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libraries_TJ_explore.R
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libraries_TJ_explore.R
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#### Basic Exploratory analytics ####
#### T.Jetka ####
tj_logreg_acc<-function(x,y){
df=data.frame(x=x,y=y)
df$y=factor(df$y)
y_levels<-levels(df$y)
tweights=rep(1,nrow(df))
for (ilevel in y_levels){
tweights[df$y==ilevel]=(1/sum(df$y==ilevel))
}
lr_model=nnet::multinom(y~x,data=df,
na.action=na.omit,weights = tweights)
prob_lr<-data.frame(fitted(lr_model))
if (length(y_levels)==2) {prob_lr=cbind(1-prob_lr,prob_lr)}
colnames(prob_lr)<-as.character(y_levels)
obs<-df$y
pred<-apply(prob_lr,1,function(x){
idmax=which.max(x)
as.character(y_levels)[idmax]
})
temp_confusionMatrix<-try(caret::confusionMatrix(factor(pred,levels=y_levels),obs),silent=TRUE)
if (length(y_levels)==2){
out=mean(temp_confusionMatrix$byClass[11],na.rm=TRUE)
} else {
out=mean(temp_confusionMatrix$byClass[,11],na.rm=TRUE)
}
if (!is.numeric(out)){
out=0
}
out
}
tj_explore_round2<-function(x){
round(x,digits=2)
}
tj_explore_round2Sc<-function(xx){
xx_out=xx
for (i in 1:length(xx)){
x=xx[i]
if ((x>1000)|x<(-10000)) {
xx_out[i]=format(x,scientific=TRUE,digits=2)
} else if ((x>0.2)|x<(-0.2)){
xx_out[i]=round(x,digits=2)
} else {
xx_out[i]=format(x,scientific=TRUE,digits=2)
}
}
xx_out
}
tj_explore_summaryNum<-function(df){
if (all(sapply(df,is.numeric))){
outdf=do.call(rbind,lapply(df,function(x){
x=x[!is.na(x)]
temp_mean = mean(x,na.rm=TRUE)
temp_sd = sd(x,na.rm=TRUE)
temp_q1 = quantile(x,probs = c(0.25),na.rm = TRUE)
temp_q3 = quantile(x,probs = c(0.75),na.rm = TRUE)
temp_irq = temp_q3-temp_q1
id_outliers1=(abs(x-temp_mean)>3*temp_sd)
id_outliers2=(x>temp_q3+1.5*temp_irq)|(x<temp_q1-1.5*temp_irq)
data.frame(NAs=sum(is.na(x)),
min=tj_explore_round2Sc(min(x,na.rm=TRUE)),
Q05=tj_explore_round2Sc(quantile(x,probs = c(0.05),na.rm = TRUE)),
Q25=tj_explore_round2Sc(temp_q1),
median=tj_explore_round2Sc(quantile(x,probs = c(0.50),na.rm = TRUE)),
Q75=tj_explore_round2Sc(temp_q3),
Q95=tj_explore_round2Sc(quantile(x,probs = c(0.95),na.rm = TRUE)),
max=tj_explore_round2Sc(max(x,na.rm=TRUE)),
mean=tj_explore_round2Sc(temp_mean),
sd=tj_explore_round2Sc(temp_sd),
irq=tj_explore_round2Sc(temp_irq),
zeros=sum(x[!is.na(x)]==0),
outliers=sum(id_outliers1|id_outliers2),
stringsAsFactors = FALSE)
}))
} else {
stop("Non-numeric variables supplied")
}
data.frame(outdf)
}
tj_explore_introduce<-function(df){
df=as.data.frame(df)
temp_classes=sapply(df,class)
temp_numeric_classes=sapply(df,is.numeric)
temp_factor_classes=sapply(df,function(x){
is.character(x)|is.factor(x)
})
temp_num_unique=sapply(df,function(x) length(unique(x)))
temp_colnames_strange=sum(str_detect(colnames(df),"[^a-zA-Z0-9_]"))
t(data.frame(rows=nrow(df),
cols=ncol(df),
cols_StrangeSymbols=temp_colnames_strange,
numeric_vars=sum(temp_numeric_classes),
short_numeric_vars=sum((temp_num_unique<7)&temp_numeric_classes),
category_vars=sum(temp_factor_classes),
long_category_vars=sum((temp_num_unique>6)&temp_factor_classes),
single_val_vars=sum(temp_num_unique==1),
unknown_class=sum(!(temp_numeric_classes|temp_factor_classes)),
total_cases=sum(sapply(df,function(x) sum(!is.na(x)))),
total_NAs=sum(sapply(df,function(x) sum(is.na(x)))),
all_NA_cols=sum(sapply(df,function(x) all(is.na(x)))),
any_NA_cols=sum(sapply(df,function(x) any(is.na(x)))),
all_NA_rows=sum(apply(df,1,function(x) all(is.na(x)))),
any_NA_rows=sum(apply(df,1,function(x) any(is.na(x)))),
complete_rows=sum(apply(df,1,function(x) all(!is.na(x)))),
stringsAsFactors = FALSE))
}
tj_explore_colsNA<-function(df){
temp_na=tj_explore_round2(sapply(df,function(x) {
mean(is.na(x))
}))
if (all(temp_na==0)){
out = list("No NA in columns")
} else {
out = list(dfNA=data.frame(colnameX=colnames(df)[temp_na>0],
perc_NA=temp_na[temp_na>0],
stringsAsFactors = FALSE),
cols_highNA=colnames(df)[temp_na>0.1])
row.names(out$dfNA)<-NULL
}
out
}
tj_explore_distribCat<-function(x,nname,path_dir){
tj_dirCreate(path_dir)
num_cases=length(unique(x))
plot = ggplot(data=data.frame(x=x),aes(x=x))+geom_bar()+
theme_bw()+ggtitle(nname)
ggsave(plot,filename = paste0(path_dir,"/",nname,".png"),height=5,width=(3+(num_cases/3)))
1
}
tj_explore_distribNum<-function(x,nname,path_dir){
tj_dirCreate(path_dir)
num_cases=length(unique(x))
x=x[!is.na(x)]
temp_mean = mean(x,na.rm=TRUE)
temp_sd = sd(x,na.rm=TRUE)
temp_q1 = quantile(x,probs = c(0.25),na.rm = TRUE)
temp_q3 = quantile(x,probs = c(0.75),na.rm = TRUE)
temp_irq = temp_q3-temp_q1
id_outliers1=(abs(x-temp_mean)>3*temp_sd)
id_outliers2=(x>temp_q3+1.5*temp_irq)|(x<temp_q1-1.5*temp_irq)
id_outliers=id_outliers1|id_outliers2
if (sum(id_outliers)>0){
plot1 = ggplot(data=data.frame(x=x),aes(y=x,x=1))+geom_boxplot(size=1.5)+
theme_bw()+coord_flip()+ggtitle(nname)+
theme(axis.title.x = element_blank(),axis.ticks.y = element_blank(),axis.text.y = element_blank())+
scale_x_continuous("",limits=c(0,2))
plot2 = ggplot(data=data.frame(x=x),aes(x=x))+geom_density(fill="gray",size=1.5,alpha=0.6)+
theme_bw()+theme(axis.title.x = element_blank())
plot3 = ggplot(data=data.frame(x=x[!id_outliers]),aes(y=x,x=1))+geom_boxplot(size=1.5)+
theme_bw()+coord_flip()+ggtitle("without outliers")+
theme(axis.title.x = element_blank(),axis.ticks.y = element_blank(),axis.text.y = element_blank())+
scale_x_continuous("",limits=c(0,2))
plot4 = ggplot(data=data.frame(x=x[!id_outliers]),aes(x=x))+geom_density(fill="gray",size=1.5,alpha=0.6)+
theme_bw()+theme(axis.title.x = element_blank())
plot5 = ggplot(data=data.frame(x=x),aes(x=x))+
geom_histogram(bins = min(100,num_cases),colour="black",fill="gray",alpha=0.8 )+
theme_bw()+ggtitle("Histogram")+theme(axis.title.x = element_blank())
plot6 = ggplot(data=data.frame(x=x),aes(sample=x))+stat_qq()+stat_qq_line()+
ggtitle("QQ plot")
plot=grid.arrange(plot1,plot3,plot5,plot2,plot4,plot6,nrow=2,ncol=3)
ggsave(plot,filename =paste0(path_dir,"/",nname,".png"),height=5,width=10)
} else {
plot1 = ggplot(data=data.frame(x=x),aes(y=x,x=1))+geom_boxplot(size=1.5)+
theme_bw()+coord_flip()+ggtitle(nname)+
theme(axis.title.x = element_blank(),axis.ticks.y = element_blank(),axis.text.y = element_blank())+
scale_x_continuous("",limits=c(0,2))
plot2 = ggplot(data=data.frame(x=x),aes(x=x))+geom_density(fill="gray",size=1.5,alpha=0.6)+
theme_bw()
plot5 = ggplot(data=data.frame(x=x),aes(x=x))+
geom_histogram(bins = min(100,num_cases),colour="black",fill="gray",alpha=0.8)+theme_bw()+
ggtitle("Histogram")
plot6 = ggplot(data=data.frame(x=x),aes(sample=x))+stat_qq()+stat_qq_line()+theme_bw()+
ggtitle("QQ plot")
plot=grid.arrange(plot1,plot5,plot2,plot6,nrow=2,ncol=2)
ggsave(plot,filename =paste0(path_dir,"/",nname,".png"),height=5,width=7)
}
1
}
tj_explore_distrib<-function(df,explore_dir){
tj_dirCreate(explore_dir)
df=as.data.frame(df)
temp_numeric_classes=sapply(df,is.numeric)
temp_factor_classes=sapply(df,function(x){
is.character(x)|is.factor(x)
})
temp_num_unique=sapply(df,function(x) length(unique(x)))
numeric_vars=colnames(df)[((temp_num_unique>6)&temp_numeric_classes)]
short_numeric_vars=colnames(df)[((temp_num_unique<7)&temp_numeric_classes)]
category_vars=colnames(df)[(temp_num_unique<7)&(temp_factor_classes)]
long_category_vars=colnames(df)[((temp_num_unique>6)&temp_factor_classes)]
explore_dir_0=paste0(explore_dir,"/short_numeric/")
for (icol in short_numeric_vars){
tj_explore_distribCat(df[[icol]],icol,path_dir=explore_dir_0)
}
explore_dir_0=paste0(explore_dir,"/main_numeric/")
num_summary=tj_explore_summaryNum(df[,numeric_vars])
num_summary$metric=row.names(num_summary)
tj_write.table(num_summary,paste0(explore_dir,"/numeric_summary.txt"))
for (icol in numeric_vars){
tj_explore_distribNum(df[[icol]],icol,path_dir=explore_dir_0)
}
explore_dir_0=paste0(explore_dir,"/main_category/")
for (icol in category_vars){
tj_explore_distribCat(df[[icol]],icol,path_dir=explore_dir_0)
}
explore_dir_0=paste0(explore_dir,"/long_category/")
for (icol in long_category_vars){
tj_explore_distribCat(df[[icol]],icol,path_dir=explore_dir_0)
}
1
}
tj_explore_correlation<-function(df,output_dir){
tj_dirCreate(output_dir)
temp_numeric_classes=sapply(df,is.numeric)
temp_factor_classes=sapply(df,function(x){
is.character(x)|is.factor(x)
})
temp_num_unique=sapply(df,function(x) length(unique(x)))
numeric_vars=colnames(df)[((temp_num_unique>6)&temp_numeric_classes)]
short_numeric_vars=colnames(df)[((temp_num_unique<7)&temp_numeric_classes)]
category_vars=colnames(df)[(temp_num_unique<7)&(temp_factor_classes)]
long_category_vars=colnames(df)[((temp_num_unique>6)&temp_factor_classes)]
allNA_cols=sapply(df,function(x) all(is.na(x)))
singleVal_cols=(temp_num_unique==1)
for (icol in long_category_vars){
temp_table=table(df[[icol]])
temp_table=temp_table/sum(temp_table)
temp_names_to_cut=names(temp_table)[temp_table<0.01]
for (itemp in temp_names_to_cut){
df[[icol]][df[[icol]]==itemp]="other"
}
}
cat_vars=c(short_numeric_vars,category_vars,long_category_vars)
num_vars=numeric_vars
cat_vars=cat_vars[!cat_vars%in%c(allNA_cols,singleVal_cols)]
num_vars=num_vars[!num_vars%in%c(allNA_cols,singleVal_cols)]
df_cor_num=cor(df[,num_vars],use = 'pairwise.complete.obs')
df_cor_cat=matrix(0,length(cat_vars),length(cat_vars))
for (i in 1:(length(cat_vars)-1)){
t_namei=cat_vars[i]
for (j in (i+1):length(cat_vars)){
t_namej=cat_vars[j]
df_cor_cat[i,j]=cramerV( df[[t_namei]],df[[t_namej]] ,bias.correct = TRUE)
}
}
df_cor_cat=df_cor_cat+t(df_cor_cat)
for (i in 1:(length(cat_vars))){
df_cor_cat[i,i]=1
}
df_cor_numcat=matrix(0,length(cat_vars),length(num_vars))
for (i in 1:length(cat_vars)){
t_namei=cat_vars[i]
for (j in 1:length(num_vars)){
t_namej=num_vars[j]
df_cor_numcat[i,j]=tj_logreg_acc( df[[t_namej]],df[[t_namei]])
}
}
colnames(df_cor_numcat)=num_vars
row.names(df_cor_numcat)=cat_vars
plot1=ggcorr(data = NULL, cor_matrix = df_cor_num, label = TRUE,hjust=1)
plot2=ggcorr(data = NULL, cor_matrix = df_cor_cat, label = TRUE,hjust=1)
ggsave(plot1,filename =paste0(output_dir,"/","num_correlations.png"))
ggsave(plot2,filename =paste0(output_dir,"/","cat_correlations.png"))
png(paste0(output_dir,"/","num_cat_correlations.png"))
corrplot::corrplot(corr = df_cor_numcat,is.corr=FALSE)
dev.off()
df_cor_poly=hetcor(df[,c(cat_vars,num_vars)],
use="pairwise.complete.obs")
plot4=ggcorr(data = NULL, cor_matrix = df_cor_poly$correlations, label = TRUE,hjust=1)
ggsave(plot4,filename =paste0(output_dir,"/","poly_correlations.png"))
png(paste0(output_dir,"/aux_DE_corr.png"))
plot_correlation(na.omit(df[,c(cat_vars,num_vars)]), maxcat = 5L)
dev.off()
tj_write.table(df_cor_num,paste0(output_dir,"/","num_corr_matrix.txt"))
tj_write.table(df_cor_cat,paste0(output_dir,"/","cat_corr_matrix.txt"))
tj_write.table(df_cor_numcat,paste0(output_dir,"/","numcat_corr_matrix.txt"))
tj_write.table(df_cor_poly$correlations,paste0(output_dir,"/","poly_corr_matrix.txt"))
1
}
tj_explore_pca<-function(df,output_dir){
tj_dirCreate(output_dir)
# temp_numeric_classes=sapply(df,is.numeric)
# temp_factor_classes=sapply(df,function(x){
# is.character(x)|is.factor(x)
# })
# temp_num_unique=sapply(df,function(x) length(unique(x)))
# numeric_vars=colnames(df)[((temp_num_unique>6)&temp_numeric_classes)]
# short_numeric_vars=colnames(df)[((temp_num_unique<7)&temp_numeric_classes)]
# category_vars=colnames(df)[(temp_num_unique<7)&(temp_factor_classes)]
# long_category_vars=colnames(df)[((temp_num_unique>6)&temp_factor_classes)]
# allNA_cols=sapply(df,function(x) all(is.na(x)))
# singleVal_cols=(temp_num_unique==1)
# for (icol in long_category_vars){
# temp_table=table(df[[icol]])
# temp_table=temp_table/sum(temp_table)
# temp_names_to_cut=names(temp_table)[temp_table<0.01]
# for (itemp in temp_names_to_cut){
# df[[icol]][df[[icol]]==itemp]="other"
# }
# }
# cat_vars=c(short_numeric_vars,category_vars,long_category_vars)
# num_vars=numeric_vars
# cat_vars=cat_vars[!cat_vars%in%c(allNA_cols,singleVal_cols)]
# num_vars=num_vars[!num_vars%in%c(allNA_cols,singleVal_cols)]
tempFA=FAMD(base = as.data.frame(df),
graph = FALSE,ncp = min(10,ncol(df)))
temp_plot=data.frame(tempFA$eig)
temp_plot$x=factor(row.names(temp_plot),levels = row.names(temp_plot))
colnames(temp_plot)=c("eig","perc_Var","cuml_Var","component")
temp_plot$eig=NULL
temp_plot=melt(temp_plot,id.vars="component")
plot1=ggplot(data=temp_plot,aes(x=component,y=value,fill=variable))+
geom_col(position = "dodge")+theme_bw()+scale_y_continuous("Variance %")
temp_plot=data.frame(tempFA$var$coord[,1:5])
temp_plot$x=as.character(row.names(temp_plot))
temp_vars_chosen=c()
for (i in 1:5){
temp_vars_chosen=c(temp_vars_chosen,
temp_plot$x[rank(-abs(temp_plot[[i]]))<6])
}
temp_plot=temp_plot[temp_plot$x%in%temp_vars_chosen,]
temp_plot=melt(temp_plot,id.vars = "x")
plot2=ggplot(data=temp_plot,aes(x=x,y=value))+geom_col()+coord_flip()+facet_grid(.~variable)
temp_plot=data.frame(tempFA$ind$coord[,1:3])
plot3=ggpairs(temp_plot)+theme_bw()
ggsave(plot1,filename =paste0(output_dir,"/variance_decomp.png"))
ggsave(plot2,filename =paste0(output_dir,"/variables_importance.png"))
ggsave(plot3,filename =paste0(output_dir,"/pca_comp.png"))
1
}
#### Dependance ####
tj_explore_summary_numericAux<-function(x){
x=x[!is.na(x)]
if (length(x)>3){
data.frame(mean=round2Sc(mean(x)),SD=round2Sc(sd(x)),mad=round2Sc(mad(x)),
min=tj_explore_round2Sc(min(x)),max=round2Sc(max(x)),
q01=tj_explore_round2Sc(quantile(x,probs = 0.01)),
q05=tj_explore_round2Sc(quantile(x,probs = 0.05)),
q25=tj_explore_round2Sc(quantile(x,probs = 0.25)),
q50=tj_explore_round2Sc(quantile(x,probs = 0.50)),
q75=tj_explore_round2Sc(quantile(x,probs = 0.75)),
q95=tj_explore_round2Sc(quantile(x,probs = 0.95)),
q99=tj_explore_round2Sc(quantile(x,probs = 0.99)),
nobs=length(x),stringsAsFactors = FALSE
)
} else {
data.frame(media=NA,SD=NA,mad=NA,
min=NA,max=NA,
q01=NA,
q05=NA,
q25=NA,
q50=NA,
q75=NA,
q95=NA,
q99=NA,
nmuestra=length(x),stringsAsFactors = FALSE
)
}
}
tj_explore_summary_numeric<-function(df){
do.call(rbind,lapply(colnames(df),function(xn){
x=df[[xn]]
data.frame(varnum=xn,tj_explore_summary_numericAux(x))
}))
}
tj_explore_summary_numericBY<-function(df,byvar){
do.call(rbind,lapply(colnames(df),function(xn){
outdf=data.frame(varnum=xn,do.call(rbind,by(df,byvar,function(y){
x=y[[xn]]
x=as.numeric(x)
data.frame(tj_explore_summary_numericAux(x),stringsAsFactors = FALSE)
})),stringsAsFactors = FALSE)
outdf$groupname=row.names(outdf)
outdf
}))
}
tj_explore_NumVsCategory<-function(x,y,dir_output,nname,nnameGroup){
cols1=colsG1[1:length(unique(y))]
dir.create(dir_output,recursive = TRUE)
temp_df=data.frame(x=x,y=y)
df_summary=do.call(rbind,by(temp_df,temp_df$y,function(x){
tj_explore_summary_numericAux(x$x)
}))
df_summary$var=row.names(df_summary)
anova_test=oneway.test(data = temp_df,x~y)
kruskal_test=kruskal.test(data = temp_df,x~y)
if (length(unique(temp_df$y))==2){
effect_size=round2(abs(mean(temp_df$x[temp_df$y==unique(temp_df$y)[1]])-
mean(temp_df$x[temp_df$y==unique(temp_df$y)[2]]))/sd(temp_df$x))
} else {
effect_size=""
}
df_summary$param_testPV=formatSc(anova_test$p.value)
df_summary$nonparam_testPV=formatSc(kruskal_test$p.value)
df_print=df_summary[,c("var","q50","media","SD","min","max","nmuestra")]
colnames(df_print)<-c("Grupo","mediana","media","SD","min","max","nmuestra")
tj_write.table(df_summary,paste0(dir_output,"/summaryTable_",nname,".txt"))
textGG=textGrob(nname)
emptyGG=textGrob("")
plot1=ggplot(data=temp_df,aes(y=x,x=y,group=y,colour=y))+geom_boxplot()+theme_bw()+
scale_x_discrete(nnameGroup)+scale_y_continuous(nname)+scale_color_manual(nnameGroup,values = cols1)
plot2=ggplot(data=temp_df,aes(y=x,x=y,group=y,colour=y))+geom_violin(size=2)+theme_bw()+
scale_x_discrete(nnameGroup)+scale_y_continuous(nname)+scale_color_manual(nnameGroup,values = cols1)
plot3=ggplot(data=temp_df,aes(y=..density..,x=x,fill=y,colour=y))+geom_density(alpha=0.4)+theme_bw()+
scale_fill_manual(nnameGroup,values = cols1)+scale_x_continuous(nname)+scale_color_manual(nnameGroup,values = cols1)+
scale_y_continuous("densidad")
tableGG=tableGrob(df_print)
ggpv1=textGrob("")#textGrob(paste0("Parametric Test: ", df_summary$param_testPV))
ggpv2=textGrob(paste0("Kruskall-Wallis Test: ", df_summary$nonparam_testPV))
gges=textGrob("")#textGrob(paste0("Tamano del efecto: ", effect_size))
plotAll=grid.arrange(textGG,plot1,plot2,plot3,tableGG,ggpv1,ggpv2,emptyGG,gges,
layout_matrix=matrix(c(1,2,2,2,2,5,5,5,
1,2,2,2,2,5,5,5,
1,2,2,2,2,5,5,5,
1,2,2,2,2,5,5,5,
8,3,3,3,3,5,5,5,
8,3,3,3,3,5,5,5,
8,3,3,3,3,5,5,5,
8,3,3,3,3,5,5,5,
8,4,4,4,4,6,7,9,
8,4,4,4,4,6,7,9,
8,4,4,4,4,6,7,9,
8,4,4,4,4,6,7,9),
8,12))
ggsave(plotAll,filename =paste0(dir_output,"/mainPlot_",nname,".png"),width = 12,height=8)
1
}
tj_explore_CatVsCategory<-function(x,y,dir_output,nname,nnameGroup){
cols1=colsG1[1:length(unique(y))]
dir.create(dir_output,recursive = TRUE)
temp_df=data.frame(x=x,y=y)
temp_df$x=factor(temp_df$x)
temp_df$y=factor(temp_df$y)
temp_table=table(temp_df$x,temp_df$y)
temp_tableDF=dcast(data.frame(temp_table),Var1~Var2,value.var="Freq")
colnames(temp_tableDF)[1]=nname
colnames(temp_tableDF)[-1]=paste0(nnameGroup,"_",colnames(temp_tableDF)[-1])
tj_write.table(temp_tableDF,paste0(dir_output,"/countTable_",nname,".txt"))
if (nrow(temp_table)>1&ncol(temp_table)>1){
fisher_test=formatSc(fisher.test(temp_table,simulate.p.value = TRUE)$p.value)
Mantel_Haenszel_test=formatSc(MHChisqTest(temp_table)$p.value)
G_test=formatSc(GTest(temp_table)$p.value)
chisq_test=formatSc(chisq.test(temp_table)$p.value)
y_levels=unique(y)
tweights=rep(1,nrow(temp_df))
for (ilevel in y_levels){
tweights[temp_df$y==ilevel]=1/sum(temp_df$y==ilevel)
}
logreg=tj_calculate_classifier(data_main = temp_df,ind_variables = "x" ,d_variable = "y",tweights=tweights)
if (class(logreg$confusionMatrix)=="try-error"){
logreg_test=""
} else {
temp_acc_sd=3*mean(c(abs(logreg$confusionMatrix$overall[4]-logreg$confusionMatrix$overall[1]),
abs(logreg$confusionMatrix$overall[3]-logreg$confusionMatrix$overall[1])))
temp_pv=2*(1-pnorm(logreg$confusionMatrix$byClass[11],mean = 0.5, sd=temp_acc_sd))
logreg_test=paste0("Exact.: ",round(logreg$confusionMatrix$byClass[11],digits=2),", ",
"Exact_PV:",formatSc(temp_pv))
}
} else {
fisher_test=NA
logreg_test=NA
Mantel_Haenszel_test=NA
G_test=NA
chisq_test=NA
}
temp_freq1=do.call(rbind,lapply(unique(temp_df$x),function(xx){
x=temp_df[temp_df$x==xx,]
data.frame(varBy=xx,table(x$y)/sum(table(x$y)))
}))
colnames(temp_freq1)<-c(nname,nnameGroup,"freq")
temp_freq2=do.call(rbind,lapply(unique(temp_df$y),function(xx){
x=temp_df[temp_df$y==xx,]
data.frame(varBy=xx,table(x$x)/sum(table(x$x)))
}))
colnames(temp_freq2)<-c(nnameGroup,nname,"freq")
textGG=textGrob(paste0(nname," vs ", nnameGroup))
textGGE=textGrob("")
textTest_2=textGrob(paste0("Fishers Test: ",fisher_test ))
textTest_3=textGrob(paste0("Mantel-Haenszel Test: ",Mantel_Haenszel_test ))
textTest_4=textGrob(paste0("G Test: ",G_test ))
textTest_1=textGrob(paste0("Chi-Sq Test: ",chisq_test ))
textTest_5=textGrob(paste0("LogReg Test: ",logreg_test ))
plot1=ggplot(data=temp_df,aes(x=x,group=y,fill=y))+stat_count(position = "dodge")+theme_bw()+
scale_x_discrete(nname)+scale_fill_manual(nnameGroup,values = cols1)+scale_y_continuous("Cuenta")+
ggtitle("A1) Cuenta absoluta")
plot2=ggplot(data=temp_df,aes(x=y,group=x,fill=x))+stat_count(position = "dodge")+theme_bw()+
scale_fill_brewer(nname,palette="Dark2")+scale_x_discrete(nnameGroup)+scale_y_continuous("Cuenta")+
ggtitle("A2) Cuenta absoluta")
plot3=ggplot(data=temp_freq2,aes_string(y="freq",x=nname,group=nnameGroup,fill=nnameGroup))+
geom_col(position = "dodge")+theme_bw()+
scale_x_discrete(nname)+scale_fill_manual(nnameGroup,values =cols1)+ggtitle(paste0("B) Cuenta relativa a ",nnameGroup))+
scale_y_continuous("Frec")
plot4=ggplot(data=temp_freq1,aes_string(y="freq",x=nnameGroup,group=nname,fill=nname))+
geom_col(position = "dodge")+theme_bw()+
scale_x_discrete(nnameGroup)+scale_fill_brewer(nname,palette="Dark2")+ggtitle(paste0("C) Cuenta relativa a ",nname))+
scale_y_continuous("Frec")
textGG_t1=textGrob("A) Cuenta absoluta")
textGG_t3=textGrob(paste0("C) Cuenta relativa a ",nname))
textGG_t4=textGrob(paste0("B) Cuenta relativa a ",nnameGroup))
#textGG_t2=textGrob(paste0("Cuenta relativa"))
tabGG1=tableGrob(temp_table)
#tabGG2=tableGrob(round2(temp_table/sum(temp_table)))
temptab=dcast(temp_freq1,as.formula(paste0(nname,"~",nnameGroup)),value.var="freq")
temptab[,-1]=round2(temptab[,-1])
colnames(temptab)[1]="var"
row.names(temptab)=temptab[[1]]
tabGG3=tableGrob(temptab[,-1])
temptab=dcast(temp_freq2,as.formula(paste0(nname,"~",nnameGroup)),value.var="freq")
temptab[,-1]=round2(temptab[,-1])
colnames(temptab)[1]="var"
row.names(temptab)=temptab[[1]]
tabGG4=tableGrob(temptab[,-1])
padding <- unit(5,"mm")
xtabGG1 <- gtable_add_rows(
tabGG1,
heights = grobHeight(textGG_t1) + padding,
pos = 0)
xtabGG3 <- gtable_add_rows(
tabGG3,
heights = grobHeight(textGG_t3) + padding,
pos = 0)
xtabGG4 <- gtable_add_rows(
tabGG4,
heights = grobHeight(textGG_t4) + padding,
pos = 0)
tabGG1=gtable_add_grob(xtabGG1, textGG_t1, 1, 1, 1, ncol(xtabGG1),clip="off")
tabGG3=gtable_add_grob(xtabGG3, textGG_t3, 1, 1, 1, ncol(xtabGG3),clip="off")
tabGG4=gtable_add_grob(xtabGG4, textGG_t4, 1, 1, 1, ncol(xtabGG4),clip="off")
plotAll=grid.arrange(textGGE,#textGG_t1,textGG_t2,textGG_t3,textGG_t4,
plot1,plot2,plot3,plot4,
tabGG1,
#tabGG2,
tabGG4,
tabGG3,
textTest_1,textTest_5,
layout_matrix= matrix(c(3,3,3,3,5,5,5,5,7,7,7,7,10,
3,3,3,3,5,5,5,5,7,7,7,7,10,
3,3,3,3,5,5,5,5,8,8,8,8,2,
3,3,3,3,5,5,5,5,8,8,8,8,2,
4,4,4,4,6,6,6,6,8,8,8,8,11,
4,4,4,4,6,6,6,6,9,9,9,9,11,
4,4,4,4,6,6,6,6,9,9,9,9,11,
4,4,4,4,6,6,6,6,9,9,9,9,11)-1,13,8))
ggsave(plotAll,filename = paste0(dir_output,"/mainPlot_",nname,".png"),width = 10,height=12)
1
}