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Copy file name to clipboardexpand all lines: _episodes/05-Group-By-Example.md
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```output
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dataResourceName count
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0 FrogID 39840
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1 NSW BioNet Atlas 4882
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2 iNaturalist Australia 2578
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3 NatureMapr 249
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4 Earth Guardians Weekly Feed151
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1 NSW BioNet Atlas 4884
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2 iNaturalist Australia 2664
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3 Earth Guardians Weekly Feed150
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4 NatureMapr 133
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5 ALA species sightings and OzAtlas 16
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6 Victorian Biodiversity Atlas 10
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7 FrogWatch SA 6
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11 SA Fauna 2
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```
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We can see that there are 12 data resources that have provided the ALA observations of *Litoria peronii*, and surprisingly, FrogID provides the second most observations!
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We can see that there are 12 data resources that have provided the ALA observations of *Litoria peronii*.
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Now, in the query above, we specified that we want records since 2018. However, we can also see how many records came from each year by adding `year` to the `group_by` arguments.
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```output
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dataResourceName year count
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0 FrogID - 39840
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1 NSW BioNet Atlas - 4882
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2 iNaturalist Australia - 2578
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3 NatureMapr - 249
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4 Earth Guardians Weekly Feed - 151
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1 NSW BioNet Atlas - 4884
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2 iNaturalist Australia - 2664
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3 Earth Guardians Weekly Feed - 150
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4 NatureMapr - 133
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5 ALA species sightings and OzAtlas - 16
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6 Victorian Biodiversity Atlas - 10
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7 FrogWatch SA - 6
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8 Australian Museum provider for OZCAM - 4
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9 BowerBird - 3
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10 Melbourne Water Frog Census - 2
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11 SA Fauna - 2
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12 - 2018 5200
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13 - 2019 5469
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14 - 2020 13358
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15 - 2021 14469
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16 - 2022 7506
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17 - 2023 817
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18 - 2024 762
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19 - 2025 162
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12 - 2018 5181
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13 - 2019 5447
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14 - 2020 13334
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15 - 2021 14458
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16 - 2022 7496
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17 - 2023 800
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18 - 2024 753
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19 - 2025 245
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```
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Now, we not only have the data resources providing observations of *Litoria peronii*, we can also see how many observations there were per year.
Copy file name to clipboardexpand all lines: _episodes/06-Make-a-Map.md
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```
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```output
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totalRecords
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0 27969
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0 39840
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```
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All we have to do to download occurrences is to change the function name `atlas_counts` to `atlas_occurrences`, but first, we need to provide an email registered with the ALA to `galah-python`:
All of this data for each occurrence record is great! However, say you want to only get specific columns of the table, like `decimalLatitude`,`decimalLongitude` and `scientificName`. You can specify column names in the `fields` argument of `atlas_occurrences`:
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```
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```output
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scientificName decimalLatitude decimalLongitude
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0 Litoria peronii -32.303061 151.687980
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1 Litoria peronii -32.809788 151.353210
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2 Litoria peronii -29.929163152.008692
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3 Litoria peronii -36.354229150.075424
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4 Litoria peronii -34.496047150.777103
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0 Litoria peronii -33.624100 151.323000
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1 Litoria peronii -33.718800 151.003000
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2 Litoria peronii -33.324700151.365000
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3 Litoria peronii -33.572700148.436000
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4 Litoria peronii -35.115900147.981000
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... ... ... ...
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27964 Litoria peronii -30.101165153.161719
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27965 Litoria peronii -34.053162 151.086362
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27966 Litoria peronii -33.614032150.697754
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27967 Litoria peronii -34.643233150.325404
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27968 Litoria peronii -34.314148150.918278
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39835 Litoria peronii -33.817474151.177367
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39836 Litoria peronii -33.948932 151.251668
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39837 Litoria peronii -33.930552151.237679
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39838 Litoria peronii -33.686587151.096895
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39839 Litoria peronii -33.448529151.375129
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```
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# Make a map of *Litoria peronii* occurrence records since 2018 in New South Wales
scientificName scientificNameAuthorship taxonConceptID rank ... order family genus issues
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0 Morganella Zeller https://id.biodiversity.org.au/node/fungi/6009... genus ... Agaricales Agaricaceae Morganella noIssue
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scientificName scientificNameAuthorship rank ... order family genus issues
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0 Morganella Zeller genus ... Agaricales Agaricaceae Morganella noIssue
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```
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This disambiguation of the *Morganella* taxa can then be used by `atlas_counts()`, `atlas_occurrences()`, `atlas_species()` or `atlas_media()` by providing the keyword `scientific_name` to any of these functions.
2 Petroica boodang South-western Scarlet Robin 211
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3 Petroica boodang Tasmanian Scarlet Robin 91
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4 Petroica goodenovii Red-capped Robin 120523
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5 Petroica multicolor Pacific Robin 6795
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6 Petroica phoenicea Flame Robin 88884
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3 Petroica boodang Tasmanian Scarlet Robin 93
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4 Petroica goodenovii Red-capped Robin 120947
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5 Petroica multicolor Pacific Robin 6856
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6 Petroica phoenicea Flame Robin 82751
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7 Petroica rodinogaster Mainland Pink Robin 69
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8 Petroica rodinogaster Pink Robin 15753
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9 Petroica rodinogaster Tasmanian Pink Robin 45
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10 Petroica rosea Rose Robin 60276
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8 Petroica rodinogaster Pink Robin 15608
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9 Petroica rodinogaster Tasmanian Pink Robin 47
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10 Petroica rosea Rose Robin 60552
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```
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This can be useful in searching for [paraphyletic](https://en.wikipedia.org/wiki/Paraphyly) or [polyphyletic](http://en.wikipedia.org/wiki/Polyphyly) groups. For example, to get counts of non-chordates:
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```
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```output
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phylum count
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0 Acanthocephala 481
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1 Annelida 329585
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2 Arthropoda 10086467
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3 Brachiopoda 11574
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4 Bryozoa 32837
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0 Acanthocephala 482
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1 Annelida 332234
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2 Arthropoda 10135041
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3 Brachiopoda 11634
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4 Bryozoa 32937
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```
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# OPTIONAL: Deciding between `filters=`, `search_taxa()`, and taxonomic ranks
If, for instance, you have the correct species or subspecies name, then searching for matches against the species and subspecies fields, respectively, will provide more precise results. This is because the field `scientificName` may include subgenera. If you’ve used `search_taxa()` to get the ALA-matched name of a taxon and only want records identified to a particular level of classification, searching for matches against `scientificName` is recommended.
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