README.md
In seananderson/ramlegacy: Download and Cache the RAM Legacy Stock Assessment Database
Import the RAM Legacy Stock Assessment Database into R
Please use Carl Boettiger and Gupta Kshitiz’s ramlegacy package
https://github.com/ropensci/ramlegacy
instead.
This package does one thing: it downloads a Microsoft Access copy of the
RAM Legacy Stock Assessment Database and
converts it to a local sqlite3 database. This makes it easy to work
with
dplyr/dbplyr,
for example. The make_ramlegacy() function also leaves a copy of
.csv files for each table in the database in the R working directory
if you’d prefer to work with those.
Note that you must have the utility mdb-tables installed and in
your path from mdbtools. This utility provides tools for extracting
Access databases. You can find installation instructions at
http://mdbtools.sourceforge.net.
If you’re on OS X and using homebrew, you can install it with
brew install mdbtools.
Example use
Install the package:
# install.packages("devtools")
devtools::install_github("seananderson/ramlegacy")
Cache and convert the database:
library("ramlegacy")
make_ramlegacy()
#> Converting table area
#> Converting table assessment
#> Converting table assessmethod
#> Converting table assessor
#> Converting table biometrics
#> Converting table bioparams
#> Converting table bioparams_units_views
#> Converting table bioparams_values_views
#> Converting table management
#> Converting table model_results
#> Converting table stock
#> Converting table taxonomy
#> Converting table timeseries
#> Converting table timeseries_units_views
#> Converting table timeseries_values_views
#> Converting table tsmetrics
Work with the data:
library("dplyr")
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
ram <- src_sqlite("ramlegacy.sqlite3")
ram # see the available tables
#> src: sqlite 3.19.3 [/Users/seananderson/src/ramlegacy/ramlegacy.sqlite3]
#> tbls: area, assessment, assessmethod, assessor, biometrics, bioparams,
#> bioparams_units_views, bioparams_values_views, management,
#> model_results, sqlite_stat1, sqlite_stat4, stock, taxonomy, timeseries,
#> timeseries_units_views, timeseries_values_views, tsmetrics
Access the area table:
tbl(ram, "area")
#> # Source: table<area> [?? x 6]
#> # Database: sqlite 3.19.3
#> # [/Users/seananderson/src/ramlegacy/ramlegacy.sqlite3]
#> country areatype areacode areaname alternateareaname areaid
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Argentina CFP ARG-N Northern Argen… <NA> Argentin…
#> 2 Argentina CFP ARG-S Southern Argen… <NA> Argentin…
#> 3 Australia AFMA CASCADE Cascade Plateau <NA> Australi…
#> 4 Australia AFMA ESE Eastern half o… <NA> Australi…
#> 5 Australia AFMA GAB Great Australi… <NA> Australi…
#> 6 Australia AFMA MI Macquarie Isla… "" Australi…
#> 7 Australia AFMA NAUST Northern Austr… <NA> Australi…
#> 8 Australia AFMA NSWWA New South Wale… <NA> Australi…
#> 9 Australia AFMA SE Southeast Aust… <NA> Australi…
#> 10 Australia AFMA TAS Tasmania <NA> Australi…
#> # ... with more rows
Join the time series ts and stock tables on the stockid column:
ts <- tbl(ram, "timeseries")
stock <- tbl(ram, "stock")
select(stock, stockid, scientificname, commonname, region) %>%
inner_join(ts)
#> Joining, by = "stockid"
#> # Source: lazy query [?? x 9]
#> # Database: sqlite 3.19.3
#> # [/Users/seananderson/src/ramlegacy/ramlegacy.sqlite3]
#> stockid scientificname commonname region assessid stocklong tsid
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 2 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 3 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 4 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 5 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 6 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 7 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 8 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 9 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 10 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> # ... with more rows, and 2 more variables: tsyear <dbl>, tsvalue <dbl>
Note that because you are working with dplyr and a database, you will
need to use dplyr::collect() once you want to load the data into a
local data frame. For example:
tbl(ram, "area") %>%
collect()
#> # A tibble: 275 x 6
#> country areatype areacode areaname alternateareaname areaid
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Argentina CFP ARG-N Northern Argen… <NA> Argentin…
#> 2 Argentina CFP ARG-S Southern Argen… <NA> Argentin…
#> 3 Australia AFMA CASCADE Cascade Plateau <NA> Australi…
#> 4 Australia AFMA ESE Eastern half o… <NA> Australi…
#> 5 Australia AFMA GAB Great Australi… <NA> Australi…
#> 6 Australia AFMA MI Macquarie Isla… "" Australi…
#> 7 Australia AFMA NAUST Northern Austr… <NA> Australi…
#> 8 Australia AFMA NSWWA New South Wale… <NA> Australi…
#> 9 Australia AFMA SE Southeast Aust… <NA> Australi…
#> 10 Australia AFMA TAS Tasmania <NA> Australi…
#> # ... with 265 more rows
For safety, you may want to use dplyr::collect(n = Inf) to return all
rows of data, not just the minimum default number. In this case it won’t
make a difference.
tbl(ram, "area") %>%
collect(n = Inf)
#> # A tibble: 275 x 6
#> country areatype areacode areaname alternateareaname areaid
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Argentina CFP ARG-N Northern Argen… <NA> Argentin…
#> 2 Argentina CFP ARG-S Southern Argen… <NA> Argentin…
#> 3 Australia AFMA CASCADE Cascade Plateau <NA> Australi…
#> 4 Australia AFMA ESE Eastern half o… <NA> Australi…
#> 5 Australia AFMA GAB Great Australi… <NA> Australi…
#> 6 Australia AFMA MI Macquarie Isla… "" Australi…
#> 7 Australia AFMA NAUST Northern Austr… <NA> Australi…
#> 8 Australia AFMA NSWWA New South Wale… <NA> Australi…
#> 9 Australia AFMA SE Southeast Aust… <NA> Australi…
#> 10 Australia AFMA TAS Tasmania <NA> Australi…
#> # ... with 265 more rows
seananderson/ramlegacy documentation built on May 12, 2020, 11:41 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Import the RAM Legacy Stock Assessment Database into R
Please use Carl Boettiger and Gupta Kshitiz’s ramlegacy package https://github.com/ropensci/ramlegacy instead.
This package does one thing: it downloads a Microsoft Access copy of the
RAM Legacy Stock Assessment Database and
converts it to a local sqlite3 database. This makes it easy to work
with
dplyr/dbplyr,
for example. The make_ramlegacy() function also leaves a copy of
.csv files for each table in the database in the R working directory
if you’d prefer to work with those.
Note that you must have the utility mdb-tables installed and in
your path from mdbtools. This utility provides tools for extracting
Access databases. You can find installation instructions at
http://mdbtools.sourceforge.net.
If you’re on OS X and using homebrew, you can install it with
brew install mdbtools.
Example use
Install the package:
# install.packages("devtools")
devtools::install_github("seananderson/ramlegacy")
Cache and convert the database:
library("ramlegacy")
make_ramlegacy()
#> Converting table area
#> Converting table assessment
#> Converting table assessmethod
#> Converting table assessor
#> Converting table biometrics
#> Converting table bioparams
#> Converting table bioparams_units_views
#> Converting table bioparams_values_views
#> Converting table management
#> Converting table model_results
#> Converting table stock
#> Converting table taxonomy
#> Converting table timeseries
#> Converting table timeseries_units_views
#> Converting table timeseries_values_views
#> Converting table tsmetrics
Work with the data:
library("dplyr")
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
ram <- src_sqlite("ramlegacy.sqlite3")
ram # see the available tables
#> src: sqlite 3.19.3 [/Users/seananderson/src/ramlegacy/ramlegacy.sqlite3]
#> tbls: area, assessment, assessmethod, assessor, biometrics, bioparams,
#> bioparams_units_views, bioparams_values_views, management,
#> model_results, sqlite_stat1, sqlite_stat4, stock, taxonomy, timeseries,
#> timeseries_units_views, timeseries_values_views, tsmetrics
Access the area table:
tbl(ram, "area")
#> # Source: table<area> [?? x 6]
#> # Database: sqlite 3.19.3
#> # [/Users/seananderson/src/ramlegacy/ramlegacy.sqlite3]
#> country areatype areacode areaname alternateareaname areaid
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Argentina CFP ARG-N Northern Argen… <NA> Argentin…
#> 2 Argentina CFP ARG-S Southern Argen… <NA> Argentin…
#> 3 Australia AFMA CASCADE Cascade Plateau <NA> Australi…
#> 4 Australia AFMA ESE Eastern half o… <NA> Australi…
#> 5 Australia AFMA GAB Great Australi… <NA> Australi…
#> 6 Australia AFMA MI Macquarie Isla… "" Australi…
#> 7 Australia AFMA NAUST Northern Austr… <NA> Australi…
#> 8 Australia AFMA NSWWA New South Wale… <NA> Australi…
#> 9 Australia AFMA SE Southeast Aust… <NA> Australi…
#> 10 Australia AFMA TAS Tasmania <NA> Australi…
#> # ... with more rows
Join the time series ts and stock tables on the stockid column:
ts <- tbl(ram, "timeseries")
stock <- tbl(ram, "stock")
select(stock, stockid, scientificname, commonname, region) %>%
inner_join(ts)
#> Joining, by = "stockid"
#> # Source: lazy query [?? x 9]
#> # Database: sqlite 3.19.3
#> # [/Users/seananderson/src/ramlegacy/ramlegacy.sqlite3]
#> stockid scientificname commonname region assessid stocklong tsid
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 2 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 3 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 4 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 5 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 6 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 7 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 8 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 9 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> 10 ACADRED… Sebastes fasci… Acadian r… US Ea… NEFSC-AC… Acadian re… BdivB…
#> # ... with more rows, and 2 more variables: tsyear <dbl>, tsvalue <dbl>
Note that because you are working with dplyr and a database, you will
need to use dplyr::collect() once you want to load the data into a
local data frame. For example:
tbl(ram, "area") %>%
collect()
#> # A tibble: 275 x 6
#> country areatype areacode areaname alternateareaname areaid
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Argentina CFP ARG-N Northern Argen… <NA> Argentin…
#> 2 Argentina CFP ARG-S Southern Argen… <NA> Argentin…
#> 3 Australia AFMA CASCADE Cascade Plateau <NA> Australi…
#> 4 Australia AFMA ESE Eastern half o… <NA> Australi…
#> 5 Australia AFMA GAB Great Australi… <NA> Australi…
#> 6 Australia AFMA MI Macquarie Isla… "" Australi…
#> 7 Australia AFMA NAUST Northern Austr… <NA> Australi…
#> 8 Australia AFMA NSWWA New South Wale… <NA> Australi…
#> 9 Australia AFMA SE Southeast Aust… <NA> Australi…
#> 10 Australia AFMA TAS Tasmania <NA> Australi…
#> # ... with 265 more rows
For safety, you may want to use dplyr::collect(n = Inf) to return all
rows of data, not just the minimum default number. In this case it won’t
make a difference.
tbl(ram, "area") %>%
collect(n = Inf)
#> # A tibble: 275 x 6
#> country areatype areacode areaname alternateareaname areaid
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Argentina CFP ARG-N Northern Argen… <NA> Argentin…
#> 2 Argentina CFP ARG-S Southern Argen… <NA> Argentin…
#> 3 Australia AFMA CASCADE Cascade Plateau <NA> Australi…
#> 4 Australia AFMA ESE Eastern half o… <NA> Australi…
#> 5 Australia AFMA GAB Great Australi… <NA> Australi…
#> 6 Australia AFMA MI Macquarie Isla… "" Australi…
#> 7 Australia AFMA NAUST Northern Austr… <NA> Australi…
#> 8 Australia AFMA NSWWA New South Wale… <NA> Australi…
#> 9 Australia AFMA SE Southeast Aust… <NA> Australi…
#> 10 Australia AFMA TAS Tasmania <NA> Australi…
#> # ... with 265 more rows
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