Tidy Tuesday 2025 Week 35

Code

library(tidytuesdayR)
tuesdata <- tidytuesdayR::tt_load(2025, week=35)

frog <- tuesdata$frogID_data
names <- tuesdata$frog_names

Code

library(dplyr)
library(ggplot2)
unique(frog$stateProvince)

[1] "New South Wales"              "Australian Capital Territory"
[3] "Western Australia"            "South Australia"             
[5] "Northern Territory"           "Queensland"                  
[7] "Victoria"                     "Tasmania"                    
[9] "Other Territories"

Code

sum(frog$stateProvince == "New South Wales")

[1] 58749

Code

#I want to ask the research question: Where are the most common frogs located physically? 
#To start, let's mark graph them via latitude and longitude.

ggplot(frog, aes(x=decimalLatitude, y=decimalLongitude, color=stateProvince)) +
  geom_point()

Code

frog %>%
  count(scientificName, sort=TRUE)

# A tibble: 186 × 2
   scientificName                 n
   <chr>                      <int>
 1 Crinia signifera           33630
 2 Limnodynastes peronii      17462
 3 Litoria fallax              8572
 4 Litoria peronii             8565
 5 Limnodynastes tasmaniensis  7372
 6 Litoria ewingii             6471
 7 Litoria verreauxii          5824
 8 Crinia parinsignifera       4339
 9 Limnodynastes dumerilii     3289
10 Litoria caerulea            3011
# ℹ 176 more rows

Code

#let's rank the top 3 most common frogs and add only them to our new dataset

frog2 <- frog %>% 
  group_by(scientificName) %>%
  mutate(n = n()) %>%
  ungroup() %>%
  mutate(rank = dense_rank(desc(n))) %>%
  filter(rank <= 3)

Code

#Time for our final plot!

ggplot(frog2, aes(x=decimalLatitude, y=decimalLongitude, color=stateProvince)) +
  geom_point() +
  facet_wrap(~scientificName) +
  xlim(-50,-15) +
  ylim(135,155) +
  labs(title = "Top 3 Most Common Frogs in Australia",
       x = "Latitude",
       y = "Longitude",
       color = "Province")