## ----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)

## ----setup--------------------------------------------------------------------
#  library(ColOpenData)

## ----list datasets------------------------------------------------------------
#  datasets <- list_datasets(language = "EN")
#  
#  head(datasets)

## ----list demographic datasets------------------------------------------------
#  demographic_datasets <- list_datasets(module = "demographic", language = "EN")
#  
#  head(demographic_datasets)

## ----list datasets with information by age------------------------------------
#  age_datasets <- look_up(keywords = "age")
#  
#  head(age_datasets)

## ----list datasets with information by area and sex in demographic module-----
#  area_sex_datasets <- look_up(
#    keywords = c("area", "sex"),
#    module = "demographic",
#    logic = "and",
#    language = "EN"
#  )
#  
#  head(area_sex_datasets)

## ----dictionary for MGNCNPV at municipalities---------------------------------
#  dict_mpio <- geospatial_dictionary(
#    spatial_level = "municipality",
#    language = "EN"
#  )
#  
#  head(dict_mpio)

## ----dicionary for climate data-----------------------------------------------
#  dict_climate <- get_climate_tags(language = "EN")
#  
#  head(dict_climate)

## ----divipola-table-----------------------------------------------------------
#  divipola <- divipola_table()
#  head(divipola)

## ----cordoba------------------------------------------------------------------
#  name_to_code_dep(department_name = "Guajira")

## ----divipola tunja-----------------------------------------------------------
#  name_to_code_mun(
#    department_name = "Boyacá",
#    municipality_name = "Tunja"
#  )

## ----tunja name---------------------------------------------------------------
#  code_to_name_mun(municipality_code = "15001")

