inst/docker/parallel_cloudrun/plumber/api.R
In MarkEdmondson1234/googleCloudRunner: R Scripts in the Google Cloud via Cloud Run, Cloud Build and Cloud Scheduler
if (Sys.getenv("PORT") == "") Sys.setenv(PORT = 8000)
#auth via BQ_AUTH_FILE environment argument
library(bigQueryR)
library(xts)
library(forecast)
#' @get /
#' @html
function() {
"<html><h1>It works!</h1></html>"
}
#' @get /hello
#' @html
function() {
"<html><h1>hello world</h1></html>"
}
#' @get /covid_traffic
#' @param industry the industry to filter results down to e.g "Software"
#' @param region the region to filter results down to e.g "Europe"
function(region=NULL, industry=NULL, bqds = NULL, bqtbl = NULL) {
if(any(is.null(region), is.null(industry))){
stop("Must supply region and industry parameters")
}
# to handle spaces
region <- URLdecode(region)
industry <- URLdecode(industry)
sql <- sprintf("SELECT date, industry, percent_of_baseline
FROM `bigquery-public-data.covid19_geotab_mobility_impact.commercial_traffic_by_industry`
WHERE region = '%s' order by date LIMIT 1000", region)
message("Query: ", sql)
traffic <- bqr_query(
query = sql,
datasetId = "covid19_geotab_mobility_impact",
useLegacySql = FALSE,
maxResults = 10000
)
# filter to industry in R this time
test_data <- traffic[traffic$industry == industry,
c("date", "percent_of_baseline")]
tts <- xts(test_data$percent_of_baseline,
order.by = test_data$date,
frequency = 7)
# replace with long running sophisticated analysis
model <- forecast(auto.arima(tts))
# output a list that can be turned into JSON via jsonlite::toJSON
o <- list(
params = c(region, industry),
x = model$x,
mean = model$mean,
lower = model$lower,
upper = model$upper
)
message("Return: ", jsonlite::toJSON(o))
if (is.null(bqds) || is.null(bqtbl)) {
# return data directly
return(o)
}
# we upload result to bigQuery
bqr_upload_data(
datasetId = bqds,
tableId = bqtbl,
upload_data = list(o),
sourceFormat = "NEWLINE_DELIMITED_JSON",
wait = FALSE,
autodetect = TRUE
)
}
MarkEdmondson1234/googleCloudRunner documentation built on Feb. 5, 2023, 5:45 p.m.
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if (Sys.getenv("PORT") == "") Sys.setenv(PORT = 8000)
#auth via BQ_AUTH_FILE environment argument
library(bigQueryR)
library(xts)
library(forecast)
#' @get /
#' @html
function() {
"<html><h1>It works!</h1></html>"
}
#' @get /hello
#' @html
function() {
"<html><h1>hello world</h1></html>"
}
#' @get /covid_traffic
#' @param industry the industry to filter results down to e.g "Software"
#' @param region the region to filter results down to e.g "Europe"
function(region=NULL, industry=NULL, bqds = NULL, bqtbl = NULL) {
if(any(is.null(region), is.null(industry))){
stop("Must supply region and industry parameters")
}
# to handle spaces
region <- URLdecode(region)
industry <- URLdecode(industry)
sql <- sprintf("SELECT date, industry, percent_of_baseline
FROM `bigquery-public-data.covid19_geotab_mobility_impact.commercial_traffic_by_industry`
WHERE region = '%s' order by date LIMIT 1000", region)
message("Query: ", sql)
traffic <- bqr_query(
query = sql,
datasetId = "covid19_geotab_mobility_impact",
useLegacySql = FALSE,
maxResults = 10000
)
# filter to industry in R this time
test_data <- traffic[traffic$industry == industry,
c("date", "percent_of_baseline")]
tts <- xts(test_data$percent_of_baseline,
order.by = test_data$date,
frequency = 7)
# replace with long running sophisticated analysis
model <- forecast(auto.arima(tts))
# output a list that can be turned into JSON via jsonlite::toJSON
o <- list(
params = c(region, industry),
x = model$x,
mean = model$mean,
lower = model$lower,
upper = model$upper
)
message("Return: ", jsonlite::toJSON(o))
if (is.null(bqds) || is.null(bqtbl)) {
# return data directly
return(o)
}
# we upload result to bigQuery
bqr_upload_data(
datasetId = bqds,
tableId = bqtbl,
upload_data = list(o),
sourceFormat = "NEWLINE_DELIMITED_JSON",
wait = FALSE,
autodetect = TRUE
)
}
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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