README.md

In stp4/stp25formula: Formulas Transformation Functions

stp25formula

Interne Funktionen um Formel und Daten für Evaluationen vorzubereiten. Die daten werden dabei ins tibble-Format konvertiert und angeforderte Transformationen ausgeführt.

Overview

Usage

dat <- data.frame(
  sex = 1:2,
  treatment = 1:2,
  m1 = 1:2,  m2 = 1:2,  m3 = 1:2,  m4 = 1:2,  m5 = 1:2,  m6 = 1:2
)
x <- stp25formula::prepare_data2( ~ m1 + m2 + m3 + m4, dat)
str(x)
#> List of 14
#>  $ data           : tibble [2 x 4] (S3: tbl_df/tbl/data.frame)
#>   ..$ m1: int [1:2] 1 2
#>   ..$ m2: int [1:2] 1 2
#>   ..$ m3: int [1:2] 1 2
#>   ..$ m4: int [1:2] 1 2
#>   ..- attr(*, "terms")=Classes 'terms', 'formula'  language ~m1 + m2 + m3 + m4
#>   .. .. ..- attr(*, "variables")= language list(m1, m2, m3, m4)
#>   .. .. ..- attr(*, "factors")= int [1:4, 1:4] 1 0 0 0 0 1 0 0 0 0 ...
#>   .. .. .. ..- attr(*, "dimnames")=List of 2
#>   .. .. .. .. ..$ : chr [1:4] "m1" "m2" "m3" "m4"
#>   .. .. .. .. ..$ : chr [1:4] "m1" "m2" "m3" "m4"
#>   .. .. ..- attr(*, "term.labels")= chr [1:4] "m1" "m2" "m3" "m4"
#>   .. .. ..- attr(*, "order")= int [1:4] 1 1 1 1
#>   .. .. ..- attr(*, "intercept")= int 1
#>   .. .. ..- attr(*, "response")= int 0
#>   .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
#>   .. .. ..- attr(*, "predvars")= language list(m1, m2, m3, m4)
#>   .. .. ..- attr(*, "dataClasses")= Named chr [1:4] "numeric" "numeric" "numeric" "numeric"
#>   .. .. .. ..- attr(*, "names")= chr [1:4] "m1" "m2" "m3" "m4"
#>  $ measure.vars   : chr [1:4] "m1" "m2" "m3" "m4"
#>  $ group.vars     : NULL
#>  $ condition.vars : NULL
#>  $ formula        :Class 'formula'  language ~m1 + m2 + m3 + m4
#>   .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
#>  $ by             : chr "1"
#>  $ measure        : Named chr [1:4] "integer" "integer" "integer" "integer"
#>   ..- attr(*, "names")= chr [1:4] "m1" "m2" "m3" "m4"
#>  $ row_name       : Named chr [1:4] "m1" "m2" "m3" "m4"
#>   ..- attr(*, "names")= chr [1:4] "m1" "m2" "m3" "m4"
#>  $ col_name       : Named chr(0) 
#>   ..- attr(*, "names")= chr(0) 
#>  $ measure.class  : Named chr [1:4] "integer" "integer" "integer" "integer"
#>   ..- attr(*, "names")= chr [1:4] "m1" "m2" "m3" "m4"
#>  $ group.class    : NULL
#>  $ condition.class: NULL
#>  $ digits         : Named num [1:4] 2 2 2 2
#>   ..- attr(*, "names")= chr [1:4] "m1" "m2" "m3" "m4"
#>  $ N              : int 2
#>  - attr(*, "class")= chr [1:2] "stp25data" "list"

prepare_data2(dat, 2:5)
#> 
#> formula: ~treatment + m1 + m2 + m3
#> <environment: 0x0000000014e628c8>
#> 
#> measure.vars:  treatment, m1, m2, m3
#> measure:  integer, integer, integer, integer
#> measure.class:  integer, integer, integer, integer
#> digits:  2, 2, 2, 2
#> row_name:  treatment, m1, m2, m3
#> by: [1] "1"
#> 
#> group.vars:   
#> # A tibble: 2 x 4
#>   treatment    m1    m2    m3
#>       <int> <int> <int> <int>
#> 1         1     1     1     1
#> 2         2     2     2     2

prepare_data2(dat, m1, m2, m3)
#> 
#> formula: ~m1 + m2 + m3
#> <environment: 0x000000001388dde0>
#> 
#> measure.vars:  m1, m2, m3
#> measure:  integer, integer, integer
#> measure.class:  integer, integer, integer
#> digits:  2, 2, 2
#> row_name:  m1, m2, m3
#> by: [1] "1"
#> 
#> group.vars:   
#> # A tibble: 2 x 3
#>      m1    m2    m3
#>   <int> <int> <int>
#> 1     1     1     1
#> 2     2     2     2

Lokal transformieren. Interne Evaluation mit stats::model.frame die Variablen Namen bleiben aber so erhalten wie sie im orinalem data.frame waren.

 prepare_data2(~ log(m1) + m2 + m3 + m4, dat)
#> 
#> formula: ~log(m1) + m2 + m3 + m4
#> 
#> measure.vars:  m1, m2, m3, m4
#> measure:  integer, integer, integer, integer
#> measure.class:  integer, integer, integer, integer
#> digits:  2, 2, 2, 2
#> row_name:  m1, m2, m3, m4
#> by: [1] "1"
#> 
#> group.vars:   
#> # A tibble: 2 x 4
#>      m1    m2    m3    m4
#>   <dbl> <int> <int> <int>
#> 1 0         1     1     1
#> 2 0.693     2     2     2

Metainformation wie digits und Berechnungsmethoden bereitstellen

prepare_data2( ~ m1[1] + m2 + m3[4, median] + m4, dat)
#> 
#> formula: ~m1 + m2 + m3 + m4
#> <environment: 0x000000001662a470>
#> 
#> measure.vars:  m1, m2, m3, m4
#> measure:  integer, integer, median, integer
#> measure.class:  integer, integer, integer, integer
#> digits:  1, 2, 4, 2
#> row_name:  m1, m2, m3, m4
#> by: [1] "1"
#> 
#> group.vars:   
#> # A tibble: 2 x 4
#>      m1    m2    m3    m4
#>   <int> <int> <int> <int>
#> 1     1     1     1     1
#> 2     2     2     2     2

prepare_data2(dat, m1[1, freq], m2[2, mean], m3[3, median])
#> 
#> formula: ~m1 + m2 + m3
#> <environment: 0x000000001584da80>
#> 
#> measure.vars:  m1, m2, m3
#> measure:  freq, mean, median
#> measure.class:  integer, integer, integer
#> digits:  1, 2, 3
#> row_name:  m1, m2, m3
#> by: [1] "1"
#> 
#> group.vars:   
#> # A tibble: 2 x 3
#>      m1    m2    m3
#>   <int> <int> <int>
#> 1     1     1     1
#> 2     2     2     2

Gruppierung

prepare_data2(m1 ~ sex, dat)
#> 
#> formula: m1 ~ sex
#> 
#> measure.vars:  m1
#> measure:  integer
#> measure.class:  integer
#> digits:  2
#> row_name:  m1
#> by: ~sex
#> <environment: 0x0000000012d8fdb8>
#> 
#> group.vars:  sex 
#> # A tibble: 2 x 2
#>      m1   sex
#>   <int> <int>
#> 1     1     1
#> 2     2     2

prepare_data2(dat, m1, by = ~ sex, )
#> 
#> formula: m1 ~ sex
#> <environment: 0x000000001c466418>
#> 
#> measure.vars:  m1
#> measure:  integer
#> measure.class:  integer
#> digits:  2
#> row_name:  m1
#> by: ~sex
#> <environment: 0x000000001c477848>
#> 
#> group.vars:  sex 
#> # A tibble: 2 x 2
#>      m1   sex
#>   <int> <int>
#> 1     1     1
#> 2     2     2
x <- prepare_data2(m1 ~ sex | treatment, dat)
x
#> 
#> formula: m1 ~ sex
#> 
#> measure.vars:  m1
#> measure:  integer
#> measure.class:  integer
#> digits:  2
#> row_name:  m1
#> by: ~sex
#> <environment: 0x000000001c8020b8>
#> 
#> group.vars:  sex 
#> # A tibble: 2 x 3
#>      m1   sex treatment
#>   <int> <int>     <int>
#> 1     1     1         1
#> 2     2     2         2
x$condition.vars
#> [1] "treatment"

stp4/stp25formula documentation built on Sept. 17, 2021, 2:04 p.m.