DS Labs

#Load all libraries and datasets
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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✔ forcats   1.0.0     ✔ stringr   1.5.1
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✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library("dslabs")
data(package="dslabs")
list.files(system.file("script", package = "dslabs"))

 [1] "make-admissions.R"                   
 [2] "make-brca.R"                         
 [3] "make-brexit_polls.R"                 
 [4] "make-calificaciones.R"               
 [5] "make-death_prob.R"                   
 [6] "make-divorce_margarine.R"            
 [7] "make-gapminder-rdas.R"               
 [8] "make-greenhouse_gases.R"             
 [9] "make-historic_co2.R"                 
[10] "make-mice_weights.R"                 
[11] "make-mnist_127.R"                    
[12] "make-mnist_27.R"                     
[13] "make-movielens.R"                    
[14] "make-murders-rda.R"                  
[15] "make-na_example-rda.R"               
[16] "make-nyc_regents_scores.R"           
[17] "make-olive.R"                        
[18] "make-outlier_example.R"              
[19] "make-polls_2008.R"                   
[20] "make-polls_us_election_2016.R"       
[21] "make-pr_death_counts.R"              
[22] "make-reported_heights-rda.R"         
[23] "make-research_funding_rates.R"       
[24] "make-stars.R"                        
[25] "make-temp_carbon.R"                  
[26] "make-tissue-gene-expression.R"       
[27] "make-trump_tweets.R"                 
[28] "make-weekly_us_contagious_diseases.R"
[29] "save-gapminder-example-csv.R"        

data(mice_weigths)

p1 <-  mice_weights |>
  ggplot(aes(x = bone_density, y = body_weight, color = sex))+
  geom_point()+
  geom_smooth(method = lm, se=FALSE, color = "purple")+
  labs(x = "Bone Density", y = "Body Weight", title = "Mice body weight vs bone density by sex", color = "Sex")+
  theme_minimal()+
  geom_smooth(method = lm, se=FALSE)
  
  
p1 

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 4 rows containing non-finite outside the scale range
(`stat_smooth()`).

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 4 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning: Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).

I made a basic Scatter Plot comparing Bone Density to Body weight for mice, and I split them by gender, adding a line of best fit for each gender plus the graph as a whole. It seemed unreasonable to add interactivity with the quanitity of data so I kept it fairly simple. I used geom smooth twice once before the labs so it would give me a line for the graph as a whole, and then once after the labs so it would give me an individual line for each sex.