overview

Rveg is capable of fast transcription, editing, and viewing of your relevés. It creates a database composed of two CSV files (comma-separated values): one for header data (environmental variables, file ending with HEAD.csv) and a second for abundance data (file ending with REL.csv). Values in both files can be edited outside of Rveg in any spreadsheet software while keeping compatibility with Rveg, but don’t edit row names or column names.

The Header file consists of preset variables in rows and relevés in columns. It is advised not to use commas in values, as commas are used as separators.

The Relevés file consists of species number codes as row names and relevés in columns. The first column represents the species shortcut code combined with an underscore and the vegetation layer. It is based on the external checklist of species (Danihelka et al. 2012), which assigns a shortcut code (e.g., TRIFPRA for Trifolium pratense) to each species to prevent typos and speed up the process of writing. A REL table might look like this:

The Rveg package is based entirely around working with these two files, which together are referred to as the Rveg database. This is achieved primarily by the addReleve function, but Rveg also contains some functions with extra features. Rveg includes the following functions:

1. addReleve()
2. RvegCombine()
3. RvegCheck()
4. RvegToJuice()
5. RvegMerge()
6. tvToRveg()
7. RvegToTv()
8. CreateChecklist()
9. RvegLoad()

To understand each function, view the next chapter or run help("function_name").

relevé digitizing

Install the latest version of the Rveg:

Assuming you have successfully installed Rveg, we can start digitizing right away. We start with the core function for digitizing, addReleve(). For our first database to be created, we need the argument SAVE, which specifies where the database will be stored on our disk. Let’s say we want to have our database stored in the home directory and named “MyFirstDatabase”.

By forming the function this way, we will immediately start by filling the first relevé. The process starts by filling header fields. Enter a value for each field; formats are not mandatory to follow. You can leave the fields empty as well.

Here I show you the first three fields, where I have filled the DATE and LOCALITY but left SPRINGDATE empty, as we have not sampled spring aspect vegetation. The last field is NOTE for any additional remarks. After you complete the first header, Rveg starts to ask you if you want to add species. Let’s add our first species, Trifolium pratense, with a 30% cover.

At this moment we are editing first reléve, herb layer in percentage and Rveg awaits our response if we want to add another specie (our first). Species are inserted by the shortname species codes, that is usually created by 4 letters of Genus and 3 letters of species, therefore for Trifolium pratense we type TrifPra. After we type the abundance, we are aske to doublecheck if the species is correct. This is also useful for exceptions of shortnames, as some codes could be duplicite or for other reasons, the species code might except the 4 genus 3 species letters rule. We confirm Trifolium pratense and Rveg list our relevé, which consist of one species now and already awaits for another species. The id row names are not important for us. Lets add few more species, Arrhenatherum elatius, Rumex acetosa and Bellis perennis.

In the first option, we confirmed we want to add/edit a new vegetation layer; we selected one which represents the herb layer and then we selected percentage as cover values. Note that Rveg suggests capital letters as answers, and while R is case-sensitive, Rveg capitalizes the text in the background, so you can write in lowercase as well. At this moment, we are editing the first relevé, herb layer in percentage, and Rveg awaits our response if we want to add another species (our first). Species are inserted by the shortname species codes, which are usually created by 4 letters of the genus and 3 letters of the species; therefore, for Trifolium pratense we type trifpra. After we type the abundance, we are asked to double-check if the species is correct. This is also useful for exceptions of shortnames, as some codes could be duplicates or for other reasons the species code might not follow the 4 genus and 3 species letters rule. We confirm Trifolium pratense, and Rveg lists our relevé, which consists of one species now and already awaits another species. The ID row names are not important for us. Let’s add a few more species: Arrhenatherum elatius, Rumex acetosa, and Bellis perennis.

Now we hit a wall: Rveg does not know any species with the shortname bellper. Therefore, we must decline that the name is correct, which is followed by a question about the first three letters of the species full name. Rveg then lists all species with corresponding name starts, also with the correct ShortName codes.

Now we see that according to the checklist, the correct shortname is BELIPER.

Now we have successfully added Bellis perennis to the herb layer; however, at our plot, we also found a shrub of Sambucus nigra. Let’s add it to our relevé, and we can try the Braun-Blanquet scale. We will select layer two (shrub layer) and abundance +.

You can see Sambucus nigra is now in our list within the second layer, and the cover code was converted from + to 2%. In case you want to adjust the abundance or remove the species, we can just add the species again with a different cover; zero to remove. Let’s try it and change the abundance of A. elatius to 35%. Therefore, we need to edit layer one again.

Now we assume our first relevé is finished. Congratulations! Therefore, we decline adding new species and adding new layers.

Now we end up in a menu where we can select different prompts. For a detailed explanation, check the addReleve section in the FUNCTIONS chapter. We will now be satisfied with PRINTREL and PRINTHEAD. If we want to see our database state, we prompt PRINTREL for relevés and PRINTHEAD for headers. Prompting Y will start a new relevé with header as before.

addReleve

ARGUMENTS

• DATABASE is set by default to "NEW", which indicates that the user is creating a new database. You will need to change this value only if you want to edit or add new records to your database created before. In that case, you will enter the name of the database (without the HEAD.csv, REL.csv suffix).
• SAVE sets the path and name of your database. It is therefore the only required argument when creating a new database. When you are editing an already existing database, you can select to keep SAVE the same as DATABASE, which will overwrite the loaded database, or you can choose a different SAVE value to create a new database as well as keep the old one (e.g., backup or versioning). If kept on default, the temporary files will be used in the tmp directory.
• checklist is set by default to "default", which makes Rveg use the provided checklist by Danihelka et al. (2012). At the moment, there are no more checklists, but you are able to edit or create your own checklists. Insert the full path to the custom checklist. A custom checklist can be structured to the required format with the function CreateChecklist().
• extrahead is set by default to NULL, meaning there are no extra header characteristics. To add other fields, insert the character vector of the characteristics.
• start Is logical value if the user wants to start digitizing right away by default method. Works only when creating a new database.

DESCRIPTION

The most important function, which is the core of the whole package. It allows you to digitize relevés, therefore creating and editing Rveg databases which consist of two CSV files: one for evironmental (header) data and another for abundance (relevé). The whole process is based on communication with the user. After starting the function, the digitizing process begins and runs until closed; until then, the R session cannot be used in other ways. You can create a database for your project with preset header data, but it also allows you to add your own characteristics. There are several ways of digitizing using this function, each explained below. This function includes several mechanisms to make digitizing comfortable, efficient, and error-free. First, the database is continuously saved during the digitizing process after every step (e.g., after every relevé digitized); therefore, in an unexpected closure of the software, most of the data are not lost. Be aware that R needs rights to write the files onto the disk; therefore, ensure the files are not open in other software (e.g., Microsoft Excel), as it might cause errors. To prevent typographical mistakes and speed up the digitizing process, the species are inserted using 7-letter codes and require double-checking. Thanks to the species checklist (Danihelka et al. 2012) for the Czech Republic, provided within the package, Rveg will connect species names to the 7-letter codes. If you ever find the checklist insufficient, you can use custom checklists. In future updates, more checklists might be included. The structure of the checklist is a TXT file, a dataframe with 3 columns. The first column, Number, represents the ID of the species. Rveg will recalculate IDs by itself, so there is no real use for this column; you can use it for your own sorting and orientation. If you don’t want to think of a number, you can simply enter 99999, or any other number. The second column, ShortName, is the already mentioned species shortcut. It is written in uppercase and consists of 4 letters of the genus and 3 letters of the species. It is important to keep uppercase letters and ensure every shortname is unique and present only once. To access the checklist for modifying, you can run:

The last column, FullName, contains full species names. Columns are separated by a tabulator. I recommend editing the file in a text editor rather than in R, but be careful as some text editors can alter tabulators with spaces.

Default header includes following fields:

1. ID (YYYY/MM/DD) - generated automatically, unique value for each relevé
2. DATE (YYYY/MM/DD) - date of data sampling
3. SpringDATE - date of sampling spring aspect
4. LOCALITY - place where relevé was taken
5. FieldCODE - internal code for distinguishing relevés from the same locality
6. Authors - name of the sampler
7. PlotSize (m²) - size of the sampled area
8. Latitude & Longitude - XY coordinates
9. Accuracy - accuracy of coordinate measurement
10. CRS - used coordinate reference system
11. Slope (degrees) - slope of the plot
12. Exposure - exposure direction
13. E3, E2, E1, Ejuv, E0 (%) - percentage cover of each layer (tree, shrub, herb, juvenile, and moss, respectively)
14. Note - any extra note you might need to record

Example of a relevé header:

Some characteristics have a recommended format; however, it is completely up to you and your preferences how you fill them. Everything is converted to string (text) format. You can notice that some of the fields remain empty. Every database contains this set of header fields, but it is up to the user if they want to fill them.

RvegCombine

This function allows us to combine all species from different layers into one or to merge two different species in all relevés.

ARGUMENTS

• database: Path to the database we want to edit.
• export: Name of our edited database (save). If kept on default export, the file will be saved in the tmp directory.
• checklist: Used checklist.

DESCRIPTION

Let’s imagine the following example: we have Alnus glutinosa present in the second relevé, both in the tree and shrub layers, but we decided to distinguish only the herb layer and tree layer in our database.

We received a combination of Alnus glutinosa from the shrub layer and tree layer, calculated by the following formula (taken from Tichý 2011, p. 29):

A + (B * (1 − (A/100)))

Where A and B are covers in percentage. The second possible use of this function is to merge two species together using the same formula. Let’s combine Fragaria vesca with Poa riphaea.

Do not confuse this function with RvegMerge().

RvegMerge

ARGUMENTS

• x path to the first joining database
• y path to the second joining database
• save name of the exported database. If kept on default export_merge, the file will be saved in the tmp directory.
• head logical value, deciding if we want to join headers files as well

DESCRIPTION

This function allows you to join two databases together. It is useful when multiple people are working on the same project or when you have a large database and want to work with it in parts. When your database gets significantly bigger (e.g., 100 relevés), you might notice the software slows down a bit. If this reaches an unacceptable level, you can start a new database and merge them later with this function. It will automatically create new database files without any additional action needed. This function uses functions from the dplyr package (Wickham et al. 2022).

RvegCheck

ARGUMENTS

• DATABASE path to the database you want to check
• fullnames logical value if you want to include fullnames of species in the export
• export name of the exported table. If kept on default export, the file will be saved in the tmp directory.
• checklist checklist used to match shortnames with fullnames

DESCRIPTION

This function will be removed in later versions of the package as it will be efficiently replaced by RvegLoad(). It is able to check for duplicates, fix them, and will create and output full names for further analysis and presentations. As mentioned at the beginning of this manual, it is important to have a unique ShortName code for every species. However, some species might theoretically have more than one ShortName code in the checklist. If this happens and you find you used two different ShortName codes for one species, this function will detect the duplicate species and solve the issue for you by keeping the highest value in each relevé from both (in case you enter both ShortName codes in one relevé). Also, there is an option to include full names in the exported table; this might be useful when you want to browse data manually or present your data. However, be careful, as the table with full names will not be backward compatible with Rveg and thus uneditable in Rveg.

If it finds any duplicates, you can choose how to solve each one individually. Otherwise, it will just generate a new table without any additional action needed.

Resulting in:

RvegToJuice

ARGUMENTS

• Data path to the database you want to convert
• checklist checklist used to match ShortNames with FullNames
• export name of the exported table. If kept on default export, the file will be saved in the tmp directory.

DESCRIPTION

This function will convert the Rveg database format to the Juice-compatible format. Juice (Tichý 2002) is software for editing, classification, and analysis of large phytosociological tables and databases. After running the function, the table will be converted without any additional action needed. However, importing it might not be that intuitive. Let’s go through a step-by-step guide on how to load the output of the function into Juice.

1. File -> Import -> Table -> from Spreadsheet File
2. Select the file
3. Press Next and select Comma as separator and use second column as layer information
4. Check if table size is correct; next, you will see a preview of the table
5. Select Percentage as Cover values
6. Finish

This way, the relevés will be imported into Juice; however, we might want to add headers to the relevés.

1. File -> Import -> Header Data -> From Comma Delimited File
2. Select the header file

TvToRveg

ARGUMENTS

• tv Path to the Turboveg csv export
• checklist Checklist used to match ShortNames with FullNames
• export Name of the exported table. If kept on default export, the files will be saved in the tmp directory.

DESCRIPTION

This function will convert CSV exports from Turboveg software into the Rveg-compatible format. Turboveg (Hennekens and Schaminée 2001) is software for the processing of phytosociological data. Since many members of our team used Turboveg, it influenced and inspired the creation of Rveg; you could say that Rveg is a simpler alternative to Turboveg. This function lets you edit or continue your work from Turboveg in Rveg. It will automatically create new database files without any additional action needed. The Turboveg CSV export has to include a header and relevés but should not include the Ellenberg indicator values. The possible cause of error might be wrong formating of CSV file, if you are having trouble check the example file at:

Guide for Turboveg export:

1. Select relevés
2. Export
3. Spreadsheet table
4. Select Format - Comma delimited
5. Select header data; don’t select ecological data
6. Export

Then insert the path to the tv parameter of the function. The abundance can be either in percentage or Braun-Blanquet scale. Then you have to manually connect the species that were not recognized in the Rveg checklist; for example, Salix fragilis was not recognized in our data because the checklist uses the synonym Salix euxina. You can use a custom checklist as well. Also, you can check for each species what code is given to them according to the checklist, or you can skip this quickly by holding the enter button. When you know the correct ShortName code you can fill it right away.

RvegToTv

ARGUMENTS

• database Path to the Rveg database to export
• export Name of the exported table / files. If kept on default export, the file will be saved in the tmp directory.
• ver Version of turvobeg to upload to, turboveg 3 can import one single file but turboveg 2 needs import of headers and relevés separately
• checklist Checklist used to match ShortNames with FullNames

DESCRIPTION

This function creates files importable to Turboveg 2 or Turboveg 3. The files will be created without any user input; however, uploading them to Turboveg might be challenging. Let’s take a look at how to import such files into Turboveg 2. Two files with suffix R.csv (relevé) and H.csv (header) will be created.

1. In Turboveg, open or start a new database
2. Select Import
3. Select Free format species data table
4. Select the Relevé data
5. Now select Species column #1, Layer column #2, click Next
6. Species have been matched; now select percentage (%) as cover scale
7. Select all relevés, click Next, then Complete

This way, we have inserted the relevés into the Turboveg software; now we have to match header data.

1. Select Import
2. Select Free format header table
3. Select the header
4. Click Next; the header table should now appear visible
5. Select field names, field alias names in row 1; check ‘Add new fields automatically’ box
6. Press Next; ignore the warning messages. Most of the fields Turboveg cannot recognize and will be created as new fields
7. Select the first row ‘ID’ and change it to ‘releve_nr’ and mark it as key field (other fields are optional to match with Turboveg known fields)
8. Press Complete; headers should be matched

CreateChecklist

ARGUMENTS

• specieslist Path to the list of species, which will be used to create checklist
• export Name and path to the export of the checklist. If kept on default export, the file will be saved in the tmp directory.

DESCRIPTION

This function allows you to form a checklist in Rveg-readable format from a character vector of species names. There are certain rules in the process, so we recommend editing full names according to these, which are in the original checklist. Therefore:

1. Hybrids name with ‘x’ (e.g., Galium x pomeranicum = GALU*PO); the shortname will contain ‘*’ sign
2. Unrecognized species with suffix ‘species’ (e.g., Corylus species = CORL-SP)
3. Subspecies have a dash in their species name (e.g., Primula veris ssp. veris = PRIMV-V)
4. Aggregates of species have a hashtag in their species name (e.g., Carex flava agg. = CARE#FL)
5. Complicated genera might have sections starting with ‘SE’ (e.g., Taraxacum sect. Taraxacum = TARASEO)

We can show how the species list will be created from a few species.

The checklist then will be usable in addReleve().

RvegLoad

ARGUMENTS

• DATABASE Path to your database. Default option default will load example database.
• CustomScale Boolean if custom not precoded scale was used during the digitizing
• checklist TXT checklist used during creation of the database

DESCRIPTION

This function loads the Rveg database into the R environment and is ready for analysis. Also, when custom scales were used during the digitizing, here it will allow you to transfer to percentage values. Let’s use our previously created database, where we used percentages, but we will pretend it was a cover scale from 0-60. The output is a data.frame in R.