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New columns from extra attributes

Source: R/extra_attrs.R
extra_attrs_to_cols.Rd

New columns from extra attributes

Usage

extra_attrs_to_cols(data, ..., flatten = FALSE, keep_empty = TRUE)

Arguments

data

An interaction data frame.

...

The names of the extra attributes; NSE is supported. Custom column names can be provided as argument names.

flatten

Logical: unnest the list column even if some records have multiple values for the attributes; these will yield multiple records in the resulted data frame.

keep_empty

Logical: if `flatten` is `TRUE`, shall we keep the records which do not have the attribute?

Value

Data frame with the new column created; the new column is list type if one interaction might have multiple values of the attribute, or character type if

See also

Examples

i <- import_omnipath_interactions(fields = 'extra_attrs')
extra_attrs_to_cols(i, Cellinker_type, Macrophage_type)
#> # A tibble: 40,014 × 18
#>    source target sourc…¹ targe…² is_di…³ is_st…⁴ is_in…⁵ conse…⁶ conse…⁷ conse…⁸
#>    <chr>  <chr>  <chr>   <chr>     <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#>  1 P0DP25 P48995 CALM3   TRPC1         1       0       1       1       0       1
#>  2 P0DP23 P48995 CALM1   TRPC1         1       0       1       1       0       1
#>  3 P0DP24 P48995 CALM2   TRPC1         1       0       1       1       0       1
#>  4 Q03135 P48995 CAV1    TRPC1         1       1       0       1       1       0
#>  5 P14416 P48995 DRD2    TRPC1         1       1       0       1       1       0
#>  6 Q99750 P48995 MDFI    TRPC1         1       0       1       1       0       1
#>  7 Q14571 P48995 ITPR2   TRPC1         1       1       0       1       1       0
#>  8 P29966 P48995 MARCKS  TRPC1         1       0       1       1       0       1
#>  9 Q13255 P48995 GRM1    TRPC1         1       1       0       1       1       0
#> 10 Q13586 P48995 STIM1   TRPC1         1       1       0       1       1       0
#> # … with 40,004 more rows, 8 more variables: extra_attrs <list>, sources <chr>,
#> #   references <chr>, curation_effort <dbl>, n_references <dbl>,
#> #   n_resources <int>, Cellinker_type <chr>, Macrophage_type <list>, and
#> #   abbreviated variable names ¹​source_genesymbol, ²​target_genesymbol,
#> #   ³​is_directed, ⁴​is_stimulation, ⁵​is_inhibition, ⁶​consensus_direction,
#> #   ⁷​consensus_stimulation, ⁸​consensus_inhibition
extra_attrs_to_cols(
    i,
    Cellinker_type,
    Macrophage_type,
    flatten = TRUE,
    keep_empty = FALSE
)
#> # A tibble: 4,202 × 18
#>    source target sourc…¹ targe…² is_di…³ is_st…⁴ is_in…⁵ conse…⁶ conse…⁷ conse…⁸
#>    <chr>  <chr>  <chr>   <chr>     <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#>  1 Q16539 P49137 MAPK14  MAPKAP…       1       1       0       1       1       0
#>  2 Q16539 P49137 MAPK14  MAPKAP…       1       1       0       1       1       0
#>  3 O60674 P19235 JAK2    EPOR          1       1       0       1       1       0
#>  4 Q9Y219 P46531 JAG2    NOTCH1        1       1       1       1       1       0
#>  5 O00548 P46531 DLL1    NOTCH1        1       1       0       1       1       0
#>  6 O15111 P19838 CHUK    NFKB1         1       1       1       1       0       1
#>  7 P05019 P08069 IGF1    IGF1R         1       1       0       1       1       0
#>  8 P78504 P46531 JAG1    NOTCH1        1       1       1       1       1       0
#>  9 Q14164 Q92985 IKBKE   IRF7          1       1       0       1       1       0
#> 10 Q13490 P42574 BIRC2   CASP3         1       0       1       1       0       1
#> # … with 4,192 more rows, 8 more variables: extra_attrs <list>, sources <chr>,
#> #   references <chr>, curation_effort <dbl>, n_references <dbl>,
#> #   n_resources <int>, Cellinker_type <chr>, Macrophage_type <chr>, and
#> #   abbreviated variable names ¹​source_genesymbol, ²​target_genesymbol,
#> #   ³​is_directed, ⁴​is_stimulation, ⁵​is_inhibition, ⁶​consensus_direction,
#> #   ⁷​consensus_stimulation, ⁸​consensus_inhibition