library(palmerpenguins)
## 
## Attaching package: 'palmerpenguins'
## The following objects are masked from 'package:datasets':
## 
##     penguins, penguins_raw
data("penguins")

#Is there a relationship between and species and Island?

head(penguins)
## # A tibble: 6 × 8
##   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
##   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
## 1 Adelie  Torgersen           39.1          18.7               181        3750
## 2 Adelie  Torgersen           39.5          17.4               186        3800
## 3 Adelie  Torgersen           40.3          18                 195        3250
## 4 Adelie  Torgersen           NA            NA                  NA          NA
## 5 Adelie  Torgersen           36.7          19.3               193        3450
## 6 Adelie  Torgersen           39.3          20.6               190        3650
## # ℹ 2 more variables: sex <fct>, year <int>
tail(penguins)
## # A tibble: 6 × 8
##   species   island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
##   <fct>     <fct>           <dbl>         <dbl>             <int>       <int>
## 1 Chinstrap Dream            45.7          17                 195        3650
## 2 Chinstrap Dream            55.8          19.8               207        4000
## 3 Chinstrap Dream            43.5          18.1               202        3400
## 4 Chinstrap Dream            49.6          18.2               193        3775
## 5 Chinstrap Dream            50.8          19                 210        4100
## 6 Chinstrap Dream            50.2          18.7               198        3775
## # ℹ 2 more variables: sex <fct>, year <int>
View(penguins)
str(penguins)
## tibble [344 × 8] (S3: tbl_df/tbl/data.frame)
##  $ species          : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ island           : Factor w/ 3 levels "Biscoe","Dream",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ bill_length_mm   : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
##  $ bill_depth_mm    : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
##  $ flipper_length_mm: int [1:344] 181 186 195 NA 193 190 181 195 193 190 ...
##  $ body_mass_g      : int [1:344] 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ...
##  $ sex              : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ...
##  $ year             : int [1:344] 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...
levels(penguins$species)
## [1] "Adelie"    "Chinstrap" "Gentoo"
#dataframe
penguin_df <- penguins[, c("species", "island")] 

#missing data 
penguin_df_final <- na.omit(penguin_df)
#making into table
penguin_df_final <- table(penguins$species, penguins$island)

chisq_test_result <- chisq.test(penguin_df_final)
chisq_test_result
## 
##  Pearson's Chi-squared test
## 
## data:  penguin_df_final
## X-squared = 299.55, df = 4, p-value < 2.2e-16

#There is a relationship between species and Island because the p-value is lower than 0.05 and it rejected the null hypothesis

formatted_p_value <- format(chisq_test_result$p.value, scientific = FALSE)
formatted_p_value
## [1] "0.000000000000000000000000000000000000000000000000000000000000001354574"