CoronaNetR is a database on government responses to the COVID-19. To date, this database provides the most comprehensive and granular documentation of such government policies in the world, capturing data for 18 broad policy categories alongside many other dimensions, including the initiator, target, and timing of a policy. This R package offers efficient and user-friendly access to the CoronaNet data via an HTTP API back-end.
If you prefer to access the end point yourself, the API access point is postgrest-1572524110.us-east-2.elb.amazonaws.com/public_release_allvars and can be queried using read-only HTTP requests with the postgrest API syntax.
Version 0.2.0 includes the first public beta API access
using the get_event() function, offering users easy access
to the CoronaNet Event Dataset as R data frames.
You can install the development version of CoronaNetR as follows:
devtools::install_github("CoronaNetDataScience/CoronaNetR")get_event()Access CoronaNet’s Event Dataset. The function allows you to filter
by policy type (type), policy subtype
(type_sub_cat), and date (from and
to).
head(get_event(countries = "All", type = "All", type_sub_cat = "All", from = "2019-12-31", to = "2020-01-10"))## Rows: 64 Columns: 28
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (23): record_id, policy_id, entry_type, update_type, update_level, upda...
## lgl (2): target_intl_org, target_other
## date (3): date_announced, date_start, date_end
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## # A tibble: 6 × 28
## record_id polic…¹ entry…² updat…³ updat…⁴ updat…⁵ date_ann…⁶ date_start
## <chr> <chr> <chr> <chr> <chr> <chr> <date> <date>
## 1 R_3n788do2QRzmN… 8472080 new_en… <NA> <NA> <NA> 2020-03-14 2020-01-03
## 2 R_1Leg47dGNydFw… 8856106 new_en… <NA> <NA> <NA> 2020-01-06 2020-01-06
## 3 R_3kidvyxTvdHn0… 2967891 new_en… <NA> <NA> <NA> NA 2019-12-31
## 4 R_1GJJ7V1shrFkv… 2971618 new_en… <NA> <NA> <NA> 2020-01-01 2020-01-01
## 5 R_2AMF4PcSVHjxT… 1884124 new_en… <NA> <NA> <NA> 2020-02-25 2020-01-01
## 6 R_2CdDjoSFr0msV… 8936839 new_en… <NA> <NA> <NA> 2019-12-31 2019-12-31
## # … with 20 more variables: date_end <date>, date_end_spec <chr>,
## # country <chr>, init_country_level <chr>, province <chr>,
## # target_init_same <chr>, target_country <chr>, target_province <chr>,
## # target_city <chr>, target_intl_org <lgl>, target_other <lgl>,
## # target_who_what <chr>, target_who_gen <chr>, target_direction <chr>,
## # compliance <chr>, enforcer <chr>, city <chr>, type <chr>,
## # type_sub_cat <chr>, description <chr>, and abbreviated variable names …There are a lot of records that do not yet have end dates, so these
are included by default. To exclude them, set the
include_no_end_date argument to FALSE.
By default a set of columns is included that can identify each
record, but only includes the policy target, type and description. To
change this default, you can pass a character vector to the
default_columns argument. However, it is better to leave
that argument unchanged and add any additional desired columns as a
character vector to the additional_columns argument. A full
list of columns is available in our
codebook.
get_policy_scoresYou can download our six different policy intensity indexes, which
are covered in this
paper, and aggregate our data into daily country-level policy
intensity scores using the get_policy_scores function:
head(get_policy_scores(from="2020-01-01",
to="2020-01-10"))## Rows: 11010 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): country, modtype
## dbl (4): med_est, high_est, low_est, sd_est
## date (1): date_policy
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## # A tibble: 6 × 7
## country modtype date_policy med_est high_est low_est sd_est
## <chr> <chr> <date> <dbl> <dbl> <dbl> <dbl>
## 1 Afghanistan Business Restrictions 2020-01-01 -0.186 -0.149 -0.213 0.0194
## 2 Afghanistan Health Monitoring 2020-01-01 0.00669 0.124 -0.0364 0.0451
## 3 Afghanistan Health Resources 2020-01-01 -0.0384 0.0580 -0.0918 0.0428
## 4 Afghanistan Mask Policies 2020-01-01 0.0250 0.0836 -0.0778 0.0450
## 5 Afghanistan School Restrictions 2020-01-01 -0.00681 0.0742 -0.0649 0.0426
## 6 Afghanistan Social Distancing 2020-01-01 -0.172 -0.122 -0.217 0.0292These scores are estimated with measurement error, both the standard deviation of the uncertainty of the scores and a high/low uncertainty interval. This information is useful for checking results for robustness to coding and other kinds of errors in the scores. These indexes are periodically updated as we add more records to our CoronaNet database. The indexes currently run from January 1st, 2020 to April 29th, 2021 for over 180 countries.
type) and Subtype Options
(type_sub_cat)The CoronaNet dataset has 20 main policy types and 258 policy
sub-types (not all sub types are available for all main types). By
default, the get_events() function will select all policy
types and sub-types, but more specific criteria can be set by passing a
character vector of relevant types/sub-types to the type
and type_sub_cat arguments. To see a list of available
policy types, please see our
codebook.
You can select a subset of the records using the from
and to arguments (both must be specified) in YYYY-MM-DD
format as a character value. These arguments correspond to the coded
policy begin and end dates for a given policy record.
You can select a specific country to access records using the
country argument. This will also pull sub-national policies
for that country. A sub-national record will have a value in either the
province or city column of the record.