04-06]
Range1945-07-20 to 2019-07-03
Age at start-Median [Q25 - Q75]0 [0 - 0]
Mean (SD)0.00 (0.00)
Range0 to 0
Age at end-Median [Q25 - Q75]57 [47 - 67]
Range31 to 110
SexFemaleN%1,373 (50.97)
MaleN%1,321 (49.03)
Prior observation-Median [Q25 - Q75]0 [0 - 0]
Mean (SD)0.00 (0.00)
Range0 to 0
Future observation-Median [Q25 - Q75]20,870 [17,494 - 24,701]
Mean (SD)21,601.60 (5,460.69)
Range11,396 to 40,348
-
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z2h=^8(<2|$flxWq1->qf)SeK^JQDZ9&@8}V?m|*V2U!p{Ho{z(V&|R~NXRYtFu8IX zH)%4YftRc1J+#u-AsEVtOfeE)?9X?t|1xN|o?6fx4LSeP)yv_n@O`$E!-3s7JCBq8 E9sZrUn*aa+ From 19ca138a7232fc619f3c716ae2dc12619b5ad86e Mon Sep 17 00:00:00 2001 From: cecicampanile Date: Fri, 20 Dec 2024 09:48:25 +0000 Subject: [PATCH 4/6] date interval in x ax --- R/plotInObservation.R | 4 +--- R/plotRecordCount.R | 23 +++++++++++++---------- man/figures/README-unnamed-chunk-3-1.png | Bin 102394 -> 102041 bytes man/figures/README-unnamed-chunk-5-1.png | Bin 53391 -> 63772 bytes man/figures/README-unnamed-chunk-6-1.png | Bin 37643 -> 46388 bytes 5 files changed, 14 insertions(+), 13 deletions(-) diff --git a/R/plotInObservation.R b/R/plotInObservation.R index 9fc6227e..f0ae0053 100644 --- a/R/plotInObservation.R +++ b/R/plotInObservation.R @@ -60,7 +60,6 @@ plotInObservation <- function(result, # plot if(length(unique(result$additional_level)) > 1 ){ p <- result |> - dplyr::mutate(additional_level = as.character(gsub("-01-01$","",as.Date(gsub(" to.*","",.data$additional_level))))) |> dplyr::filter(.data$estimate_name == "count") |> visOmopResults::scatterPlot( x = "time_interval", @@ -81,8 +80,7 @@ plotInObservation <- function(result, p$data <- p$data |> dplyr::arrange(time_interval) |> dplyr::mutate( - show_label = seq_along(time_interval) %% - (10 * nrow(p$data|>dplyr::distinct(.data$omop_table))) == 0 # Show every 10th label + show_label = seq_along(time_interval) %% ceiling(nrow(p$data) / 20) == 0 ) p <- p + diff --git a/R/plotRecordCount.R b/R/plotRecordCount.R index 0af399e1..dbfbabb1 100644 --- a/R/plotRecordCount.R +++ b/R/plotRecordCount.R @@ -59,27 +59,30 @@ plotRecordCount <- function(result, y = "Number records", x = "Date" ) - p$data <- p$data |> - dplyr::mutate( - time_interval = as.character( - gsub("-01-01$", "", as.Date(gsub(" to.*", "", time_interval))) - ) - ) |> dplyr::arrange(time_interval) |> + dplyr::group_by(omop_table) |> dplyr::mutate( - show_label = seq_along(time_interval) %% - (10 * nrow(p$data|>dplyr::distinct(.data$omop_table))) == 0 # Show every 10th label - ) + show_label = if (dplyr::cur_group_id() == 1) { + seq_along(time_interval) %% ceiling(n() / 20) == 0 + } else { + FALSE + } + ) |> + dplyr::ungroup() + # Modify the plot p <- p + ggplot2::scale_x_discrete( - breaks = p$data$time_interval[p$data$show_label] + breaks = p$data$time_interval[p$data$show_label], + labels = p$data$time_interval[p$data$show_label] ) + ggplot2::theme( axis.text.x = ggplot2::element_text(angle = 90, hjust = 1, size = 8), plot.margin = ggplot2::margin(t = 5, r = 5, b = 30, l = 5) ) + + }else{ p <- result |> visOmopResults::barPlot(x = "variable_name", diff --git a/man/figures/README-unnamed-chunk-3-1.png b/man/figures/README-unnamed-chunk-3-1.png index bb2cd146e7804ab7b272f3b62883e57a4bc86627..e25b42c36aae158f6691d6679b9207c644487f8c 100644 GIT binary patch literal 102041 zcmd43bySq?+b&EfhzLlFASfw`Up(r68GIU5vj)EXP 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f0ae0053..f79b9304 100644 --- a/R/plotInObservation.R +++ b/R/plotInObservation.R @@ -78,9 +78,9 @@ plotInObservation <- function(result, x = "Date" ) p$data <- p$data |> - dplyr::arrange(time_interval) |> + dplyr::arrange(.data$time_interval) |> dplyr::mutate( - show_label = seq_along(time_interval) %% ceiling(nrow(p$data) / 20) == 0 + show_label = seq_along(.data$time_interval) %% ceiling(nrow(p$data) / 20) == 0 ) p <- p + diff --git a/R/plotRecordCount.R b/R/plotRecordCount.R index dbfbabb1..37299602 100644 --- a/R/plotRecordCount.R +++ b/R/plotRecordCount.R @@ -60,11 +60,11 @@ plotRecordCount <- function(result, x = "Date" ) p$data <- p$data |> - dplyr::arrange(time_interval) |> - dplyr::group_by(omop_table) |> + dplyr::arrange(.data$time_interval) |> + dplyr::group_by(.data$omop_table) |> dplyr::mutate( show_label = if (dplyr::cur_group_id() == 1) { - seq_along(time_interval) %% ceiling(n() / 20) == 0 + seq_along(.data$time_interval) %% ceiling(dplyr::n() / 20) == 0 } else { FALSE } From c8ceb00e790d64f64710c85316d2ceea7071dc5c Mon Sep 17 00:00:00 2001 From: cecicampanile Date: Fri, 20 Dec 2024 16:27:57 +0000 Subject: [PATCH 6/6] Update mockOmopSketch.R --- R/mockOmopSketch.R | 28 +++++++++++++++++++++------- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/R/mockOmopSketch.R b/R/mockOmopSketch.R index 6c776f20..3cf81984 100644 --- a/R/mockOmopSketch.R +++ b/R/mockOmopSketch.R @@ -59,19 +59,31 @@ mockOmopSketch <- function(con = NULL, } checkColumns <- function(cdm_local){ - info <- omopgenerics::omopTableFields() + info <- omopgenerics::omopTableFields() |> + dplyr::filter(.data$type == "cdm_table") |> + dplyr::mutate(cdm_datatype = dplyr::case_when( + .data$cdm_datatype == "integer" ~ "NA_integer_", + grepl("varchar", .data$cdm_datatype) ~ "NA_character_", + .default = "NA" + )) for (table in names(cdm_local)){ cols <- info |> dplyr::filter(.data$cdm_table_name == table)|> - dplyr::distinct(.data$cdm_field_name)|> - dplyr::pull() + dplyr::select(cdm_field_name,cdm_datatype) - missing_cols <- setdiff(cols, colnames(cdm_local[[table]])) + missing_cols <- cols|> + dplyr::filter(!(cdm_field_name %in% colnames(cdm_local[[table]]))) + if (nrow(missing_cols) > 0) { - if (length(missing_cols) > 0) { - - missing_tbl <- tibble::tibble(!!!rlang::set_names(rep(list(NA_character_), length(missing_cols)), missing_cols)) + missing_tbl <- tibble::tibble( + !!!rlang::set_names( + lapply(missing_cols$cdm_datatype, function(datatype) { + eval(parse(text = datatype)) + }), + missing_cols$cdm_field_name + ) + ) cdm_local[[table]] <- dplyr::bind_cols(cdm_local[[table]], missing_tbl) @@ -79,3 +91,5 @@ checkColumns <- function(cdm_local){ } return(cdm_local) } + +