,
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]| Title: | Preprocessing Plant Trait Data |
|---|---|
| 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. |
| Authors: | 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] |
| Maintainer: | Olee Hoi Ying Lam <[email protected]> |
| License: | CC BY 4.0 |
| Version: | 1.1.0 |
| Built: | 2025-02-11 03:41:43 UTC |
| Source: | https://github.com/mpi-bgc-functional-biogeography/rtry |
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.
data_coordinatesdata_coordinates
A data frame with 20 rows and 2 variables:
Latitude, in WGS84 projection.
Longitude, in WGS84 projection.
A data frame with 20 rows and 2 variables of sample coordinates data.
A dataset containing 20 location information.
The raw dataset (data_locations.csv) is also provided in the directory testdata.
data_locationsdata_locations
A data frame with 20 rows and 3 variables:
Country code.
Full name of a country.
Specific location, e.g. town or city name.
A data frame with 20 rows and 3 variables of sample locations data.
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.
data_TRY_15160data_TRY_15160
A data frame with 1782 rows and 28 variables:
Surname of data contributor.
First name of data contributor.
Unique identifier of contributed dataset.
Name of contributed dataset
Original name of species.
Unique identifier of consolidated species name.
Consolidated species name.
Unique identifier for each observation in TRY.
Unique identifier for each row in the TRY data table, either trait record or ancillary data.
Unique identifier for traits (only if the record is a trait).
Name of trait (only if the record is a trait).
Unique identifier for each DataName (either sub-trait or ancillary data).
Name of sub-trait or ancillary data.
Original name of sub-trait or ancillary data.
Original value of trait or ancillary data.
Original unit of trait or ancillary data.
Value kind (single measurement, mean, median, etc.).
Original uncertainty.
Kind of uncertainty (standard deviation, standard error, etc.).
Number of replicates.
Standardized trait value: available for frequent continuous traits.
Standard unit: available for frequent continuous traits.
Relative uncertainty in %.
Unique identifier for duplicate trait records.
Indication for outlier trait values: distance to mean in standard deviations.
Reference to be cited if trait record is used in analysis.
Explanation for the OriglName in the contributed dataset.
Empty, an artifact due to different interpretation of column separator by MySQL and R.
A data frame with 1782 rows and 28 variables of sample TRY data (Request 15160).
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.
data_TRY_15161data_TRY_15161
A data frame with 4627 rows and 28 variables:
Surname of data contributor.
First name of data contributor.
Unique identifier of contributed dataset.
Name of contributed dataset
Original name of species.
Unique identifier of consolidated species name.
Consolidated species name.
Unique identifier for each observation in TRY.
Unique identifier for each row in the TRY data table, either trait record or ancillary data.
Unique identifier for traits (only if the record is a trait).
Name of trait (only if the record is a trait).
Unique identifier for each DataName (either sub-trait or ancillary data).
Name of sub-trait or ancillary data.
Original name of sub-trait or ancillary data.
Original value of trait or ancillary data.
Original unit of trait or ancillary data.
Value kind (single measurement, mean, median, etc.).
Original uncertainty.
Kind of uncertainty (standard deviation, standard error, etc.).
Number of replicates.
Standardized trait value: available for frequent continuous traits.
Standard unit: available for frequent continuous traits.
Relative uncertainty in %.
Unique identifier for duplicate trait records.
Indication for outlier trait values: distance to mean in standard deviations.
Reference to be cited if trait record is used in analysis.
Explanation for the OriglName in the contributed dataset.
Empty, an artifact due to different interpretation of column separator by MySQL and R.
A data frame with 4627 rows and 28 variables of sample TRY data (Request 15161).
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.
rtry_bind_col(..., showOverview = TRUE)rtry_bind_col(..., showOverview = TRUE)
... |
A list of data frames or data tables to be combined by columns. |
showOverview |
Default |
An object of the same type as the first input.
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.
This function makes use of the bind_cols function within the dplyr package.
rtry_bind_row, rtry_join_left, rtry_join_outer
# 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# 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
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.
rtry_bind_row(..., showOverview = TRUE)rtry_bind_row(..., showOverview = TRUE)
... |
A list of data frames or data tables to be combined by rows. |
showOverview |
Default |
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.
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.
This function makes use of the bind_rows function within the dplyr package.
rtry_bind_col, rtry_join_left, rtry_join_outer
# 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# 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
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.
rtry_exclude(input, ..., baseOn, showOverview = TRUE)rtry_exclude(input, ..., baseOn, showOverview = TRUE)
input |
Input data frame or data table. |
... |
Criteria for excluding. |
baseOn |
The attribute on which excluding is based on. If it is set to |
showOverview |
Default |
An object of the same type as the input data after excluding.
This function makes use of the subset function
within the base package.
# 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").# 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").
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.
rtry_explore(input, ..., sortBy = "", showOverview = TRUE)rtry_explore(input, ..., sortBy = "", showOverview = TRUE)
input |
Data frame or data table, e.g. from |
... |
Attribute names to group together. |
sortBy |
(Optional) Default |
showOverview |
Default |
A data frame of unique values grouped and sorted by the specified attribute(s).
This function makes use of the group_by, summarise
and arrange functions within the dplyr package.
# 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").# 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").
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.
rtry_export(data, output, quote = TRUE, encoding = "UTF-8")rtry_export(data, output, quote = TRUE, encoding = "UTF-8")
data |
The data to be saved. |
output |
Output path. |
quote |
Default |
encoding |
Default |
No return value, called for exporting a .csv file.
This function makes use of the write.csv function
within the utils package.
# 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# 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
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.
rtry_geocoding(address, email)rtry_geocoding(address, email)
address |
String of an address. |
email |
String of an email address. |
A data frame that contains latitudes (lat) and longitudes (lon) in WGS84 projection.
## 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 = "[email protected]") # 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").## 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 = "[email protected]") # 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").
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.
rtry_import( input, separator = "\t", encoding = "Latin-1", quote = "", showOverview = TRUE )rtry_import( input, separator = "\t", encoding = "Latin-1", quote = "", showOverview = TRUE )
input |
Path to the data file. |
separator |
Default |
encoding |
Default |
quote |
Default |
showOverview |
Default |
A data.table.
This function makes use of the fread function
within the data.table package.
# 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# 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
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.
rtry_join_left(x, y, baseOn, showOverview = TRUE)rtry_join_left(x, y, baseOn, showOverview = TRUE)
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 |
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.
This function makes use of the merge function
within the base package.
rtry_join_outer, rtry_bind_col, rtry_bind_row
# 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# 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
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.
rtry_join_outer(x, y, baseOn, showOverview = TRUE)rtry_join_outer(x, y, baseOn, showOverview = TRUE)
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 |
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.
This function makes use of the merge function
within the base package.
rtry_join_left, rtry_bind_col, rtry_bind_row
# 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# 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
This function removes specified columns from the imported data for further processing.
rtry_remove_col(input, ..., showOverview = TRUE)rtry_remove_col(input, ..., showOverview = TRUE)
input |
Input data frame or data table. |
... |
Names of columns to be removed separated by commas. The operator |
showOverview |
Default |
An object of the same type as the input data.
This function makes use of the select function
within the dplyr package.
# 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 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
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.
rtry_remove_dup(input, showOverview = TRUE)rtry_remove_dup(input, showOverview = TRUE)
input |
Input data frame or data table. |
showOverview |
Default |
An object of the same type as the input data after removing the duplicates.
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.
This function makes use of the subset function
within the base package.
# 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# 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
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.
rtry_revgeocoding(lat_lon, email)rtry_revgeocoding(lat_lon, email)
lat_lon |
A data frame containing latitude and longitude in WGS84 projection. |
email |
String of an email address. |
A data frame that contains address.
## 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 = "[email protected]") # 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").## 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 = "[email protected]") # 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").
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.
rtry_select_anc(input, ..., showOverview = TRUE)rtry_select_anc(input, ..., showOverview = TRUE)
input |
Input data frame or data table. |
... |
The IDs of the ancillary data ( |
showOverview |
Default |
An object of the same type as the input data.
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.
# 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# 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
This function selects the specified columns from the input data.
rtry_select_col(input, ..., showOverview = TRUE)rtry_select_col(input, ..., showOverview = TRUE)
input |
Input data frame or data table. |
... |
Column names to be selected. |
showOverview |
Default |
An object of the same type as the input data.
This function makes use of the select function
within the dplyr package.
# 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 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
This function selects rows based on specified criteria
and the corresponding ObservationID from the imported data for further processing.
rtry_select_row( input, ..., getAncillary = FALSE, rmDuplicates = FALSE, showOverview = TRUE )rtry_select_row( input, ..., getAncillary = FALSE, rmDuplicates = FALSE, showOverview = TRUE )
input |
Input data frame or data table. |
... |
Criteria for row selection. |
getAncillary |
Default |
rmDuplicates |
Default |
showOverview |
Default |
An object of the same type as the input data.
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.
This function makes use of the unique and subset functions
within the base package. It also uses the function rtry_remove_dup.
# 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# 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
This function transforms the original long table format of the data into a wide table format.
rtry_trans_wider( input, names_from = NULL, values_from = NULL, values_fn = NULL, showOverview = TRUE )rtry_trans_wider( input, names_from = NULL, values_from = NULL, values_fn = NULL, showOverview = TRUE )
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 |
A data frame of the transformed wide table.
This function makes use of the pivot_wider function
within the tidyr package.
rtry_select_row, rtry_select_col, rtry_select_anc,
rtry_join_left
# 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")# 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")