m <- dataset_metadata(title = "Motor Trend Car Road Tests",
name = "mtcars",
notes = "The data was extracted from the 1974 Motor Trend
US magazine, and comprises fuel consumption and 10 aspects
of automobile design and performance for 32 automobiles
(1973–74 models).",
owner_org = "americas",
visibility = "public",
geographies = "UNSPECIFIED",
external_access_level = "open_access",
data_collector = "Motor Trend",
keywords = keywords[c("Environment", "Other")],
unit_of_measurement = "car",
data_collection_technique = "oth",
archived = "False")
m
#> $title
#> [1] "Motor Trend Car Road Tests"
#>
#> $name
#> [1] "mtcars"
#>
#> $notes
#> [1] "The data was extracted from the 1974 Motor Trend \n US magazine, and comprises fuel consumption and 10 aspects\n of automobile design and performance for 32 automobiles \n (1973–74 models)."
#>
#> $owner_org
#> [1] "americas"
#>
#> $visibility
#> [1] "public"
#>
#> $external_access_level
#> [1] "open_access"
#>
#> $data_collector
#> [1] "Motor Trend"
#>
#> $keywords
#> Environment Other
#> "11" "54"
#>
#> $unit_of_measurement
#> [1] "car"
#>
#> $geographies
#> [1] "UNSPECIFIED"
#>
#> $data_collection_technique
#> [1] "oth"
#>
#> $archived
#> [1] "False"m <- dataset_metadata(title = "Motor Trend Car Road Tests",
name = "mtcars",
notes = "The data was extracted from the 1974 Motor Trend
US magazine, and comprises fuel consumption and 10 aspects
of automobile design and performance for 32 automobiles
(1973–74 models).",
owner_org = "americas", ## becarefull- all lower case!!!
visibility = "public",
geographies = "UNSPECIFIED",
external_access_level = "open_access",
data_collector = "Motor Trend",
keywords = keywords[c("Environment", "Other")],
unit_of_measurement = "car",
data_collection_technique = "oth",
archived = "False")
m1 <- dataset_tibblify(m)
m1
#> # A tibble: 1 × 12
#> title name notes owner_org visibility external_access_level data_collector
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Motor T… mtca… "The… americas public open_access Motor Trend
#> # ℹ 5 more variables: keywords <list>, unit_of_measurement <chr>,
#> # geographies <chr>, data_collection_technique <chr>, archived <chr>
#-----
# test search in prod
Sys.unsetenv("USE_UAT")
# riddle::dataset_show(id = "unhcr-cbi-americas-quarterly-report")
#
# p <- riddle::dataset_show('rms_v4')
# list_of_ressources <- p[["resources"]][[1]]
# list_of_ressources
#-----
# Test create in UAT
Sys.setenv(USE_UAT=1)
m <- riddle::dataset_metadata(title = "Testing Riddle Interface",
name = "riddleapitest",
notes = "Making an API test",
owner_org = "americas", ## be careful- all lower case!!!
visibility = "public",
geographies = "UNSPECIFIED",
external_access_level = "open_access",
data_collector = "Motor Trend",
keywords = keywords[c("Environment", "Other")],
unit_of_measurement = "car",
data_collection_technique = "oth",
archived = "False")
# ## For the above to work - you need to make sure you have at least editor access
# to the corresponding container - i.e. owner_org = "exercise-container"
# p <- dataset_create(metadata = m)
# The return value is a representation of the dataset we just created in
# RIDL that you could inspect like any other R object.
# p
## Now deleting this!
# dataset_delete(id = p$id)
#-----
# Test create in prod
Sys.unsetenv("USE_UAT")
# m1 <- riddle::dataset_metadata(title = "Test",
# name = "Test",
# notes = "The data was extracted from kobo.",
# owner_org = "americas-regional-dataset",
# visibility = "public",
# geographies = "UNSPECIFIED",
# external_access_level = "open_access",
# data_collector = "UNHCR",
# keywords = keywords[c("Environment", "Other")],
# unit_of_measurement = "car",
# data_collection_technique = "oth",
# archived = "False")
# p <- riddle::dataset_create(metadata = m1)#resource_metadata()
m <- riddle::resource_metadata(type = "data",
url = "mtcars.csv",
name = "mtcars.csv",
format = "csv",
file_type = "microdata",
date_range_start = "1973-01-01",
date_range_end = "1973-12-31",
version = "1",
visibility = "public",
process_status = "raw",
identifiability = "anonymized_public")
m
#> $type
#> [1] "data"
#>
#> $url
#> [1] "mtcars.csv"
#>
#> $name
#> [1] "mtcars.csv"
#>
#> $format
#> [1] "csv"
#>
#> $file_type
#> [1] "microdata"
#>
#> $date_range_start
#> [1] "1973-01-01"
#>
#> $date_range_end
#> [1] "1973-12-31"
#>
#> $visibility
#> [1] "public"
#>
#> $version
#> [1] "1"
#>
#> $process_status
#> [1] "raw"
#>
#> $identifiability
#> [1] "anonymized_public"m <- riddle::resource_metadata(type = "data",
url = "mtcars.csv",
# upload = httr::upload_file(system.file("extdata/mtcars.csv", package = "readr")),
name = "mtcars.csv",
format = "csv",
file_type = "microdata",
date_range_start = "1973-01-01",
date_range_end = "1973-12-31",
version = "1",
visibility = "public",
process_status = "raw",
identifiability = "anonymized_public")
m1 <- riddle::resource_tibblify(m)
m1
#> # A tibble: 1 × 11
#> type url name format file_type date_range_start date_range_end visibility
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 data mtcar… mtca… csv microdata 1973-01-01 1973-12-31 public
#> # ℹ 3 more variables: version <chr>, process_status <chr>,
#> # identifiability <chr># ## Full example available with the fetch function..
#-----
# ## Test search in prod
# Sys.unsetenv("USE_UAT")
# p <- dataset_search("rms_v4")
# p
# list_of_resources <- p[["resources"]][[1]]
# knitr::kable(list_of_resources)
#-----
# ## Test search in uat
# Sys.setenv(USE_UAT=1)
# p <- dataset_search("tests")
# p
# ##take the first one
# ridlid <- as.character(p[9, c("id")])
#-----
# ## Test resource in UAT
# Sys.setenv(USE_UAT=1)
# m <- riddle::dataset_metadata(title = "Testing Riddle Interface",
# name = "riddleapitest",
# notes = "Making an API test",
# owner_org = "americas", ## be careful- all lower case!!!
# visibility = "public",
# geographies = "UNSPECIFIED",
# external_access_level = "open_access",
# data_collector = "myself",
# keywords = keywords[c("Environment", "Other")],
# unit_of_measurement = "byte",
# data_collection_technique = "oth",
# archived = "False")
# ## For the above to work - you need to make sure you have at least editor access
# ## to the corresponding container - i.e. owner_org = "exercise-container"
# p <- dataset_create(metadata = m)
# p <- dataset_show('riddleapitest')
# ## Now testing adding the file "resource.R" as an attachment
# new_attachment <- riddle::resource_metadata(type = "attachment",
# url = "resourceR",
# upload = httr::upload_file(here::here("R","resource.R") ),
# name = "Rscript",
# format = "R",
# file_type = "report",
# version = "1",
# visibility = "public" )
# r <- resource_create(package_id = p$id, res_metadata = new_attachment )
# resource_create(package_id = p$name, res_metadata = new_attachment )
# ## Like before, the return value is a tibble representation of the resource.
# r
# ## Another example with a data ressource
# m <- riddle::resource_metadata(type = "data",
# url = "mtcars.csv",
# upload = httr::upload_file(system.file("extdata/mtcars.csv", package = "readr")),
# name = "mtcars.csv",
# format = "csv",
# file_type = "microdata",
# date_range_start = "1973-01-01",
# date_range_end = "1973-12-31",
# version = "1",
# visibility = "public",
# process_status = "raw",
# identifiability = "anonymized_public")
# r <- resource_create(package_id = p$id,
# res_metadata = m )
# ## let's get again the details of the dataset we want to add the resource in..
# r
# ## and now can search for it - checking it is correctly there...
# resource_search("name:mtcarsriddle")
# ## And once we’re done experimenting with the API, we should take down our
# ## toy dataset since we don’t really need it on RIDL.
# dataset_delete(p$id)
# The return value is a representation of the dataset we just created in
# RIDL that you could inspect like any other R object.
# p
## Now deleting this!
# dataset_delete(id = p$id)
## Example 1: with a direct URL
#-----
# Test search in prod
# Sys.unsetenv("USE_UAT")
# resource_fetch(url = 'https://ridl.unhcr.org/dataset/a60f4b79-8acc-4893-8fb9-d52f94416b19/resource/daa2b9e4-bf97-4302-86a5-08bb62a5a937/download/df_age_2022.csv',
# path = tempfile())
## Example 2: Let's try to identify a resource - then fetch it locally and update it back... as from here
# https://github.com/unhcr-americas/darien_gap_human_mobility/blob/main/report.Rmd#L38
# Sys.unsetenv("USE_UAT")
# ## Get the dataset metadata based on its canonical name
# p <- riddle::dataset_show('rms_v4')
# ## Let's get the fifth resource within this dataset
# test_ressources <- p[["resources"]][[1]] |> dplyr::slice(5)
#
# ## Download the resource locally in a file name file..
# resource_fetch(url = test_ressources$url, path = here::here("file"))
# test_ressources$url
# # Rebuild the metadata
# m <- resource_metadata(type = test_ressources$type, #"data",
# url = "df_gender_2020.csv",
# upload = httr::upload_file(here::here("file")),
# name = test_ressources$name,
# "Irregular entries by gender in 2022",
# format = test_ressources$format, #"csv",
# file_type = test_ressources$file_type, #"microdata",
# visibility = test_ressources$visibility, # "public",
# date_range_start = test_ressources$date_range_start,
# "2022-01-01",
# date_range_end = test_ressources$date_range_end, #as.character(floor_date(today('America/Panama'), "month") - days(1)),
#end day of last month
# version = test_ressources$version, # "0",
# process_status = test_ressources$process_status,
#"anonymized",
# identifiability = test_ressources$identifiability, #"anonymized_public"
# )
#r <- resource_update(id = test_ressources$id, res_metadata = m)
## Time to archive your work once done!!
# used in the RIDL_Notebook markdown template in the package
# if( params$publish == "yes"){
# namethisfile = basename(rstudioapi::getSourceEditorContext()$path )
# riddle_notebook(ridl = params$ridl,
# datafolder = params$datafolder,
# namethisfile = namethisfile ,
# visibility = params$visibility ) }