https://github.com/openwashdata/ploswater
Repository with data on publications from PLOS Water Journal
https://github.com/openwashdata/ploswater
Last synced: 4 months ago
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Repository with data on publications from PLOS Water Journal
- Host: GitHub
- URL: https://github.com/openwashdata/ploswater
- Owner: openwashdata
- License: cc-by-4.0
- Created: 2025-01-08T11:48:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-08T15:44:43.000Z (over 1 year ago)
- Last Synced: 2025-09-04T23:52:53.376Z (9 months ago)
- Language: R
- Homepage: https://openwashdata.github.io/ploswater/
- Size: 3.83 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
---
output: github_document
always_allow_html: true
editor_options:
markdown:
wrap: 72
chunk_output_type: console
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
message = FALSE,
warning = FALSE,
fig.retina = 2,
fig.align = 'center'
)
```
# ploswater
[](https://creativecommons.org/licenses/by/4.0/)
[](https://github.com/openwashdata/ploswater/actions/workflows/R-CMD-check.yaml)
[](https://zenodo.org/doi/10.5281/zenodo.14616993)
The goal of ploswater is to make available data on publication trends from the [PLOS Water Journal](https://journals.plos.org/water/).
## Installation
You can install the development version of ploswater from
[GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("openwashdata/ploswater")
```
```{r}
## Run the following code in console if you don't have the packages
## install.packages(c("dplyr", "knitr", "readr", "stringr", "gt", "kableExtra"))
library(dplyr)
library(knitr)
library(readr)
library(stringr)
library(gt)
library(kableExtra)
```
Alternatively, you can download the individual datasets as a CSV or XLSX
file from the table below.
1. Click Download CSV. A window opens that displays the CSV in
your browser.
2. Right-click anywhere inside the window and select "Save Page As...".
3. Save the file in a folder of your choice.
```{r, echo=FALSE, message=FALSE, warning=FALSE}
extdata_path <- "https://github.com/openwashdata/ploswater/raw/main/inst/extdata/"
read_csv("data-raw/dictionary.csv") |>
distinct(file_name) |>
dplyr::mutate(file_name = str_remove(file_name, ".rda")) |>
dplyr::rename(dataset = file_name) |>
mutate(
CSV = paste0("[Download CSV](", extdata_path, dataset, ".csv)"),
XLSX = paste0("[Download XLSX](", extdata_path, dataset, ".xlsx)")
) |>
knitr::kable()
```
## Data
The package provides access to data from publications in the [PLOS Water Journal](https://journals.plos.org/water/). This data includes information on publication types, SDGs targeted, authors, institutions, and more.
```{r}
library(ploswater)
```
### ploswater
The dataset `ploswater` contains data about ... It has
`r nrow(ploswater)` observations and `r ncol(ploswater)`
variables
```{r}
ploswater |>
select(1:10) |> # Select first 10 columns
head(3) |>
gt::gt() |>
gt::as_raw_html()
```
For an overview of the variable names, see the following table.
```{r echo=FALSE, message=FALSE, warning=FALSE}
readr::read_csv("data-raw/dictionary.csv") |>
dplyr::filter(file_name == "ploswater.rda") |>
dplyr::select(variable_name:description) |>
head(10) |>
knitr::kable() |>
kableExtra::kable_styling("striped") |>
kableExtra::scroll_box(height = "200px")
```
## Example
```{r}
library(ploswater)
library(ggplot2)
library(lubridate)
# Convert publication_date to Date type
ploswater$publication_date <- as.Date(ploswater$publication_date)
# Create year-month column and count publications
monthly_counts <- ploswater %>%
mutate(publication_month = floor_date(publication_date, "month")) %>%
count(publication_month) %>%
arrange(publication_month)
# Create bar chart
ggplot(monthly_counts, aes(x = publication_month, y = n)) +
geom_bar(stat = "identity", fill = "steelblue") +
labs(x = "",
y = "Number of Publications",
title = "Number of Publications per Month") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_x_date(date_breaks = "3 month",
date_labels = "%b %Y")
```
```{r}
# Create citation impact visualization
ggplot(ploswater, aes(x = publication_date, y = cited_by_count)) +
geom_point(aes(size = fwci), alpha = 0.6) +
scale_size_continuous(name = "Field-Weighted\nCitation Impact") +
labs(x = "Publication Date",
y = "Number of Citations",
title = "Citation Impact Over Time") +
theme_minimal()
```
```{r}
library(gt)
# Compute summary statistics
summary_stats <- data.frame(
Statistic = c(
"Mean Citations",
"Median Citations",
"Mean FWCI",
"Total Publications",
"Correlation: Citations & FWCI"
),
Value = c(
mean(ploswater$cited_by_count, na.rm = TRUE),
median(ploswater$cited_by_count, na.rm = TRUE),
mean(ploswater$fwci, na.rm = TRUE),
round(nrow(ploswater), 0),
cor(ploswater$cited_by_count, ploswater$fwci, use = "complete.obs")
)
)
# Create and print the table using gt
summary_stats %>%
gt() %>%
tab_header(
title = "Summary Statistics"
) %>%
gt::as_raw_html()
```
## License
Data are available as
[CC-BY](https://github.com/openwashdata/%7B%7B%7Bpackagename%7D%7D%7D/blob/main/LICENSE.md).
## Citation
Please cite this package using:
```{r}
citation("ploswater")
```