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Normalise a Ped object dataframe

Source: R/norm_data.R
norm_ped.Rd

Normalise dataframe for a Ped object

Usage

norm_ped(
  ped_df,
  na_strings = c("NA", ""),
  missid = NA_character_,
  try_num = FALSE,
  cols_used_del = FALSE,
  date_pattern = "%Y-%m-%d"
)

Arguments

ped_df

A data.frame with the individuals informations. The minimum columns required are:

  • id individual identifiers

  • dadid biological fathers identifiers

  • momid biological mothers identifiers

  • sex of the individual

The famid column, if provided, will be merged to the ids field separated by an underscore using the upd_famid() function.

The following columns are also recognize and will be transformed with the vect_to_binary() function:

  • deceased status -> is the individual dead

  • avail status -> is the individual available

  • evaluated status -> has the individual a documented evaluation

  • consultand status -> is the individual the consultand

  • proband status -> is the individual the proband

  • carrier status -> is the individual a carrier

  • asymptomatic status -> is the individual asymptomatic

  • adopted status -> is the individual adopted

The values recognized for those columns are 1 or 0, TRUE or FALSE.

The fertility column will be transformed to a factor using the fertility_to_factor() function. infertile_choice_na, infertile, fertile

The miscarriage column will be transformed to a using the miscarriage_to_factor() function. SAB, TOP, ECT, FALSE

The dateofbirth and dateofdeath columns will be transformed to a date object using the char_to_date() function.

na_strings

Vector of strings to be considered as NA values.

missid

A character vector with the missing values identifiers. All the id, dadid and momid corresponding to those values will be set to NA_character_.

try_num

Boolean defining if the function should try to convert all the columns to numeric.

cols_used_del

Boolean defining if the columns that will be used should be deleted.

date_pattern

The pattern of the date

Value

A dataframe with different variable correctly standardized and with the errors identified in the error column

Details

Normalise a dataframe and check for columns correspondance to be able to use it as an input to create a Ped object. Multiple test are done and errors are checked.

Will be considered available any individual with no 'NA' values in the available column. Duplicated id will nullify the relationship of the individual. All individuals with errors will be remove from the dataframe and will be transfered to the error dataframe.

A number of checks are done to ensure the dataframe is correct:

On identifiers:

  • All ids (id, dadid, momid, famid) are not empty (!= "")

  • All id are unique (no duplicated)

  • All dadid and momid are unique in the id column (no duplicated)

  • id is not the same as dadid or momid

  • Either have both parents or none

On sex:

  • All sex code are either male, female, or unknown.

  • No parents are infertile or aborted

  • All fathers are male

  • All mothers are female

See also

Ped() Ped Pedigree()

Examples

df <- data.frame(
    id = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
    dadid = c("A", 0, 1, 3, 0, 4, 1, 0, 6, 6),
    momid = c(0, 0, 2, 2, 0, 5, 2, 0, 8, 8),
    famid = c(1, 1, 1, 1, 1, 1, 1, 2, 2, 2),
    sex = c(1, 2, "m", "man", "f", "male", "m", 3, NA, "f"),
    fertility = c(
      "TRUE", "FALSE", TRUE, FALSE, 1,
      0, "fertile", "infertile", 1, "TRUE"
    ),
    miscarriage = c("TOB", "SAB", NA, FALSE, "ECT", "other", 1, 0, 1, 0),
    deceased = c("TRUE", "FALSE", TRUE, FALSE, 1, 0, 1, 0, 1, 0),
    avail = c("A", "1", 0, NA, 1, 0, 1, 0, 1, 0),
    evalutated = c(
        "TRUE", "FALSE", TRUE, FALSE, 1, 0, NA, "NA", "other", "0"
    ),
    consultand = c(
        "TRUE", "FALSE", TRUE, FALSE, 1, 0, NA, "NA", "other", "0"
    ),
    proband = c("TRUE", "FALSE", TRUE, FALSE, 1, 0, NA, "NA", "other", "0"),
    carrier = c("TRUE", "FALSE", TRUE, FALSE, 1, 0, NA, "NA", "other", "0"),
    asymptomatic = c(
        "TRUE", "FALSE", TRUE, FALSE, 1, 0, NA, "NA", "other", "0"
    ),
    adopted = c("TRUE", "FALSE", TRUE, FALSE, 1, 0, NA, "NA", "other", "0"),
    dateofbirth = c(
         "1978-01-01", "1980-01-01", "1982-01-01", "1984-01-01",
         "1986-01-01", "1988-01-01", "1990-01-01", "1992-01-01",
         "1994-01-01", "1996-01-01"
    ),
    dateofdeath = c(
        "2000-01-01", "2002-01-01", "2004-01-01", NA, "date-not-recognize",
        "NA", "", NA, "2008/01/01", NA
    )
)
tryCatch(
    norm_ped(df),
    error = function(e) print(e)
)
#> Warning: NAs introduced by coercion
#> Warning: NAs introduced by coercion
#> Warning: NAs introduced by coercion
#> Warning: NAs introduced by coercion
#> Warning: NAs introduced by coercion
#> Warning: NAs introduced by coercion
#>      id dadid momid famid     sex fertility miscarriage deceased avail
#> 1   1_1   1_A   1_0     1    male   fertile       FALSE     TRUE    NA
#> 2   1_2   1_0   1_0     1  female infertile         SAB    FALSE  TRUE
#> 3   1_3   1_1   1_2     1    male   fertile       FALSE     TRUE FALSE
#> 4   1_4   1_3   1_2     1    male infertile       FALSE    FALSE    NA
#> 5   1_5   1_0   1_0     1  female   fertile         ECT     TRUE  TRUE
#> 6   1_6   1_4   1_5     1    male infertile       FALSE    FALSE FALSE
#> 7   1_7   1_1   1_2     1    male   fertile       FALSE     TRUE  TRUE
#> 8   2_8   2_0   2_0     2  female infertile       FALSE    FALSE FALSE
#> 9   2_9   2_6   2_8     2 unknown   fertile       FALSE     TRUE  TRUE
#> 10 2_10   2_6   2_8     2  female   fertile       FALSE    FALSE FALSE
#>    evalutated consultand proband carrier asymptomatic adopted dateofbirth
#> 1        TRUE       TRUE    TRUE    TRUE         TRUE    TRUE  1978-01-01
#> 2       FALSE      FALSE   FALSE   FALSE        FALSE   FALSE  1980-01-01
#> 3        TRUE       TRUE    TRUE    TRUE         TRUE    TRUE  1982-01-01
#> 4       FALSE      FALSE   FALSE   FALSE        FALSE   FALSE  1984-01-01
#> 5           1       TRUE    TRUE    TRUE         TRUE    TRUE  1986-01-01
#> 6           0      FALSE   FALSE   FALSE        FALSE   FALSE  1988-01-01
#> 7        <NA>      FALSE   FALSE      NA           NA   FALSE  1990-01-01
#> 8        <NA>      FALSE   FALSE      NA           NA   FALSE  1992-01-01
#> 9       other      FALSE   FALSE      NA           NA   FALSE  1994-01-01
#> 10          0      FALSE   FALSE   FALSE        FALSE   FALSE  1996-01-01
#>    dateofdeath
#> 1   2000-01-01
#> 2   2002-01-01
#> 3   2004-01-01
#> 4         <NA>
#> 5         <NA>
#> 6         <NA>
#> 7         <NA>
#> 8         <NA>
#> 9         <NA>
#> 10        <NA>
#>                                                                               error
#> 1                                                                              <NA>
#> 2  is-infertile-but-is-parent_is-aborted-but-is-parent_is-aborted-but-has-fertility
#> 3                                                                              <NA>
#> 4                                                        is-infertile-but-is-parent
#> 5                                                          is-aborted-but-is-parent
#> 6                                                                              <NA>
#> 7                                                                              <NA>
#> 8                                                        is-infertile-but-is-parent
#> 9                                                                              <NA>
#> 10                                                                             <NA>
#>    evaluated
#> 1      FALSE
#> 2      FALSE
#> 3      FALSE
#> 4      FALSE
#> 5      FALSE
#> 6      FALSE
#> 7      FALSE
#> 8      FALSE
#> 9      FALSE
#> 10     FALSE