Codes for Descriptive Statistics.^[See childRmd/_11descriptives.Rmd
file for other codes]
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$Sex %>% summarytools::freq(cumul = FALSE, report.nas = FALSE) %>% summarytools::tb()
mydata %>% explore::describe() %>% dplyr::filter(unique < 5)
mydata %>% explore::describe() %>% dplyr::filter(na > 0)
mydata %>% explore::describe()
Use R/gc_desc_cat.R
to generate gc_desc_cat.Rmd
containing descriptive statistics for categorical variables
source(here::here("R", "gc_desc_cat.R"))
tab <- mydata %>% dplyr::select( -keycolumns ) %>% tableone::CreateTableOne(data = .) ?print.CatTable tab$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$Group
library(summarytools) grouped_freqs <- stby(data = mydata$Smoker, INDICES = mydata$Sex, FUN = freq, cumul = FALSE, report.nas = FALSE) grouped_freqs %>% tb(order = 2)
summarytools::stby( list(x = mydata$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") ))
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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()
grouped_descr <- summarytools::stby(data = mydata, INDICES = mydata$Sex, FUN = summarytools::descr, stats = "common") # grouped_descr %>% summarytools::tb(order = 2) grouped_descr %>% summarytools::tb()
mydata %>% group_by(US) %>% dlookr::describe(Sales, Income) carseats %>% group_by(US, Urban) %>% dlookr::describe(Sales, Income)
categ <- dlookr::target_by(carseats, US) cat_num <- dlookr::relate(categ, Sales) cat_num summary(cat_num) plot(cat_num)
summarytools::stby(data = mydata, INDICES = mydata$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 )
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