Okay, So I will answer my question. I tried using Rselenium because PlasmoDB is Java-based Based so elements like data tables I was interested in would load only when clicked. Since rvest is only for static HTML pages, this can only be done by Rselenium. After spending a day installing it in Windows 11, I was able to write down the following code that works for me (but it might not be one of the most elegant solutions)

library(RSelenium)
library(netstat)
library(wdman)
library(rvest)

## Before installation of selenium drivers first download openjdk from Azul: https://www.azul.com/downloads/#zulu and install it in the default directory whichever it chooses

## Installation
## First run Selenium Command to install the necessary drivers
# selenium()
# 
# ## Now check where these drivers have been installed
# obj <- selenium(retcommand = T,check = F)

## Now you will see 3 directories containing drivers of version <115. For our chorme which is most updated we will first download new driver manually from https://googlechromelabs.github.io/chrome-for-testing/#stable (you can check which chrome version you are running by typing chrome://version/ in the browser). This is the most crucial step. Post downloading use the numeric value to create the folder say 126.0.6478.126 here in Users\rohit_satyam\AppData\Local\binman\binman_chromedriver\win32\ folder. Remember that AppData is a hidden folder and will require you to click on View > Show ? Hidden Items in Windows 11.


getTables <- function(gids){
  urls <- paste0("https://plasmodb.org/plasmo/app/record/gene/",gids,"#ExpressionGraphs")
  rD1 <- rsDriver(browser = "chrome", port = free_port(),chromever =  "latest")
  remdr <- rD1$client

  results <- lapply(1:length(urls), function(i){
    remdr$navigate(urls[i]) ## Go to the page
    Sys.sleep(5)
    ## Now click all the buttons to open the transcriptomics field
    firstTask <- remdr$findElements(using = "xpath","//th[contains(@class,'wdk-DataTableCell')]")
    lapply(firstTask, function(x){x$clickElement()})

    Sys.sleep(5)
    ## Now within each transcriptomics study open the data table
    secondTask <- remdr$findElements(using = "xpath","//h4[contains(@class,'eupathdb-ExpressionGraphsDataTableContainerHeader')]")
    lapply(secondTask, function(x){x$clickElement()})


    ## Let's store the information of the name of the studies. 
    thirdTask <- remdr$findElements(using = "xpath","//div[contains(@class,'Cell HtmlCell Cell-short_attribution')]")
    studyname <- as.character(lapply(1:length(thirdTask), function(x){thirdTask[[x]]$getElementText()[[1]]}))

    ## Let's get the study description because the study name is not available for all data tables
    forthTask <- remdr$findElements(using = "xpath","//div[contains(@class,'Cell HtmlCell Cell-summary')]")
    description <- as.character(lapply(1:length(forthTask), function(x){forthTask[[x]]$getElementText()[[1]]}))

    ## Remove Cao et al since there are no data table for it

    ## Because there is no data table for Cao et al
    remdesc <- which(studyname=="Caro et al.")
    studyname <- studyname[!studyname=="Caro et al."]
    description <- description[-remdesc]
    Sys.sleep(10)
    ## Get all the datatables now

    fifthTask <- remdr$findElements(using = "xpath","//div[starts-with(@class,'DataTable')]")
    dt <- fifthTask[[1]]$getPageSource()
    page <- read_html(dt %>% unlist()) 
    df <- html_table(page)
    df <- df[2:length(df)] ## Since first data table is always a superset of all the DTs in our case

    ## Add the information of study and the description
    modifydf <- lapply(1:length(df), function(x){
      df[[x]]$studyname <- studyname[x]
      df[[x]]$description <-description[x]
      df[[x]]
    })

    dfFinal <- do.call("rbind", modifydf)
  })
  return(results)
}

## Testing with some random genes

res <- getTables(c("PF3D7_0518900","PF3D7_0602800","PF3D7_0624600"))

I wanted this solution because PlasmoDB doesn't provide the functionality to download these data tables from multiple Expression Studies. Sometimes my Lab members request me to get these values, and I must copy-paste them. And if the number of genes are high this becomes a frustrating job.