The goal of emberr is to provide functions to query and retrieve data from the Ember API.
You can view the API documentation for details. Please note the license information provided by Ember:
*Our data is published using the CC-BY-4.0 license. Anyone is able to use our data for any purpose (personal, commercial, etc.). The only requirements by this license are that:
- Ember is cited as the data source. E.g. ‘Monthly electricity generation data, Ember’
- You may not add any additional legal/technological restrictions to the data.*
See this blog post for a description and examples of the package.
You can install the development version of emberr from GitHub with:
# install.packages("devtools")
devtools::install_github("andypicke/emberr")You will need to sign up for a free API key.
The package assumes you have stored your API key in your .Renviron file as EMBER_API_KEY
Get monthly electricity generation data for 2021-2023. By Default this returns data for all countries/regions (“entities”).
library(emberr)
gen <- emberr::get_ember_data(dataset = "electricity-generation", temporal_resolution = "monthly", min_date = 2021, max_date = 2023)
head(gen)
#> entity entity_code is_aggregate_entity date series
#> 1 Africa <NA> TRUE 2021-01-01 Bioenergy
#> 2 Africa <NA> TRUE 2021-01-01 Clean
#> 3 Africa <NA> TRUE 2021-01-01 Coal
#> 4 Africa <NA> TRUE 2021-01-01 Demand
#> 5 Africa <NA> TRUE 2021-01-01 Fossil
#> 6 Africa <NA> TRUE 2021-01-01 Gas
#> is_aggregate_series generation_twh share_of_generation_pct
#> 1 FALSE 0.11 0.16
#> 2 TRUE 16.22 24.00
#> 3 FALSE 20.04 29.65
#> 4 TRUE 67.58 100.00
#> 5 TRUE 51.36 76.00
#> 6 FALSE 29.30 43.36Get options for the entity parameter:
options <- emberr::get_ember_options(dataset = "electricity-generation", filter_name = "entity")
#> No encoding supplied: defaulting to UTF-8.
str(options)
#> chr [1:228] "ASEAN" "Afghanistan" "Africa" "Albania" "Algeria" ...Retrieve data for just one country/region:
df_usa <- emberr::get_ember_data(entity = "United States")
str(df_usa)
#> 'data.frame': 153 obs. of 8 variables:
#> $ entity : chr "United States" "United States" "United States" "United States" ...
#> $ entity_code : chr "USA" "USA" "USA" "USA" ...
#> $ is_aggregate_entity : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
#> $ date : chr "2015" "2015" "2015" "2015" ...
#> $ series : chr "Bioenergy" "Clean" "Coal" "Demand" ...
#> $ is_aggregate_series : logi FALSE TRUE FALSE TRUE TRUE FALSE ...
#> $ generation_twh : num 63.6 1353.7 1352.4 4150.7 2730.3 ...
#> $ share_of_generation_pct: num 1.56 33.15 33.11 101.63 66.85 ...You can also retrieve data for multiple countries/regions:
df <- get_ember_data(min_date = 2020, entity = "United States,United Kingdom")
str(df)
#> 'data.frame': 132 obs. of 8 variables:
#> $ entity : chr "United Kingdom" "United Kingdom" "United Kingdom" "United Kingdom" ...
#> $ entity_code : chr "GBR" "GBR" "GBR" "GBR" ...
#> $ is_aggregate_entity : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
#> $ date : chr "2020" "2020" "2020" "2020" ...
#> $ series : chr "Bioenergy" "Clean" "Coal" "Demand" ...
#> $ is_aggregate_series : logi FALSE TRUE FALSE TRUE TRUE FALSE ...
#> $ generation_twh : num 39.36 184.78 5.49 330.37 127.24 ...
#> $ share_of_generation_pct: num 12.61 59.22 1.76 105.88 40.78 ...