https://github.com/jsta/nesr

Scrape Data from the National Eutrophication Survey
https://github.com/jsta/nesr

epa rstats water-quality

Last synced: about 2 months ago
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Scrape Data from the National Eutrophication Survey

• Host: GitHub
• URL: https://github.com/jsta/nesr
• Owner: jsta
• License: gpl-3.0
• Created: 2016-12-13T14:40:40.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2023-10-10T18:03:24.000Z (over 1 year ago)
• Last Synced: 2024-06-11T17:08:54.117Z (8 months ago)
• Topics: epa, rstats, water-quality
• Language: R
• Homepage: https://dx.doi.org/10.5063/F1KK98R5
• Size: 25.1 MB
• Stars: 1
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE

Awesome Lists containing this project

README

        
---

output: github_document

bibliography: 

  - inst/references.bib

nocite:  |

  @brett_review_2007, @reckhow_empirical_1988, @StachelekNationalEutrophicationSurvey2017

csl: inst/ecology.csl

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```

```{r knitcitations, echo=FALSE, cache = FALSE}

library(knitcitations)

cleanbib()   

cite_options(citation_format = "pandoc")

```

# nesR

Code to rescue (scrape) data from the National Eutrophication Survey archival PDF.

## Installation

### Prerequites

Until `magick` can handle local adaptive thresholding. This package requires you to be able to call the `imagemagick` `convert` command with `system()`.

You can install nesR from github with:

```{r gh-installation, eval = FALSE}

# install.packages("devtools")

devtools::install_github("jsta/nesR")

```

## Usage

### Load package

```{r }

library(nesR)

```

### Get data 

```{r eval=TRUE}

nes_file <- system.file("extdata/national-eutrophication-survey_477.PDF", 

                        package = "nesR")

res <- nes_get(nes_file, 89)

parse_nes(res)

```

### Build database

As written, building the NES database requires GNU Make and the ability to run `R` commands using the `Rscript` command-line utility (aka doesn't work on Windows). For best results, use a machine with at least 4 GB RAM.

```{bash eval=FALSE}

make PDFSOURCE=474 all

make PDFSOURCE=475 all

make PDFSOURCE=476 all

make PDFSOURCE=477 all

```

# References

```{r write_citations, cache=FALSE, include=FALSE,eval=FALSE}

write.bibtex(file="inst/references.bib")

```