Bring your own data to start 2023!

#TidyTuesday - Edition 2023

This time I’ll use the {oregonfrogs} package.

Rana Pretiosa, a rare species of frog, is found on various locations around Crane Prairie Reservoir lake in Oregon. Such as: pond, river, North, North East, South East, and West part of the Reservoir.

library(tidyverse)
library(oregonfrogs)
data("oregonfrogs")
oregonfrogs::oregonfrogs %>% count(subsite)

Scientists monitoring the frogs have found interesting patterns in the density distribution between male and female frogs. Through the use of radio frequencies, they are able to track the movements of the frogs and study their behaviors. However, as their habitat is threatened, the scientists must race against time to protect the unique Rana Pretiosa population.

oregonfrogs::oregonfrogs %>% head

oregonfrogs %>%
  mutate(sex = as.factor(sex)) %>%
  ggplot(aes(doy, detection, group = sex, color = sex)) +
  geom_point() +
  facet_wrap( ~ subsite)+
  hrbrthemes::scale_color_ipsum()+
  hrbrthemes::theme_tinyhand()

oregonfrogs%>%
  mutate(sex=as.factor(sex),
         subsite=case_when(subsite=="Cow Camp Pond"~"Pond",
                           subsite=="Cow Camp Pond"~"Pond",
                           TRUE~subsite))%>%
  ggplot(aes(frequency,group=sex,color=sex))+
  geom_density()+
  facet_wrap(~subsite)

Let’s have a look at the ggplot2 extensions: https://exts.ggplot2.tidyverse.org/

library(hrbrthemes)
hrbrthemes.loadfonts=TRUE
hrbrthemes::import_tinyhand()
hrbrthemes::import_roboto_condensed()
extrafont::loadfonts()

oregonfrogs %>%
  mutate(sex = as.factor(sex),
         sex=ifelse(sex==0,"Female","Male"),
         frequency=round(frequency,2)) %>%
  ggplot(aes(frequency, group = sex, color = sex)) +
  geom_density(linewidth=1,key_glyph = "point") +
  facet_wrap( ~ subsite) +
  coord_cartesian(clip = 'off')+
  labs(title = "Oregonfrogs: Rana Pretiosa",
       subtitle = "Located frogs by sex density distributions",
       caption = "DataSource: #TidyTuesday 2023 week1 - BYO data: Oregonfrogs\nDataViz: Federica Gazzelloni #30DayChartChallenge 2023 Day3 - fauna/flora\n") +
  hrbrthemes::scale_color_ipsum() +
  hrbrthemes::theme_tinyhand(strip_text_size = 9,
                             plot_title_size = 13,
                             subtitle_size = 7,
                             subtitle_margin = 5,
                             caption_margin = 7,
                             caption_family = "Roboto Condensed",
                             axis_title_size = 9,
                             axis_text_size = 7,
                             plot_margin = margin(10, 10, 10, 10))

ggsave("w1_byo.png")

date: ‘2023-01-03’ image-alt: ’’ description: ‘Networks’ output: html_document execute: eval: false

---

#TidyTuesday - Edition 2023

This time I’ll use the {oregonfrogs} package.

Rana Pretiosa, a rare species of frog, is found on various locations around Crane Prairie Reservoir lake in Oregon. Such as: pond, river, North, North East, South East, and West part of the Reservoir. ::: {.cell}

library(tidyverse)
library(oregonfrogs)
data("oregonfrogs")
oregonfrogs::oregonfrogs %>% count(subsite)

:::

Scientists monitoring the frogs have found interesting patterns in the density distribution between male and female frogs. Through the use of radio frequencies, they are able to track the movements of the frogs and study their behaviors. However, as their habitat is threatened, the scientists must race against time to protect the unique Rana Pretiosa population.

oregonfrogs::oregonfrogs %>% head

oregonfrogs %>%
  mutate(sex = as.factor(sex)) %>%
  ggplot(aes(doy, detection, group = sex, color = sex)) +
  geom_point() +
  facet_wrap( ~ subsite)+
  hrbrthemes::scale_color_ipsum()+
  hrbrthemes::theme_tinyhand()

oregonfrogs%>%
  mutate(sex=as.factor(sex),
         subsite=case_when(subsite=="Cow Camp Pond"~"Pond",
                           subsite=="Cow Camp Pond"~"Pond",
                           TRUE~subsite))%>%
  ggplot(aes(frequency,group=sex,color=sex))+
  geom_density()+
  facet_wrap(~subsite)

Let’s have a look at the ggplot2 extensions: https://exts.ggplot2.tidyverse.org/

library(hrbrthemes)
hrbrthemes.loadfonts=TRUE
hrbrthemes::import_tinyhand()
hrbrthemes::import_roboto_condensed()
extrafont::loadfonts()

oregonfrogs %>%
  mutate(sex = as.factor(sex),
         sex=ifelse(sex==0,"Female","Male"),
         frequency=round(frequency,2)) %>%
  ggplot(aes(frequency, group = sex, color = sex)) +
  geom_density(linewidth=1,key_glyph = "point") +
  facet_wrap( ~ subsite) +
  coord_cartesian(clip = 'off')+
  labs(title = "Oregonfrogs: Rana Pretiosa",
       subtitle = "Located frogs by sex density distributions",
       caption = "DataSource: #TidyTuesday 2023 week1 - BYO data: Oregonfrogs\nDataViz: Federica Gazzelloni #30DayChartChallenge 2023 Day3 - fauna/flora\n") +
  hrbrthemes::scale_color_ipsum() +
  hrbrthemes::theme_tinyhand(strip_text_size = 9,
                             plot_title_size = 13,
                             subtitle_size = 7,
                             subtitle_margin = 5,
                             caption_margin = 7,
                             caption_family = "Roboto Condensed",
                             axis_title_size = 9,
                             axis_text_size = 7,
                             plot_margin = margin(10, 10, 10, 10))

ggsave("w1_byo.png")