CMT01

Concomitant Medications (GNEDrug Legacy Coding)

---

Standard Table

Preview

# Define the split function
split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one treatment",
      nonunique = "Total number of treatments"
    )
  ) %>%
  split_rows_by("CMCLAS",
    split_fun = split_fun,
    child_labels = "visible",
    nested = FALSE,
    indent_mod = 1L,
    label_pos = "topleft",
    split_label = obj_label(adcm_con$CMCLAS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one treatment",
      nonunique = "Total number of treatments"
    )
  ) %>%
  count_occurrences(vars = "CMDECOD", .indent_mods = -1L) %>%
  append_varlabels(adcm_con, "CMDECOD", indent = 1L)

result <- build_table(
  lyt = lyt,
  df = adcm_con,
  alt_counts_df = adsl
) %>%
  prune_table() %>%
  sort_at_path(
    path = c("CMCLAS", "*", "CMDECOD"),
    scorefun = score_occurrences
  )

result

Medication Class                                            A: Drug X    B: Placebo    C: Combination
  Standardized Medication Name                               (N=134)       (N=134)        (N=132)    
—————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one treatment       117 (87.3%)   116 (86.6%)    116 (87.9%)  
Total number of treatments                                     415           414            460      
  medcl A                                                                                            
    Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  
    Total number of treatments                                 134           137            143      
    medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  
    medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  
  medcl B                                                                                            
    Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  
    Total number of treatments                                 141           137            162      
    medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  
    medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  
  medcl C                                                                                            
    Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  
    Total number of treatments                                 140           140            155      
    medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  
    medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  

Try this using WebR

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Table Showing Medications Prior to the Start of the Study

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one treatment",
      nonunique = "Total number of treatments"
    )
  ) %>%
  split_rows_by("CMCLAS",
    split_fun = split_fun,
    child_labels = "visible",
    nested = FALSE,
    label_pos = "topleft",
    split_label = obj_label(adcm_con$CMCLAS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one treatment",
      nonunique = "Total number of treatments"
    )
  ) %>%
  count_occurrences(vars = "CMDECOD", .indent_mods = -1L) %>%
  append_varlabels(adcm_prior, "CMDECOD", indent = 1L)

result <- build_table(
  lyt = lyt,
  df = adcm_prior,
  alt_counts_df = adsl
) %>%
  prune_table() %>%
  sort_at_path(
    path = c("CMCLAS", "*", "CMDECOD"),
    scorefun = score_occurrences
  )

result

Medication Class                                         A: Drug X    B: Placebo   C: Combination
  Standardized Medication Name                            (N=134)      (N=134)        (N=132)    
—————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one treatment     89 (66.4%)   95 (70.9%)    106 (80.3%)  
Total number of treatments                                  194          208            243      
medcl A                                                                                          
  Total number of patients with at least one treatment   54 (40.3%)   49 (36.6%)     69 (52.3%)  
  Total number of treatments                                 71           70             99      
  medname A_1/3                                          54 (40.3%)   49 (36.6%)     69 (52.3%)  
medcl B                                                                                          
  Total number of patients with at least one treatment   76 (56.7%)   80 (59.7%)     81 (61.4%)  
  Total number of treatments                                123          138            144      
  medname B_2/4                                          52 (38.8%)   55 (41.0%)     56 (42.4%)  
  medname B_3/4                                          47 (35.1%)   47 (35.1%)     52 (39.4%)  

Try this using WebR

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Table with Subtotal Per Medication Class Suppressed

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one treatment",
      nonunique = "Total number of treatments"
    )
  ) %>%
  split_rows_by("CMCLAS",
    split_fun = split_fun, child_labels = "visible",
    nested = FALSE,
    label_pos = "topleft",
    split_label = obj_label(adcm_con$CMCLAS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .labels = c(unique = "Total number of patients with at least one treatment"),
    .stats = "unique"
  ) %>%
  count_occurrences(vars = "CMDECOD", .indent_mods = -1L) %>%
  append_varlabels(adcm_con, "CMDECOD", indent = 1L)

result <- build_table(
  lyt = lyt,
  df = adcm_con,
  alt_counts_df = adsl
) %>%
  prune_table() %>%
  sort_at_path(
    path = c("CMCLAS", "*", "CMDECOD"),
    scorefun = score_occurrences
  )

result

Medication Class                                          A: Drug X    B: Placebo    C: Combination
  Standardized Medication Name                             (N=134)       (N=134)        (N=132)    
———————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  
Total number of treatments                                   415           414            460      
medcl A                                                                                            
  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  
  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  
  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  
medcl B                                                                                            
  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  
  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  
  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  
medcl C                                                                                            
  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  
  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  
  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  

Try this using WebR

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Table Sorted by Total Column Showing Additional “All Patients” Column

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  add_overall_col("All Patients") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      "Total number of patients with at least one treatment (%)",
      "Total number of treatments"
    )
  ) %>%
  split_rows_by(
    "CMCLAS",
    split_fun = split_fun,
    child_labels = "visible",
    nested = FALSE,
    label_pos = "topleft",
    split_label = obj_label(adcm_con$CMCLAS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      "Total number of patients with at least one treatment (%)",
      "Total number of treatments"
    )
  ) %>%
  count_occurrences(vars = "CMDECOD", .indent_mods = -1L) %>%
  append_varlabels(adcm_con, "CMDECOD", indent = 1L)

result <- build_table(
  lyt = lyt,
  df = adcm_con,
  alt_counts_df = adsl
) %>%
  prune_table() %>%
  sort_at_path(
    path = c("CMCLAS", "*", "CMDECOD"),
    scorefun = score_occurrences,
    decreasing = TRUE
  ) %>%
  sort_at_path(
    path = c("CMCLAS"),
    scorefun = cont_n_allcols,
    decreasing = TRUE
  )

result

Medication Class                                              A: Drug X    B: Placebo    C: Combination   All Patients
  Standardized Medication Name                                 (N=134)       (N=134)        (N=132)         (N=400)   
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one treatment (%)     117 (87.3%)   116 (86.6%)    116 (87.9%)     349 (87.2%) 
Total number of treatments                                       415           414            460             1289    
medcl C                                                                                                               
  Total number of patients with at least one treatment (%)   82 (61.2%)    84 (62.7%)      89 (67.4%)     255 (63.7%) 
  Total number of treatments                                     140           140            155             435     
  medname C_2/2                                              52 (38.8%)    58 (43.3%)      60 (45.5%)     170 (42.5%) 
  medname C_1/2                                              51 (38.1%)    50 (37.3%)      56 (42.4%)     157 (39.2%) 
medcl B                                                                                                               
  Total number of patients with at least one treatment (%)   83 (61.9%)    74 (55.2%)      88 (66.7%)     245 (61.3%) 
  Total number of treatments                                     141           137            162             440     
  medname B_1/4                                              52 (38.8%)    57 (42.5%)      59 (44.7%)     168 (42.0%) 
  medname B_4/4                                              50 (37.3%)    45 (33.6%)      55 (41.7%)     150 (37.5%) 
medcl A                                                                                                               
  Total number of patients with at least one treatment (%)   75 (56.0%)    79 (59.0%)      81 (61.4%)     235 (58.8%) 
  Total number of treatments                                     134           137            143             414     
  medname A_2/3                                              53 (39.6%)    50 (37.3%)      56 (42.4%)     159 (39.8%) 
  medname A_3/3                                              45 (33.6%)    54 (40.3%)      48 (36.4%)     147 (36.8%) 

Try this using WebR

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Data Setup

library(dplyr)
library(tern)

# The following tables require ADCM data structure that follows the legacy concomitant medication coding.
# WHO coding is not used.
adsl <- random.cdisc.data::cadsl
adcm <- random.cdisc.data::cadcm

# Below step is to set up example data based on legacy standard.
adcm <- adcm %>%
  select(-starts_with("ATC")) %>%
  unique()

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adcm <- df_explicit_na(adcm)

adcm_con <- adcm %>%
  filter(ATIREL == "CONCOMITANT")

adcm_prior <- adcm %>%
  filter(ATIREL == "PRIOR")

teal App

Preview

Here, adcm is processed to keep only one path per treatment.

library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  library(dplyr)

  ADSL <- random.cdisc.data::cadsl
  ADCM <- random.cdisc.data::cadcm

  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
  ADSL <- df_explicit_na(ADSL)
  ADCM <- df_explicit_na(ADCM)

  ADCM <- ADCM %>%
    group_by(STUDYID, USUBJID, CMDECOD) %>%
    slice(1) %>%
    ungroup()
})
datanames <- c("ADSL", "ADCM")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADCM <- data[["ADCM"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_events(
      label = "Concomitant Medication Table",
      dataname = "ADCM",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      llt = choices_selected(
        choices = variable_choices(ADCM, "CMDECOD"),
        selected = "CMDECOD"
      ),
      hlt = choices_selected(
        choices = variable_choices(ADCM, "CMCLAS"),
        selected = c("CMCLAS")
      ),
      add_total = TRUE,
      event_type = "treatment"
    )
  )
)

shinyApp(app$ui, app$server)

Try this using shinylive

Experimental use!

shinylive allow you to modify to run shiny application entirely in the web browser. Modify the code below and click re-run the app to see the results. The performance is slighly worse and some of the features (e.g. downloading) might not work at all.

#| standalone: true
#| viewerHeight: 800
#| editorHeight: 200
#| components: [viewer, editor]
#| layout: vertical

# -- WEBR HELPERS --
options(webr_pkg_repos = c("r-universe" = "https://pharmaverse.r-universe.dev", getOption("webr_pkg_repos")))

# -- APP CODE --
library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  library(dplyr)

  ADSL <- random.cdisc.data::cadsl
  ADCM <- random.cdisc.data::cadcm

  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
  ADSL <- df_explicit_na(ADSL)
  ADCM <- df_explicit_na(ADCM)

  ADCM <- ADCM %>%
    group_by(STUDYID, USUBJID, CMDECOD) %>%
    slice(1) %>%
    ungroup()
})
datanames <- c("ADSL", "ADCM")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADCM <- data[["ADCM"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_events(
      label = "Concomitant Medication Table",
      dataname = "ADCM",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      llt = choices_selected(
        choices = variable_choices(ADCM, "CMDECOD"),
        selected = "CMDECOD"
      ),
      hlt = choices_selected(
        choices = variable_choices(ADCM, "CMCLAS"),
        selected = c("CMCLAS")
      ),
      add_total = TRUE,
      event_type = "treatment"
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 18:05:00 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14)
 os       Ubuntu 22.04.5 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2024-11-02
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version     date (UTC) lib source
 backports               1.5.0       2024-05-23 [1] CRAN (R 4.4.1)
 brio                    1.1.5       2024-04-24 [1] CRAN (R 4.4.1)
 broom                   1.0.7       2024-09-26 [1] CRAN (R 4.4.1)
 bslib                   0.8.0       2024-07-29 [1] CRAN (R 4.4.1)
 cachem                  1.1.0       2024-05-16 [1] CRAN (R 4.4.1)
 callr                   3.7.6       2024-03-25 [1] CRAN (R 4.4.1)
 checkmate               2.3.2       2024-07-29 [1] CRAN (R 4.4.1)
 chromote                0.3.1       2024-08-30 [1] CRAN (R 4.4.1)
 cli                     3.6.3       2024-06-21 [1] CRAN (R 4.4.1)
 coda                    0.19-4.1    2024-01-31 [1] CRAN (R 4.4.1)
 codetools               0.2-20      2024-03-31 [2] CRAN (R 4.4.1)
 colorspace              2.1-1       2024-07-26 [1] CRAN (R 4.4.1)
 digest                  0.6.37      2024-08-19 [1] CRAN (R 4.4.1)
 dplyr                 * 1.1.4       2023-11-17 [1] CRAN (R 4.4.1)
 emmeans                 1.10.5      2024-10-14 [1] CRAN (R 4.4.1)
 estimability            1.5.1       2024-05-12 [1] CRAN (R 4.4.1)
 evaluate                1.0.1       2024-10-10 [1] CRAN (R 4.4.1)
 fansi                   1.0.6       2023-12-08 [1] CRAN (R 4.4.1)
 fastmap                 1.2.0       2024-05-15 [1] CRAN (R 4.4.1)
 fontawesome             0.5.2       2023-08-19 [1] CRAN (R 4.4.1)
 forcats                 1.0.0       2023-01-29 [1] CRAN (R 4.4.1)
 formatR                 1.14        2023-01-17 [1] CRAN (R 4.4.1)
 formatters            * 0.5.9.9003  2024-11-02 [1] git2r (https://github.com/insightsengineering/formatters.git@84e8c3e)
 geepack                 1.3.12      2024-09-23 [1] CRAN (R 4.4.1)
 generics                0.1.3       2022-07-05 [1] CRAN (R 4.4.1)
 ggplot2                 3.5.1       2024-04-23 [1] CRAN (R 4.4.1)
 glue                    1.8.0       2024-09-30 [1] CRAN (R 4.4.1)
 gtable                  0.3.5       2024-04-22 [1] CRAN (R 4.4.1)
 htmltools               0.5.8.1     2024-04-04 [1] CRAN (R 4.4.1)
 htmlwidgets             1.6.4       2023-12-06 [1] CRAN (R 4.4.1)
 httpuv                  1.6.15      2024-03-26 [1] CRAN (R 4.4.1)
 jquerylib               0.1.4       2021-04-26 [1] CRAN (R 4.4.1)
 jsonlite                1.8.9       2024-09-20 [1] CRAN (R 4.4.1)
 knitr                   1.48        2024-07-07 [1] CRAN (R 4.4.1)
 later                   1.3.2       2023-12-06 [1] CRAN (R 4.4.1)
 lattice                 0.22-6      2024-03-20 [2] CRAN (R 4.4.1)
 lifecycle               1.0.4       2023-11-07 [1] CRAN (R 4.4.1)
 logger                  0.3.0       2024-03-05 [1] CRAN (R 4.4.1)
 magrittr              * 2.0.3       2022-03-30 [1] CRAN (R 4.4.1)
 MASS                    7.3-61      2024-06-13 [2] CRAN (R 4.4.1)
 Matrix                  1.7-0       2024-04-26 [1] CRAN (R 4.4.1)
 memoise                 2.0.1       2021-11-26 [1] CRAN (R 4.4.1)
 mime                    0.12        2021-09-28 [1] CRAN (R 4.4.1)
 multcomp                1.4-26      2024-07-18 [1] CRAN (R 4.4.1)
 munsell                 0.5.1       2024-04-01 [1] CRAN (R 4.4.1)
 mvtnorm                 1.3-1       2024-09-03 [1] CRAN (R 4.4.1)
 nestcolor               0.1.2.9017  2024-11-02 [1] git2r (https://github.com/insightsengineering/nestcolor.git@3d2ce62)
 nlme                    3.1-166     2024-08-14 [2] CRAN (R 4.4.1)
 pillar                  1.9.0       2023-03-22 [1] CRAN (R 4.4.1)
 pkgconfig               2.0.3       2019-09-22 [1] CRAN (R 4.4.1)
 processx                3.8.4       2024-03-16 [1] CRAN (R 4.4.1)
 promises                1.3.0       2024-04-05 [1] CRAN (R 4.4.1)
 ps                      1.8.0       2024-09-12 [1] CRAN (R 4.4.1)
 purrr                   1.0.2       2023-08-10 [1] CRAN (R 4.4.1)
 R6                      2.5.1       2021-08-19 [1] CRAN (R 4.4.1)
 random.cdisc.data       0.3.16.9001 2024-11-02 [1] git2r (https://github.com/insightsengineering/random.cdisc.data.git@a65bfbe)
 rbibutils               2.3         2024-10-04 [1] CRAN (R 4.4.1)
 Rcpp                    1.0.13      2024-07-17 [1] CRAN (R 4.4.1)
 Rdpack                  2.6.1       2024-08-06 [1] CRAN (R 4.4.1)
 rlang                   1.1.4       2024-06-04 [1] CRAN (R 4.4.1)
 rmarkdown               2.28        2024-08-17 [1] CRAN (R 4.4.1)
 rtables               * 0.6.10.9002 2024-11-02 [1] git2r (https://github.com/insightsengineering/rtables.git@d2cc7a0)
 sandwich                3.1-1       2024-09-15 [1] CRAN (R 4.4.1)
 sass                    0.4.9       2024-03-15 [1] CRAN (R 4.4.1)
 scales                  1.3.0       2023-11-28 [1] CRAN (R 4.4.1)
 sessioninfo             1.2.2       2021-12-06 [1] CRAN (R 4.4.1)
 shiny                 * 1.9.1       2024-08-01 [1] CRAN (R 4.4.1)
 shinycssloaders         1.1.0       2024-07-30 [1] CRAN (R 4.4.1)
 shinyjs                 2.1.0       2021-12-23 [1] CRAN (R 4.4.1)
 shinyvalidate           0.1.3       2023-10-04 [1] CRAN (R 4.4.1)
 shinyWidgets            0.8.7       2024-09-23 [1] CRAN (R 4.4.1)
 stringi                 1.8.4       2024-05-06 [1] CRAN (R 4.4.1)
 stringr                 1.5.1       2023-11-14 [1] CRAN (R 4.4.1)
 survival                3.7-0       2024-06-05 [2] CRAN (R 4.4.1)
 teal                  * 0.15.2.9079 2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.git@53c423d)
 teal.code             * 0.5.0.9012  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.code.git@86aa265)
 teal.data             * 0.6.0.9015  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.data.git@eca9695)
 teal.logger             0.3.0.9000  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.logger.git@cd9cc7e)
 teal.modules.clinical * 0.9.1.9030  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.modules.clinical.git@5cf76ac)
 teal.reporter           0.3.1.9016  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.reporter.git@4fefa7e)
 teal.slice            * 0.5.1.9015  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.slice.git@25f4101)
 teal.transform        * 0.5.0.9015  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.transform.git@0b855e3)
 teal.widgets            0.4.2.9022  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.widgets.git@8a39dd4)
 tern                  * 0.9.6.9013  2024-11-02 [1] git2r (https://github.com/insightsengineering/tern.git@435fd51)
 tern.gee                0.1.5.9004  2024-11-02 [1] git2r (https://github.com/insightsengineering/tern.gee.git@458172d)
 testthat                3.2.1.1     2024-04-14 [1] CRAN (R 4.4.1)
 TH.data                 1.1-2       2023-04-17 [1] CRAN (R 4.4.1)
 tibble                  3.2.1       2023-03-20 [1] CRAN (R 4.4.1)
 tidyr                   1.3.1       2024-01-24 [1] CRAN (R 4.4.1)
 tidyselect              1.2.1       2024-03-11 [1] CRAN (R 4.4.1)
 utf8                    1.2.4       2023-10-22 [1] CRAN (R 4.4.1)
 vctrs                   0.6.5       2023-12-01 [1] CRAN (R 4.4.1)
 webshot                 0.5.5       2023-06-26 [1] CRAN (R 4.4.1)
 webshot2                0.1.1       2023-08-11 [1] CRAN (R 4.4.1)
 websocket               1.4.2       2024-07-22 [1] CRAN (R 4.4.1)
 withr                   3.0.1       2024-07-31 [1] CRAN (R 4.4.1)
 xfun                    0.48        2024-10-03 [1] CRAN (R 4.4.1)
 xtable                  1.8-4       2019-04-21 [1] CRAN (R 4.4.1)
 yaml                    2.3.10      2024-07-26 [1] CRAN (R 4.4.1)
 zoo                     1.8-12      2023-04-13 [1] CRAN (R 4.4.1)

 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

──────────────────────────────────────────────────────────────────────────────

.lock file

Download the .lock file and use renv::restore() on it to recreate environment used to generate this website.

Download