From 83471fd7b499eedacb7f8a8d319e43d3359e7f4d Mon Sep 17 00:00:00 2001 From: Conor Flynn Date: Mon, 27 Mar 2023 23:38:28 -0400 Subject: [PATCH] Push updated r bridge code --- .../aave-protocol-dated-func-v1.Rmd | 175 -- .../aave-protocol-dated-func-v2.Rmd | 85 - R-Code-Samples/aave-protocol-dated-func.html | 1989 ----------------- R-Code-Samples/aave-protocol-dated-func.pdf | Bin 192710 -> 0 bytes R-Code-Samples/data-engine-r-functions.Rmd | 134 ++ 5 files changed, 134 insertions(+), 2249 deletions(-) delete mode 100644 R-Code-Samples/aave-protocol-dated-func-v1.Rmd delete mode 100644 R-Code-Samples/aave-protocol-dated-func-v2.Rmd delete mode 100644 R-Code-Samples/aave-protocol-dated-func.html delete mode 100644 R-Code-Samples/aave-protocol-dated-func.pdf create mode 100644 R-Code-Samples/data-engine-r-functions.Rmd diff --git a/R-Code-Samples/aave-protocol-dated-func-v1.Rmd b/R-Code-Samples/aave-protocol-dated-func-v1.Rmd deleted file mode 100644 index e6a51955..00000000 --- a/R-Code-Samples/aave-protocol-dated-func-v1.Rmd +++ /dev/null @@ -1,175 +0,0 @@ ---- -title: "DeFi Engine Use Example" -subtitle: "aave-protocol-dated function" -author: "Kacy Adams" -date: "12/15/2022" -output: - pdf_document: default - html_document: - toc: true - number_sections: true - df_print: paged ---- -# Start by loading the proper libraries: -```{r setup, include=FALSE} -# Set the default CRAN repository -local({r <- getOption("repos") - r["CRAN"] <- "http://cran.r-project.org" - options(repos=r) -}) - -# Set code chunk defaults -knitr::opts_chunk$set(echo = TRUE) - -# Load required packages; install if necessary -# CAUTION: DO NOT interrupt R as it installs packages!! -if (!require("ggplot2")) { - install.packages("ggplot2") - library(ggplot2) -} - -if (!require("httr")) { - install.packages("httr") - library(httr) -} -if (!require("jsonlite")) { - install.packages("jsonlite") - library(jsonlite) -} - -if (!require("lubridate")) { - install.packages("lubridate") - library(lubridate) -} -if(!require("dplyr")){ - install.packages("dplyr") - library(dplyr) -} -if(!require("stringr")){ - install.packages("stringr") - library(stringr) -} -if(!require("tidyr")){ - install.packages("tidyr") - library(tidyr) -} -if(!require("knitr")){ - install.packages("knitr") - library(knitr) -} - - -``` - - -We provide a function to request and parse data from our DeFi data engine living on the IDEA Cluster. This initializes a data stream from the Amber Data API, opens a socket, requests data, listens on the socket, and then parses the received data. The finished dataframe is as close as possible to the schema of the cold-storage data we currently use. -```{R} - -getJson <- function(startdate, enddate) { - - #Initialize data stream with Amber Data API key - #It is ok to do this multiple times, will always return the same key - out <- httr::GET(url = "http://defi-de.idea.rpi.edu:8080/defi/v1/rest/initialize?source=amber_data&auth_data=key,UAK7ed69235426c360be22bfc2bde1809b6") - out <- content(out, "parsed") - engine_key <- out$data - - - #Create socket and get socket_key which tells the engine where to put the data - socket <- socketConnection("defi-de.idea.rpi.edu", 61200, blocking=TRUE) - socket_key <- readLines(socket, 1) - - - #Build the request delimited by &&& - #Similar to a GET request in the way we handle parameters - reqString <- paste( - "SRC&&&RQST&&&destination&&&", socket_key, - "&&&key&&&", engine_key, "&&&", - "start_date&&&", startdate, "&&&", - "end_date&&&", enddate, "&&&", - "query&&&aave-protocol-dated&&&", - "request&&&aave-protocol-dated\n", sep="") - - #Write this request back to the socket to tell engine what we want - writeLines(reqString, socket) - - #Now the engine will begin feeding us data - #We grab the first to initialize the data var and then we continue listening\ - data <- readLines(socket, 1) - while (TRUE) { - temp <- readLines(socket, 1) - data <- paste(data, temp, "") - if (grepl("<<>>", temp, fixed=TRUE)) {break} - } - - - data <- str_replace(data, " <<>> ", "") #We begin parsing by removing the end character - data <- str_replace_all(data, " ", "") #We remove all spaces - data <- str_replace_all(data, "\\}\\{", "\\},\\{") #We add commas in between every object - data <- paste("[", data, "]", sep="") #and format the string as a json list - output <- fromJSON(data) #and then it is finally ready to be parsed as a json - - #We then need to match this with our current cold-storage data schema - - output <- output[,-(colnames(output) == "_id")] #We remove the Mongodb data engine ID - colnames(output)[colnames(output) == "action"] = "type" #Rename the 'action' column to 'type' - output$type <- as.factor(output$type) #and make this column a factor for a faster df - - #We calculate the date and time via the UNIX epoch timestamp provided in the data - output <- output %>% mutate(datetime = as_datetime(as.numeric(substr(timestamp, 1, nchar(timestamp)-3)))) - - #We rename to match the desired schema - colnames(output)[colnames(output) == "assetId"] = "reserveId" - colnames(output)[colnames(output) == "assetSymbol"] = "reserve" - - #We convert these from chars to numbers (doubles) for obvious reasons - output$amount <- as.double(output$amount) - output$borrowRate <- as.double(output$borrowRate) - output$principalAmount <- as.double(output$principalAmount) - - #We rename to match desired schema - colnames(output)[colnames(output) == "user"] = "userId" - - #We unite these columns as they are only present in specific actions - #and our old data refers to them purely as 'onBehalfOfId' - output <- output %>% unite(col = "onBehalfOfId", onBehalfOf,repayer,initiator, na.rm = TRUE, sep = "") - - #We rename to match desired schema - colnames(output)[colnames(output) == "collateralAssetId"] = "collateralReserveId" - colnames(output)[colnames(output) == "collateralAssetSymbol"] = "collateralReserve" - colnames(output)[colnames(output) == "principalAssetId"] = "principalReserveId" - colnames(output)[colnames(output) == "principalAssetSymbol"] = "principalReserve" - - #We remove column 15 as it is a mongoDB timestamp from the engine that is no longer needed - output <- output[,-15] - - return(output) -} - -``` - -With this function, we can now get our data. -```{R} - -#We make a sample call to the function which will return all transactions 2022 August 1st to August 3rd -df <- getJson("2022-08-01", "2022-08-04") -kable(head(df), "simple") - -``` - -Now that we have our dataframe, let's show that it is usable. We'll plot the daily count of transactions over the dataframe. -```{r} -library(dplyr) - -dailySummaries <- df %>% - mutate(day = floor_date(datetime, unit = "day")) %>% # Add a new column that rounds the date of each transaction down to the nearest week - group_by(day) %>% # Group the transactions together by the week they were performed. - summarise(transactionCount = n()) # Count the number of transactions in each group. -``` - -With these daily summaries computed, we can simply plot the week on the x-axis and the transaction count on the y-axis to visualize this. -```{r} -dailyTransactionsPlot <- ggplot(data = dailySummaries, aes(x = day, y = transactionCount)) + geom_line() -dailyTransactionsPlot -``` -We see that we received transactions for each of the three days requested. Remember that the engine parameter 'end_date' is exclusive and as such we receive zero transactions for August 4th. - diff --git a/R-Code-Samples/aave-protocol-dated-func-v2.Rmd b/R-Code-Samples/aave-protocol-dated-func-v2.Rmd deleted file mode 100644 index 52a3deae..00000000 --- a/R-Code-Samples/aave-protocol-dated-func-v2.Rmd +++ /dev/null @@ -1,85 +0,0 @@ ---- -title: "DFDE-Method" -author: "Conor Flynn" -date: "2022-12-15" -output: html_document ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` - -```{r} -library("httr") -library("jsonlite") -library("lubridate") -library(stringr) -library(tidyverse) - -getJson <- function(startdate, enddate) { - - httr::GET(url = "http://defi-de.idea.rpi.edu:8080/defi/v1/rest/initialize?source=amber_data&auth_data=key,UAK7ed69235426c360be22bfc2bde1809b6") - - engine_key <- "b6c810a7f35f4fa0d28258278325b4b5ab82ba79868ab33d01d5c878e13872ec129a91a3fbf702e59c2404f0fb4a53420a3ffb50130c35b4d06b32d81e56c1f4" - - socket <- socketConnection("defi-de.idea.rpi.edu", 61200, blocking=TRUE) - ss <- readLines(socket, 1) - ss - - reqString <- paste( - "SRC&&&RQST&&&destination&&&", ss, - "&&&key&&&", engine_key, "&&&", - "start_date&&&", startdate, "&&&", - "end_date&&&", enddate, "&&&", - "query&&&aave-protocol-dated&&&", - "request&&&aave-protocol-dated\n", sep="") - - writeLines(reqString, socket) - - data <- readLines(socket, 1) - - while (TRUE) { - temp <- readLines(socket, 1) - data <- paste(data, temp, "") - if (grepl("<<>>", temp, fixed=TRUE)) {break} - } - data - - data <- str_replace(data, " <<>> ", "") - data <- str_replace_all(data, " ", "") - data <- str_replace_all(data, "\\}\\{", "\\},\\{") - data <- paste("[", data, "]", sep="") - output <- fromJSON(data) - - output <- output[,-(colnames(output) == "_id")] - colnames(output)[colnames(output) == "action"] = "type" - output$type <- as.factor(output$type) - output <- output %>% mutate(datetime = as_datetime(as.numeric(substr(timestamp, 1, nchar(timestamp)-3)))) - - colnames(output)[colnames(output) == "assetId"] = "reserveId" - colnames(output)[colnames(output) == "assetSymbol"] = "reserve" - - output$amount <- as.double(output$amount) - output$borrowRate <- as.double(output$borrowRate) - - colnames(output)[colnames(output) == "user"] = "userId" - - output <- output %>% unite(col = "onBehalfOfId", onBehalfOf,repayer,initiator, na.rm = TRUE, sep = "") - - - colnames(output)[colnames(output) == "collateralAssetId"] = "collateralReserveId" - colnames(output)[colnames(output) == "collateralAssetSymbol"] = "collateralReserve" - colnames(output)[colnames(output) == "principalAssetId"] = "principalReserveId" - colnames(output)[colnames(output) == "principalAssetSymbol"] = "principalReserve" - - output$principalAmount <- as.double(output$principalAmount) - output <- output[,-15] - output - - return(output) -} - - -temp <- getJson("2022-08-01", "2022-08-02") -temp -``` \ No newline at end of file diff --git a/R-Code-Samples/aave-protocol-dated-func.html b/R-Code-Samples/aave-protocol-dated-func.html deleted file mode 100644 index ad15a684..00000000 --- a/R-Code-Samples/aave-protocol-dated-func.html +++ /dev/null @@ -1,1989 +0,0 @@ - - - - - - - - - - - - - - - -DeFi Engine Use Example - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - - - -
-

1 Start by loading the -proper libraries:

-

We provide a function to request and parse data from our DeFi data -engine living on the IDEA Cluster. This initializes a data stream from -the Amber Data API, opens a socket, requests data, listens on the -socket, and then parses the received data. The finished dataframe is as -close as possible to the schema of the cold-storage data we currently -use.

-
getJson <- function(startdate, enddate) {
-  
-  #Initialize data stream with Amber Data API key
-  #It is ok to do this multiple times, will always return the same key
-  out <- httr::GET(url = "http://defi-de.idea.rpi.edu:8080/defi/v1/rest/initialize?source=amber_data&auth_data=key,UAK7ed69235426c360be22bfc2bde1809b6")
-  out <- content(out, "parsed")
-  engine_key <- out$data
-
-  
-  #Create socket and get socket_key which tells the engine where to put the data
-  socket <- socketConnection("defi-de.idea.rpi.edu", 61200, blocking=TRUE)
-  socket_key <- readLines(socket, 1)
-  
-  
-  #Build the request delimited by &&&
-  #Similar to a GET request in the way we handle parameters
-  reqString <- paste(
-                "SRC&&&RQST&&&destination&&&", socket_key, 
-                "&&&key&&&", engine_key, "&&&",
-                "start_date&&&", startdate, "&&&",
-                "end_date&&&", enddate, "&&&",
-                "query&&&aave-protocol-dated&&&",
-                "request&&&aave-protocol-dated\n", sep="")
-  
-  #Write this request back to the socket to tell engine what we want
-  writeLines(reqString, socket)
-  
-  #Now the engine will begin feeding us data
-  #We grab the first to initialize the data var and then we continue listening\
-  data <- readLines(socket, 1)
-  while (TRUE) {
-    temp <- readLines(socket, 1)
-    data <- paste(data, temp, "")
-    if (grepl("<<<end>>>", temp, fixed=TRUE)) {break}
-  }
-  
-  
-  data <- str_replace(data, "  <<<end>>> ", "") #We begin parsing by removing the end character
-  data <- str_replace_all(data, " ", "") #We remove all spaces
-  data <- str_replace_all(data, "\\}\\{", "\\},\\{") #We add commas in between every object
-  data <- paste("[", data, "]", sep="")              #and format the string as a json list
-  output <- fromJSON(data)                           #and then it is finally ready to be parsed as a json
-  
-  #We then need to match this with our current cold-storage data schema
-  
-  output <- output[,-(colnames(output) == "_id")] #We remove the Mongodb data engine ID
-  colnames(output)[colnames(output) == "action"] = "type" #Rename the 'action' column to 'type'
-  output$type <- as.factor(output$type)                   #and make this column a factor for a faster df
-  
-  #We calculate the date and time via the UNIX epoch timestamp provided in the data
-  output <- output %>% mutate(datetime = as_datetime(as.numeric(substr(timestamp, 1, nchar(timestamp)-3))))
-  
-  #We rename to match the desired schema
-  colnames(output)[colnames(output) == "assetId"] = "reserveId"
-  colnames(output)[colnames(output) == "assetSymbol"] = "reserve"
-  
-  #We convert these from chars to numbers (doubles) for obvious reasons
-  output$amount <- as.double(output$amount)
-  output$borrowRate <- as.double(output$borrowRate)
-  output$principalAmount <- as.double(output$principalAmount)
-  
-  #We rename to match desired schema
-  colnames(output)[colnames(output) == "user"] = "userId"
-  
-  #We unite these columns as they are only present in specific actions
-  #and our old data refers to them purely as 'onBehalfOfId'
-  output <- output %>% unite(col = "onBehalfOfId", onBehalfOf,repayer,initiator, na.rm = TRUE, sep = "")
-  
-  #We rename to match desired schema
-  colnames(output)[colnames(output) == "collateralAssetId"] = "collateralReserveId"
-  colnames(output)[colnames(output) == "collateralAssetSymbol"] = "collateralReserve"
-  colnames(output)[colnames(output) == "principalAssetId"] = "principalReserveId"
-  colnames(output)[colnames(output) == "principalAssetSymbol"] = "principalReserve"
-  
-  #We remove column 15 as it is a mongoDB timestamp from the engine that is no longer needed
-  output <- output[,-15]
-  
-  return(output)
-}
-

With this function, we can now get our data.

-
#We make a sample call to the function which will return all transactions 2022 August 1st to August 3rd
-df <- getJson("2022-08-01", "2022-08-04")
-kable(head(df), "simple")
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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-

Now that we have our dataframe, let’s show that it is usable. We’ll -plot the daily count of transactions over the dataframe.

-
library(dplyr)
-
-dailySummaries <- df %>%
-  mutate(day = floor_date(datetime, unit = "day")) %>% # Add a new column that rounds the date of each transaction down to the nearest week
-  group_by(day) %>% # Group the transactions together by the week they were performed.
-  summarise(transactionCount = n()) # Count the number of transactions in each group.
-

With these daily summaries computed, we can simply plot the week on -the x-axis and the transaction count on the y-axis to visualize -this.

-
dailyTransactionsPlot <- ggplot(data = dailySummaries, aes(x = day, y = transactionCount)) + geom_line()
-dailyTransactionsPlot
-

-We see that we received transactions for each of the three days -requested. Remember that the engine parameter ‘end_date’ is exclusive -and as such we receive zero transactions for August 4th.

-
- - - - -
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zzGMb&8$IMdE9vdo?^<1a*v%oy{~OS}c{#EgOG(X36BDw21CKAYX_~VM@iNupe?sn% zaMM~t=3byM>mnM&TPB3gl|57zLFRgXKfF^Hqty=0FXgA-q%pmRv`(vuIKgf`w3UX&_ApyJ35~yAaNrap>96@cQ7miyF-0!n@Q!$C7rrtKwiR@girr3MEUdHn@E4$(+Y zN;msE_`ht$ZN%;?F4Yzi9RB9IJr)aBD%!w(Mt8Iig6*03()4RrrX00(#k)OF#9?Xfju+ty1;wzzQOELP?CYzVdw z0KRH|PfF9S0QDP-F}U~92XDZnUo&_i;yd~LWg<+i#C^EQws|$QqXg$NRnmKWsmFO! z^xDoyBva_CWys1r1(jB;JH@}S?%3Rnn$NZ^PpQBq{UJK&dx>UXsxUav1o0z9%GdVF zSJHceNtuqDSBhUtTRommGRv%Gb?X9enuN#If=i4P>;hHxme`(Vk@>|FmtE3dgm*a( zX_px*xxTd>Qlw`CG{Ez7!@&82-{Z<^widn}c(0FBN2_~}7R*23#RvRs|At^Grp5bi zplH@!5$@K=tByJprgje8a**o0)Hm7+^XM%-~R2?1N$hdgk>Z`ufEgZ?X-WQbb zhB%p?0H-Jik0=K>E4L&sFP9{@q_`Nj7&j{iFQ){%2#+WWmk`%R{4mz;~Rr&!Ke7s(iKR=nTO(0rWg8A{pNL(}>NJ?+FNN%ei zE&s%A|73U_@PdJ@yujQg$NCBbs_cx6!1M|QTo(Wql|$5cLR`zlpqZ>>", temp, fixed=TRUE)) {break} + temp.df <- as.data.frame(fromJSON(temp)) + df <- rbind.fill(df, temp.df) + } + + return(df) +} + +``` + +With this function, we can now get our data. +```{R} + +#We make a sample call to the function which will return all transactions 2022 August 1st to August 3rd +df <- request("amberdata-aave-protocol", "", "x-api-key,UAK7ed69235426c360be22bfc2bde1809b6", "2022-08-02", "2022-08-03") +kable(head(df), "simple") + +``` \ No newline at end of file