library(readr)
library(tableone)

options(max.print = 99999)
setwd("E:/xx")
whkxz<-read.csv("inter_group differences.csv")
whkxz<-na.omit(whkxz)
whkxz$TYPE <- factor(whkxz$TYPE, levels = c(0,1,2,3,4,5,6),
                    labels = c("Cyst","Type A","Type AB","Type B1","Type B2","Type B3","Type C"))
whkxz$SEX<-factor(whkxz$SEX, levels = c(0,1),
                    labels = c("female","male"))
whkxz$HOSPITAL<-factor(whkxz$HOSPITAL, levels = c(0,1,2),
                  labels = c("czyy","hayy","hxyy"))
summary(whkxz)
str(whkxz)
names(whkxz)
x1<-c("AGE","diagnostics_Image_original_Mean","diagnostics_Image_original_Maximum","diagnostics_Mask_original_VoxelNum","original_firstorder_10Percentile","original_firstorder_90Percentile","original_firstorder_Energy","original_firstorder_Entropy","original_firstorder_InterquartileRange","original_firstorder_Kurtosis","original_firstorder_Maximum","original_firstorder_MeanAbsoluteDeviation","original_firstorder_Mean","original_firstorder_Median","original_firstorder_Minimum","original_firstorder_Range","original_firstorder_RobustMeanAbsoluteDeviation","original_firstorder_RootMeanSquared","original_firstorder_Skewness","original_firstorder_TotalEnergy","original_firstorder_Uniformity","original_firstorder_Variance","original_glcm_Autocorrelation","original_glcm_ClusterProminence","original_glcm_ClusterShade","original_glcm_ClusterTendency","original_glcm_Contrast","original_glcm_Correlation","original_glcm_DifferenceAverage","original_glcm_DifferenceEntropy","original_glcm_DifferenceVariance","original_glcm_Id","original_glcm_Idm","original_glcm_Idmn","original_glcm_Idn","original_glcm_Imc1","original_glcm_Imc2","original_glcm_InverseVariance","original_glcm_JointAverage","original_glcm_JointEnergy","original_glcm_JointEntropy","original_glcm_MCC","original_glcm_MaximumProbability","original_glcm_SumAverage","original_glcm_SumEntropy","original_glcm_SumSquares","original_gldm_DependenceEntropy","original_gldm_DependenceNonUniformity","original_gldm_DependenceNonUniformityNormalized","original_gldm_DependenceVariance","original_gldm_GrayLevelNonUniformity","original_gldm_GrayLevelVariance","original_gldm_HighGrayLevelEmphasis","original_gldm_LargeDependenceEmphasis","original_gldm_LargeDependenceHighGrayLevelEmphasis","original_gldm_LargeDependenceLowGrayLevelEmphasis","original_gldm_LowGrayLevelEmphasis","original_gldm_SmallDependenceEmphasis","original_gldm_SmallDependenceHighGrayLevelEmphasis","original_gldm_SmallDependenceLowGrayLevelEmphasis","original_glrlm_GrayLevelNonUniformity","original_glrlm_GrayLevelNonUniformityNormalized","original_glrlm_GrayLevelVariance","original_glrlm_HighGrayLevelRunEmphasis","original_glrlm_LongRunEmphasis","original_glrlm_LongRunHighGrayLevelEmphasis","original_glrlm_LongRunLowGrayLevelEmphasis","original_glrlm_LowGrayLevelRunEmphasis","original_glrlm_RunEntropy","original_glrlm_RunLengthNonUniformity","original_glrlm_RunLengthNonUniformityNormalized","original_glrlm_RunPercentage","original_glrlm_RunVariance","original_glrlm_ShortRunEmphasis","original_glrlm_ShortRunHighGrayLevelEmphasis","original_glrlm_ShortRunLowGrayLevelEmphasis","original_glszm_GrayLevelNonUniformity","original_glszm_GrayLevelNonUniformityNormalized","original_glszm_GrayLevelVariance","original_glszm_HighGrayLevelZoneEmphasis","original_glszm_LargeAreaEmphasis","original_glszm_LargeAreaHighGrayLevelEmphasis","original_glszm_LargeAreaLowGrayLevelEmphasis","original_glszm_LowGrayLevelZoneEmphasis","original_glszm_SizeZoneNonUniformity","original_glszm_SizeZoneNonUniformityNormalized","original_glszm_SmallAreaEmphasis","original_glszm_SmallAreaHighGrayLevelEmphasis","original_glszm_SmallAreaLowGrayLevelEmphasis","original_glszm_ZoneEntropy","original_glszm_ZonePercentage","original_glszm_ZoneVariance","original_ngtdm_Busyness","original_ngtdm_Coarseness","original_ngtdm_Complexity","original_ngtdm_Contrast","original_ngtdm_Strength")
x2<-c("HOSPITAL","SEX")
library(tableone)
table1<-CreateTableOne(vars = c(x1, x2), 
                       data = whkxz, 
                       factorVars = x2, 
                       strata = "TYPE", addOverall = T)
results1<-print(table1,  showalllevels = FALSE)