Setting up my Environment

Note: First of all , installing necessary packages Tidyverse and palmerpenguins , also loading them by library function.

Main point to observe and to keep in mind

This data is of three years – 2007-2009

summary(penguins)
##       species          island    bill_length_mm  bill_depth_mm  
##  Adelie   :152   Biscoe   :168   Min.   :32.10   Min.   :13.10  
##  Chinstrap: 68   Dream    :124   1st Qu.:39.23   1st Qu.:15.60  
##  Gentoo   :124   Torgersen: 52   Median :44.45   Median :17.30  
##                                  Mean   :43.92   Mean   :17.15  
##                                  3rd Qu.:48.50   3rd Qu.:18.70  
##                                  Max.   :59.60   Max.   :21.50  
##                                  NA's   :2       NA's   :2      
##  flipper_length_mm  body_mass_g       sex           year     
##  Min.   :172.0     Min.   :2700   female:165   Min.   :2007  
##  1st Qu.:190.0     1st Qu.:3550   male  :168   1st Qu.:2007  
##  Median :197.0     Median :4050   NA's  : 11   Median :2008  
##  Mean   :200.9     Mean   :4202                Mean   :2008  
##  3rd Qu.:213.0     3rd Qu.:4750                3rd Qu.:2009  
##  Max.   :231.0     Max.   :6300                Max.   :2009  
##  NA's   :2         NA's   :2
penguins %>% group_by(island , species)%>% drop_na() %>% 
  summarise(max_bl = max(bill_length_mm), mean_bl = mean(bill_length_mm))
## `summarise()` has grouped output by 'island'. You can override using the
## `.groups` argument.
## # A tibble: 5 × 4
## # Groups:   island [3]
##   island    species   max_bl mean_bl
##   <fct>     <fct>      <dbl>   <dbl>
## 1 Biscoe    Adelie      45.6    39.0
## 2 Biscoe    Gentoo      59.6    47.6
## 3 Dream     Adelie      44.1    38.5
## 4 Dream     Chinstrap   58      48.8
## 5 Torgersen Adelie      46      39.0

Here is one abbreviation ‘bl’ means ‘bill_length_mm’
There are three island and two of them have two types of species, and 3rd has only one ,named - ‘Adelie’.
Adelie is common in all three islands.

Ploting flipper_length vs body_mass by Species

This graph shows that there is smoth relation between flipper_length and body_mass - As flipper length increase , they becomes havier, or vice versa

ggplot(data=penguins)+geom_point(mapping = aes(x=flipper_length_mm, y=body_mass_g,
                                               color=species))

ggplot(data = penguins, aes(x=flipper_length_mm, y=body_mass_g))+
  geom_point(aes(color=species))+
  facet_wrap(~species)

Flipper length and body mass by sex and species

ggplot(data = penguins, aes(x=flipper_length_mm, y=body_mass_g))+
  geom_point(aes(color=species))+
  facet_grid(sex~species)