Title: Many Global Governance Datacubes
Version: 1.1.3
Date: 2025-09-30
Description: This is the core package offering a portal to the many packages universe. It includes functions to help researchers access, work across, and maintain ensembles of datasets on global governance called datacubes.
License: CC BY 4.0
URL: https://www.manydata.ch/
BugReports: https://github.com/globalgov/manydata/issues
Depends: R (≥ 3.5.0), cli, dplyr, messydates (≥ 0.5.0)
Imports: caret, dtplyr, ggplot2 (≥ 3.4.0), glmnet, httr, jsonlite, purrr, remotes, stringr, text2vec, tidyr
Suggests: testthat, readr, knitr, rmarkdown, ggVennDiagram, rlang
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.3
Config/Needs/build: roxygen2, devtools
Config/Needs/check: covr, lintr, spelling
Config/Needs/website: pkgdown
Config/testthat/parallel: true
Config/testthat/edition: 3
Config/testthat/start-first: compare
NeedsCompilation: no
Packaged: 2025-09-30 11:30:16 UTC; hollway
Author: James Hollway ORCID iD [cre, aut, ctb] (IHEID), Henrique Sposito ORCID iD [aut, ctb] (IHEID), Bernhard Bieri ORCID iD [ctb] (IHEID), Esther Peev ORCID iD [ctb] (IHEID), Jael Tan ORCID iD [ctb] (IHEID)
Maintainer: James Hollway <james.hollway@graduateinstitute.ch>
Repository: CRAN
Date/Publication: 2025-09-30 12:50:02 UTC

Call, download, and update many* packages

Description

call_packages() finds and download other packages that belong to the many universe of packages. It allows users to rapidly access the names and other descriptive information of these packages. If users intend to download and install a package listed, they can type the package name within the function.

Usage

call_packages(package, develop = FALSE)

Arguments

package

A character vector of package name. For multiple packages, please declare package names as a vector (e.g. c("package1", "package2")).

develop

Would you like to download the develop version of the package? FALSE by default.

Value

call_packages() returns a tibble with the 'many packages' currently available. If one or more package names are provided, these will be installed from Github.

See Also

Other call_: call_releases(), call_treaties()

Examples


#call_packages()
#call_packages("manyenviron")

Call releases historical milestones/releases

Description

The function will take a data frame that details this information, or more usefully, a Github repository listing.

Usage

call_releases(repo, begin = NULL, end = NULL)

Arguments

repo

the github repository to track, e.g. "globalgov/manydata"

begin

When to begin tracking repository milestones. By default NULL, two months before the first release.

end

When to end tracking repository milestones. By default NULL, two months after the latest release.

Details

The function creates a project timeline graphic using ggplot2 with historical milestones and milestone statuses gathered from a specified GitHub repository.

Value

A ggplot graph object

Source

https://benalexkeen.com/creating-a-timeline-graphic-using-r-and-ggplot2/

See Also

Other call_: call_packages(), call_treaties()

Examples


#call_releases("globalgov/manydata")
#call_releases("manypkgs")

Call sources and citations

Description

These functions call any source or citation information that is available for a datacube or dataset. The function can be used on its own to the console, called during another function call such as consolidate() or pluck(), or is used to automatically and consistently populate help files.

Usage

call_sources(x)

call_citations(x, output = c("console", "help"))

Arguments

x

A datacube or dataset

output

Whether the output should be formatted for "console" or the "help" page.

Call treaties from 'many' datasets

Description

Call treaties from 'many' datasets

Usage

call_treaties(
  dataset,
  treaty_type = NULL,
  variable = NULL,
  actor = NULL,
  key = "manyID"
)

Arguments

dataset

A dataset in a datacube from one of the many packages. NULL by default. That is, all datasets in the datacube are used. For multiple datasets, please declare datasets as a vector (e.g. c("dataset1", "dataset2")).

treaty_type

The type of treaties to be returned. NULL, by default. Other options are "bilateral" or "multilateral".

variable

Would you like to get one, or more, specific variables present in one or more datasets in the 'many' datacube? NULL by default. For multiple variables, please declare variable names as a vector.

actor

An actor variable in dataset. NULL by default. If declared, a tibble of the treaties and their member actors is returned.

key

A variable key to join datasets. 'manyID' by default.

Details

Certain datasets, or consolidated datacubes, in 'many' packages contains information on treaties which can be retrieved with call_treaties().

Value

call_treaties() returns a tibble with a list of the agreements.

See Also

Other call_: call_packages(), call_releases()

Examples


membs <- dplyr::tibble(manyID = c("ROU-RUS[RFP]_1901A",
"ROU-RUS[RFP]_1901A", "GD16FI_1901A"),
stateID = c("ROU", "RUS", "DNK"),
Title = c("Convention Between Roumania And Russia Concerning Fishing
In The Danube And The Pruth",
"Convention Between Roumania And Russia Concerning Fishing
In The Danube And The Pruth",
"Convention Between The Governments Of Denmark And
The United Kingdom Of Great Britain
And Northern Ireland For Regulating The Fisheries
Of Their Respective Subjects Outside
Territorial Waters In The Ocean Surrounding The Faroe Islands"),
Begin = c("1901-02-22", "1901-02-22", "1901-06-24"))
call_treaties(membs)
call_treaties(membs, treaty_type = "bilateral",
variable = c("Title", "Begin"))
call_treaties(membs, variable = c("Title", "Begin"), actor = "stateID")

Extending codes

Description

These functions use text embeddings and multinomial logistic regression to suggest missing codes or flag potentially incorrect codes based on text data. Two approaches are provided: one using GloVe embeddings trained on the input text, and another using pre-trained BERT embeddings via the {text} package. Both functions require a vector of text (e.g., titles or descriptions) and a corresponding vector of categorical codes, with NA or empty strings indicating missing codes to be inferred. The functions train a multinomial logistic regression model using glmnet on the text embeddings of the entries with known codes, and then predict codes for the entries with missing codes. The functions also validate the model's performance on a holdout set and report per-class precision, recall, and F1-score. If no missing codes are present, the functions instead check existing codes for potential mismatches and report them.

Usage

code_extend_glove(titles, var, req_f1 = 0.8, rarity_threshold = 8)

code_extend_bert(titles, var, req_f1 = 0.8, rarity_threshold = 8, emb_texts)

Arguments

titles

A character vector of text entries (e.g., titles or descriptions).

var

A character vector of (categorical) codes that might be coded from the titles or texts. Entries with missing codes should be NA_character_ or empty strings. The function will suggest codes for these entries. If no missing codes are present, the function will check existing codes for potential mismatches.

req_f1

The required macro-F1 score on the validation set before proceeding with inference. Default is 0.80.

rarity_threshold

Minimum number of occurrences for a code to be included in training. Codes with fewer occurrences are excluded from training to ensure sufficient data for learning. Default is 8.

emb_texts

For code_extend_bert(), pre-computed embeddings from text::textEmbed(). This avoids re-computing embeddings if they have already been computed. A Hugging Face model can be specified via the model argument. Default is "sentence-transformers/all-MiniLM-L6-v2". Other models can be used, but they should produce sentence-level embeddings.

Examples

titles <- paste(emperors$Wikipedia$CityBirth,
                emperors$Wikipedia$ProvinceBirth,
                emperors$Wikipedia$Rise,
                emperors$Wikipedia$Dynasty,
                emperors$Wikipedia$Cause)
var <- emperors$Wikipedia$Killer
var[var=="Unknown"] <- NA
var[var %in% c("Senate","Court Officials","Opposing Army")] <- "Enemies"
var[var %in% c("Fire","Lightning","Aneurism","Heart Failure")] <- "God"
var[var %in% c("Wife","Usurper","Praetorian Guard","Own Army")] <- "Friends"
glo <- code_extend_glove(titles, 
           var)

Compare categories in 'many' datacubes

Description

Compare categories in 'many' datacubes

Usage

compare_categories(
  datacube,
  dataset = "all",
  key = "manyID",
  variable = "all",
  category = "all"
)

Arguments

datacube

A datacube from one of the many packages.

dataset

A dataset in a datacube from one of the many packages. By default "all". That is, all datasets in the datacube are used. To select two or more datasets, please declare them as a vector.

key

A variable key to join datasets. 'manyID' by default.

variable

Would you like to focus on one, or more, specific variables present in one or more datasets in the 'many' datacube? By default "all". For multiple variables, please declare variable names as a vector.

category

Would you like to focus on one specific code category? By default "all" are returned. Other options include "confirmed", "unique", "missing", "conflict", or "majority". For multiple variables, please declare categories as a vector.

Details

Confirmed values are the same in all datasets in datacube. Unique values appear once in datasets in datacube. Missing values are missing in all datasets in datacube. Conflict values are different in the same number of datasets in datacube. Majority values have the same value in multiple, but not all, datasets in datacube.

See Also

Other compare_: compare_dimensions(), compare_missing(), compare_overlap()

Examples


compare_categories(emperors, key = "ID")
compare_categories(datacube = emperors, dataset = c("wikipedia", "UNRV"),
key = "ID", variable = c("Beg", "End"), category = c("conflict", "unique"))
plot(compare_categories(emperors, key = "ID"))
plot(compare_categories(datacube = emperors, dataset = c("wikipedia", "UNRV"),
key = "ID", variable = c("Beg", "End"), category = c("conflict", "unique")))

Compare two datasets for differences

Description

Compare two datasets for differences

Usage

compare_new(.data1, .data2, by = "ID")

compare_diff(
  .data1,
  .data2,
  by = "ID",
  exclude = c("Title", "Coder", "Comments"),
  diff_threshold = 0
)

Arguments

.data1

First dataset to compare

.data2

Second dataset to compare

by

Column name to join on (default is "ID")

exclude

Character vector of column names to exclude from comparison. By default, "Title", "Coder", and "Comments" are excluded.

diff_threshold

Integer specifying the minimum number of differing columns for a row to be included in the output. Default is 0, meaning any difference will be included. Set to 3 to only show rows with at least 3 differing columns.

Details

This function uses dplyr::anti_join to find rows in .data1 that are not present in .data2. If no differences are found, a message is printed and NULL is returned. If differences are found, they are returned as a data frame.

Value

A data frame with the differences found

Examples

## Not run: 
df1 <- data.frame(ID = 1:5, Value = letters[1:5])
df2 <- data.frame(ID = 3:7, Value = letters[3:7])
compare_new(df1, df2)
compare_new(df1, df1)

## End(Not run)
compare_diff(emperors$Wikipedia, emperors$Britannica)

Compare dimensions for 'many' data

Description

Compare dimensions for 'many' data

Usage

compare_dimensions(datacube, dataset = "all")

Arguments

datacube

A datacube from one of the many packages.

dataset

A dataset in a datacube from one of the many packages. By default, "all". That is, all datasets in the datacube are used. To select two or more datasets, please declare them as a vector.

Details

compare_dimensions() compares the number of observations, variables, the earliest date, and the latest date in all observations for datasets in a 'many' datacube.

Value

compare_dimensions() returns a tibble with information about each dataset including the number of observations, the number of variables, the earliest date, and the latest date in all observations.

See Also

Other compare_: compare_categories(), compare_missing(), compare_overlap()

Examples


compare_dimensions(emperors)

Compare missing observations for 'many' data

Description

Compare missing observations for 'many' data

Usage

compare_missing(datacube, dataset = "all", variable = "all")

Arguments

datacube

A datacube from one of the many packages.

dataset

A dataset in a datacube from one of the many packages. NULL by default. That is, all datasets in the datacube are used. To select two or more datasets, please declare them as a vector.

variable

Would you like to focus on one, or more, specific variables present in one or more datasets in the 'many' datacube? By default "all". For multiple variables, please declare variable names as a vector.

Details

compare_missing() compares the missing observations for variables in each dataset in a 'many' datacube.

Value

compare_missing() returns a tibble with information about each dataset including the number of observations, the number of variables, the earliest date, and the latest date in all observations.

See Also

Other compare_: compare_categories(), compare_dimensions(), compare_overlap()

Examples


compare_missing(emperors)
plot(compare_missing(emperors))

Compare the overlap between datasets in 'many' datacubes

Description

Compare the overlap between datasets in 'many' datacubes

Usage

compare_overlap(datacube, dataset = "all", key = NULL)

Arguments

datacube

A datacube from one of the many packages.

dataset

A dataset in a datacube from one of the many packages. By default "all". That is, all datasets in the datacube are used.

key

A variable key to join datasets. 'manyID' by default.

Details

compare_overlap() compares the overlap between "key" observations in each dataset in a 'many' datacube.

Value

compare_overlap() returns a tibble with information about each dataset and the number of overlapping observations.

See Also

Other compare_: compare_categories(), compare_dimensions(), compare_missing()

Examples


compare_overlap(emperors, key = "ID")
plot(compare_overlap(emperors, key = "ID"))

Consolidate datacube into a single dataset

Description

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.

Value

A single tibble/data frame.

Examples


consolidate(emperors, join = "full", resolve = "coalesce", key = "ID")
consolidate(emperors, join = "inner", resolve = "min", key = "ID")
consolidate(emperors, join = "left", resolve = "max", key = "ID")

Functions that have been renamed, superseded, or are no longer working

Description

[Deprecated] Generally these functions have been superseded or renamed. Upon using them, a message is provided directing the user to the new function. However, at this stage of package development, we generally clear older defunct functions at each minor release, and so you are strongly encouraged to use the new functions/names/syntax wherever possible and update your scripts accordingly.

Usage

coalesce_compatible(.data)

network_map(.data)

db_plot(.data)

db_comp(.data)

retrieve_bilaterals(.data)

retrieve_multilaterals(.data)

retrieve_links(.data)

retrieve_membership_list(.data)

data_source(.data)

get_packages(.data)

open_codebook(.data)

data_contrast(.data)

data_evolution(.data)

plot_releases(.data)

Functions

Data reports for datacubes and datasets with 'mdate' variables

Description

These functions provide meta level descriptions of datacubes or datasets. mreport() creates a properly formatted data report for datasets which contain 'mdate' class objects, alongside other object classes. describe_datacube() prints a text description of the datasets in a datacube.

Usage

mreport(data)

describe_datacube(datacube)

Arguments

data

A {tibble} or a {data.frame}.

datacube

A datacube

Details

'mreport' displays the variable's name, the variable type, the number of observations per variable, the number of missing observations for variable, and the percentage of missing observations in variable.

Value

A data report of class 'mreport'.

Examples

mreport(emperors)

Emperors datacube documentation

Description

The emperors datacube is a list containing 3 datasets: Wikipedia, UNRV, and Britannica

Usage

emperors

Format

Wikipedia:

A dataset with 68 observations and the following 15 variables: ID, Begin, End, FullName, Birth, Death, CityBirth, ProvinceBirth, Rise, Cause, Killer, Dynasty, Era, Notes, Verif.

UNRV:

A dataset with 99 observations and the following 7 variables: ID, Begin, End, Birth, Death, FullName, Dynasty.

Britannica:

A dataset with 87 observations and the following 3 variables: ID, Begin, End.

Details

#> $Wikipedia
#> --------------------------------------------------------
#> |  Variable   |  Class  |  Obs  |  Missing  |  Miss %  |
#> --------------------------------------------------------
#> |ID           |character|     69|          0|         0|
#> |Begin        |mdate    |     69|          0|         0|
#> |End          |mdate    |     69|          0|         0|
#> |FullName     |character|     68|          1|      1.45|
#> |Birth        |mdate    |     63|          6|       8.7|
#> |Death        |mdate    |     68|          1|      1.45|
#> |CityBirth    |character|     51|         18|     26.09|
#> |ProvinceBirth|character|     68|          1|      1.45|
#> |Rise         |character|     68|          1|      1.45|
#> |Cause        |character|     68|          1|      1.45|
#> |Killer       |character|     68|          1|      1.45|
#> |Dynasty      |character|     68|          1|      1.45|
#> |Era          |character|     68|          1|      1.45|
#> |Notes        |character|     46|         23|     33.33|
#> --------------------------------------------------------
#> 
#> 
#> $UNRV
#> -------------------------------------------------------
#> |  Variable  |  Class  |  Obs  |  Missing  |  Miss %  |
#> -------------------------------------------------------
#> |ID          |character|     98|          0|         0|
#> |Begin       |mdate    |     98|          0|         0|
#> |End         |mdate    |     98|          0|         0|
#> |Birth       |mdate    |     74|         24|     24.49|
#> |Death       |mdate    |     98|          0|         0|
#> |FullName    |character|     93|          5|       5.1|
#> |Dynasty     |character|     61|         37|     37.76|
#> -------------------------------------------------------
#> 
#> 
#> $Britannica
#> -------------------------------------------------------
#> |  Variable  |  Class  |  Obs  |  Missing  |  Miss %  |
#> -------------------------------------------------------
#> |ID          |character|     87|          0|         0|
#> |Begin       |mdate    |     87|          0|         0|
#> |End         |mdate    |     87|          0|         0|
#> -------------------------------------------------------

URL

Mapping

from to
name ID
reign.start Begin
reign.end End
name.full FullName
birth Birth
death Death
birth.cty CityBirth
birth.prv ProvinceBirth
rise Rise
cause Cause
killer Killer
dynasty Dynasty
era Era
notes Notes
verif.who Verif
from to
'Common Name' ID
Beg Begin
'Full Name/Imperial Name' FullName
'Dynasty/Class/Notes' Dynasty
from to
Name ID
reign_start Begin
reign_end End

Source

Filtering datacube datasets to a certain date

Description

Filtering datacube datasets to a certain date

Usage

filter_datacube(datacube, date = Sys.Date())

Arguments

datacube

A datacube, i.e. a list of data frames with Begin and End date variables.

date

A date (of class Date or character) at which to filter the datacube.

Examples

filter_datacube(emperors, date = "0100")

Find elements within manydata

Description

Find elements within manydata

Usage

find_ID(df, id_col = "ID")

find_common_ID(..., id_col = "ID")

find_duplicates(df, id_col = "ID")

Arguments

df

A data frame to be scored.

id_col

The name of the column containing IDs. Default is "ID".

...

Data frames to compare

Examples

find_duplicates(emperors$Wikipedia)

Creates Numerical IDs from Signature Dates

Description

Agreements should have a unique identification number that is meaningful, we condense their signature dates to produce this number.

Usage

find_year(date)

Arguments

date

A date variable

Value

A character vector with condensed dates

Examples

## Not run: 
IEADB <- dplyr::slice_sample(manyenviron::agreements$IEADB, n = 10)
code_dates(IEADB$Title)

## End(Not run)

Selects a single dataset from a datacube

Description

This function is reexported/wrapped from the {purrr} package. It allows users to select a single dataset from one of the datacubes available across the 'many* packages'. It additionally invites users to cite the selected dataset.

Usage

pluck(.x, ..., .default = NULL)

Arguments

.x

The datacube

...

The name of the dataset in the datacube

.default

Value to use if target is NULL or absent.

Value

The selected dataset

Examples


pluck(emperors, "UNRV")

Pastes unique string vectors

Description

For use with dplyr::summarise, for example

Usage

recollect(x, collapse = "_")

Arguments

x

A vector

collapse

String indicating how elements separated

Details

This function operates similarly to reunite, but instead of operating on columns/observations, it pastes together unique rows/observations.

Value

A single value

Examples


data <- data.frame(ID = c(1,2,3,3,2,1))
data1 <- data.frame(ID = c(1,2,3,3,2,1), One = c(1,NA,3,NA,2,NA))
recollect(data$ID)
recollect(data1$One)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

dplyr

%>%

Fills missing data by lookup

Description

Fills missing data where known by other observations with the same id/index

Usage

repaint(df, id, var)

Arguments

df

a dataframe

id

a string identifying a column in the dataframe for indexing

var

a string identifying a column or columns in the dataframe to be filled

Value

A dataframe

Examples


data <- data.frame(ID = c(1,2,3,3,2,1),
                    One = c(1,NA,3,NA,2,NA),
                    Two = c(NA,"B",NA,"C",NA,"A"))
repaint(data, "ID", c("One","Two"))

Resolving multiple observations of the same variable into one

Description

This family of functions provides row-wise summarization for data frames or tibbles, returning a single value per row based on specified columns. They are useful for tasks like extracting typical or summary values from multiple variables, simplifying wide data structures, and imputing representative values.

Usage

resolve_unite(.data, vars, na.rm = TRUE)

resolve_coalesce(.data, vars)

resolve_min(.data, vars, na.rm = TRUE)

resolve_max(.data, vars, na.rm = TRUE)

resolve_random(.data, vars, na.rm = TRUE)

resolve_precision(.data, vars)

resolve_mean(.data, vars, na.rm = TRUE)

resolve_mode(.data, vars, na.rm = TRUE)

resolve_median(.data, vars, na.rm = TRUE)

resolve_consensus(.data, vars, na.rm = TRUE)

Arguments

.data

A data frame or tibble containing the variables.

vars

A vector of variables from .data to be resolved or converged. If this argument is left unspecified, then all variables will be merged together.

na.rm

Logical whether missing values (NAs) should be removed before operation of the function. Note that unlike how the na.rm argument operates in functions in base R, e.g. max(), here the default is TRUE.

Unite

Uniting returns all the unique values as a set, separated by commas and contained within braces. Note that uniting always returns a character/string vector, which enables it to accommodate different classes of variables. The order of the values reflects their first appearance; that is, they are not ordered by increasing value.

Coalesce

Coalescing returns a vector of the first non-missing values found when reading the variables from left to right. That is, missing values in the first vector may be filled by observations in the second vector, or later vectors if the second vector also misses an observation for that cell. Variables can be reordered manually.

Min and Max

These functions return a vector containing each row's minimum or maximum value. Note that these functions work not only on numeric and date vectors, but also on character string vectors. For character data, these functions will return the shortest or longest strings, respectively, in each row.

Random

This function returns a vector of values selected randomly from among the values contained in each row. Note that by default na.rm = TRUE, which means that missing data will not be selected at random by default, which can also change the probability distribution by each row. Where na.rm = FALSE, the probability of each value being selected is uniform.

Precision

This function returns a vector that maximises the precision of the values in each row. For numeric vectors, precision is expressed in significant digits, such that 1.01 would be more precise than 1. For character vectors, precision is expressed in terms of the character length proportional to the max character length in the row. This applies also to messydates, meaning precision is expressed in the lowest level date component specified, such that 2008-10 would be more precise than 2008, and 2008-10-10 would be more precise still.

Mean and median

These functions return a vector of the means or medians, respectively, of the values in each row.

Consensus

This function returns a vector of consensus values, i.e. where there is no variation in values by each row. If the values (excluding missing values by default) are not equivalent, then an NA is returned for that row.

Examples

test <- data.frame(preferred_dataset = c(1,6,NA), 
                   more_comprehensive = c(1,3,3), 
                   precise_where_available = c(NA,3.3,4.1))
test
resolve_unite(test)
resolve_coalesce(test)
resolve_min(test)
resolve_max(test)
resolve_random(test)
resolve_precision(test)
resolve_mean(test)
resolve_mode(test)
resolve_median(test)
resolve_consensus(test)

Pastes unique string vectors

Description

A vectorised function for use with dplyr's mutate, etc

Usage

reunite(..., sep = "_")

Arguments

...

Variables to pass to the function, currently only two at a time

sep

Separator when vectors reunited, by default "_"

Value

A single vector with unique non-missing information

Examples


data <- data.frame(fir=c(NA, "two", "three", NA),
                   sec=c("one", NA, "three", NA), stringsAsFactors = FALSE)
transmutate(data, single = reunite(fir, sec))

Scoring Functions for Data Quality Checks

Description

A set of functions to assess various aspects of data quality. including a comprehensive dataset score as well as individual scores for specific data quality dimensions such as date consistency, duplicates, recency, frequency, time, coding, comments, sources, missing values, and variables.

According to the literature, data quality can be assessed by checking for consistency, completeness, accuracy, timeliness, and uniqueness of the data. Consistency means that the data is logically coherent, completeness means that all required data is present, accuracy means that the data is correct and reliable, timeliness means that the data is up-to-date, and uniqueness means that there are no duplicate records.

Usage

score_dataset(df)

score_obs_no(df)

score_var_no(df)

score_completeness(df)

score_date_consistency(df)

score_date_scope(df)

score_obs_info(df, id_col = "ID")

score_coding(df)

score_comments(df)

score_var_info(df)

Arguments

df

A data frame to be scored.

id_col

The name of the column containing IDs. Default is "ID".

Details

These functions are designed to help assess the quality of data in a data frame. Each function checks a specific aspect of the data and returns a score or a message indicating the quality of that aspect. The functions include:

References

Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-34.

Examples

score_dataset(emperors)
score_obs_no(emperors)
score_var_no(emperors)
score_completeness(emperors)
score_date_consistency(emperors)
score_date_scope(emperors)
score_obs_info(emperors)
score_var_info(emperors)

Drop only columns used in formula

Description

A function between dplyr's transmute and mutate

Usage

transmutate(.data, ...)

Arguments

.data

Data frame to pass to the function

...

Variables to pass to the function

Value

Data frame with mutated variables and none of the variables used in the mutations, but, unlike dplyr::transmute(), all other unnamed variables.

Source

https://stackoverflow.com/questions/51428156/dplyr-mutate-transmute-drop-only-the-columns-used-in-the-formula

Examples


pluck(emperors, "Wikipedia")
transmutate(emperors$Wikipedia, Beginning = Begin)