MNG01

Mean Plot

Output

Preview
Try this using WebR

Code

plot <- g_lineplot(
  df = adlb_f,
  alt_counts_df = adsl_f,
  subtitle = "Laboratory Test:"
)
plot

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.

Preview
Try this using WebR

Code

plot <- g_lineplot(
  df = advs_f,
  alt_counts_df = adsl_f,
  variables = control_lineplot_vars(y = "CHG"),
  title = "Plot of Mean and 95% Confidence Intervals of Change from Baseline by Visit",
  subtitle = "Assessment:"
)
plot

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.

Preview
Try this using WebR

Code

plot <- g_lineplot(
  df = adlb_f,
  alt_counts_df = adsl_f,
  interval = "mean_sdi",
  whiskers = c("mean_sdi_lwr", "mean_sdi_upr"),
  title = "Plot of Mean (+/-SD)",
  subtitle = "Laboratory Test:"
)
plot

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.

Preview
Try this using WebR

Code

plot <- g_lineplot(
  df = adlb_f,
  alt_counts_df = adsl_f,
  control = control_analyze_vars(conf_level = 0.80),
  title = "Plot of Mean and 80% Confidence Limits by Visit",
  subtitle = "Laboratory Test:"
)
plot

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.

Preview
Try this using WebR

Code

plot <- g_lineplot(
  df = adlb_f,
  alt_counts_df = adsl_f,
  table = "n",
  subtitle = "Laboratory Test:"
)
plot

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.

Preview
Try this using WebR

Code

plot <- g_lineplot(
  df = adlb_f,
  alt_counts_df = adsl_f,
  table = c("n", "mean", "mean_ci"),
  subtitle = "Laboratory Test:"
)
plot

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.

This option is not supported.

Preview
Try this using WebR

Code

plot <- g_lineplot(
  df = adlb_f,
  alt_counts_df = adsl_f,
  whiskers = "mean_ci_upr",
  subtitle = "Laboratory Test:"
)
plot

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.

Code

library(dplyr)
library(tern)
library(nestcolor)

adsl <- random.cdisc.data::cadsl
adlb <- random.cdisc.data::cadlb
advs <- random.cdisc.data::cadvs

adsl_f <- adsl %>%
  filter(SAFFL == "Y") %>%
  df_explicit_na()

adlb_f <- adlb %>%
  mutate(AVISIT = forcats::fct_reorder(AVISIT, AVISITN, min)) %>%
  dplyr::filter(
    SAFFL == "Y",
    ANL01FL == "Y",
    ONTRTFL == "Y",
    PARAMCD == "ALT",
    AVISIT != "SCREENING"
  ) %>%
  droplevels() %>%
  df_explicit_na()

advs_f <- advs %>%
  mutate(AVISIT = forcats::fct_reorder(AVISIT, AVISITN, min)) %>%
  dplyr::filter(
    SAFFL == "Y",
    ONTRTFL == "Y",
    PARAMCD == "DIABP",
    AVISIT != "SCREENING"
  ) %>%
  droplevels() %>%
  df_explicit_na()

Code

library(teal.modules.clinical)

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

  ADSL <- random.cdisc.data::cadsl
  ADLB <- random.cdisc.data::cadlb %>%
    mutate(AVISIT = fct_reorder(AVISIT, AVISITN, min))
})
datanames <- c("ADSL", "ADLB")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADSL <- data[["ADSL"]]
ADLB <- data[["ADLB"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_g_lineplot(
      label = "LINE PLOT",
      dataname = "ADLB",
      strata = choices_selected(
        variable_choices(ADSL, c("ARM", "ARMCD", "ACTARMCD")),
        "ARM"
      ),
      y = choices_selected(
        variable_choices(ADLB, c("AVAL", "BASE", "CHG", "PCHG")),
        "AVAL"
      ),
      param = choices_selected(
        value_choices(ADLB, "PARAMCD", "PARAM"),
        "ALT"
      )
    )
  )
)

shinyApp(app$ui, app$server)

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.

#| '!! shinylive warning !!': |
#|   shinylive does not work in self-contained HTML documents.
#|   Please set `embed-resources: false` in your metadata.
#| standalone: true
#| viewerHeight: 800
#| editorHeight: 200
#| components: [viewer, editor]
#| layout: vertical

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

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

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

  ADSL <- random.cdisc.data::cadsl
  ADLB <- random.cdisc.data::cadlb %>%
    mutate(AVISIT = fct_reorder(AVISIT, AVISITN, min))
})
datanames <- c("ADSL", "ADLB")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADSL <- data[["ADSL"]]
ADLB <- data[["ADLB"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_g_lineplot(
      label = "LINE PLOT",
      dataname = "ADLB",
      strata = choices_selected(
        variable_choices(ADSL, c("ARM", "ARMCD", "ACTARMCD")),
        "ARM"
      ),
      y = choices_selected(
        variable_choices(ADLB, c("AVAL", "BASE", "CHG", "PCHG")),
        "AVAL"
      ),
      param = choices_selected(
        value_choices(ADLB, "PARAMCD", "PARAM"),
        "ALT"
      )
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-12-21 17:31:59 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     2024-12-21
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package           * version date (UTC) lib source
 backports           1.5.0   2024-05-23 [1] RSPM
 brio                1.1.5   2024-04-24 [1] RSPM
 broom               1.0.7   2024-09-26 [1] RSPM
 checkmate           2.3.2   2024-07-29 [1] RSPM
 cli                 3.6.3   2024-06-21 [1] RSPM
 codetools           0.2-20  2024-03-31 [2] CRAN (R 4.4.1)
 colorspace          2.1-1   2024-07-26 [1] RSPM
 cowplot             1.1.3   2024-01-22 [1] RSPM
 curl                6.0.1   2024-11-14 [1] RSPM
 digest              0.6.37  2024-08-19 [1] RSPM
 dplyr             * 1.1.4   2023-11-17 [1] RSPM
 evaluate            1.0.1   2024-10-10 [1] RSPM
 farver              2.1.2   2024-05-13 [1] RSPM
 fastmap             1.2.0   2024-05-15 [1] RSPM
 forcats             1.0.0   2023-01-29 [1] RSPM
 formatters        * 0.5.9   2024-09-12 [1] RSPM
 generics            0.1.3   2022-07-05 [1] RSPM
 ggplot2             3.5.1   2024-04-23 [1] RSPM
 glue                1.8.0   2024-09-30 [1] RSPM
 gtable              0.3.6   2024-10-25 [1] RSPM
 htmltools           0.5.8.1 2024-04-04 [1] RSPM
 htmlwidgets         1.6.4   2023-12-06 [1] RSPM
 jsonlite            1.8.9   2024-09-20 [1] RSPM
 knitr               1.49    2024-11-08 [1] RSPM
 labeling            0.4.3   2023-08-29 [1] RSPM
 lattice             0.22-6  2024-03-20 [2] CRAN (R 4.4.1)
 lifecycle           1.0.4   2023-11-07 [1] RSPM
 magrittr          * 2.0.3   2022-03-30 [1] RSPM
 Matrix              1.7-1   2024-10-18 [1] RSPM
 munsell             0.5.1   2024-04-01 [1] RSPM
 nestcolor         * 0.1.2   2023-06-09 [1] RSPM
 pillar              1.10.0  2024-12-17 [1] RSPM
 pkgcache            2.2.3   2024-09-12 [1] RSPM
 pkgconfig           2.0.3   2019-09-22 [1] RSPM
 processx            3.8.4   2024-03-16 [1] RSPM
 ps                  1.8.1   2024-10-28 [1] RSPM
 purrr               1.0.2   2023-08-10 [1] RSPM
 R6                  2.5.1   2021-08-19 [1] RSPM
 random.cdisc.data   0.3.16  2024-10-10 [1] RSPM
 rbibutils           2.3     2024-10-04 [1] RSPM
 Rdpack              2.6.2   2024-11-15 [1] RSPM
 rlang               1.1.4   2024-06-04 [1] RSPM
 rmarkdown           2.29    2024-11-04 [1] RSPM
 rtables           * 0.6.10  2024-09-20 [1] RSPM
 scales              1.3.0   2023-11-28 [1] RSPM
 sessioninfo         1.2.2   2021-12-06 [1] any (@1.2.2)
 stringi             1.8.4   2024-05-06 [1] RSPM
 stringr             1.5.1   2023-11-14 [1] RSPM
 survival            3.8-3   2024-12-17 [1] RSPM
 tern              * 0.9.6   2024-09-24 [1] RSPM
 testthat            3.2.2   2024-12-10 [1] RSPM
 tibble              3.2.1   2023-03-20 [1] RSPM
 tidyr               1.3.1   2024-01-24 [1] RSPM
 tidyselect          1.2.1   2024-03-11 [1] RSPM
 vctrs               0.6.5   2023-12-01 [1] RSPM
 withr               3.0.2   2024-10-28 [1] RSPM
 xfun                0.49    2024-10-31 [1] RSPM
 yaml                2.3.10  2024-07-26 [1] RSPM

 [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

Output

teal App

Reproducibility

Timestamp

Session Info

.lock file

`teal` App

`.lock` file