daily.R
In GHCNr: Download Weather Station Data from GHCNd

## ----include=FALSE------------------------------------------------------------
# rmarkdown::render("vignettes/daily.Rmd")
# rmarkdown::render("vignettes/daily.Rmd", rmarkdown::github_document())
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup--------------------------------------------------------------------
library(GHCNr)
library(terra)  # for handling countries geometries

## ----download-inventory, eval=FALSE-------------------------------------------
#  inventory_file <- download_inventory("~/Downloads/ghcn-inventory.txt")
#  s <- stations(
#    inventory_file,
#    variables = "TMAX",
#    first_year = 1990,
#    last_year = 2000
#  )

## ----read-inventory, eval=FALSE-----------------------------------------------
#  s <- stations(variables = "TMAX", first_year = 1990, last_year = 2000)
#  s
#  # A tibble: 16,763 × 6
#     station     latitude longitude variable firstYear lastYear
#     <chr>          <dbl>     <dbl> <chr>        <dbl>    <dbl>
#   1 AE000041196     25.3     55.5  TMAX          1944     2024
#   2 AEM00041194     25.3     55.4  TMAX          1983     2024
#   3 AEM00041217     24.4     54.7  TMAX          1983     2024
#   4 AFM00040938     34.2     62.2  TMAX          1973     2020
#   5 AFM00040948     34.6     69.2  TMAX          1966     2021
#   6 AFM00040990     31.5     65.8  TMAX          1973     2020
#   7 AG000060390     36.7      3.25 TMAX          1940     2024
#   8 AG000060590     30.6      2.87 TMAX          1940     2024
#   9 AG000060611     28.0      9.63 TMAX          1958     2024
#  10 AG000060680     22.8      5.43 TMAX          1940     2004
#  # ℹ 16,753 more rows
#  # ℹ Use `print(n = ...)` to see more rows

## ----filter-by-year, eval=FALSE-----------------------------------------------
#  
#  Spatial filters can also be easily applied.
#  Spatial boundaries of countries can be downloaded from <https://www.geoboundaries.org/> using the `get_countr(couuntry_code = ...)` function, where `country_code` is the ISO3 code.

## ----get-country, eval=FALSE--------------------------------------------------
#  italy <- get_country("ITA")

## ----spatial-filter, eval=FALSE-----------------------------------------------
#  s <- filter_stations(s, italy)
#  s
#  # A tibble: 41 × 6
#     station     latitude longitude variable firstYear lastYear
#     <chr>          <dbl>     <dbl> <chr>        <dbl>    <dbl>
#   1 IT000016090     45.4     10.9  TMAX          1951     2024
#   2 IT000016134     44.2     10.7  TMAX          1951     2024
#   3 IT000016232     42       15    TMAX          1975     2024
#   4 IT000016239     41.8     12.6  TMAX          1951     2024
#   5 IT000016320     40.6     17.9  TMAX          1951     2024
#   6 IT000016560     39.2      9.05 TMAX          1951     2024
#   7 IT000160220     46.2     11.0  TMAX          1951     2024
#   8 IT000162240     42.1     12.2  TMAX          1954     2024
#   9 IT000162580     41.7     16.0  TMAX          1951     2024
#  10 ITE00100554     45.5      9.19 TMAX          1763     2008
#  # ℹ 31 more rows
#  # ℹ Use `print(n = ...)` to see more rows

## ----daily, eval=FALSE--------------------------------------------------------
#  daily_ts <- daily(
#    station_id = "CA003076680",
#    start_date = paste("2002", "11", "01", sep = "-"),
#    end_date = paste("2024", "04", "22", sep = "-"),
#    variables = "tmax"
#  )
#  daily_ts

## ----daily-saved, echo=FALSE--------------------------------------------------
daily_ts <- CA003076680[, c("date", "station", "tmax", "tmax_flag")]
daily_ts

## ----multidaily, eval=FALSE---------------------------------------------------
#  daily_ts <- daily(
#    station_id = c("CA003076680", "USC00010655"),
#    start_date = paste("2002", "11", "01", sep = "-"),
#    end_date = paste("2024", "04", "22", sep = "-"),
#    variables = "tmax"
#  )
#  plot(daily_ts, "tmax")

## ----multidaily-saved, echo=FALSE---------------------------------------------
daily_ts <- rbind(CA003076680, USC00010655)[, c("date", "station", "tmax", "tmax_flag")]
plot(daily_ts, "tmax")

## ----flags, echo=FALSE--------------------------------------------------------
as.list(GHCNr:::.flags(strict = TRUE))

## ----flags-strict, echo=FALSE-------------------------------------------------
as.list(GHCNr:::.flags(strict = FALSE))

## ----remove-flagged-----------------------------------------------------------
daily_ts <- remove_flagged(daily_ts)
plot(daily_ts, "tmax")

## ----daily-coverage-----------------------------------------------------------
station_coverage <- coverage(daily_ts)
station_coverage

## ----low-coverage-------------------------------------------------------------
unique(station_coverage[
  station_coverage$annual_coverage_tmax < .95,
  c("station", "year", "annual_coverage_tmax")
])

## ----monthly------------------------------------------------------------------
monthly_ts <- monthly(daily_ts)
monthly_ts
plot(monthly_ts, "tmax")

## ----quarterly----------------------------------------------------------------
quarterly_ts <- quarterly(daily_ts)
quarterly_ts
plot(quarterly_ts, "tmax")

## ----annual-------------------------------------------------------------------
annual_ts <- annual(daily_ts)
annual_ts
plot(annual_ts, "tmax")