Import data
# excel file
data <- read_excel("../00_data/MKmyData1.xlsx")
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>,
## # prey.per.month <dbl>, reproductive.condition <chr>, sex <chr>,
## # hours.indoor.per.day <dbl>, cats.in.house <dbl>, dry.food <lgl>,
## # wet.food <lgl>, other.food <chr>, study.location <chr>, age <chr>
Filter rows
filter(data, wet.food == "FALSE")
## # A tibble: 30 × 17
## Column1 id.on.tag animal.name scientific.name `tag deployment.start`
## <dbl> <chr> <chr> <chr> <chr>
## 1 9 Morpheus-Tag Morpheus Felis catus 2017-07-01T01:02:00.0000001…
## 2 10 Nettle-Tag Nettle Felis catus 2017-07-01T01:05:00.0000001…
## 3 12 Friday Friday Felis catus 2017-07-02T07:28:00.0000002…
## 4 13 Carbonel-Tag Carbonel Felis catus 2017-07-07T01:00:59.9999999…
## 5 18 Indie-Tag Indie Felis catus 2017-07-09T01:02:59.9999997…
## 6 20 Rusty-Tag Rusty Felis catus 2017-07-09T02:19:00.0000001…
## 7 22 Wilfred-Tag Wilfred Felis catus 2017-07-13T01:00:59.9999999…
## 8 25 Pussy-Tag Pussy Felis catus 2017-07-13T01:05:00.0000001…
## 9 38 Merlin-Tag Merlin Felis catus 2017-07-20T09:24:59.9999998…
## 10 41 Sid-Tag Sid Felis catus 2017-07-22T01:03:59.9999999…
## # ℹ 20 more rows
## # ℹ 12 more variables: `tag deployment.end` <chr>, hunt <chr>,
## # prey.per.month <dbl>, reproductive.condition <chr>, sex <chr>,
## # hours.indoor.per.day <dbl>, cats.in.house <dbl>, dry.food <lgl>,
## # wet.food <lgl>, other.food <chr>, study.location <chr>, age <chr>
Arrange rows
arrange(data, scientific.name, animal.name)
## # A tibble: 101 × 17
## Column1 id.on.tag animal.name scientific.name `tag deployment.start`
## <dbl> <chr> <chr> <chr> <chr>
## 1 83 Abba-Tag Abba Felis catus 2017-09-25T01:18:59.999999734…
## 2 93 Alfie-Tag Alfie Felis catus 2017-10-23T01:02:59.999999748…
## 3 43 Amber-Tag Amber Felis catus 2017-07-28T01:02:59.999999748…
## 4 3 Ares Ares Felis catus 2017-06-24T01:02:59.999999748…
## 5 2 Athena Athena Felis catus 2017-06-24T01:02:00.000000181…
## 6 76 Balu-Tag Balu Felis catus 2017-09-18T01:02:59.999999748…
## 7 16 Barney-Tag Barney Felis catus 2017-07-08T01:15:59.999999776…
## 8 40 Beanie-Tag Beanie Felis catus 2017-07-22T01:02:00.000000181…
## 9 35 Bear-Tag Bear Felis catus 2017-07-20T01:02:59.999999748…
## 10 30 Bella-Tag Bella Felis catus 2017-07-16T01:45:59.999999986…
## # ℹ 91 more rows
## # ℹ 12 more variables: `tag deployment.end` <chr>, hunt <chr>,
## # prey.per.month <dbl>, reproductive.condition <chr>, sex <chr>,
## # hours.indoor.per.day <dbl>, cats.in.house <dbl>, dry.food <lgl>,
## # wet.food <lgl>, other.food <chr>, study.location <chr>, age <chr>
Select columns
select(data, prey.per.month, reproductive.condition:sex)
## # A tibble: 101 × 3
## prey.per.month reproductive.condition sex
## <dbl> <chr> <chr>
## 1 12.5 Neutered m
## 2 3 Spayed f
## 3 0 Neutered m
## 4 3 Spayed f
## 5 3 Neutered m
## 6 3 Spayed f
## 7 3 Neutered m
## 8 17.5 Neutered m
## 9 3 Neutered m
## 10 7.5 Spayed f
## # ℹ 91 more rows
Add columns
data_sml <- select(data, prey.per.month, hours.indoor.per.day)
mutate(data_sml,
prey.per.day = prey.per.month / hours.indoor.per.day)
## # A tibble: 101 × 3
## prey.per.month hours.indoor.per.day prey.per.day
## <dbl> <dbl> <dbl>
## 1 12.5 12.5 1
## 2 3 7.5 0.4
## 3 0 7.5 0
## 4 3 17.5 0.171
## 5 3 12.5 0.24
## 6 3 12.5 0.24
## 7 3 12.5 0.24
## 8 17.5 7.5 2.33
## 9 3 2.5 1.2
## 10 7.5 12.5 0.6
## # ℹ 91 more rows
Summarize by groups
by_age <- group_by(data, prey.per.month, hours.indoor.per.day, cats.in.house)
summarise(by_age, na.rim = TRUE)
## # A tibble: 46 × 4
## # Groups: prey.per.month, hours.indoor.per.day [20]
## prey.per.month hours.indoor.per.day cats.in.house na.rim
## <dbl> <dbl> <dbl> <lgl>
## 1 0 2.5 1 TRUE
## 2 0 7.5 1 TRUE
## 3 0 7.5 2 TRUE
## 4 0 7.5 4 TRUE
## 5 0 12.5 1 TRUE
## 6 0 12.5 2 TRUE
## 7 0 12.5 3 TRUE
## 8 0 17.5 1 TRUE
## 9 0 17.5 2 TRUE
## 10 0 17.5 4 TRUE
## # ℹ 36 more rows