inst/doc/retrieve_downloads.R

In EDIutils: An API Client for the Environmental Data Initiative Repository

## ----eval=FALSE---------------------------------------------------------------
#  library(EDIutils)
#  library(dplyr)
#  library(tidyr)
#  library(ggplot2)
#  library(lubridate)

## ----eval=FALSE---------------------------------------------------------------
#  login()
#  #> userID: "my_name"
#  #> userPass: "my_secret"

## ----eval=FALSE---------------------------------------------------------------
#  # Construct the query
#  query <- paste(
#    "category=info",
#    "serviceMethod=readDataEntity",
#    "resourceId=knb-lter-ntl",
#    "fromTime=2018-12-12T00:00:00",
#    "toTime=2022-05-24T00:00:00",
#    sep = "&"
#  )
#  
#  # Get the report
#  df_report <- get_audit_report(query)
#  
#  logout()

## ----eval=FALSE---------------------------------------------------------------
#  df_results <- df_report %>%
#    filter(user != "robot") %>%
#    filter(userAgent != "DataONE-Python/3.4.7 +http://dataone.org/") %>%
#    filter(nchar(resourceId) > 0) %>%
#    separate(entryTime, into = c("date", NA), sep = "T") %>%
#    separate(
#      resourceId,
#      into = c(NA, NA, NA, NA, NA, NA, "scope", "identifier", "revision", NA),
#      sep = "/"
#    )
#  
#  df_results$date <- ymd(df_results$date)

## ----eval=FALSE---------------------------------------------------------------
#  df_downloads <- df_results %>%
#    group_by(identifier) %>%
#    summarise(n = n())

## ----eval=FALSE---------------------------------------------------------------
#  top20 <- arrange(df_downloads, desc(n)) %>% slice(1:20)
#  
#  ggplot(top20, aes(x = reorder(identifier, -n), y = n)) +
#    geom_bar(stat = "identity") +
#    labs(
#      y = "Number of Downloads",
#      x = "Data Package Identifier",
#      title = "Downloads by Identifier"
#    )

## ----eval=FALSE---------------------------------------------------------------
#  df_downloads_per_month <- df_results %>%
#    mutate(month = month(date)) %>%
#    group_by(month) %>%
#    summarise(n = n())

## ----eval=FALSE---------------------------------------------------------------
#  df_downloads_daily <- df_results %>%
#    group_by(date) %>%
#    arrange(date) %>%
#    summarise(n = n())
#  
#  ggplot(df_downloads_daily, aes(x = date, y = n, group = 1)) +
#    geom_line() +
#    labs(
#      y = "Number of Downloaded Entities",
#      x = "Date",
#      title = "Daily Downloads"
#    )