Import data

Apply the following dplyr verbs to your data

Filter rows

## # A tibble: 32,383 × 11
##    occurrenceID eventID decimalLatitude decimalLongitude scientificName    
##           <dbl>   <dbl>           <dbl>            <dbl> <chr>             
##  1        23852  525543           -37.3             144. Litoria raniformis
##  2        23853  526178           -38.4             144. Litoria raniformis
##  3        23854  526288           -38               147. Litoria aurea     
##  4        23855  526289           -38               147. Litoria aurea     
##  5        23856  526518           -38.1             147. Litoria raniformis
##  6        23857  526518           -38.1             147. Litoria aurea     
##  7        23858  526533           -38.1             147. Litoria raniformis
##  8        23859  526533           -38.1             147. Litoria aurea     
##  9        23860  526536           -38.1             147. Litoria aurea     
## 10        23861  526536           -38.1             147. Litoria raniformis
## # ℹ 32,373 more rows
## # ℹ 6 more variables: eventDate <dttm>, eventTime <dttm>, timezone <chr>,
## #   coordinateUncertaintyInMeters <dbl>, recordedBy <dbl>, stateProvince <chr>

Arrange rows

## # A tibble: 136,621 × 11
##    occurrenceID eventID decimalLatitude decimalLongitude scientificName 
##           <dbl>   <dbl>           <dbl>            <dbl> <chr>          
##  1        12850  529492           -30.3             153  Adelotus brevis
##  2        12855  529496           -30.3             153  Adelotus brevis
##  3        12860  529693           -30.4             153. Adelotus brevis
##  4        12863  529694           -30.4             153. Adelotus brevis
##  5        12866  529720           -30.4             153. Adelotus brevis
##  6        12905  538071           -30.4             153  Adelotus brevis
##  7        12908  538073           -30.4             153  Adelotus brevis
##  8        12909  538077           -30.4             153  Adelotus brevis
##  9        12959  543519           -30.4             153  Adelotus brevis
## 10        12977  547069           -30.4             153  Adelotus brevis
## # ℹ 136,611 more rows
## # ℹ 6 more variables: eventDate <dttm>, eventTime <dttm>, timezone <chr>,
## #   coordinateUncertaintyInMeters <dbl>, recordedBy <dbl>, stateProvince <chr>

Select columns

## # A tibble: 136,621 × 2
##    scientificName           eventDate          
##    <chr>                    <dttm>             
##  1 Philoria loveridgei      2023-01-01 00:00:00
##  2 Heleioporus australiacus 2023-01-02 00:00:00
##  3 Mixophyes iteratus       2023-01-02 00:00:00
##  4 Mixophyes fasciolatus    2023-01-02 00:00:00
##  5 Litoria latopalmata      2023-01-02 00:00:00
##  6 Assa darlingtoni         2023-01-04 00:00:00
##  7 Assa darlingtoni         2023-01-04 00:00:00
##  8 Litoria nasuta           2023-01-06 00:00:00
##  9 Mixophyes iteratus       2023-01-06 00:00:00
## 10 Litoria gracilenta       2023-01-06 00:00:00
## # ℹ 136,611 more rows

Add columns

## # A tibble: 136,621 × 12
##    occurrenceID eventID decimalLatitude decimalLongitude scientificName         
##           <dbl>   <dbl>           <dbl>            <dbl> <chr>                  
##  1        12832  525618           -28.5             153. Philoria loveridgei    
##  2        12833  526341           -33.7             151. Heleioporus australiac…
##  3        12834  526673           -28.7             153. Mixophyes iteratus     
##  4        12835  526673           -28.7             153. Mixophyes fasciolatus  
##  5        12836  526673           -28.7             153. Litoria latopalmata    
##  6        12837  527056           -30.4             153. Assa darlingtoni       
##  7        12838  527058           -30.4             153. Assa darlingtoni       
##  8        12839  528103           -30.4             153  Litoria nasuta         
##  9        12840  528103           -30.4             153  Mixophyes iteratus     
## 10        12841  528103           -30.4             153  Litoria gracilenta     
## # ℹ 136,611 more rows
## # ℹ 7 more variables: eventDate <dttm>, eventTime <dttm>, timezone <chr>,
## #   coordinateUncertaintyInMeters <dbl>, recordedBy <dbl>, stateProvince <chr>,
## #   test <dbl>

Summarize by groups

## # A tibble: 9 × 2
##   stateProvince                Total
##   <chr>                        <int>
## 1 Australian Capital Territory  2082
## 2 New South Wales              58749
## 3 Northern Territory            2380
## 4 Other Territories              129
## 5 Queensland                   23334
## 6 South Australia               4158
## 7 Tasmania                      2562
## 8 Victoria                     32383
## 9 Western Australia            10844