filter-dm.md
In dm: Relational Data Models

dm_filter() deprecations

Code
  dm_filter(dm_for_filter(), tf_1, a > 4)
Condition
  Warning:
  The `table` argument of `dm_filter()` is deprecated as of dm 1.0.0.
  `dm_filter()` now takes named filter expressions, the names correspond to the tables to be filtered. You no longer need to call `dm_apply_filters()` to materialize the filters.
Output
  -- Metadata --------------------------------------------------------------------
  Tables: `tf_1`, `tf_2`, `tf_3`, `tf_4`, `tf_5`, `tf_6`
  Columns: 20
  Primary keys: 6
  Foreign keys: 5
  -- Filters ---------------------------------------------------------------------
  tf_1: a > 4
Code
  dm_filter(dm = dm_for_filter(), tf_1, a > 4)
Condition
  Warning:
  The `dm` argument of `dm_filter()` is deprecated as of dm 1.0.0.
  Please use the `.dm` argument instead.
  Warning:
  The `table` argument of `dm_filter()` is deprecated as of dm 1.0.0.
  `dm_filter()` now takes named filter expressions, the names correspond to the tables to be filtered. You no longer need to call `dm_apply_filters()` to materialize the filters.
Output
  -- Metadata --------------------------------------------------------------------
  Tables: `tf_1`, `tf_2`, `tf_3`, `tf_4`, `tf_5`, `tf_6`
  Columns: 20
  Primary keys: 6
  Foreign keys: 5
  -- Filters ---------------------------------------------------------------------
  tf_1: a > 4
Code
  dm_filter(dm_for_filter(), tf_1, a > 4) %>% dm_apply_filters()
Condition
  Warning:
  The `table` argument of `dm_filter()` is deprecated as of dm 1.0.0.
  `dm_filter()` now takes named filter expressions, the names correspond to the tables to be filtered. You no longer need to call `dm_apply_filters()` to materialize the filters.
Output
  -- Metadata --------------------------------------------------------------------
  Tables: `tf_1`, `tf_2`, `tf_3`, `tf_4`, `tf_5`, `tf_6`
  Columns: 20
  Primary keys: 6
  Foreign keys: 5
Code
  dm_filter(dm_for_filter(), tf_1 = a > 4) %>% dm_apply_filters()
Condition
  Warning:
  `dm_apply_filters()` was deprecated in dm 1.0.0.
  Calling `dm_apply_filters()` after `dm_filter()` is no longer necessary.
Output
  -- Metadata --------------------------------------------------------------------
  Tables: `tf_1`, `tf_2`, `tf_3`, `tf_4`, `tf_5`, `tf_6`
  Columns: 20
  Primary keys: 6
  Foreign keys: 5
Code
  dm_filter(dm_for_filter(), tf_1, a > 4) %>% dm_apply_filters_to_tbl(tf_2)
Condition
  Warning:
  The `table` argument of `dm_filter()` is deprecated as of dm 1.0.0.
  `dm_filter()` now takes named filter expressions, the names correspond to the tables to be filtered. You no longer need to call `dm_apply_filters()` to materialize the filters.
Output
  # A tibble: 3 x 4
    c         d e        e1
    <chr> <int> <chr> <int>
  1 worm      5 G         7
  2 dog       6 E         5
  3 cat       7 F         6
Code
  dm_filter(dm_for_filter(), tf_1 = a > 4) %>% dm_apply_filters_to_tbl(tf_2)
Condition
  Warning:
  `dm_apply_filters_to_tbl()` was deprecated in dm 1.0.0.
  Access tables directly after `dm_filter()`.
Output
  # A tibble: 3 x 4
    c         d e        e1
    <chr> <int> <chr> <int>
  1 worm      5 G         7
  2 dog       6 E         5
  3 cat       7 F         6
Code
  dm_filter(dm_for_filter(), tf_1, a > 4) %>% dm_get_filters()
Condition
  Warning:
  The `table` argument of `dm_filter()` is deprecated as of dm 1.0.0.
  `dm_filter()` now takes named filter expressions, the names correspond to the tables to be filtered. You no longer need to call `dm_apply_filters()` to materialize the filters.
Output
  # A tibble: 1 x 3
    table filter     zoomed
    <chr> <list>     <lgl> 
  1 tf_1  <language> FALSE 
Code
  dm_filter(dm_for_filter(), tf_1 = a > 4) %>% dm_get_filters()
Condition
  Warning:
  `dm_get_filters()` was deprecated in dm 1.0.0.
  Filter conditions are no longer stored with the dm object.
Output
  # A tibble: 0 x 3
  # ... with 3 variables: table <chr>, filter <list>, zoomed <lgl>

data structure

Code
  dm_more_complex() %>% dm_paste(options = c("select", "keys"))
Message
  dm::dm(
    tf_1,
    tf_2,
    tf_3,
    tf_4,
    tf_5,
    tf_6,
    tf_7,
    tf_6_2,
    tf_4_2,
    a,
    b,
    c,
    d,
    e,
  ) %>%
    dm::dm_select(tf_1, a, b) %>%
    dm::dm_select(tf_2, c, d, e, e1) %>%
    dm::dm_select(tf_3, f, f1, g) %>%
    dm::dm_select(tf_4, h, i, j, j1) %>%
    dm::dm_select(tf_5, ww, k, l, m) %>%
    dm::dm_select(tf_6, zz, n, o) %>%
    dm::dm_select(tf_7, p, q) %>%
    dm::dm_select(tf_6_2, p, f, f1) %>%
    dm::dm_select(tf_4_2, r, s, t) %>%
    dm::dm_select(a, a_1, a_2) %>%
    dm::dm_select(b, b_1, b_2, b_3) %>%
    dm::dm_select(c, c_1) %>%
    dm::dm_select(d, d_1, b_1) %>%
    dm::dm_select(e, e_1, b_1) %>%
    dm::dm_add_pk(tf_1, a) %>%
    dm::dm_add_pk(tf_2, c) %>%
    dm::dm_add_pk(tf_3, c(f, f1)) %>%
    dm::dm_add_pk(tf_4, h) %>%
    dm::dm_add_pk(tf_5, k) %>%
    dm::dm_add_pk(tf_6, n) %>%
    dm::dm_add_pk(tf_6_2, p) %>%
    dm::dm_add_pk(tf_4_2, r) %>%
    dm::dm_add_pk(a, a_1) %>%
    dm::dm_add_pk(b, b_1) %>%
    dm::dm_add_pk(c, c_1) %>%
    dm::dm_add_pk(d, d_1) %>%
    dm::dm_add_pk(e, e_1) %>%
    dm::dm_add_fk(tf_2, d, tf_1) %>%
    dm::dm_add_fk(tf_2, c(e, e1), tf_3) %>%
    dm::dm_add_fk(tf_4, c(j, j1), tf_3) %>%
    dm::dm_add_fk(tf_6_2, c(f, f1), tf_3) %>%
    dm::dm_add_fk(tf_5, l, tf_4) %>%
    dm::dm_add_fk(tf_5, m, tf_6) %>%
    dm::dm_add_fk(tf_5, l, tf_4_2) %>%
    dm::dm_add_fk(b, b_2, a) %>%
    dm::dm_add_fk(d, b_1, b) %>%
    dm::dm_add_fk(e, b_1, b) %>%
    dm::dm_add_fk(b, b_3, c)

we get filtered/unfiltered tables with respective funs

Code
  dm_for_filter() %>% dm_filter(tf_1 = a > 3 & a < 8) %>% dm_get_tables() %>% map(
    harmonize_tbl)
Output
  $tf_1
  # A tibble: 4 x 2
        a b    
    <int> <chr>
  1     4 D    
  2     5 E    
  3     6 F    
  4     7 G

  $tf_2
  # A tibble: 4 x 4
    c         d e        e1
    <chr> <int> <chr> <int>
  1 cat       7 F         6
  2 dog       6 E         5
  3 seal      4 F         6
  4 worm      5 G         7

  $tf_3
  # A tibble: 3 x 3
    f        f1 g    
    <chr> <int> <chr>
  1 E         5 four 
  2 F         6 five 
  3 G         7 six

  $tf_4
  # A tibble: 3 x 4
    h     i     j        j1
    <chr> <chr> <chr> <int>
  1 c     five  E         5
  2 d     six   F         6
  3 e     seven F         6

  $tf_5
  # A tibble: 3 x 4
       ww     k l     m         
    <int> <int> <chr> <chr>     
  1     2     2 c     tree      
  2     2     3 d     streetlamp
  3     2     4 e     streetlamp

  $tf_6
  # A tibble: 2 x 3
       zz n          o    
    <int> <chr>      <chr>
  1     1 streetlamp h    
  2     1 tree       f

dm_filter() works as intended for reversed dm

Code
  dm_for_filter_rev() %>% dm_filter(tf_1 = a < 8 & a > 3) %>% dm_get_tables() %>%
    map(harmonize_tbl)
Output
  $tf_6
  # A tibble: 2 x 3
       zz n          o    
    <int> <chr>      <chr>
  1     1 streetlamp h    
  2     1 tree       f

  $tf_5
  # A tibble: 3 x 4
       ww     k l     m         
    <int> <int> <chr> <chr>     
  1     2     2 c     tree      
  2     2     3 d     streetlamp
  3     2     4 e     streetlamp

  $tf_4
  # A tibble: 3 x 4
    h     i     j        j1
    <chr> <chr> <chr> <int>
  1 c     five  E         5
  2 d     six   F         6
  3 e     seven F         6

  $tf_3
  # A tibble: 3 x 3
    f        f1 g    
    <chr> <int> <chr>
  1 E         5 four 
  2 F         6 five 
  3 G         7 six

  $tf_2
  # A tibble: 4 x 4
    c         d e        e1
    <chr> <int> <chr> <int>
  1 cat       7 F         6
  2 dog       6 E         5
  3 seal      4 F         6
  4 worm      5 G         7

  $tf_1
  # A tibble: 4 x 2
        a b    
    <int> <chr>
  1     4 D    
  2     5 E    
  3     6 F    
  4     7 G

dm_filter() works as intended for inbetween table

Code
  dm_for_filter() %>% dm_filter(tf_3 = g == "five") %>% dm_get_tables() %>% map(
    harmonize_tbl)
Output
  $tf_1
  # A tibble: 2 x 2
        a b    
    <int> <chr>
  1     4 D    
  2     7 G

  $tf_2
  # A tibble: 2 x 4
    c         d e        e1
    <chr> <int> <chr> <int>
  1 cat       7 F         6
  2 seal      4 F         6

  $tf_3
  # A tibble: 1 x 3
    f        f1 g    
    <chr> <int> <chr>
  1 F         6 five

  $tf_4
  # A tibble: 2 x 4
    h     i     j        j1
    <chr> <chr> <chr> <int>
  1 d     six   F         6
  2 e     seven F         6

  $tf_5
  # A tibble: 2 x 4
       ww     k l     m         
    <int> <int> <chr> <chr>     
  1     2     3 d     streetlamp
  2     2     4 e     streetlamp

  $tf_6
  # A tibble: 1 x 3
       zz n          o    
    <int> <chr>      <chr>
  1     1 streetlamp h

dm_filter() output for compound keys

Code
  nyc_comp() %>% dm_filter(flights = sched_dep_time <= 1200) %>% dm_nrow()
Output
  airlines airports  flights   planes  weather 
        14       63      672      502       47 
Code
  nyc_comp() %>% dm_filter(weather = pressure < 1020) %>% dm_nrow()
Output
  airlines airports  flights   planes  weather 
         0        0        0        0        0