Fix log shiny inputs@main #86

gogonzo · 2024-07-23T14:01:51Z

This fixes:

logger observer doesn't trigger when anything changes (observe -> observeEvent)
logger observer is set only if log_level is set TRACE or higher
don't log NULL -> NULL anymore.

follow up:

log_trace -> log_debug teal.transform#215

To test:
set options(teal.log_level = "TRACE") before attaching packages and run teal.gallery apps to see the difference between main and {this} branch

github-actions · 2024-07-23T14:02:06Z

✅ All contributors have signed the CLA
_{Posted by the CLA Assistant Lite bot.}

github-actions · 2024-07-23T14:05:10Z

Code Coverage Summary

Filename                       Stmts    Miss  Cover    Missing
---------------------------  -------  ------  -------  ---------
R/log_formatter.R                 12       9  25.00%   11-19
R/log_shiny_input_changes.R       27      27  0.00%    43-74
R/register_handlers.R             39       0  100.00%
R/register_logger.R               56       1  98.21%   115
R/supress_logs.R                  13      13  0.00%    17-29
R/utils.R                         20       0  100.00%
TOTAL                            167      50  70.06%

Diff against main

Filename                       Stmts    Miss  Cover
---------------------------  -------  ------  --------
R/log_formatter.R                +12      +9  +25.00%
R/log_shiny_input_changes.R       +3      +3  +100.00%
TOTAL                            +15     +12  -4.94%

Results for commit: 24b01c8

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

R/log_shiny_input_changes.R

gogonzo · 2024-07-24T11:55:46Z

I have read the CLA Document and I hereby sign the CLA

github-actions · 2024-07-24T11:57:11Z

Unit Tests Summary

1 files 3 suites 0s ⏱️
25 tests 25 ✅ 0 💤 0 ❌
37 runs 37 ✅ 0 💤 0 ❌

Results for commit 1ce985d.

github-actions · 2024-07-24T11:57:12Z

Unit Tests Summary

1 files 4 suites 0s ⏱️
33 tests 33 ✅ 0 💤 0 ❌
46 runs 46 ✅ 0 💤 0 ❌

Results for commit 24b01c8.

♻️ This comment has been updated with latest results.

R/log_shiny_input_changes.R

m7pr · 2024-07-29T10:13:22Z

Hey, taking a look at teal.gallery apps

m7pr · 2024-07-29T12:26:38Z

I'm seeing

Warning: Error in bindingIsLocked: no binding for "shiny_input_values"

for efficacy app when I go to Demographic Table

Code


options(teal.log_level = 'TRACE')
library(teal.modules.general)
library(teal.modules.clinical)
options(shiny.useragg = FALSE)

nest_logo <- "https://raw.githubusercontent.com/insightsengineering/hex-stickers/main/PNG/nest.png"

## Data reproducible code ----
data <- teal_data()
data <- within(data, {
  library(dplyr)
  library(scda)
  library(scda.2022)
  library(nestcolor)
  # optional libraries
  library(sparkline)

  ADSL <- synthetic_cdisc_dataset("latest", "adsl")
  adsl_labels <- teal.data::col_labels(ADSL, fill = FALSE)

  char_vars_asl <- names(Filter(isTRUE, sapply(ADSL, is.character)))

  adsl_labels <- c(
    adsl_labels,
    AGEGR1 = "Age Group"
  )
  ADSL <- ADSL %>%
    mutate(
      AGEGR1 = factor(case_when(
        AGE < 45 ~ "<45",
        AGE >= 45 ~ ">=45"
      ))
    ) %>%
    mutate_at(char_vars_asl, factor)

  teal.data::col_labels(ADSL) <- adsl_labels

  ADTTE <- synthetic_cdisc_dataset("latest", "adtte")

  ADRS <- synthetic_cdisc_dataset("latest", "adrs")
  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_labels <- teal.data::col_labels(ADQS, fill = FALSE)
  ADQS <- ADQS %>%
    filter(ABLFL != "Y" & ABLFL2 != "Y") %>%
    filter(AVISIT %in% c("WEEK 1 DAY 8", "WEEK 2 DAY 15", "WEEK 3 DAY 22")) %>%
    mutate(
      AVISIT = as.factor(AVISIT),
      AVISITN = rank(AVISITN) %>%
        as.factor() %>%
        as.numeric() %>%
        as.factor()
    )
  teal.data::col_labels(ADQS) <- adqs_labels
})

# set datanames
datanames <- c("ADSL", "ADTTE", "ADRS", "ADQS")
datanames(data) <- datanames

# set join_keys
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADSL <- data[["ADSL"]]
ADTTE <- data[["ADTTE"]]
ADRS <- data[["ADRS"]]
ADQS <- data[["ADQS"]]
char_vars_asl <- data[["char_vars_asl"]]

arm_vars <- c("ARMCD", "ARM")
strata_vars <- c("STRATA1", "STRATA2")
facet_vars <- c("AGEGR1", "BMRKR2", "SEX", "COUNTRY")
cov_vars <- c("AGE", "SEX", "BMRKR1", "BMRKR2", "REGION1")
visit_vars <- c("AVISIT", "AVISITN")

cs_arm_var <- choices_selected(
  choices = variable_choices(ADSL, subset = arm_vars),
  selected = "ARM"
)

cs_strata_var <- choices_selected(
  choices = variable_choices(ADSL, subset = strata_vars),
  selected = "STRATA1"
)

cs_facet_var <- choices_selected(
  choices = variable_choices(ADSL, subset = facet_vars),
  selected = "AGEGR1"
)

cs_cov_var <- choices_selected(
  choices = variable_choices(ADSL, subset = cov_vars),
  selected = "AGE"
)

cs_paramcd_tte <- choices_selected(
  choices = value_choices(ADTTE, "PARAMCD", "PARAM"),
  selected = "OS"
)

cs_paramcd_rsp <- choices_selected(
  choices = value_choices(ADRS, "PARAMCD", "PARAM"),
  selected = "BESRSPI"
)

cs_paramcd_qs <- choices_selected(
  choices = value_choices(ADQS, "PARAMCD", "PARAM"),
  selected = "FKSI-FWB"
)

cs_visit_var_qs <- choices_selected(
  choices = variable_choices(ADQS, subset = visit_vars),
  selected = "AVISIT"
)

fact_vars_asl <- names(Filter(isTRUE, sapply(ADSL, is.factor)))
fact_vars_asl_orig <- fact_vars_asl[!fact_vars_asl %in% char_vars_asl]

date_vars_asl <- names(ADSL)[vapply(ADSL, function(x) inherits(x, c("Date", "POSIXct", "POSIXlt")), logical(1))]
demog_vars_asl <- names(ADSL)[!(names(ADSL) %in% c("USUBJID", "STUDYID", date_vars_asl))]

# reference & comparison arm selection when switching the arm variable
# ARMCD is given in a delayed fashion using value choices and
# ARM is given with the ref and comp levels supplied explicitly
arm_ref_comp <- list(
  ARMCD = list(
    ref = value_choices("ADSL", var_choices = "ARMCD", var_label = "ARM", subset = "ARM A"),
    comp = value_choices("ADSL", var_choices = "ARMCD", var_label = "ARM", subset = c("ARM B", "ARM C"))
  ),
  ARM = list(ref = "A: Drug X", comp = c("B: Placebo", "C: Combination"))
)

## Setup App
app <- init(
  data = data,
  filter = teal_slices(
    count_type = "all",
    teal_slice(dataname = "ADSL", varname = "ITTFL", selected = "Y"),
    teal_slice(dataname = "ADSL", varname = "SEX"),
    teal_slice(dataname = "ADSL", varname = "AGE")
  ),
  modules = modules(
    tm_front_page(
      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"
      ),
      tables = list(`NEST packages used in this demo app` = data.frame(
        Packages = c("teal.modules.general", "teal.modules.clinical", "scda", "scda.2022")
      ))
    ),
    tm_data_table("Data Table"),
    tm_variable_browser("Variable Browser"),
    tm_t_summary(
      label = "Demographic Table",
      dataname = "ADSL",
      arm_var = cs_arm_var,
      summarize_vars = choices_selected(
        choices = variable_choices(ADSL, demog_vars_asl),
        selected = c("SEX", "AGE", "RACE")
      )
    ),
    modules(
      label = "Forest Plots",
      tm_g_forest_tte(
        label = "Survival Forest Plot",
        dataname = "ADTTE",
        arm_var = cs_arm_var,
        strata_var = cs_strata_var,
        subgroup_var = cs_facet_var,
        paramcd = cs_paramcd_tte,
        plot_height = c(800L, 200L, 4000L)
      ),
      tm_g_forest_rsp(
        label = "Response Forest Plot",
        dataname = "ADRS",
        arm_var = cs_arm_var,
        strata_var = cs_strata_var,
        subgroup_var = cs_facet_var,
        paramcd = cs_paramcd_rsp,
        plot_height = c(800L, 200L, 4000L)
      )
    ),
    tm_g_km(
      label = "Kaplan Meier Plot",
      dataname = "ADTTE",
      arm_var = cs_arm_var,
      arm_ref_comp = arm_ref_comp,
      paramcd = cs_paramcd_tte,
      facet_var = cs_facet_var,
      strata_var = cs_strata_var,
      plot_height = c(1800L, 200L, 4000L)
    ),
    tm_t_binary_outcome(
      label = "Response Table",
      dataname = "ADRS",
      arm_var = cs_arm_var,
      arm_ref_comp = arm_ref_comp,
      paramcd = cs_paramcd_rsp,
      strata_var = cs_strata_var,
      rsp_table = TRUE
    ),
    tm_t_tte(
      label = "Time To Event Table",
      dataname = "ADTTE",
      arm_var = cs_arm_var,
      paramcd = cs_paramcd_tte,
      strata_var = cs_strata_var,
      time_points = choices_selected(c(182, 365, 547), 182),
      event_desc_var = choices_selected(
        choices = variable_choices("ADTTE", "EVNTDESC"),
        selected = "EVNTDESC",
        fixed = TRUE
      )
    ),
    tm_t_crosstable(
      "Cross Table",
      x = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          choices = variable_choices(ADSL, fact_vars_asl_orig),
          selected = fact_vars_asl_orig[1]
        )
      ),
      y = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          choices = variable_choices(ADSL, fact_vars_asl_orig),
          selected = fact_vars_asl_orig[4]
        )
      )
    ),
    tm_t_coxreg(
      label = "Cox Reg",
      dataname = "ADTTE",
      arm_var = cs_arm_var,
      arm_ref_comp = arm_ref_comp,
      paramcd = cs_paramcd_tte,
      strata_var = cs_strata_var,
      cov_var = cs_cov_var
    ),
    tm_t_logistic(
      label = "Logistic Reg",
      dataname = "ADRS",
      arm_var = cs_arm_var,
      arm_ref_comp = NULL,
      paramcd = cs_paramcd_rsp,
      cov_var = cs_cov_var
    ),
    tm_a_mmrm(
      label = "MMRM",
      dataname = "ADQS",
      aval_var = choices_selected(c("AVAL", "CHG"), "AVAL"),
      id_var = choices_selected(c("USUBJID", "SUBJID"), "USUBJID"),
      arm_var = cs_arm_var,
      visit_var = cs_visit_var_qs,
      arm_ref_comp = arm_ref_comp,
      paramcd = cs_paramcd_qs,
      cov_var = choices_selected(c("BASE", "AGE", "SEX", "BASE:AVISIT"), NULL),
      conf_level = choices_selected(c(0.95, 0.9, 0.8), 0.95)
    ),
    tm_t_binary_outcome(
      label = "Binary Response",
      dataname = "ADRS",
      arm_var = cs_arm_var,
      paramcd = cs_paramcd_rsp,
      strata_var = cs_strata_var
    ),
    tm_t_ancova(
      label = "ANCOVA",
      dataname = "ADQS",
      avisit = choices_selected(value_choices(ADQS, "AVISIT"), "WEEK 1 DAY 8"),
      arm_var = cs_arm_var,
      arm_ref_comp = arm_ref_comp,
      aval_var = choices_selected(variable_choices(ADQS, c("AVAL", "CHG", "PCHG")), "CHG"),
      cov_var = choices_selected(variable_choices(ADQS, c("BASE", "STRATA1", "SEX")), "STRATA1"),
      paramcd = cs_paramcd_qs
    )
  ),
  title = build_app_title("Efficacy Analysis Teal Demo App", nest_logo),
  header = tags$span(
    style = "display: flex; align-items: center; justify-content: space-between; margin: 10px 0 10px 0;",
    tags$span(
      style = "font-size: 30px;",
      "Example teal app focusing on efficacy analysis of clinical trial data with teal.modules.clinical"
    ),
    tags$span(
      style = "display: flex; align-items: center;",
      tags$img(src = nest_logo, alt = "NEST logo", height = "45px", style = "margin-right:10px;"),
      tags$span(style = "font-size: 24px;", "NEST @ Roche")
    )
  ),
  footer = tags$p(
    actionLink("showAboutModal", "About,"),
    tags$a(
      href = "https://github.com/insightsengineering/teal.gallery/tree/main/efficacy",
      target = "_blank",
      "Source Code,"
    ),
    tags$a(
      href = "https://github.com/insightsengineering/teal.gallery/issues",
      target = "_blank",
      "Report Issues"
    )
  )
)

body(app$server)[[length(body(app$server)) + 1]] <- quote(
  observeEvent(input$showAboutModal, {
    showModal(modalDialog(
      tags$p(
        "This teal app is brought to you by the NEST Team at Roche/Genentech.
        For more information, please visit:"
      ),
      tags$ul(
        tags$li(tags$a(
          href = "https://github.com/insightsengineering", "Insights Engineering",
          target = "blank"
        )),
        tags$li(tags$a(
          href = "https://pharmaverse.org", "Pharmaverse",
          target = "blank"
        ))
      ),
      easyClose = TRUE
    ))
  })
)

shinyApp(app$ui, app$server)

R/log_shiny_input_changes.R

…main

m7pr · 2024-09-03T10:04:13Z

Hey, will investigate with teal.gallery apps

m7pr · 2024-09-03T10:52:59Z

Hey, while testing on efficacy app from teal.gallery this is the amount of logs that I see when I uncheck Female in Sex variable on the filter panel. Is this the expected behavior?

Below is on Variable Browser

[TRACE] 2024-09-03 12:50:24.0627 pid:26108 token:[450bea7b] teal.modules.general teal-teal_modules-variable_browser-module Shiny input change detected in variable_browser_ADSL_rows_current: 1:10 -> NULL
[TRACE] 2024-09-03 12:50:24.0651 pid:26108 token:[450bea7b] teal.modules.general teal-teal_modules-variable_browser-module Shiny input change detected in variable_browser_ADSL_rows_all: 1:56 -> NULL
[TRACE] 2024-09-03 12:50:24.0680 pid:26108 token:[450bea7b] teal.modules.general teal-teal_modules-variable_browser-module Shiny input change detected in variable_browser_ADSL_state: list(time = 1725360614844, start = 0, length = 10, order = list(), search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE), columns = list(list(visible = TRUE, search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE)), list(visible = TRUE, search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE)), list(visible = TRUE, search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE)), list(visible = TRUE, search = list(
    search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE)), list(visible = TRUE, search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE)))) -> NULL
[TRACE] 2024-09-03 12:50:24.0706 pid:26108 token:[450bea7b] teal.modules.general teal-teal_modules-variable_browser-module Shiny input change detected in variable_summary_table_rows_current: 1 -> NULL
[TRACE] 2024-09-03 12:50:24.0730 pid:26108 token:[450bea7b] teal.modules.general teal-teal_modules-variable_browser-module Shiny input change detected in variable_summary_table_rows_all: 1 -> NULL
[TRACE] 2024-09-03 12:50:24.0756 pid:26108 token:[450bea7b] teal.modules.general teal-teal_modules-variable_browser-module Shiny input change detected in variable_summary_table_state: list(time = 1725360614738, start = 0, length = 10, order = list(), search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE), columns = list(list(visible = TRUE, search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE)), list(visible = TRUE, search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE)))) -> NULL
[TRACE] 2024-09-03 12:50:24.1447 pid:26108 token:[450bea7b] teal.modules.general teal-teal_modules-variable_browser-module Shiny input change detected in variable_summary_table_rows_current: NULL -> 1
[TRACE] 2024-09-03 12:50:24.1474 pid:26108 token:[450bea7b] teal.modules.general teal-teal_modules-variable_browser-module Shiny input change detected in variable_summary_table_rows_all: NULL -> 1
[TRACE] 2024-09-03 12:50:24.1614 pid:26108 token:[450bea7b] teal.modules.general teal-teal_modules-variable_browser-module Shiny input change detected in variable_summary_table_state: NULL -> list(time = 1725360624073, start = 0, length = 10, order = list(), search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE), columns = list(list(visible = TRUE, search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE)), list(visible = TRUE, search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE))))
[TRACE] 2024-09-03 12:50:24.2086 pid:26108 token:[450bea7b] teal.modules.general teal-teal_modules-variable_browser-module Shiny input change detected in variable_browser_ADSL_rows_current: NULL -> 1:10
[TRACE] 2024-09-03 12:50:24.2125 pid:26108 token:[450bea7b] teal.modules.general teal-teal_modules-variable_browser-module Shiny input change detected in variable_browser_ADSL_rows_all: NULL -> 1:56
[TRACE] 2024-09-03 12:50:24.2160 pid:26108 token:[450bea7b] teal.modules.general teal-teal_modules-variable_browser-module Shiny input change detected in variable_browser_ADSL_state: NULL -> list(time = 1725360624152, start = 0, length = 10, order = list(), search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE), columns = list(list(visible = TRUE, search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE)), list(visible = TRUE, search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE)), list(visible = TRUE, search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE)), list(visible = TRUE, search = list(
    search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE)), list(visible = TRUE, search = list(search = "", smart = TRUE, regex = FALSE, caseInsensitive = TRUE))))

Looks like a lot of lines.

However, when it comes to changes on the encodings panel: 3 changes yield 3 lines which is ideal. Below is on Demographic Table.

[TRACE] 2024-09-03 12:52:09.1345 pid:26108 token:[450bea7b] teal.modules.clinical teal-teal_modules-demographic_table-module Shiny input change detected in useNA: ifany -> no
[TRACE] 2024-09-03 12:52:12.7041 pid:26108 token:[450bea7b] teal.modules.clinical teal-teal_modules-demographic_table-module Shiny input change detected in numeric_stats: c("n", "mean_sd", "mean_ci", "geom_mean", "median", "median_ci", "quantiles", "range") -> c("n", "mean_ci", "geom_mean", "median", "median_ci", "quantiles", "range")
[TRACE] 2024-09-03 12:52:14.1469 pid:26108 token:[450bea7b] teal.modules.clinical teal-teal_modules-demographic_table-module Shiny input change detected in numeric_stats: c("n", "mean_ci", "geom_mean", "median", "median_ci", "quantiles", "range") -> c("n", "mean_ci", "median", "median_ci", "quantiles", "range")

m7pr · 2024-09-03T10:55:58Z

On another module Demographic Table when I change Sex filter on filter panel I do see a lot of teal.transform modules.
Maybe we should discard teal.transform changes at all?

[TRACE] 2024-09-03 12:53:43.0371 pid:26108 token:[450bea7b] teal.transform merge_datasets called with: ADSL datasets; arm_var, summarize_vars selectors; dplyr::inner_join merge function.
[TRACE] 2024-09-03 12:53:43.0419 pid:26108 token:[450bea7b] teal.transform merge_selectors called with: arm_var, summarize_vars selectors.
[TRACE] 2024-09-03 12:53:43.0446 pid:26108 token:[450bea7b] teal.transform get_dplyr_call_data called with: arm_var selectors.
[TRACE] 2024-09-03 12:53:43.0476 pid:26108 token:[450bea7b] teal.transform get_merge_key_grid called with: arm_var selectors.
[TRACE] 2024-09-03 12:53:43.0512 pid:26108 token:[450bea7b] teal.transform get_merge_key_pair called with: arm_var, arm_var selectors; STUDYID, USUBJID keys.
[TRACE] 2024-09-03 12:53:43.0538 pid:26108 token:[450bea7b] teal.transform get_dropped_filters called with arm_var selector.
[TRACE] 2024-09-03 12:53:43.0566 pid:26108 token:[450bea7b] teal.transform get_merge_key_pair returns STUDYID, USUBJID merge keys.
[TRACE] 2024-09-03 12:53:43.0595 pid:26108 token:[450bea7b] teal.transform get_dplyr_call called with: ADSL datasets; arm_var selectors.
[TRACE] 2024-09-03 12:53:43.0625 pid:26108 token:[450bea7b] teal.transform get_filter_call called with: ADSL dataset;  filters.
[TRACE] 2024-09-03 12:53:43.0666 pid:26108 token:[450bea7b] teal.transform get_select_call called with: STUDYID, USUBJID, ARM, AGE, SEX, RACE columns.
[TRACE] 2024-09-03 12:53:43.0733 pid:26108 token:[450bea7b] teal.transform get_merge_call called with: arm_var selectors; dplyr::inner_join merge function.
[TRACE] 2024-09-03 12:53:43.0777 pid:26108 token:[450bea7b] teal.transform get_relabel_cols called with: arm_var, summarize_vars columns_source.
[TRACE] 2024-09-03 12:53:43.0808 pid:26108 token:[450bea7b] teal.transform get_anl_relabel_call called with: arm_var, summarize_vars columns_source; ANL merged dataset.
[TRACE] 2024-09-03 12:53:43.4158 pid:26108 token:[450bea7b] teal.transform get_relabel_call called with: Description of Planned Arm Age Sex Race labels.
[TRACE] 2024-09-03 12:53:43.4185 pid:26108 token:[450bea7b] teal.transform merge_datasets merge code executed resulting in ANL dataset.
[TRACE] 2024-09-03 12:53:43.4314 pid:26108 token:[450bea7b] teal.transform merge_datasets called with: ADSL datasets; arm_var selectors; dplyr::full_join merge function.
[TRACE] 2024-09-03 12:53:43.4342 pid:26108 token:[450bea7b] teal.transform merge_selectors called with: arm_var selectors.
[TRACE] 2024-09-03 12:53:43.4367 pid:26108 token:[450bea7b] teal.transform get_dplyr_call_data called with: arm_var selectors.
[TRACE] 2024-09-03 12:53:43.4393 pid:26108 token:[450bea7b] teal.transform get_merge_key_grid called with: arm_var selectors.
[TRACE] 2024-09-03 12:53:43.4427 pid:26108 token:[450bea7b] teal.transform get_merge_key_pair called with: arm_var, arm_var selectors; STUDYID, USUBJID keys.
[TRACE] 2024-09-03 12:53:43.4450 pid:26108 token:[450bea7b] teal.transform get_dropped_filters called with arm_var selector.
[TRACE] 2024-09-03 12:53:43.4471 pid:26108 token:[450bea7b] teal.transform get_merge_key_pair returns STUDYID, USUBJID merge keys.
[TRACE] 2024-09-03 12:53:43.4496 pid:26108 token:[450bea7b] teal.transform get_dplyr_call called with: ADSL datasets; arm_var selectors.
[TRACE] 2024-09-03 12:53:43.4519 pid:26108 token:[450bea7b] teal.transform get_filter_call called with: ADSL dataset;  filters.
[TRACE] 2024-09-03 12:53:43.4539 pid:26108 token:[450bea7b] teal.transform get_select_call called with: STUDYID, USUBJID, ARM columns.
[TRACE] 2024-09-03 12:53:43.4560 pid:26108 token:[450bea7b] teal.transform get_merge_call called with: arm_var selectors; dplyr::full_join merge function.
[TRACE] 2024-09-03 12:53:43.4581 pid:26108 token:[450bea7b] teal.transform get_relabel_cols called with: arm_var columns_source.
[TRACE] 2024-09-03 12:53:43.4597 pid:26108 token:[450bea7b] teal.transform get_anl_relabel_call called with: arm_var columns_source; ANL_ADSL merged dataset.
[TRACE] 2024-09-03 12:53:43.4633 pid:26108 token:[450bea7b] teal.transform get_relabel_call called with: Description of Planned Arm labels.
[TRACE] 2024-09-03 12:53:43.4656 pid:26108 token:[450bea7b] teal.transform merge_datasets merge code executed resulting in ANL_ADSL dataset.

R/log_shiny_input_changes.R

m7pr · 2024-09-03T11:16:24Z

Just flagging that changes on Reporter Previewer are not registered (unsure if they should)

m7pr · 2024-09-03T11:21:33Z

Besides the reduction of teal.transform logs we could consider reducing those that come from teal.widgets

gogonzo · 2024-09-03T11:48:14Z

Just flagging that changes on Reporter Previewer are not registered (unsure if they should)

Probably should if teal.reporter include log_shiny_input_changes

Besides the reduction of teal.transform logs we could consider reducing those that come from teal.widgets

I think merge should be entirely rewritten and changing logs won't change anything. I think reducing logs in transform isn't worth it now

Hey, while testing on efficacy app from teal.gallery this is the amount of logs that I see when I uncheck Female in Sex variable on the filter panel. Is this the expected behavior?

Expected but not desired. datatable has many inputs wnd they are changed every change of the data. Could be excluded where log_shiny_input_changes is called.

gogonzo · 2024-09-04T11:27:16Z

@pawelru any more comments?

R/log_shiny_input_changes.R

pawelru

I have completed the review and submitted my comments. Please request me once again if you want me to look at this again.

R/zzz.R

github-actions · 2024-09-06T09:52:12Z

Unit Test Performance Difference

Test Suite	$Status$	Time on `main`	$±Time$	$±Tests$	$±Skipped$	$±Failures$	$±Errors$
log_formatter	👶		$+0.09$	$+9$	$0$	$0$	$0$

Additional test case details

Test Suite	$Status$	$±Time$	Test Case
log_formatter	👶	$+0.01$	teal.logger_formats_NA_asis
log_formatter	👶	$+0.01$	teal.logger_formats_NULL_asis
log_formatter	👶	$+0.04$	teal.logger_formats_character_0_asis
log_formatter	👶	$+0.01$	teal.logger_formats_list_as_a_list_literal
log_formatter	👶	$+0.00$	teal.logger_formats_nested_list_as_a_named_list_array_literal
log_formatter	👶	$+0.01$	teal.logger_formats_scalar_asis
log_formatter	👶	$+0.00$	teal.logger_formats_two_vectors_in_a_single_log
log_formatter	👶	$+0.00$	teal.logger_formats_vector_as_an_array_literal

Results for commit 1b9c449

♻️ This comment has been updated with latest results.

R/log_formatter.R

R/zzz.R

pawelru

Thanks 👍

Co-authored-by: Pawel Rucki <[email protected]> Signed-off-by: Dawid Kałędkowski <[email protected]>

gogonzo added 2 commits July 23, 2024 15:50

trigger only when the input changes

832d6a5

don't log NULL -> NULL

bf483fc

gogonzo marked this pull request as draft July 23, 2024 14:02

[skip roxygen] [skip vbump] Roxygen Man Pages Auto Update

cc2241e

improvements

3481ced

gogonzo commented Jul 24, 2024

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fix checks

1ce985d

gogonzo marked this pull request as ready for review July 24, 2024 11:53

gogonzo added the core label Jul 24, 2024

gogonzo commented Jul 24, 2024

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donyunardi mentioned this pull request Jul 26, 2024

[Release]: 0.2.0 insightsengineering/teal.goshawk#297

Closed

30 tasks

pawelru reviewed Jul 31, 2024

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pawelru reviewed Aug 6, 2024

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gogonzo added 5 commits September 2, 2024 12:13

Merge remote-tracking branch 'origin/main' into fix_log_shiny_inputs@…

dc29584

…main

fix asserts

cc13880

@ruckip comment

852f1c9

fix intersect

bab8468

checkmate to stopifnot

0433987

m7pr reviewed Sep 3, 2024

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pawelru reviewed Sep 5, 2024

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pawelru reviewed Sep 5, 2024

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tighten assertion

1eb7961

pawelru reviewed Sep 5, 2024

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pawelru reviewed Sep 5, 2024

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fix glue_formatter to handle NULL and vectors

42e16d3