Running ‘testthat.R’ [3m/12m]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > # This file is part of the standard setup for testthat.
  > # It is recommended that you do not modify it.
  > #
  > # Where should you do additional test configuration?
  > # Learn more about the roles of various files in:
  > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
  > # * https://testthat.r-lib.org/articles/special-files.html
  > 
  > library(testthat)
  > library(evanverse)
  > 
  > test_check("evanverse")
  x RDS file not found: '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf6173dff2.rds'
  
  -- Compiling Color Palettes (JSON -> RDS) --------------------------------------
  v Added 'demo_palette' (Type: qualitative, 2 colors)
  v Saved RDS: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf7ddd602c.rds
  
  -- Compilation Summary --
  
  i Sequential:   0
  i Diverging:    0
  i Qualitative:  1
  v All palettes compiled successfully!
  
  -- Compiling Color Palettes (JSON -> RDS) --------------------------------------
  ! Failed to parse JSON: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/palettes_982bf2d98810d/qualitative/invalid.json
  v Saved RDS: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf6261d60a.rds
  
  -- Compilation Summary --
  
  i Sequential:   0
  i Diverging:    0
  i Qualitative:  0
  v All palettes compiled successfully!
  
  -- Compiling Color Palettes (JSON -> RDS) --------------------------------------
  ! Missing fields (colors) in: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/palettes_982bf20681186/qualitative/incomplete.json
  v Saved RDS: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf2717149c.rds
  
  -- Compilation Summary --
  
  i Sequential:   0
  i Diverging:    0
  i Qualitative:  0
  v All palettes compiled successfully!
  
  -- Compiling Color Palettes (JSON -> RDS) --------------------------------------
  v Added 'seq_pal' (Type: sequential, 2 colors)
  v Added 'div_pal' (Type: diverging, 2 colors)
  v Saved RDS: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf2bb5e5.rds
  
  -- Compilation Summary --
  
  i Sequential:   1
  i Diverging:    1
  i Qualitative:  0
  v All palettes compiled successfully!
  i Converting symbols to UPPERCASE (human standard)
  i Creating standardized column: symbol_upper
  i Creating standardized column: symbol_lower
  i Converting symbols to UPPERCASE (human standard)
  i Converting symbols to UPPERCASE (human standard)
  i Directory created: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/palette_test_982bf223b0f85/qualitative
  v Palette saved: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/palette_test_982bf223b0f85/qualitative/testset.json
  i Directory created: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/reuse_test_982bfb2029d8/diverging
  v Palette saved: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/reuse_test_982bfb2029d8/diverging/reused.json
  ! Palette already exists: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/reuse_test_982bfb2029d8/diverging/reused.json
  i Directory created: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/log_test_982bf3ba92981/qualitative
  v Palette saved: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/log_test_982bf3ba92981/qualitative/logtest.json
  Saving _problems/test-download_geo_data-30.R
  Saving _problems/test-download_geo_data-94.R
  Saving _problems/test-download_geo_data-131.R
  Saving _problems/test-download_geo_data-162.R
  Saving _problems/test-download_geo_data-193.R
  Saving _problems/test-download_geo_data-220.R
  Saving _problems/test-download_geo_data-263.R
  
  -- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf69c7cdc1 ----
  +-- test.txt
  
  -- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf59a06e02 ----
  +-- file1.txt
  +-- subdir
      +-- file2.txt
  
  -- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf1324b699 ----
  +-- level1
  
  -- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf1324b699 ----
  +-- level1
      +-- level2
          +-- deep_file.txt
  
  -- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf10201e03 ----
  +-- example.txt
  v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf10201e03/logs/tree/test.log
  
  -- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf16886e3d ----
  +-- sample.txt
  v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf16886e3d/logs/content_test.log
  
  -- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf6a5face6 ----
  +-- test.txt
  v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf6a5face6/logs/file_tree_20260304_194341.log
  
  -- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf792b3ba1 ----
  +-- file1.txt
  v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf7b6b6d9/overwrite_test.log
  
  -- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf792b3ba1 ----
  +-- file1.txt
  v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf7b6b6d9/overwrite_test.log
  
  -- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf3894f0ea ----
  +-- file1.txt
  v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf71676207/append_test.log
  
  -- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf3894f0ea ----
  +-- file1.txt
  v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf71676207/append_test.log
  x Directory does not exist: nonexistent/directory/path
  
  -- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf7b2da8e2 ----
  
  -- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf8a4ea2d -----
  +-- test.txt
  v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf8a4ea2d/new/log/path/test.log
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  i #FF8000 -> RGB: c(255, 128, 0)
  i #00FF00 -> RGB: c(0, 255, 0)
  i Mapping completed for 'id': 1 unmatched value(s) assigned to default.
  i Mapping completed for 'id': 1 unmatched value(s) assigned to default.
  i Mapping completed for 'group': 1 unmatched value(s) assigned to default.
  i Mapping completed for 'id': 1 unmatched value(s) assigned to default.
  -- Objects are NOT identical ---------------------------------------------------
  x Type mismatch: integer vs double
  x Class mismatch: integer vs numeric
  x Values differ at 1 position(s), e.g., index 3: 3 vs 4
  -- Objects are NOT identical ---------------------------------------------------
  x Values differ at 1 position(s), e.g., index 2: b vs c
  -- Objects are NOT identical ---------------------------------------------------
  x Type mismatch: integer vs double
  x Values differ at 1 cell(s), e.g., [2,2]: 4 vs 5
  -- Objects are NOT identical ---------------------------------------------------
  x Values differ in column 'y' at 1 row(s), e.g., row 2: b vs c
  -- Objects are NOT identical ---------------------------------------------------
  x Names differ: a, b vs b, a
  x Values differ at 2 position(s), e.g., index 1: 1 vs 2
  -- Objects are NOT identical ---------------------------------------------------
  x Column names differ: x, y vs y, x
  -- Objects are NOT identical ---------------------------------------------------
  x Dimnames differ: (r1|r2; c1|c2) vs (r1|r2; cX|c2)
  -- Objects are NOT identical ---------------------------------------------------
  -- Objects are NOT identical ---------------------------------------------------
  -- Objects are NOT identical ---------------------------------------------------
  x Dimension mismatch: 0x0 vs 1x0
  -- Objects are NOT identical ---------------------------------------------------
  x Length mismatch: 2 vs 3
  -- Objects are NOT identical ---------------------------------------------------
  x Dimension mismatch: 2x2 vs 1x4
  -- Objects are NOT identical ---------------------------------------------------
  x Column names differ: x vs y
  x Unsupported type for 'a': list
  x Unsupported type for 'b': function
  ! Large n (25) may cause numeric overflow. Consider using the 'gmp' package for arbitrary precision.
  ! Large n (171) may cause numeric overflow. Consider using the 'gmp' package for arbitrary precision.
  
  -- Configuring cran mirror --
  
  v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
  i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring cran mirror --
  
  v CRAN mirror set to: <https://mirrors.ustc.edu.cn/CRAN>
  i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring cran mirror --
  
  v CRAN mirror set to: <https://mirrors.westlake.edu.cn/CRAN>
  i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring bioc mirror --
  
  v Bioconductor mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/bioconductor>
  i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring bioc mirror --
  
  v Bioconductor mirror set to: <https://mirrors.ustc.edu.cn/bioconductor>
  i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring bioc mirror --
  
  v Bioconductor mirror set to: <https://mirrors.westlake.edu.cn/bioconductor>
  i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring all mirror --
  
  v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
  v Bioconductor mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/bioconductor>
  i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
  i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring all mirror --
  
  v CRAN mirror set to: <https://mirrors.ustc.edu.cn/CRAN>
  v Bioconductor mirror set to: <https://mirrors.ustc.edu.cn/bioconductor>
  i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
  i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring cran mirror --
  
  
  -- Configuring bioc mirror --
  
  
  -- Configuring cran mirror --
  
  v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
  i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring all mirror --
  
  v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
  v Bioconductor mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/bioconductor>
  i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
  i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring cran mirror --
  
  i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring bioc mirror --
  
  i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring cran mirror --
  
  v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring cran mirror --
  
  v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
  i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
  
  -- Configuring cran mirror --
  
  v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
  i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring bioc mirror --
  
  
  -- Configuring all mirror --
  
  
  -- Configuring cran mirror --
  
  v CRAN mirror set to: <https://cloud.r-project.org>
  i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  
  -- Configuring bioc mirror --
  
  v Bioconductor mirror set to: <https://bioconductor.org>
  i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
  i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
  v Installed: stats
  v Installed: cli
  v Installed: ggplot2
  v Installed: stats
  v Installed: utils
  
  -- Package: stats --
  
  i Matched exported names: 465
  .MFclass
  .checkMFClasses
  .getXlevels
  .lm.fit
  .nknots.smspl
  .preformat.ts
  .vcov.aliased
  AIC
  ARMAacf
  ARMAtoMA
  BIC
  Box.test
  C
  D
  DF2formula
  Gamma
  HoltWinters
  IQR
  KalmanForecast
  KalmanLike
  KalmanRun
  KalmanSmooth
  NLSstAsymptotic
  NLSstClosestX
  NLSstLfAsymptote
  NLSstRtAsymptote
  PP.test
  Pair
  SSD
  SSasymp
  SSasympOff
  SSasympOrig
  SSbiexp
  SSfol
  SSfpl
  SSgompertz
  SSlogis
  SSmicmen
  SSweibull
  StructTS
  TukeyHSD
  acf
  acf2AR
  add.scope
  add1
  addmargins
  aggregate
  aggregate.data.frame
  aggregate.ts
  alias
  anova
  ansari.test
  aov
  approx
  approxfun
  ar
  ar.burg
  ar.mle
  ar.ols
  ar.yw
  arima
  arima.sim
  arima0
  arima0.diag
  as.dendrogram
  as.dist
  as.formula
  as.hclust
  as.stepfun
  as.ts
  asOneSidedFormula
  ave
  bandwidth.kernel
  bartlett.test
  binom.test
  binomial
  biplot
  bw.SJ
  bw.bcv
  bw.nrd
  bw.nrd0
  bw.ucv
  cancor
  case.names
  ccf
  chisq.test
  cmdscale
  coef
  coefficients
  complete.cases
  confint
  confint.default
  confint.lm
  constrOptim
  contr.SAS
  contr.helmert
  contr.poly
  contr.sum
  contr.treatment
  contrasts
  contrasts<-
  convolve
  cooks.distance
  cophenetic
  cor
  cor.test
  cov
  cov.wt
  cov2cor
  covratio
  cpgram
  cutree
  cycle
  dbeta
  dbinom
  dcauchy
  dchisq
  decompose
  delete.response
  deltat
  dendrapply
  density
  density.default
  deriv
  deriv3
  deviance
  dexp
  df
  df.kernel
  df.residual
  dfbeta
  dfbetas
  dffits
  dgamma
  dgeom
  dhyper
  diffinv
  dist
  dlnorm
  dlogis
  dmultinom
  dnbinom
  dnorm
  dpois
  drop.scope
  drop.terms
  drop1
  dsignrank
  dt
  dummy.coef
  dummy.coef.lm
  dunif
  dweibull
  dwilcox
  ecdf
  eff.aovlist
  effects
  embed
  end
  estVar
  expand.model.frame
  extractAIC
  factanal
  factor.scope
  family
  fft
  filter
  fisher.test
  fitted
  fitted.values
  fivenum
  fligner.test
  formula
  free1way
  frequency
  friedman.test
  ftable
  gaussian
  getCall
  getInitial
  get_all_vars
  glm
  glm.control
  glm.fit
  hasTsp
  hat
  hatvalues
  hclust
  heatmap
  influence
  influence.measures
  integrate
  interaction.plot
  inverse.gaussian
  is.empty.model
  is.leaf
  is.mts
  is.stepfun
  is.ts
  is.tskernel
  isoreg
  kernapply
  kernel
  kmeans
  knots
  kruskal.test
  ks.test
  ksmooth
  lag
  lag.plot
  line
  lm
  lm.fit
  lm.influence
  lm.wfit
  loadings
  loess
  loess.control
  loess.smooth
  logLik
  loglin
  lowess
  ls.diag
  ls.print
  lsfit
  mad
  mahalanobis
  make.link
  makeARIMA
  makepredictcall
  manova
  mantelhaen.test
  mauchly.test
  mcnemar.test
  median
  median.default
  medpolish
  model.extract
  model.frame
  model.frame.default
  model.matrix
  model.matrix.default
  model.matrix.lm
  model.offset
  model.response
  model.tables
  model.weights
  monthplot
  mood.test
  mvfft
  na.action
  na.contiguous
  na.exclude
  na.fail
  na.omit
  na.pass
  napredict
  naprint
  naresid
  nextn
  nlm
  nlminb
  nls
  nls.control
  nobs
  numericDeriv
  offset
  oneway.test
  optim
  optimHess
  optimise
  optimize
  order.dendrogram
  p.adjust
  p.adjust.methods
  pacf
  pairwise.prop.test
  pairwise.t.test
  pairwise.table
  pairwise.wilcox.test
  pbeta
  pbinom
  pbirthday
  pcauchy
  pchisq
  pexp
  pf
  pgamma
  pgeom
  phyper
  plclust
  plnorm
  plogis
  plot.ecdf
  plot.spec.coherency
  plot.spec.phase
  plot.stepfun
  plot.ts
  pnbinom
  pnorm
  poisson
  poisson.test
  poly
  polym
  power
  power.anova.test
  power.free1way.test
  power.prop.test
  power.t.test
  ppoints
  ppois
  ppplot
  ppr
  prcomp
  predict
  predict.glm
  predict.lm
  preplot
  princomp
  printCoefmat
  profile
  proj
  promax
  prop.test
  prop.trend.test
  psignrank
  psmirnov
  pt
  ptukey
  punif
  pweibull
  pwilcox
  qbeta
  qbinom
  qbirthday
  qcauchy
  qchisq
  qexp
  qf
  qgamma
  qgeom
  qhyper
  qlnorm
  qlogis
  qnbinom
  qnorm
  qpois
  qqline
  qqnorm
  qqplot
  qr.influence
  qsignrank
  qsmirnov
  qt
  qtukey
  quade.test
  quantile
  quasi
  quasibinomial
  quasipoisson
  qunif
  qweibull
  qwilcox
  r2dtable
  rWishart
  rbeta
  rbinom
  rcauchy
  rchisq
  read.ftable
  rect.hclust
  reformulate
  relevel
  reorder
  replications
  reshape
  resid
  residuals
  residuals.glm
  residuals.lm
  rexp
  rf
  rfree1way
  rgamma
  rgeom
  rhyper
  rlnorm
  rlogis
  rmultinom
  rnbinom
  rnorm
  rpois
  rsignrank
  rsmirnov
  rstandard
  rstudent
  rt
  runif
  runmed
  rweibull
  rwilcox
  scatter.smooth
  screeplot
  sd
  se.contrast
  selfStart
  setNames
  shapiro.test
  sigma
  simulate
  smooth
  smooth.spline
  smoothEnds
  sortedXyData
  spec.ar
  spec.pgram
  spec.taper
  spectrum
  spline
  splinefun
  splinefunH
  start
  stat.anova
  step
  stepfun
  stl
  summary.aov
  summary.glm
  summary.lm
  summary.manova
  summary.stepfun
  supsmu
  symnum
  t.test
  termplot
  terms
  terms.formula
  time
  toeplitz
  toeplitz2
  ts
  ts.intersect
  ts.plot
  ts.union
  tsSmooth
  tsdiag
  tsp
  tsp<-
  uniroot
  update
  update.default
  update.formula
  var
  var.test
  variable.names
  varimax
  vcov
  weighted.mean
  weighted.residuals
  weights
  wilcox.test
  window
  window<-
  write.ftable
  xtabs
  
  -- Package: stats --
  
  i Matched exported names: 465
  .MFclass
  .checkMFClasses
  .getXlevels
  .lm.fit
  .nknots.smspl
  .preformat.ts
  .vcov.aliased
  AIC
  ARMAacf
  ARMAtoMA
  BIC
  Box.test
  C
  D
  DF2formula
  Gamma
  HoltWinters
  IQR
  KalmanForecast
  KalmanLike
  KalmanRun
  KalmanSmooth
  NLSstAsymptotic
  NLSstClosestX
  NLSstLfAsymptote
  NLSstRtAsymptote
  PP.test
  Pair
  SSD
  SSasymp
  SSasympOff
  SSasympOrig
  SSbiexp
  SSfol
  SSfpl
  SSgompertz
  SSlogis
  SSmicmen
  SSweibull
  StructTS
  TukeyHSD
  acf
  acf2AR
  add.scope
  add1
  addmargins
  aggregate
  aggregate.data.frame
  aggregate.ts
  alias
  anova
  ansari.test
  aov
  approx
  approxfun
  ar
  ar.burg
  ar.mle
  ar.ols
  ar.yw
  arima
  arima.sim
  arima0
  arima0.diag
  as.dendrogram
  as.dist
  as.formula
  as.hclust
  as.stepfun
  as.ts
  asOneSidedFormula
  ave
  bandwidth.kernel
  bartlett.test
  binom.test
  binomial
  biplot
  bw.SJ
  bw.bcv
  bw.nrd
  bw.nrd0
  bw.ucv
  cancor
  case.names
  ccf
  chisq.test
  cmdscale
  coef
  coefficients
  complete.cases
  confint
  confint.default
  confint.lm
  constrOptim
  contr.SAS
  contr.helmert
  contr.poly
  contr.sum
  contr.treatment
  contrasts
  contrasts<-
  convolve
  cooks.distance
  cophenetic
  cor
  cor.test
  cov
  cov.wt
  cov2cor
  covratio
  cpgram
  cutree
  cycle
  dbeta
  dbinom
  dcauchy
  dchisq
  decompose
  delete.response
  deltat
  dendrapply
  density
  density.default
  deriv
  deriv3
  deviance
  dexp
  df
  df.kernel
  df.residual
  dfbeta
  dfbetas
  dffits
  dgamma
  dgeom
  dhyper
  diffinv
  dist
  dlnorm
  dlogis
  dmultinom
  dnbinom
  dnorm
  dpois
  drop.scope
  drop.terms
  drop1
  dsignrank
  dt
  dummy.coef
  dummy.coef.lm
  dunif
  dweibull
  dwilcox
  ecdf
  eff.aovlist
  effects
  embed
  end
  estVar
  expand.model.frame
  extractAIC
  factanal
  factor.scope
  family
  fft
  filter
  fisher.test
  fitted
  fitted.values
  fivenum
  fligner.test
  formula
  free1way
  frequency
  friedman.test
  ftable
  gaussian
  getCall
  getInitial
  get_all_vars
  glm
  glm.control
  glm.fit
  hasTsp
  hat
  hatvalues
  hclust
  heatmap
  influence
  influence.measures
  integrate
  interaction.plot
  inverse.gaussian
  is.empty.model
  is.leaf
  is.mts
  is.stepfun
  is.ts
  is.tskernel
  isoreg
  kernapply
  kernel
  kmeans
  knots
  kruskal.test
  ks.test
  ksmooth
  lag
  lag.plot
  line
  lm
  lm.fit
  lm.influence
  lm.wfit
  loadings
  loess
  loess.control
  loess.smooth
  logLik
  loglin
  lowess
  ls.diag
  ls.print
  lsfit
  mad
  mahalanobis
  make.link
  makeARIMA
  makepredictcall
  manova
  mantelhaen.test
  mauchly.test
  mcnemar.test
  median
  median.default
  medpolish
  model.extract
  model.frame
  model.frame.default
  model.matrix
  model.matrix.default
  model.matrix.lm
  model.offset
  model.response
  model.tables
  model.weights
  monthplot
  mood.test
  mvfft
  na.action
  na.contiguous
  na.exclude
  na.fail
  na.omit
  na.pass
  napredict
  naprint
  naresid
  nextn
  nlm
  nlminb
  nls
  nls.control
  nobs
  numericDeriv
  offset
  oneway.test
  optim
  optimHess
  optimise
  optimize
  order.dendrogram
  p.adjust
  p.adjust.methods
  pacf
  pairwise.prop.test
  pairwise.t.test
  pairwise.table
  pairwise.wilcox.test
  pbeta
  pbinom
  pbirthday
  pcauchy
  pchisq
  pexp
  pf
  pgamma
  pgeom
  phyper
  plclust
  plnorm
  plogis
  plot.ecdf
  plot.spec.coherency
  plot.spec.phase
  plot.stepfun
  plot.ts
  pnbinom
  pnorm
  poisson
  poisson.test
  poly
  polym
  power
  power.anova.test
  power.free1way.test
  power.prop.test
  power.t.test
  ppoints
  ppois
  ppplot
  ppr
  prcomp
  predict
  predict.glm
  predict.lm
  preplot
  princomp
  printCoefmat
  profile
  proj
  promax
  prop.test
  prop.trend.test
  psignrank
  psmirnov
  pt
  ptukey
  punif
  pweibull
  pwilcox
  qbeta
  qbinom
  qbirthday
  qcauchy
  qchisq
  qexp
  qf
  qgamma
  qgeom
  qhyper
  qlnorm
  qlogis
  qnbinom
  qnorm
  qpois
  qqline
  qqnorm
  qqplot
  qr.influence
  qsignrank
  qsmirnov
  qt
  qtukey
  quade.test
  quantile
  quasi
  quasibinomial
  quasipoisson
  qunif
  qweibull
  qwilcox
  r2dtable
  rWishart
  rbeta
  rbinom
  rcauchy
  rchisq
  read.ftable
  rect.hclust
  reformulate
  relevel
  reorder
  replications
  reshape
  resid
  residuals
  residuals.glm
  residuals.lm
  rexp
  rf
  rfree1way
  rgamma
  rgeom
  rhyper
  rlnorm
  rlogis
  rmultinom
  rnbinom
  rnorm
  rpois
  rsignrank
  rsmirnov
  rstandard
  rstudent
  rt
  runif
  runmed
  rweibull
  rwilcox
  scatter.smooth
  screeplot
  sd
  se.contrast
  selfStart
  setNames
  shapiro.test
  sigma
  simulate
  smooth
  smooth.spline
  smoothEnds
  sortedXyData
  spec.ar
  spec.pgram
  spec.taper
  spectrum
  spline
  splinefun
  splinefunH
  start
  stat.anova
  step
  stepfun
  stl
  summary.aov
  summary.glm
  summary.lm
  summary.manova
  summary.stepfun
  supsmu
  symnum
  t.test
  termplot
  terms
  terms.formula
  time
  toeplitz
  toeplitz2
  ts
  ts.intersect
  ts.plot
  ts.union
  tsSmooth
  tsdiag
  tsp
  tsp<-
  uniroot
  update
  update.default
  update.formula
  var
  var.test
  variable.names
  varimax
  vcov
  weighted.mean
  weighted.residuals
  weights
  wilcox.test
  window
  window<-
  write.ftable
  xtabs
  
  -- Package: stats --
  
  i Matched exported names: 24
  .lm.fit
  KalmanForecast
  KalmanLike
  KalmanRun
  KalmanSmooth
  confint.lm
  contr.helmert
  dummy.coef.lm
  glm
  glm.control
  glm.fit
  lm
  lm.fit
  lm.influence
  lm.wfit
  model.matrix.lm
  nlm
  nlminb
  predict.glm
  predict.lm
  residuals.glm
  residuals.lm
  summary.glm
  summary.lm
  
  -- Package: stats --
  
  i Matched exported names: 24
  .lm.fit
  KalmanForecast
  KalmanLike
  KalmanRun
  KalmanSmooth
  confint.lm
  contr.helmert
  dummy.coef.lm
  glm
  glm.control
  glm.fit
  lm
  lm.fit
  lm.influence
  lm.wfit
  model.matrix.lm
  nlm
  nlminb
  predict.glm
  predict.lm
  residuals.glm
  residuals.lm
  summary.glm
  summary.lm
  
  -- Package: stats --
  
  i Matched exported names: 0
  ! No exported names matched keyword: "zzzzzz"
  i s1 had 75 duplicates, now de-duplicated.
  i s2 had 94 duplicates, now de-duplicated.
  i s1 had 75 duplicates, now de-duplicated.
  i s2 had 94 duplicates, now de-duplicated.
  i a had 26 duplicates, now de-duplicated.
  i b had 22 duplicates, now de-duplicated.
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  ! Could not load palette "qual_bold". Using defaults.
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  
  -- Quick ANOVA Results --
  
  
  i Method: anova
  v Significant group differences (p < 0.001)
  
  
  -- Descriptive Statistics 
  
  
  -- Post-hoc Summary (tukey) 
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  
  -- Detailed Quick ANOVA Summary --
  
  -- Omnibus Test 
  
  i Effect sizes: eta_squared 0.448, omega_squared 0.430
  
  -- Descriptive Statistics 
  
  -- Normality Checks (Shapiro-Wilk) 
  A: n = 25, p = 0.5812
  B: n = 25, p = 0.8166
  C: n = 25, p = 0.9928
  
  i Decision: Small sample size (n < 30). Data appears normal (Shapiro p >= 0.05). Using t-test.
  
  -- Variance Equality (Levene) 
  Levene's test: p = 0.9887
  Equal variance: TRUE
  
  -- Post-hoc Comparisons (tukey) 
  Analysis performed: 2026-03-04 19:47:54
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  
  -- Automatic Method Selection --
  
  i Checking normality for each group...
  v Data appears normal (Shapiro-Wilk p >= 0.05).
  A: n = 25, p = 0.581
  B: n = 25, p = 0.817
  C: n = 25, p = 0.993
  v Variances appear equal (Levene's test, p = 0.989)
  
  -- Omnibus Test --
  
  v Completed classical one-way ANOVA (p = 0.0000)
  i Applied Tukey HSD post-hoc comparisons.
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  i var1 converted to factor with 1 level.
  i var2 converted to factor with 2 levels.
  i var2 converted to factor with 2 levels.
  ! Failed to load palette 'qual_vivid': Palette "qual_vivid" not found under "sequential", but exists under "qualitative". Try: `get_palette("qual_vivid", type = "qualitative")`. Using default colors.
  ! Removed 10 rows with missing values.
  i var1 converted to factor with 3 levels.
  i var2 converted to factor with 2 levels.
  ! Failed to load palette 'qual_vivid': Palette "qual_vivid" not found under "sequential", but exists under "qualitative". Try: `get_palette("qual_vivid", type = "qualitative")`. Using default colors.
  ! Pearson residuals not available. Using grouped bar chart.
  
  -- Data Preparation --
  
  i Automatically selected 6 numeric columns.
  
  -- Computing Correlations --
  
  i Found 1 significant correlation out of 10 tests
  
  -- Creating Heatmap --
  
  v Analysis complete!
  
  -- Data Preparation --
  
  i Automatically selected 5 numeric columns.
  
  -- Computing Correlations --
  
  i Found 1 significant correlation out of 10 tests
  
  -- Creating Heatmap --
  
  v Analysis complete!
  
  -- Data Preparation --
  
  
  -- Data Preparation --
  
  i Automatically selected 1 numeric column.
  
  -- Data Preparation --
  
  
  -- Data Preparation --
  
  
  -- Data Preparation --
  
  i Automatically selected 3 numeric columns.
  
  -- Computing Correlations --
  
  i Found 0 significant correlations out of 3 tests
  
  -- Creating Heatmap --
  
  v Analysis complete!
  
  -- Quick Correlation Analysis Results --
  
  
  i Method: pearson
  i Variables: 5
  i Significant pairs: 1
  
  
  -- Top 5 Significant Correlations 
  
  Use `summary()` for detailed results.
  
  
  -- Detailed Correlation Analysis Summary --
  
  
  -- Analysis Parameters 
  Correlation method: pearson
  Missing value handling: pairwise.complete.obs
  P-value adjustment: none
  Number of variables: 5
  
  
  -- Descriptive Statistics 
   variable  n      mean       sd    median       min      max
       var1 50 10.068807 1.851740  9.854719  6.066766 14.33791
       var2 50 15.439225 2.716342 15.457737  8.072493 21.56200
       var3 50 18.984398 3.957336 18.780520 11.787011 28.40044
       var4 50 25.194034 4.654875 24.616200 18.445992 41.20520
       var5 50  8.046525 1.578258  8.027126  4.130347 12.53476
  
  
  -- Correlation Summary 
  Min correlation: -0.156
  Max correlation: 0.81
  Mean |correlation|: 0.144
  
  
  -- Significant Correlations 
  i Significant pairs are based on unadjusted p-values
  Significant pairs: 1 out of 10 tests
  
  All significant pairs:
   var1 var2 correlation      p_value
   var1 var5   0.8100595 1.041235e-12
  
  Analysis performed: 2026-03-04 19:50:32
  
  
  -- Detailed Correlation Analysis Summary --
  
  
  -- Analysis Parameters 
  Correlation method: pearson
  Missing value handling: pairwise.complete.obs
  P-value adjustment: bonferroni
  Number of variables: 5
  
  
  -- Descriptive Statistics 
   variable  n      mean       sd    median       min      max
       var1 50 10.068807 1.851740  9.854719  6.066766 14.33791
       var2 50 15.439225 2.716342 15.457737  8.072493 21.56200
       var3 50 18.984398 3.957336 18.780520 11.787011 28.40044
       var4 50 25.194034 4.654875 24.616200 18.445992 41.20520
       var5 50  8.046525 1.578258  8.027126  4.130347 12.53476
  
  
  -- Correlation Summary 
  Min correlation: -0.156
  Max correlation: 0.81
  Mean |correlation|: 0.144
  
  
  -- Significant Correlations 
  i Significant pairs are based on adjusted p-values (method: bonferroni)
  Significant pairs: 1 out of 10 tests
  
  All significant pairs:
   var1 var2 correlation      p_value
   var1 var5   0.8100595 1.041235e-11
  
  Analysis performed: 2026-03-04 19:50:35
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  ! Could not load palette "qual_bold". Using defaults.
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  
  -- Quick t-test Results --
  
  
  i Method: t.test
  v Significant difference (p < 0.001)
  
  
  -- Descriptive Statistics 
  
  Use `summary()` for detailed results.
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  
  -- Detailed Quick t-test Summary --
  
  -- Test Method 
  Method used: t.test
  Paired: FALSE
  Alternative: two.sided
  Equal variance: TRUE
  
  -- Test Result 
  
  -- Descriptive Statistics 
  
  -- Normality Tests (Shapiro-Wilk) 
  Control: n = 25, p = 0.5812
  Treatment: n = 25, p = 0.8166
  
  i Decision: Small sample size (n < 30). Data appears normal (Shapiro p >= 0.05). Using t-test.
  
  -- Variance Equality Test (Levene) 
  Levene's test: p = 0.9863
  Equal variances: TRUE
  Analysis performed: 2026-03-04 19:51:42
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  
  -- Automatic Method Selection --
  
  i Checking normality for each group...
  v Data appears normal (Shapiro-Wilk p >= 0.05).
  Control: n = 25, p = 0.581
  Treatment: n = 25, p = 0.817
  v Variances appear equal (Levene's test, p = 0.986)
  v Using Student's t-test (equal variances assumed)
  
  -- Statistical Test --
  
  v Significant difference detected (p < 0.001)
  
  -- Creating Visualization --
  
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Analysis complete!
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  v Loaded palette "qual_vivid" ("qualitative"), 9 colors
  
  -- Reading Excel file --
  
  i Sheets in '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf1a04a8a2.xlsx': Sheet1
  v Column names cleaned with janitor::clean_names().
  v Successfully read sheet 1 from '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf1a04a8a2.xlsx'.
  
  -- Reading Excel file --
  
  i Sheets in '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf379022f3.xlsx': Sheet1
  v Column names cleaned with janitor::clean_names().
  v Successfully read sheet 1 from '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf379022f3.xlsx'.
  
  -- Reading Excel file --
  
  i Sheets in '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf130f833b.xlsx': Sheet1
  v Successfully read sheet 1 from '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf130f833b.xlsx'.
  
  -- Reading Excel file --
  
  i Sheets in '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf5fc367ed.xlsx': Sheet1
  v Column names cleaned with janitor::clean_names().
  v Successfully read sheet 1 from '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf5fc367ed.xlsx'.
  
  
  Path: '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf7a200709.csv'
  Separator: "," | Encoding: "UTF-8"
    OpenMP version (_OPENMP)       202011
    omp_get_num_procs()            24
    R_DATATABLE_NUM_PROCS_PERCENT  unset (default 50)
    R_DATATABLE_NUM_THREADS        unset
    R_DATATABLE_THROTTLE           unset (default 1024)
    omp_get_thread_limit()         2
    omp_get_max_threads()          24
    OMP_THREAD_LIMIT               2
    OMP_NUM_THREADS                unset
    RestoreAfterFork               true
    data.table is using 2 threads with throttle==1024. See ?setDTthreads.
  freadR.c has been passed a filename: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf7a200709.csv
  [01] Check arguments
    Using 2 threads (omp_get_max_threads()=24, nth=2)
    NAstrings = [<<NA>>]
    None of the NAstrings look like numbers.
    show progress = 1
    0/1 column will be read as integer
    Y/N column will be read as character
  [02] Opening the file
    Opening file /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf7a200709.csv
    File opened, size = 235 bytes.
    Memory mapped ok
  [03] Detect and skip BOM
  [04] Arrange mmap to be \0 terminated
    \n has been found in the input (counts: 0 \r by themselves vs 6 [\r]*\n) and different lines can end with different line endings (e.g. mixed \n and \r\n in one file). This is common and ideal.
  [05] Skipping initial rows if needed
    Positioned on line 1 starting: <<mpg,cyl,disp,hp,drat,wt,qsec,v>>
  [06] Detect separator, quoting rule, and ncolumns
    Using supplied sep ','
    sep=','  with 6 lines of 11 fields using quote rule 0
    Detected 11 columns on line 1. This line is either column names or first data row. Line starts as: <<mpg,cyl,disp,hp,drat,wt,qsec,v>>
    Quote rule picked = 0
    fill=false and the most number of columns found is 11
  [07] Detect column types, dec, good nrow estimate and whether first row is column names
    'header' changed by user from 'auto' to true
    sep=',' so dec set to '.'
    Number of sampling jump points = 1 because (234 bytes from row 1 to eof) / (2 * 234 jump0size) == 0
    Type codes (jump 000)    : 97779997777  Quote rule 0
    All rows were sampled since file is small so we know nrow=5 exactly
  [08] Assign column names
  [09] Apply user overrides on column types
    After 0 type and 0 drop user overrides : 97779997777
  [10] Allocate memory for the datatable
    Allocating 11 column slots (11 - 0 dropped) with 5 rows
  [11] Read the data
    jumps=[0..1), chunk_size=1048576, total_size=189
  Read 5 rows x 11 columns from 235 bytes file in 00:00.000 wall clock time
  [12] Finalizing the datatable
    Type counts:
           7 : int32     '7'
           4 : float64   '9'
  =============================
     0.000s ( 31%) Memory map 0.000GiB file
     0.000s ( 47%) sep=',' ncol=11 and header detection
     0.000s (  6%) Column type detection using 5 sample rows
     0.000s (  4%) Allocation of 5 rows x 11 cols (0.000GiB) of which 5 (100%) rows used
     0.000s ( 13%) Reading 1 chunks (0 swept) of 1.000MiB (each chunk 5 rows) using 1 threads
     +    0.000s (  2%) Parse to row-major thread buffers (grown 0 times)
     +    0.000s (  0%) Transpose
     +    0.000s ( 10%) Waiting
     0.000s (  0%) Rereading 0 columns due to out-of-sample type exceptions
     0.000s        Total
  v File loaded successfully (5 rows x 11 cols)
  
  -- glimpse 
  `glimpse(df)` from dplyr/tibble gives a compact overview.
  
  -- glimpse 
  `glimpse(df)` from dplyr/tibble gives a compact overview.
  
  -- read_excel 
  `readxl::read_excel("yourfile.xlsx")` reads Excel files. Supports `sheet =`, `range =`, etc.
  
  -- glimpse 
  `glimpse(df)` from dplyr/tibble gives a compact overview.
  
  -- Usage Examples --------------------------------------------------------------
  
  -- glimpse 
  `glimpse(df)` from dplyr/tibble gives a compact overview.
  
  -- read_excel 
  `readxl::read_excel("yourfile.xlsx")` reads Excel files. Supports `sheet =`, `range =`, etc.
  
  -- droplevels 
  `droplevels(df)` removes unused factor levels from a data frame or factor.
  
  -- modifyList 
  `modifyList(x, y)` merges two lists; elements in `y` overwrite those in `x`.
  
  -- do.call 
  `do.call(fun, args)` calls a function with arguments in a list: `do.call(plot, list(x = 1:10))`.
  
  -- sprintf 
  `sprintf("Hello, %s!", name)` formats strings with `%s`, `%d`, etc.
  
  -- scRNAseq 
  `scRNAseq` (Bioconductor) provides scRNA-seq datasets, e.g., `ZeiselBrainData()`.
  
  -- basename 
  `basename(path)` extracts the filename from a full path. See also `dirname()`.
  
  -- here 
  `here::here("data", "raw", "sample1.rds")` builds a path from project root.
  
  -- stopifnot 
  `stopifnot(cond1, cond2, ...)` throws if any condition is FALSE.
  
  -- object.size 
  `object.size(x)` estimates memory size; use `format()` to pretty-print.
  
  -- slice 
  `slice(df, 1:3)` selects rows by position; see `slice_head()`, `slice_tail()`, `slice_max()`.
  
  -- unzip 
  `unzip("file.zip", exdir = "dir")` extracts ZIP archives.
  
  -- gunzip 
  `R.utils::gunzip("file.csv.gz", remove = FALSE)` decompresses .gz files.
  
  -- untar 
  `untar("file.tar.gz", exdir = "dir")` extracts .tar or .tar.gz archives.
  
  -- NoLegend 
  `NoLegend()` removes legends from ggplot2/Seurat plots.
  
  -- RotatedAxis 
  `RotatedAxis()` rotates x-axis text for readability in dot plots.
  
  -- guides 
  `guides(fill = "none")` customizes or removes legends (with `scale_*`).
  
  -- log2 
  `log2(x)` base-2 logarithm (often for fold change).
  
  -- log 
  `log(x, base = exp(1))` natural log by default; set `base = 10` or `2` for others.
  
  -- log10 
  `log10(x)` base-10 logarithm (orders of magnitude).
  
  -- round 
  `round(x, digits = 0)` rounds; use `signif()` for significant digits.
  
  -- floor 
  `floor(x)` greatest integer <= x (e.g., `floor(2.8)` -> 2).
  
  -- ceiling 
  `ceiling(x)` smallest integer >= x (e.g., `ceiling(2.1)` -> 3).
  
  -- cut 
  `cut(x, breaks)` bins numeric vector; `breaks = 3` or custom; `labels = FALSE` for group indices.
  
  -- cumsum 
  `cumsum(x)` cumulative sum.
  
  -- cumprod 
  `cumprod(x)` cumulative product.
  
  -- cummin 
  `cummin(x)` running minimum.
  
  -- cummax 
  `cummax(x)` running maximum.
  
  -- row_number 
  `row_number(x)` order rank (ties broken arbitrarily).
  
  -- min_rank 
  `min_rank(x)` ties get the same minimum rank.
  
  -- dense_rank 
  `dense_rank(x)` like `min_rank()` but without gaps.
  
  -- percent_rank 
  `percent_rank(x)` relative rank in [0,1], normalized by n-1.
  
  -- cume_dist 
  `cume_dist(x)` cumulative proportion of values <= x.
  
  -- str_view 
  `stringr::str_view(string, pattern)` highlights regex matches; `str_view_all()` for all.
  
  -- str_c 
  `stringr::str_c(...)` concatenates; use `sep`/`collapse` as needed.
  
  -- str_glue 
  `glue::glue("Hello, {name}!")` inline expressions with `{}`.
  
  -- str_flatten 
  `stringr::str_flatten(x, collapse = ", ")` join a character vector.
  
  -- str_length 
  `stringr::str_length(x)` string lengths.
  
  -- str_sub 
  `stringr::str_sub(x, start, end)` extract/replace substrings (supports negative indices).
  
  -- today 
  `lubridate::today()` current Date (no time).
  
  -- now 
  `lubridate::now()` current POSIXct date-time.
  
  -- Sys.timezone 
  `Sys.timezone()` system time zone name.
  
  -- skimr 
  `skimr::skim(df)` compact, readable data summaries.
  
  -- par 
  `par(mfrow = c(m, n))` split plotting area (e.g., 2x2).
  
  -- layout 
  `layout(matrix, widths, heights)` flexible plot arrangement.
  
  -- datatable 
  `DT::datatable(data)` interactive table (search/filter/sort/paginate).
  
  -- windowsFonts 
  `windowsFonts()` register system fonts (Windows).
  
  -- sign 
  `sign(x)` returns -1/0/1 for negative/zero/positive.
  
  -- reactable 
  `reactable::reactable(data)` modern interactive table.
  
  -- trimws 
  `trimws(x)` removes leading and trailing whitespace.
  
  -- cranlogs 
  `cranlogs::cran_downloads('pkgname', from = 'last-month')` gets CRAN download stats; use `'last-week'`, `'last-day'`, or specific dates.
  
  -- dlstats 
  `dlstats::cran_stats('pkgname')` shows CRAN download trends with plots; supports Bioconductor via `source = 'bioc'`.
  -- Available Keywords ----------------------------------------------------------
  `glimpse, read_excel, droplevels, modifyList, do.call, sprintf, scRNAseq,
  basename, here, stopifnot, object.size, slice, unzip, gunzip, untar, NoLegend,
  RotatedAxis, guides, log2, log, log10, round, floor, ceiling, cut, cumsum,
  cumprod, cummin, cummax, row_number, min_rank, dense_rank, percent_rank,
  cume_dist, str_view, str_c, str_glue, str_flatten, str_length, str_sub, today,
  now, Sys.timezone, skimr, par, layout, datatable, windowsFonts, sign,
  reactable, trimws, cranlogs, dlstats`
  x No match found for keyword: "notakeyword"
  
  -- min_rank 
  `min_rank(x)` ties get the same minimum rank.
  
  -- dense_rank 
  `dense_rank(x)` like `min_rank()` but without gaps.
  
  -- percent_rank 
  `percent_rank(x)` relative rank in [0,1], normalized by n-1.
  v Palette removed from qualitative: '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/palettes_test_982bf41596a56/qualitative/test_palette.json'
  ! Palette not found in any type: nonexistent
  v Palette removed from diverging: '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/palettes_test_982bf13083d9b/diverging/test_palette.json'
  v Palette removed from qualitative: '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/custom_palettes_982bfa8e89c6/qualitative/custom_test.json'
  v RGB: c(255, 128, 0) -> HEX: #FF8000
  v RGB: c(0, 0, 0) -> HEX: #000000
  v RGB: c(255, 255, 255) -> HEX: #FFFFFF
  v Converted 3 RGB values to HEX.
  i RGB: c(255, 128, 0) -> HEX: #FF8000
  i RGB: c(0, 255, 0) -> HEX: #00FF00
  i RGB: c(0, 0, 255) -> HEX: #0000FF
  v RGB: c(0, 1, 255) -> HEX: #0001FF
  x An error occurred: non-numeric argument to mathematical function
  
  -- Statistical Power Analysis --------------------------------------------------
  
  i Test: t.test (two.sample)
  i Effect size: 0.500
  i Sample size: 30 (per group)
  i Significance level (alpha): 0.050
  i Alternative: two.sided
  
  
  -- Result --
  
  x Statistical Power: "47.79%" (Very Low)
  
  This study has only 47.8% power, meaning there is a 47.8% chance of detecting a
  true effect of size 0.50. This is considered very low power.
  
  i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group.
  
  
  -- Statistical Power Analysis --------------------------------------------------
  
  i Test: t.test (two.sample)
  i Effect size: 0.500
  i Sample size: 30 (per group)
  i Significance level (alpha): 0.050
  i Alternative: two.sided
  
  
  -- Result --
  
  x Statistical Power: "47.79%" (Very Low)
  
  This study has only 47.8% power, meaning there is a 47.8% chance of detecting a
  true effect of size 0.50. This is considered very low power.
  
  i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group.
  
  
  -- Statistical Power Analysis --------------------------------------------------
  
  i Test: t.test (two.sample)
  i Effect size: 0.500
  i Sample size: 30 (per group)
  i Significance level (alpha): 0.050
  i Alternative: two.sided
  
  
  -- Result --
  
  x Statistical Power: "47.79%" (Very Low)
  
  This study has only 47.8% power, meaning there is a 47.8% chance of detecting a
  true effect of size 0.50. This is considered very low power.
  
  i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group.
  
  
  -- Sample Size Estimation ------------------------------------------------------
  
  i Test: t.test (two.sample)
  i Target power: 0.80
  i Effect size: 0.500
  i Significance level (alpha): 0.050
  i Alternative: two.sided
  
  
  -- Result --
  
  v Sample size per group: 64
  v Total sample size: 128
  
  To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects
  per group (128 total).
  
  i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations.
  
  
  -- Sample Size Estimation ------------------------------------------------------
  
  i Test: t.test (two.sample)
  i Target power: 0.80
  i Effect size: 0.500
  i Significance level (alpha): 0.050
  i Alternative: two.sided
  
  
  -- Result --
  
  v Sample size per group: 64
  v Total sample size: 128
  
  To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects
  per group (128 total).
  
  i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations.
  
  
  -- Sample Size Estimation ------------------------------------------------------
  
  i Test: t.test (two.sample)
  i Target power: 0.80
  i Effect size: 0.500
  i Significance level (alpha): 0.050
  i Alternative: two.sided
  
  
  -- Result --
  
  v Sample size per group: 64
  v Total sample size: 128
  
  To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects
  per group (128 total).
  
  i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations.
  
  i Square test started at 2026-03-04 19:53:18
  v Square test completed in 0.000 seconds
  i Silent task started at 2026-03-04 19:53:18
  v Silent task completed in 0.001 seconds
  v Excel file written to '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf30958dff.xlsx'
  v Excel file written to '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf73bddaa1.xlsx'
  v Excel file written to '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf30f7af20.xlsx'
  ! File already exists and will be overwritten: '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf30f7af20.xlsx'
  v Excel file written to '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf30f7af20.xlsx'
  v Excel file written to '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/test-write-982bf35b58fdb_2026-03-04.xlsx'
  [ FAIL 7 | WARN 0 | SKIP 68 | PASS 2100 ]
  
  ══ Skipped tests (68) ══════════════════════════════════════════════════════════
  • On CRAN (68): 'test-download_batch.R:19:3', 'test-download_batch.R:39:3',
    'test-download_gene_ref.R:66:3', 'test-download_gene_ref.R:109:3',
    'test-download_gene_ref.R:150:3', 'test-download_gene_ref.R:174:3',
    'test-download_gene_ref.R:198:3', 'test-download_gene_ref.R:218:3',
    'test-download_gene_ref.R:237:3', 'test-download_gene_ref.R:264:3',
    'test-download_gene_ref.R:292:3', 'test-download_gene_ref.R:320:3',
    'test-download_gene_ref.R:339:3', 'test-download_gene_ref.R:359:3',
    'test-download_url.R:206:3', 'test-download_url.R:238:3',
    'test-download_url.R:266:3', 'test-download_url.R:296:3',
    'test-download_url.R:319:3', 'test-download_url.R:342:3',
    'test-download_url.R:365:3', 'test-file_info.R:12:3',
    'test-file_info.R:26:3', 'test-file_info.R:48:3', 'test-file_info.R:59:3',
    'test-file_info.R:71:3', 'test-pkg.R:389:3', 'test-pkg.R:528:3',
    'test-pkg.R:536:3', 'test-pkg.R:546:3', 'test-pkg.R:561:3',
    'test-plot_forest.R:61:3', 'test-plot_forest.R:77:3',
    'test-plot_forest.R:96:3', 'test-plot_forest.R:113:3',
    'test-plot_forest.R:134:3', 'test-plot_forest.R:149:3',
    'test-plot_forest.R:164:3', 'test-plot_forest.R:183:3',
    'test-plot_forest.R:200:3', 'test-plot_forest.R:217:3',
    'test-plot_forest.R:235:3', 'test-plot_forest.R:253:3',
    'test-plot_forest.R:275:3', 'test-plot_forest.R:292:3',
    'test-plot_forest.R:310:3', 'test-plot_forest.R:327:3',
    'test-plot_forest.R:350:3', 'test-plot_forest.R:368:3',
    'test-plot_forest.R:389:3', 'test-plot_forest.R:410:3',
    'test-plot_forest.R:427:3', 'test-plot_forest.R:444:3',
    'test-plot_forest.R:475:3', 'test-plot_forest.R:493:3',
    'test-plot_forest.R:515:3', 'test-plot_forest.R:532:3',
    'test-plot_forest.R:553:3', 'test-plot_forest.R:570:3',
    'test-plot_forest.R:594:3', 'test-plot_forest.R:611:3',
    'test-plot_forest.R:632:3', 'test-plot_forest.R:662:3',
    'test-plot_forest.R:696:3', 'test-plot_forest.R:730:3',
    'test-plot_forest.R:756:3', 'test-plot_forest.R:772:3',
    'test-plot_forest.R:789:3'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-download_geo_data.R:27:3'): download_geo_data() validates gse_id parameter ──
  Error in `download_geo_data("invalid", dest_dir = temp_dir)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
  Backtrace:
      ▆
   1. ├─testthat::expect_error(...) at test-download_geo_data.R:27:3
   2. │ └─testthat:::expect_condition_matching_(...)
   3. │   └─testthat:::quasi_capture(...)
   4. │     ├─testthat (local) .capture(...)
   5. │     │ └─base::withCallingHandlers(...)
   6. │     └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
   7. └─evanverse::download_geo_data("invalid", dest_dir = temp_dir)
   8.   └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
   9.     └─rlang::abort(...)
  ── Error ('test-download_geo_data.R:91:3'): download_geo_data() validates dest_dir parameter ──
  Error in `download_geo_data("GSE12345", dest_dir = 123)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
  Backtrace:
      ▆
   1. ├─testthat::expect_error(...) at test-download_geo_data.R:91:3
   2. │ └─testthat:::expect_condition_matching_(...)
   3. │   └─testthat:::quasi_capture(...)
   4. │     ├─testthat (local) .capture(...)
   5. │     │ └─base::withCallingHandlers(...)
   6. │     └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
   7. └─evanverse::download_geo_data("GSE12345", dest_dir = 123)
   8.   └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
   9.     └─rlang::abort(...)
  ── Error ('test-download_geo_data.R:128:3'): download_geo_data() validates overwrite parameter ──
  Error in `download_geo_data("GSE12345", dest_dir = temp_dir, overwrite = "yes")`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
  Backtrace:
      ▆
   1. ├─testthat::expect_error(...) at test-download_geo_data.R:128:3
   2. │ └─testthat:::expect_condition_matching_(...)
   3. │   └─testthat:::quasi_capture(...)
   4. │     ├─testthat (local) .capture(...)
   5. │     │ └─base::withCallingHandlers(...)
   6. │     └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
   7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, overwrite = "yes")
   8.   └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
   9.     └─rlang::abort(...)
  ── Error ('test-download_geo_data.R:159:3'): download_geo_data() validates log parameter ──
  Error in `download_geo_data("GSE12345", dest_dir = temp_dir, log = "yes")`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
  Backtrace:
      ▆
   1. ├─testthat::expect_error(...) at test-download_geo_data.R:159:3
   2. │ └─testthat:::expect_condition_matching_(...)
   3. │   └─testthat:::quasi_capture(...)
   4. │     ├─testthat (local) .capture(...)
   5. │     │ └─base::withCallingHandlers(...)
   6. │     └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
   7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, log = "yes")
   8.   └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
   9.     └─rlang::abort(...)
  ── Error ('test-download_geo_data.R:190:3'): download_geo_data() validates log_file parameter ──
  Error in `download_geo_data("GSE12345", dest_dir = temp_dir, log_file = 123)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
  Backtrace:
      ▆
   1. ├─testthat::expect_error(...) at test-download_geo_data.R:190:3
   2. │ └─testthat:::expect_condition_matching_(...)
   3. │   └─testthat:::quasi_capture(...)
   4. │     ├─testthat (local) .capture(...)
   5. │     │ └─base::withCallingHandlers(...)
   6. │     └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
   7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, log_file = 123)
   8.   └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
   9.     └─rlang::abort(...)
  ── Error ('test-download_geo_data.R:217:3'): download_geo_data() validates retries parameter ──
  Error in `download_geo_data("GSE12345", dest_dir = temp_dir, retries = -1)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
  Backtrace:
      ▆
   1. ├─testthat::expect_error(...) at test-download_geo_data.R:217:3
   2. │ └─testthat:::expect_condition_matching_(...)
   3. │   └─testthat:::quasi_capture(...)
   4. │     ├─testthat (local) .capture(...)
   5. │     │ └─base::withCallingHandlers(...)
   6. │     └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
   7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, retries = -1)
   8.   └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
   9.     └─rlang::abort(...)
  ── Error ('test-download_geo_data.R:260:3'): download_geo_data() validates timeout parameter ──
  Error in `download_geo_data("GSE12345", dest_dir = temp_dir, timeout = 0)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
  Backtrace:
      ▆
   1. ├─testthat::expect_error(...) at test-download_geo_data.R:260:3
   2. │ └─testthat:::expect_condition_matching_(...)
   3. │   └─testthat:::quasi_capture(...)
   4. │     ├─testthat (local) .capture(...)
   5. │     │ └─base::withCallingHandlers(...)
   6. │     └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
   7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, timeout = 0)
   8.   └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
   9.     └─rlang::abort(...)
  
  [ FAIL 7 | WARN 0 | SKIP 68 | PASS 2100 ]
  Error:
  ! Test failures.
  Execution halted