EE-BD-FU2-tables.Rmd

---
title: "Supplementary Tables"
output:
  pdf_document: default
  html_document: default
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, fig.width=12, fig.height=20, message=FALSE, warning =FALSE)
```

```{r, include=FALSE}
rm(list=ls())
library(tidyverse)
library(knitr)
library(here)
load("C:/Users/andre/Dropbox/WASHB-EE-analysis/WBB-EE-analysis/Results/Andrew/FU2_results.RDATA")
load("C:/Users/andre/Dropbox/WASHB-EE-analysis/WBB-EE-analysis/Results/Andrew/FU2_pooled_results.Rdata")

#Plot parameters
scaleFUN <- function(x) sprintf("%.2f", x)

#Plot themes
theme_set(theme_bw())

#hbgdki pallet
tableau10 <- c("#1F77B4","#FF7F0E","#2CA02C","#D62728",
  "#9467BD","#8C564B","#E377C2","#7F7F7F","#BCBD22","#17BECF")


#make clean_table function to clean up column names
clean_table <- function(tab){
  tab <- tab %>% as.data.frame() %>% rename(`Lower 95% CI`=ci.l, `Upper 95% CI`=ci.u)
  try(tab <- tab %>% as.data.frame() %>% rename( `P-value`=Pvalue))
  tab <- tab[,!(colnames(tab) %in% c("variance","logRR", "logRR_var"))]
  
rownames(tab) <- gsub("T1", "3 months", rownames(tab))
rownames(tab) <- gsub("T2", "1 year", rownames(tab))
rownames(tab) <- gsub("T3", "2 years", rownames(tab))

  
  if("Outcome" %in% colnames(tab)){
  tab$Outcome <- gsub("T1", "3 months", tab$Outcome)
  tab$Outcome <- gsub("T2", "1 year", tab$Outcome)
  tab$Outcome <- gsub("T3", "2 years", tab$Outcome)
  }

  return(tab)
}



```


## Supplementary Table 1: N's by treatment arm

```{r, echo=F}

colnames(summary_res) <- c("Treatment Arm","FUT2 Status", "Number of Children", "Number of Mothers")
summary_res<-summary_res[!is.na(summary_res$`FUT2 Status`),]
summary_res$`FUT2 Status` <- factor(summary_res$`FUT2 Status`, levels=c("Positive","Negative","Inconclusive"))
summary_res <- summary_res %>% arrange(`Treatment Arm`, `FUT2 Status`)


knitr::kable(summary_res)
```



## Supplementary Table 2: Table of associations between child FUT2 status and continuous  growth outcomes 

```{r, echo=F}

  knitr::kable(clean_table(pooled_childFU2_growthRF_cont), digits=3)
  cat("\n\n\n")
  

```

## Supplementary Table 3: Table of associations between child FUT2 status and categorical growth outcomes 

```{r, echo=F}

  knitr::kable(clean_table(pooled_childFU2_growthRF_bin), digits=3)
  cat("\n\n\n")
  

```

## Supplementary Table 4: Table of associations between maternal FUT2 status and continuous growth outcomes 

```{r, echo=F}
  knitr::kable(clean_table(pooled_motherFU2_growthRF_cont), digits=3)
  cat("\n\n\n")

```

## Supplementary Table 5:  Table of associations between maternal FUT2 status and categorical growth outcomes 

```{r, echo=F}
  knitr::kable(clean_table(pooled_motherFU2_growthRF_bin), digits=3)
  cat("\n\n\n")

```







## Supplementary Table 6: Table of associations between child FUT2 status and EED outcomes 

```{r, echo=F}

  knitr::kable(clean_table(pooled_childFU2_RF_adj), digits=3)
  cat("\n\n\n")
  

```

## Supplementary Table 7: Table of associations between maternal FUT2 status and EED outcomes 

```{r, echo=F}
  knitr::kable(clean_table(pooled_motherFU2_RF_adj), digits=3)
  cat("\n\n\n")

```






## Supplementary Table 8: Table of associations between child FUT2 status and continuous  growth outcomes stratified by measurement round

```{r, echo=F}

  knitr::kable(clean_table(res_childFU2_growthRF_cont), digits=3)
  cat("\n\n\n")
  

```

## Supplementary Table 9:  Table of associations between child FUT2 status and categorical growth outcomes stratified by measurement round


```{r, echo=F}

  knitr::kable(clean_table(res_childFU2_growthRF_bin), digits=3)
  cat("\n\n\n")
  

```

## Supplementary Table 10: Table of associations between maternal FUT2 status and continuous  growth outcomes stratified by measurement round


```{r, echo=F}
  knitr::kable(clean_table(res_motherFU2_growthRF_cont), digits=3)
  cat("\n\n\n")

```

## Supplementary Table 11: Table of associations between maternal FUT2 status and categorical growth outcomes stratified by measurement round


```{r, echo=F}
  knitr::kable(clean_table(res_motherFU2_growthRF_bin), digits=3)
  cat("\n\n\n")

```




## Supplementary Table 12: Table of associations between child FUT2 status and EED outcomes stratified by measurement round


```{r, echo=F}

  knitr::kable(clean_table(res_childFU2_RF_adj), digits=3)
  cat("\n\n\n")
  

```

## Supplementary Table 13: Table of associations between maternal FUT2 status and EED outcomes stratified by measurement round


```{r, echo=F}
  knitr::kable(clean_table(res_motherFU2_RF_adj), digits=3)
  cat("\n\n\n")

```



## Supplementary Table 14: Table of WASH Benefits intervention effects, stratified by child FUT2 status

```{r, echo=F, results='asis'}

  tab_childFU2_EM_unadj=list_rbind(res_childFU2_EM_unadj, names_to="Outcome")
    tab_childFU2_EM_unadj <- tab_childFU2_EM_unadj %>% subset(., select= -c(`Std. Error`,`z value`))  %>%
          rename(Pvalue=Pval, `Interaction P-value`=InteractionPval)

  print(knitr::kable(clean_table(tab_childFU2_EM_unadj), digits=3))
  
  cat("\n\n\n")
  

```

## Supplementary Table 15: Table of WASH Benefits intervention effects, stratified by maternal FUT2 status

```{r, echo=F, results='asis'}
  #To do:
  #clean up ci.l and ci.u for all tables
  #Combine tables together
  #Add outcome names as column
  #(drop Z-value)
  
  tab_motherFU2_EM_unadj=list_rbind(res_motherFU2_EM_unadj, names_to="Outcome")
  tab_motherFU2_EM_unadj <- tab_motherFU2_EM_unadj %>% subset(., select= -c(`Std. Error`,`z value`)) %>%
          rename(Pvalue=Pval, `Interaction P-value`=InteractionPval)



  print(knitr::kable(clean_table(tab_motherFU2_EM_unadj), digits=3))
  
  cat("\n\n\n")

```