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README.Rmd
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---
output:
github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
warning = FALSE,
message = FALSE,
eval = FALSE,
fig.width = 10,
fig.path = "man/figures",
comment = "#> "
)
```
# ridl <img src="man/figures/hex_ridl.png" align="right" width="140" />
[](http://www.repostatus.org/#wip)
[](https://gitlab.com/dickoa/ridl/pipelines)
[](https://codecov.io/gh/dickoa/ridl)
[](https://cran.r-project.org/package=ridl)
[](https://opensource.org/licenses/MIT)
`ridl` is an R client for the [UNHCR Raw Internal Data Library (RIDL) platform](https://ridl.unhcr.org).
### Introduction
The [UNHCR RIDL platform](https://ridl.unhcr.org) is UNHCR internal platform to easily store, find and analyze raw data.
#### RIDL concepts
In order to easily use the `ridl` package, it's important to understand some key concepts of this platform. RIDL documentation is available [here](https://im.unhcr.org/ridl) for more details.
##### `Container`
A `container` is a placeholder where we can share data on `RIDL`.
A `container` is represented in the `ridl` package as a `RIDLContainer` object and can hold zero or multiple `datasets`.
Most functions are prefixed by `ridl_container` or `rc`
+ Get metadata of a container: `ridl_container_show` or `rc_show`
+ Get the list of the names of all containers in the server: `ridl_container_list` or `rc_list`
##### `Dataset`
A `dataset` is a placeholder where we can share data files (`resources`). In a dataset page there's some metadata that give you enough context and information to properly store the data files and use them. A data file e.g an Excel file is called a `resource` and many of them can be shared in `dataset` page. In the `ridl` package, a `RIDLDataset` object is used to represent a `dataset`.
Most functions are prefixed by `ridl_dataset` or `rd`:
+ Get metadata of a dataset and its resources: `ridl_dataset_show` or `rd_show`
+ Get the list of the names of all datasets in the server: `ridl_dataset_list` or `rd_list`
+ Check whether a dataset exists or not: `ridl_dataset_exist` or `rd_exist`
+ Searches for datasets satisfying a given search criteria: `ridl_datasets_search` or `rd_search`
+ Get the `Container` object in which you have the dataset: `ridl_datasets_container_get` or `rd_container_get`
##### `Resource`
A `resource` is a file shared in `dataset` page, it includes `microdata` and supporting documents like reports or survey forms. A `RIDLResource` class is used to implement all the logic needed to manipulate RIDL `resource`.
Most functions are prefixed by `ridl_resource` or `rr`
+ Get metadata of a resource: `ridl_resource_show` or `rr_show`
### Installation
This package is not on yet on CRAN and to install it, you will need the [`remotes`](https://github.com/r-lib/remotes) package.
You can get `ridl` from Gitlab or Github (mirror)
```{r}
## install.packages("remotes")
remotes::install_gitlab("dickoa/ridl")
```
### ridl: A quick tutorial
```{r}
library("ridl")
```
The `ridl` package requires you to add your API token and store it for further use. It is preferred option, you no longer need to use the API key.
In order to have an API token, you need to generate one, by going to the following this URL: "ridl-server-url/user/@your-user-name/api-tokens".
You give it a name, and generate a token. Once generated, you can store it in your `.Renviron` file which is automatically read by R on startup.
You can either edit directly the `.Renviron` file or access it by calling `usethis::edit_r_environ()` (assuming you have the `usethis` package installed) and entering:
```{bash, eval=FALSE, engine="sh"}
RIDL_API_TOKEN=xxxxxxxxxxxxxxxxxx
```
Once the environment variable is set you will need to restart your session.
```{r}
library("ridl")
ridl_config_get()
## <RIDL Configuration>
## RIDL site url: https://ridl.unhcr.org
## RIDL API token: xxxxxxxxxxxxxxxxxx
```
If you plan to use RIDL testing environment (https://ridl-uat.unhcr.org), you'll need to also setup the `RIDL_UAT_API_TOKEN` variable.
```{bash, eval=FALSE, engine="sh"}
RIDL_UAT_API_TOKEN=xxxxxxxxxxxxxxxxxx
```
You can also configure directly the `ridl` package using the `ridl_config_setup` function and check the config using `ridl_config_get` but it's not persistent if you close your session.
```{r}
ridl_config_setup(site = "test",
token = "xxxxxxxxxxxxxxxxxx")
ridl_config_get()
## <RIDL Configuration>
## RIDL site: https://ridl-uat.unhcr.org/
## RIDL API token: xxxxxxxxxxxxxxxxxx
```
Now that we are connected to RIDL, we can search for dataset using `ridl_dataset_search`.
```{r}
ridl_config_setup(site = "prod")
ridl_dataset_search("mali", visibility = "public", rows = 2) ## search internally public dataset in RIDL, limit the results to two rows
## [[1]]
## <RIDL Dataset> 6f37029d-0ec2-4322-88ed-6447b2eebf3a
## Title: Socio-economic assessment of Malian refugees in Burkina Faso 2016
## Name: unhcr-bfa-2016-sea-1-1
## Visibility: public
## Resources (up to 5): DDI XML, DDI RDF, UNHCR_BFA_2016_SEA_household_v1_1, UNHCR_BFA_2016_SEA_individual_v1_1, UNHCR_BFA_2016_final report
## [[2]]
## <RIDL Dataset> 59573073-aef6-42c1-a9db-efae3f95051c
## Title: Socio-economic assessment of refugees in Mauritania's Mberra camp 2017
## Name: unhcr-mrt-2017-sea-1-1
## Visibility: public
## Resources (up to 5): DDI XML, DDI RDF, UNHCR_MRT_2017_SEA_household_v1_1, UNHCR_MRT_2017_SEA_individual_v1_1, UNHCR_MRT_2017_SEA_questionnaire
## attr(,"class")
## [1] "ridl_datasets_list"
```
We can select a particular `dataset` from the list (a `ridl_dataset_list` is a list) of `dataset` using `R` function to access elements from list (e.g `[[`). In this example, we can use either `purrr::pluck` or `dplyr::nth` since they both play well with the pipe operator `%>%`. Once the dataset selected, it's possible to list all its `resource` objects using `ridl_resource_list`.
```{r, eval = FALSE}
library(tidyverse)
ridl_dataset_search("mali", visibility = "public", rows = 2) |>
nth(1) |>
ridl_resource_list(format = "stata")
## <RIDL Resource> 026f9547-d7b2-4ec3-bbaa-5096837b1f01
## Name: UNHCR_BFA_2016_SEA_household_v1_1
## Description: BFA SEA household level data
## Type: microdata
## Size: 1278720
## Format: Stata
## [[2]]
## <RIDL Resource> 30ab9f7a-9b84-4695-88ba-7504a4aed9e2
## Name: UNHCR_BFA_2016_SEA_individual_v1_1
## Description: BFA SEA individual data
## Type: microdata
## Size: 143744
## Format: Stata
## attr(,"class")
## [1] "ridl_resource_list"
```
A `ridl_resource_list` is a simple `R` `list` and can be manipulated using `purrr::pluck` or `dplyr::nth` to select the one you want to `read` into your `R` session or `download`.
```{r, eval = FALSE}
library(tidyverse)
ridl_dataset_search("mali", visibility = "public", rows = 2) |>
nth(1) |>
ridl_dataset_resource_get_all(format = "stata") |>
nth(1) |>
ridl_resource_read()
## + # A tibble: 1,690 x 459
## hhid q002a q006 q008 q102 q113 q200 q201
## <dbl> <dbl+l> <dbl+l> <dbl+l> <dbl> <dbl> <dbl+l> <dbl+l>
## 1 10004 1 [Cam… 1 [Oui] 1 [Pré… 3 0 2 [Dou… 1 [For…
## 2 10008 1 [Cam… 1 [Oui] 1 [Pré… 3 1 2 [Dou… 1 [For…
## 3 10012 1 [Cam… 1 [Oui] 1 [Pré… 7 1 2 [Dou… 1 [For…
## 4 10016 1 [Cam… 1 [Oui] 1 [Pré… 2 1 2 [Dou… 1 [For…
## 5 10020 1 [Cam… 1 [Oui] 1 [Pré… 6 1 2 [Dou… 1 [For…
## 6 10024 1 [Cam… 1 [Oui] 1 [Pré… 3 1 2 [Dou… 1 [For…
## 7 10028 1 [Cam… 1 [Oui] 1 [Pré… 5 1 2 [Dou… 1 [For…
## 8 10032 1 [Cam… 1 [Oui] 1 [Pré… 7 1 2 [Dou… 1 [For…
## 9 10036 1 [Cam… 1 [Oui] 1 [Pré… 4 3 2 [Dou… 1 [For…
## 10 10040 1 [Cam… 1 [Oui] 1 [Pré… 2 1 2 [Dou… 1 [For…
## # … with 1,680 more rows, and 451 more variables:
## # q202 <dbl+lbl>, q203 <dbl>, q204 <dbl+lbl>, q205 <dbl+lbl>,
## # q206_1 <dbl+lbl>, q206_2 <dbl+lbl>, q206_3 <dbl+lbl>,
## # q206_4 <dbl+lbl>, q206_5 <dbl+lbl>, q206_6 <dbl+lbl>,
## # q207 <dbl+lbl>, q208 <dbl+lbl>, q209 <dbl+lbl>, q210 <dbl>,
## # q211 <dbl+lbl>, q21201 <dbl+lbl>, q21202 <dbl+lbl>,
## # q21203 <dbl+lbl>, q213 <dbl+lbl>, q214 <dbl>,
## # q215 <dbl+lbl>, q216 <dbl>, q217 <dbl+lbl>, q218 <dbl>,
## # q219 <dbl+lbl>, q220 <dbl+lbl>, q221 <dbl+lbl>,
## # q222 <dbl+lbl>, q223 <dbl+lbl>, q224 <dbl+lbl>, q225 <dbl>,
## # q226 <dbl+lbl>, q227 <dbl>, q22801 <dbl+lbl>,
## # q22802 <dbl+lbl>, q22803 <dbl+lbl>, q22804 <dbl+lbl>,
## # q22805 <dbl+lbl>, q22806 <dbl+lbl>, q22807 <dbl+lbl>,
## # q22808 <dbl+lbl>, q22809 <dbl+lbl>, q22810 <dbl+lbl>,
## # q22811 <dbl+lbl>, q229 <dbl+lbl>, q230 <dbl>,
## # q231 <dbl+lbl>, q232 <dbl>, q23301 <dbl+lbl>,
## # q23302 <dbl+lbl>, q23303 <dbl+lbl>, q23304 <dbl+lbl>,
## # q23305 <dbl+lbl>, q23306 <dbl+lbl>, q23307 <dbl+lbl>,
## # q23308 <dbl+lbl>, q23309 <dbl+lbl>, q23310 <dbl+lbl>,
## # q23311 <dbl+lbl>, q234 <dbl+lbl>, q23501 <dbl+lbl>,
## # q23502 <dbl+lbl>, q23503 <dbl+lbl>, q23504 <dbl+lbl>,
## # q23505 <dbl+lbl>, q23506 <dbl+lbl>, q23507 <dbl+lbl>,
## # q23508 <dbl+lbl>, q23509 <dbl+lbl>, q23510 <dbl+lbl>,
## # q23511 <dbl+lbl>, q23512 <dbl+lbl>, q23513 <dbl+lbl>,
## # q23514 <dbl+lbl>, q23515 <dbl+lbl>, q23516 <dbl+lbl>,
## # q23517 <dbl+lbl>, q23518 <dbl+lbl>, q23601 <dbl+lbl>,
## # q23602 <dbl+lbl>, q23603 <dbl+lbl>, q23604 <dbl+lbl>,
## # q23605 <dbl+lbl>, q23606 <dbl+lbl>, q23607 <dbl+lbl>,
## # q23608 <dbl+lbl>, q23609 <dbl+lbl>, q23610 <dbl+lbl>,
## # q23611 <dbl+lbl>, q23612 <dbl+lbl>, q23613 <dbl+lbl>,
## # q23614 <dbl+lbl>, q237 <dbl+lbl>, q238 <dbl+lbl>,
## # q23901 <dbl+lbl>, q23902 <dbl+lbl>, q23903 <dbl+lbl>,
## # q23904 <dbl+lbl>, q23909 <dbl+lbl>, q240 <dbl+lbl>, …
```
`read` will not work with all resources in RIDL, so far the following format are supported: `csv`, `xlsx`, `xls`, `dta` (`Stata`).
I will consider adding more data types in the future, feel free to file an issue if it doesn't work as expected or you want to add a support for a new format.
For Excel files (`xlsx` and `xls`), you can also use `get_sheets` to list available sheets and use the `sheet` paramater in `read` to specify the sheet you want to read (default is to read the first sheet).
#### Reading dataset directly
We can also use `ridl_dataset_show` to directly read and access a dataset object.
```{r, eval = FALSE}
dataset_name <- "official-cross-border-figures-of-venezuelan-individuals"
rd_show(dataset_name) |>
rd_resource_get_all() |>
nth(1) |>
rr_read()
## + Reading sheet: VEN_Official Borders Figures
## # A tibble: 1,314 x 5
## Country `Mov Type` `Border Point` Month_Year Total_individua…
## <chr> <chr> <chr> <chr> <dbl>
## 1 Ecuador Entry from… Aeropuerto In… January-20 0
## 2 Ecuador Entry from… Aeropuerto In… February-… 1
## 3 Ecuador Entry from… Aeropuerto In… March-20 0
## 4 Ecuador Entry from… Aeropuerto In… April-20 0
## 5 Ecuador Entry from… Aeropuerto In… May-20 0
## 6 Ecuador Entry from… Aeropuerto In… June-20 2
## 7 Ecuador Entry from… Aeropuerto In… July-20 2
## 8 Ecuador Entry from… Aeropuerto In… August-20 2
## 9 Ecuador Entry from… Aeropuerto In… September… NA
## 10 Ecuador Entry from… Aeropuerto In… January-20 0
# … with 1,304 more rows
```
If you know the id of a `RIDL Resource` object you can also use directly `ridl_resource_show` to access directly the desired resource.
```{r, eval = FALSE}
rd_show(dataset_name) |>
rd_resource_get_all() |>
nth(1)
## + <RIDL Resource> 68e39d44-88ae-49f9-b492-3635341c92be
## Name: VEN_OfficialFiguresBorders
## Description: Compilation of official figures on Venezuelan population per month per entry-exit point.
## Type: microdata
## Size: 39998
## Format: XLSX
ridl_resource_show("68e39d44-88ae-49f9-b492-3635341c92be") |>
ridl_resource_read()
## + Reading sheet: VEN_Official Borders Figures
## # A tibble: 1,314 x 5
## Country `Mov Type` `Border Point` Month_Year Total_individua…
## <chr> <chr> <chr> <chr> <dbl>
## 1 Ecuador Entry from… Aeropuerto In… January-20 0
## 2 Ecuador Entry from… Aeropuerto In… February-… 1
## 3 Ecuador Entry from… Aeropuerto In… March-20 0
## 4 Ecuador Entry from… Aeropuerto In… April-20 0
## 5 Ecuador Entry from… Aeropuerto In… May-20 0
## 6 Ecuador Entry from… Aeropuerto In… June-20 2
## 7 Ecuador Entry from… Aeropuerto In… July-20 2
## 8 Ecuador Entry from… Aeropuerto In… August-20 2
## 9 Ecuador Entry from… Aeropuerto In… September… NA
## 10 Ecuador Entry from… Aeropuerto In… January-20 0
# … with 1,304 more rows
```
### Some other handy functions
```{r, eval = FALSE}
ct <- ridl_container_list(sort = "package_count")
head(ct)
## [1] "ethiopia-sens" "data-deposit" "kenya-sens"
## [4] "afghanistan" "bangladesh-sens" "south-sudan-sens"
grep("niger-", ct, ignore.case = TRUE, value = TRUE)
## [1] "niger-protection" "niger-sens"
ridl_container_show("niger-protection")
## <RIDL Container> d341942e-547e-404b-bcdf-c72b2cd85530
## Name: niger-protection
## Display name: Niger: Protection
## No. Datasets: 5
## No. Members: 3
ridl_container_show("niger-protection") |>
ridl_dataset_list()
## [1] "enrolement-pdi-tillaberi-tillaberi-niger-2020"
## [2] "identify-asylum-seekers-in-migration-flow-agadez-niger-2018-2019-2020"
## [3] "monitoring-the-migration-flow-1-agadez-niger-2019-2020"
## [4] "enrolement-pdi-tahoua-aout-2020-tahoua-niger-2020"
## [5] "enrolement-pdi-maradi-maradi-niger-2020"
```
### Create a dataset
It's possible to create a `RIDLDataset` object we can manipulate and upload to the RIDL platform.
```{r, eval = FALSE}
ridl_dataset(name = "test-dataset-pen",
title = "Test Dataset PEN",
notes = "Some description",
owner_org = "africa",
data_collector = "unhcr",
keywords = list(3, 4),
unit_of_measurement = "kg",
data_collection_technique = "f2f",
archived = FALSE,
visibility = "restricted",
external_access_level = "data_enclave")
## <RIDL Dataset>
## Title: Test Dataset PEN
## Name: test-dataset-pen
## Visibility: restricted
## Container: Africa
## Resources (up to 5):
```
```{r, eval = FALSE}
ds <- ridl_dataset(name = "test-dataset",
title = "Test Dataset",
notes = "An example dataset",
owner_org = "west-africa",
data_collector = "ACF, UNHCR",
keywords = list(3, 4),
unit_of_measurement = "individual",
data_collection_technique = "f2f",
sampling_procedure = "nonprobability",
operational_purpose_of_data = "cartography",
archived = "False",
visibility = "restricted",
external_access_level = "open_access")
ds
## <RIDL Dataset>
## Title: Test Dataset
## Name: test-dataset
## Visibility: public
## Resources (up to 5):
```
`ridl_resource` can also be used to create a `RIDLResource`.
```{r, eval = FALSE}
rs <- ridl_resource(name = "Test resource",
type = "data",
format = "CSV",
file_type = "microdata",
identifiability = "anonymized_public",
date_range_start = "2018-01-01",
date_range_end = "2019-01-01",
process_status = "anonymized",
visibility = "public",
version = 1L)
rs
## <RIDL Resource>
## Name: Test resource
## Description:
## Type: microdata
## Size:
## Format: CSV
```
We can add the resource to the dataset and upload it to the RIDL platform.
```{r, eval = FALSE}
ds |>
ridl_dataset_resource_add(rs)
ds
## <RIDL Dataset>
## Title: Test Dataset
## Name: test-dataset
## Visibility: restricted
## Resources (up to 5): Test resource
```
## Meta
* Please [report any issues or bugs](https://gitlab.com/dickoa/ridl/-/issues).
* License: MIT
* Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms.