Import data
# csv file
data <- read_csv("data/mydata.csv")
## New names:
## Rows: 29787 Columns: 23
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (18): animal_id, animal_name, animal_type, primary_color, secondary_colo... dbl
## (3): ...1, latitude, longitude lgl (2): outcome_is_dead, was_outcome_alive
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
data
## # A tibble: 29,787 × 23
## ...1 animal_id animal_name animal_type primary_color secondary_color sex
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1 A693708 *charlien dog white <NA> Female
## 2 2 A708149 <NA> reptile brown green Unknown
## 3 3 A638068 <NA> bird green red Unknown
## 4 4 A639310 <NA> bird white gray Unknown
## 5 5 A618968 *morgan cat black white Female
## 6 6 A730385 *brandon rabbit black white Neuter…
## 7 7 A646202 <NA> bird black <NA> Unknown
## 8 8 A628138 <NA> other gray black Unknown
## 9 9 A597464 <NA> cat black <NA> Unknown
## 10 10 A734321 sophie dog cream <NA> Spayed
## # ℹ 29,777 more rows
## # ℹ 16 more variables: dob <chr>, intake_date <chr>, intake_condition <chr>,
## # intake_type <chr>, intake_subtype <chr>, reason_for_intake <chr>,
## # outcome_date <chr>, crossing <chr>, jurisdiction <chr>, outcome_type <chr>,
## # outcome_subtype <chr>, latitude <dbl>, longitude <dbl>,
## # outcome_is_dead <lgl>, was_outcome_alive <lgl>, geopoint <chr>
Plot prices
data %>%
ggplot(aes(animal_type)) +
geom_bar()
