Import data

# excel file
data <- read_excel("../00_data/MKmyData.xlsx") %>%
    janitor::clean_names() %>%
    mutate(age = as.numeric(age))
data
## # A tibble: 101 × 17
##    column1 id_on_tag    animal_name scientific_name tag_deployment_start        
##      <dbl> <chr>        <chr>       <chr>           <chr>                       
##  1       1 Tommy-Tag    Tommy       Felis catus     2017-06-03T01:02:00.0000001…
##  2       2 Athena       Athena      Felis catus     2017-06-24T01:02:00.0000001…
##  3       3 Ares         Ares        Felis catus     2017-06-24T01:02:59.9999997…
##  4       4 Lola         Lola        Felis catus     2017-06-24T01:18:00.0000001…
##  5       5 Maverick     Maverick    Felis catus     2017-06-25T01:03:59.9999999…
##  6       6 Coco         Coco        Felis catus     2017-06-28T01:02:00.0000001…
##  7       7 Charlie      Charlie     Felis catus     2017-06-28T01:02:59.9999997…
##  8       8 Jago         Jago        Felis catus     2017-06-28T04:09:59.9999998…
##  9       9 Morpheus-Tag Morpheus    Felis catus     2017-07-01T01:02:00.0000001…
## 10      10 Nettle-Tag   Nettle      Felis catus     2017-07-01T01:05:00.0000001…
## # ℹ 91 more rows
## # ℹ 12 more variables: tag_deployment_end <chr>, hunt <chr>,
## #   number_prey_per_month <dbl>, reproductive_condition <chr>, sex <chr>,
## #   hours_indoor_per_day <dbl>, number_of_cats_in_house <dbl>, dry_food <lgl>,
## #   wet_food <lgl>, other_food <chr>, study_location <chr>, age <dbl>

State one question

The longer the number of hours indoor per day, the older the cat is # Plot data

ggplot(data = data) + 
    geom_point(mapping = aes(x = age, y = hours_indoor_per_day))

Interpret

There seems to be preliminary evidence of no clear positive relationship between the number of hours indoor per day and the age of the cat.