updated some plots functions for the new versions of the functions

DESCRIPTION

@@ -77,5 +77,4 @@ Depends:
 URL: https://OHDSI.github.io/OmopSketch/
 BugReports: https://github.com/OHDSI/OmopSketch/issues
 VignetteBuilder: knitr
-Remotes: 
 

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") |>

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
 }
+

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.