# Sourcing climatic data from AREAdata

## Introduction

Often when presented with a set of population dynamic data, we may wish to investigate the abiotic conditions such as temperature or humidity that these dynamics occurred under.

Ideally this sort of data would have been collected at the same time as the dynamics data. However with data collected for one project and then repurposed for another, this is often not possible.

In such scenarios it is possible to link the location of the data with other measurements of abiotic variables in the area.

These can be retrieved from raster files provided by projects such as the ERA5 data store from the Copernicus project, however often this is a large amount of data for a small number of datapoints.

## AREAdata

An alternative method for gathering this data is through the AREAdata project, which provides historical and forecast data aggregated at different spatial scales.

### Setting up the cache

The downloads for AREAdata are generally quite large, but rarely updated. As such it’s worthwhile keeping these files around if you’ll use them regularly.

To do this let’s run a command to set up a local cache.

    library(ohvbd)
    set_default_ohvbd_cache()

Now you’ve done this, your cached data will not be deleted when you close R (though you must run this command once per session).

When you run this on your own computer, you will receive a message giving instructions on how to permanently set this using your .Rprofile file.

### Data

Now you have set up your cache, let’s take a look at the format of data retrieved from AREAdata (we will deconstruct this command properly in a bit):

    ad_df <- fetch_ad(metric = "temp", gid = 0, use_cache = TRUE)
    # Display only a brief summary of the data
    summary(ad_df)

    ## <ohvbd.ad.matrix>
    ## Areadata matrix for temp at gid level 0 .
    ## Cached: FALSE
    ## Dates: 2020-01-01 -> 2025-12-31 (2192)
    ## Locations: 256

So that is quite a lot of data! Way too much to print here, given that it’s 561152 entries, however if you want to print all the data rather than just a summary, you can use the print command:

    print(ad_df)

AREAdata outputs are formatted as matrices. Here the rows correspond to locations (identified by GADM codes):

    head(rownames(ad_df))

    ## [1] "Aruba"       "Afghanistan" "Angola"      "Anguilla"    "Åland"
    ## [6] "Albania"

Meanwhile the column names correspond to the day that the data refers to:

    head(colnames(ad_df))

    ## [1] "2020-01-01" "2020-01-02" "2020-01-03" "2020-01-04" "2020-01-05"
    ## [6] "2020-01-06"

So the value at `ad_df["Angola", "2020-01-01"]` corresponds to the value of the metric (in this case temperature) in Angola on 2020-01-01:

    ad_df["Angola", "2020-01-01"]

    ## [1] 29.33611

## Deconstructing `fetch_ad()`

That call above to `fetch_ad()` used some slightly odd parameters, so let’s break that down a bit.

The arguments we provided were

1. metric = “temp”
2. gid = 0
3. use_cache = TRUE
4. cache_location = cachepath

### `metric`

The metric argument tells `fetch_ad()` what type of data it should retrieve from AREAdata.

There are many valid options for this including temperature and humidity. (See AREAdata or run `?fetch_ad()` in the R console for a full list).

### `gid`

The gid argument tells `fetch_ad()` the spatial scale to download (with higher numbers corresponding to finer scales, at the cost of larger download sizes).

### `use_cache` and `cache_location`

As the AREAdata files are large and monolithic, it is often advantageous to download data at an given spatial scale only once.

We can do this by caching the files and retrieving them as needed. Whilst you could do this manually, `ohvbd` already has the functionality to perform this behind the scenes.

If we set `use_cache = TRUE` then instead of downloading from the repository immediately, `ohvbd` will first try to load any cached version that may be present.

Typically, the cache location is a folder in your user directory, obtained from `tools::R_user_dir()`. However, if you want to overwrite it (such as if you want to share the cache between different scripts) you can provide an alternative path to save data to.

### Example

So given the above, let’s construct another function call to download relative humidity data at the province level (gid 1), caching in the same folder.

    temp_gid1<- fetch_ad(metric = "relhumid", gid = 1, use_cache = TRUE)

## Extracting specific data

Extracting data from this matrix can be fiddly, but luckily `ohvbd` also provides a function to aid in that: `glean_ad()`.

So let’s again try to extract the same data as previously, the temperature in Angola on 2020-01-01:

    ad_df |> glean_ad(targetdate = "2020-01-01", places = "Angola")

    ## <ohvbd.ad.matrix>
    ## Areadata matrix for temp at gid level 0 .
    ## Cached: FALSE
    ## Dates: 2020-01-01 -> 2020-01-01 (1)
    ## Locations: 1
    ## Data:
    ##        2020-01-01
    ## Angola   29.33611
    ## attr(,"metric")
    ## [1] "temp"
    ## attr(,"gid")
    ## [1] 0
    ## attr(,"cached")
    ## [1] FALSE
    ## attr(,"db")
    ## [1] "ad"
    ## attr(,"writetime")
    ## [1] "2026-02-05 15:31:57 GMT"
    ## attr(,"class")
    ## [1] "ohvbd.ad.matrix" "matrix"          "array"

Note: ignore the `attr` part of the output, this data just allows `ohvbd` to ensure consistency between its functions.

Wonderful! But what if we want a range of dates, or many places? What if the dates fall outside the dates that we have data for, or the places are not actually present in the data?

This is where `glean_ad()` really shines.

### Dates and date ranges

Let’s first look at the flexibility of the date arguments to `glean_ad()`.

If we (for example) want not just one day, we can specify either a vector of dates, or a less exact date as `targetdate`

    ad_df |> glean_ad(
      targetdate = c("2020-01-01", "2020-01-02", "2020-01-05"),
      places = "Angola"
    )

    ## <ohvbd.ad.matrix>
    ## Areadata matrix for temp at gid level 0 .
    ## Cached: FALSE
    ## Dates: 2020-01-01 -> 2020-01-05 (3)
    ## Locations: 1
    ## Data:
    ##        2020-01-01 2020-01-02 2020-01-05
    ## Angola   29.33611   28.77514   28.13407
    ## attr(,"metric")
    ## [1] "temp"
    ## attr(,"gid")
    ## [1] 0
    ## attr(,"cached")
    ## [1] FALSE
    ## attr(,"db")
    ## [1] "ad"
    ## attr(,"writetime")
    ## [1] "2026-02-05 15:31:57 GMT"
    ## attr(,"class")
    ## [1] "ohvbd.ad.matrix" "matrix"          "array"

    ad_df |> glean_ad(targetdate = "2020-01", places = "Angola")

    ## <ohvbd.ad.matrix>
    ## Areadata matrix for temp at gid level 0 .
    ## Cached: FALSE
    ## Dates: 2020-01-01 -> 2020-01-31 (31)
    ## Locations: 1
    ## Data:
    ##        2020-01-01 2020-01-02 2020-01-03 2020-01-04 2020-01-05 2020-01-06
    ## Angola   29.33611   28.77514   28.88312    27.4325   28.13407   28.64046
    ##        2020-01-07 2020-01-08 2020-01-09 2020-01-10 2020-01-11 2020-01-12
    ## Angola   28.09983   28.40937   28.71222   27.21757   26.85815   28.92744
    ##        2020-01-13 2020-01-14 2020-01-15 2020-01-16 2020-01-17 2020-01-18
    ## Angola    29.5375   28.88849   28.81174   29.81402   29.89741    29.5939
    ##        2020-01-19 2020-01-20 2020-01-21 2020-01-22 2020-01-23 2020-01-24
    ## Angola   29.87222   30.28832   30.18386   29.98904   29.97556   29.63855
    ##        2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29 2020-01-30
    ## Angola   29.53917   29.00056   28.81855   29.47186   28.44818   28.64745
    ##        2020-01-31
    ## Angola   28.34594
    ## attr(,"metric")
    ## [1] "temp"
    ## attr(,"gid")
    ## [1] 0
    ## attr(,"cached")
    ## [1] FALSE
    ## attr(,"db")
    ## [1] "ad"
    ## attr(,"writetime")
    ## [1] "2026-02-05 15:31:57 GMT"
    ## attr(,"class")
    ## [1] "ohvbd.ad.matrix" "matrix"          "array"

    # 366 days of data return as 2020 is a leap year
    length(ad_df |> glean_ad(targetdate = "2020", places = "Angola"))

    ## [1] 366

If we would instead like to specify a date range, we can add the argument `enddate`.

Note: the end date is defined exclusively, meaning that any date before it (and on or after the target date) is considered to be “in”.

    ad_df |> glean_ad(
      targetdate = "2020-01-01",
      enddate = "2020-01-04",
      places = "Angola"
    )

    ## <ohvbd.ad.matrix>
    ## Areadata matrix for temp at gid level 0 .
    ## Cached: FALSE
    ## Dates: 2020-01-01 -> 2020-01-03 (3)
    ## Locations: 1
    ## Data:
    ##        2020-01-01 2020-01-02 2020-01-03
    ## Angola   29.33611   28.77514   28.88312
    ## attr(,"metric")
    ## [1] "temp"
    ## attr(,"gid")
    ## [1] 0
    ## attr(,"cached")
    ## [1] FALSE
    ## attr(,"db")
    ## [1] "ad"
    ## attr(,"writetime")
    ## [1] "2026-02-05 15:31:57 GMT"
    ## attr(,"class")
    ## [1] "ohvbd.ad.matrix" "matrix"          "array"

### Places

Multiple places to extract data for can also be provided as a vector of places:

    ad_df |> glean_ad(
      targetdate = "2020-01-01",
      places = c("Angola", "Latvia", "United Kingdom")
    )

    ## <ohvbd.ad.matrix>
    ## Areadata matrix for temp at gid level 0 .
    ## Cached: FALSE
    ## Dates: 2020-01-01 -> 2020-01-01 (1)
    ## Locations: 3
    ## Data:
    ##                2020-01-01
    ## Angola          29.336107
    ## Latvia           1.765062
    ## United_Kingdom   5.113141
    ## attr(,"metric")
    ## [1] "temp"
    ## attr(,"gid")
    ## [1] 0
    ## attr(,"cached")
    ## [1] FALSE
    ## attr(,"db")
    ## [1] "ad"
    ## attr(,"writetime")
    ## [1] "2026-02-05 15:31:57 GMT"
    ## attr(,"class")
    ## [1] "ohvbd.ad.matrix" "matrix"          "array"

This can be combined with the date filters above to create a more restrictive extraction:

    ad_df |> glean_ad(
      targetdate = "2020-01-01",
      enddate = "2020-01-04",
      places = c("Angola", "Latvia", "United Kingdom")
    )

    ## <ohvbd.ad.matrix>
    ## Areadata matrix for temp at gid level 0 .
    ## Cached: FALSE
    ## Dates: 2020-01-01 -> 2020-01-03 (3)
    ## Locations: 3
    ## Data:
    ##                2020-01-01 2020-01-02 2020-01-03
    ## Angola          29.336107  28.775145  28.883123
    ## Latvia           1.765062   3.268600   2.733880
    ## United_Kingdom   5.113141   8.206171   6.198989
    ## attr(,"metric")
    ## [1] "temp"
    ## attr(,"gid")
    ## [1] 0
    ## attr(,"cached")
    ## [1] FALSE
    ## attr(,"db")
    ## [1] "ad"
    ## attr(,"writetime")
    ## [1] "2026-02-05 15:31:57 GMT"
    ## attr(,"class")
    ## [1] "ohvbd.ad.matrix" "matrix"          "array"

### Resiliance

The `glean_ad()` function is also relatively resilient to the use of dates or places that are not in AREAdata:

    ad_df |> glean_ad(
      targetdate = "2023-12-30",
      enddate = "2024-01-02",
      places = c("Atlantis", "Latvia", "United Kingdom")
    )

    ## <ohvbd.ad.matrix>
    ## Areadata matrix for temp at gid level 0 .
    ## Cached: FALSE
    ## Dates: 2023-12-30 -> 2024-01-01 (3)
    ## Locations: 2
    ## Data:
    ##                2023-12-30 2023-12-31 2024-01-01
    ## United_Kingdom   7.007509   7.715774   7.124182
    ## Latvia           4.098629  -2.789258  -7.665634
    ## attr(,"metric")
    ## [1] "temp"
    ## attr(,"gid")
    ## [1] 0
    ## attr(,"cached")
    ## [1] FALSE
    ## attr(,"db")
    ## [1] "ad"
    ## attr(,"writetime")
    ## [1] "2026-02-05 15:31:57 GMT"
    ## attr(,"class")
    ## [1] "ohvbd.ad.matrix" "matrix"          "array"

## Associating AREAdata climatic variables with other data

On top of providing a download interface for AREAdata, `ohvbd` provides tools for easing the process of associating your own data with data from AREAdata.

This is performed through the use of the `assoc_ad()` function.

For example, let’s imagine we have some data and their associate locations:

      lonlatdf <- data.frame(
        Country = c("UK", "DE", "US", "ES", "IT", "BG", "US"),
        Latitude = c(
          53.813727033336384, 50.94133730102917, 41.502614374776414, 37.09584576240546,
          46.190816816324634, 43.40408987260468, 34.02921305111613
        ),
        Longitude = c(
          -1.5631510640531983, 6.95792487354786, -73.96228644647785,
          -2.0967211553577694, 11.135228310071971, 28.148385835241964,
          -84.36091597146886
        ),
        count = c(6, 36, 340, 202, 802, 541, 325)
      )

    lonlatdf

    ##   Country Latitude  Longitude count
    ## 1      UK 53.81373  -1.563151     6
    ## 2      DE 50.94134   6.957925    36
    ## 3      US 41.50261 -73.962286   340
    ## 4      ES 37.09585  -2.096721   202
    ## 5      IT 46.19082  11.135228   802
    ## 6      BG 43.40409  28.148386   541
    ## 7      US 34.02921 -84.360916   325

If we know that we collected samples between the 4th and 5th of August 2021, we can associate the temperature values from these regions on these days at a given spatial scale.

Let’s do this at the country scale:

    areadata <- fetch_ad(metric="temp", gid=0, use_cache=TRUE)
    ad_extract_working <- assoc_ad(lonlatdf, areadata,
                                        targetdate = c("2021-08-04"), enddate=c("2021-08-06"),
                                        gid=0, lonlat_names = c("Longitude", "Latitude"))
    # Suppress lonlat just to make the output easier to read
    ad_extract_working |> dplyr::select(!c("Longitude", "Latitude"))

    ##   Country count temp_2021.08.04 temp_2021.08.05
    ## 1      UK     6        15.98993        16.09847
    ## 2      DE    36        17.17353        18.25541
    ## 3      US   340        26.12418        26.02746
    ## 4      ES   202        26.00963        27.42062
    ## 5      IT   802        26.21531        24.99068
    ## 6      BG   541        29.31433        31.18788
    ## 7      US   325        26.12418        26.02746

If we prefer a finer resolution (e.g. county-level), we could change the `gid` argument to a higher number (indicating a finer scale, up to `2`):

    areadata_gid2 <- fetch_ad(metric="temp", gid=2, use_cache=TRUE)
    ad_extract_working_gid2 <- assoc_ad(lonlatdf, areadata_gid2,
                                        targetdate = c("2021-08-04"), enddate=c("2021-08-06"),
                                        gid=2, lonlat_names = c("Longitude", "Latitude"))
    ad_extract_working_gid2 |> dplyr::select(!c("Longitude", "Latitude"))

    ##   Country count temp_2021.08.04 temp_2021.08.05
    ## 1      UK     6        16.35346        16.46636
    ## 2      DE    36        16.62339        18.94270
    ## 3      US   340        20.59405        20.87954
    ## 4      ES   202        29.42114        31.26401
    ## 5      IT   802        20.82837        23.16447
    ## 6      BG   541        25.01201        25.38486
    ## 7      US   325        23.69151        24.70063

We can see here that by using a finer spatial scale, the two entries from the US now have different associated temperatures.

## Managing the AREAdata cache

Interacting with AREAdata requires the caching of a significant amount of data.

This is generally managed by `ohvbd` itself, and in most cases you should not need to worry about it.

However if you want to free up some space, use guaranteed new data, or see what data `ohvbd` has currently stored on your computer, you have a few options available to you.

### `refresh_cache` for `fetch_ad()`

The `fetch_ad()` function has an argument called `refresh_cache`. If this is set to `TRUE`, this will force a refresh of the cache at download time (and suppress loading from said cache):

    test_ad <- fetch_ad("temp", gid = 0)
    attr(test_ad, "writetime")

    ## [1] "2026-02-05 15:31:57 GMT"

    test_ad <- fetch_ad("temp", gid = 0, refresh_cache = TRUE)
    attr(test_ad, "writetime")

    ## [1] "2026-02-05 15:32:46 GMT"

### Cache management tools

Alternatively you can use the provided tools `list_ohvbd_cache()` and `clean_ohvbd_cache()` to explore and delete files within the ohvbd cache:

    list_ohvbd_cache()

    ── Cached files ────────────────────────
    Cache location: ../cache/R/ohvbd

    ── <root>: 0 files ──

    none

    ── adcache: 3 files ──

    • relhumid-1.rda
    • temp-0.rda
    • temp-2.rda

    ── gadmcache: 6 files ──

    • gadm-counties.gpkg
    • gadm-countries.gpkg
    • gadm-states.gpkg

    ── vecdyn: 1 file ──

    • vd_meta_table.rds

You can filter this by subdirectory, so let’s just look at the data downloaded from AREAdata (in `adcache`):

    list_ohvbd_cache(subdir = "adcache")

    ── Cached files ────────────────────────
    Cache location: ../cache/R/ohvbd

    ── adcache: 3 files ────────────────────

    • relhumid-1.rda
    • temp-0.rda
    • temp-2.rda

If we wished to delete these files, we could simply run the following:

    clean_ohvbd_cache(subdir = "adcache")

    ── Cached files  ────────────────────────
    Cache location: ../cache/R/ohvbd

    ── adcache: 3 files ──

    • relhumid-1.rda
    • temp-0.rda
    • temp-2.rda

    ! This will permanently delete files from adcache
    ℹ Are you sure? [y/N]
    >> y

    ℹ Clearing files from ../cache/R/ohvbd/adcache
    ✔ Removed 3 files

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