inst/doc/climate_data.R

In ColOpenData: Download Colombian Demographic, Climate and Geospatial Data

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = identical(tolower(Sys.getenv("NOT_CRAN")), "true")
)

## ----IDEAM table, echo = FALSE------------------------------------------------
#  tags <- c(
#    "TSSM_CON", "THSM_CON", "TMN_CON", "TMX_CON", "TSTG_CON", "HR_CAL",
#    "HRHG_CON", "TV_CAL", "TPR_CAL", "PTPM_CON", "PTPG_CON", "EVTE_CON",
#    "FA_CON", "NB_CON", "RCAM_CON", "BSHG_CON", "VVAG_CON", "DVAG_CON",
#    "VVMXAG_CON", "DVMXAG_CON"
#  )
#  variable <- c(
#    "Dry-bulb Temperature", "Wet-bulb Temperature",
#    "Minimum Temperature", "Maximum Temperature",
#    "Dry-bulb Temperature (Termograph)", "Relative Humidity",
#    "Relative Humidity (Hydrograph)", "Vapour Pressure", "Dew Point",
#    "Precipitation (Daily)", "Precipitation (Hourly)", "Evaporation",
#    "Atmospheric Phenomenon", "Cloudiness", "Wind Trajectory",
#    "Sunshine Duration", "Wind Speed", "Wind Direction",
#    "Maximum Wind Speed", "Maximum Wind Direction"
#  )
#  
#  IDEAM_tags <- data.frame(
#    Tags = tags, Variable = variable,
#    stringsAsFactors = FALSE
#  )
#  knitr::kable(IDEAM_tags)

## ----library imports, results = "hide", warning = FALSE, message = FALSE------
#  library(ColOpenData)
#  library(dplyr)
#  library(sf)
#  library(leaflet)
#  library(ggplot2)

## ----polygon creation---------------------------------------------------------
#  lat <- c(4.263744, 4.263744, 4.078156, 4.078156, 4.263744)
#  lon <- c(-75.042067, -74.777022, -74.777022, -75.042067, -75.042067)
#  polygon <- st_polygon(x = list(cbind(lon, lat))) %>% st_sfc()
#  roi <- st_as_sf(polygon)

## ----polygon plot-------------------------------------------------------------
#  leaflet(roi) %>%
#    addProviderTiles("OpenStreetMap") %>%
#    addPolygons(
#      stroke = TRUE,
#      weight = 2,
#      color = "#2e6930",
#      fillColor = "#2e6930",
#      opacity = 0.6
#    )

## ----stations in roi----------------------------------------------------------
#  stations <- stations_in_roi(geometry = roi)
#  
#  head(stations)

## ----stations filtered--------------------------------------------------------
#  cw_stations <- stations %>%
#    filter(
#      as.Date(fecha_suspension) > as.Date("2013-01-01") | estado == "Activa",
#      categoria %in% c("Climática Principal", "Climática Ordinaria")
#    )
#  
#  head(cw_stations)

## ----download climate stations------------------------------------------------
#  tssm_stations <- download_climate_stations(
#    stations = cw_stations,
#    start_date = "2013-01-01",
#    end_date = "2016-12-31",
#    tag = "TSSM_CON"
#  )
#  
#  head(tssm_stations)

## ----plot temperatures stations-----------------------------------------------
#  ggplot(data = tssm_stations) +
#    geom_line(aes(x = date, y = value, group = station), color = "#106ba0") +
#    ggtitle("Dry-bulb Temperature in Espinal by station") +
#    xlab("Date") +
#    ylab("Temperature [°C]") +
#    facet_grid(rows = vars(station)) +
#    theme_minimal() +
#    theme(
#      plot.background = element_rect(fill = "white", colour = "white"),
#      panel.background = element_rect(fill = "white", colour = "white"),
#      plot.title = element_text(hjust = 0.5)
#    )

## ----plot monthly-------------------------------------------------------------
#  tssm_month <- tssm_stations %>% aggregate_climate(frequency = "month")
#  
#  ggplot(data = tssm_month) +
#    geom_line(aes(x = date, y = value, group = station), color = "#106ba0") +
#    ggtitle("Dry-bulb Temperature in Espinal by station") +
#    xlab("Date") +
#    ylab("Dry-bulb temperature [C]") +
#    facet_grid(rows = vars(station)) +
#    theme_minimal() +
#    theme(
#      plot.background = element_rect(fill = "white", colour = "white"),
#      panel.background = element_rect(fill = "white", colour = "white"),
#      plot.title = element_text(hjust = 0.5)
#    )

## ----download climate data, eval = FALSE--------------------------------------
#  tssm_roi <- download_climate_geom(
#    geometry = roi,
#    start_date = "2013-01-01",
#    end_date = "2016-12-31",
#    tag = "TSSM_CON"
#  ) %>% aggregate_climate(frequency = "month")

## ----municipality code--------------------------------------------------------
#  espinal_code <- name_to_code_mun("Tolima", "Espinal")
#  espinal_code

## ----download climate mpio, eval = FALSE--------------------------------------
#  tssm_mpio <- download_climate(
#    code = espinal_code,
#    start_date = "2013-01-01",
#    end_date = "2016-12-31",
#    tag = "TMX_CON"
#  ) %>% aggregate_climate(frequency = "month")