In sbalci/histopathology-template: Template of R Codes Used in Histopathology Research.

Codes for Descriptive Statistics.^[See childRmd/_11descriptives.Rmd file for other codes]

Table One

Report Data properties via report 📦

mydata %>%
  dplyr::select(-dplyr::contains("Date")) %>%
  report::report()

Table 1 via arsenal 📦

# cat(names(mydata), sep = " + \n")
library(arsenal)
tab1 <- arsenal::tableby(
  ~ Sex +
    Age +
    Race +
    PreinvasiveComponent +
    LVI +
    PNI +
    Death +
    Group +
    Grade +
    TStage +
    # `Anti-X-intensity` +
    # `Anti-Y-intensity` +
    LymphNodeMetastasis +
    Valid +
    Smoker +
    Grade_Level
  ,
  data = mydata 
)
summary(tab1)

Table 1 via tableone 📦

library(tableone)
mydata %>% 
  dplyr::select(-keycolumns,
         -dateVariables
         ) %>% 
tableone::CreateTableOne(data = .)

# CreateTableOne(vars = myVars, data = mydata, factorVars = characterVariables)

# tab <- CreateTableOne(vars = myVars, data = pbc, factorVars = catVars)
# print(tab, showAllLevels = TRUE)
# ?print.TableOne
# summary(tab)

# print(tab, nonnormal = biomarkers)

# print(tab, nonnormal = biomarkers, exact = "stage", quote = TRUE, noSpaces = TRUE)

# tab3Mat <- print(tab3, nonnormal = biomarkers, exact = "stage", quote = FALSE, noSpaces = TRUE, printToggle = FALSE)
# write.csv(tab3Mat, file = "myTable.csv")

Descriptive Statistics of Continuous Variables

mydata %>% 
  dplyr::select(
    continiousVariables,
    numericVariables,
    integerVariables
  ) %>% 
summarytools::descr(., style = 'rmarkdown')

print(summarytools::descr(mydata), method = 'render', table.classes = 'st-small')

mydata %>% 
  summarytools::descr(.,
                      stats = "common",
                      transpose = TRUE,
                      headings = FALSE
                      )

mydata %>% 
  summarytools::descr(stats = "common") %>%
  summarytools::tb()

mydata$Sex %>% 
  summarytools::freq(cumul = FALSE, report.nas = FALSE) %>%
  summarytools::tb()

mydata %>%
  explore::describe() %>%
  dplyr::filter(unique < 5)

mydata %>%
  explore::describe() %>%
  dplyr::filter(na > 0)

mydata %>% explore::describe()

Categorical Variables

Use R/gc_desc_cat.R to generate gc_desc_cat.Rmd containing descriptive statistics for categorical variables

source(here::here("R", "gc_desc_cat.R"))

tab <- 
  mydata %>% 
  dplyr::select(
    -keycolumns
    ) %>% 
  tableone::CreateTableOne(data = .)
?print.CatTable
tab$CatTable

race_stats <- summarytools::freq(mydata$Race) 
print(race_stats,
      report.nas = FALSE,
      totals = FALSE,
      display.type = FALSE,
      Variable.label = "Race Group"
      )

mydata %>% explore::describe(PreinvasiveComponent)

## Frequency or custom tables for categorical variables
SmartEDA::ExpCTable(
  mydata,
  Target = NULL,
  margin = 1,
  clim = 10,
  nlim = 5,
  round = 2,
  bin = NULL,
  per = T
)

inspectdf::inspect_cat(mydata)

inspectdf::inspect_cat(mydata)$levels$Group

Split-Group Stats Categorical

library(summarytools)

grouped_freqs <- stby(data = mydata$Smoker, 
                      INDICES = mydata$Sex, 
                      FUN = freq, cumul = FALSE, report.nas = FALSE)

grouped_freqs %>% tb(order = 2)

Grouped Categorical

summarytools::stby(
  list(x = mydata$LVI, y = mydata$LymphNodeMetastasis), 
  mydata$PNI,
  summarytools::ctable
  )

with(mydata, 
     summarytools::stby(
       list(x = LVI, y = LymphNodeMetastasis), PNI,
       summarytools::ctable
       )
     )

SmartEDA::ExpCTable(
  mydata,
  Target = "Sex",
  margin = 1,
  clim = 10,
  nlim = NULL,
  round = 2,
  bin = 4,
  per = F
)

mydata %>% 
  dplyr::select(characterVariables) %>% 
  dplyr::select(PreinvasiveComponent,
         PNI,
         LVI
         ) %>% 
reactable::reactable(data = ., groupBy = c("PreinvasiveComponent", "PNI"), columns = list(
  LVI = reactable::colDef(aggregate = "count")
))

\pagebreak

---

Continious Variables

questionr:::icut()

source(here::here("R", "gc_desc_cont.R"))

tab <- tableone::CreateTableOne(data = mydata)
# ?print.ContTable
tab$ContTable
print(tab$ContTable, nonnormal = c("Anti-X-intensity"))

mydata %>% explore::describe(Age)

mydata %>% 
  dplyr::select(continiousVariables) %>% 
SmartEDA::ExpNumStat(
  data = .,
  by = "A",
  gp = NULL,
  Qnt = seq(0, 1, 0.1),
  MesofShape = 2,
  Outlier = TRUE,
  round = 2
)

inspectdf::inspect_num(mydata, breaks = 10)

inspectdf::inspect_num(mydata)$hist$Age

inspectdf::inspect_num(mydata, breaks = 10) %>%
  inspectdf::show_plot()

Split-Group Stats Continious

grouped_descr <- summarytools::stby(data = mydata, 
                      INDICES = mydata$Sex, 
                      FUN = summarytools::descr, stats = "common")
# grouped_descr %>% summarytools::tb(order = 2)
grouped_descr %>% summarytools::tb()

mydata %>%
  group_by(US) %>% 
  dlookr::describe(Sales, Income) 

carseats %>%
  group_by(US, Urban) %>% 
  dlookr::describe(Sales, Income) 

categ <- dlookr::target_by(carseats, US)
cat_num <- dlookr::relate(categ, Sales)
cat_num
summary(cat_num)
plot(cat_num)

Grouped Continious

   summarytools::stby(data = mydata, 
                               INDICES = mydata$PreinvasiveComponent, 
                               FUN = summarytools::descr,
                      stats = c("mean", "sd", "min", "med", "max"), 
                               transpose = TRUE)

with(mydata, 
     summarytools::stby(Age, PreinvasiveComponent, summarytools::descr), 
                   stats = c("mean", "sd", "min", "med", "max"),
                   transpose = TRUE
                   )

mydata %>% 
  group_by(PreinvasiveComponent) %>% 
  summarytools::descr(stats = "fivenum")

## Summary statistics by – category
SmartEDA::ExpNumStat(
  mydata,
  by = "GA",
  gp = "PreinvasiveComponent",
  Qnt = seq(0, 1, 0.1),
  MesofShape = 2,
  Outlier = TRUE,
  round = 2
)

\pagebreak

sbalci/histopathology-template documentation built on June 29, 2023, 5:52 a.m.