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API & Integration

Seamless access to Wilderlab’s eDNA data

Getting started

We provide comprehensive API documentation, code samples, and technical support to help you connect effortlessly. Contact our team to request access and discuss your integration needs.

Harness the power of Wilderlab’s environmental DNA insights directly within your digital ecosystem—making data-driven decisions faster and smarter.

What our API offers

  • Real-time data access: Retrieve up-to-date sampling results and metadata directly from Wilderlab’s database.
  • Flexible integration: Easily incorporate eDNA data into your own software platforms, dashboards, or research tools.
  • Custom queries: Filter data by species, location, date, or project to suit your specific needs.
  • Automated workflows: Streamline your data processing and reporting through automated API calls, reducing manual handling.
  • Secure and reliable: Built with robust security protocols to protect sensitive data and ensure service reliability.

Who can benefit?

  • Research institutions seeking to automate data retrieval
  • Environmental consultants integrating monitoring results
  • Government agencies tracking biosecurity risks
  • Developers building innovative eDNA tools and applications

How to get connected

  • Reach out to the team at info@wilderlab.co to receive your unique API access codes
  • Follow our detailed instructions available below (DO WE WANT TO KEEP THIS? / HAVE A LINK TO ANOTHER PAGE WITH THE FULL INSTRUCTIONS?)

Wilderlab API user manual

Environmental DNA (eDNA) testing generates a lot of data. Over the past three years our clients have detected over a million different DNA sequences from thousands of different species. While our primary method of disseminating data is in xlsx format, static result tables do not reflect continuous updates to taxon IDs due to the availability of new reference sequences. Furthermore, static result tables are not always shared between departments, resulting in fragmented data and reporting.

To remedy this, Wilderlab has developed an API (Application Programming Interface) that enables clients to access their data in real time, facilitating the export of eDNA results to environmental data storage platforms such as Kisters and Hilltop.

This tutorial provides a brief introduction to accessing the connect.wilderlab API using the R programming language and the the wilderlab R package. Please contact us if you would like us to generate API access keys for your organisation, or if you would like any further help.

1. Installation

The wilderlab R package contains functions for importing and exporting eDNA data. To download the package from GitHub, ensure that devtools is installed. The insect package will also be required later on in this tutorial:

if(!("devtools" %in% list.files(.libPaths()))) install.packages("devtools")
if(!("insect" %in% list.files(.libPaths()))) install.packages("insect")

Then run:

devtools::install_github("wilderlabnz/wilderlab") 
library(wilderlab)

2. Load access keys

When signing up to the Wilderlab API, your unique log in information will be securely sent to you. This will include three access keys: an API access key id, key; a secret access key, secret; and a X-API-Key, xapikey.
Copy and paste this unique information into the appropriate slots in the following code to load them into your R session.

key <- "*****************"
secret <- "***************************************"
xapikey <- "***************************************"

3. get_wilderdata function

Description

Wrapper functions for creating URLs and authorisation headers to download job, sample, taxa, and record information from the connect.wilderlab API.

Usage

get_wilderdata(tb, key, secret, xapikey, JobID = NULL)

Arguments

ArgumentDescription
tba character string specifying the table required. Accepeted values are jobs, samples, taxa, and records.
keya string specifying the API access key for the client account. Please contact info@wilderlab.co if you would like access keys generated for your account.
secreta string specifying the API secret access key for the client account. Please contact info@wilderlab.co if you would like access keys generated for your account.
xapikeya string specifying the X-API-Key value for the client account. Please contact info@wilderlab.co if you would like access keys generated for your account.
JobIDa 6 digit integer specifying a Wilderlab job number. Required only for accessing the records table.

Details

The Wilderlab API is designed for clients to access up-to-date eDNA data for their internal data storage platforms and geospatial applications. Clients can access their job, sample, taxon and eDNA records data at any time by querying the API with a valid URL and authorization header. The get_wilderdata function is a wrapper that enables these URLs and headers to be compiled with minimal effort.

 

4. Get data tables

Pulling information from the job, samples, and taxa tables is very straightforward. All the information associated with a unique API access key is accessed as a whole:

jobs <- get_wilderdata("jobs", key = key, secret = secret, xapikey = xapikey)
samples <- get_wilderdata("samples", key = key, secret = secret, xapikey = xapikey)
taxa <- get_wilderdata("taxa", key = key, secret = secret, xapikey = xapikey)

Pulling from the records table is done on a specified JobID thus multiple calls must be made to get a full records table. After accessing the jobs table, the relevant JobIDs can be iterated over, the corresponding records pulled through the API, and finally the gathered records combined into a complete records table. The following code chunk is an example of how this can be done.

records <- vector(mode = "list", length = nrow(jobs))
for(i in seq_along(records)){
  records[[i]] <- get_wilderdata("records", JobID = jobs$JobID[i],
                                 key = key, secret = secret, xapikey = xapikey)
}
records <- do.call("rbind", records)

5. Fill lineage information

The final step in completing the records table is to add lineage information and sample metadata. This is an optional step but can improve interpretation of the results.

tdb <- taxa[, 1:4]
colnames(tdb) <- c("taxID", "parent_taxID", "rank", "name")
lineages = insect::get_lineage(records$TaxID, tdb)
records$phylum <- sapply(lineages, "[", "phylum")
records$class <- sapply(lineages, "[", "class")
records$order <- sapply(lineages, "[", "order")
records$family <- sapply(lineages, "[", "family")
records$genus <- sapply(lineages, "[", "genus")
records$species <- sapply(lineages, "[", "species")
records$Latitude <- samples$Latitude[match(records$UID,samples$UID)]
records$Longitude <- samples$Longitude[match(records$UID,samples$UID)]
records$ClientSampleID <- samples$ClientSampleID[match(records$UID,samples$UID)]

Example output

For the purposes of this manual, we will use example access keys as follows. This information will allow us to produce an example of what to expect from connecting to the Wilderlab API. Feel free to try out the API with these access keys first to ensure it is working as expected.

key <- "AKIATVYXGCYLWADFJVEX"
secret <- "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"
xapikey <- "7CCm580l5vgeKbalwIEy565uFhbEudTauAq80B38"

Jobs

Getting information from the jobs table:

jobs <- get_wilderdata("jobs", key = key, secret = secret, xapikey = xapikey)
jobs
   JobID SubmissionDate     ContactName    ContactEmail PurchaseOrder                          JobReference JobNotes NumberOfSamples          AssayPanel MakeDataPublic PassCode JobStatus                                                                                                      ResultsLink InvoiceNo
1 601833     2021-07-04 Shaun Wilkinson api@example.com               Passive sampler validation experiment                        3 Comprehensive panel              1  W1E0638  Complete https://s3.ap-southeast-2.amazonaws.com/wilderlab.results/c03d243992534adcf4ffecd39c43d6e8/WLM601833_250626.xlsx  INTERNAL
2 601834     2021-07-04 Shaun Wilkinson api@example.com               Passive sampler validation experiment                        3 Comprehensive panel              1  W1E0638  Complete https://s3.ap-southeast-2.amazonaws.com/wilderlab.results/a26f9576e52ce92c689070da34865104/WLM601834_250626.xlsx  INTERNAL

Samples

Getting information from the samples table:

samples <- get_wilderdata("samples", key = key, secret = secret, xapikey = xapikey)
samples
samples <- get_wilderdata("samples", key = key, secret = secret, xapikey = xapikey)
samples
 SID  JobID    UID CollectionDate     CollectedBy   ClientSampleID   Latitude  Longitude VolumeFilteredML DeploymentDuration EnvironmentType   TICI TICINoSeqs TICIQuantile        TICIVersion   ClientNotes      AccountName MakeDataPublic                                                                                     Report
1 8145 601833 507875     2021-07-03 Shaun Wilkinson Ruakokoputuna C3 -41.312665 175.449873             1000                       River/Stream 105.80        109         0.57        Riverine V1 Control rep 3 Wilderlab NZ Ltd              1 https://s3.ap-southeast-2.amazonaws.com/wilderlab.openwaters/reports/c62322f1b3162646.html
2 8143 601833 507877     2021-07-03 Shaun Wilkinson Ruakokoputuna C1 -41.312665 175.449873             1000                       River/Stream   0.00          0         0.00 NC - Low seq count Control rep 1 Wilderlab NZ Ltd              1 https://s3.ap-southeast-2.amazonaws.com/wilderlab.openwaters/reports/c62322f1b3162646.html
3 8144 601833 510042     2021-07-03 Shaun Wilkinson Ruakokoputuna C2 -41.312665 175.449873             1000                       River/Stream 100.08        159         0.51        Riverine V1 Control rep 2 Wilderlab NZ Ltd              1 https://s3.ap-southeast-2.amazonaws.com/wilderlab.openwaters/reports/c62322f1b3162646.html
4 8146 601834 510897     2021-07-03 Shaun Wilkinson Ruakokoputuna P1 -41.312665 175.449873                                  24    River/Stream 100.97        391         0.52        Riverine V1 Passive rep 1 Wilderlab NZ Ltd              1 https://s3.ap-southeast-2.amazonaws.com/wilderlab.openwaters/reports/0b6f40128711495a.html
5 8147 601834 510898     2021-07-03 Shaun Wilkinson Ruakokoputuna P2 -41.312665 175.449873                                  24    River/Stream 100.88        381         0.52        Riverine V1 Passive rep 2 Wilderlab NZ Ltd              1 https://s3.ap-southeast-2.amazonaws.com/wilderlab.openwaters/reports/0b6f40128711495a.html
6 8148 601834 510899     2021-07-03 Shaun Wilkinson Ruakokoputuna P3 -41.312665 175.449873                                  24    River/Stream  99.81        320         0.50        Riverine V1 Passive rep 3 Wilderlab NZ Ltd              1 https://s3.ap-southeast-2.amazonaws.com/wilderlab.openwaters/reports/0b6f40128711495a.html

Taxa

Getting information from the taxa table:

taxa <- get_wilderdata("taxa", key = key, secret = secret, xapikey = xapikey)
head(taxa, 10)
 TaxID ParentTaxID    Rank             Name                CommonName MaoriName
1      1           0 no rank             root              Unidentified          
2      2      131567  domain         Bacteria                Eubacteria          
3      6      335928   genus     Azorhizobium                                    
4     10     1706371   genus       Cellvibrio                                    
5     13      203488   genus     Dictyoglomus                                    
6     18      213421   genus       Pelobacter                                    
7     20       76892   genus Phenylobacterium                                    
8     22      267890   genus       Shewanella                                    
9     29       32015   order     Myxococcales Fruiting gliding bacteria          
10    31       80811  family    Myxococcaceae

Records

Getting information from the records table:

records <- vector(mode = "list", length = nrow(jobs))
for(i in seq_along(records)){
  records[[i]] <- get_wilderdata("records", JobID = jobs$JobID[i],
                                 key = key, secret = secret, xapikey = xapikey)
}
records <- do.call("rbind", records)

head(records, 10)
       HID    UID TaxID   Rank               Name CommonName    Group Count
1  2557713 507875    10  genus         Cellvibrio            Bacteria    37
2  2679827 510042    10  genus         Cellvibrio            Bacteria    43
3  2557718 507875   237  genus     Flavobacterium            Bacteria    62
4  2679837 510042   237  genus     Flavobacterium            Bacteria    21
5  2557710 507875   444 family     Legionellaceae            Bacteria    42
6  2557724 507875   543 family Enterobacteriaceae            Bacteria    20
7  2557748 507877   543 family Enterobacteriaceae            Bacteria     7
8  2679838 510042   543 family Enterobacteriaceae            Bacteria    20
9  2679848 510042   642  genus          Aeromonas            Bacteria     6
10 2679844 510042   978  genus          Cytophaga            Bacteria     9

Adding lineage information and sample metadata information into the records table:

tdb <- taxa[, 1:4]
colnames(tdb) <- c("taxID", "parent_taxID", "rank", "name")
lineages = insect::get_lineage(records$TaxID, tdb)
records$phylum <- sapply(lineages, "[", "phylum")
records$class <- sapply(lineages, "[", "class")
records$order <- sapply(lineages, "[", "order")
records$family <- sapply(lineages, "[", "family")
records$genus <- sapply(lineages, "[", "genus")
records$species <- sapply(lineages, "[", "species")
records$Latitude <- samples$Latitude[match(records$UID, samples$UID)]
records$Longitude <- samples$Longitude[match(records$UID, samples$UID)]
records$ClientSampleID <- samples$ClientSampleID[match(records$UID, samples$UID)]

head(records, 10)
       HID    UID TaxID   Rank               Name CommonName    Group Count         phylum               class            order             family          genus species   Latitude  Longitude   ClientSampleID
1  2557713 507875    10  genus         Cellvibrio            Bacteria    37 Pseudomonadota Gammaproteobacteria  Cellvibrionales   Cellvibrionaceae     Cellvibrio    <NA> -41.312665 175.449873 Ruakokoputuna C3
2  2679827 510042    10  genus         Cellvibrio            Bacteria    43 Pseudomonadota Gammaproteobacteria  Cellvibrionales   Cellvibrionaceae     Cellvibrio    <NA> -41.312665 175.449873 Ruakokoputuna C2
3  2557718 507875   237  genus     Flavobacterium            Bacteria    62   Bacteroidota      Flavobacteriia Flavobacteriales  Flavobacteriaceae Flavobacterium    <NA> -41.312665 175.449873 Ruakokoputuna C3
4  2679837 510042   237  genus     Flavobacterium            Bacteria    21   Bacteroidota      Flavobacteriia Flavobacteriales  Flavobacteriaceae Flavobacterium    <NA> -41.312665 175.449873 Ruakokoputuna C2
5  2557710 507875   444 family     Legionellaceae            Bacteria    42 Pseudomonadota Gammaproteobacteria    Legionellales     Legionellaceae           <NA>    <NA> -41.312665 175.449873 Ruakokoputuna C3
6  2557724 507875   543 family Enterobacteriaceae            Bacteria    20 Pseudomonadota Gammaproteobacteria Enterobacterales Enterobacteriaceae           <NA>    <NA> -41.312665 175.449873 Ruakokoputuna C3
7  2557748 507877   543 family Enterobacteriaceae            Bacteria     7 Pseudomonadota Gammaproteobacteria Enterobacterales Enterobacteriaceae           <NA>    <NA> -41.312665 175.449873 Ruakokoputuna C1
8  2679838 510042   543 family Enterobacteriaceae            Bacteria    20 Pseudomonadota Gammaproteobacteria Enterobacterales Enterobacteriaceae           <NA>    <NA> -41.312665 175.449873 Ruakokoputuna C2
9  2679848 510042   642  genus          Aeromonas            Bacteria     6 Pseudomonadota Gammaproteobacteria    Aeromonadales     Aeromonadaceae      Aeromonas    <NA> -41.312665 175.449873 Ruakokoputuna C2
10 2679844 510042   978  genus          Cytophaga            Bacteria     9   Bacteroidota          Cytophagia     Cytophagales      Cytophagaceae      Cytophaga    <NA> -41.312665 175.449873 Ruakokoputuna C2

Issues

If you experience a problem using this software please feel free to raise it as an issue on GitHub.

:first-child"); var i, h, a; for (i = 0; i < hs.length; i++) { h = hs[i]; if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 a = h.attributes; while (a.length > 0) h.removeAttribute(a[0].name); } });

Environmental DNA (eDNA) testing generates a lot of data. Over the past three years our clients have detected over a million different DNA sequences from thousands of different species. While our primary method of disseminating data is in xlsx format, static result tables do not reflect continuous updates to taxon IDs due to the availability of new reference sequences. Furthermore, static result tables are not always shared between departments, resulting in fragmented data and reporting.

To remedy this, Wilderlab has developed an API (Application Programming Interface) that enables clients to access their data in real time, facilitating the export of eDNA results to environmental data storage platforms such as Kisters and Hilltop.

This tutorial provides a brief introduction to accessing the connect.wilderlab API using the R programming language and the the wilderlab R package. Please contact us if you would like us to generate API access keys for your organisation, or if you would like any further help.

Wilderlab API Instructions

1. Installation

The wilderlab R package contains functions for importing and exporting eDNA data. To download the package from GitHub, ensure that devtools is installed. The insect package will also be required later on in this tutorial:

if(!("devtools" %in% list.files(.libPaths()))) install.packages("devtools")
if(!("insect" %in% list.files(.libPaths()))) install.packages("insect")

Then run:

devtools::install_github("wilderlabnz/wilderlab") 
library(wilderlab)


2. Load access keys

When signing up to the Wilderlab API, your unique log in information will be securely sent to you. This will include three access keys: an API access key id, key; a secret access key, secret; and a X-API-Key, xapikey.
Copy and paste this unique information into the appropriate slots in the following code to load them into your R session.

key <- "*****************"
secret <- "***************************************"
xapikey <- "***************************************"


3. get_wilderdata function

Description

Wrapper functions for creating URLs and authorisation headers to download job, sample, taxa, and record information from the connect.wilderlab API.

Usage

get_wilderdata(tb, key, secret, xapikey, JobID = NULL)

Arguments

Argument Description
tb a character string specifying the table required. Accepted values are jobs, samples, taxa, and records.
key a string specifying the API access key for the client account. Please contact if you would like access keys generated for your account.
secret a string specifying the API secret access key for the client account. Please contact if you would like access keys generated for your account.
xapikey a string specifying the X-API-Key value for the client account. Please contact if you would like access keys generated for your account.
JobID a 6 digit integer specifying a Wilderlab job number. Required only for accessing the records table.

Details

The Wilderlab API is designed for clients to access up-to-date eDNA data for their internal data storage platforms and geospatial applications. Clients can access their job, sample, taxon and eDNA records data at any time by querying the API with a valid URL and authorization header. The get_wilderdata function is a wrapper that enables these URLs and headers to be compiled with minimal effort.


4. Get data tables

Pulling information from the job, samples, and taxa tables is very straightforward. All the information associated with a unique API access key is accessed as a whole:

jobs <- get_wilderdata("jobs", key = key, secret = secret, xapikey = xapikey)
samples <- get_wilderdata("samples", key = key, secret = secret, xapikey = xapikey)
taxa <- get_wilderdata("taxa", key = key, secret = secret, xapikey = xapikey)

Pulling from the records table is done on a specified JobID thus multiple calls must be made to get a full records table. After accessing the jobs table, the relevant JobIDs can be iterated over, the corresponding records pulled through the API, and finally the gathered records combined into a complete records table. The following code chunk is an example of how this can be done.

records <- vector(mode = "list", length = nrow(jobs))
for(i in seq_along(records)){
  records[[i]] <- get_wilderdata("records", JobID = jobs$JobID[i],
                                 key = key, secret = secret, xapikey = xapikey)
}
records <- do.call("rbind", records)


5. Fill lineage information

The final step in completing the records table is to add lineage information and sample metadata. This is an optional step but can improve interpretation of the results.

tdb <- taxa[, 1:4]
colnames(tdb) <- c("taxID", "parent_taxID", "rank", "name")
lineages = insect::get_lineage(records$TaxID, tdb)
records$phylum <- sapply(lineages, "[", "phylum")
records$class <- sapply(lineages, "[", "class")
records$order <- sapply(lineages, "[", "order")
records$family <- sapply(lineages, "[", "family")
records$genus <- sapply(lineages, "[", "genus")
records$species <- sapply(lineages, "[", "species")
records$Latitude <- samples$Latitude[match(records$UID,samples$UID)]
records$Longitude <- samples$Longitude[match(records$UID,samples$UID)]
records$ClientSampleID <- samples$ClientSampleID[match(records$UID,samples$UID)]

Example output

For the purposes of this manual, we will use example access keys as follows. This information will allow us to produce an example of what to expect from connecting to the Wilderlab API. Feel free to try out the API with these access keys first to ensure it is working as expected.

key <- "AKIATVYXGCYLWADFJVEX"
secret <- "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"
xapikey <- "7CCm580l5vgeKbalwIEy565uFhbEudTauAq80B38"


Jobs

Getting information from the jobs table:

jobs <- get_wilderdata("jobs", key = key, secret = secret, xapikey = xapikey)
jobs
   JobID SubmissionDate     ContactName    ContactEmail PurchaseOrder                          JobReference JobNotes NumberOfSamples          AssayPanel MakeDataPublic PassCode JobStatus                                                                                                      ResultsLink InvoiceNo
1 601833     2021-07-04 Shaun Wilkinson api@example.com               Passive sampler validation experiment                        3 Comprehensive panel              1  W1E0638  Complete https://s3.ap-southeast-2.amazonaws.com/wilderlab.results/c03d243992534adcf4ffecd39c43d6e8/WLM601833_250626.xlsx  INTERNAL
2 601834     2021-07-04 Shaun Wilkinson api@example.com               Passive sampler validation experiment                        3 Comprehensive panel              1  W1E0638  Complete https://s3.ap-southeast-2.amazonaws.com/wilderlab.results/a26f9576e52ce92c689070da34865104/WLM601834_250626.xlsx  INTERNAL


Samples

Getting information from the samples table:

samples <- get_wilderdata("samples", key = key, secret = secret, xapikey = xapikey)
samples
   SID  JobID    UID CollectionDate     CollectedBy   ClientSampleID   Latitude  Longitude VolumeFilteredML DeploymentDuration EnvironmentType   TICI TICINoSeqs TICIQuantile        TICIVersion   ClientNotes      AccountName MakeDataPublic                                                                                     Report
1 8145 601833 507875     2021-07-03 Shaun Wilkinson Ruakokoputuna C3 -41.312665 175.449873             1000                       River/Stream 105.80        109         0.57        Riverine V1 Control rep 3 Wilderlab NZ Ltd              1 https://s3.ap-southeast-2.amazonaws.com/wilderlab.openwaters/reports/c62322f1b3162646.html
2 8143 601833 507877     2021-07-03 Shaun Wilkinson Ruakokoputuna C1 -41.312665 175.449873             1000                       River/Stream   0.00          0         0.00 NC - Low seq count Control rep 1 Wilderlab NZ Ltd              1 https://s3.ap-southeast-2.amazonaws.com/wilderlab.openwaters/reports/c62322f1b3162646.html
3 8144 601833 510042     2021-07-03 Shaun Wilkinson Ruakokoputuna C2 -41.312665 175.449873             1000                       River/Stream 100.08        159         0.51        Riverine V1 Control rep 2 Wilderlab NZ Ltd              1 https://s3.ap-southeast-2.amazonaws.com/wilderlab.openwaters/reports/c62322f1b3162646.html
4 8146 601834 510897     2021-07-03 Shaun Wilkinson Ruakokoputuna P1 -41.312665 175.449873                                  24    River/Stream 100.97        391         0.52        Riverine V1 Passive rep 1 Wilderlab NZ Ltd              1 https://s3.ap-southeast-2.amazonaws.com/wilderlab.openwaters/reports/0b6f40128711495a.html
5 8147 601834 510898     2021-07-03 Shaun Wilkinson Ruakokoputuna P2 -41.312665 175.449873                                  24    River/Stream 100.88        381         0.52        Riverine V1 Passive rep 2 Wilderlab NZ Ltd              1 https://s3.ap-southeast-2.amazonaws.com/wilderlab.openwaters/reports/0b6f40128711495a.html
6 8148 601834 510899     2021-07-03 Shaun Wilkinson Ruakokoputuna P3 -41.312665 175.449873                                  24    River/Stream  99.81        320         0.50        Riverine V1 Passive rep 3 Wilderlab NZ Ltd              1 https://s3.ap-southeast-2.amazonaws.com/wilderlab.openwaters/reports/0b6f40128711495a.html


Taxa

Getting information from the taxa table:

taxa <- get_wilderdata("taxa", key = key, secret = secret, xapikey = xapikey)
head(taxa, 10)
   TaxID ParentTaxID    Rank             Name                CommonName MaoriName
1      1           0 no rank             root              Unidentified          
2      2      131567  domain         Bacteria                Eubacteria          
3      6      335928   genus     Azorhizobium                                    
4     10     1706371   genus       Cellvibrio                                    
5     13      203488   genus     Dictyoglomus                                    
6     18      213421   genus       Pelobacter                                    
7     20       76892   genus Phenylobacterium                                    
8     22      267890   genus       Shewanella                                    
9     29       32015   order     Myxococcales Fruiting gliding bacteria          
10    31       80811  family    Myxococcaceae


Records

Getting information from the records table:

records <- vector(mode = "list", length = nrow(jobs))
for(i in seq_along(records)){
  records[[i]] <- get_wilderdata("records", JobID = jobs$JobID[i],
                                 key = key, secret = secret, xapikey = xapikey)
}
records <- do.call("rbind", records)

head(records, 10)
       HID    UID TaxID   Rank               Name CommonName    Group Count
1  2557713 507875    10  genus         Cellvibrio            Bacteria    37
2  2679827 510042    10  genus         Cellvibrio            Bacteria    43
3  2557718 507875   237  genus     Flavobacterium            Bacteria    62
4  2679837 510042   237  genus     Flavobacterium            Bacteria    21
5  2557710 507875   444 family     Legionellaceae            Bacteria    42
6  2557724 507875   543 family Enterobacteriaceae            Bacteria    20
7  2557748 507877   543 family Enterobacteriaceae            Bacteria     7
8  2679838 510042   543 family Enterobacteriaceae            Bacteria    20
9  2679848 510042   642  genus          Aeromonas            Bacteria     6
10 2679844 510042   978  genus          Cytophaga            Bacteria     9

Adding lineage information and sample metadata information into the records table:

tdb <- taxa[, 1:4]
colnames(tdb) <- c("taxID", "parent_taxID", "rank", "name")
lineages = insect::get_lineage(records$TaxID, tdb)
records$phylum <- sapply(lineages, "[", "phylum")
records$class <- sapply(lineages, "[", "class")
records$order <- sapply(lineages, "[", "order")
records$family <- sapply(lineages, "[", "family")
records$genus <- sapply(lineages, "[", "genus")
records$species <- sapply(lineages, "[", "species")
records$Latitude <- samples$Latitude[match(records$UID, samples$UID)]
records$Longitude <- samples$Longitude[match(records$UID, samples$UID)]
records$ClientSampleID <- samples$ClientSampleID[match(records$UID, samples$UID)]

head(records, 10)
       HID    UID TaxID   Rank               Name CommonName    Group Count         phylum               class            order             family          genus species   Latitude  Longitude   ClientSampleID
1  2557713 507875    10  genus         Cellvibrio            Bacteria    37 Pseudomonadota Gammaproteobacteria  Cellvibrionales   Cellvibrionaceae     Cellvibrio    <NA> -41.312665 175.449873 Ruakokoputuna C3
2  2679827 510042    10  genus         Cellvibrio            Bacteria    43 Pseudomonadota Gammaproteobacteria  Cellvibrionales   Cellvibrionaceae     Cellvibrio    <NA> -41.312665 175.449873 Ruakokoputuna C2
3  2557718 507875   237  genus     Flavobacterium            Bacteria    62   Bacteroidota      Flavobacteriia Flavobacteriales  Flavobacteriaceae Flavobacterium    <NA> -41.312665 175.449873 Ruakokoputuna C3
4  2679837 510042   237  genus     Flavobacterium            Bacteria    21   Bacteroidota      Flavobacteriia Flavobacteriales  Flavobacteriaceae Flavobacterium    <NA> -41.312665 175.449873 Ruakokoputuna C2
5  2557710 507875   444 family     Legionellaceae            Bacteria    42 Pseudomonadota Gammaproteobacteria    Legionellales     Legionellaceae           <NA>    <NA> -41.312665 175.449873 Ruakokoputuna C3
6  2557724 507875   543 family Enterobacteriaceae            Bacteria    20 Pseudomonadota Gammaproteobacteria Enterobacterales Enterobacteriaceae           <NA>    <NA> -41.312665 175.449873 Ruakokoputuna C3
7  2557748 507877   543 family Enterobacteriaceae            Bacteria     7 Pseudomonadota Gammaproteobacteria Enterobacterales Enterobacteriaceae           <NA>    <NA> -41.312665 175.449873 Ruakokoputuna C1
8  2679838 510042   543 family Enterobacteriaceae            Bacteria    20 Pseudomonadota Gammaproteobacteria Enterobacterales Enterobacteriaceae           <NA>    <NA> -41.312665 175.449873 Ruakokoputuna C2
9  2679848 510042   642  genus          Aeromonas            Bacteria     6 Pseudomonadota Gammaproteobacteria    Aeromonadales     Aeromonadaceae      Aeromonas    <NA> -41.312665 175.449873 Ruakokoputuna C2
10 2679844 510042   978  genus          Cytophaga            Bacteria     9   Bacteroidota          Cytophagia     Cytophagales      Cytophagaceae      Cytophaga    <NA> -41.312665 175.449873 Ruakokoputuna C2

Issues

If you experience a problem using this software please feel free to raise it as an issue on GitHub.