Help for package rtry

Title:

Preprocessing Plant Trait Data

Version:

1.1.0

Description:

Designed to support the application of plant trait data providing easy applicable functions for the basic steps of data preprocessing, e.g. data import, data exploration, selection of columns and rows, excluding trait data according to different attributes, geocoding, long- to wide-table transformation, and data export. 'rtry' was initially developed as part of the TRY R project to preprocess trait data received via the TRY database.

License:

CC BY 4.0

Depends:

R (≥ 4.0.0)

Imports:

utils, data.table, dplyr, tidyr, jsonlite, curl, magrittr

URL:

https://github.com/MPI-BGC-Functional-Biogeography/rtry, https://www.try-db.org/TryWeb/Home.php

BugReports:

https://github.com/MPI-BGC-Functional-Biogeography/rtry/issues

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.2.3

Suggests:

knitr, rmarkdown, testthat

VignetteBuilder:

knitr

Maintainer:

Olee Hoi Ying Lam <hlam9@wisc.edu>

NeedsCompilation:

Packaged:

2023-08-09 20:01:58 UTC; hlam9

Author:

Olee Hoi Ying Lam

[aut, cre], Susanne Tautenhahn

[aut], Gabriel Walther

[aut], Gerhard Boenisch [aut], Pramod Baddam

[aut], Jens Kattge

[aut], Fellowship Group Functional Biogeography at MPI-BGC, Jena, Germany [cph]

Repository:

CRAN

Date/Publication:

2023-08-09 20:40:21 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling rhs(lhs).

Sample TRY data (Request 15160)

Description

A dataset requested from the TRY Database. The request ID of this dataset is 15160, which contains TraitID: 3115, 3116 and AccSpeciesID: 10773, 35846, 45737. The raw dataset (data_TRY_15160.txt) is also provided in the directory testdata.

Usage

data_TRY_15160

Format

A data frame with 1782 rows and 28 variables:

LastName: Surname of data contributor.
FirstName: First name of data contributor.
DatasetID: Unique identifier of contributed dataset.
Dataset: Name of contributed dataset
SpeciesName: Original name of species.
AccSpeciesID: Unique identifier of consolidated species name.
AccSpeciesName: Consolidated species name.
ObservationID: Unique identifier for each observation in TRY.
ObsDataID: Unique identifier for each row in the TRY data table, either trait record or ancillary data.
TraitID: Unique identifier for traits (only if the record is a trait).
TraitName: Name of trait (only if the record is a trait).
DataID: Unique identifier for each DataName (either sub-trait or ancillary data).
DataName: Name of sub-trait or ancillary data.
OriglName: Original name of sub-trait or ancillary data.
OrigValueStr: Original value of trait or ancillary data.
OrigUnitStr: Original unit of trait or ancillary data.
ValueKindName: Value kind (single measurement, mean, median, etc.).
OrigUncertaintyStr: Original uncertainty.
UncertaintyName: Kind of uncertainty (standard deviation, standard error, etc.).
Replicates: Number of replicates.
StdValue: Standardized trait value: available for frequent continuous traits.
UnitName: Standard unit: available for frequent continuous traits.
RelUncertaintyPercent: Relative uncertainty in %.
OrigObsDataID: Unique identifier for duplicate trait records.
ErrorRisk: Indication for outlier trait values: distance to mean in standard deviations.
Reference: Reference to be cited if trait record is used in analysis.
Comment: Explanation for the OriglName in the contributed dataset.
V28: Empty, an artifact due to different interpretation of column separator by MySQL and R.

Value

A data frame with 1782 rows and 28 variables of sample TRY data (Request 15160).

Sample TRY data (Request 15161)

Description

A dataset requested from the TRY Database. The request ID of this dataset is 15161, which contains TraitID: 3117 and AccSpeciesID: 10773, 35846, 45737. The raw dataset (data_TRY_15161.txt) is also provided in the directory testdata.

Usage

data_TRY_15161

Format

A data frame with 4627 rows and 28 variables:

LastName: Surname of data contributor.
FirstName: First name of data contributor.
DatasetID: Unique identifier of contributed dataset.
Dataset: Name of contributed dataset
SpeciesName: Original name of species.
AccSpeciesID: Unique identifier of consolidated species name.
AccSpeciesName: Consolidated species name.
ObservationID: Unique identifier for each observation in TRY.
ObsDataID: Unique identifier for each row in the TRY data table, either trait record or ancillary data.
TraitID: Unique identifier for traits (only if the record is a trait).
TraitName: Name of trait (only if the record is a trait).
DataID: Unique identifier for each DataName (either sub-trait or ancillary data).
DataName: Name of sub-trait or ancillary data.
OriglName: Original name of sub-trait or ancillary data.
OrigValueStr: Original value of trait or ancillary data.
OrigUnitStr: Original unit of trait or ancillary data.
ValueKindName: Value kind (single measurement, mean, median, etc.).
OrigUncertaintyStr: Original uncertainty.
UncertaintyName: Kind of uncertainty (standard deviation, standard error, etc.).
Replicates: Number of replicates.
StdValue: Standardized trait value: available for frequent continuous traits.
UnitName: Standard unit: available for frequent continuous traits.
RelUncertaintyPercent: Relative uncertainty in %.
OrigObsDataID: Unique identifier for duplicate trait records.
ErrorRisk: Indication for outlier trait values: distance to mean in standard deviations.
Reference: Reference to be cited if trait record is used in analysis.
Comment: Explanation for the OriglName in the contributed dataset.
V28: Empty, an artifact due to different interpretation of column separator by MySQL and R.

Value

A data frame with 4627 rows and 28 variables of sample TRY data (Request 15161).

Sample coordinates data

Description

A dataset containing 20 sets of latitudes and longitudes in WGS84 projection. The raw dataset (data_coordinates.csv) is also provided in the directory testdata.

Usage

data_coordinates

Format

A data frame with 20 rows and 2 variables:

Latitude: Latitude, in WGS84 projection.
Longitude: Longitude, in WGS84 projection.

Value

A data frame with 20 rows and 2 variables of sample coordinates data.

Sample locations data

Description

A dataset containing 20 location information. The raw dataset (data_locations.csv) is also provided in the directory testdata.

Usage

data_locations

Format

A data frame with 20 rows and 3 variables:

Country code: Country code.
Country: Full name of a country.
Location: Specific location, e.g. town or city name.

Value

A data frame with 20 rows and 3 variables of sample locations data.

Bind data by columns

Description

This function takes a list of data frames or data tables and combines them by columns. The data have to have the same number and sequence of rows.

Usage

rtry_bind_col(..., showOverview = TRUE)

Arguments

...

A list of data frames or data tables to be combined by columns.

showOverview

Default TRUE displays the dimension and column names of the combined data.

Value

An object of the same type as the first input.

Note

A common attribute is not necessary (difference to the function rtry_join_left and rtry_join_outer): the binding process simply puts the data side-by-side.

References

This function makes use of the bind_cols function within the dplyr package.

Examples

# Assuming a user has selected different columns as separated data tables
# and later on would like to combine them as one for further processing.
data1 <- rtry_select_col(data_TRY_15160,
           ObsDataID, ObservationID, AccSpeciesID, AccSpeciesName, ValueKindName,
           TraitID, TraitName, DataID, DataName, OrigObsDataID, ErrorRisk, Comment)

data2 <- rtry_select_col(data_TRY_15160,
           OriglName, OrigValueStr, OrigUnitStr, StdValue, UnitName)

data <- rtry_bind_col(data1, data2)

# Expected messages:
# dim:   1782 12
# col:   ObsDataID ObservationID AccSpeciesID AccSpeciesName ValueKindName TraitID
#        TraitName DataID DataName OrigObsDataID ErrorRisk Comment
#
# dim:   1782 5
# col:   OriglName OrigValueStr OrigUnitStr StdValue UnitName
#
# dim:   1782 17
# col:   ObsDataID ObservationID AccSpeciesID AccSpeciesName ValueKindName TraitID
#        TraitName DataID DataName OrigObsDataID ErrorRisk Comment OriglName
#        OrigValueStr OrigUnitStr StdValue UnitName

Bind data by rows

Description

This function takes a list of data frames or data tables and combines them by rows, it adds the rows of the second data below the rows of the first one.

Usage

rtry_bind_row(..., showOverview = TRUE)

Arguments

...

A list of data frames or data tables to be combined by rows.

showOverview

Default TRUE displays the dimension and column names of the combined data.

Value

An object of the same type as the first input. The object will contain a column if that column appears in any of the inputs.

Note

A common attribute is not necessary (difference to the function rtry_join_left and rtry_join_outer): the binding process simply puts the data one after another while matching the column names, and any missing columns will be filled with NA.

References

This function makes use of the bind_rows function within the dplyr package.

Examples

# Combine the two provided sample data (data_TRY_15160 and data_TRY_15161)
data <- rtry_bind_row(data_TRY_15160, data_TRY_15161)

# Expected message:
# dim:   6409 28
# col:   LastName FirstName DatasetID Dataset SpeciesName AccSpeciesID AccSpeciesName
#        ObservationID ObsDataID TraitID TraitName DataID DataName OriglName
#        OrigValueStr OrigUnitStr ValueKindName OrigUncertaintyStr UncertaintyName
#        Replicates StdValue UnitName RelUncertaintyPercent OrigObsDataID ErrorRisk
#        Reference Comment V28

Exclude (remove) data

Description

This function takes the input data frame or data table and excludes all records (rows) with the same value in the attribute specified in the argument baseOn if the criteria specified in the arguments for excluding (...) are fulfilled for one of those records.

Usage

rtry_exclude(input, ..., baseOn, showOverview = TRUE)

Arguments

input

Input data frame or data table.

...

Criteria for excluding.

baseOn

The attribute on which excluding is based on. If it is set to ObservationID, the function excludes all records with the respective ObservationID if the specified criteria for excluding is fulfilled for one record. Alternatively, use ObsDataID to exclude only the record (row) for which the specified criterion is fulfilled. Other reasonable parameter values are TraitID, DataID or AccSpeciesID.

showOverview

Default TRUE displays the dimension of the data after excluding.

Value

An object of the same type as the input data after excluding.

References

This function makes use of the subset function within the base package.

Examples

# Example 1: Exclude observations on juvenile plants or unknown state:
# Identify observations where the plant developmental status (DataID 413) is either
# "juvenile" or "unknown", and exclude the whole observation
data_filtered <- rtry_exclude(data_TRY_15160,
                   (DataID %in% 413) & (OrigValueStr %in% c("juvenile", "unknown")),
                   baseOn = ObservationID)

# Expected message:
# dim:   1618 28

# Example 2: Exclude outliers:
# Identify the outliers, i.e. trait records where the ErrorRisk is larger than 4
# and exclude these records (not the whole observation)
data_filtered <- rtry_exclude(data_TRY_15160,
                   ErrorRisk > 4,
                   baseOn = ObsDataID)

# Expected message:
# dim:   1778 28

# Learn more applications of the excluding function via the vignette (Workflow for
# general data preprocessing using rtry): vignette("rtry-workflow-general").

Explore data

Description

This function takes a data frame or data table and converts it into a grouped data frame of unique values based on the specified column names. A column (Count) is added, which shows the number of records within each group. The data are grouped by the first attribute if not specified with the argument sortBy.

Usage

rtry_explore(input, ..., sortBy = "", showOverview = TRUE)

Arguments

input

Data frame or data table, e.g. from rtry_import().

...

Attribute names to group together.

sortBy

(Optional) Default "" indicates no sorting is applied to the grouped data. Specify the attribute name used to re-order the rows in ascending order.

showOverview

Default TRUE displays the dimension of the result data table.

Value

A data frame of unique values grouped and sorted by the specified attribute(s).

References

This function makes use of the group_by, summarise and arrange functions within the dplyr package.

Examples

# Explore the unique values in the provided sample data (data_TRY_15160)
# based on the attributes AccSpeciesID, AccSpeciesName, TraitID, TraitName, DataID
# and DataName, sorted by TraitID
data_explore <- rtry_explore(data_TRY_15160,
                  AccSpeciesID, AccSpeciesName, TraitID, TraitName, DataID, DataName,
                  sortBy = TraitID)

# Expected message:
# dim:   235 7

# Learn more applications of the explore function via the vignette (Workflow for
# general data preprocessing using rtry): vignette("rtry-workflow-general").

Export preprocessed data

Description

This function exports the preprocessed data as comma separated values to a .csv file. If the specified output directory does not exist, it will be created.

Usage

rtry_export(data, output, quote = TRUE, encoding = "UTF-8")

Arguments

data

The data to be saved.

output

Output path.

quote

Default TRUE inserts double quotes around any character or factor columns.

encoding

Default "UTF-8". File encoding.

Value

No return value, called for exporting a .csv file.

References

This function makes use of the write.csv function within the utils package.

Examples

# Export the preprocessed data to a specific location
rtry_export(data_TRY_15160, file.path(tempdir(), "TRYdata_unprocessed.csv"))

# Expected message:
# File saved at: C:\Users\user\AppData\Local\Temp\Rtmp4wJAvQ/TRYdata_unprocessed.csv

Perform geocoding

Description

This function uses Nominatim, a search engine for OpenStreetMap (OSM) data, to perform geocoding, i.e. converting an address into coordinates (latitudes, longitudes). The data provided by OSM is free to use for any purpose, including commercial use, and is governed by the distribution license ODbL.

Usage

rtry_geocoding(address, email)

Arguments

address

String of an address.

email

String of an email address.

Value

A data frame that contains latitudes (lat) and longitudes (lon) in WGS84 projection.

Examples

## Not run: 
# Convert the address of MPI-BGC ("Hans-Knoell-Strasse 10, 07745 Jena, Germany")
# into coordinates in latitudes and longitudes
# Note: Please change to your own email address when executing this function
rtry_geocoding("Hans-Knoell-Strasse 10, 07745 Jena, Germany",
   email = "john.doe@example.com")

# Expected message:
#        lat      lon
# 1 50.9101 11.56674

## End(Not run)

# Learn to perform geocoding to a list of locations via the vignette (Workflow for
# geocoding using rtry): vignette("rtry-workflow-geocoding").

Import data

Description

This function imports a data file as a data.table for further processing. The default arguments are set to import tabulartor delimited data files in text format (.txt) exported from the TRY database. It can also be used to import other file formats, such as .csv files with comma separated values.

Usage

rtry_import(
  input,
  separator = "\t",
  encoding = "Latin-1",
  quote = "",
  showOverview = TRUE
)

Arguments

input

Path to the data file.

separator

Default "\t" for the TRY data output. Data separator.

encoding

Default "Latin-1". File encoding.

quote

Default "" reads the fields as is. If the fields in the data file are by a double quote, use "\"" instead.

showOverview

Default TRUE displays the input path, the dimension and the column names of the imported data.

Value

A data.table.

References

This function makes use of the fread function within the data.table package.

Examples

# Example 1: Import data exported from the TRY database
# Specify file path to the raw data provided within the rtry package
input_path <- system.file("testdata", "data_TRY_15160.txt", package = "rtry")

# For own data and Windows users the path might rather look similar to this:
# input_path <- "C:/Users/User/Desktop/data_TRY_15160.txt"

# Import data file using rtry_import
input <- rtry_import(input_path)

# Explicit notation:
# input <- rtry_import(input_path, separator = "\t", encoding = "Latin-1",
#            quote = "", showOverview = TRUE)

# Expected message:
# input: ~/R/R-4.0.5/library/rtry/testdata/data_TRY_15160.txt
# dim:   1782 28
# col:   LastName FirstName DatasetID Dataset SpeciesName AccSpeciesID AccSpeciesName
#        ObservationID ObsDataID TraitID TraitName DataID DataName OriglName
#        OrigValueStr OrigUnitStr ValueKindName OrigUncertaintyStr UncertaintyName
#        Replicates StdValue UnitName RelUncertaintyPercent OrigObsDataID ErrorRisk
#        Reference Comment V28

# Example 2: Import CSV file
# Specify file path to the raw data provided within the rtry package
input_path <- system.file("testdata", "data_locations.csv", package = "rtry")

# Import data file using rtry_import
input <- rtry_import(input_path, separator = ",", encoding = "UTF-8",
           quote = "\"", showOverview = TRUE)

# Expected message:
# input: ~/R/R-4.0.5/library/rtry/testdata/data_locations.csv
# dim:   20 3
# col:   Country code Country Location

Left join for two data frames

Description

This function merges two data frames or data tables based on a specified common column and returns all records from the left data frame (x) together with the matched records from the right data frame (y), while discards all the records in the right data frame that does not exist in the left data frame. In other words, this function performs a left join on the two provided data frames or data tables.

Usage

rtry_join_left(x, y, baseOn, showOverview = TRUE)

Arguments

x

A data frame or data table to be coerced and will be considered as the data on the left.

y

A data frame or data table to be coerced and will be considered as the data on the right.

baseOn

The common column used for merging.

showOverview

Default TRUE displays the dimension and column names of the merged data.

Value

An object of the same type of the input data. The merged data is by default lexicographically sorted on the common column. The columns are the common column followed by the remaining columns in x and then those in y.

References

This function makes use of the merge function within the base package.

Examples

# Assume a user has obtained two unique data tables, one with the ancillary data
# Longitude and one with Latitude (e.g. using rtry_select_anc()), and would like to
# add a column Latitude to the data table with Longitude based on the common
# identifier ObservationID
lon <- rtry_select_anc(data_TRY_15160, 60)
lat <- rtry_select_anc(data_TRY_15160, 59)

georef <- rtry_join_left(lon, lat, baseOn = ObservationID)

# Expected messages:
# dim:   97 2
# col:   ObservationID Longitude
#
# dim:   98 2
# col:   ObservationID Latitude
#
# dim:   97 3
# col:   ObservationID Longitude Latitude

Outer join for two data frames

Description

This function merges two data frames or data tables based on a specified common column and returns all rows from both data, join records from the left (x) which have matching keys in the right data frame (y). In order words, this functions performs an outer join on the two provided data frames, i.e. the join table will contain all records from both data frames or data tables.

Usage

rtry_join_outer(x, y, baseOn, showOverview = TRUE)

Arguments

x

A data frame or data table to be coerced and will be considered as the data on the left.

y

A data frame or data table to be coerced and will be considered as the data on the right.

baseOn

The common column used for merging.

showOverview

Default TRUE displays the dimension and column names of the merged data.

Value

References

This function makes use of the merge function within the base package.

Examples

# Assume a user has obtained two unique data tables, one with the ancillary data
# Longitude and one with Latitude (e.g. using rtry_select_anc()), and would like to
# merge two data tables into one according to the common identifier ObservationID.
# It does not matter if either Longitude or Latitude data has no record
lon <- rtry_select_anc(data_TRY_15160, 60)
lat <- rtry_select_anc(data_TRY_15160, 59)

georef <- rtry_join_outer(lon, lat, baseOn = ObservationID)

# Expected messages:
# dim:   97 2
# col:   ObservationID Longitude
#
# dim:   98 2
# col:   ObservationID Latitude
#
# dim:   98 3
# col:   ObservationID Longitude Latitude

Remove columns

Description

This function removes specified columns from the imported data for further processing.

Usage

rtry_remove_col(input, ..., showOverview = TRUE)

Arguments

input

Input data frame or data table.

...

Names of columns to be removed separated by commas. The operator : can be used for selecting a range of consecutive variables.

showOverview

Default TRUE displays the dimension of the remaining data.

Value

An object of the same type as the input data.

References

This function makes use of the select function within the dplyr package.

Examples

# Remove certain columns from the provided sample data (data_TRY_15160)
data_rm_col <- rtry_remove_col(data_TRY_15160,
                 LastName, FirstName, DatasetID, Dataset, SpeciesName,
                 OrigUncertaintyStr, UncertaintyName, Replicates,
                 RelUncertaintyPercent, Reference, V28)

# Expected message:
# dim:   1782 17
# col:   AccSpeciesID AccSpeciesName ObservationID ObsDataID TraitID TraitName
#        DataID DataName OriglName OrigValueStr OrigUnitStr ValueKindName
#        StdValue UnitName OrigObsDataID ErrorRisk Comment

Remove duplicates in data

Description

This function removes the duplicates from the input data using the duplicate identifier OrigObsDataID provided within the TRY data. Once the function is called and executed, the number of duplicates removed will be displayed on the console as reference.

Usage

rtry_remove_dup(input, showOverview = TRUE)

Arguments

input

Input data frame or data table.

showOverview

Default TRUE displays the the dimension of the data after removing the duplicates.

Value

An object of the same type as the input data after removing the duplicates.

Note

This function depends on the duplicate identifier OrigObsDataID listed in the data exported from the TRY database, therefore, if the column OrigObsDataID has been removed, this function will not work. Also, if the original value of an indicated duplicate is a restricted value, which has not been requested from the TRY database (if only public data were requested), the duplicate will be removed and this may result in data loss.

References

This function makes use of the subset function within the base package.

Examples

# Remove the duplicates within the provided sample data (data_TRY_15160)
data_rm_dup <- rtry_remove_dup(data_TRY_15160)

# Expected message:
# 45 duplicates removed.
# dim:   1737 28

Perform reverse geocoding

Description

This function uses Nominatim, a search engine for OpenStreetMap data, to perform reverse geocoding, i.e. converting coordinates (latitudes, longitudes) into an address. The data provided by OSM is free to use for any purpose, including commercial use, and is governed by the distribution license ODbL.

Usage

rtry_revgeocoding(lat_lon, email)

Arguments

lat_lon

A data frame containing latitude and longitude in WGS84 projection.

email

String of an email address.

Value

A data frame that contains address.

Examples

## Not run: 
# Convert the coordinates of MPI-BGC (50.9101, 11.56674) into an address
# Note: Please change to your own email address when executing this function
rtry_revgeocoding(data.frame(50.9101, 11.56674),
  email = "john.doe@example.com")

# Expected message:
#               full_address town city country country_code
# 1 Jena, Thuringia, Germany   NA Jena Germany           de

## End(Not run)

# Learn to perform reverse geocoding to a list of coordinates via the vignette
# (Workflow for geocoding using rtry): vignette("rtry-workflow-geocoding").

Select ancillary data in wide table format

Description

This function selects one specified ancillary data together with the ObservationID from the imported data and transforms it into a wide table format for further processing.

Usage

rtry_select_anc(input, ..., showOverview = TRUE)

Arguments

input

Input data frame or data table.

...

The IDs of the ancillary data (DataID in the TRY data) to be selected.

showOverview

Default TRUE displays the dimension and column names of the selected data.

Value

An object of the same type as the input data.

References

This function makes use of the subset and distinct functions within the base and dplyr packages respectively. It also uses the functions rtry_select_col and rtry_remove_col, as well as the function rtry_join_outer to select and combine the extracted ancillary data with the ObservationID.

Examples

# Obtain a list of ObservationID and the corresponding ancillary data of interest
# using the specified DataID (e.g. DataID 60 for longitude and 59 for latitude) from
# the provided sample data (e.g. data_TRY_15160)
georef <- rtry_select_anc(data_TRY_15160, 60, 59)

# Expected message:
# dim:   98 3
# col:   ObservationID Longitude Latitude


# Obtain a list of ObservationID and one corresponding ancillary data of interest
# using the specified DataID (e.g. DataID 61 for altitude) from the provided sample
# data (e.g. data_TRY_15160)
alt <- rtry_select_anc(data_TRY_15160, 61)

# Expected message:
# dim:   23 2
# col:   ObservationID Altitude

Select columns

Description

This function selects the specified columns from the input data.

Usage

rtry_select_col(input, ..., showOverview = TRUE)

Arguments

input

Input data frame or data table.

...

Column names to be selected.

showOverview

Default TRUE displays the dimension and column names of the selected columns.

Value

An object of the same type as the input data.

References

This function makes use of the select function within the dplyr package.

Examples

# Select certain columns from the provided sample data (data_TRY_15160)
data_selected <- rtry_select_col(data_TRY_15160,
                   ObsDataID, ObservationID, AccSpeciesID, AccSpeciesName,
                   ValueKindName, TraitID, TraitName, DataID, DataName, OriglName,
                   OrigValueStr, OrigUnitStr, StdValue, UnitName, OrigObsDataID,
                   ErrorRisk, Comment)

# Expected message:
# dim:   1782 17
# col:   ObsDataID ObservationID AccSpeciesID AccSpeciesName ValueKindName TraitID
#        TraitName DataID DataName OriglName OrigValueStr OrigUnitStr StdValue
#        UnitName OrigObsDataID ErrorRisk Comment

Select rows

Description

This function selects rows based on specified criteria and the corresponding ObservationID from the imported data for further processing.

Usage

rtry_select_row(
  input,
  ...,
  getAncillary = FALSE,
  rmDuplicates = FALSE,
  showOverview = TRUE
)

Arguments

input

Input data frame or data table.

...

Criteria for row selection.

getAncillary

Default FALSE, set to TRUE selects all ancillary data based on the row selection criteria.

rmDuplicates

Default FALSE, set to TRUE calls the function rtry_remove_dup.

showOverview

Default TRUE displays the dimension of the data after row selection.

Value

An object of the same type as the input data.

Note

This function by default filters data based on the unique identifier ObservationID listed in the TRY data, therefore, if the column ObservationID has been removed, this function will not work.

References

This function makes use of the unique and subset functions within the base package. It also uses the function rtry_remove_dup.

Examples

# Within the provided sample data (data_TRY_15160) select the georeferenced traits
# records together with records for Latitude and Longitude (DataID 59 and 60) and
# exclude duplicate trait records
data_selected <- rtry_select_row(data_TRY_15160,
                   (TraitID > 0) | (DataID %in% c(59, 60)),
                   getAncillary = TRUE,
                   rmDuplicates = TRUE)

# Expected message:
# 45 duplicates removed.
# dim:   1737 28

Transform data from long to wide table

Description

This function transforms the original long table format of the data into a wide table format.

Usage

rtry_trans_wider(
  input,
  names_from = NULL,
  values_from = NULL,
  values_fn = NULL,
  showOverview = TRUE
)

Arguments

input

Input data frame or data table.

names_from

The column(s) from which the output column names to be obtained.

values_from

The column(s) from which the output values to be obtained.

values_fn

(Optional) Function to be applied to the output values.

showOverview

Default TRUE displays the dimension of the wide table.

Value

A data frame of the transformed wide table.

References

This function makes use of the pivot_wider function within the tidyr package.

Examples

# Provide the standardized trait values per observation, together with species names
# and the georeferences of the sampling site (Latitude and Longtude), if availalbe,
# in a wide table format. Several steps are necessary:

# 1. Select only the trait records that have standardized numeric values.
#    The complete.cases() is used to ensure the cases are complete, i.e. have no
#    missing values.
num_traits <- rtry_select_row(data_TRY_15160,
                complete.cases(TraitID) & complete.cases(StdValue))

# 2. Select the relevant columns for transformation.
num_traits <- rtry_select_col(num_traits,
                ObservationID, AccSpeciesID, AccSpeciesName, TraitID, TraitName,
                StdValue, UnitName)

# 3. Extract the values of georeferences and the corresponding ObservationID.
lat <- rtry_select_anc(data_TRY_15160, 59)
lon <- rtry_select_anc(data_TRY_15160, 60)

# 4. Merge the relevant data frames based on the ObservationID using rtry_join_left().
num_traits_georef <- rtry_join_left(num_traits, lat, baseOn = ObservationID)
num_traits_georef <- rtry_join_left(num_traits_georef, lon, baseOn = ObservationID)

# 5. Perform wide table transformation of TraitID, TraitName and UnitName based on
#    ObservationID, AccSpeciesID and AccSpeciesName with cell values from StdValue.
#    If several records with StdValue were provided for one trait with the same
#    ObservationID, AccSpeciesID and AccSpeciesName, calculate their mean.
num_traits_georef_wider <- rtry_trans_wider(num_traits_georef,
                             names_from = c(TraitID, TraitName, UnitName),
                             values_from = c(StdValue),
                             values_fn = list(StdValue = mean))

# Expected messages:
# dim:   150 28
# dim:   150 7
# col:   ObservationID AccSpeciesID AccSpeciesName TraitID TraitName
#        StdValue UnitName
#
# dim:   98 2
# col:   ObservationID Latitude
#
# dim:   97 2
# col:   ObservationID Longitude
#
# dim:   150 8
# col:   ObservationID AccSpeciesID AccSpeciesName TraitID TraitName
#        StdValue UnitName Latitude
#
# dim:   150 9
# col:   ObservationID AccSpeciesID AccSpeciesName TraitID TraitName
#        StdValue UnitName Latitude Longitude
#
# dim:   146 7

# Learn more via the vignette (Workflow for general data preprocessing using rtry):
# vignette("rtry-workflow-general")

Pipe operator

Description

Usage

Arguments

Value

Sample TRY data (Request 15160)

Description

Usage

Format

Value

Sample TRY data (Request 15161)

Description

Usage

Format

Value

Sample coordinates data

Description

Usage

Format

Value

Sample locations data

Description

Usage

Format

Value

Bind data by columns

Description

Usage

Arguments

Value

Note

References

See Also

Examples

Bind data by rows

Description

Usage

Arguments

Value

Note

References

See Also

Examples

Exclude (remove) data

Description

Usage

Arguments

Value

References

Examples

Explore data

Description

Usage

Arguments

Value

References

Examples

Export preprocessed data

Description

Usage

Arguments

Value

References

Examples

Perform geocoding

Description

Usage

Arguments

Value

See Also

Examples

Import data

Description

Usage

Arguments

Value

References

Examples

Left join for two data frames

Description