Replace scda with random.cdisc.data

.github/workflows/deploy.yml

@@ -60,7 +60,7 @@ jobs:
         run: |
           echo "BRANCH_NAME=dev" >> $GITHUB_ENV
           if [ "${{ matrix.channel }}" == "stable" ]; then
-            echo "BRANCH_NAME=main" >> $GITHUB_ENV
+            echo "BRANCH_NAME=154-use-rcd@main" >> $GITHUB_ENV
           fi
 
       - name: Setup job token 🔑

RNA-seq/app.R

@@ -7,9 +7,11 @@ nest_logo <- "https://raw.githubusercontent.com/insightsengineering/hex-stickers
 ## Data reproducible code ----
 data <- teal_data()
 data <- within(data, {
-  library(scda.2022)
   library(nestcolor)
-  ADTTE <- scda::synthetic_cdisc_dataset("rcd_2022_06_27", "adtte") %>%
+  library(random.cdisc.data)
+
+  ADSL <- radsl(seed = 1)
+  ADTTE <- radtte(ADSL, seed = 1) %>%
     dplyr::mutate(is_event = CNSR == 0)
   MAE <- hermes::multi_assay_experiment
 })
@@ -21,9 +23,9 @@ app <- init(
   modules = modules(
     tm_front_page(
       label = "App Info",
-      header_text = c("Info about input data source" = "This app uses MultiplAssayExperiment data object randomly generated by `scda`, `scda.2022` & `hermes` R packages"), # nolint: line_length.
+      header_text = c("Info about input data source" = "This app uses MultiplAssayExperiment data object randomly generated by `random.cdisc.data` & `hermes` R packages"), # nolint: line_length.
       tables = list(`NEST packages used in this demo app` = data.frame(
-        Packages = c("teal.modules.hermes", "teal.modules.general", "scda", "scda.2022", "hermes")
+        Packages = c("teal.modules.hermes", "teal.modules.general", "random.cdisc.data", "hermes")
       ))
     ),
     tm_g_quality(

RNA-seq/renv.lock

@@ -2053,6 +2053,19 @@
       ],
       "Hash": "c145edf05cc128e6ffcfa5d872c46329"
     },
+    "lubridate": {
+      "Package": "lubridate",
+      "Version": "1.9.3",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "generics",
+        "methods",
+        "timechange"
+      ],
+      "Hash": "680ad542fbcf801442c83a6ac5a2126c"
+    },
     "magrittr": {
       "Package": "magrittr",
       "Version": "2.0.3",
@@ -2513,6 +2526,26 @@
       ],
       "Hash": "082e1a198e3329d571f4448ef0ede4bc"
     },
+    "random.cdisc.data": {
+      "Package": "random.cdisc.data",
+      "Version": "0.3.15",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "checkmate",
+        "dplyr",
+        "lifecycle",
+        "lubridate",
+        "magrittr",
+        "rlang",
+        "stringr",
+        "tibble",
+        "tidyr",
+        "yaml"
+      ],
+      "Hash": "c92edb70a24230a0b30f961c431c9fdf"
+    },
     "rappdirs": {
       "Package": "rappdirs",
       "Version": "0.3.3",
@@ -2660,38 +2693,6 @@
       ],
       "Hash": "c19df082ba346b0ffa6f833e92de34d1"
     },
-    "scda": {
-      "Package": "scda",
-      "Version": "0.1.6",
-      "Source": "GitHub",
-      "RemoteType": "github",
-      "RemoteHost": "api.github.com",
-      "RemoteUsername": "insightsengineering",
-      "RemoteRepo": "scda",
-      "RemoteRef": "v0.1.6",
-      "RemoteSha": "665d4540a8f3c9e8d9271c33b5dd70296b49636c",
-      "Remotes": "insightsengineering/scda.2022@*release",
-      "Requirements": [
-        "R",
-        "utils"
-      ],
-      "Hash": "050ae235c739506fb50990d8e03518bb"
-    },
-    "scda.2022": {
-      "Package": "scda.2022",
-      "Version": "0.1.5",
-      "Source": "GitHub",
-      "RemoteType": "github",
-      "RemoteHost": "api.github.com",
-      "RemoteUsername": "insightsengineering",
-      "RemoteRepo": "scda.2022",
-      "RemoteRef": "v0.1.5",
-      "RemoteSha": "5b08051312fababe15476f21323d17bcbb0543cf",
-      "Requirements": [
-        "R"
-      ],
-      "Hash": "0e04543b6e8a5f658699c9cdc50534c8"
-    },
     "shape": {
       "Package": "shape",
       "Version": "1.4.6.1",
@@ -3342,6 +3343,17 @@
       ],
       "Hash": "829f27b9c4919c16b593794a6344d6c0"
     },
+    "timechange": {
+      "Package": "timechange",
+      "Version": "0.3.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "cpp11"
+      ],
+      "Hash": "c5f3c201b931cd6474d17d8700ccb1c8"
+    },
     "tinytex": {
       "Package": "tinytex",
       "Version": "0.50",

early-dev/app.R

@@ -8,14 +8,13 @@ nest_logo <- "https://raw.githubusercontent.com/insightsengineering/hex-stickers
 ## Data reproducible code ----
 data <- teal_data()
 data <- within(data, {
-  library(scda)
-  library(scda.2022)
+  library(random.cdisc.data)
   library(dplyr)
   library(nestcolor)
   # optional libraries
   library(sparkline)
 
-  ADSL <- synthetic_cdisc_dataset("latest", "adsl")
+  ADSL <- radsl(seed = 1)
 
   # derive ADSL treatment duration
   adsl_labels <- teal.data::col_labels(ADSL, fill = FALSE)
@@ -34,7 +33,7 @@ data <- within(data, {
     droplevels()
   teal.data::col_labels(ADSL)[c(names(adsl_labels))] <- adsl_labels
 
-  ADAE <- synthetic_cdisc_dataset("latest", "adae")
+  ADAE <- radae(ADSL, seed = 1)
 
   # derive common flags for AEs
   adae_labels <- teal.data::col_labels(ADAE, fill = FALSE)
@@ -67,7 +66,7 @@ data <- within(data, {
       AELLT = "Lowest Level Term"
     )
 
-  ADCM <- synthetic_cdisc_dataset("latest", "adcm")
+  ADCM <- radcm(ADSL, seed = 1)
 
   # process ADCM
   ADCM <- ADCM %>%
@@ -80,7 +79,7 @@ data <- within(data, {
       AENDT = "Analysis End Date"
     )
 
-  ADEX <- synthetic_cdisc_dataset("latest", "adex")
+  ADEX <- radex(ADSL, seed = 1)
 
   # process ADEX
   ADEX <- ADEX %>%
@@ -93,7 +92,7 @@ data <- within(data, {
       AENDT = "Analysis End Date"
     )
 
-  ADTR <- synthetic_cdisc_dataset("latest", "adtr")
+  ADTR <- radtr(ADSL, seed = 1)
 
   # process ADTR
   adtr_labels <- teal.data::col_labels(ADTR, fill = FALSE)
@@ -113,19 +112,19 @@ data <- within(data, {
 
 
   # process ADRS
-  ADRSSWIM <- synthetic_cdisc_dataset("latest", "adrs") %>%
+  ADRSSWIM <- radrs(ADSL, seed = 1) %>%
     filter(PARAMCD == "OVRINV") %>%
     arrange(USUBJID)
 
-  ADRS <- synthetic_cdisc_dataset("latest", "adrs")
+  ADRS <- radrs(ADSL, seed = 1)
   adrs_labels <- teal.data::col_labels(ADRS, fill = FALSE)
   ADRS <- ADRS %>%
     filter(PARAMCD %in% c("BESRSPI", "INVET")) %>%
     mutate(ADT = as.Date(ADTM)) %>%
     droplevels()
   teal.data::col_labels(ADRS)["ADT"] <- "Analysis Date"
 
-  ADLB <- synthetic_cdisc_dataset("latest", "adlb")
+  ADLB <- radlb(ADSL, seed = 1)
 
   # process ADLB
   ADLB <- ADLB %>%
@@ -231,11 +230,11 @@ app <- init(
       label = "App Info",
       header_text = c(
         "Info about input data source" =
-          "This app uses CDISC ADaM datasets randomly generated by `scda` & `scda.2022` R packages"
+          "This app uses CDISC ADaM datasets randomly generated by `random.cdisc.data` R packages"
       ),
       tables = list(
         `NEST packages used in this demo app` =
-          data.frame(Packages = c("teal.modules.general", "teal.modules.clinical", "teal.osprey", "scda", "scda.2022"))
+          data.frame(Packages = c("teal.modules.general", "teal.modules.clinical", "teal.osprey", "random.cdisc.data"))
       )
     ),
     tm_data_table("Data Table"),

early-dev/renv.lock

@@ -1565,6 +1565,19 @@
       ],
       "Hash": "c145edf05cc128e6ffcfa5d872c46329"
     },
+    "lubridate": {
+      "Package": "lubridate",
+      "Version": "1.9.3",
+      "Source": "Repository",
+      "Repository": "RSPM",
+      "Requirements": [
+        "R",
+        "generics",
+        "methods",
+        "timechange"
+      ],
+      "Hash": "680ad542fbcf801442c83a6ac5a2126c"
+    },
     "magrittr": {
       "Package": "magrittr",
       "Version": "2.0.3",
@@ -2093,6 +2106,26 @@
       ],
       "Hash": "082e1a198e3329d571f4448ef0ede4bc"
     },
+    "random.cdisc.data": {
+      "Package": "random.cdisc.data",
+      "Version": "0.3.15",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "checkmate",
+        "dplyr",
+        "lifecycle",
+        "lubridate",
+        "magrittr",
+        "rlang",
+        "stringr",
+        "tibble",
+        "tidyr",
+        "yaml"
+      ],
+      "Hash": "c92edb70a24230a0b30f961c431c9fdf"
+    },
     "rappdirs": {
       "Package": "rappdirs",
       "Version": "0.3.3",
@@ -2312,38 +2345,6 @@
       ],
       "Hash": "c19df082ba346b0ffa6f833e92de34d1"
     },
-    "scda": {
-      "Package": "scda",
-      "Version": "0.1.6",
-      "Source": "GitHub",
-      "RemoteType": "github",
-      "RemoteHost": "api.github.com",
-      "RemoteUsername": "insightsengineering",
-      "RemoteRepo": "scda",
-      "RemoteRef": "v0.1.6",
-      "RemoteSha": "665d4540a8f3c9e8d9271c33b5dd70296b49636c",
-      "Remotes": "insightsengineering/scda.2022@*release",
-      "Requirements": [
-        "R",
-        "utils"
-      ],
-      "Hash": "050ae235c739506fb50990d8e03518bb"
-    },
-    "scda.2022": {
-      "Package": "scda.2022",
-      "Version": "0.1.5",
-      "Source": "GitHub",
-      "RemoteType": "github",
-      "RemoteHost": "api.github.com",
-      "RemoteUsername": "insightsengineering",
-      "RemoteRepo": "scda.2022",
-      "RemoteRef": "v0.1.5",
-      "RemoteSha": "5b08051312fababe15476f21323d17bcbb0543cf",
-      "Requirements": [
-        "R"
-      ],
-      "Hash": "0e04543b6e8a5f658699c9cdc50534c8"
-    },
     "shape": {
       "Package": "shape",
       "Version": "1.4.6.1",
@@ -3051,6 +3052,17 @@
       ],
       "Hash": "829f27b9c4919c16b593794a6344d6c0"
     },
+    "timechange": {
+      "Package": "timechange",
+      "Version": "0.3.0",
+      "Source": "Repository",
+      "Repository": "RSPM",
+      "Requirements": [
+        "R",
+        "cpp11"
+      ],
+      "Hash": "c5f3c201b931cd6474d17d8700ccb1c8"
+    },
     "tinytex": {
       "Package": "tinytex",
       "Version": "0.50",

efficacy/app.R

@@ -8,13 +8,12 @@ nest_logo <- "https://raw.githubusercontent.com/insightsengineering/hex-stickers
 data <- teal_data()
 data <- within(data, {
   library(dplyr)
-  library(scda)
-  library(scda.2022)
+  library(random.cdisc.data)
   library(nestcolor)
   # optional libraries
   library(sparkline)
 
-  ADSL <- synthetic_cdisc_dataset("latest", "adsl")
+  ADSL <- radsl(seed = 1)
   adsl_labels <- teal.data::col_labels(ADSL, fill = FALSE)
 
   char_vars_asl <- names(Filter(isTRUE, sapply(ADSL, is.character)))
@@ -34,14 +33,14 @@ data <- within(data, {
 
   teal.data::col_labels(ADSL) <- adsl_labels
 
-  ADTTE <- synthetic_cdisc_dataset("latest", "adtte")
+  ADTTE <- radtte(ADSL, seed = 1)
 
-  ADRS <- synthetic_cdisc_dataset("latest", "adrs")
+  ADRS <- radrs(ADSL, seed = 1)
   adrs_labels <- teal.data::col_labels(ADRS, fill = FALSE)
   ADRS <- filter(ADRS, PARAMCD == "BESRSPI" | AVISIT == "FOLLOW UP")
   teal.data::col_labels(ADRS) <- adrs_labels
 
-  ADQS <- synthetic_cdisc_dataset("latest", "adqs")
+  ADQS <- radqs(ADSL, seed = 1)
   adqs_labels <- teal.data::col_labels(ADQS, fill = FALSE)
   ADQS <- ADQS %>%
     filter(ABLFL != "Y" & ABLFL2 != "Y") %>%
@@ -147,10 +146,10 @@ app <- init(
       label = "App Info",
       header_text = c(
         "Info about input data source" =
-          "This app uses CDISC ADaM datasets randomly generated by `scda` & `scda.2022` R packages"
+          "This app uses CDISC ADaM datasets randomly generated by `random.cdisc.data` R packages"
       ),
       tables = list(`NEST packages used in this demo app` = data.frame(
-        Packages = c("teal.modules.general", "teal.modules.clinical", "scda", "scda.2022")
+        Packages = c("teal.modules.general", "teal.modules.clinical", "random.cdisc.data")
       ))
     ),
     tm_data_table("Data Table"),