This function consolidates a set of datasets in a 'many*' package datacube into a single dataset with some combination of the rows, columns, and observations of the datasets in the datacube.
Usage
consolidate(
datacube,
join = c("full", "inner", "left"),
resolve = "coalesce",
key = NULL
)Arguments
- datacube
A datacube from one of the many packages
- join
Which join procedure to use. By default "full" so that all observations are retained, but other options include "left" for basing the consolidated dataset on observations present in the first dataset (reorder the datasets to favour another dataset), and "inner" for a consolidated dataset that includes only observations that are present in all datasets.
- resolve
Choice how (potentially conflicting) values from shared variables should be resolved. Options include:
"coalesce" (default): uses first non-NA value (if available) for each observation, essentially favouring the order the datasets are in in the datacube.
"unite": combines the unique values for each observation across datasets as a set (separated by commas and surrounded by braces), which can be useful for retaining information.
"random": selects values at random from among the observations from each dataset that observed that variable, of particular use for exploring the implications of dataset-related variation.
"precise": selects the value that has the highest precision from among the observations from each dataset (see
resolving_precision()), which favours more precise data."min", "max": these options return the minimum or maximum values respectively, which can be useful for conservative temporal fixing.
To resolve variables by different functions, pass the argument a vector (e.g.
resolve = c(var1 = "min", var2 = "max")). Unnamed variables will be resolved according to the default ("coalesce").- key
An ID column to collapse by. By default "manyID". Users can also specify multiple key variables in a list. For multiple key variables, the key variables must be present in all the datasets in the datacube (e.g.
key = c("key1", "key2")). For equivalent key columns with different names across datasets, matching is possible if keys are declared (e.g.key = c("key1" = "key2")). Missing observations in the key variable are removed.
Details
The function includes separate arguments for the rows and columns, as well as for how to resolve conflicts for observations across datasets. This provides users with considerable flexibility in how they combine data. For example, users may wish to stick to units that appear in every dataset but include variables coded in any dataset, or units that appear in any dataset but only those variables that appear in every dataset. Even then there may be conflicts, as the actual unit-variable observations may differ from dataset to dataset. We offer a number of resolve methods that enable users to choose how conflicts between observations are resolved.
Text variables are dropped for more efficient consolidation.
Examples
# \donttest{
consolidate(emperors, join = "full", resolve = "coalesce", key = "ID")
#> Using `ID` for matching observations across datasets...
#> ✔ Matched 151 observations by `ID` variable in `emperors` datasets.
#> Using `ID` for matching observations across datasets...
#> Joining 3 datasets using a full join...
#> ✔ Joined 3 datasets using a full join.
#> Joining 3 datasets using a full join...
#> Found 12 conflicts to resolve by `coalesce()`...
#> ✔ Resolved 3 related variables into `Begin`.
#> Found 12 conflicts to resolve by `coalesce()`...
#> ✔ Resolved 3 related variables into `End`.
#> Found 12 conflicts to resolve by `coalesce()`...
#> ✔ Resolved 2 related variables into `FullName`.
#> Found 12 conflicts to resolve by `coalesce()`...
#> ✔ Resolved 2 related variables into `Birth`.
#> Found 12 conflicts to resolve by `coalesce()`...
#> ✔ Resolved 2 related variables into `Death`.
#> Found 12 conflicts to resolve by `coalesce()`...
#> ✔ Resolved 2 related variables into `Dynasty`.
#> Found 12 conflicts to resolve by `coalesce()`...
#> # A tibble: 103 × 14
#> ID Begin End FullName Birth Death CityBirth ProvinceBirth Rise Cause
#> <chr> <mda> <mda> <chr> <mda> <mda> <chr> <chr> <chr> <chr>
#> 1 Aemilian 0253… 0253… Caesar … 0207… 0253… NA Africa Appo… Assa…
#> 2 Allectus 0293… 0297… ? NA … 0297… NA NA NA NA
#> 3 Anastas… 0491… 0518… Flavius… 0430… 0518… NA NA NA NA
#> 4 Anthemi… 0467… 0472… Procopi… 0420… 0472… NA NA NA NA
#> 5 Antonin… 0138… 0161… Caesar … 0086… 0161… Lanuvium Italia Birt… Natu…
#> 6 Arcadius 0395… 0408… Flavius… 0377… 0408… NA NA NA NA
#> 7 Augustus -002… 0014… Imperat… -006… 0014… Rome Italia Birt… Assa…
#> 8 Aurelian 0270… 0275… Caesar … 0214… 0275… Sirmium Pannonia Appo… Assa…
#> 9 Avitus 0455… 0456… Marcus … 0395… 0456… NA NA NA NA
#> 10 Balbinus 0238… 0238… Caesar … 0178… 0238… NA Unknown Appo… Assa…
#> # ℹ 93 more rows
#> # ℹ 4 more variables: Killer <chr>, Dynasty <chr>, Era <chr>, Notes <chr>
consolidate(emperors, join = "inner", resolve = "min", key = "ID")
#> Using `ID` for matching observations across datasets...
#> ✔ Matched 151 observations by `ID` variable in `emperors` datasets.
#> Using `ID` for matching observations across datasets...
#> Joining 3 datasets using a inner join...
#> ✔ Joined 3 datasets using a inner join.
#> Joining 3 datasets using a inner join...
#> Found 12 conflicts to resolve by `min()`...
#> ✔ Resolved 3 related variables into `Begin`.
#> Found 12 conflicts to resolve by `min()`...
#> ✔ Resolved 3 related variables into `End`.
#> Found 12 conflicts to resolve by `min()`...
#> ✔ Resolved 2 related variables into `FullName`.
#> Found 12 conflicts to resolve by `min()`...
#> Warning: There were 4 warnings in `mutate()`.
#> The first warning was:
#> ℹ In argument: `Birth = get(funny)(out, all_of(vars))`.
#> Caused by warning in `min()`:
#> ! no non-missing arguments, returning NA
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 3 remaining warnings.
#> ✔ Resolved 2 related variables into `Birth`.
#> Found 12 conflicts to resolve by `min()`...
#> ✔ Resolved 2 related variables into `Death`.
#> Found 12 conflicts to resolve by `min()`...
#> ✔ Resolved 2 related variables into `Dynasty`.
#> Found 12 conflicts to resolve by `min()`...
#> # A tibble: 65 × 14
#> ID Begin End FullName Birth Death CityBirth ProvinceBirth Rise Cause
#> <chr> <mda> <mda> <chr> <mda> <mda> <chr> <chr> <chr> <chr>
#> 1 Augustus -002… -0014 Gaius J… -006… 0014 Rome Italia Birt… Assa…
#> 2 Tiberius 0014… 0037 Tiberiu… -004… 0037 Rome Italia Birt… Assa…
#> 3 Caligula 0037… 0041 Gaius C… 0012… 0014 Antitum Italia Birt… Assa…
#> 4 Claudius 0041… 0054 Tiberiu… -000… 0041 Lugdunum Gallia Lugdu… Birt… Assa…
#> 5 Nero 0054… 0068 Claudiu… 0037… 0068 Antitum Italia Birt… Suic…
#> 6 Galba 0068… 0069 Servius… -000… 0069 Terracina Italia Seiz… Assa…
#> 7 Otho 0069… 0069 Marcus … 0032… 0069 Terentin… Italia Appo… Suic…
#> 8 Vitelli… 0069… 0069 Aulus V… 0015… 0069 Rome Italia Seiz… Assa…
#> 9 Vespasi… 0069… 0079 Titus F… 0009… 0079 Falacrine Italia Seiz… Natu…
#> 10 Titus 0079… 0081 Titus F… 0039… 0079 Rome Italia Birt… Natu…
#> # ℹ 55 more rows
#> # ℹ 4 more variables: Killer <chr>, Dynasty <chr>, Era <chr>, Notes <chr>
consolidate(emperors, join = "left", resolve = "max", key = "ID")
#> Using `ID` for matching observations across datasets...
#> ✔ Matched 151 observations by `ID` variable in `emperors` datasets.
#> Using `ID` for matching observations across datasets...
#> Joining 3 datasets using a left join...
#> ✔ Joined 3 datasets using a left join.
#> Joining 3 datasets using a left join...
#> Found 12 conflicts to resolve by `max()`...
#> ✔ Resolved 3 related variables into `Begin`.
#> Found 12 conflicts to resolve by `max()`...
#> ✔ Resolved 3 related variables into `End`.
#> Found 12 conflicts to resolve by `max()`...
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `FullName = get(funny)(out, all_of(vars))`.
#> Caused by warning in `max()`:
#> ! no non-missing arguments, returning NA
#> ✔ Resolved 2 related variables into `FullName`.
#> Found 12 conflicts to resolve by `max()`...
#> Warning: There were 6 warnings in `mutate()`.
#> The first warning was:
#> ℹ In argument: `Birth = get(funny)(out, all_of(vars))`.
#> Caused by warning in `max()`:
#> ! no non-missing arguments, returning NA
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 5 remaining warnings.
#> ✔ Resolved 2 related variables into `Birth`.
#> Found 12 conflicts to resolve by `max()`...
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `Death = get(funny)(out, all_of(vars))`.
#> Caused by warning in `max()`:
#> ! no non-missing arguments, returning NA
#> ✔ Resolved 2 related variables into `Death`.
#> Found 12 conflicts to resolve by `max()`...
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `Dynasty = get(funny)(out, all_of(vars))`.
#> Caused by warning in `max()`:
#> ! no non-missing arguments, returning NA
#> ✔ Resolved 2 related variables into `Dynasty`.
#> Found 12 conflicts to resolve by `max()`...
#> # A tibble: 69 × 14
#> ID Begin End FullName Birth Death CityBirth ProvinceBirth Rise Cause
#> <chr> <mda> <mda> <chr> <mda> <mda> <chr> <chr> <chr> <chr>
#> 1 Augustus -003… 0014… Imperat… -006… 0014… Rome Italia Birt… Assa…
#> 2 Tiberius 0014… 0037… Tiberiv… -004… 0037… Rome Italia Birt… Assa…
#> 3 Caligula 0037… 0041… Gaivs I… 0012… 0041… Antitum Italia Birt… Assa…
#> 4 Claudius 0041… 0054… Tiberiv… -001… 0054… Lugdunum Gallia Lugdu… Birt… Assa…
#> 5 Nero 0054… 0068… Nero Cl… 0037… 0068… Antitum Italia Birt… Suic…
#> 6 Galba 0068… 0069… Servivs… -000… 0069… Terracina Italia Seiz… Assa…
#> 7 Otho 0069… 0069… Marcvs … 0032… 0069… Terentin… Italia Appo… Suic…
#> 8 Vitelli… 0069… 0069… Avlvs V… 0015… 0069… Rome Italia Seiz… Assa…
#> 9 Vespasi… 0069… 0079… Titvs F… 0009… 0079… Falacrine Italia Seiz… Natu…
#> 10 Titus 0079… 0081… Titvs F… 0039… 0081… Rome Italia Birt… Natu…
#> # ℹ 59 more rows
#> # ℹ 4 more variables: Killer <chr>, Dynasty <chr>, Era <chr>, Notes <chr>
# }