update r code engine samples

R-Code-Samples/aave-protocol-dated-func.Rmd

@@ -4,55 +4,46 @@ library("httr")
 library("jsonlite")
 library("lubridate")
 
-```
-
-This function sets up an Engine socket and makes the get request. The engine feeds the socket while
-the function listens and parses the data, and then returns it as a data frame.
-```{R}
-
-getDF <- function(startdate, enddate) {
-  engine_key <- "b6c810a7f35f4fa0d28258278325b4b5ab82ba79868ab33d01d5c878e13872ec129a91a3fbf702e59c2404f0fb4a53420a3ffb50130c35b4d06b32d81e56c1f4"
+getJson <- function(startdate, enddate) {
   
-  socket <- make.socket("defi-de.idea.rpi.edu", 61200)
-  
-  ss <- read.socket(socket)
-  ss <- gsub("[\r\n]", "", ss)
+  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
   
-  httr::GET(
-    url = "http://defi-de.idea.rpi.edu:8080/defi/v1/rest/request_dated",
-    query = list(
-      destination = ss,
-      key = engine_key,
-      request = "aave-protocol-dated",
-      query = "get_all,aave-protocol_dated",
-      start_date = startdate,
-      end_date = enddate
-    )
-  ) 
+  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="")
   
-  data <- read.socket(socket)
+  writeLines(reqString, socket)
   
+  data <- readLines(socket, 1)
   
   while (TRUE) {
-    temp <- read.socket(socket)
+    temp <- readLines(socket, 1)
     data <- paste(data, temp, "")
     if (grepl("<<<end>>>", temp, fixed=TRUE)) {break}
   }
+  data
   
-  data <- str_replace(data, "\n<<<end>>>\n ", "")
-  data <- str_replace_all(data, "\n", ",")
-  data <- sub(',', '', data)
+  data <- str_replace(data, "  <<<end>>> ", "")
   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(timestamp)))
-
+  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"
   
@@ -62,28 +53,40 @@ getDF <- function(startdate, enddate) {
   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", "2022-09-01")
+temp
+
 ```
 
+```{r}
+library(dplyr)
 
-We make a sample call to the function which takes the start date and end date and receive back
-a data frame containing the aave data.
-```{R}
+weeklySummaries <- temp %>%
+  mutate(week = floor_date(datetime, unit = "day")) %>% # Add a new column that rounds the date of each transaction down to the nearest week
+  group_by(week) %>% # Group the transactions together by the week they were performed.
+  summarise(transactionCount = n()) # Count the number of transactions in each group.
+```
 
-temp <- getDF("2022-08-01", "2022-08-04")
+With these weekly summaries computed, we can simply plot the week on the x-axis and the transaction count on the y-axis to visualize this new representation:
 
+```{r}
+weeklyTransactionsPlot <- ggplot(data = weeklySummaries, aes(x = week, y = transactionCount)) + geom_line()
+weeklyTransactionsPlot
 ```
+
+