NB: This vignette is (still) work-in-progress and not yet complete.

## Short intro on hashes

TBD

## Difference between `digest()` and `sha1()`

R FAQ 7.31 illustrates potential problems with floating point arithmetic. Mathematically the equality $x = \sqrt{x}^2$ should hold. But the precision of floating points numbers is finite. Hence some rounding is done, leading to numbers which are no longer identical.

An illustration:

    > a0 <- 2

    > b <- sqrt(a0)

    > a1 <- b^2

    > identical(a0, a1)
    [1] FALSE

    > a0 - a1
    [1] -4.440892e-16

    > a <- c(a0, a1)

    > sprintf("%a", a)
    [1] "0x1p+1"               "0x1.0000000000001p+1"

Although the difference is small, any difference will result in different hash when using the `digest()` function. However, the `sha1()` function tackles this problem by using the hexadecimal representation of the numbers and truncates that representation to a certain number of digits prior to calculating the hash function.

    > library(digest)

    > sapply(a, digest, algo = "sha1")
    [1] "315a5aa84aa6cfa4f3fb4b652a596770be0365e8"
    [2] "5e3999bf79c230f7430e97d7f30070a7eff5ec92"

    > sapply(a, sha1)
    [1] "8a938d8f5fb9b8ccb6893aa1068babcc517f32d4"
    [2] "8a938d8f5fb9b8ccb6893aa1068babcc517f32d4"

    > sapply(a, sha1, digits = 15)
    [1] "98eb1dc9fada00b945d3ef01c77049ee5a4b7b9c"
    [2] "5a173e2445df1134908037f69ac005fbd8afee74"

    > sapply(a, sha1, digits = 13)
    [1] "43b3b465c975af322c85473190a9214b79b79bf6"
    [2] "43b3b465c975af322c85473190a9214b79b79bf6"

    > sapply(a, sha1, digits = 10)
    [1] "6b537a9693c750ed535ca90527152f06e358aa3a"
    [2] "6b537a9693c750ed535ca90527152f06e358aa3a"

    > c(sha1(pi), sha1(pi, digits = 13), sha1(pi, digits = 10))
    [1] "169388cf1ce60dc4e9904a22bc934c5db33d975b"
    [2] "20dc38536b6689d5f2d053f30efb09c44af78378"
    [3] "3a727417bd1807af2f0148cf3de69abff32c23ec"

The result of floating point arithematic on 32-bit and 64-bit can be slightly different. E.g. `print(pi ^ 11, 22)` returns `294204.01797389047` on 32-bit and `294204.01797389053` on 64-bit. Note that only the last 2 digits are different.

command | 32-bit | 64-bit
--- | --- | ---
`print(pi ^ 11, 22)` | `294204.01797389047` | `294204.01797389053`
`sprintf("%a", pi ^ 11)` | `"0x1.1f4f01267bf5fp+18"` | `"0x1.1f4f01267bf6p+18"`
`digest(pi ^ 11, algo = "sha1")` | `"c5efc7f167df1bb402b27cf9b405d7cebfba339a"` | `"b61f6fea5e2a7952692cefe8bba86a00af3de713"`
`sha1(pi ^ 11, digits = 14)` | `"5c7740500b8f78ec2354ea6af58ea69634d9b7b1"` | `"4f3e296b9922a7ddece2183b1478d0685609a359"`
`sha1(pi ^ 11, digits = 13)` | `"372289f87396b0877ccb4790cf40bcb5e658cad7"` | `"372289f87396b0877ccb4790cf40bcb5e658cad7"`
`sha1(pi ^ 11, digits = 10)` | `"c05965af43f9566bfb5622f335817f674abfc9e4"` | `"c05965af43f9566bfb5622f335817f674abfc9e4"`

## Choosing `digest()` or `sha1()`

TBD

## Creating a sha1 method for other classes

### How to

1. Identify the relevant components for the hash.
2. Determine the class of each relevant component and check if they are handled by `sha1()`.
   - Write a method for each component class not yet handled by `sha1`.
3. Extract the relevant components.
4. Combine the relevant components into a list. Not required in case of a single component.
5. Apply `sha1()` on the (list of) relevant component(s).
6. Turn this into a function with name sha1._classname_.
7. sha1._classname_ needs exactly the same arguments as `sha1()`
8. Choose sensible defaults for the arguments
   - `zapsmall = 7` is recommended.
   - `digits = 14` is recommended in case all numerics are data.
   - `digits = 4` is recommended in case some numerics stem from floating point arithmetic.

### summary.lm

Let’s illustrate this using the summary of a simple linear regression. Suppose that we want a hash that takes into account the coefficients, their standard error and sigma.

    > lm_SR <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)

    > lm_sum <- summary(lm_SR)

    > class(lm_sum)
    [1] "summary.lm"

    > str(lm_sum)
    List of 11
     $ call         : language lm(formula = sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
     $ terms        :Classes 'terms', 'formula'  language sr ~ pop15 + pop75 + dpi + ddpi
      .. ..- attr(*, "variables")= language list(sr, pop15, pop75, dpi, ddpi)
      .. ..- attr(*, "factors")= int [1:5, 1:4] 0 1 0 0 0 0 0 1 0 0 ...
      .. .. ..- attr(*, "dimnames")=List of 2
      .. .. .. ..$ : chr [1:5] "sr" "pop15" "pop75" "dpi" ...
      .. .. .. ..$ : chr [1:4] "pop15" "pop75" "dpi" "ddpi"
      .. ..- attr(*, "term.labels")= chr [1:4] "pop15" "pop75" "dpi" "ddpi"
      .. ..- attr(*, "order")= int [1:4] 1 1 1 1
      .. ..- attr(*, "intercept")= int 1
      .. ..- attr(*, "response")= int 1
      .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
      .. ..- attr(*, "predvars")= language list(sr, pop15, pop75, dpi, ddpi)
      .. ..- attr(*, "dataClasses")= Named chr [1:5] "numeric" "numeric" "numeric" "numeric" ...
      .. .. ..- attr(*, "names")= chr [1:5] "sr" "pop15" "pop75" "dpi" ...
     $ residuals    : Named num [1:50] 0.864 0.616 2.219 -0.698 3.553 ...
      ..- attr(*, "names")= chr [1:50] "Australia" "Austria" "Belgium" "Bolivia" ...
     $ coefficients : num [1:5, 1:4] 28.566087 -0.461193 -1.691498 -0.000337 0.409695 ...
      ..- attr(*, "dimnames")=List of 2
      .. ..$ : chr [1:5] "(Intercept)" "pop15" "pop75" "dpi" ...
      .. ..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"
     $ aliased      : Named logi [1:5] FALSE FALSE FALSE FALSE FALSE
      ..- attr(*, "names")= chr [1:5] "(Intercept)" "pop15" "pop75" "dpi" ...
     $ sigma        : num 3.8
     $ df           : int [1:3] 5 45 5
     $ r.squared    : num 0.338
     $ adj.r.squared: num 0.28
     $ fstatistic   : Named num [1:3] 5.76 4 45
      ..- attr(*, "names")= chr [1:3] "value" "numdf" "dendf"
     $ cov.unscaled : num [1:5, 1:5] 3.74 -7.24e-02 -4.46e-01 -7.86e-05 -1.88e-02 ...
      ..- attr(*, "dimnames")=List of 2
      .. ..$ : chr [1:5] "(Intercept)" "pop15" "pop75" "dpi" ...
      .. ..$ : chr [1:5] "(Intercept)" "pop15" "pop75" "dpi" ...
     - attr(*, "class")= chr "summary.lm"

    > coef_sum <- coef(lm_sum)[, c("Estimate", "Std. Error")]

    > sigma <- lm_sum$sigma

    > class(coef_sum)
    [1] "matrix" "array"

    > class(sigma)
    [1] "numeric"

    > sha1(coef_sum, digits = 4)
    [1] "3f0b0c552f94d753fcc8deb4d3e9fc11a83197af"

    > sha1(sigma, digits = 4)
    [1] "cbc83d1791ef1eeadd824ea9a038891b5889056b"

    > sha1(list(coef_sum, sigma), digits = 4)
    [1] "476d27265365cd41662eedf059b335d38a221cc2"

    > sha1.summary.lm <- function(x, digits = 4, zapsmall = 7) {
    +     coef_sum <- coef(x)[, c("Estimate", "Std. Error")]
    +     sigma <- x$sigma
    +     combined <- list(coef_sum, sigma)
    +     sha1(combined, digits = digits, zapsmall = zapsmall)
    + }

    > sha1(lm_sum)
    [1] "476d27265365cd41662eedf059b335d38a221cc2"

    > LCS2 <- LifeCycleSavings[rownames(LifeCycleSavings) !=
    +     "Zambia", ]

    > lm_SR2 <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LCS2)

    > sha1(summary(lm_SR2))
    [1] "90beb028833bf0542997fde7c3f19e5b9fdfeef4"

### lm

Let’s illustrate this using the summary of a simple linear regression. Suppose that we want a hash that takes into account the coefficients, their standard error and sigma.

    > class(lm_SR)
    [1] "lm"

    > str(lm_SR)
    List of 12
     $ coefficients : Named num [1:5] 28.566087 -0.461193 -1.691498 -0.000337 0.409695
      ..- attr(*, "names")= chr [1:5] "(Intercept)" "pop15" "pop75" "dpi" ...
     $ residuals    : Named num [1:50] 0.864 0.616 2.219 -0.698 3.553 ...
      ..- attr(*, "names")= chr [1:50] "Australia" "Austria" "Belgium" "Bolivia" ...
     $ effects      : Named num [1:50] -68.38 -14.29 7.3 -3.52 -7.94 ...
      ..- attr(*, "names")= chr [1:50] "(Intercept)" "pop15" "pop75" "dpi" ...
     $ rank         : int 5
     $ fitted.values: Named num [1:50] 10.57 11.45 10.95 6.45 9.33 ...
      ..- attr(*, "names")= chr [1:50] "Australia" "Austria" "Belgium" "Bolivia" ...
     $ assign       : int [1:5] 0 1 2 3 4
     $ qr           :List of 5
      ..$ qr   : num [1:50, 1:5] -7.071 0.141 0.141 0.141 0.141 ...
      .. ..- attr(*, "dimnames")=List of 2
      .. .. ..$ : chr [1:50] "Australia" "Austria" "Belgium" "Bolivia" ...
      .. .. ..$ : chr [1:5] "(Intercept)" "pop15" "pop75" "dpi" ...
      .. ..- attr(*, "assign")= int [1:5] 0 1 2 3 4
      ..$ qraux: num [1:5] 1.14 1.17 1.16 1.15 1.05
      ..$ pivot: int [1:5] 1 2 3 4 5
      ..$ tol  : num 1e-07
      ..$ rank : int 5
      ..- attr(*, "class")= chr "qr"
     $ df.residual  : int 45
     $ xlevels      : Named list()
     $ call         : language lm(formula = sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
     $ terms        :Classes 'terms', 'formula'  language sr ~ pop15 + pop75 + dpi + ddpi
      .. ..- attr(*, "variables")= language list(sr, pop15, pop75, dpi, ddpi)
      .. ..- attr(*, "factors")= int [1:5, 1:4] 0 1 0 0 0 0 0 1 0 0 ...
      .. .. ..- attr(*, "dimnames")=List of 2
      .. .. .. ..$ : chr [1:5] "sr" "pop15" "pop75" "dpi" ...
      .. .. .. ..$ : chr [1:4] "pop15" "pop75" "dpi" "ddpi"
      .. ..- attr(*, "term.labels")= chr [1:4] "pop15" "pop75" "dpi" "ddpi"
      .. ..- attr(*, "order")= int [1:4] 1 1 1 1
      .. ..- attr(*, "intercept")= int 1
      .. ..- attr(*, "response")= int 1
      .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
      .. ..- attr(*, "predvars")= language list(sr, pop15, pop75, dpi, ddpi)
      .. ..- attr(*, "dataClasses")= Named chr [1:5] "numeric" "numeric" "numeric" "numeric" ...
      .. .. ..- attr(*, "names")= chr [1:5] "sr" "pop15" "pop75" "dpi" ...
     $ model        :'data.frame':  50 obs. of  5 variables:
      ..$ sr   : num [1:50] 11.43 12.07 13.17 5.75 12.88 ...
      ..$ pop15: num [1:50] 29.4 23.3 23.8 41.9 42.2 ...
      ..$ pop75: num [1:50] 2.87 4.41 4.43 1.67 0.83 2.85 1.34 0.67 1.06 1.14 ...
      ..$ dpi  : num [1:50] 2330 1508 2108 189 728 ...
      ..$ ddpi : num [1:50] 2.87 3.93 3.82 0.22 4.56 2.43 2.67 6.51 3.08 2.8 ...
      ..- attr(*, "terms")=Classes 'terms', 'formula'  language sr ~ pop15 + pop75 + dpi + ddpi
      .. .. ..- attr(*, "variables")= language list(sr, pop15, pop75, dpi, ddpi)
      .. .. ..- attr(*, "factors")= int [1:5, 1:4] 0 1 0 0 0 0 0 1 0 0 ...
      .. .. .. ..- attr(*, "dimnames")=List of 2
      .. .. .. .. ..$ : chr [1:5] "sr" "pop15" "pop75" "dpi" ...
      .. .. .. .. ..$ : chr [1:4] "pop15" "pop75" "dpi" "ddpi"
      .. .. ..- attr(*, "term.labels")= chr [1:4] "pop15" "pop75" "dpi" "ddpi"
      .. .. ..- attr(*, "order")= int [1:4] 1 1 1 1
      .. .. ..- attr(*, "intercept")= int 1
      .. .. ..- attr(*, "response")= int 1
      .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
      .. .. ..- attr(*, "predvars")= language list(sr, pop15, pop75, dpi, ddpi)
      .. .. ..- attr(*, "dataClasses")= Named chr [1:5] "numeric" "numeric" "numeric" "numeric" ...
      .. .. .. ..- attr(*, "names")= chr [1:5] "sr" "pop15" "pop75" "dpi" ...
     - attr(*, "class")= chr "lm"

    > lm_model <- lm_SR$model

    > lm_terms <- lm_SR$terms

    > class(lm_model)
    [1] "data.frame"

    > class(lm_terms)
    [1] "terms"   "formula"

    > sha1.formula <- function(x, digits = 14, zapsmall = 7,
    +     ..., algo = "sha1") {
    +     sha1(as.character(x), digits = digits, zapsmall = zapsmall,
    +         algo = algo)
    + }

    > sha1(lm_terms)
    [1] "2737d209720aa7d1c0555050ad06ebe89f3850cd"

    > sha1(lm_model)
    [1] "27b7dd9e3e09b9577da6947b8473b63a1d0b6eb4"

    > sha1.lm <- function(x, digits = 14, zapsmall = 7,
    +     ..., algo = "sha1") {
    +     lm_model <- x$model
    +     lm_terms <- x$terms
    +     combined <- list(lm_model, lm_terms)
    +     sha1(combined, digits = digits, zapsmall = zapsmall, ...,
    +         algo = algo)
    + }

    > sha1(lm_SR)
    [1] "7eda2a9d58e458c8e782e40ce140d62b836b2a2f"

    > sha1(lm_SR2)
    [1] "4d3abdb1f17bd12fdf9d9b91a2ad04c07824fe4a"

## Using hashes to track changes in analysis

Use case

- automated analysis

- update frequency of the data might be lower than the frequency of automated analysis

- similar analyses on many datasets (e.g. many species in ecology)

- analyses that require a lot of computing time

  - not rerunning an analysis because nothing has changed saves enough resources to compensate the overhead of tracking changes

- Bundle all relevant information on an analysis in a class

  - data
  - method
  - formula
  - other metadata
  - resulting model

- calculate `sha1()`

  file fingerprint ~ `sha1()` on the stable parts

  status fingerprint ~ `sha1()` on the parts that result for the model

1. Prepare analysis objects
2. Store each analysis object in a rda file which uses the file fingerprint as filename
   - File will already exist when no change in analysis
   - Don’t overwrite existing files
3. Loop over all rda files
   - Do nothing if the analysis was run
   - Otherwise run the analysis and update the status and status fingerprint