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README.md
In stellaRbase: A 'Stellar' Client
stellaRbase - an R library for Stellar
Disclaimer
This is not a project maintained by or officially endorsed by the Stellar Development Foundation.
Why?
Community-developed SDKs exist for Python, C#, Java, Go and other languages of choice. This R library acts as an interface to Stellar's Horizon API and hopes to open up the Stellar network to avid R users interested in:
- Performing their own descriptive analytics of the data on the public ledger.
- Building predictive models through techiques such as machine learning models or neural networks, or performing prescriptive analytics.
- Building general or web (R shiny) applications that allow Stellar users to engage with the network and improve XLM adoption.
Using stellaRbase
The library acts as a wrapper and allows you to call the full range of Horizon API endpoints and resources from your R project. All requests currently do not require you to have any special permissions to use the network, and there is no need to register an application beforehand.
You can use stellaRbase to:
- Create large data sets with data about ledgers, transactions, effects, operations and payments.
- Use the streaming capability to watch ledgers get confirmed in near to real-time, or monitor specific accounts.
- Get details about a specific transaction, account, payment etc.
Data
Data is delivered as a structured tabular format (a data.table) or as a semi-structured list for you to work with the complete set of values. See the examples below for more information.
Installing stellaRbase
To install directly from CRAN:
install.packages("stellaRbase")
To install the beta version directly from this Github repo, install the devtools library and do the following:
devtools::install_github("froocpu/stellaRbase") # install.packages("devtools")
TODO
Needs improving/further testing
getAssets()findPaymentPaths()
Known issues
- Fix the streaming capability to keep the stream open for longer than a second.
Examples and walkthrough
Loading the library
To use the libary in your R code (once you've successfully downloaded it):
library(stellaRbase)
Making requests
You can use the data.table parameter on a number of functions to toggle whether you received a list object or a tabular format. data.table = TRUE will return a data.table for you to work with. FALSE will return a list, parsed using the fromJSON function.
For example, you can get Effects events as a table:
getEffects(limit = 5)[, c(1,2,4,5,6)]
# id paging_token type type_i starting_balance
#1: 0000000012884905985-0000000001 12884905985-1 account_created 0 20.0000000
#2: 0000000012884905985-0000000002 12884905985-2 account_debited 3 NA
#3: 0000000012884905985-0000000003 12884905985-3 signer_created 10 NA
#4: 0000000012884905986-0000000001 12884905986-1 account_credited 2 NA
#5: 0000000012884905986-0000000002 12884905986-2 account_debited 3 NA
Or as a list:
getEffects(limit = 1, data.table = FALSE)[['_embedded']][['records']]
#[[1]]$id
#[1] "0000000012884905985-0000000001"
#[[1]]$paging_token
#[1] "12884905985-1"
#[[1]]$account
#[1] "GALPCCZN4YXA3YMJHKL6CVIECKPLJJCTVMSNYWBTKJW4K5HQLYLDMZTB"
#[[1]]$type
#[1] "account_created"
#[[1]]$type_i
#[1] 0
#[[1]]$starting_balance
#[1] "20.0000000"
The collect function
If you need more than 200 ledger records to work with, you can call a function from the "collect" family of functions native to stellaRbase, or in this case the collect() function. Like so:
ledgers = collect(endpoint = "ledgers", n = 100) # This will make multiple calls to the API, collecting 200 rows at a time.
str(ledgers[, 4:12, with=FALSE]) # get an overview of the data frame (minus the hashes, ids etc.)
# Classes ‘data.table’ and 'data.frame': 19528 obs. of 9 variables:
# $ sequence : int 1 2 3 4 5 6 7 8 9 10 ...
# $ transaction_count : int 0 0 1 0 0 0 0 0 0 0 ...
# $ operation_count : int 0 0 3 0 0 0 0 0 0 0 ...
# $ closed_at : Factor w/ 19528 levels "1970-01-01T00:00:00Z",..: 1 2 3 4 5 6 7 8 9 10 ...
# $ total_coins : Factor w/ 4 levels "100000000000.0000000",..: 1 1 1 1 1 1 1 1 1 1 ...
# $ fee_pool : num 0 0 0.00003 0.00003 0.00003 0.00003 0.00003 0.00003 0.00003 0.00003 ...
# $ base_fee_in_stroops : int 100 100 100 100 100 100 100 100 100 100 ...
# $ base_reserve_in_stroops: int 100000000 100000000 100000000 100000000 100000000 100000000 100000000 100000000 100000000 100000000 ...
# $ max_tx_set_size : int 100 500 500 500 500 500 500 500 500 500 ...
If you would rather with the list object (and treat it like semi-structured data) then you can pass the parameter data.table = FALSE.
Streams
You can stream events in real time from the Horizon server by passing a stream = TRUE to a number of functions. For example, this will produce a live stream of ledgers and new records will appear as they are created:
getLedgers(stream = TRUE)
# $retry
# [1] "1000"
#
# $event
# [1] "open"
#
# $data
# [1] "\"hello\""
#
# $id
# [1] "4294967296"
#
# $data
# $data$self
# href
# "https://horizon.stellar.org/ledgers/1"
#
# $data$transactions
# $data$transactions$href
# [1] "https://horizon.stellar.org/ledgers/1/transactions{?cursor,limit,order}"
# $data$transactions$templated
# [1] TRUE
The result will always be a list. TODO: Work out how the streaming connection can be kept open for a longer period of time. Passing headers like Connection: keep-alive do not seem to work.
Transactions
Get the latest 10 transactions:
getTransactions(limit = 10)
Or get details about a specific transaction:
hash = "b957fd83d5377402ee995d1c3ff4834357f48cbe9a6d42477baad52f1351c155"
getTransactionDetail(hash)
Or pull all of the transactions connected to an address:
address = "GCO2IP3MJNUOKS4PUDI4C7LGGMQDJGXG3COYX3WSB4HHNAHKYV5YL3VC"
getTransactions_Account(address)
Trades
Get the latest trade events:
getTrades(limit = 10, order = "desc")
Accounts
Get information about a particular account on the ledger:
getAccount(address)
Ledgers
Get the latest n ledgers in a single, one-off call:
getLedgers(limit = 10, order = "desc")
Or you can get the transactions appended to a specific ledger:
getTransactions_Ledger(17008514)
Trades and trade aggregations
Get the latest trades:
getTrades()
Or trade aggregations, direct from the API:
start_time <- "1512689100000"
end_time <- "1512775500000"
resolution <- "300000"
base_asset_type <- "native"
counter_asset_type <- "credit_alphanum4"
counter_asset_code <- "BTC"
counter_asset_issuer <- "GATEMHCCKCY67ZUCKTROYN24ZYT5GK4EQZ65JJLDHKHRUZI3EUEKMTCH"
getTradeAggregations(start_time, end_time, resolution,
base_asset_type = base_asset_type,
counter_asset_type = counter_asset_type,
counter_asset_code = counter_asset_code,
counter_asset_issuer = counter_asset_issuer,
data.table = TRUE)
Payments
Get payments data:
getPayments(limit = 20, order = "desc")
Or payments from a specific account:
test_address = "GA2HGBJIJKI6O4XEM7CZWY5PS6GKSXL6D34ERAJYQSPYA6X6AI7HYW36"
getPayments_Account(test_address, data.table = TRUE)
Using the collect function:
collect(endpoint = "payments", n = 15)
Sample data sets
You can experiment with sample responses from the Horizon data sets with the .rda files in the /data directory. They are exported with the main functions in the R package, so you can reference them like so:
summary(stellaRbase::test_effects)
# Length Class Mode
#_links 3 -none- list
#_embedded 1 -none- list
summary(listToDF(stellaRbase::test_ledgers)[, 4:12, with=FALSE])
# sequence transaction_count operation_count closed_at total_coins
# Min. : 1.00 Min. :0.0 Min. :0.0 1970-01-01T00:00:00Z:1 100000000000.0000000:10
# 1st Qu.: 3.25 1st Qu.:0.0 1st Qu.:0.0 2015-09-30T16:46:54Z:1
# Median : 5.50 Median :0.0 Median :0.0 2015-09-30T17:15:54Z:1
# Mean : 5.50 Mean :0.1 Mean :0.3 2015-09-30T17:15:59Z:1
# 3rd Qu.: 7.75 3rd Qu.:0.0 3rd Qu.:0.0 2015-09-30T17:16:04Z:1
# Max. :10.00 Max. :1.0 Max. :3.0 2015-09-30T17:16:09Z:1
(Other) :4
# fee_pool base_fee_in_stroops base_reserve_in_stroops max_tx_set_size
# Min. :0.000000 Min. :100 Min. :100000000 Min. :100
# 1st Qu.:0.000030 1st Qu.:100 1st Qu.:100000000 1st Qu.:500
# Median :0.000030 Median :100 Median :100000000 Median :500
# Mean :0.000024 Mean :100 Mean :100000000 Mean :460
# 3rd Qu.:0.000030 3rd Qu.:100 3rd Qu.:100000000 3rd Qu.:500
# Max. :0.000030 Max. :100 Max. :100000000 Max. :500
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stellaRbase documentation built on April 11, 2018, 5:03 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
stellaRbase - an R library for Stellar
Disclaimer
This is not a project maintained by or officially endorsed by the Stellar Development Foundation.
Why?
Community-developed SDKs exist for Python, C#, Java, Go and other languages of choice. This R library acts as an interface to Stellar's Horizon API and hopes to open up the Stellar network to avid R users interested in:
- Performing their own descriptive analytics of the data on the public ledger.
- Building predictive models through techiques such as machine learning models or neural networks, or performing prescriptive analytics.
- Building general or web (R shiny) applications that allow Stellar users to engage with the network and improve XLM adoption.
Using stellaRbase
The library acts as a wrapper and allows you to call the full range of Horizon API endpoints and resources from your R project. All requests currently do not require you to have any special permissions to use the network, and there is no need to register an application beforehand.
You can use stellaRbase to:
- Create large data sets with data about ledgers, transactions, effects, operations and payments.
- Use the streaming capability to watch ledgers get confirmed in near to real-time, or monitor specific accounts.
- Get details about a specific transaction, account, payment etc.
Data
Data is delivered as a structured tabular format (a data.table) or as a semi-structured list for you to work with the complete set of values. See the examples below for more information.
Installing stellaRbase
To install directly from CRAN:
install.packages("stellaRbase")
To install the beta version directly from this Github repo, install the devtools library and do the following:
devtools::install_github("froocpu/stellaRbase") # install.packages("devtools")
TODO
Needs improving/further testing
getAssets()findPaymentPaths()
Known issues
- Fix the streaming capability to keep the stream open for longer than a second.
Examples and walkthrough
Loading the library
To use the libary in your R code (once you've successfully downloaded it):
library(stellaRbase)
Making requests
You can use the data.table parameter on a number of functions to toggle whether you received a list object or a tabular format. data.table = TRUE will return a data.table for you to work with. FALSE will return a list, parsed using the fromJSON function.
For example, you can get Effects events as a table:
getEffects(limit = 5)[, c(1,2,4,5,6)]
# id paging_token type type_i starting_balance
#1: 0000000012884905985-0000000001 12884905985-1 account_created 0 20.0000000
#2: 0000000012884905985-0000000002 12884905985-2 account_debited 3 NA
#3: 0000000012884905985-0000000003 12884905985-3 signer_created 10 NA
#4: 0000000012884905986-0000000001 12884905986-1 account_credited 2 NA
#5: 0000000012884905986-0000000002 12884905986-2 account_debited 3 NA
Or as a list:
getEffects(limit = 1, data.table = FALSE)[['_embedded']][['records']]
#[[1]]$id
#[1] "0000000012884905985-0000000001"
#[[1]]$paging_token
#[1] "12884905985-1"
#[[1]]$account
#[1] "GALPCCZN4YXA3YMJHKL6CVIECKPLJJCTVMSNYWBTKJW4K5HQLYLDMZTB"
#[[1]]$type
#[1] "account_created"
#[[1]]$type_i
#[1] 0
#[[1]]$starting_balance
#[1] "20.0000000"
The collect function
If you need more than 200 ledger records to work with, you can call a function from the "collect" family of functions native to stellaRbase, or in this case the collect() function. Like so:
ledgers = collect(endpoint = "ledgers", n = 100) # This will make multiple calls to the API, collecting 200 rows at a time.
str(ledgers[, 4:12, with=FALSE]) # get an overview of the data frame (minus the hashes, ids etc.)
# Classes ‘data.table’ and 'data.frame': 19528 obs. of 9 variables:
# $ sequence : int 1 2 3 4 5 6 7 8 9 10 ...
# $ transaction_count : int 0 0 1 0 0 0 0 0 0 0 ...
# $ operation_count : int 0 0 3 0 0 0 0 0 0 0 ...
# $ closed_at : Factor w/ 19528 levels "1970-01-01T00:00:00Z",..: 1 2 3 4 5 6 7 8 9 10 ...
# $ total_coins : Factor w/ 4 levels "100000000000.0000000",..: 1 1 1 1 1 1 1 1 1 1 ...
# $ fee_pool : num 0 0 0.00003 0.00003 0.00003 0.00003 0.00003 0.00003 0.00003 0.00003 ...
# $ base_fee_in_stroops : int 100 100 100 100 100 100 100 100 100 100 ...
# $ base_reserve_in_stroops: int 100000000 100000000 100000000 100000000 100000000 100000000 100000000 100000000 100000000 100000000 ...
# $ max_tx_set_size : int 100 500 500 500 500 500 500 500 500 500 ...
If you would rather with the list object (and treat it like semi-structured data) then you can pass the parameter data.table = FALSE.
Streams
You can stream events in real time from the Horizon server by passing a stream = TRUE to a number of functions. For example, this will produce a live stream of ledgers and new records will appear as they are created:
getLedgers(stream = TRUE)
# $retry
# [1] "1000"
#
# $event
# [1] "open"
#
# $data
# [1] "\"hello\""
#
# $id
# [1] "4294967296"
#
# $data
# $data$self
# href
# "https://horizon.stellar.org/ledgers/1"
#
# $data$transactions
# $data$transactions$href
# [1] "https://horizon.stellar.org/ledgers/1/transactions{?cursor,limit,order}"
# $data$transactions$templated
# [1] TRUE
The result will always be a list. TODO: Work out how the streaming connection can be kept open for a longer period of time. Passing headers like Connection: keep-alive do not seem to work.
Transactions
Get the latest 10 transactions:
getTransactions(limit = 10)
Or get details about a specific transaction:
hash = "b957fd83d5377402ee995d1c3ff4834357f48cbe9a6d42477baad52f1351c155"
getTransactionDetail(hash)
Or pull all of the transactions connected to an address:
address = "GCO2IP3MJNUOKS4PUDI4C7LGGMQDJGXG3COYX3WSB4HHNAHKYV5YL3VC"
getTransactions_Account(address)
Trades
Get the latest trade events:
getTrades(limit = 10, order = "desc")
Accounts
Get information about a particular account on the ledger:
getAccount(address)
Ledgers
Get the latest n ledgers in a single, one-off call:
getLedgers(limit = 10, order = "desc")
Or you can get the transactions appended to a specific ledger:
getTransactions_Ledger(17008514)
Trades and trade aggregations
Get the latest trades:
getTrades()
Or trade aggregations, direct from the API:
start_time <- "1512689100000"
end_time <- "1512775500000"
resolution <- "300000"
base_asset_type <- "native"
counter_asset_type <- "credit_alphanum4"
counter_asset_code <- "BTC"
counter_asset_issuer <- "GATEMHCCKCY67ZUCKTROYN24ZYT5GK4EQZ65JJLDHKHRUZI3EUEKMTCH"
getTradeAggregations(start_time, end_time, resolution,
base_asset_type = base_asset_type,
counter_asset_type = counter_asset_type,
counter_asset_code = counter_asset_code,
counter_asset_issuer = counter_asset_issuer,
data.table = TRUE)
Payments
Get payments data:
getPayments(limit = 20, order = "desc")
Or payments from a specific account:
test_address = "GA2HGBJIJKI6O4XEM7CZWY5PS6GKSXL6D34ERAJYQSPYA6X6AI7HYW36"
getPayments_Account(test_address, data.table = TRUE)
Using the collect function:
collect(endpoint = "payments", n = 15)
Sample data sets
You can experiment with sample responses from the Horizon data sets with the .rda files in the /data directory. They are exported with the main functions in the R package, so you can reference them like so:
summary(stellaRbase::test_effects)
# Length Class Mode
#_links 3 -none- list
#_embedded 1 -none- list
summary(listToDF(stellaRbase::test_ledgers)[, 4:12, with=FALSE])
# sequence transaction_count operation_count closed_at total_coins
# Min. : 1.00 Min. :0.0 Min. :0.0 1970-01-01T00:00:00Z:1 100000000000.0000000:10
# 1st Qu.: 3.25 1st Qu.:0.0 1st Qu.:0.0 2015-09-30T16:46:54Z:1
# Median : 5.50 Median :0.0 Median :0.0 2015-09-30T17:15:54Z:1
# Mean : 5.50 Mean :0.1 Mean :0.3 2015-09-30T17:15:59Z:1
# 3rd Qu.: 7.75 3rd Qu.:0.0 3rd Qu.:0.0 2015-09-30T17:16:04Z:1
# Max. :10.00 Max. :1.0 Max. :3.0 2015-09-30T17:16:09Z:1
(Other) :4
# fee_pool base_fee_in_stroops base_reserve_in_stroops max_tx_set_size
# Min. :0.000000 Min. :100 Min. :100000000 Min. :100
# 1st Qu.:0.000030 1st Qu.:100 1st Qu.:100000000 1st Qu.:500
# Median :0.000030 Median :100 Median :100000000 Median :500
# Mean :0.000024 Mean :100 Mean :100000000 Mean :460
# 3rd Qu.:0.000030 3rd Qu.:100 3rd Qu.:100000000 3rd Qu.:500
# Max. :0.000030 Max. :100 Max. :100000000 Max. :500
Try the stellaRbase package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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