nepal_wildlife

First, load the required packages:

library(Nepalwildlife)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(ggplot2)

Let’s take a look at the dataset:

# check the bottom and top rows
head(nepal_wildlife)
#> # A tibble: 6 x 4
#>   `Scientific Name`     `Common Name`               Family      `Taxonomic Grou~
#>   <chr>                 <chr>                       <chr>       <chr>           
#> 1 Apus apus             Common Swift                Apodidae    Birds           
#> 2 Eurystomus orientalis Eastern Broad-Billed Roller Coraciidae  Birds           
#> 3 Calidris minuta       Little Stint                Scolopacid~ Birds           
#> 4 Saxicoloides fulicat~ Indian Robin                Muscicapid~ Birds           
#> 5 Falco tinnunculus     Common Kestrel              Falconidae  Birds           
#> 6 Abroscopus schistice~ Black-Faced Flycatcher-War~ Sylviidae   Birds
tail(nepal_wildlife)
#> # A tibble: 6 x 4
#>   `Scientific Name`  `Common Name`   Family       `Taxonomic Group`
#>   <chr>              <chr>           <chr>        <chr>            
#> 1 Juniperus communis Common Juniper  Cupressaceae Conifers         
#> 2 Larix potaninii    Chinese Larch   Pinaceae     Conifers         
#> 3 Juniperus pingii   Ping's Juniper  Cupressaceae Conifers         
#> 4 Juniperus tibetica Tibetan Juniper Cupressaceae Conifers         
#> 5 Cedrus deodara     Deodar          Pinaceae     Conifers         
#> 6 Juniperus squamata Flaky Juniper   Cupressaceae Conifers
# take a glimpse at the overall dataset
glimpse(nepal_wildlife)
#> Rows: 1,409
#> Columns: 4
#> $ `Scientific Name` <chr> "Apus apus", "Eurystomus orientalis", "Calidris m...
#> $ `Common Name`     <chr> "Common Swift", "Eastern Broad-Billed Roller", "L...
#> $ Family            <chr> "Apodidae", "Coraciidae", "Scolopacidae", "Muscic...
#> $ `Taxonomic Group` <chr> "Birds", "Birds", "Birds", "Birds", "Birds", "Bir...

We can calculate the number of species for each Taxonomic Group, and create a bar chart out of it

nepal_wildlife %>% 
  group_by(`Taxonomic Group`) %>% 
  summarize(n= n()) %>% 
  
  ggplot(aes(x = `Taxonomic Group`, y = n)) +
  geom_bar(stat = "identity")
#> `summarise()` ungrouping output (override with `.groups` argument)

How many species have the common name leopard?

nepal_wildlife %>% 
  select(c(`Common Name`, Family)) %>% 
  mutate(has_leopard = stringr::str_detect(`Common Name`, stringr::regex("leopard", ignore_case = TRUE))) %>% 
  filter(has_leopard == TRUE)
#> # A tibble: 4 x 3
#>   `Common Name`   Family  has_leopard
#>   <chr>           <chr>   <lgl>      
#> 1 Leopard         Felidae TRUE       
#> 2 Snow Leopard    Felidae TRUE       
#> 3 Clouded Leopard Felidae TRUE       
#> 4 Leopard Cat     Felidae TRUE

Among these, Leopard and Snow Leopard are considered to be big cats.