This vignette describes which changes are necessary to adapt your code when updating the {dm} package version from a version 0.0.5 or lower to 0.0.6 or higher.
0.0.5 to 0.0.6
cdm with dm
During this update the prevalent prefix cdm was discarded in favor of dm. The old prefix would still do its job, but a warning message would be issued each time a function beginning with cdm was being used, informing that the function is soft-deprecated and suggesting the use of its newer version.
If you have a script which is based on an older {dm} version, it should still work with the newer version, albeit complaining each time an outdated function is being used. This can be repaired by:
cdm by dm in this script. This can e.g. be done in RStudio using “Find” or in the terminal using sed -e 's/cdm/dm/g' path-to-file on Windows or sed -i '' -e 's/cdm/dm/g' path-to-file on a Mac. If the script errors after this step, you will need to check where exactly the error happens and manually repair the damage.dm: tbl, [[, $
Furthermore, you need to pay attention if you used one of tbl.dm(), [[.dm(), $.dm(). During the same update the implementation for those methods changed as well, and here you don’t get the convenient warning messages. The change was, that before the update, the mentioned methods would return the table after “filtering” it to just contain the rows with values that relate via foreign key relations to other tables that were filtered earlier. After the update just the table as is would be returned. If you want to retain the former behavior, you need to replace each of the methods with the function dm_apply_filters_to_tbl(), which was made available with the update.
The methods are of course not to be avoided in general, if no filters are set anyway the result will not change after the update.
Here a short example for the different cases:
Formerly you would access the “filtered” tables using the following syntax:
library(dm)
flights_dm <- dm_nycflights13()
tbl(flights_dm, "airports")#> # A tibble: 1,458 x 8
#> faa name lat lon alt tz dst tzone
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 04G Lansdowne Airport 41.1 -80.6 1044 -5 A America/New_…
#> 2 06A Moton Field Municipa… 32.5 -85.7 264 -6 A America/Chic…
#> 3 06C Schaumburg Regional 42.0 -88.1 801 -6 A America/Chic…
#> 4 06N Randall Airport 41.4 -74.4 523 -5 A America/New_…
#> 5 09J Jekyll Island Airport 31.1 -81.4 11 -5 A America/New_…
#> 6 0A9 Elizabethton Municip… 36.4 -82.2 1593 -5 A America/New_…
#> 7 0G6 Williams County Airp… 41.5 -84.5 730 -5 A America/New_…
#> 8 0G7 Finger Lakes Regiona… 42.9 -76.8 492 -5 A America/New_…
#> 9 0P2 Shoestring Aviation … 39.8 -76.6 1000 -5 U America/New_…
#> 10 0S9 Jefferson County Intl 48.1 -123. 108 -8 A America/Los_…
#> # … with 1,448 more rows
flights_dm$planes#> # A tibble: 3,322 x 9
#> tailnum year type manufacturer model engines seats speed engine
#> <chr> <int> <chr> <chr> <chr> <int> <int> <int> <chr>
#> 1 N10156 2004 Fixed win… EMBRAER EMB-1… 2 55 NA Turbo…
#> 2 N102UW 1998 Fixed win… AIRBUS INDUS… A320-… 2 182 NA Turbo…
#> 3 N103US 1999 Fixed win… AIRBUS INDUS… A320-… 2 182 NA Turbo…
#> 4 N104UW 1999 Fixed win… AIRBUS INDUS… A320-… 2 182 NA Turbo…
#> 5 N10575 2002 Fixed win… EMBRAER EMB-1… 2 55 NA Turbo…
#> 6 N105UW 1999 Fixed win… AIRBUS INDUS… A320-… 2 182 NA Turbo…
#> 7 N107US 1999 Fixed win… AIRBUS INDUS… A320-… 2 182 NA Turbo…
#> 8 N108UW 1999 Fixed win… AIRBUS INDUS… A320-… 2 182 NA Turbo…
#> 9 N109UW 1999 Fixed win… AIRBUS INDUS… A320-… 2 182 NA Turbo…
#> 10 N110UW 1999 Fixed win… AIRBUS INDUS… A320-… 2 182 NA Turbo…
#> # … with 3,312 more rows
flights_dm[["weather"]]#> # A tibble: 861 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
#> # … with 851 more rows, and 5 more variables: wind_gust <dbl>,
#> # precip <dbl>, pressure <dbl>, visib <dbl>, time_hour <dttm>
After the update the same result is achieved by this type of function call:
dm_apply_filters_to_tbl(flights_dm, airlines)#> # A tibble: 16 x 2
#> carrier name
#> <chr> <chr>
#> 1 9E Endeavor Air Inc.
#> 2 AA American Airlines Inc.
#> 3 AS Alaska Airlines Inc.
#> 4 B6 JetBlue Airways
#> 5 DL Delta Air Lines Inc.
#> 6 EV ExpressJet Airlines Inc.
#> 7 F9 Frontier Airlines Inc.
#> 8 FL AirTran Airways Corporation
#> 9 HA Hawaiian Airlines Inc.
#> 10 MQ Envoy Air
#> 11 OO SkyWest Airlines Inc.
#> 12 UA United Air Lines Inc.
#> 13 US US Airways Inc.
#> 14 VX Virgin America
#> 15 WN Southwest Airlines Co.
#> 16 YV Mesa Airlines Inc.