mudata2

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The mudata2 package provides tools to read, write, and document multi-parameter spatiotemporal data.

Installation

You can install mudata2 from CRAN with:

install.packages("mudata2")

Or alternatively, you can the development version from github with:

# install.packages("remotes")
remotes::install_github("paleolimbot/mudata")

What is mudata?

The mudata format is a data table in a specific form, with data dictionaries for locations, parameters, and datasets contained in the data table. The term “mudata” refers to a (mostly) universal data structure; the term “multi-parameter spatiotemporal data” is a mouthful, but just means that you measured a bunch of things (parameters) in a bunch of places (locations) at a bunch of different times. The best example of this is historical climate data, because it is usually set up in such a way that there are climate stations (locations) that measure some things (parameters, like temperature, precipitation, wind, etc.) at various points in time. This package is designed primarily for climate data and sediment core data, however the format can be applied to many other types of data where parameters are measured along one or more common axes (time, depth, etc.).

A few examples from this package can be found in the ns_climate, second_lake_temp, and alta_lake datasets:

ns_climate
#> A mudata object aligned along "date"
#>   distinct_datasets():  "ecclimate_monthly"
#>   distinct_locations(): "ANNAPOLIS ROYAL 6289", "BADDECK 6297" ... and 13 more
#>   distinct_params():    "dir_of_max_gust", "extr_max_temp" ... and 9 more
#>   src_tbls():           "data", "locations" ... and 3 more
#> 
#> tbl_data() %>% head():
#> # A tibble: 6 x 7
#>   dataset        location       param      date       value flag  flag_text
#>   <chr>          <chr>          <chr>      <date>     <dbl> <chr> <chr>    
#> 1 ecclimate_mon… SABLE ISLAND … mean_max_… 1897-01-01    NA M     Missing  
#> 2 ecclimate_mon… SABLE ISLAND … mean_max_… 1897-02-01    NA M     Missing  
#> 3 ecclimate_mon… SABLE ISLAND … mean_max_… 1897-03-01    NA M     Missing  
#> 4 ecclimate_mon… SABLE ISLAND … mean_max_… 1897-04-01    NA M     Missing  
#> 5 ecclimate_mon… SABLE ISLAND … mean_max_… 1897-05-01    NA M     Missing  
#> 6 ecclimate_mon… SABLE ISLAND … mean_max_… 1897-06-01    NA M     Missing
second_lake_temp
#> A mudata object aligned along "datetime", "depth"
#>   distinct_datasets():  "second_lake_temp"
#>   distinct_locations(): "Second Lake"
#>   distinct_params():    "temp"
#>   src_tbls():           "data", "locations" ... and 3 more
#> 
#> tbl_data() %>% head():
#> # A tibble: 6 x 6
#>   dataset          location    param datetime            depth value
#>   <chr>            <chr>       <chr> <dttm>              <dbl> <dbl>
#> 1 second_lake_temp Second Lake temp  2013-07-10 07:14:56     0  23.9
#> 2 second_lake_temp Second Lake temp  2013-07-10 08:14:56     0  23.8
#> 3 second_lake_temp Second Lake temp  2013-07-10 09:29:56     0  23.7
#> 4 second_lake_temp Second Lake temp  2013-07-10 09:44:56     0  23.8
#> 5 second_lake_temp Second Lake temp  2013-07-10 10:14:56     0  23.7
#> 6 second_lake_temp Second Lake temp  2013-07-10 11:29:56     0  23.9
alta_lake
#> A mudata object aligned along "depth", "age"
#>   distinct_datasets():  "alta_lake16"
#>   distinct_locations(): "ALGC2"
#>   distinct_params():    "As", "C" ... and 12 more
#>   src_tbls():           "data", "locations" ... and 3 more
#> 
#> tbl_data() %>% head():
#> # A tibble: 6 x 10
#>   dataset     location param depth   age value stdev units     n zone  
#>   <chr>       <chr>    <chr> <dbl> <dbl> <dbl> <dbl> <chr> <int> <chr> 
#> 1 alta_lake16 ALGC2    As     0.25 2015.  23    NA   ppm       1 Zone 3
#> 2 alta_lake16 ALGC2    As     0.75 2011.  24.3  23.0 ppm       3 Zone 3
#> 3 alta_lake16 ALGC2    As     1.25 2008.  60    NA   ppm       1 Zone 3
#> 4 alta_lake16 ALGC2    As     1.75 2003.  60    NA   ppm       1 Zone 3
#> 5 alta_lake16 ALGC2    As     2.5  1998.  43.4  NA   ppm       1 Zone 3
#> 6 alta_lake16 ALGC2    As     3.5  1982.  42    NA   ppm       1 Zone 3

For examples of using and creating mudata objects, see the mudata and mudata_create vignettes.

Why do I need it?

There are plenty of ways to store “multi-parameter spatiotemporal data”, but few of them are good at keeping metadata like what method was used to measure a parameter, the latitude and longitude of a sample location, or the level of uncertainty of a parameter measurement. These metadata are rarely used directly in analyses, but are invaluable to correctly interpret the results (and to correctly choose the analysis).

When do I need it?

We have used the mudata format in the following situations:

More information

For more examples of mudata usage, see the package vignettes: vignette("mudata", package = "mudata2"), and vignette("mudata_create", package = "mudata2")