Simulation methods

DataFakeR offers various methods for defining how exactly each column should be simulated.

We can highlight the below four simulation method types:

Deterministic (formula or constraint-based) simulation.
Special method simulation.
Restricted simulation.
Default simulation.

The package tries to simulate the column with each method, with preserving the order of the above list.

That means, the package will try to simulate the column using special method first and when such method cannot be executed (the conditions for running such method are not met) then, the next method is applied. More to that when one of the methods successfully generated the data, the following ones are skipped.

The goal of this document is to describe each method providing required details and, if applied, provide an information how each method can be customized.

Deterministic (formula or constraint-based) simulation.

Let’s imagine we want to generate tables that reflects standard database used in the library. One of the tables used there, can be the one describing all the books owned by the library.

The YAML configuration of such table can have a form:

# schema-books.yml
public:
  tables:
    books:
      columns:
        book_id:
          type: char(12)
        author:
          type: varchar
        title:
          type: varchar
        genre:
          type: varchar
        bought:
          type: date
        amount:
          type: smallint
        purchase_id:
          type: varchar

While running the standard simulation process we get the following result:

set.seed(123)
sch <- schema_source(system.file("extdata", "schema-books.yml", package = "DataFakeR"))
sch <- schema_simulate(sch)
schema_get_table(sch, "books")
#> # A tibble: 10 × 7
#>    book_id  author   title    genre      bought     amount purchase_id
#>    <chr>    <chr>    <chr>    <chr>      <date>      <int> <chr>      
#>  1 OoVtwCbu QJoEYa   XLaS     xljCUY     1971-02-17 -28970 ZUYlCSp    
#>  2 cXxXjdFu fRZxZuvf h        dkvg       1982-12-06  -6823 rMSssP     
#>  3 MCRxukhz VHwPDx   TSbmLV   WcDNUKrH   1999-06-19 -28513 H          
#>  4 ikcePHyu lHcxeV   NgJ      pci        2018-07-04 -17965 kFBzBRvgOm 
#>  5 jpBYnLQM hQQLTzI  smigTbte IgQl       2012-03-14 -29188 rQZkhhyVGb 
#>  6 HVVTHHMY E        rQk      Uyyl       1983-05-07  11159 L          
#>  7 NsCWpGdK Hjguim   NeZNdvuO NLeNbo     1996-09-13 -13255 ZTzULgHyaI 
#>  8 GnuTiETO bi       Q        I          2016-05-26 -26167 g          
#>  9 qXqqpWng q        zgwYViH  VYtwtjxadR 1994-08-08 -28056 ziVQrHKuQS 
#> 10 kAYLTfSF oy       dMyfaUt  SBat       2010-05-15  24932 oKAVZ

Even though many columns don’t look realistic let’s take care of book_id and purchase_id columns. From the rules followed in the library, we know that:

book_id is the concatenation of first 4 letters from author, title and bought,
purchase_id is concatenation of word ‘purchase_’ and column bought (such rule is checked by SQL purchase_id = 'purchase_' || bought check constraint).

If we want to preserve such rules, we have two options:

store the rules as check_constraints rewritten in R code,
define book_id and purchase_id using formula.

Let’s describe purchase_id using the first method:

# schema-books_2.yml
public:
  tables:
    books:
      columns:
        book_id:
          type: char(12)
        author:
          type: varchar
        title:
          type: varchar
        genre:
          type: varchar
        bought:
          type: date
        amount:
          type: smallint
        purchase_id:
          type: varchar
      check_constraints:
        purchase_id_check:
          column: purchase_id
          expression: !expr purchase_id == paste0('purchase_', bought)

let’s update the source and plot column dependencies:

sch <- schema_update_source(sch, file = system.file("extdata", "schema-books_2.yml", package = "DataFakeR"))
schema_plot_deps(sch, "books")

As you can see DataFakeR detected dependency between purchase_id and bought column. More to that the package will assure bought column will be simulated before purchase_id.

Let’s take a quick look at the data:

sch <- schema_simulate(sch)
schema_get_table(sch, "books")
#> # A tibble: 10 × 7
#>    book_id  author     title      genre     bought     amount
#>    <chr>    <chr>      <chr>      <chr>     <date>      <int>
#>  1 obYnWkze leZ        eH         wuPcdqB   1974-04-22  31400
#>  2 suceMctW DIrDylVHb  Sh         dN        1996-09-09  25250
#>  3 JKZRpDRU jYbVn      hTSTv      jKJuYWecg 2016-03-15  32340
#>  4 SjiJcVUe NsuEksi    nHRMIyFD   gHPn      2009-12-05  -8172
#>  5 VCfOjSus noTSZCjxmI mGAsRZdF   gh        1988-05-11 -10032
#>  6 RwvBDkNJ CawvEUAyr  EtaZmIBeJK wfmIn     1971-06-20  18227
#>  7 EngSilEF AD         xINeIh     oKVd      1973-07-07 -23221
#>  8 axJdjADg ZV         KbRddOMueZ ZfONo     2000-12-14    202
#>  9 pzZxHxYI rEk        nCggoULhXd cIy       1976-06-19  15893
#> 10 UqSzDjOO BdRqmjgd   mHbGe      ZILkFHVh  2015-05-15 -19569
#>    purchase_id        
#>    <chr>              
#>  1 purchase_1974-04-22
#>  2 purchase_1996-09-09
#>  3 purchase_2016-03-15
#>  4 purchase_2009-12-05
#>  5 purchase_1988-05-11
#>  6 purchase_1971-06-20
#>  7 purchase_1973-07-07
#>  8 purchase_2000-12-14
#>  9 purchase_1976-06-19
#> 10 purchase_2015-05-15

As we can see check constraint rule was applied to correctly create purchase_id column.

Note: Check constraint expressions are used to detect column dependency and to create the checked column, only when the ones are equality expressions (that means the expression contains == operator). Such expression assures the column definition is deterministic. In the future releases (depending on the user’s needs) the check expression rule can be extended to the cases where expression constains < and > operators.

Now let’s take care to assure that book_id is the first 8 letters of concatenation of author, title and bought-year. Such example can be also described using check constraint, but we’ll use a different method that opens various options for defining column rules.

If you want to create a column using custom expression, you pass it as a formula parameter in yaml configuration file. In our case, we want book_id be created with expression paste0(substr(author, 1, 4), substr(title, 1, 4), substr(bought, 1, 4)). Let’s put it in configuration as formula parameter:

# schema-books_3.yml
public:
  tables:
    books:
      columns:
        book_id:
          type: char(12)
          formula: !expr paste0(substr(author, 1, 4), substr(title, 1, 4), substr(bought, 1, 4))
        author:
          type: varchar
        title:
          type: varchar
        genre:
          type: varchar
        bought:
          type: date
        amount:
          type: smallint
        purchase_id:
          type: varchar
      check_constraints:
        purchase_id_check:
          column: purchase_id
          expression: !expr purchase_id == paste0('purchase_', bought)

Again, update the source and plot column dependencies:

sch <- schema_update_source(sch, file = system.file("extdata", "schema-books_3.yml", package = "DataFakeR"))
schema_plot_deps(sch, "books")

The column dependencies are detected correctly. Let’s move then to simulation step:

sch <- schema_simulate(sch)
schema_get_table(sch, "books")
#> # A tibble: 10 × 7
#>    book_id      author     title     genre      bought     amount
#>    <chr>        <chr>      <chr>     <chr>      <date>      <int>
#>  1 vTcK2007     vTc        K         vwHCWAF    2007-06-14 -17869
#>  2 oYJHqbnQ1997 oYJHEpyDP  qbnQ      a          1997-03-16  11240
#>  3 PyfNq2007    Pyf        Nq        wEcoZjiczZ 2007-03-16 -15864
#>  4 QDiNxcpm1979 QDiNktG    xcpmbzYN  Nm         1979-05-20 -32045
#>  5 vLKIkQH1974  vLK        IkQH      ipc        1974-04-06    638
#>  6 yvZMNMnt1982 yvZMs      NMnt      s          1982-07-03  24789
#>  7 wCQqZYEh2014 wCQqGTzWP  ZYEhJMaXH dZt        2014-01-05  -4823
#>  8 PdYrIPlM1988 PdYrd      IPlM      GebYogVMQy 1988-05-13 -30463
#>  9 jCWJ2003     jCW        J         ayYLMBSykj 2003-03-16 -18411
#> 10 KKjBfTy1977  KKjBXmcVTU fTy       qEq        1977-10-21  18874
#>    purchase_id        
#>    <chr>              
#>  1 purchase_2007-06-14
#>  2 purchase_1997-03-16
#>  3 purchase_2007-03-16
#>  4 purchase_1979-05-20
#>  5 purchase_1974-04-06
#>  6 purchase_1982-07-03
#>  7 purchase_2014-01-05
#>  8 purchase_1988-05-13
#>  9 purchase_2003-03-16
#> 10 purchase_1977-10-21

As we can see the result is again as expected.

Note The formula expression is passed to dplyr::mutate in the implementation, which means you may use in the formula any dplyr-specific functions, such as n().

Note Formula-based column definition was classified as a ‘Deterministic simulation’ method, but you may also define formulas with random sampling inside. For example assuring that column end_date have values larger than start_date you may define: formula: start_date + sample(1:10, dplyr::n(), replace = TRUE).

Special method simulation

Let’s take a look at the last simulated data:

schema_get_table(sch, "books")
#> # A tibble: 10 × 7
#>    book_id      author     title     genre      bought     amount
#>    <chr>        <chr>      <chr>     <chr>      <date>      <int>
#>  1 vTcK2007     vTc        K         vwHCWAF    2007-06-14 -17869
#>  2 oYJHqbnQ1997 oYJHEpyDP  qbnQ      a          1997-03-16  11240
#>  3 PyfNq2007    Pyf        Nq        wEcoZjiczZ 2007-03-16 -15864
#>  4 QDiNxcpm1979 QDiNktG    xcpmbzYN  Nm         1979-05-20 -32045
#>  5 vLKIkQH1974  vLK        IkQH      ipc        1974-04-06    638
#>  6 yvZMNMnt1982 yvZMs      NMnt      s          1982-07-03  24789
#>  7 wCQqZYEh2014 wCQqGTzWP  ZYEhJMaXH dZt        2014-01-05  -4823
#>  8 PdYrIPlM1988 PdYrd      IPlM      GebYogVMQy 1988-05-13 -30463
#>  9 jCWJ2003     jCW        J         ayYLMBSykj 2003-03-16 -18411
#> 10 KKjBfTy1977  KKjBXmcVTU fTy       qEq        1977-10-21  18874
#>    purchase_id        
#>    <chr>              
#>  1 purchase_2007-06-14
#>  2 purchase_1997-03-16
#>  3 purchase_2007-03-16
#>  4 purchase_1979-05-20
#>  5 purchase_1974-04-06
#>  6 purchase_1982-07-03
#>  7 purchase_2014-01-05
#>  8 purchase_1988-05-13
#>  9 purchase_2003-03-16
#> 10 purchase_1977-10-21

As we mentioned in the previous section, we’re not happy with the result of multiple column values. In this section we’ll take of the result of author and title columns.

In case of author column, we’d like the values to be random, human readable names. Across R packages, there are many that offer such functionality. In case of DataFakeR, it’s just enough to define spec: name for the column definition:

# schema-books_4.yml
public:
  tables:
    books:
      columns:
        book_id:
          type: char(12)
          formula: !expr paste0(substr(author, 1, 4), substr(title, 1, 4), substr(bought, 1, 4))
        author:
          type: varchar
          spec: name
        title:
          type: varchar
        genre:
          type: varchar
        bought:
          type: date
        amount:
          type: smallint
        purchase_id:
          type: varchar
      check_constraints:
        purchase_id_check:
          column: purchase_id
          expression: !expr purchase_id == paste0('purchase_', bought)

Again, update the source and plot column dependencies:

sch <- schema_update_source(sch, file = system.file("extdata", "schema-books_4.yml", package = "DataFakeR"))
sch <- schema_simulate(sch)
schema_get_table(sch, "books")
#> # A tibble: 10 × 7
#>    book_id      author            title      genre     bought     amount
#>    <chr>        <chr>             <chr>      <chr>     <date>      <int>
#>  1 Mr. eQDr1993 Mr. Elmore Hane   eQDr       rXovvKYq  1993-06-21   7005
#>  2 SallOOPF1984 Sally Lubowitz    OOPFG      HN        1984-10-31   3054
#>  3 AgushEpx1980 Agustus Schmidt   hEpx       X         1980-09-29 -20733
#>  4 Dr. PdAf1972 Dr. Fabiola Huels PdAfHpFVNp RoTqbvVY  1972-07-07  29393
#>  5 Dr. SUrn1988 Dr. Harper Kunze  SUrn       Hx        1988-05-10 -24386
#>  6 DonnEDO1973  Donny Wuckert     EDO        PLVA      1973-04-22 -18597
#>  7 AnicJ2013    Anice Jones PhD   J          cejZnE    2013-12-28  15596
#>  8 TavaLUwY2021 Tavaris Dicki     LUwYDHD    NcfFH     2021-06-03  18248
#>  9 Hughbsga2018 Hugh Corkery      bsgaYOAM   KHvuAXgM  2018-12-30  -7676
#> 10 ParajAXf1973 Paralee King      jAXf       QmNicoQmP 1973-03-18 -14651
#>    purchase_id        
#>    <chr>              
#>  1 purchase_1993-06-21
#>  2 purchase_1984-10-31
#>  3 purchase_1980-09-29
#>  4 purchase_1972-07-07
#>  5 purchase_1988-05-10
#>  6 purchase_1973-04-22
#>  7 purchase_2013-12-28
#>  8 purchase_2021-06-03
#>  9 purchase_2018-12-30
#> 10 purchase_1973-03-18

Voila!

How does it work?

Whenever you precise spec parameter to the column, DataFakeR will look for the simulation options if the special method with such name was defined. For the specific column type, such option is defined at options$opt_simul_spec_<column-type>.

So for the default options and character column type, we have:

default_faker_opts$opt_simul_spec_character
#> $name
#> function (n, not_null, unique, default, spec_params, na_ratio, 
#>     levels_ratio, ...) 
#> {
#>     call_args <- names(sys.call())
#>     if (!"spec_params" %in% call_args) {
#>         spec_params <- list()
#>     }
#>     spec_params$n <- n
#>     unique_sample(do.call(charlatan::ch_name, spec_params), spec_params = spec_params, 
#>         unique = unique) %>% levels_rand(unique = unique, levels_ratio = levels_ratio) %>% 
#>         na_rand(not_null = not_null, na_ratio = na_ratio)
#> }
#> <bytecode: 0x556429d76c80>
#> <environment: namespace:DataFakeR>

The name method was defined and we were able to use it in the simulation. Looking at the function body, we may see it uses charlatan::ch_name function to simulate human-readable names.

What else can we spot regarding the method definition? The below points are worth notice:

the method have n parameter. This is obligatory parameter for each simulation method.
the method parameters also contain standard column ones from YAML file. Whenever you need access to column parameters, just define them while creating the method. DataFakeR passes all the column parameters (not only the default ones) to each method.
if the parameter is not directly defined in the function it’s still possible to access all of them by parsing ellipsis (…). Please make sure ellipsis is always added as a function parameter.
by defining spec_params yaml configuration parameter you may access and parse them directly in the method definition. Such parameter allows to resolve conflicts with standard parameters names when duplication exist.

Now, let’s take care of preparing human-readable title. For this case we’ll create our custom function. The titles will consist of combination of four words from predefined values:

books <- function(n) {
  first <- c("Learning", "Amusing", "Hiding", "Symbols", "Hunting", "Smile")
  second <- c("Of", "On", "With", "From", "In", "Before")
  third <- c("My", "Your", "The", "Common", "Mysterious", "A")
  fourth <- c("Future", "South", "Technology", "Forest", "Storm", "Dreams")
  paste(sample(first, n), sample(second, n), sample(third, n), sample(fourth, n))
}

Let’s check a few possible results:

books(3)
#> [1] "Hiding On Common South"       "Smile With My Forest"        
#> [3] "Amusing Of Mysterious Dreams"

Perfect!

In order to present how spec_params can be used let’s add an option to skip the second word in the result:

books <- function(n, add_second = FALSE) {
  first <- c("Learning", "Amusing", "Hiding", "Symbols", "Hunting", "Smile")
  second <- c("Of", "On", "With", "From", "In", "Before")
  third <- c("My", "Your", "The", "Common", "Mysterious", "A")
  fourth <- c("Future", "South", "Technology", "Forest", "Storm", "Dreams")
  second_res <- NULL
  if (add_second) {
    second_res <- sample(second, n, replace = TRUE)
  }
  paste(
    sample(first, n, replace = TRUE), second_res, 
    sample(third, n, replace = TRUE), sample(fourth, n, replace = TRUE)
  )
}

Now, let’s create the final method to use in the workflow.

The function needs to:

take n parameter,
optional standard parameter unique (we’ll use it to assure unique results are returned),
spec_params that allows us to modify special method parameters

simul_spec_character_book <- function(n, unique, spec_params, ...) {
  spec_params$n <- n
  
  DataFakeR::unique_sample(
    do.call(books, spec_params), 
    spec_params = spec_params, unique = unique
  )
}

Before we run the example, let’s explain code blocks in function definition.

We’re evaluating books using do.call. That’s why we need to store all the parameters in the list passed to do.call. The only missing one is n, so:

spec_params$n <- n

We allowed our method to respect unique parameter, that’s why we want to assure the returned sample is unique. We may achieve this using DataFakeR::unique_sample.

The function evaluates sampling expression multiple times, replacing duplicated values with the new ones. It’s worth to mention some of the function parameters:

sim_expr - simulation expression to be evaluated,
... - parameters values used in sim_expr,
unique - if TRUE, the function will try to generate unique values,
n_iter - number of iteration to try generate unique value.

So it’s enough to define:

DataFakeR::unique_sample(
  sim_expr = do.call(books, spec_params), 
  spec_params = spec_params, unique = unique
)

Let’s modify configuration file with the book method (with using spec_params to simulate full title):

schema-books_5.yml
public:
  tables:
    books:
      columns:
        book_id:
          type: char(8)
          formula: !expr paste0(substr(author, 1, 4), substr(title, 1, 4), substr(bought, 1, 4))
        author:
          type: varchar
          spec: name
        title:
          type: varchar
          spec: book
          spec_params:
            add_second: true
        genre:
          type: varchar
        bought:
          type: date
        amount:
          type: smallint
        purchase_id:
          type: varchar
      check_constraints:
        purchase_id_check:
          column: purchase_id
          expression: !expr purchase_id == paste0('purchase_', bought)

and define the new method in the package options:

my_opts <- set_faker_opts(
  opt_simul_spec_character = opt_simul_spec_character(book = simul_spec_character_book)
)
sch <- schema_source(
  system.file("extdata", "schema-books_5.yml", package = "DataFakeR"), 
  faker_opts = my_opts
)
sch <- schema_simulate(sch)
schema_get_table(sch, "books")
#> # A tibble: 10 × 7
#>    book_id      author                   title                        
#>    <chr>        <chr>                    <chr>                        
#>  1 AliaSymb2005 Alia Thiel               Symbols With My Storm        
#>  2 FlorHidi1973 Florida Gottlieb         Hiding From Common Technology
#>  3 RitaSmil1978 Rita Volkman PhD         Smile On The Future          
#>  4 Mrs.Amus1974 Mrs. Prudence Deckow DDS Amusing From Common Future   
#>  5 AlviSymb2004 Alvis Wolf               Symbols Before Common Forest 
#>  6 LindSmil1985 Lindell Abshire          Smile In Your Dreams         
#>  7 KareHidi1976 Kareem Kshlerin IV       Hiding On A Storm            
#>  8 CarrSmil1985 Carrie McDermott         Smile Before Common South    
#>  9 DevoLear2013 Devonte Hansen           Learning Before The Storm    
#> 10 AleaHunt1987 Alease Pouros            Hunting From The Future      
#>    genre      bought     amount purchase_id        
#>    <chr>      <date>      <int> <chr>              
#>  1 mKVIeGGTy  2005-05-12  15135 purchase_2005-05-12
#>  2 dUnXYSEI   1973-04-24  -5641 purchase_1973-04-24
#>  3 npNBcTycf  1978-02-13  27090 purchase_1978-02-13
#>  4 DostQ      1974-05-05 -24378 purchase_1974-05-05
#>  5 CmGBYUpbPE 2004-03-03  29926 purchase_2004-03-03
#>  6 mqIf       1985-08-30   5654 purchase_1985-08-30
#>  7 kYodnY     1976-08-03 -31808 purchase_1976-08-03
#>  8 tmPdYwb    1985-07-31  19944 purchase_1985-07-31
#>  9 RSqpcXzQa  2013-12-18  19069 purchase_2013-12-18
#> 10 Wc         1987-04-18  27308 purchase_1987-04-18

Great! We’ve managed to use our custom special method to simulate human-readable titles.

For the last part of this section it’s worth to mention the remaining special methods for each column type. For numeric, integer, logical and Date column classes, DataFakeR offer spec: distr method that allow to simulate column from the selected distribution. The method requires to provide:

spec_params:
  method: <method-name>

where method name is the name of simulation function such as rnorm, rbinom etc.

For example, in order to simulate the column from normal distribution with mean = 10 and sd = 5 we should define:

spec: distr
spec_params:
  method: rnorm
  mean: 10
  sd: 5

Restricted simulation

Restricted simulation methods allow to simulate data considering exceptional parameters defined for each column.

Such parameters can be for example:

values - possible column values for the target column
range - possible column values range for the target column

All the restricted methods offered by DataFakeR for column type are defined in default_faker_opts$opt_simul_restricted_<column-type>.

Let’s take a look what methods are offered for integer columns:

default_faker_opts$opt_simul_restricted_integer
#> $f_key
#> function (n, not_null, unique, default, type, values, na_ratio, 
#>     levels_ratio, ...) 
#> {
#>     if (isTRUE(not_null)) {
#>         values <- values[!is.na(values)]
#>     }
#>     if (isTRUE(unique)) {
#>         warning("Requested to simulate foreign key having unique values. Make sure config is correctly defined.")
#>     }
#>     sample(values, n, replace = !unique) %>% na_rand(not_null = not_null, 
#>         na_ratio = na_ratio)
#> }
#> <bytecode: 0x556429d9e520>
#> <environment: namespace:DataFakeR>
#> 
#> $in_set
#> function (n, not_null, unique, default, type, values, na_ratio, 
#>     levels_ratio, ...) 
#> {
#>     if (!missing(values)) {
#>         if (isTRUE(not_null)) {
#>             values <- values[!is.na(values)]
#>         }
#>         return(sample(values, n, replace = !unique) %>% na_rand(not_null = not_null, 
#>             na_ratio = na_ratio))
#>     }
#>     return(NULL)
#> }
#> <bytecode: 0x556429da12e8>
#> <environment: namespace:DataFakeR>
#> 
#> $range
#> function (n, not_null, unique, default, type, range, na_ratio, 
#>     levels_ratio, ...) 
#> {
#>     if (!missing(range)) {
#>         return(unique_sample(round(stats::runif(n, range[1], 
#>             range[2])), range = range, n = n, unique = unique) %>% 
#>             na_rand(not_null = not_null, na_ratio = na_ratio))
#>     }
#>     return(NULL)
#> }
#> <bytecode: 0x556429da0210>
#> <environment: namespace:DataFakeR>

We can see there are three methods defined:

f_key - restricted method responsible for simulating foreign keys (we’ll come back to this one in the last part of the section),
range - method taking range parameter. When range is missing NULL is returned, otherwise integers between range[1] and range[2] are returned,
in_set- method taking values parameter. When values is missing NULL is returned, otherwise sampled values from values are returned.

Let’s highlight the general rule for restricted methods used by the package (excluding f_key one):

each method has n as obligatory parameter that determines number of returned values,
standard column parameters (not_null, unique, type) are optional but respected,
the method usually have defined ‘restriction’ parameter that determines whether the method should be applied or not,
when restriction condition is not met (for example ‘restriction’ parameter is missing) the method should return NULL.

The last point allows DataFakeR to scan across all the restriction methods. When a method returns NULL result, the package moves to simulate from the next method defined in opt_simul_restricted_<column-type> setting.

Similar to special methods, you may also define a set of your custom restricted methods. You may achieve this by setting up new methods with:

set_faker_opts(
  opt_simul_restricted_<column-type> = opt_simul_restricted_<column-type>(my_method = method, ...)
)

Note: DataFakeR allow also to rewrite currently existing method. So whenever you need to rewrite for example in_set method, just specify it as above.

Having the knowledge about restricted methods, let’s use them to generate amount column from values between 1 and 99. We’ll do it by using range method for amount column:

# schema-books_6.yml
public:
  tables:
    books:
      columns:
        book_id:
          type: char(8)
          formula: !expr paste0(substr(author, 1, 4), substr(title, 1, 4), substr(bought, 1, 4))
        author:
          type: varchar
          spec: name
        title:
          type: varchar
          spec: book
          spec_params:
            add_second: true
        genre:
          type: varchar
        bought:
          type: date
        amount:
          type: smallint
          range: [1, 99]
        purchase_id:
          type: varchar
      check_constraints:
        purchase_id_check:
          column: purchase_id
          expression: !expr purchase_id == paste0('purchase_', bought)

sch <- schema_update_source(sch, system.file("extdata", "schema-books_6.yml", package = "DataFakeR"))
sch <- schema_simulate(sch)
schema_get_table(sch, "books")
#> # A tibble: 10 × 7
#>    book_id      author                    title                          
#>    <chr>        <chr>                     <chr>                          
#>  1 BranLear1997 Branson Kerluke           Learning From A South          
#>  2 AutrSmil1998 Autry Brakus              Smile From A Storm             
#>  3 Ms. Lear1970 Ms. Delphia Yundt DVM     Learning From Your Storm       
#>  4 TeniHidi2003 Tenisha Beier             Hiding From Mysterious South   
#>  5 MilfSmil1997 Milford Stanton-VonRueden Smile With Mysterious South    
#>  6 IvorHidi1996 Ivory Fahey               Hiding Of My Forest            
#>  7 MelbLear1984 Melba Lehner              Learning With Common Technology
#>  8 Mrs.Amus2017 Mrs. Malaya Wolf DVM      Amusing On My Storm            
#>  9 Dr. Symb2013 Dr. Tabetha Ferry         Symbols Of Your Forest         
#> 10 MeyeSymb2002 Meyer Stamm-Hintz         Symbols From Mysterious Storm  
#>    genre      bought     amount purchase_id        
#>    <chr>      <date>      <int> <chr>              
#>  1 ysosUqWUZ  1997-10-07     75 purchase_1997-10-07
#>  2 CFDcnO     1998-01-05     52 purchase_1998-01-05
#>  3 ATojacy    1970-11-01     97 purchase_1970-11-01
#>  4 QZnAjphst  2003-03-03     11 purchase_2003-03-03
#>  5 RQTDi      1997-07-04     59 purchase_1997-07-04
#>  6 lfwZjb     1996-08-26     86 purchase_1996-08-26
#>  7 TyqsEy     1984-02-23     32 purchase_1984-02-23
#>  8 oRjRQTg    2017-09-10     64 purchase_2017-09-10
#>  9 nTyuinThgz 2013-09-09     66 purchase_2013-09-09
#> 10 ToWVIacj   2002-08-16     92 purchase_2002-08-16

Awesome! Let’s simulate now human-readable book genre from a set of defined values. We can achieve this using in_set method for character column type:

default_faker_opts$opt_simul_restricted_character$in_set
#> function (n, not_null, unique, default, nchar, type, values, 
#>     na_ratio, levels_ratio, ...) 
#> {
#>     if (!missing(values)) {
#>         if (isTRUE(not_null)) {
#>             values <- values[!is.na(values)]
#>         }
#>         return(sample(values, n, replace = !unique) %>% na_rand(not_null = not_null, 
#>             na_ratio = na_ratio))
#>     }
#>     return(NULL)
#> }
#> <bytecode: 0x556429d80548>
#> <environment: namespace:DataFakeR>

So let’s assume we want the genre to be simulated from set: Fantasy, Adventure, Horror, Romance.

Let’s define such set as possible values for genre column:

schema-books_7.yml
public:
  tables:
    books:
      columns:
        book_id:
          type: char(8)
          formula: !expr paste0(substr(author, 1, 4), substr(title, 1, 4), substr(bought, 1, 4))
        author:
          type: varchar
          spec: name
        title:
          type: varchar
          spec: book
          spec_params:
            add_second: true
        genre:
          type: varchar
          values: [Fantasy, Adventure, Horror, Romance]
        bought:
          type: date
        amount:
          type: smallint
          range: [1, 99]
        purchase_id:
          type: varchar
      check_constraints:
        purchase_id_check:
          column: purchase_id
          expression: !expr purchase_id == paste0('purchase_', bought)

sch <- schema_update_source(sch, system.file("extdata", "schema-books_7.yml", package = "DataFakeR"))
sch <- schema_simulate(sch)
schema_get_table(sch, "books")
#> # A tibble: 10 × 7
#>    book_id      author                   title                         
#>    <chr>        <chr>                    <chr>                         
#>  1 RylaHidi1970 Rylan Prosacco           Hiding From The Dreams        
#>  2 TripAmus1979 Tripp Armstrong          Amusing From Mysterious Future
#>  3 TainSmil1999 Taina Steuber            Smile In Mysterious Storm     
#>  4 Ova Lear2020 Ova Hettinger            Learning With The Technology  
#>  5 Dr. Lear1994 Dr. Alphonse Watsica Sr. Learning In A Technology      
#>  6 DevoSmil1975 Devonte McClure          Smile Before The Forest       
#>  7 ConaLear2019 Conard Volkman-Walker    Learning In A Future          
#>  8 EloySymb1982 Eloy Ratke III           Symbols Before A Forest       
#>  9 PhoeSymb1975 Phoebe Swaniawski        Symbols On Mysterious Dreams  
#> 10 AdelSymb1997 Adeline Block PhD        Symbols With Common Dreams    
#>    genre     bought     amount purchase_id        
#>    <chr>     <date>      <int> <chr>              
#>  1 Adventure 1970-09-14     98 purchase_1970-09-14
#>  2 Romance   1979-11-11     46 purchase_1979-11-11
#>  3 Horror    1999-12-14     44 purchase_1999-12-14
#>  4 Romance   2020-02-18     19 purchase_2020-02-18
#>  5 Adventure 1994-06-04     39 purchase_1994-06-04
#>  6 Fantasy   1975-06-09     95 purchase_1975-06-09
#>  7 Romance   2019-06-17     17 purchase_2019-06-17
#>  8 Adventure 1982-02-11     38 purchase_1982-02-11
#>  9 Romance   1975-05-07      7 purchase_1975-05-07
#> 10 Romance   1997-02-05     28 purchase_1997-02-05

Let’s add the last improvement by specifying date range for books:

schema-books_8.yml
public:
  tables:
    books:
      columns:
        book_id:
          type: char(8)
          formula: !expr paste0(substr(author, 1, 4), substr(title, 1, 4), substr(bought, 1, 4))
        author:
          type: varchar
          spec: name
        title:
          type: varchar
          spec: book
          spec_params:
            add_second: true
        genre:
          type: varchar
          values: [Fantasy, Adventure, Horror, Romance]
        bought:
          type: date
          range: ['2020-01-02', '2021-06-01']
        amount:
          type: smallint
          range: [1, 99]
        purchase_id:
          type: varchar
      check_constraints:
        purchase_id_check:
          column: purchase_id
          expression: !expr purchase_id == paste0('purchase_', bought)

sch <- schema_update_source(sch, system.file("extdata", "schema-books_8.yml", package = "DataFakeR"))
sch <- schema_simulate(sch)
schema_get_table(sch, "books")
#> # A tibble: 10 × 7
#>    book_id      author                  title                          
#>    <chr>        <chr>                   <chr>                          
#>  1 DomoHunt2021 Domonique Hettinger     Hunting In Mysterious Storm    
#>  2 ShaeHidi2020 Shae Daugherty          Hiding Before Mysterious Future
#>  3 HopeHunt2020 Hope Murphy-Cremin      Hunting On Common Forest       
#>  4 MalaSmil2020 Malaya Rippin-Feil      Smile Of Your Technology       
#>  5 ChriSmil2021 Christoper Kerluke      Smile Of My Dreams             
#>  6 LeeaLear2020 Leeann Rice             Learning In Your Storm         
#>  7 Mrs.Hidi2020 Mrs. Alys Dickinson DVM Hiding On A Storm              
#>  8 ChriAmus2020 Christin Padberg DVM    Amusing In Common South        
#>  9 DeboSmil2020 Debora Schamberger      Smile Of The South             
#> 10 EzzaHidi2020 Ezzard Keeling          Hiding On Your Forest          
#>    genre     bought     amount purchase_id        
#>    <chr>     <date>      <int> <chr>              
#>  1 Adventure 2021-01-02     86 purchase_2021-01-02
#>  2 Fantasy   2020-04-12     10 purchase_2020-04-12
#>  3 Adventure 2020-07-11     15 purchase_2020-07-11
#>  4 Adventure 2020-06-11     78 purchase_2020-06-11
#>  5 Adventure 2021-04-16     48 purchase_2021-04-16
#>  6 Horror    2020-06-05     65 purchase_2020-06-05
#>  7 Romance   2020-08-17      3 purchase_2020-08-17
#>  8 Fantasy   2020-08-02     69 purchase_2020-08-02
#>  9 Horror    2020-05-20     68 purchase_2020-05-20
#> 10 Adventure 2020-09-15     19 purchase_2020-09-15

Here we are.

For the last part of restricted methods, let’s add a few words about f_key restricted method.

As mentioned the method is responsible for simulating foreign key columns. When the column is defined in schema as a foreign key, DataFakeR will source possible values from the parent table and pass such values set as a values parameter to f_key method.

Simulating foreign key will skip execution of the remaining restricted methods defined in options.

To see it in action, let’s extend our schema definition by adding a new borrowed table (let’s also precise nrows for each table).

# schema-books_9.yml
public:
  tables:
    books:
      nrows: 10
      columns:
        book_id:
          type: char(8)
          formula: !expr paste0(substr(author, 1, 4), substr(title, 1, 4), substr(bought, 1, 4))
        author:
          type: varchar
          spec: name
        title:
          type: varchar
          spec: book
          spec_params:
            add_second: true
        genre:
          type: varchar
          values: [Fantasy, Adventure, Horror, Romance]
        bought:
          type: date
          range: ['2020-01-02', '2021-06-01']
        amount:
          type: smallint
          range: [1, 99]
        purchase_id:
          type: varchar
      check_constraints:
        purchase_id_check:
          column: purchase_id
          expression: !expr purchase_id == paste0('purchase_', bought)
    borrowed:
      nrows: 30
      columns:
        book_id:
          type: char(8)
        user_id:
          type: char(10)
      foreign_keys:
        book_id_fkey:
          columns: book_id
          references:
            columns: book_id
            table: books

Let’s update the file and check table dependencies:

sch <- schema_update_source(sch, system.file("extdata", "schema-books_9.yml", package = "DataFakeR"))
schema_plot_deps(sch)

As shown, DataFakeR detected dependency between books and borrowed, and will generate books table first to get possible values for foreign key column.

Let’s simulate the data and compare simumlated book ids:

sch <- schema_simulate(sch)
schema_get_table(sch, "books")
#> # A tibble: 10 × 7
#>    book_id      author                 title                         genre    
#>    <chr>        <chr>                  <chr>                         <chr>    
#>  1 ThedSymb2020 Theda Hamill           Symbols In The Future         Horror   
#>  2 QueeLear2021 Queenie Klein-Predovic Learning Of Mysterious Forest Romance  
#>  3 AdamAmus2020 Adam Hills             Amusing From Common Storm     Fantasy  
#>  4 BranHunt2021 Brandin Zboncak DDS    Hunting On Common Technology  Adventure
#>  5 Mrs.Hidi2020 Mrs. Louise Vandervort Hiding From My South          Horror   
#>  6 Dr. Symb2021 Dr. Clarence Beier     Symbols With The Storm        Romance  
#>  7 ShanHunt2020 Shannon Bode           Hunting Of Your Dreams        Romance  
#>  8 DarrHunt2020 Darrien Schuster       Hunting From A Forest         Fantasy  
#>  9 MariHunt2020 Maritza Mosciski       Hunting In The Forest         Adventure
#> 10 Mr. Amus2020 Mr. Clinton Herzog DVM Amusing Of Mysterious Future  Romance  
#>    bought     amount purchase_id        
#>    <date>      <int> <chr>              
#>  1 2020-09-02     62 purchase_2020-09-02
#>  2 2021-01-10     83 purchase_2021-01-10
#>  3 2020-07-22     17 purchase_2020-07-22
#>  4 2021-02-16     49 purchase_2021-02-16
#>  5 2020-01-23      9 purchase_2020-01-23
#>  6 2021-01-20     36 purchase_2021-01-20
#>  7 2020-08-16     52 purchase_2020-08-16
#>  8 2020-06-18     14 purchase_2020-06-18
#>  9 2020-01-19     54 purchase_2020-01-19
#> 10 2020-08-23     89 purchase_2020-08-23

unique(schema_get_table(sch, "borrowed")$book_id)
#> [1] "QueeLear2021" "Mr. Amus2020" NA             "AdamAmus2020" "MariHunt2020"
#> [6] "ThedSymb2020" "Mrs.Hidi2020" "BranHunt2021" "DarrHunt2020"

So the values were correctly inherited from parent table.

Default simulation

When none of the above methods were applied DataFakeR will simulate the column using the default method. For each column type you may find the default methods defined at default_faker_opts$default_faker_opts$opt_simul_default_fun_<column-type>.

In order to overwrite such method it’s just enough to:

set_faker_opts(
  opt_simul_default_fun_<column-type> = my_custom_method
)

Krystian Igras

2023-02-08

Deterministic (formula or constraint-based) simulation.

Special method simulation

Restricted simulation

Default simulation