Nothing
tests/testthat/_snaps/dm.md
In dm: Relational Data Models
dm() API
Code
dm(a = tibble(), a = tibble(), .name_repair = "unique")
Message
New names:
* `a` -> `a...1`
* `a` -> `a...2`
Output
-- Metadata --------------------------------------------------------------------
Tables: `a...1`, `a...2`
Columns: 0
Primary keys: 0
Foreign keys: 0
Code
dm(a = tibble(), a = tibble(), .name_repair = "unique", .quiet = TRUE)
Output
-- Metadata --------------------------------------------------------------------
Tables: `a...1`, `a...2`
Columns: 0
Primary keys: 0
Foreign keys: 0
Code
dm(a = tibble(), a = tibble())
Condition
Error in `dm()`:
! Names must be unique.
x These names are duplicated:
* "a" at locations 1 and 2.
Code
dm(a = dm())
Condition
Error in `dm()`:
! All dm objects passed to `dm()` must be unnamed.
i Argument 1 has name `a`.
Code
dm(a = tibble(), dm_zoom_to(dm_for_filter(), tf_1))
Condition
Error in `dm()`:
! All dm objects passed to `dm()` must be unzoomed.
i Argument 2 is a zoomed dm.
dm() works for adding tables
Code
dm(dm_for_filter(), tf_1 = data_card_1(), .name_repair = "check_unique")
Condition
Error in `dm()`:
! Names must be unique.
x These names are duplicated:
* "tf_1" at locations 1 and 7.
dm() for adding tables with compound keys
Code
dm(dm_for_flatten(), res_flat = result_from_flatten()) %>% dm_paste(options = c(
"select", "keys"))
Message
dm::dm(
fact,
dim_1,
dim_2,
dim_3,
dim_4,
res_flat,
) %>%
dm::dm_select(fact, fact, dim_1_key_1, dim_1_key_2, dim_2_key, dim_3_key, dim_4_key, something) %>%
dm::dm_select(dim_1, dim_1_pk_1, dim_1_pk_2, something) %>%
dm::dm_select(dim_2, dim_2_pk, something) %>%
dm::dm_select(dim_3, dim_3_pk, something) %>%
dm::dm_select(dim_4, dim_4_pk, something) %>%
dm::dm_select(res_flat, fact, dim_1_key_1, dim_1_key_2, dim_2_key, dim_3_key, dim_4_key, fact.something, dim_1.something, dim_2.something, dim_3.something, dim_4.something) %>%
dm::dm_add_pk(dim_1, c(dim_1_pk_1, dim_1_pk_2)) %>%
dm::dm_add_pk(dim_2, dim_2_pk) %>%
dm::dm_add_pk(dim_3, dim_3_pk) %>%
dm::dm_add_pk(dim_4, dim_4_pk) %>%
dm::dm_add_fk(fact, c(dim_1_key_1, dim_1_key_2), dim_1) %>%
dm::dm_add_fk(fact, dim_2_key, dim_2) %>%
dm::dm_add_fk(fact, dim_3_key, dim_3) %>%
dm::dm_add_fk(fact, dim_4_key, dim_4)
errors: duplicate table names, src mismatches
Code
dm(dm_for_filter(), dm_for_flatten(), dm_for_filter())
Condition
Error in `dm()`:
! Names must be unique.
x These names are duplicated:
* "tf_1" at locations 1 and 12.
* "tf_2" at locations 2 and 13.
* "tf_3" at locations 3 and 14.
* "tf_4" at locations 4 and 15.
* "tf_5" at locations 5 and 16.
* ...
test error output for src mismatches
Code
writeLines(conditionMessage(expect_error(dm(dm_for_flatten(),
dm_for_filter_duckdb()))))
Output
All `dm` objects need to share the same `src`.
output for dm() with dm
Code
dm()
Output
dm()
Code
dm(empty_dm())
Output
dm()
Code
dm(dm_for_filter()) %>% collect()
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(dm_for_filter(), dm_for_flatten(), dm_for_filter(), .name_repair = "unique",
.quiet = TRUE) %>% collect()
Output
-- Metadata --------------------------------------------------------------------
Tables: `tf_1...1`, `tf_2...2`, `tf_3...3`, `tf_4...4`, `tf_5...5`, ... (17 total)
Columns: 56
Primary keys: 16
Foreign keys: 14
output for dm() with dm (2)
Code
dm(dm_for_filter(), dm_for_flatten(), dm_for_filter())
Condition
Error in `dm()`:
! Names must be unique.
x These names are duplicated:
* "tf_1" at locations 1 and 12.
* "tf_2" at locations 2 and 13.
* "tf_3" at locations 3 and 14.
* "tf_4" at locations 4 and 15.
* "tf_5" at locations 5 and 16.
* ...
output for dm() with dm (3)
Code
dm(dm_for_filter(), dm_for_flatten(), dm_for_filter(), .name_repair = "unique") %>%
collect()
Message
New names:
* `tf_1` -> `tf_1...1`
* `tf_2` -> `tf_2...2`
* `tf_3` -> `tf_3...3`
* `tf_4` -> `tf_4...4`
* `tf_5` -> `tf_5...5`
* `tf_6` -> `tf_6...6`
* `tf_1` -> `tf_1...12`
* `tf_2` -> `tf_2...13`
* `tf_3` -> `tf_3...14`
* `tf_4` -> `tf_4...15`
* `tf_5` -> `tf_5...16`
* `tf_6` -> `tf_6...17`
Output
-- Metadata --------------------------------------------------------------------
Tables: `tf_1...1`, `tf_2...2`, `tf_3...3`, `tf_4...4`, `tf_5...5`, ... (17 total)
Columns: 56
Primary keys: 16
Foreign keys: 14
output dm() for dm for compound keys
Code
dm(dm_for_filter(), dm_for_flatten()) %>% dm_paste(options = c("select", "keys"))
Message
dm::dm(
tf_1,
tf_2,
tf_3,
tf_4,
tf_5,
tf_6,
fact,
dim_1,
dim_2,
dim_3,
dim_4,
) %>%
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(fact, fact, dim_1_key_1, dim_1_key_2, dim_2_key, dim_3_key, dim_4_key, something) %>%
dm::dm_select(dim_1, dim_1_pk_1, dim_1_pk_2, something) %>%
dm::dm_select(dim_2, dim_2_pk, something) %>%
dm::dm_select(dim_3, dim_3_pk, something) %>%
dm::dm_select(dim_4, dim_4_pk, something) %>%
dm::dm_add_pk(tf_1, a, autoincrement = TRUE) %>%
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, o) %>%
dm::dm_add_pk(dim_1, c(dim_1_pk_1, dim_1_pk_2)) %>%
dm::dm_add_pk(dim_2, dim_2_pk) %>%
dm::dm_add_pk(dim_3, dim_3_pk) %>%
dm::dm_add_pk(dim_4, dim_4_pk) %>%
dm::dm_add_uk(tf_3, g) %>%
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_5, l, tf_4, on_delete = "cascade") %>%
dm::dm_add_fk(tf_5, m, tf_6, n) %>%
dm::dm_add_fk(fact, c(dim_1_key_1, dim_1_key_2), dim_1) %>%
dm::dm_add_fk(fact, dim_2_key, dim_2) %>%
dm::dm_add_fk(fact, dim_3_key, dim_3) %>%
dm::dm_add_fk(fact, dim_4_key, dim_4)
Code
dm(dm_for_flatten(), dm_for_filter()) %>% dm_paste(options = c("select", "keys"))
Message
dm::dm(
fact,
dim_1,
dim_2,
dim_3,
dim_4,
tf_1,
tf_2,
tf_3,
tf_4,
tf_5,
tf_6,
) %>%
dm::dm_select(fact, fact, dim_1_key_1, dim_1_key_2, dim_2_key, dim_3_key, dim_4_key, something) %>%
dm::dm_select(dim_1, dim_1_pk_1, dim_1_pk_2, something) %>%
dm::dm_select(dim_2, dim_2_pk, something) %>%
dm::dm_select(dim_3, dim_3_pk, something) %>%
dm::dm_select(dim_4, dim_4_pk, something) %>%
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_add_pk(dim_1, c(dim_1_pk_1, dim_1_pk_2)) %>%
dm::dm_add_pk(dim_2, dim_2_pk) %>%
dm::dm_add_pk(dim_3, dim_3_pk) %>%
dm::dm_add_pk(dim_4, dim_4_pk) %>%
dm::dm_add_pk(tf_1, a, autoincrement = TRUE) %>%
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, o) %>%
dm::dm_add_uk(tf_3, g) %>%
dm::dm_add_fk(fact, c(dim_1_key_1, dim_1_key_2), dim_1) %>%
dm::dm_add_fk(fact, dim_2_key, dim_2) %>%
dm::dm_add_fk(fact, dim_3_key, dim_3) %>%
dm::dm_add_fk(fact, dim_4_key, dim_4) %>%
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_5, l, tf_4, on_delete = "cascade") %>%
dm::dm_add_fk(tf_5, m, tf_6, n)
Code
dm(dm_for_flatten(), dm_for_flatten(), .name_repair = "unique") %>% dm_paste(
options = c("select", "keys"))
Message
New names:
* `fact` -> `fact...1`
* `dim_1` -> `dim_1...2`
* `dim_2` -> `dim_2...3`
* `dim_3` -> `dim_3...4`
* `dim_4` -> `dim_4...5`
* `fact` -> `fact...6`
* `dim_1` -> `dim_1...7`
* `dim_2` -> `dim_2...8`
* `dim_3` -> `dim_3...9`
* `dim_4` -> `dim_4...10`
dm::dm(
fact...1,
dim_1...2,
dim_2...3,
dim_3...4,
dim_4...5,
fact...6,
dim_1...7,
dim_2...8,
dim_3...9,
dim_4...10,
) %>%
dm::dm_select(fact...1, fact, dim_1_key_1, dim_1_key_2, dim_2_key, dim_3_key, dim_4_key, something) %>%
dm::dm_select(dim_1...2, dim_1_pk_1, dim_1_pk_2, something) %>%
dm::dm_select(dim_2...3, dim_2_pk, something) %>%
dm::dm_select(dim_3...4, dim_3_pk, something) %>%
dm::dm_select(dim_4...5, dim_4_pk, something) %>%
dm::dm_select(fact...6, fact, dim_1_key_1, dim_1_key_2, dim_2_key, dim_3_key, dim_4_key, something) %>%
dm::dm_select(dim_1...7, dim_1_pk_1, dim_1_pk_2, something) %>%
dm::dm_select(dim_2...8, dim_2_pk, something) %>%
dm::dm_select(dim_3...9, dim_3_pk, something) %>%
dm::dm_select(dim_4...10, dim_4_pk, something) %>%
dm::dm_add_pk(dim_1...2, c(dim_1_pk_1, dim_1_pk_2)) %>%
dm::dm_add_pk(dim_2...3, dim_2_pk) %>%
dm::dm_add_pk(dim_3...4, dim_3_pk) %>%
dm::dm_add_pk(dim_4...5, dim_4_pk) %>%
dm::dm_add_pk(dim_1...7, c(dim_1_pk_1, dim_1_pk_2)) %>%
dm::dm_add_pk(dim_2...8, dim_2_pk) %>%
dm::dm_add_pk(dim_3...9, dim_3_pk) %>%
dm::dm_add_pk(dim_4...10, dim_4_pk) %>%
dm::dm_add_fk(fact...1, c(dim_1_key_1, dim_1_key_2), dim_1...2) %>%
dm::dm_add_fk(fact...1, dim_2_key, dim_2...3) %>%
dm::dm_add_fk(fact...1, dim_3_key, dim_3...4) %>%
dm::dm_add_fk(fact...1, dim_4_key, dim_4...5) %>%
dm::dm_add_fk(fact...6, c(dim_1_key_1, dim_1_key_2), dim_1...7) %>%
dm::dm_add_fk(fact...6, dim_2_key, dim_2...8) %>%
dm::dm_add_fk(fact...6, dim_3_key, dim_3...9) %>%
dm::dm_add_fk(fact...6, dim_4_key, dim_4...10)
'compute.dm()' fails with temporary = FALSE (#2059)
Code
dm_for_filter_duckdb() %>% compute(temporary = FALSE)
Condition
Error in `compute()`:
! `compute.dm()` does not support `temporary = FALSE`.
output
Code
print(dm())
Output
dm()
Code
nyc_flights_dm <- dm_nycflights_small()
collect(nyc_flights_dm)
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 53
Primary keys: 3
Foreign keys: 3
Code
nyc_flights_dm %>% format()
Output
dm: 5 tables, 53 columns, 3 primary keys, 3 foreign keys
Code
nyc_flights_dm %>% dm_filter(flights = (origin == "EWR")) %>% collect()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 53
Primary keys: 3
Foreign keys: 3
output for compound keys
Code
copy_to(nyc_comp(), mtcars, "car_table")
Condition
Warning:
`copy_to.dm()` was deprecated in dm 0.2.0.
i Use `copy_to(dm_get_con(dm), ...)` and `dm()`.
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`, `car_table`
Columns: 64
Primary keys: 4
Foreign keys: 4
Code
dm(nyc_comp(), car_table)
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`, `car_table`
Columns: 64
Primary keys: 4
Foreign keys: 4
Code
nyc_comp() %>% collect()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 53
Primary keys: 4
Foreign keys: 4
Code
nyc_comp() %>% dm_filter(flights = (day == 10)) %>% collect() %>% dm_get_def() %>%
select(-uuid)
Output
# A tibble: 5 x 10
table data segment display pks uks fks filters zoom
<chr> <list> <chr> <chr> <list<tibble> <list<> <list<> <list<> <list>
1 airlines <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
2 airports <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
3 flights <tibble> <NA> <NA> [0 x 2] [0 x 1] [0 x 4] [0 x 2] <NULL>
4 planes <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
5 weather <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
# i 1 more variable: col_tracker_zoom <list>
Code
nyc_comp() %>% dm_zoom_to(weather) %>% mutate(origin_new = paste0(origin,
" airport")) %>% compute() %>% dm_update_zoomed() %>% collect() %>%
dm_get_def() %>% select(-uuid)
Output
# A tibble: 5 x 10
table data segment display pks uks fks filters zoom
<chr> <list> <chr> <chr> <list<tibble> <list<> <list<> <list<> <list>
1 airlines <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
2 airports <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
3 flights <tibble> <NA> <NA> [0 x 2] [0 x 1] [0 x 4] [0 x 2] <NULL>
4 planes <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
5 weather <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
# i 1 more variable: col_tracker_zoom <list>
Code
nyc_comp() %>% dm_zoom_to(weather) %>% collect()
Message
Detaching table from dm.
i Use `. %>% pull_tbl() %>% collect()` instead to silence this message.
Output
# A tibble: 144 x 15
origin year month day hour temp dewp humid wind_dir wind_speed
<chr> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 EWR 2013 1 10 0 41 32 70.1 230 8.06
2 EWR 2013 1 10 1 39.0 30.0 69.9 210 9.21
3 EWR 2013 1 10 2 39.0 28.9 66.8 230 6.90
4 EWR 2013 1 10 3 39.9 27.0 59.5 270 5.75
5 EWR 2013 1 10 4 41 26.1 55.0 320 6.90
6 EWR 2013 1 10 5 41 26.1 55.0 300 12.7
7 EWR 2013 1 10 6 39.9 25.0 54.8 280 6.90
8 EWR 2013 1 10 7 41 25.0 52.6 330 6.90
9 EWR 2013 1 10 8 43.0 25.0 48.7 330 8.06
10 EWR 2013 1 10 9 45.0 23 41.6 320 17.3
# i 134 more rows
# i 5 more variables: wind_gust <dbl>, precip <dbl>, pressure <dbl>,
# visib <dbl>, time_hour <chr>
Code
pull_tbl(nyc_comp(), weather)
Output
# A tibble: 144 x 15
origin year month day hour temp dewp humid wind_dir wind_speed
<chr> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 EWR 2013 1 10 0 41 32 70.1 230 8.06
2 EWR 2013 1 10 1 39.0 30.0 69.9 210 9.21
3 EWR 2013 1 10 2 39.0 28.9 66.8 230 6.90
4 EWR 2013 1 10 3 39.9 27.0 59.5 270 5.75
5 EWR 2013 1 10 4 41 26.1 55.0 320 6.90
6 EWR 2013 1 10 5 41 26.1 55.0 300 12.7
7 EWR 2013 1 10 6 39.9 25.0 54.8 280 6.90
8 EWR 2013 1 10 7 41 25.0 52.6 330 6.90
9 EWR 2013 1 10 8 43.0 25.0 48.7 330 8.06
10 EWR 2013 1 10 9 45.0 23 41.6 320 17.3
# i 134 more rows
# i 5 more variables: wind_gust <dbl>, precip <dbl>, pressure <dbl>,
# visib <dbl>, time_hour <chr>
Code
nyc_comp() %>% dm_zoom_to(weather) %>% pull_tbl()
Output
# A tibble: 144 x 15
origin year month day hour temp dewp humid wind_dir wind_speed
<chr> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 EWR 2013 1 10 0 41 32 70.1 230 8.06
2 EWR 2013 1 10 1 39.0 30.0 69.9 210 9.21
3 EWR 2013 1 10 2 39.0 28.9 66.8 230 6.90
4 EWR 2013 1 10 3 39.9 27.0 59.5 270 5.75
5 EWR 2013 1 10 4 41 26.1 55.0 320 6.90
6 EWR 2013 1 10 5 41 26.1 55.0 300 12.7
7 EWR 2013 1 10 6 39.9 25.0 54.8 280 6.90
8 EWR 2013 1 10 7 41 25.0 52.6 330 6.90
9 EWR 2013 1 10 8 43.0 25.0 48.7 330 8.06
10 EWR 2013 1 10 9 45.0 23 41.6 320 17.3
# i 134 more rows
# i 5 more variables: wind_gust <dbl>, precip <dbl>, pressure <dbl>,
# visib <dbl>, time_hour <chr>
glimpse.dm() works
Code
glimpse(empty_dm())
Output
dm of 0 tables
Code
glimpse(dm_for_disambiguate())
Output
dm of 3 tables: `iris_1`, `iris_2`, `iris_3`
--------------------------------------------------------------------------------
Table: `iris_1`
Primary key: `key`
Rows: 150
Columns: 6
$ key <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.~
$ Species <chr> "setosa", "setosa", "setosa", "setosa", "setosa", "setosa~
--------------------------------------------------------------------------------
Table: `iris_2`
1 outgoing foreign key(s):
`key` -> `iris_1$key` no_action
Rows: 150
Columns: 7
$ key <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.~
$ Species <chr> "setosa", "setosa", "setosa", "setosa", "setosa", "setosa~
$ other_col <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
--------------------------------------------------------------------------------
Table: `iris_3`
Rows: 150
Columns: 8
$ key <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.~
$ Species <chr> "setosa", "setosa", "setosa", "setosa", "setosa", "setosa~
$ other_col <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
$ one_more_col <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
--------------------------------------------------------------------------------
Code
glimpse(dm_for_disambiguate(), width = 40)
Output
dm of 3 tables: `iris_1`, `iris_2`, `iri...
--------------------------------------------------------------------------------
Table: `iris_1`
Primary key: `key`
Rows: 150
Columns: 6
$ key <int> 1, 2, 3, 4, 5, 6,~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.~
$ Species <chr> "setosa", "setosa~
--------------------------------------------------------------------------------
Table: `iris_2`
1 outgoing foreign key(s):
`key` -> `iris_1$key` no_action
Rows: 150
Columns: 7
$ key <int> 1, 2, 3, 4, 5, 6,~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.~
$ Species <chr> "setosa", "setosa~
$ other_col <int> 1, 1, 1, 1, 1, 1,~
--------------------------------------------------------------------------------
Table: `iris_3`
Rows: 150
Columns: 8
$ key <int> 1, 2, 3, 4, 5, 6,~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.~
$ Species <chr> "setosa", "setosa~
$ other_col <int> 1, 1, 1, 1, 1, 1,~
$ one_more_col <dbl> 1, 1, 1, 1, 1, 1,~
--------------------------------------------------------------------------------
Code
getOption("width")
Output
[1] 80
Code
glimpse(dm_for_disambiguate() %>% dm_rename(iris_1,
gdsjgiodsjgdisogjdsiogjdsigjsdiogjisdjgiodsjgiosdjgiojsdiogjgrjihjrehoierjhiorejhrieojhreiojhieorhjioerjhierjhioerjhioerjhioerjiohjeriosdiogjsdjigjsd = key) %>%
dm_rename_tbl(
gdsjgiodsjgdisogjdsiogjdsigjsdiogjisdjgiodsjgiosdjgiojsdiogjgrjihjrehoierjhiorejhrieojhreiojhieorhjioerjhierjhioerjhioerjhioerjiohjeriosdiogjsdjigjsd = iris_1))
Output
dm of 3 tables: `gdsjgiodsjgdisogjdsiogjdsigjsdiogjisdjgiodsjgiosdjgiojsdiogjgrj...
--------------------------------------------------------------------------------
Table: `gdsjgiodsjgdisogjdsiogjdsigjsdiogjisdjgiodsjgiosdjgiojsdiogjgrjihjrehoie...
Primary key: `gdsjgiodsjgdisogjdsiogjdsigjsdiogjisdjgiodsjgiosdjgiojsdiogjgrjihj...
Rows: 150
Columns: 6
$ gdsjgiodsjgdisogjdsiogjdsigjsdiogjisdjgiodsjgiosdjgiojsdiogjgrjihjrehoierjhiorejhrieojhreiojhieorhjioerjhierjhioerjhioerjhioerjiohjeriosdiogjsdjigjsd <int> ~
$ Sepal.Length <dbl> ~
$ Sepal.Width <dbl> ~
$ Petal.Length <dbl> ~
$ Petal.Width <dbl> ~
$ Species <chr> ~
--------------------------------------------------------------------------------
Table: `iris_2`
1 outgoing foreign key(s):
`key` -> `gdsjgiodsjgdisogjdsiogjdsigjsdiogjisdjgiodsjgiosdjgiojsdiogjgrjihjre...
Rows: 150
Columns: 7
$ key <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.~
$ Species <chr> "setosa", "setosa", "setosa", "setosa", "setosa", "setosa~
$ other_col <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
--------------------------------------------------------------------------------
Table: `iris_3`
Rows: 150
Columns: 8
$ key <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.~
$ Species <chr> "setosa", "setosa", "setosa", "setosa", "setosa", "setosa~
$ other_col <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
$ one_more_col <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
--------------------------------------------------------------------------------
Code
dm_nycflights13() %>% dm_select_tbl(weather) %>% dm_select(weather, -origin) %>%
glimpse()
Output
dm of 1 tables: `weather`
--------------------------------------------------------------------------------
Table: `weather`
Rows: 144
Columns: 14
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013,~
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,~
$ day <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,~
$ hour <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1~
$ temp <dbl> 41.00, 39.02, 39.02, 39.92, 41.00, 41.00, 39.92, 41.00, 42.~
$ dewp <dbl> 32.00, 30.02, 28.94, 26.96, 26.06, 26.06, 24.98, 24.98, 24.~
$ humid <dbl> 70.08, 69.86, 66.85, 59.50, 54.97, 54.97, 54.81, 52.56, 48.~
$ wind_dir <dbl> 230, 210, 230, 270, 320, 300, 280, 330, 330, 320, 320, 330,~
$ wind_speed <dbl> 8.05546, 9.20624, 6.90468, 5.75390, 6.90468, 12.65858, 6.90~
$ wind_gust <dbl> NA, NA, NA, NA, NA, 20.71404, 17.26170, NA, NA, 26.46794, N~
$ precip <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
$ pressure <dbl> 1024.6, 1025.9, 1026.9, 1027.5, 1028.2, 1029.0, 1030.0, 103~
$ visib <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,~
$ time_hour <dttm> 2013-01-10 00:00:00, 2013-01-10 01:00:00, 2013-01-10 02:00~
--------------------------------------------------------------------------------
glimpse.dm_zoomed() works
Code
dm_nycflights13() %>% dm_zoom_to(airports) %>% glimpse()
Output
dm of 5 tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Zoomed table: `airports`
Primary key: `faa`
Rows: 86
Columns: 8
$ faa <chr> "ALB", "ATL", "AUS", "BDL", "BHM", "BNA", "BOS", "BTV", "BUF", "~
$ name <chr> "Albany Intl", "Hartsfield Jackson Atlanta Intl", "Austin Bergst~
$ lat <dbl> 42.74827, 33.63672, 30.19453, 41.93889, 33.56294, 36.12447, 42.3~
$ lon <dbl> -73.80169, -84.42807, -97.66989, -72.68322, -86.75355, -86.67819~
$ alt <dbl> 285, 1026, 542, 173, 644, 599, 19, 335, 724, 778, 146, 236, 1228~
$ tz <dbl> -5, -5, -6, -5, -6, -6, -5, -5, -5, -8, -5, -5, -5, -5, -5, -5, ~
$ dst <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",~
$ tzone <chr> "America/New_York", "America/New_York", "America/Chicago", "Amer~
Code
dm_nycflights13() %>% dm_zoom_to(flights) %>% glimpse(width = 100)
Output
dm of 5 tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Zoomed table: `flights`
4 outgoing foreign key(s):
`carrier` -> `airlines$carrier` no_action
`origin` -> `airports$faa` no_action
`tailnum` -> `planes$tailnum` no_action
(`origin`, `time_hour`) -> (`weather$origin`, `weather$time_hour`) no_action
Rows: 1,761
Columns: 19
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 201~
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
$ day <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,~
$ dep_time <int> 3, 16, 450, 520, 530, 531, 535, 546, 549, 550, 553, 553, 553, 553, 555, 555~
$ sched_dep_time <int> 2359, 2359, 500, 525, 530, 540, 540, 600, 600, 600, 600, 600, 600, 600, 600~
$ dep_delay <dbl> 4, 17, -10, -5, 0, -9, -5, -14, -11, -10, -7, -7, -7, -7, -5, -10, -5, -4, ~
$ arr_time <int> 426, 447, 634, 813, 824, 832, 1015, 645, 652, 649, 711, 837, 834, 733, 733,~
$ sched_arr_time <int> 437, 444, 648, 820, 829, 850, 1017, 709, 724, 703, 715, 910, 859, 759, 745,~
$ arr_delay <dbl> -11, 3, -14, -7, -5, -18, -2, -24, -32, -14, -4, -33, -25, -26, -12, -19, 2~
$ carrier <chr> "B6", "B6", "US", "UA", "UA", "AA", "B6", "B6", "EV", "US", "EV", "AA", "B6~
$ flight <int> 727, 739, 1117, 1018, 404, 1141, 725, 380, 6055, 2114, 5716, 707, 507, 731,~
$ tailnum <chr> "N571JB", "N564JB", "N171US", "N35204", "N815UA", "N5EAAA", "N784JB", "N337~
$ origin <chr> "JFK", "JFK", "EWR", "EWR", "LGA", "JFK", "JFK", "EWR", "LGA", "LGA", "JFK"~
$ dest <chr> "BQN", "PSE", "CLT", "IAH", "IAH", "MIA", "BQN", "BOS", "IAD", "BOS", "IAD"~
$ air_time <dbl> 183, 191, 78, 215, 210, 149, 191, 39, 48, 36, 51, 201, 144, 85, 126, 94, 42~
$ distance <dbl> 1576, 1617, 529, 1400, 1416, 1089, 1576, 200, 229, 184, 228, 1389, 1065, 50~
$ hour <dbl> 23, 23, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6~
$ minute <dbl> 59, 59, 0, 25, 30, 40, 40, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, ~
$ time_hour <dttm> 2013-01-10 23:00:00, 2013-01-10 23:00:00, 2013-01-10 05:00:00, 2013-01-10 ~
Code
dm_nycflights13() %>% dm_zoom_to(weather) %>% select(-origin) %>% glimpse()
Output
dm of 5 tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Zoomed table: `weather`
Rows: 144
Columns: 14
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013,~
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,~
$ day <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,~
$ hour <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1~
$ temp <dbl> 41.00, 39.02, 39.02, 39.92, 41.00, 41.00, 39.92, 41.00, 42.~
$ dewp <dbl> 32.00, 30.02, 28.94, 26.96, 26.06, 26.06, 24.98, 24.98, 24.~
$ humid <dbl> 70.08, 69.86, 66.85, 59.50, 54.97, 54.97, 54.81, 52.56, 48.~
$ wind_dir <dbl> 230, 210, 230, 270, 320, 300, 280, 330, 330, 320, 320, 330,~
$ wind_speed <dbl> 8.05546, 9.20624, 6.90468, 5.75390, 6.90468, 12.65858, 6.90~
$ wind_gust <dbl> NA, NA, NA, NA, NA, 20.71404, 17.26170, NA, NA, 26.46794, N~
$ precip <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
$ pressure <dbl> 1024.6, 1025.9, 1026.9, 1027.5, 1028.2, 1029.0, 1030.0, 103~
$ visib <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,~
$ time_hour <dttm> 2013-01-10 00:00:00, 2013-01-10 01:00:00, 2013-01-10 02:00~
Code
dm_nycflights13() %>% dm_zoom_to(weather) %>% rename(origin_location = origin) %>%
glimpse()
Output
dm of 5 tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Zoomed table: `weather`
Primary key: (`origin_location`, `time_hour`)
Rows: 144
Columns: 15
$ origin_location <chr> "EWR", "EWR", "EWR", "EWR", "EWR", "EWR", "EWR", "EWR"~
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, ~
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
$ day <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10~
$ hour <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, ~
$ temp <dbl> 41.00, 39.02, 39.02, 39.92, 41.00, 41.00, 39.92, 41.00~
$ dewp <dbl> 32.00, 30.02, 28.94, 26.96, 26.06, 26.06, 24.98, 24.98~
$ humid <dbl> 70.08, 69.86, 66.85, 59.50, 54.97, 54.97, 54.81, 52.56~
$ wind_dir <dbl> 230, 210, 230, 270, 320, 300, 280, 330, 330, 320, 320,~
$ wind_speed <dbl> 8.05546, 9.20624, 6.90468, 5.75390, 6.90468, 12.65858,~
$ wind_gust <dbl> NA, NA, NA, NA, NA, 20.71404, 17.26170, NA, NA, 26.467~
$ precip <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~
$ pressure <dbl> 1024.6, 1025.9, 1026.9, 1027.5, 1028.2, 1029.0, 1030.0~
$ visib <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10~
$ time_hour <dttm> 2013-01-10 00:00:00, 2013-01-10 01:00:00, 2013-01-10 ~
Code
dm_nycflights13() %>% dm_zoom_to(flights) %>% select(-carrier) %>% glimpse()
Output
dm of 5 tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Zoomed table: `flights`
3 outgoing foreign key(s):
`origin` -> `airports$faa` no_action
`tailnum` -> `planes$tailnum` no_action
(`origin`, `time_hour`) -> (`weather$origin`, `weather$time_hour`) no_action
Rows: 1,761
Columns: 18
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2~
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
$ day <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,~
$ dep_time <int> 3, 16, 450, 520, 530, 531, 535, 546, 549, 550, 553, 553~
$ sched_dep_time <int> 2359, 2359, 500, 525, 530, 540, 540, 600, 600, 600, 600~
$ dep_delay <dbl> 4, 17, -10, -5, 0, -9, -5, -14, -11, -10, -7, -7, -7, -~
$ arr_time <int> 426, 447, 634, 813, 824, 832, 1015, 645, 652, 649, 711,~
$ sched_arr_time <int> 437, 444, 648, 820, 829, 850, 1017, 709, 724, 703, 715,~
$ arr_delay <dbl> -11, 3, -14, -7, -5, -18, -2, -24, -32, -14, -4, -33, -~
$ flight <int> 727, 739, 1117, 1018, 404, 1141, 725, 380, 6055, 2114, ~
$ tailnum <chr> "N571JB", "N564JB", "N171US", "N35204", "N815UA", "N5EA~
$ origin <chr> "JFK", "JFK", "EWR", "EWR", "LGA", "JFK", "JFK", "EWR",~
$ dest <chr> "BQN", "PSE", "CLT", "IAH", "IAH", "MIA", "BQN", "BOS",~
$ air_time <dbl> 183, 191, 78, 215, 210, 149, 191, 39, 48, 36, 51, 201, ~
$ distance <dbl> 1576, 1617, 529, 1400, 1416, 1089, 1576, 200, 229, 184,~
$ hour <dbl> 23, 23, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,~
$ minute <dbl> 59, 59, 0, 25, 30, 40, 40, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0~
$ time_hour <dttm> 2013-01-10 23:00:00, 2013-01-10 23:00:00, 2013-01-10 0~
Code
dm_nycflights13() %>% dm_zoom_to(flights) %>% select(-origin) %>% glimpse()
Output
dm of 5 tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Zoomed table: `flights`
2 outgoing foreign key(s):
`carrier` -> `airlines$carrier` no_action
`tailnum` -> `planes$tailnum` no_action
Rows: 1,761
Columns: 18
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2~
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
$ day <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,~
$ dep_time <int> 3, 16, 450, 520, 530, 531, 535, 546, 549, 550, 553, 553~
$ sched_dep_time <int> 2359, 2359, 500, 525, 530, 540, 540, 600, 600, 600, 600~
$ dep_delay <dbl> 4, 17, -10, -5, 0, -9, -5, -14, -11, -10, -7, -7, -7, -~
$ arr_time <int> 426, 447, 634, 813, 824, 832, 1015, 645, 652, 649, 711,~
$ sched_arr_time <int> 437, 444, 648, 820, 829, 850, 1017, 709, 724, 703, 715,~
$ arr_delay <dbl> -11, 3, -14, -7, -5, -18, -2, -24, -32, -14, -4, -33, -~
$ carrier <chr> "B6", "B6", "US", "UA", "UA", "AA", "B6", "B6", "EV", "~
$ flight <int> 727, 739, 1117, 1018, 404, 1141, 725, 380, 6055, 2114, ~
$ tailnum <chr> "N571JB", "N564JB", "N171US", "N35204", "N815UA", "N5EA~
$ dest <chr> "BQN", "PSE", "CLT", "IAH", "IAH", "MIA", "BQN", "BOS",~
$ air_time <dbl> 183, 191, 78, 215, 210, 149, 191, 39, 48, 36, 51, 201, ~
$ distance <dbl> 1576, 1617, 529, 1400, 1416, 1089, 1576, 200, 229, 184,~
$ hour <dbl> 23, 23, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,~
$ minute <dbl> 59, 59, 0, 25, 30, 40, 40, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0~
$ time_hour <dttm> 2013-01-10 23:00:00, 2013-01-10 23:00:00, 2013-01-10 0~
Code
dm_nycflights13() %>% dm_zoom_to(flights) %>% rename(origin_location = origin) %>%
glimpse()
Output
dm of 5 tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Zoomed table: `flights`
4 outgoing foreign key(s):
`carrier` -> `airlines$carrier` no_action
`origin_location` -> `airports$faa` no_action
`tailnum` -> `planes$tailnum` no_action
(`origin_location`, `time_hour`) -> (`weather$origin`, `weather$time_hour`) no...
Rows: 1,761
Columns: 19
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, ~
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
$ day <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10~
$ dep_time <int> 3, 16, 450, 520, 530, 531, 535, 546, 549, 550, 553, 55~
$ sched_dep_time <int> 2359, 2359, 500, 525, 530, 540, 540, 600, 600, 600, 60~
$ dep_delay <dbl> 4, 17, -10, -5, 0, -9, -5, -14, -11, -10, -7, -7, -7, ~
$ arr_time <int> 426, 447, 634, 813, 824, 832, 1015, 645, 652, 649, 711~
$ sched_arr_time <int> 437, 444, 648, 820, 829, 850, 1017, 709, 724, 703, 715~
$ arr_delay <dbl> -11, 3, -14, -7, -5, -18, -2, -24, -32, -14, -4, -33, ~
$ carrier <chr> "B6", "B6", "US", "UA", "UA", "AA", "B6", "B6", "EV", ~
$ flight <int> 727, 739, 1117, 1018, 404, 1141, 725, 380, 6055, 2114,~
$ tailnum <chr> "N571JB", "N564JB", "N171US", "N35204", "N815UA", "N5E~
$ origin_location <chr> "JFK", "JFK", "EWR", "EWR", "LGA", "JFK", "JFK", "EWR"~
$ dest <chr> "BQN", "PSE", "CLT", "IAH", "IAH", "MIA", "BQN", "BOS"~
$ air_time <dbl> 183, 191, 78, 215, 210, 149, 191, 39, 48, 36, 51, 201,~
$ distance <dbl> 1576, 1617, 529, 1400, 1416, 1089, 1576, 200, 229, 184~
$ hour <dbl> 23, 23, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6~
$ minute <dbl> 59, 59, 0, 25, 30, 40, 40, 0, 0, 0, 0, 0, 0, 0, 0, 5, ~
$ time_hour <dttm> 2013-01-10 23:00:00, 2013-01-10 23:00:00, 2013-01-10 ~
Try the dm package in your browser
Any scripts or data that you put into this service are public.
dm documentation built on Nov. 2, 2023, 6:07 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
dm() API
Code
dm(a = tibble(), a = tibble(), .name_repair = "unique")
Message
New names:
* `a` -> `a...1`
* `a` -> `a...2`
Output
-- Metadata --------------------------------------------------------------------
Tables: `a...1`, `a...2`
Columns: 0
Primary keys: 0
Foreign keys: 0
Code
dm(a = tibble(), a = tibble(), .name_repair = "unique", .quiet = TRUE)
Output
-- Metadata --------------------------------------------------------------------
Tables: `a...1`, `a...2`
Columns: 0
Primary keys: 0
Foreign keys: 0
Code
dm(a = tibble(), a = tibble())
Condition
Error in `dm()`:
! Names must be unique.
x These names are duplicated:
* "a" at locations 1 and 2.
Code
dm(a = dm())
Condition
Error in `dm()`:
! All dm objects passed to `dm()` must be unnamed.
i Argument 1 has name `a`.
Code
dm(a = tibble(), dm_zoom_to(dm_for_filter(), tf_1))
Condition
Error in `dm()`:
! All dm objects passed to `dm()` must be unzoomed.
i Argument 2 is a zoomed dm.
dm() works for adding tables
Code
dm(dm_for_filter(), tf_1 = data_card_1(), .name_repair = "check_unique")
Condition
Error in `dm()`:
! Names must be unique.
x These names are duplicated:
* "tf_1" at locations 1 and 7.
dm() for adding tables with compound keys
Code
dm(dm_for_flatten(), res_flat = result_from_flatten()) %>% dm_paste(options = c(
"select", "keys"))
Message
dm::dm(
fact,
dim_1,
dim_2,
dim_3,
dim_4,
res_flat,
) %>%
dm::dm_select(fact, fact, dim_1_key_1, dim_1_key_2, dim_2_key, dim_3_key, dim_4_key, something) %>%
dm::dm_select(dim_1, dim_1_pk_1, dim_1_pk_2, something) %>%
dm::dm_select(dim_2, dim_2_pk, something) %>%
dm::dm_select(dim_3, dim_3_pk, something) %>%
dm::dm_select(dim_4, dim_4_pk, something) %>%
dm::dm_select(res_flat, fact, dim_1_key_1, dim_1_key_2, dim_2_key, dim_3_key, dim_4_key, fact.something, dim_1.something, dim_2.something, dim_3.something, dim_4.something) %>%
dm::dm_add_pk(dim_1, c(dim_1_pk_1, dim_1_pk_2)) %>%
dm::dm_add_pk(dim_2, dim_2_pk) %>%
dm::dm_add_pk(dim_3, dim_3_pk) %>%
dm::dm_add_pk(dim_4, dim_4_pk) %>%
dm::dm_add_fk(fact, c(dim_1_key_1, dim_1_key_2), dim_1) %>%
dm::dm_add_fk(fact, dim_2_key, dim_2) %>%
dm::dm_add_fk(fact, dim_3_key, dim_3) %>%
dm::dm_add_fk(fact, dim_4_key, dim_4)
errors: duplicate table names, src mismatches
Code
dm(dm_for_filter(), dm_for_flatten(), dm_for_filter())
Condition
Error in `dm()`:
! Names must be unique.
x These names are duplicated:
* "tf_1" at locations 1 and 12.
* "tf_2" at locations 2 and 13.
* "tf_3" at locations 3 and 14.
* "tf_4" at locations 4 and 15.
* "tf_5" at locations 5 and 16.
* ...
test error output for src mismatches
Code
writeLines(conditionMessage(expect_error(dm(dm_for_flatten(),
dm_for_filter_duckdb()))))
Output
All `dm` objects need to share the same `src`.
output for dm() with dm
Code
dm()
Output
dm()
Code
dm(empty_dm())
Output
dm()
Code
dm(dm_for_filter()) %>% collect()
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(dm_for_filter(), dm_for_flatten(), dm_for_filter(), .name_repair = "unique",
.quiet = TRUE) %>% collect()
Output
-- Metadata --------------------------------------------------------------------
Tables: `tf_1...1`, `tf_2...2`, `tf_3...3`, `tf_4...4`, `tf_5...5`, ... (17 total)
Columns: 56
Primary keys: 16
Foreign keys: 14
output for dm() with dm (2)
Code
dm(dm_for_filter(), dm_for_flatten(), dm_for_filter())
Condition
Error in `dm()`:
! Names must be unique.
x These names are duplicated:
* "tf_1" at locations 1 and 12.
* "tf_2" at locations 2 and 13.
* "tf_3" at locations 3 and 14.
* "tf_4" at locations 4 and 15.
* "tf_5" at locations 5 and 16.
* ...
output for dm() with dm (3)
Code
dm(dm_for_filter(), dm_for_flatten(), dm_for_filter(), .name_repair = "unique") %>%
collect()
Message
New names:
* `tf_1` -> `tf_1...1`
* `tf_2` -> `tf_2...2`
* `tf_3` -> `tf_3...3`
* `tf_4` -> `tf_4...4`
* `tf_5` -> `tf_5...5`
* `tf_6` -> `tf_6...6`
* `tf_1` -> `tf_1...12`
* `tf_2` -> `tf_2...13`
* `tf_3` -> `tf_3...14`
* `tf_4` -> `tf_4...15`
* `tf_5` -> `tf_5...16`
* `tf_6` -> `tf_6...17`
Output
-- Metadata --------------------------------------------------------------------
Tables: `tf_1...1`, `tf_2...2`, `tf_3...3`, `tf_4...4`, `tf_5...5`, ... (17 total)
Columns: 56
Primary keys: 16
Foreign keys: 14
output dm() for dm for compound keys
Code
dm(dm_for_filter(), dm_for_flatten()) %>% dm_paste(options = c("select", "keys"))
Message
dm::dm(
tf_1,
tf_2,
tf_3,
tf_4,
tf_5,
tf_6,
fact,
dim_1,
dim_2,
dim_3,
dim_4,
) %>%
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(fact, fact, dim_1_key_1, dim_1_key_2, dim_2_key, dim_3_key, dim_4_key, something) %>%
dm::dm_select(dim_1, dim_1_pk_1, dim_1_pk_2, something) %>%
dm::dm_select(dim_2, dim_2_pk, something) %>%
dm::dm_select(dim_3, dim_3_pk, something) %>%
dm::dm_select(dim_4, dim_4_pk, something) %>%
dm::dm_add_pk(tf_1, a, autoincrement = TRUE) %>%
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, o) %>%
dm::dm_add_pk(dim_1, c(dim_1_pk_1, dim_1_pk_2)) %>%
dm::dm_add_pk(dim_2, dim_2_pk) %>%
dm::dm_add_pk(dim_3, dim_3_pk) %>%
dm::dm_add_pk(dim_4, dim_4_pk) %>%
dm::dm_add_uk(tf_3, g) %>%
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_5, l, tf_4, on_delete = "cascade") %>%
dm::dm_add_fk(tf_5, m, tf_6, n) %>%
dm::dm_add_fk(fact, c(dim_1_key_1, dim_1_key_2), dim_1) %>%
dm::dm_add_fk(fact, dim_2_key, dim_2) %>%
dm::dm_add_fk(fact, dim_3_key, dim_3) %>%
dm::dm_add_fk(fact, dim_4_key, dim_4)
Code
dm(dm_for_flatten(), dm_for_filter()) %>% dm_paste(options = c("select", "keys"))
Message
dm::dm(
fact,
dim_1,
dim_2,
dim_3,
dim_4,
tf_1,
tf_2,
tf_3,
tf_4,
tf_5,
tf_6,
) %>%
dm::dm_select(fact, fact, dim_1_key_1, dim_1_key_2, dim_2_key, dim_3_key, dim_4_key, something) %>%
dm::dm_select(dim_1, dim_1_pk_1, dim_1_pk_2, something) %>%
dm::dm_select(dim_2, dim_2_pk, something) %>%
dm::dm_select(dim_3, dim_3_pk, something) %>%
dm::dm_select(dim_4, dim_4_pk, something) %>%
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_add_pk(dim_1, c(dim_1_pk_1, dim_1_pk_2)) %>%
dm::dm_add_pk(dim_2, dim_2_pk) %>%
dm::dm_add_pk(dim_3, dim_3_pk) %>%
dm::dm_add_pk(dim_4, dim_4_pk) %>%
dm::dm_add_pk(tf_1, a, autoincrement = TRUE) %>%
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, o) %>%
dm::dm_add_uk(tf_3, g) %>%
dm::dm_add_fk(fact, c(dim_1_key_1, dim_1_key_2), dim_1) %>%
dm::dm_add_fk(fact, dim_2_key, dim_2) %>%
dm::dm_add_fk(fact, dim_3_key, dim_3) %>%
dm::dm_add_fk(fact, dim_4_key, dim_4) %>%
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_5, l, tf_4, on_delete = "cascade") %>%
dm::dm_add_fk(tf_5, m, tf_6, n)
Code
dm(dm_for_flatten(), dm_for_flatten(), .name_repair = "unique") %>% dm_paste(
options = c("select", "keys"))
Message
New names:
* `fact` -> `fact...1`
* `dim_1` -> `dim_1...2`
* `dim_2` -> `dim_2...3`
* `dim_3` -> `dim_3...4`
* `dim_4` -> `dim_4...5`
* `fact` -> `fact...6`
* `dim_1` -> `dim_1...7`
* `dim_2` -> `dim_2...8`
* `dim_3` -> `dim_3...9`
* `dim_4` -> `dim_4...10`
dm::dm(
fact...1,
dim_1...2,
dim_2...3,
dim_3...4,
dim_4...5,
fact...6,
dim_1...7,
dim_2...8,
dim_3...9,
dim_4...10,
) %>%
dm::dm_select(fact...1, fact, dim_1_key_1, dim_1_key_2, dim_2_key, dim_3_key, dim_4_key, something) %>%
dm::dm_select(dim_1...2, dim_1_pk_1, dim_1_pk_2, something) %>%
dm::dm_select(dim_2...3, dim_2_pk, something) %>%
dm::dm_select(dim_3...4, dim_3_pk, something) %>%
dm::dm_select(dim_4...5, dim_4_pk, something) %>%
dm::dm_select(fact...6, fact, dim_1_key_1, dim_1_key_2, dim_2_key, dim_3_key, dim_4_key, something) %>%
dm::dm_select(dim_1...7, dim_1_pk_1, dim_1_pk_2, something) %>%
dm::dm_select(dim_2...8, dim_2_pk, something) %>%
dm::dm_select(dim_3...9, dim_3_pk, something) %>%
dm::dm_select(dim_4...10, dim_4_pk, something) %>%
dm::dm_add_pk(dim_1...2, c(dim_1_pk_1, dim_1_pk_2)) %>%
dm::dm_add_pk(dim_2...3, dim_2_pk) %>%
dm::dm_add_pk(dim_3...4, dim_3_pk) %>%
dm::dm_add_pk(dim_4...5, dim_4_pk) %>%
dm::dm_add_pk(dim_1...7, c(dim_1_pk_1, dim_1_pk_2)) %>%
dm::dm_add_pk(dim_2...8, dim_2_pk) %>%
dm::dm_add_pk(dim_3...9, dim_3_pk) %>%
dm::dm_add_pk(dim_4...10, dim_4_pk) %>%
dm::dm_add_fk(fact...1, c(dim_1_key_1, dim_1_key_2), dim_1...2) %>%
dm::dm_add_fk(fact...1, dim_2_key, dim_2...3) %>%
dm::dm_add_fk(fact...1, dim_3_key, dim_3...4) %>%
dm::dm_add_fk(fact...1, dim_4_key, dim_4...5) %>%
dm::dm_add_fk(fact...6, c(dim_1_key_1, dim_1_key_2), dim_1...7) %>%
dm::dm_add_fk(fact...6, dim_2_key, dim_2...8) %>%
dm::dm_add_fk(fact...6, dim_3_key, dim_3...9) %>%
dm::dm_add_fk(fact...6, dim_4_key, dim_4...10)
'compute.dm()' fails with temporary = FALSE (#2059)
Code
dm_for_filter_duckdb() %>% compute(temporary = FALSE)
Condition
Error in `compute()`:
! `compute.dm()` does not support `temporary = FALSE`.
output
Code
print(dm())
Output
dm()
Code
nyc_flights_dm <- dm_nycflights_small()
collect(nyc_flights_dm)
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 53
Primary keys: 3
Foreign keys: 3
Code
nyc_flights_dm %>% format()
Output
dm: 5 tables, 53 columns, 3 primary keys, 3 foreign keys
Code
nyc_flights_dm %>% dm_filter(flights = (origin == "EWR")) %>% collect()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 53
Primary keys: 3
Foreign keys: 3
output for compound keys
Code
copy_to(nyc_comp(), mtcars, "car_table")
Condition
Warning:
`copy_to.dm()` was deprecated in dm 0.2.0.
i Use `copy_to(dm_get_con(dm), ...)` and `dm()`.
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`, `car_table`
Columns: 64
Primary keys: 4
Foreign keys: 4
Code
dm(nyc_comp(), car_table)
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`, `car_table`
Columns: 64
Primary keys: 4
Foreign keys: 4
Code
nyc_comp() %>% collect()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 53
Primary keys: 4
Foreign keys: 4
Code
nyc_comp() %>% dm_filter(flights = (day == 10)) %>% collect() %>% dm_get_def() %>%
select(-uuid)
Output
# A tibble: 5 x 10
table data segment display pks uks fks filters zoom
<chr> <list> <chr> <chr> <list<tibble> <list<> <list<> <list<> <list>
1 airlines <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
2 airports <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
3 flights <tibble> <NA> <NA> [0 x 2] [0 x 1] [0 x 4] [0 x 2] <NULL>
4 planes <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
5 weather <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
# i 1 more variable: col_tracker_zoom <list>
Code
nyc_comp() %>% dm_zoom_to(weather) %>% mutate(origin_new = paste0(origin,
" airport")) %>% compute() %>% dm_update_zoomed() %>% collect() %>%
dm_get_def() %>% select(-uuid)
Output
# A tibble: 5 x 10
table data segment display pks uks fks filters zoom
<chr> <list> <chr> <chr> <list<tibble> <list<> <list<> <list<> <list>
1 airlines <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
2 airports <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
3 flights <tibble> <NA> <NA> [0 x 2] [0 x 1] [0 x 4] [0 x 2] <NULL>
4 planes <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
5 weather <tibble> <NA> <NA> [1 x 2] [0 x 1] [1 x 4] [0 x 2] <NULL>
# i 1 more variable: col_tracker_zoom <list>
Code
nyc_comp() %>% dm_zoom_to(weather) %>% collect()
Message
Detaching table from dm.
i Use `. %>% pull_tbl() %>% collect()` instead to silence this message.
Output
# A tibble: 144 x 15
origin year month day hour temp dewp humid wind_dir wind_speed
<chr> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 EWR 2013 1 10 0 41 32 70.1 230 8.06
2 EWR 2013 1 10 1 39.0 30.0 69.9 210 9.21
3 EWR 2013 1 10 2 39.0 28.9 66.8 230 6.90
4 EWR 2013 1 10 3 39.9 27.0 59.5 270 5.75
5 EWR 2013 1 10 4 41 26.1 55.0 320 6.90
6 EWR 2013 1 10 5 41 26.1 55.0 300 12.7
7 EWR 2013 1 10 6 39.9 25.0 54.8 280 6.90
8 EWR 2013 1 10 7 41 25.0 52.6 330 6.90
9 EWR 2013 1 10 8 43.0 25.0 48.7 330 8.06
10 EWR 2013 1 10 9 45.0 23 41.6 320 17.3
# i 134 more rows
# i 5 more variables: wind_gust <dbl>, precip <dbl>, pressure <dbl>,
# visib <dbl>, time_hour <chr>
Code
pull_tbl(nyc_comp(), weather)
Output
# A tibble: 144 x 15
origin year month day hour temp dewp humid wind_dir wind_speed
<chr> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 EWR 2013 1 10 0 41 32 70.1 230 8.06
2 EWR 2013 1 10 1 39.0 30.0 69.9 210 9.21
3 EWR 2013 1 10 2 39.0 28.9 66.8 230 6.90
4 EWR 2013 1 10 3 39.9 27.0 59.5 270 5.75
5 EWR 2013 1 10 4 41 26.1 55.0 320 6.90
6 EWR 2013 1 10 5 41 26.1 55.0 300 12.7
7 EWR 2013 1 10 6 39.9 25.0 54.8 280 6.90
8 EWR 2013 1 10 7 41 25.0 52.6 330 6.90
9 EWR 2013 1 10 8 43.0 25.0 48.7 330 8.06
10 EWR 2013 1 10 9 45.0 23 41.6 320 17.3
# i 134 more rows
# i 5 more variables: wind_gust <dbl>, precip <dbl>, pressure <dbl>,
# visib <dbl>, time_hour <chr>
Code
nyc_comp() %>% dm_zoom_to(weather) %>% pull_tbl()
Output
# A tibble: 144 x 15
origin year month day hour temp dewp humid wind_dir wind_speed
<chr> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 EWR 2013 1 10 0 41 32 70.1 230 8.06
2 EWR 2013 1 10 1 39.0 30.0 69.9 210 9.21
3 EWR 2013 1 10 2 39.0 28.9 66.8 230 6.90
4 EWR 2013 1 10 3 39.9 27.0 59.5 270 5.75
5 EWR 2013 1 10 4 41 26.1 55.0 320 6.90
6 EWR 2013 1 10 5 41 26.1 55.0 300 12.7
7 EWR 2013 1 10 6 39.9 25.0 54.8 280 6.90
8 EWR 2013 1 10 7 41 25.0 52.6 330 6.90
9 EWR 2013 1 10 8 43.0 25.0 48.7 330 8.06
10 EWR 2013 1 10 9 45.0 23 41.6 320 17.3
# i 134 more rows
# i 5 more variables: wind_gust <dbl>, precip <dbl>, pressure <dbl>,
# visib <dbl>, time_hour <chr>
glimpse.dm() works
Code
glimpse(empty_dm())
Output
dm of 0 tables
Code
glimpse(dm_for_disambiguate())
Output
dm of 3 tables: `iris_1`, `iris_2`, `iris_3`
--------------------------------------------------------------------------------
Table: `iris_1`
Primary key: `key`
Rows: 150
Columns: 6
$ key <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.~
$ Species <chr> "setosa", "setosa", "setosa", "setosa", "setosa", "setosa~
--------------------------------------------------------------------------------
Table: `iris_2`
1 outgoing foreign key(s):
`key` -> `iris_1$key` no_action
Rows: 150
Columns: 7
$ key <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.~
$ Species <chr> "setosa", "setosa", "setosa", "setosa", "setosa", "setosa~
$ other_col <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
--------------------------------------------------------------------------------
Table: `iris_3`
Rows: 150
Columns: 8
$ key <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.~
$ Species <chr> "setosa", "setosa", "setosa", "setosa", "setosa", "setosa~
$ other_col <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
$ one_more_col <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
--------------------------------------------------------------------------------
Code
glimpse(dm_for_disambiguate(), width = 40)
Output
dm of 3 tables: `iris_1`, `iris_2`, `iri...
--------------------------------------------------------------------------------
Table: `iris_1`
Primary key: `key`
Rows: 150
Columns: 6
$ key <int> 1, 2, 3, 4, 5, 6,~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.~
$ Species <chr> "setosa", "setosa~
--------------------------------------------------------------------------------
Table: `iris_2`
1 outgoing foreign key(s):
`key` -> `iris_1$key` no_action
Rows: 150
Columns: 7
$ key <int> 1, 2, 3, 4, 5, 6,~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.~
$ Species <chr> "setosa", "setosa~
$ other_col <int> 1, 1, 1, 1, 1, 1,~
--------------------------------------------------------------------------------
Table: `iris_3`
Rows: 150
Columns: 8
$ key <int> 1, 2, 3, 4, 5, 6,~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.~
$ Species <chr> "setosa", "setosa~
$ other_col <int> 1, 1, 1, 1, 1, 1,~
$ one_more_col <dbl> 1, 1, 1, 1, 1, 1,~
--------------------------------------------------------------------------------
Code
getOption("width")
Output
[1] 80
Code
glimpse(dm_for_disambiguate() %>% dm_rename(iris_1,
gdsjgiodsjgdisogjdsiogjdsigjsdiogjisdjgiodsjgiosdjgiojsdiogjgrjihjrehoierjhiorejhrieojhreiojhieorhjioerjhierjhioerjhioerjhioerjiohjeriosdiogjsdjigjsd = key) %>%
dm_rename_tbl(
gdsjgiodsjgdisogjdsiogjdsigjsdiogjisdjgiodsjgiosdjgiojsdiogjgrjihjrehoierjhiorejhrieojhreiojhieorhjioerjhierjhioerjhioerjhioerjiohjeriosdiogjsdjigjsd = iris_1))
Output
dm of 3 tables: `gdsjgiodsjgdisogjdsiogjdsigjsdiogjisdjgiodsjgiosdjgiojsdiogjgrj...
--------------------------------------------------------------------------------
Table: `gdsjgiodsjgdisogjdsiogjdsigjsdiogjisdjgiodsjgiosdjgiojsdiogjgrjihjrehoie...
Primary key: `gdsjgiodsjgdisogjdsiogjdsigjsdiogjisdjgiodsjgiosdjgiojsdiogjgrjihj...
Rows: 150
Columns: 6
$ gdsjgiodsjgdisogjdsiogjdsigjsdiogjisdjgiodsjgiosdjgiojsdiogjgrjihjrehoierjhiorejhrieojhreiojhieorhjioerjhierjhioerjhioerjhioerjiohjeriosdiogjsdjigjsd <int> ~
$ Sepal.Length <dbl> ~
$ Sepal.Width <dbl> ~
$ Petal.Length <dbl> ~
$ Petal.Width <dbl> ~
$ Species <chr> ~
--------------------------------------------------------------------------------
Table: `iris_2`
1 outgoing foreign key(s):
`key` -> `gdsjgiodsjgdisogjdsiogjdsigjsdiogjisdjgiodsjgiosdjgiojsdiogjgrjihjre...
Rows: 150
Columns: 7
$ key <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.~
$ Species <chr> "setosa", "setosa", "setosa", "setosa", "setosa", "setosa~
$ other_col <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
--------------------------------------------------------------------------------
Table: `iris_3`
Rows: 150
Columns: 8
$ key <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17~
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.~
$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.~
$ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.~
$ Species <chr> "setosa", "setosa", "setosa", "setosa", "setosa", "setosa~
$ other_col <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
$ one_more_col <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
--------------------------------------------------------------------------------
Code
dm_nycflights13() %>% dm_select_tbl(weather) %>% dm_select(weather, -origin) %>%
glimpse()
Output
dm of 1 tables: `weather`
--------------------------------------------------------------------------------
Table: `weather`
Rows: 144
Columns: 14
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013,~
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,~
$ day <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,~
$ hour <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1~
$ temp <dbl> 41.00, 39.02, 39.02, 39.92, 41.00, 41.00, 39.92, 41.00, 42.~
$ dewp <dbl> 32.00, 30.02, 28.94, 26.96, 26.06, 26.06, 24.98, 24.98, 24.~
$ humid <dbl> 70.08, 69.86, 66.85, 59.50, 54.97, 54.97, 54.81, 52.56, 48.~
$ wind_dir <dbl> 230, 210, 230, 270, 320, 300, 280, 330, 330, 320, 320, 330,~
$ wind_speed <dbl> 8.05546, 9.20624, 6.90468, 5.75390, 6.90468, 12.65858, 6.90~
$ wind_gust <dbl> NA, NA, NA, NA, NA, 20.71404, 17.26170, NA, NA, 26.46794, N~
$ precip <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
$ pressure <dbl> 1024.6, 1025.9, 1026.9, 1027.5, 1028.2, 1029.0, 1030.0, 103~
$ visib <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,~
$ time_hour <dttm> 2013-01-10 00:00:00, 2013-01-10 01:00:00, 2013-01-10 02:00~
--------------------------------------------------------------------------------
glimpse.dm_zoomed() works
Code
dm_nycflights13() %>% dm_zoom_to(airports) %>% glimpse()
Output
dm of 5 tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Zoomed table: `airports`
Primary key: `faa`
Rows: 86
Columns: 8
$ faa <chr> "ALB", "ATL", "AUS", "BDL", "BHM", "BNA", "BOS", "BTV", "BUF", "~
$ name <chr> "Albany Intl", "Hartsfield Jackson Atlanta Intl", "Austin Bergst~
$ lat <dbl> 42.74827, 33.63672, 30.19453, 41.93889, 33.56294, 36.12447, 42.3~
$ lon <dbl> -73.80169, -84.42807, -97.66989, -72.68322, -86.75355, -86.67819~
$ alt <dbl> 285, 1026, 542, 173, 644, 599, 19, 335, 724, 778, 146, 236, 1228~
$ tz <dbl> -5, -5, -6, -5, -6, -6, -5, -5, -5, -8, -5, -5, -5, -5, -5, -5, ~
$ dst <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",~
$ tzone <chr> "America/New_York", "America/New_York", "America/Chicago", "Amer~
Code
dm_nycflights13() %>% dm_zoom_to(flights) %>% glimpse(width = 100)
Output
dm of 5 tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Zoomed table: `flights`
4 outgoing foreign key(s):
`carrier` -> `airlines$carrier` no_action
`origin` -> `airports$faa` no_action
`tailnum` -> `planes$tailnum` no_action
(`origin`, `time_hour`) -> (`weather$origin`, `weather$time_hour`) no_action
Rows: 1,761
Columns: 19
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 201~
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
$ day <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,~
$ dep_time <int> 3, 16, 450, 520, 530, 531, 535, 546, 549, 550, 553, 553, 553, 553, 555, 555~
$ sched_dep_time <int> 2359, 2359, 500, 525, 530, 540, 540, 600, 600, 600, 600, 600, 600, 600, 600~
$ dep_delay <dbl> 4, 17, -10, -5, 0, -9, -5, -14, -11, -10, -7, -7, -7, -7, -5, -10, -5, -4, ~
$ arr_time <int> 426, 447, 634, 813, 824, 832, 1015, 645, 652, 649, 711, 837, 834, 733, 733,~
$ sched_arr_time <int> 437, 444, 648, 820, 829, 850, 1017, 709, 724, 703, 715, 910, 859, 759, 745,~
$ arr_delay <dbl> -11, 3, -14, -7, -5, -18, -2, -24, -32, -14, -4, -33, -25, -26, -12, -19, 2~
$ carrier <chr> "B6", "B6", "US", "UA", "UA", "AA", "B6", "B6", "EV", "US", "EV", "AA", "B6~
$ flight <int> 727, 739, 1117, 1018, 404, 1141, 725, 380, 6055, 2114, 5716, 707, 507, 731,~
$ tailnum <chr> "N571JB", "N564JB", "N171US", "N35204", "N815UA", "N5EAAA", "N784JB", "N337~
$ origin <chr> "JFK", "JFK", "EWR", "EWR", "LGA", "JFK", "JFK", "EWR", "LGA", "LGA", "JFK"~
$ dest <chr> "BQN", "PSE", "CLT", "IAH", "IAH", "MIA", "BQN", "BOS", "IAD", "BOS", "IAD"~
$ air_time <dbl> 183, 191, 78, 215, 210, 149, 191, 39, 48, 36, 51, 201, 144, 85, 126, 94, 42~
$ distance <dbl> 1576, 1617, 529, 1400, 1416, 1089, 1576, 200, 229, 184, 228, 1389, 1065, 50~
$ hour <dbl> 23, 23, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6~
$ minute <dbl> 59, 59, 0, 25, 30, 40, 40, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, ~
$ time_hour <dttm> 2013-01-10 23:00:00, 2013-01-10 23:00:00, 2013-01-10 05:00:00, 2013-01-10 ~
Code
dm_nycflights13() %>% dm_zoom_to(weather) %>% select(-origin) %>% glimpse()
Output
dm of 5 tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Zoomed table: `weather`
Rows: 144
Columns: 14
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013,~
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,~
$ day <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,~
$ hour <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1~
$ temp <dbl> 41.00, 39.02, 39.02, 39.92, 41.00, 41.00, 39.92, 41.00, 42.~
$ dewp <dbl> 32.00, 30.02, 28.94, 26.96, 26.06, 26.06, 24.98, 24.98, 24.~
$ humid <dbl> 70.08, 69.86, 66.85, 59.50, 54.97, 54.97, 54.81, 52.56, 48.~
$ wind_dir <dbl> 230, 210, 230, 270, 320, 300, 280, 330, 330, 320, 320, 330,~
$ wind_speed <dbl> 8.05546, 9.20624, 6.90468, 5.75390, 6.90468, 12.65858, 6.90~
$ wind_gust <dbl> NA, NA, NA, NA, NA, 20.71404, 17.26170, NA, NA, 26.46794, N~
$ precip <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
$ pressure <dbl> 1024.6, 1025.9, 1026.9, 1027.5, 1028.2, 1029.0, 1030.0, 103~
$ visib <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,~
$ time_hour <dttm> 2013-01-10 00:00:00, 2013-01-10 01:00:00, 2013-01-10 02:00~
Code
dm_nycflights13() %>% dm_zoom_to(weather) %>% rename(origin_location = origin) %>%
glimpse()
Output
dm of 5 tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Zoomed table: `weather`
Primary key: (`origin_location`, `time_hour`)
Rows: 144
Columns: 15
$ origin_location <chr> "EWR", "EWR", "EWR", "EWR", "EWR", "EWR", "EWR", "EWR"~
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, ~
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
$ day <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10~
$ hour <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, ~
$ temp <dbl> 41.00, 39.02, 39.02, 39.92, 41.00, 41.00, 39.92, 41.00~
$ dewp <dbl> 32.00, 30.02, 28.94, 26.96, 26.06, 26.06, 24.98, 24.98~
$ humid <dbl> 70.08, 69.86, 66.85, 59.50, 54.97, 54.97, 54.81, 52.56~
$ wind_dir <dbl> 230, 210, 230, 270, 320, 300, 280, 330, 330, 320, 320,~
$ wind_speed <dbl> 8.05546, 9.20624, 6.90468, 5.75390, 6.90468, 12.65858,~
$ wind_gust <dbl> NA, NA, NA, NA, NA, 20.71404, 17.26170, NA, NA, 26.467~
$ precip <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~
$ pressure <dbl> 1024.6, 1025.9, 1026.9, 1027.5, 1028.2, 1029.0, 1030.0~
$ visib <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10~
$ time_hour <dttm> 2013-01-10 00:00:00, 2013-01-10 01:00:00, 2013-01-10 ~
Code
dm_nycflights13() %>% dm_zoom_to(flights) %>% select(-carrier) %>% glimpse()
Output
dm of 5 tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Zoomed table: `flights`
3 outgoing foreign key(s):
`origin` -> `airports$faa` no_action
`tailnum` -> `planes$tailnum` no_action
(`origin`, `time_hour`) -> (`weather$origin`, `weather$time_hour`) no_action
Rows: 1,761
Columns: 18
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2~
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
$ day <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,~
$ dep_time <int> 3, 16, 450, 520, 530, 531, 535, 546, 549, 550, 553, 553~
$ sched_dep_time <int> 2359, 2359, 500, 525, 530, 540, 540, 600, 600, 600, 600~
$ dep_delay <dbl> 4, 17, -10, -5, 0, -9, -5, -14, -11, -10, -7, -7, -7, -~
$ arr_time <int> 426, 447, 634, 813, 824, 832, 1015, 645, 652, 649, 711,~
$ sched_arr_time <int> 437, 444, 648, 820, 829, 850, 1017, 709, 724, 703, 715,~
$ arr_delay <dbl> -11, 3, -14, -7, -5, -18, -2, -24, -32, -14, -4, -33, -~
$ flight <int> 727, 739, 1117, 1018, 404, 1141, 725, 380, 6055, 2114, ~
$ tailnum <chr> "N571JB", "N564JB", "N171US", "N35204", "N815UA", "N5EA~
$ origin <chr> "JFK", "JFK", "EWR", "EWR", "LGA", "JFK", "JFK", "EWR",~
$ dest <chr> "BQN", "PSE", "CLT", "IAH", "IAH", "MIA", "BQN", "BOS",~
$ air_time <dbl> 183, 191, 78, 215, 210, 149, 191, 39, 48, 36, 51, 201, ~
$ distance <dbl> 1576, 1617, 529, 1400, 1416, 1089, 1576, 200, 229, 184,~
$ hour <dbl> 23, 23, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,~
$ minute <dbl> 59, 59, 0, 25, 30, 40, 40, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0~
$ time_hour <dttm> 2013-01-10 23:00:00, 2013-01-10 23:00:00, 2013-01-10 0~
Code
dm_nycflights13() %>% dm_zoom_to(flights) %>% select(-origin) %>% glimpse()
Output
dm of 5 tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Zoomed table: `flights`
2 outgoing foreign key(s):
`carrier` -> `airlines$carrier` no_action
`tailnum` -> `planes$tailnum` no_action
Rows: 1,761
Columns: 18
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2~
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
$ day <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,~
$ dep_time <int> 3, 16, 450, 520, 530, 531, 535, 546, 549, 550, 553, 553~
$ sched_dep_time <int> 2359, 2359, 500, 525, 530, 540, 540, 600, 600, 600, 600~
$ dep_delay <dbl> 4, 17, -10, -5, 0, -9, -5, -14, -11, -10, -7, -7, -7, -~
$ arr_time <int> 426, 447, 634, 813, 824, 832, 1015, 645, 652, 649, 711,~
$ sched_arr_time <int> 437, 444, 648, 820, 829, 850, 1017, 709, 724, 703, 715,~
$ arr_delay <dbl> -11, 3, -14, -7, -5, -18, -2, -24, -32, -14, -4, -33, -~
$ carrier <chr> "B6", "B6", "US", "UA", "UA", "AA", "B6", "B6", "EV", "~
$ flight <int> 727, 739, 1117, 1018, 404, 1141, 725, 380, 6055, 2114, ~
$ tailnum <chr> "N571JB", "N564JB", "N171US", "N35204", "N815UA", "N5EA~
$ dest <chr> "BQN", "PSE", "CLT", "IAH", "IAH", "MIA", "BQN", "BOS",~
$ air_time <dbl> 183, 191, 78, 215, 210, 149, 191, 39, 48, 36, 51, 201, ~
$ distance <dbl> 1576, 1617, 529, 1400, 1416, 1089, 1576, 200, 229, 184,~
$ hour <dbl> 23, 23, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,~
$ minute <dbl> 59, 59, 0, 25, 30, 40, 40, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0~
$ time_hour <dttm> 2013-01-10 23:00:00, 2013-01-10 23:00:00, 2013-01-10 0~
Code
dm_nycflights13() %>% dm_zoom_to(flights) %>% rename(origin_location = origin) %>%
glimpse()
Output
dm of 5 tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Zoomed table: `flights`
4 outgoing foreign key(s):
`carrier` -> `airlines$carrier` no_action
`origin_location` -> `airports$faa` no_action
`tailnum` -> `planes$tailnum` no_action
(`origin_location`, `time_hour`) -> (`weather$origin`, `weather$time_hour`) no...
Rows: 1,761
Columns: 19
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, ~
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
$ day <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10~
$ dep_time <int> 3, 16, 450, 520, 530, 531, 535, 546, 549, 550, 553, 55~
$ sched_dep_time <int> 2359, 2359, 500, 525, 530, 540, 540, 600, 600, 600, 60~
$ dep_delay <dbl> 4, 17, -10, -5, 0, -9, -5, -14, -11, -10, -7, -7, -7, ~
$ arr_time <int> 426, 447, 634, 813, 824, 832, 1015, 645, 652, 649, 711~
$ sched_arr_time <int> 437, 444, 648, 820, 829, 850, 1017, 709, 724, 703, 715~
$ arr_delay <dbl> -11, 3, -14, -7, -5, -18, -2, -24, -32, -14, -4, -33, ~
$ carrier <chr> "B6", "B6", "US", "UA", "UA", "AA", "B6", "B6", "EV", ~
$ flight <int> 727, 739, 1117, 1018, 404, 1141, 725, 380, 6055, 2114,~
$ tailnum <chr> "N571JB", "N564JB", "N171US", "N35204", "N815UA", "N5E~
$ origin_location <chr> "JFK", "JFK", "EWR", "EWR", "LGA", "JFK", "JFK", "EWR"~
$ dest <chr> "BQN", "PSE", "CLT", "IAH", "IAH", "MIA", "BQN", "BOS"~
$ air_time <dbl> 183, 191, 78, 215, 210, 149, 191, 39, 48, 36, 51, 201,~
$ distance <dbl> 1576, 1617, 529, 1400, 1416, 1089, 1576, 200, 229, 184~
$ hour <dbl> 23, 23, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6~
$ minute <dbl> 59, 59, 0, 25, 30, 40, 40, 0, 0, 0, 0, 0, 0, 0, 0, 5, ~
$ time_hour <dttm> 2013-01-10 23:00:00, 2013-01-10 23:00:00, 2013-01-10 ~
Try the dm package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.