README.md

In naif-alsader/KapsarcR: R Client for the King Abdullah Petroleum Studies API and Research Center (KAPSARC)

KapsarcR

This library is in early development, if you face any problem please open an issue

R Client to access the data portal’s API of King Abdullah Petroleum Studies and Research Center

The documentations of the API can be found here

Installation

devtools::install_github("naif-alsader/KapsarcR")

List Datasets

• list all datasets

library(KapsarcR)
library(tidyverse)
#> ── Attaching packages ───────────────────────────────────── tidyverse 1.3.0 ──
#> ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
#> ✓ tibble  3.0.3     ✓ dplyr   1.0.0
#> ✓ tidyr   1.1.0     ✓ stringr 1.4.0
#> ✓ readr   1.3.1     ✓ forcats 0.4.0
#> ── Conflicts ──────────────────────────────────────── tidyverse_conflicts() ──
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag()    masks stats::lag()

datasets<-list_datasets()

The dataset returned from list_datasets() is not tidy. You can use clean_dataset() to get a more tidy dataset

datasets %>%
  clean_dataset() %>%
  head()
#> # A tibble: 6 x 15
#>   dataset_id country title description themes keywords number_of_obser…
#>   <chr>      <list>  <chr> <chr>       <list> <list>              <int>
#> 1 daily-cha… <NULL>  Dail… "Sources:J… <NULL> <chr [1…              895
#> 2 balance-o… <chr [… Bala… "This data… <chr … <chr [8…             7456
#> 3 growth-ra… <chr [… Grow… "<table><t… <chr … <chr [1…              105
#> 4 the-econo… <chr [… The … "About the… <chr … <chr [4…                0
#> 5 electrici… <chr [… Elec… "This data… <chr … <chr [1…               18
#> 6 weekly-us… <chr [… Week… "This data… <chr … <chr [5…               52
#> # … with 8 more variables: count_of_attchments <int>, date_created <chr>,
#> #   modified <chr>, reference <chr>, temporal <chr>, fields_label <list>,
#> #   fields_type <list>, fields_name <list>

and the column names are

#>  [1] "dataset_id"             "country"                "title"                 
#>  [4] "description"            "themes"                 "keywords"              
#>  [7] "number_of_observations" "count_of_attchments"    "date_created"          
#> [10] "modified"               "reference"              "temporal"              
#> [13] "fields_label"           "fields_type"            "fields_name"

• Filter the search

# by Country
datasets<-list_datasets(country = "Saudi Arabia")

# by Theme
datasets<-list_datasets(theme  = "Water")

# by Keyword
datasets<-list_datasets(keyword  = "Water")

• Filter by two arguments

There are two ways filter two arguments:

1. if both arguments appear together (use operation = and) the default value

2. if either argument one or two appears (use operation = or)]

datasets<-list_datasets(keyword  = "Water", theme = "Water", operation = "or")

• Filter using The Opendatasoft Query Language (ODSQL)

datasets<-list_datasets(q =  "default.theme = 'Water' or default.theme = 'Transportation'")


datasets<-list_datasets(q =  "default.modified > '2019'")


datasets<-list_datasets(q =  "default.records_count > 1000000")

The fields that can be used in the query can be found in the meta column (its actually 4 datasets in one column) of the dataset datasets$metas$default or datasets$metas$custom

here is the complete list:

_ default.records_count_, _ default.modified_, _ default.source_domain_address_, _ default.references_, _ default.keyword_, _ default.source_domain_title_, _ default.geographic_reference_, _ default.timezone_, _ default.title_, _ default.parent_domain_, _ default.theme_, _ default.modified_updates_on_data_change_, _ default.metadata_processed_, _ default.data_processed_, _ default.territory_, _ default.description_, _ default.modified_updates_on_metadata_change_, _ default.shared_catalog_, _ default.source_domain_, _ default.attributions_, _ default.geographic_reference_auto_, _ default.publisher_, _ default.language_, _ default.license_, _ default.source_dataset_, _ default.metadata_languages_, _ default.oauth_scope_, _ default.federated_ and _ default.license_url_

_ custom.predecessor_, _ custom.restricted-comment_, _ custom.expected-next-release_, _ custom.country_, _ custom.publisher-periodicity_, _ custom.iso-region_, _ custom.last-checked-date_, _ custom.related-datasets_, _ custom.contact_, _ custom.data-classification_, _ custom.source-copyrights_, _ custom.unit-of-measure_ and _ custom.discontinued-data_

_ dcat.created_, _ dcat.issued_, _ dcat.temporal_, _ dcat.granularity_, _ dcat.contributor_, _ dcat.publisher_type_, _ dcat.contact_email_, _ dcat.accrualperiodicity_, _ dcat.spatial_, _ dcat.dataquality_, _ dcat.contact_name_ and _ dcat.creator_

Retrieve Datasets

• One dataset

df <- get_dataset(datasets$dataset_id[1])

• All datasets that matches the search

library(purrr)

df_list <- map(datasets$dataset_id, get_dataset)

List attachments

• One dataset’s attachments

attachments <- get_attachments(datasets$dataset_id[1])

• All datasets’ attachments that matches the search

attachments_list <- map(datasets$dataset_id, get_attachments)

Download attachments

• One dataset attachments

download_attachments(attachments)

• All datasets’ attachments that matches the search

map(attachments_list, download_attachments)

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

naif-alsader/KapsarcR documentation built on Aug. 17, 2020, 12:27 a.m.