{"id":38654440,"url":"https://github.com/openwashdata/washinvestments","last_synced_at":"2026-01-17T09:25:35.873Z","repository":{"id":227085237,"uuid":"770378144","full_name":"openwashdata/washinvestments","owner":"openwashdata","description":"Dataset on multilateral development bank (MDB) investment in water supply and sanitation associated with the paper \"Multilateral development banks investment behaviour in water and sanitation: Findings and lessons from 60 years of investment projects in Africa and Asia\" (Heidler et al. 2023).","archived":false,"fork":false,"pushed_at":"2024-05-16T11:35:44.000Z","size":5771,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-05T02:24:14.624Z","etag":null,"topics":["africa","asia","investment","mdb","open-data","r","sanitation","wash","water"],"latest_commit_sha":null,"homepage":"https://openwashdata.github.io/washinvestments/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc-by-4.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/openwashdata.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2024-03-11T12:58:37.000Z","updated_at":"2024-05-16T11:37:05.000Z","dependencies_parsed_at":null,"dependency_job_id":"8527c223-afbe-453f-867d-b92ac2074401","html_url":"https://github.com/openwashdata/washinvestments","commit_stats":null,"previous_names":["openwashdata/washinvestments"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/openwashdata/washinvestments","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/openwashdata%2Fwashinvestments","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/openwashdata%2Fwashinvestments/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/openwashdata%2Fwashinvestments/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/openwashdata%2Fwashinvestments/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/openwashdata","download_url":"https://codeload.github.com/openwashdata/washinvestments/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/openwashdata%2Fwashinvestments/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28505550,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-17T06:57:29.758Z","status":"ssl_error","status_checked_at":"2026-01-17T06:56:03.931Z","response_time":85,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["africa","asia","investment","mdb","open-data","r","sanitation","wash","water"],"created_at":"2026-01-17T09:25:33.829Z","updated_at":"2026-01-17T09:25:35.830Z","avatar_url":"https://github.com/openwashdata.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\nalways_allow_html: true\neditor_options: \n  markdown: \n    wrap: 72\n  chunk_output_type: console\nbibliography: references.bib\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\",\n  message = FALSE,\n  warning = FALSE,\n  fig.retina = 2,\n  fig.align = 'center'\n)\n\nlibrary(tidyverse)\nlibrary(epoxy)\nlibrary(washinvestments)\n```\n\n# washinvestments\n\n\u003c!-- badges: start --\u003e\n[![License: CC BY\n4.0](https://img.shields.io/badge/License-CC_BY_4.0-orange.svg)](https://creativecommons.org/licenses/by/4.0/)\n[![R-CMD-check](https://github.com/openwashdata/washinvestments/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/openwashdata/washinvestments/actions/workflows/R-CMD-check.yaml)\n[![DOI](https://zenodo.org/badge/770378167.svg)](https://zenodo.org/doi/10.5281/zenodo.11203434)\n\u003c!-- badges: end --\u003e\n\nThe goal of washinvestments is to provide users documentation on the data set published alongside the article \"Multilateral\ndevelopment banks investment behaviour in water and sanitation: Findings\nand lessons from 60 years of investment projects in Africa and Asia\"\n[@heidler2023multilateral].\n\n## Installation\n\nYou can install the development version of washinvestments from\n[GitHub](https://github.com/) with:\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"openwashdata/washinvestments\")\n```\n\nAlternatively, you can download the dataset as a CSV or XLSX file from\nthe table below.\n\n```{r, echo=FALSE, message=FALSE, warning=FALSE}\n\nextdata_path \u003c- \"https://github.com/openwashdata/washinvestments/raw/main/inst/extdata/\"\n\nread_csv(\"data-raw/dictionary.csv\") |\u003e \n  distinct(file_name) |\u003e \n  dplyr::mutate(file_name = str_remove(file_name, \".rda\")) |\u003e \n  dplyr::rename(dataset = file_name) |\u003e \n  mutate(\n    CSV = paste0(\"[Download CSV](\", extdata_path, dataset, \".csv)\"),\n    XLSX = paste0(\"[Download XLSX](\", extdata_path, dataset, \"_utf8.xlsx)\")\n  ) |\u003e \n  knitr::kable()\n\n```\n\n## Project goal\n\nMultilateral development banks (MDBs) significantly influence water and\nsanitation infrastructure development. However, data on their investments is\ndispersed and hard to compare. This project presents a new data set\ncompiled by drawing on 3,639 water and sanitation projects that aims at\nassessing \"territorial trends, technology choices, distribution of\nfinancial burdens, and reforms to institutional arrangements to analyze\nthe investment behaviour of the World Bank, ADB, and AfDB in water\nsupply and sanitation from their first operations in the 1960s until\n2020 and distil how they responded to trends in urbanization and the\npolicy debates about governing access to basic services.\"\n[@heidler2023multilateral]\n\n## Data\n\nThe data set includes information about water and sanitation projects\nconducted in Africa and Asia between 1960 and 2020. The package provides\naccess to one data set.\n\n```{r, echo = TRUE}\nlibrary(washinvestments)\n```\n\nThe `washinvestments` data set has `r ncol(washinvestments)` variables\nand `r nrow(washinvestments)` observations. For an overview of the\nvariable names, see the following table.\n\n```{r, eval=FALSE}\nwashinvestments\n```\n\n```{r, echo=FALSE}\nreadr::read_csv(\"data-raw/dictionary.csv\") |\u003e \n  dplyr::filter(file_name == \"washinvestments.rda\") |\u003e \n  dplyr::select(variable_name:description) |\u003e \n  knitr::kable()\n```\n\n## Example: Investment trends in Africa and Asia over 60 years\n\nHere is a basic example illustrating the analysis of financing trends in the WASH sector over 60 years, with a focus on Africa and Asia as outlined in the paper. The first plot displays the aggregated number of investment projects in the WASH sector from 1960 to 2020, grouped in 10-year intervals. It is notable that a higher number of projects in Asia have secured financing, with a significant increase observed since around the 2000s. Moreover, there is a discernible upward trend in the number of investment projects over the years. Subsequently, we explore investment trends within Asia and Africa separately. The second plot illustrates investment patterns in Asia, examining various regions within the continent. Likewise, the third plot focuses on investment trends in Africa, with a breakdown by different regions.\n\n![](man/figures/africa_asia_10.png)\n![](man/figures/asia.png)\n![](man/figures/africa.png)\n\n```{r, eval=FALSE, message=FALSE, warning=FALSE, include=TRUE, paged.print=FALSE}\nlibrary(washinvestments)\nlibrary(ggplot2)\nlibrary(countrycode)\nlibrary(dplyr)\n----------------------------------------------------------------------------------------------------------------------------\n# Add a new column for continent information as well as a new column for 10-year intervals\nwashinvestments \u003c- washinvestments |\u003e \n  mutate(continent = countrycode(iso_country_code, \"iso3c\", \"continent\"),\n         year_interval = cut(year, breaks = seq(1960, 2020, by = 10),\n          labels = seq(1960, 2010, by = 10)))\n\n# Filter the data for Africa and Asia\nwashinvestments_asia_africa \u003c- washinvestments |\u003e \n  filter(continent %in% c(\"Africa\", \"Asia\"))\n\n# Count the number of projects per continent and arrange it in descending order\ncontinent_counts \u003c- washinvestments_asia_africa |\u003e \n  group_by(continent, year_interval) |\u003e \n  summarise(count = n()) |\u003e \n  arrange(desc(year_interval))\n\n# Encode the continent column as factor with chosen levels for plotting\nwashinvestments_asia_africa$continent \u003c- factor(washinvestments_asia_africa$continent, levels = continent_counts$continent)\n----------------------------------------------------------------------------------------------------------------------------\n# Filter the data set for Asia\nwashinvestments_asia \u003c- washinvestments |\u003e\n  filter(region %in% c(\"Central Asia\", \"Eastern Asia\", \"Polynesia\", \"South-eastern Asia\", \"Southern Asia\", \"Western Asia\"))\n\n# Count the number of projects per region and arrange it in descending order\nasia_counts \u003c- washinvestments_asia |\u003e \n  group_by(region) |\u003e \n  summarise(count = n()) |\u003e \n  arrange(desc(count))\n\n# Encode the region column as factor with chosen levels for plotting\nwashinvestments_asia$region \u003c- factor(washinvestments_asia$region, levels = asia_counts$region)\n----------------------------------------------------------------------------------------------------------------------------\n# Filter the data set for Africa\nwashinvestments_africa \u003c- washinvestments |\u003e\n  filter(region %in% c(\"Northern Africa\", \"Eastern Africa\", \"Middle Africa\", \"Western Africa\", \"Southern Africa\"))\n\n# Count the number of projects per region and arrange it in descending order\nafrica_counts \u003c- washinvestments_africa |\u003e \n  group_by(region) |\u003e \n  summarise(count = n()) |\u003e \n  arrange(desc(count))\n\n# Encode the region column as factor with chosen levels for plotting\nwashinvestments_africa$region \u003c- factor(washinvestments_africa$region, levels = africa_counts$region)\n----------------------------------------------------------------------------------------------------------------------------\n# Create bar plots of investment trends\nggplot(continent_counts, aes(x = year_interval, y = count, fill = continent)) +\n  geom_bar(stat = \"identity\", position = \"dodge\") +  # Position dodge for side-by-side bars\n  labs(title = \"Investment trends in Africa and Asia (aggregated every 10 years)\",\n       x = \"Year interval\",\n       y = \"Number of investment projects\",\n       fill = \"Continent\") +\n  scale_fill_brewer(palette = \"Set2\") +\n  theme(plot.title = element_text(hjust = 0.5, face = \"bold\", color = \"#333333\"))\n\nggplot(washinvestments_asia, aes(x = year, fill = region)) +\n  geom_bar() +\n  scale_x_discrete(breaks = seq(1960, 2020, 10)) +\n  labs(title = \"Investment trends in Asia\",\n       x = \"Year\",\n       y = \"Number of investment projects\",\n       fill = \"Region\") +\n  scale_fill_brewer(palette = \"Set2\") +\n  theme(plot.title = element_text(hjust = 0.5, face = \"bold\", color = \"#333333\"))\n\nggplot(washinvestments_africa, aes(x = year, fill = region)) +\n  geom_bar() +\n  scale_x_discrete(breaks = seq(1960, 2020, 10)) +\n  labs(title = \"Investment trends in Africa\",\n       x = \"Year\",\n       y = \"Number of investment projects\",\n       fill = \"Region\") +\n  scale_fill_brewer(palette = \"Set2\") +\n  theme(plot.title = element_text(hjust = 0.5, face = \"bold\", color = \"#333333\"))\n```\n\n## License\n\nData are available as\n[CC-BY](https://github.com/openwashdata/washinvestments/LICENSE.md).\n\n## Citation\n\nTo cite this package, please use:\n\n```{r}\ncitation(\"washinvestments\")\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopenwashdata%2Fwashinvestments","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopenwashdata%2Fwashinvestments","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopenwashdata%2Fwashinvestments/lists"}