tests/testthat/_snaps/dplyr.md
In krlmlr/dm: Relational Data Models
basic test: 'join()'-methods for zoomed.dm work (2)
Code
# keys are correctly tracked if selected columns from 'y' have same name as key columns from 'x'
dm_zoomed() %>% left_join(tf_3, select = c(d = g, f, f1)) %>% dm_update_zoomed() %>%
get_all_keys()
Message
Renaming ambiguous columns: %>%
dm_rename(tf_2, d.tf_2 = d) %>%
dm_rename(tf_3, d.tf_3 = d)
Output
$pks
# A tibble: 6 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
$fks
# A tibble: 5 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d.tf_2 tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
Code
# keys are correctly tracked if selected columns from 'y' have same name as key columns from 'x'
dm_zoomed() %>% semi_join(tf_3, select = c(d = g, f, f1)) %>% dm_update_zoomed() %>%
get_all_keys()
Output
$pks
# A tibble: 6 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
$fks
# A tibble: 5 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
key tracking works
Code
# rename()
zoomed_grouped_out_dm %>% rename(c_new = c) %>% dm_update_zoomed() %>%
get_all_keys()
Output
$pks
# A tibble: 6 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c_new FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
$fks
# A tibble: 5 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
Code
zoomed_grouped_out_dm %>% rename(e_new = e) %>% dm_update_zoomed() %>%
get_all_keys()
Output
$pks
# A tibble: 6 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
$fks
# A tibble: 5 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e_new, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
Code
zoomed_grouped_in_dm %>% rename(f_new = f) %>% dm_update_zoomed() %>%
get_all_keys()
Output
$pks
# A tibble: 6 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f_new, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
$fks
# A tibble: 5 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f_new, f1 no_action
3 tf_4 j, j1 tf_3 f_new, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
Code
# summarize()
zoomed_grouped_out_dm %>% summarize(d_mean = mean(d)) %>% dm_insert_zoomed(
"new_tbl") %>% get_all_keys()
Output
$pks
# A tibble: 7 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
7 new_tbl c FALSE
$fks
# A tibble: 6 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 new_tbl e, e1 tf_3 f, f1 no_action
5 tf_5 l tf_4 h cascade
6 tf_5 m tf_6 n no_action
Code
zoomed_grouped_in_dm %>% summarize(g_list = list(g)) %>% dm_insert_zoomed(
"new_tbl") %>% get_all_keys()
Output
$pks
# A tibble: 6 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
$fks
# A tibble: 5 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
key tracking works (2)
Code
# transmute()
zoomed_grouped_out_dm %>% transmute(d_mean = mean(d)) %>% dm_insert_zoomed(
"new_tbl") %>% get_all_keys()
Output
$pks
# A tibble: 7 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
7 new_tbl c FALSE
$fks
# A tibble: 6 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 new_tbl e, e1 tf_3 f, f1 no_action
5 tf_5 l tf_4 h cascade
6 tf_5 m tf_6 n no_action
key tracking works (3)
Code
zoomed_grouped_in_dm %>% transmute(g_list = list(g)) %>% dm_insert_zoomed(
"new_tbl") %>% get_all_keys()
Output
$pks
# A tibble: 6 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
$fks
# A tibble: 5 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
key tracking works (4)
Code
# mutate()
zoomed_grouped_out_dm %>% mutate(d_mean = mean(d)) %>% select(-d) %>%
dm_insert_zoomed("new_tbl") %>% get_all_keys()
Output
$pks
# A tibble: 7 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
7 new_tbl c FALSE
$fks
# A tibble: 6 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 new_tbl e, e1 tf_3 f, f1 no_action
5 tf_5 l tf_4 h cascade
6 tf_5 m tf_6 n no_action
Code
zoomed_grouped_in_dm %>% mutate(f = paste0(g, g)) %>% dm_insert_zoomed(
"new_tbl") %>% get_all_keys()
Output
$pks
# A tibble: 7 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
7 new_tbl f, f1 FALSE
$fks
# A tibble: 7 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
6 tf_2 e, e1 new_tbl f, f1 no_action
7 tf_4 j, j1 new_tbl f, f1 no_action
Code
zoomed_grouped_in_dm %>% mutate(g_new = list(g)) %>% dm_insert_zoomed("new_tbl") %>%
get_all_keys()
Output
$pks
# A tibble: 7 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
7 new_tbl f, f1 FALSE
$fks
# A tibble: 7 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
6 tf_2 e, e1 new_tbl f, f1 no_action
7 tf_4 j, j1 new_tbl f, f1 no_action
key tracking works (5)
Code
# chain of renames & other transformations
zoomed_grouped_out_dm %>% summarize(d_mean = mean(d)) %>% ungroup() %>% rename(
e_new = e) %>% group_by(e_new, e1) %>% transmute(c = paste0(c, "_animal")) %>%
dm_insert_zoomed("new_tbl") %>% get_all_keys()
Output
$pks
# A tibble: 7 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
7 new_tbl c FALSE
$fks
# A tibble: 6 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 new_tbl e_new, e1 tf_3 f, f1 no_action
5 tf_5 l tf_4 h cascade
6 tf_5 m tf_6 n no_action
key tracking works (6)
Code
zoomed_grouped_in_dm %>% select(g_new = g) %>% get_all_keys("tf_3")
Output
$pks
# A tibble: 1 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_3 f, f1 FALSE
$fks
# A tibble: 2 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 e, e1 tf_3 f, f1 no_action
2 tf_4 j, j1 tf_3 f, f1 no_action
output for compound keys
Code
grouped_zoomed_comp_dm_1 %>% mutate(count = n()) %>% col_tracker_zoomed()
Output
origin year month day hour temp
"origin" "year" "month" "day" "hour" "temp"
dewp humid wind_dir wind_speed wind_gust precip
"dewp" "humid" "wind_dir" "wind_speed" "wind_gust" "precip"
pressure visib time_hour
"pressure" "visib" "time_hour"
Code
grouped_zoomed_comp_dm_2 %>% mutate(count = n()) %>% col_tracker_zoomed()
Output
origin year month day hour temp
"origin" "year" "month" "day" "hour" "temp"
dewp humid wind_dir wind_speed wind_gust precip
"dewp" "humid" "wind_dir" "wind_speed" "wind_gust" "precip"
pressure visib time_hour
"pressure" "visib" "time_hour"
Code
grouped_zoomed_comp_dm_1 %>% transmute(count = n()) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 41
Primary keys: 3
Foreign keys: 3
Code
grouped_zoomed_comp_dm_2 %>% transmute(count = n()) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 41
Primary keys: 4
Foreign keys: 4
Code
grouped_zoomed_comp_dm_1 %>% summarize(count = n()) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 41
Primary keys: 3
Foreign keys: 3
Code
grouped_zoomed_comp_dm_2 %>% summarize(count = n()) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 41
Primary keys: 4
Foreign keys: 4
Code
zoomed_comp_dm %>% select(time_hour, wind_dir) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 40
Primary keys: 3
Foreign keys: 3
Code
zoomed_comp_dm %>% select(time_hour, origin, wind_dir) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 41
Primary keys: 4
Foreign keys: 4
Code
zoomed_comp_dm %>% rename(th = time_hour, wd = wind_dir) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 53
Primary keys: 4
Foreign keys: 4
Code
zoomed_comp_dm %>% distinct(origin, wind_dir) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 40
Primary keys: 3
Foreign keys: 3
Code
zoomed_comp_dm %>% distinct(origin, wind_dir, time_hour) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 41
Primary keys: 4
Foreign keys: 4
Code
zoomed_comp_dm %>% filter(pressure < 1020) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 53
Primary keys: 4
Foreign keys: 4
Code
zoomed_comp_dm %>% pull(origin) %>% unique()
Output
[1] "EWR" "JFK" "LGA"
Code
zoomed_comp_dm %>% slice(c(1:3, 5:3))
Message
Keeping PK column, but `slice.dm_zoomed()` can potentially damage the uniqueness of PK columns (duplicated indices). Set argument `.keep_pk` to `TRUE` or `FALSE` to ensure the behavior you intended.
Output
# Zoomed table: weather
# A tibble: 6 x 15
origin year month day hour temp dewp humid wind_dir wind_speed wind_gust
<chr> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 EWR 2013 1 10 0 41 32 70.1 230 8.06 NA
2 EWR 2013 1 10 1 39.0 30.0 69.9 210 9.21 NA
3 EWR 2013 1 10 2 39.0 28.9 66.8 230 6.90 NA
4 EWR 2013 1 10 4 41 26.1 55.0 320 6.90 NA
5 EWR 2013 1 10 3 39.9 27.0 59.5 270 5.75 NA
6 EWR 2013 1 10 2 39.0 28.9 66.8 230 6.90 NA
# i 4 more variables: precip <dbl>, pressure <dbl>, visib <dbl>,
# time_hour <chr>
Code
zoomed_comp_dm %>% slice(c(1:3, 5:3), .keep_pk = TRUE) %>% col_tracker_zoomed()
Output
origin year month day hour temp
"origin" "year" "month" "day" "hour" "temp"
dewp humid wind_dir wind_speed wind_gust precip
"dewp" "humid" "wind_dir" "wind_speed" "wind_gust" "precip"
pressure visib time_hour
"pressure" "visib" "time_hour"
Code
zoomed_comp_dm %>% left_join(flights, multiple = "all") %>% nrow()
Message
Renaming ambiguous columns: %>%
dm_rename(weather, year.weather = year) %>%
dm_rename(weather, month.weather = month) %>%
dm_rename(weather, day.weather = day) %>%
dm_rename(weather, hour.weather = hour) %>%
dm_rename(flights, year.flights = year) %>%
dm_rename(flights, month.flights = month) %>%
dm_rename(flights, day.flights = day) %>%
dm_rename(flights, hour.flights = hour)
Output
[1] 1800
Code
zoomed_comp_dm %>% right_join(flights, multiple = "all") %>% nrow()
Message
Renaming ambiguous columns: %>%
dm_rename(weather, year.weather = year) %>%
dm_rename(weather, month.weather = month) %>%
dm_rename(weather, day.weather = day) %>%
dm_rename(weather, hour.weather = hour) %>%
dm_rename(flights, year.flights = year) %>%
dm_rename(flights, month.flights = month) %>%
dm_rename(flights, day.flights = day) %>%
dm_rename(flights, hour.flights = hour)
Output
[1] 1761
Code
zoomed_comp_dm %>% inner_join(flights, multiple = "all") %>% nrow()
Message
Renaming ambiguous columns: %>%
dm_rename(weather, year.weather = year) %>%
dm_rename(weather, month.weather = month) %>%
dm_rename(weather, day.weather = day) %>%
dm_rename(weather, hour.weather = hour) %>%
dm_rename(flights, year.flights = year) %>%
dm_rename(flights, month.flights = month) %>%
dm_rename(flights, day.flights = day) %>%
dm_rename(flights, hour.flights = hour)
Output
[1] 1761
Code
zoomed_comp_dm %>% full_join(flights, multiple = "all") %>% nrow()
Message
Renaming ambiguous columns: %>%
dm_rename(weather, year.weather = year) %>%
dm_rename(weather, month.weather = month) %>%
dm_rename(weather, day.weather = day) %>%
dm_rename(weather, hour.weather = hour) %>%
dm_rename(flights, year.flights = year) %>%
dm_rename(flights, month.flights = month) %>%
dm_rename(flights, day.flights = day) %>%
dm_rename(flights, hour.flights = hour)
Output
[1] 1800
Code
zoomed_comp_dm %>% semi_join(flights) %>% nrow()
Output
[1] 105
Code
zoomed_comp_dm %>% anti_join(flights) %>% nrow()
Output
[1] 39
Code
zoomed_comp_dm %>% nest_join(flights) %>% nrow()
Output
[1] 144
krlmlr/dm documentation built on April 19, 2024, 5:23 p.m.
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basic test: 'join()'-methods for zoomed.dm work (2)
Code
# keys are correctly tracked if selected columns from 'y' have same name as key columns from 'x'
dm_zoomed() %>% left_join(tf_3, select = c(d = g, f, f1)) %>% dm_update_zoomed() %>%
get_all_keys()
Message
Renaming ambiguous columns: %>%
dm_rename(tf_2, d.tf_2 = d) %>%
dm_rename(tf_3, d.tf_3 = d)
Output
$pks
# A tibble: 6 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
$fks
# A tibble: 5 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d.tf_2 tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
Code
# keys are correctly tracked if selected columns from 'y' have same name as key columns from 'x'
dm_zoomed() %>% semi_join(tf_3, select = c(d = g, f, f1)) %>% dm_update_zoomed() %>%
get_all_keys()
Output
$pks
# A tibble: 6 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
$fks
# A tibble: 5 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
key tracking works
Code
# rename()
zoomed_grouped_out_dm %>% rename(c_new = c) %>% dm_update_zoomed() %>%
get_all_keys()
Output
$pks
# A tibble: 6 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c_new FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
$fks
# A tibble: 5 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
Code
zoomed_grouped_out_dm %>% rename(e_new = e) %>% dm_update_zoomed() %>%
get_all_keys()
Output
$pks
# A tibble: 6 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
$fks
# A tibble: 5 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e_new, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
Code
zoomed_grouped_in_dm %>% rename(f_new = f) %>% dm_update_zoomed() %>%
get_all_keys()
Output
$pks
# A tibble: 6 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f_new, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
$fks
# A tibble: 5 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f_new, f1 no_action
3 tf_4 j, j1 tf_3 f_new, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
Code
# summarize()
zoomed_grouped_out_dm %>% summarize(d_mean = mean(d)) %>% dm_insert_zoomed(
"new_tbl") %>% get_all_keys()
Output
$pks
# A tibble: 7 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
7 new_tbl c FALSE
$fks
# A tibble: 6 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 new_tbl e, e1 tf_3 f, f1 no_action
5 tf_5 l tf_4 h cascade
6 tf_5 m tf_6 n no_action
Code
zoomed_grouped_in_dm %>% summarize(g_list = list(g)) %>% dm_insert_zoomed(
"new_tbl") %>% get_all_keys()
Output
$pks
# A tibble: 6 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
$fks
# A tibble: 5 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
key tracking works (2)
Code
# transmute()
zoomed_grouped_out_dm %>% transmute(d_mean = mean(d)) %>% dm_insert_zoomed(
"new_tbl") %>% get_all_keys()
Output
$pks
# A tibble: 7 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
7 new_tbl c FALSE
$fks
# A tibble: 6 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 new_tbl e, e1 tf_3 f, f1 no_action
5 tf_5 l tf_4 h cascade
6 tf_5 m tf_6 n no_action
key tracking works (3)
Code
zoomed_grouped_in_dm %>% transmute(g_list = list(g)) %>% dm_insert_zoomed(
"new_tbl") %>% get_all_keys()
Output
$pks
# A tibble: 6 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
$fks
# A tibble: 5 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
key tracking works (4)
Code
# mutate()
zoomed_grouped_out_dm %>% mutate(d_mean = mean(d)) %>% select(-d) %>%
dm_insert_zoomed("new_tbl") %>% get_all_keys()
Output
$pks
# A tibble: 7 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
7 new_tbl c FALSE
$fks
# A tibble: 6 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 new_tbl e, e1 tf_3 f, f1 no_action
5 tf_5 l tf_4 h cascade
6 tf_5 m tf_6 n no_action
Code
zoomed_grouped_in_dm %>% mutate(f = paste0(g, g)) %>% dm_insert_zoomed(
"new_tbl") %>% get_all_keys()
Output
$pks
# A tibble: 7 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
7 new_tbl f, f1 FALSE
$fks
# A tibble: 7 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
6 tf_2 e, e1 new_tbl f, f1 no_action
7 tf_4 j, j1 new_tbl f, f1 no_action
Code
zoomed_grouped_in_dm %>% mutate(g_new = list(g)) %>% dm_insert_zoomed("new_tbl") %>%
get_all_keys()
Output
$pks
# A tibble: 7 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
7 new_tbl f, f1 FALSE
$fks
# A tibble: 7 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 tf_5 l tf_4 h cascade
5 tf_5 m tf_6 n no_action
6 tf_2 e, e1 new_tbl f, f1 no_action
7 tf_4 j, j1 new_tbl f, f1 no_action
key tracking works (5)
Code
# chain of renames & other transformations
zoomed_grouped_out_dm %>% summarize(d_mean = mean(d)) %>% ungroup() %>% rename(
e_new = e) %>% group_by(e_new, e1) %>% transmute(c = paste0(c, "_animal")) %>%
dm_insert_zoomed("new_tbl") %>% get_all_keys()
Output
$pks
# A tibble: 7 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_1 a TRUE
2 tf_2 c FALSE
3 tf_3 f, f1 FALSE
4 tf_4 h FALSE
5 tf_5 k FALSE
6 tf_6 o FALSE
7 new_tbl c FALSE
$fks
# A tibble: 6 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 d tf_1 a no_action
2 tf_2 e, e1 tf_3 f, f1 no_action
3 tf_4 j, j1 tf_3 f, f1 no_action
4 new_tbl e_new, e1 tf_3 f, f1 no_action
5 tf_5 l tf_4 h cascade
6 tf_5 m tf_6 n no_action
key tracking works (6)
Code
zoomed_grouped_in_dm %>% select(g_new = g) %>% get_all_keys("tf_3")
Output
$pks
# A tibble: 1 x 3
table pk_col autoincrement
<chr> <keys> <lgl>
1 tf_3 f, f1 FALSE
$fks
# A tibble: 2 x 5
child_table child_fk_cols parent_table parent_key_cols on_delete
<chr> <keys> <chr> <keys> <chr>
1 tf_2 e, e1 tf_3 f, f1 no_action
2 tf_4 j, j1 tf_3 f, f1 no_action
output for compound keys
Code
grouped_zoomed_comp_dm_1 %>% mutate(count = n()) %>% col_tracker_zoomed()
Output
origin year month day hour temp
"origin" "year" "month" "day" "hour" "temp"
dewp humid wind_dir wind_speed wind_gust precip
"dewp" "humid" "wind_dir" "wind_speed" "wind_gust" "precip"
pressure visib time_hour
"pressure" "visib" "time_hour"
Code
grouped_zoomed_comp_dm_2 %>% mutate(count = n()) %>% col_tracker_zoomed()
Output
origin year month day hour temp
"origin" "year" "month" "day" "hour" "temp"
dewp humid wind_dir wind_speed wind_gust precip
"dewp" "humid" "wind_dir" "wind_speed" "wind_gust" "precip"
pressure visib time_hour
"pressure" "visib" "time_hour"
Code
grouped_zoomed_comp_dm_1 %>% transmute(count = n()) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 41
Primary keys: 3
Foreign keys: 3
Code
grouped_zoomed_comp_dm_2 %>% transmute(count = n()) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 41
Primary keys: 4
Foreign keys: 4
Code
grouped_zoomed_comp_dm_1 %>% summarize(count = n()) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 41
Primary keys: 3
Foreign keys: 3
Code
grouped_zoomed_comp_dm_2 %>% summarize(count = n()) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 41
Primary keys: 4
Foreign keys: 4
Code
zoomed_comp_dm %>% select(time_hour, wind_dir) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 40
Primary keys: 3
Foreign keys: 3
Code
zoomed_comp_dm %>% select(time_hour, origin, wind_dir) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 41
Primary keys: 4
Foreign keys: 4
Code
zoomed_comp_dm %>% rename(th = time_hour, wd = wind_dir) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 53
Primary keys: 4
Foreign keys: 4
Code
zoomed_comp_dm %>% distinct(origin, wind_dir) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 40
Primary keys: 3
Foreign keys: 3
Code
zoomed_comp_dm %>% distinct(origin, wind_dir, time_hour) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 41
Primary keys: 4
Foreign keys: 4
Code
zoomed_comp_dm %>% filter(pressure < 1020) %>% dm_update_zoomed()
Output
-- Metadata --------------------------------------------------------------------
Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
Columns: 53
Primary keys: 4
Foreign keys: 4
Code
zoomed_comp_dm %>% pull(origin) %>% unique()
Output
[1] "EWR" "JFK" "LGA"
Code
zoomed_comp_dm %>% slice(c(1:3, 5:3))
Message
Keeping PK column, but `slice.dm_zoomed()` can potentially damage the uniqueness of PK columns (duplicated indices). Set argument `.keep_pk` to `TRUE` or `FALSE` to ensure the behavior you intended.
Output
# Zoomed table: weather
# A tibble: 6 x 15
origin year month day hour temp dewp humid wind_dir wind_speed wind_gust
<chr> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 EWR 2013 1 10 0 41 32 70.1 230 8.06 NA
2 EWR 2013 1 10 1 39.0 30.0 69.9 210 9.21 NA
3 EWR 2013 1 10 2 39.0 28.9 66.8 230 6.90 NA
4 EWR 2013 1 10 4 41 26.1 55.0 320 6.90 NA
5 EWR 2013 1 10 3 39.9 27.0 59.5 270 5.75 NA
6 EWR 2013 1 10 2 39.0 28.9 66.8 230 6.90 NA
# i 4 more variables: precip <dbl>, pressure <dbl>, visib <dbl>,
# time_hour <chr>
Code
zoomed_comp_dm %>% slice(c(1:3, 5:3), .keep_pk = TRUE) %>% col_tracker_zoomed()
Output
origin year month day hour temp
"origin" "year" "month" "day" "hour" "temp"
dewp humid wind_dir wind_speed wind_gust precip
"dewp" "humid" "wind_dir" "wind_speed" "wind_gust" "precip"
pressure visib time_hour
"pressure" "visib" "time_hour"
Code
zoomed_comp_dm %>% left_join(flights, multiple = "all") %>% nrow()
Message
Renaming ambiguous columns: %>%
dm_rename(weather, year.weather = year) %>%
dm_rename(weather, month.weather = month) %>%
dm_rename(weather, day.weather = day) %>%
dm_rename(weather, hour.weather = hour) %>%
dm_rename(flights, year.flights = year) %>%
dm_rename(flights, month.flights = month) %>%
dm_rename(flights, day.flights = day) %>%
dm_rename(flights, hour.flights = hour)
Output
[1] 1800
Code
zoomed_comp_dm %>% right_join(flights, multiple = "all") %>% nrow()
Message
Renaming ambiguous columns: %>%
dm_rename(weather, year.weather = year) %>%
dm_rename(weather, month.weather = month) %>%
dm_rename(weather, day.weather = day) %>%
dm_rename(weather, hour.weather = hour) %>%
dm_rename(flights, year.flights = year) %>%
dm_rename(flights, month.flights = month) %>%
dm_rename(flights, day.flights = day) %>%
dm_rename(flights, hour.flights = hour)
Output
[1] 1761
Code
zoomed_comp_dm %>% inner_join(flights, multiple = "all") %>% nrow()
Message
Renaming ambiguous columns: %>%
dm_rename(weather, year.weather = year) %>%
dm_rename(weather, month.weather = month) %>%
dm_rename(weather, day.weather = day) %>%
dm_rename(weather, hour.weather = hour) %>%
dm_rename(flights, year.flights = year) %>%
dm_rename(flights, month.flights = month) %>%
dm_rename(flights, day.flights = day) %>%
dm_rename(flights, hour.flights = hour)
Output
[1] 1761
Code
zoomed_comp_dm %>% full_join(flights, multiple = "all") %>% nrow()
Message
Renaming ambiguous columns: %>%
dm_rename(weather, year.weather = year) %>%
dm_rename(weather, month.weather = month) %>%
dm_rename(weather, day.weather = day) %>%
dm_rename(weather, hour.weather = hour) %>%
dm_rename(flights, year.flights = year) %>%
dm_rename(flights, month.flights = month) %>%
dm_rename(flights, day.flights = day) %>%
dm_rename(flights, hour.flights = hour)
Output
[1] 1800
Code
zoomed_comp_dm %>% semi_join(flights) %>% nrow()
Output
[1] 105
Code
zoomed_comp_dm %>% anti_join(flights) %>% nrow()
Output
[1] 39
Code
zoomed_comp_dm %>% nest_join(flights) %>% nrow()
Output
[1] 144
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