Demo for tools::md5sum()

The tools::md5sum() function computes the 32-byte MD5 hashes of one or more files.

From the md5sum help page:

A MD5 ‘hash’ or ‘checksum’ or ‘message digest’ is a 128-bit summary of the file contents represented by 32 hexadecimal digits. Files with different MD5 sums are different: only very exceptionally (and usually with the intent to deceive) are those with the same sums different.

On Windows all files are read in binary mode (as the md5sum utilities there do): on other OSes the files are read in the default mode (almost always text mode where there is more than one).

MD5 sums are used as a check that R packages have been unpacked correctly and not subsequently accidentally modified.

Step 0: Load packages

The md5sum() function is in the tools package.

library(tools) 
library(data.table)

Step 1: Create some source files

Write some source test files to a temporary directory. These are the files for which we will create the source (reference) md5 hashes.

# Create a temporary directory for the source files. 
tmpDir <- paste0(tempdir(), "/testArea")

# Create a directory structure with subfolders
dir.create(paste(tmpDir, "subf1", "subf2", sep = "/"), recursive = TRUE)

# Write some text and binary files
write.csv(PlantGrowth, file = paste(tmpDir, "PlantGrowth.csv", sep = "/"))
write.csv(ChickWeight, file = paste(tmpDir, "subf1", "ChickWeight.csv", sep = "/"))
write.csv(trees, file = paste(tmpDir, "subf1", "subf2", "trees.csv", sep = "/"))
write.csv(volcano, file = paste(tmpDir, "subf1", "subf2", "volcano.csv", sep = "/"))
saveRDS(ChickWeight, file = paste(tmpDir, "subf1", "ChickWeight.Rdata", sep = "/"))
saveRDS(trees, file = paste(tmpDir, "subf1", "subf2", "trees.Rdata", sep = "/"))

List the source files.

listTmpFiles <- list.files(tmpDir, full.names = FALSE, recursive = TRUE)
listTmpFiles

## [1] "PlantGrowth.csv"         "subf1/ChickWeight.csv"  
## [3] "subf1/ChickWeight.Rdata" "subf1/subf2/trees.csv"  
## [5] "subf1/subf2/trees.Rdata" "subf1/subf2/volcano.csv"

Step 2: Create source MD5 hashes dataset

Create a dataset with MD5 check sums for the source files.

wdOrig <- getwd()
setwd(tmpDir)
MD5.orig <- data.table(fileName = listTmpFiles)[
  , md5Orig := lapply(fileName, md5sum)][
  , md5OrigText := paste0(md5Orig, " ", fileName)][
    , .(md5OrigText)]
MD5.orig

##                                                 md5OrigText
## 1:         e6064331c76ef3defb9eff852f877da4 PlantGrowth.csv
## 2:   cc7feea5a98cabc13f282007155a0f69 subf1/ChickWeight.csv
## 3: 4a04111b96586fe4f55a3ab113301c2d subf1/ChickWeight.Rdata
## 4:   eda0aee4b42aa6853f11051174de41af subf1/subf2/trees.csv
## 5: dae508a52417a808b2d0e8ff1d6f2da8 subf1/subf2/trees.Rdata
## 6: 7f75c2935f5b95e138ab62bf28bc7e32 subf1/subf2/volcano.csv

Write source MD5 hashes followed by the respective file name to a .md5 file.

md5Filename <- paste0(tmpDir, "/", "orig.md5")
writeLines(text = MD5.orig$md5OrigText, con = md5Filename)

Note: On *nix systems the file orig.md5 can be check using md5sum -c orig.md5.

Step 3: Create a corrupted version of source files to check

To simulate a user receiving a corrupted version of the source files, let’s intentionally make some changes to the source files to get MD5 hash errors during the checks below.

Change a file name to get an error when we check the MD5 hashes below.

file.rename(from = paste(tmpDir, "subf1", "subf2", "trees.csv", sep = "/"),
            to = paste(tmpDir, "subf1", "subf2", "TREES.csv", sep = "/"))

## [1] TRUE

Change the content of another file to get an error when we check the MD5 hashes below.

file.append(paste(tmpDir, "subf1", "ChickWeight.csv", sep = "/"), 
            paste(tmpDir, "PlantGrowth.csv", sep = "/"))

## [1] TRUE

setwd(wdOrig)

These are the corrupted files that the user will actually receive.

listTmpCorruptedFiles <- list.files(tmpDir, full.names = FALSE, recursive = TRUE)
listTmpCorruptedFiles

## [1] "orig.md5"                "PlantGrowth.csv"        
## [3] "subf1/ChickWeight.csv"   "subf1/ChickWeight.Rdata"
## [5] "subf1/subf2/TREES.csv"   "subf1/subf2/trees.Rdata"
## [7] "subf1/subf2/volcano.csv"

Step 4: Generate the MD5 hashes for the corrupted files

setwd(tmpDir)
# Check all files except the orig.md5 file. 
checkFiles <- list.files(tmpDir, full.names = FALSE, recursive = TRUE, 
                         pattern = "[^orig.md5]")
checkFiles

## [1] "PlantGrowth.csv"         "subf1/ChickWeight.csv"  
## [3] "subf1/ChickWeight.Rdata" "subf1/subf2/TREES.csv"  
## [5] "subf1/subf2/trees.Rdata" "subf1/subf2/volcano.csv"

MD5.check <- data.table(fileName = checkFiles)
MD5.check[, md5Check := lapply(fileName, md5sum)]
MD5.check

##                   fileName                         md5Check
## 1:         PlantGrowth.csv e6064331c76ef3defb9eff852f877da4
## 2:   subf1/ChickWeight.csv dc5251152bf9dc84d85694c3c7a8c8fb
## 3: subf1/ChickWeight.Rdata 4a04111b96586fe4f55a3ab113301c2d
## 4:   subf1/subf2/TREES.csv eda0aee4b42aa6853f11051174de41af
## 5: subf1/subf2/trees.Rdata dae508a52417a808b2d0e8ff1d6f2da8
## 6: subf1/subf2/volcano.csv 7f75c2935f5b95e138ab62bf28bc7e32

Step 5: Read in source MD5 check sums text file

Reading in the MD5 hashes for the source files from orig.md5 that we created above.

origFileMD5 <- fread(md5Filename, header = FALSE, col.names = c("md5Orig", "fileName"))
origFileMD5

##                             md5Orig                fileName
## 1: e6064331c76ef3defb9eff852f877da4         PlantGrowth.csv
## 2: cc7feea5a98cabc13f282007155a0f69   subf1/ChickWeight.csv
## 3: 4a04111b96586fe4f55a3ab113301c2d subf1/ChickWeight.Rdata
## 4: eda0aee4b42aa6853f11051174de41af   subf1/subf2/trees.csv
## 5: dae508a52417a808b2d0e8ff1d6f2da8 subf1/subf2/trees.Rdata
## 6: 7f75c2935f5b95e138ab62bf28bc7e32 subf1/subf2/volcano.csv

Step 6: Check the MD5 hashes

Verify the hashes from the orig.md5 with the hashes from the “check” MD5 sums.

MD5verify <- merge(origFileMD5, MD5.check, by = "fileName", all = TRUE)
MD5verify[, Result := fifelse(md5Orig == md5Check, "OK", "Error")]

# Print out the results
MD5verify

##                   fileName                          md5Orig                         md5Check Result
## 1:         PlantGrowth.csv e6064331c76ef3defb9eff852f877da4 e6064331c76ef3defb9eff852f877da4     OK
## 2: subf1/ChickWeight.Rdata 4a04111b96586fe4f55a3ab113301c2d 4a04111b96586fe4f55a3ab113301c2d     OK
## 3:   subf1/ChickWeight.csv cc7feea5a98cabc13f282007155a0f69 dc5251152bf9dc84d85694c3c7a8c8fb  Error
## 4:   subf1/subf2/TREES.csv                             <NA> eda0aee4b42aa6853f11051174de41af   <NA>
## 5: subf1/subf2/trees.Rdata dae508a52417a808b2d0e8ff1d6f2da8 dae508a52417a808b2d0e8ff1d6f2da8     OK
## 6:   subf1/subf2/trees.csv eda0aee4b42aa6853f11051174de41af                                   Error
## 7: subf1/subf2/volcano.csv 7f75c2935f5b95e138ab62bf28bc7e32 7f75c2935f5b95e138ab62bf28bc7e32     OK

• OK: Source and received files match.
• Error: Corrupted file or file not found.
• NA: File received but it is not in the source files.

---

The R code for this README file is available at: Demo_for_tools_md5sum.R