knitr::opts_chunk$set(echo = TRUE, eval=F) library(drake) library(econDV) library(dplyr)
library(drake) library(rmd2drake) library(dplyr) library(googleclassroom) library(readr) # library(rmdgrader) library(purrr) library(rlang) library(testthat) library(withr) .root <- function(){ "/Users/martinl/Github/course-dashboard-programming-for-data-science/" }
# initiate process instance process <- Process() # get list of code chunks process$correctAnsFilename$tryGet_list_codeChunks() process$inBatch_studentsRmds$tryGet_list_codeChunks() # If one Rmd content changed, you can update simply that student's respective elements in process instance like: # process$studentsRmds$HW8_410672033.Rmd$tryGet_list_codeChunks()
only4HW8 ={ purrr::walk( seq_along(process$studentsRmds), ~{ names(process$studentsRmds[[.x]]$codeChunksProcessed$list_codeChunks) %>% stringr::str_replace("ans12", "ans12.") %>% stringr::str_replace("ans13", "ans13.") %>% stringr::str_replace("data12", "data12.") %>% stringr::str_replace("data13", "data13.") -> names(process$studentsRmds[[.x]]$codeChunksProcessed$list_codeChunks) } ) }
After users get list_codeChunks from all Rmd files, it's time to evaluate these codes.
# Initiate an Evaluation Instance ei <- Evaluate(process) # resolve codes in batch ei$answerValues$batch$resolve$part12() ei$answerValues$batch$resolve$part13() # once resolved, answer values will show up for all Rmds such as: ei$answerValues$HW8_410672033.Rmd$values
# You can also evaluate each Rmd codes one-by-one via their own resolve methods. Once resolved, you can see the values after resolution ei$answerValues$`homework8-ans.Rmd`$resolve$part12() ei$answerValues$`homework8-ans.Rmd`$resolve$part13() ei$answerValues$`homework8-ans.Rmd`$values # resolve codes of part12 from one student ei$answerValues$HW8_410678019.Rmd$resolve$part12() # once resolved, answer values will show up below: ei$answerValues$HW8_410678019.Rmd$values
You only save neccessary answer values for grading step via save method.
ei$save(filename = "data4step3.Rdata")
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