https://github.com/seananderson/ramlegacy

R package to download, import, convert, and cache the RAM Legacy Stock Assessment Database
https://github.com/seananderson/ramlegacy

database fisheries r r-package

Last synced: 12 days ago
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R package to download, import, convert, and cache the RAM Legacy Stock Assessment Database

• Host: GitHub
• URL: https://github.com/seananderson/ramlegacy
• Owner: seananderson
• Created: 2015-01-22T04:35:21.000Z (almost 10 years ago)
• Default Branch: master
• Last Pushed: 2020-05-07T17:23:12.000Z (over 4 years ago)
• Last Synced: 2024-06-11T17:20:32.447Z (5 months ago)
• Topics: database, fisheries, r, r-package
• Language: R
• Size: 18.6 KB
• Stars: 4
• Watchers: 5
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd

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README

        
---

output:

  md_document:

    variant: markdown_github

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>"

)

```

# Import the RAM Legacy Stock Assessment Database into R

[![Travis-CI Build Status](https://travis-ci.org/seananderson/ramlegacy.svg?branch=master)](https://travis-ci.org/seananderson/ramlegacy)

**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](http://ramlegacy.org) and converts it to a local sqlite3 database. This makes it easy to [work with dplyr/dbplyr](https://cran.r-project.org/web/packages/dbplyr/vignettes/dbplyr.html), 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 . If you're on OS X and using homebrew, you can install it with `brew install mdbtools`.

### Example use

```{r knitr-options, cache=FALSE, echo=FALSE}

library("knitr")

opts_chunk$set(cache=TRUE)

```

Install the package:

```{r, eval=FALSE}

# install.packages("devtools")

devtools::install_github("seananderson/ramlegacy")

```

Cache and convert the database:

```{r}

library("ramlegacy")

make_ramlegacy()

```

Work with the data:

```{r}

library("dplyr")

ram <- src_sqlite("ramlegacy.sqlite3")

ram # see the available tables

```

Access the `area` table:

```{r}

tbl(ram, "area")

```

Join the time series `ts` and `stock` tables on the `stockid` column:

```{r}

ts <- tbl(ram, "timeseries")

stock <- tbl(ram, "stock")

select(stock, stockid, scientificname, commonname, region) %>%

  inner_join(ts)

```

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:

```{r}

tbl(ram, "area") %>% 

  collect()

```

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.

```{r}

tbl(ram, "area") %>% 

  collect(n = Inf)

```