Penguin data!

three well defined questions that you want to explore with this data set

1.) comparing flipper length difference between male and female

2.) comparing the difference in each species body mass comparing to each of the island.

3.) comparing bill_length and bill_depth to each species and see if there is a correlation with different places in the island

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
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## Skipping install of 'palmerpenguins' from a github remote, the SHA1 (69530276) has not changed since last install.
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## # A tibble: 344 × 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
##  7 Adelie  Torgersen           38.9          17.8               181        3625
##  8 Adelie  Torgersen           39.2          19.6               195        4675
##  9 Adelie  Torgersen           34.1          18.1               193        3475
## 10 Adelie  Torgersen           42            20.2               190        4250
## # … with 334 more rows, and 2 more variables: sex <fct>, year <int>
## 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 ...
## [1] "species"           "island"            "bill_length_mm"   
## [4] "bill_depth_mm"     "flipper_length_mm" "body_mass_g"      
## [7] "sex"               "year"

This is where I checked the variables and see what I have to work with with my data. I wanted to see what would be the most compelling variable, then I graphed them!

Final 5 Graphics:

## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing non-finite values (stat_density).

## Warning: Removed 2 rows containing missing values (geom_point).

Variables: Tell me about the variables used in these graphics and discuss

possible relationships.

Graph 1:

I decided to use species as the response variable for each island. I got some interesting results! I found that the Adelie species is in all 3 islands! And Chinstrap and Gentoo are on separate islands. I am now curious about their migrating patterns and if there is local and transient penguins for each species.

Graph 2:

I decided to have species be the responds variable and body mass as well as sex be the explanatory variable. You can see the difference in body mass not just with each species but with their gender as well.

Graph 3:

I decided to have even more explanatory variables! Bill length is my response and Flipper length, and color body mass. I wanted to see if there was a correlation between body mass in regards to bill and flipper length. Even though I did not use body mass as the responsive, I still wanted to have the detail of color to see how it correlates with the other 2 variables.

Graph 4:

I wanted to use qplot because I haven’t really worked with it. I really like the visual results. I decided to use bill length as the response variable and species as the explanatory variable. It was cool to see if they all crossed on one line at the same time or not! They did not but they each did simultaneously from each other. Which was interesting!

Graph 5:

I used flipper length as my response variable. And I just bill length and species as my explanatory variable. And I wanted to facet gird the year so I could see the difference by year! Kinda interesting graphs. They do shift a bit. I am wondering if each year it is the same penguins or not, I would want to investigate that. If so, might be a growing pattern!