Penguins TidyModels

Julián Avila-Jiménez 2020-07-29

Initial model

Model for sex of the tree species of penguins. Clasification model to distinguish male and female penguins.

Explore the data

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
Adelie	Torgersen	39.1	18.7	181	3750	male	2007
Adelie	Torgersen	39.5	17.4	186	3800	female	2007
Adelie	Torgersen	40.3	18.0	195	3250	female	2007
Adelie	Torgersen	NA	NA	NA	NA	NA	2007
Adelie	Torgersen	36.7	19.3	193	3450	female	2007
Adelie	Torgersen	39.3	20.6	190	3650	male	2007

Penguins data. Source: Gorman, Williams and Fraser, 2014

island	species	n
Biscoe	Adelie	44
Biscoe	Gentoo	124
Dream	Adelie	56
Dream	Chinstrap	68
Torgersen	Adelie	52

Number of individuals by Species by Island

species	sex	n
Adelie	female	73
Adelie	male	73
Adelie	NA	6
Chinstrap	female	34
Chinstrap	male	34
Gentoo	female	58
Gentoo	male	61
Gentoo	NA	5

Sex by Species

year	n
2007	110
2008	114
2009	120

The number of recods by Year

Basic exploratory analisys

penguins %>% 
  filter (!is.na(sex)) %>% 
  ggplot (aes (flipper_length_mm, bill_length_mm, 
               color= sex, size= body_mass_g))+
  geom_point (alpha= 0.7)+
  labs(x="Flipper length (mm)", y="Bill length (mm)")+
  facet_wrap (~species)

penguins_df <- penguins %>%
  filter(!is.na(sex)) %>%
  select(-year, -island)

Building a model to predict the sex by the other variables

library(tidymodels)

set.seed(123)
penguin_split <- initial_split(penguins_df, strata = sex)
penguin_train <- training(penguin_split)
penguin_test <- testing(penguin_split) #use to test perfonmance for the model

set.seed(123)
penguin_boot<- bootstraps(penguin_train) #25 different bootstraps of n=250
penguin_boot

## # Bootstrap sampling 
## # A tibble: 25 x 2
##    splits           id         
##    <list>           <chr>      
##  1 <split [250/93]> Bootstrap01
##  2 <split [250/92]> Bootstrap02
##  3 <split [250/90]> Bootstrap03
##  4 <split [250/92]> Bootstrap04
##  5 <split [250/86]> Bootstrap05
##  6 <split [250/88]> Bootstrap06
##  7 <split [250/96]> Bootstrap07
##  8 <split [250/89]> Bootstrap08
##  9 <split [250/96]> Bootstrap09
## 10 <split [250/90]> Bootstrap10
## # ... with 15 more rows

Basic logistic regression model

glm_spec <- logistic_reg() %>%
  set_engine("glm")

glm_spec

## Logistic Regression Model Specification (classification)
## 
## Computational engine: glm

Random forest model

rf_spec <- rand_forest() %>%
  set_mode("classification") %>%
  set_engine("ranger")

rf_spec

## Random Forest Model Specification (classification)
## 
## Computational engine: ranger

Preprocessor and model

penguin_wf<-workflow() %>% 
  add_formula(sex ~ .)
penguin_wf

## == Workflow ===================================
## Preprocessor: Formula
## Model: None
## 
## -- Preprocessor -------------------------------
## sex ~ .

glm_rs <- penguin_wf %>%
  add_model(glm_spec) %>%
  fit_resamples(
    resamples = penguin_boot,
    control = control_resamples(save_pred = TRUE)
  )

glm_rs

## # Resampling results
## # Bootstrap sampling 
## # A tibble: 25 x 5
##    splits           id          .metrics .notes           .predictions
##    <list>           <chr>       <list>   <list>           <list>      
##  1 <split [250/93]> Bootstrap01 <NULL>   <tibble [1 x 1]> <NULL>      
##  2 <split [250/92]> Bootstrap02 <NULL>   <tibble [1 x 1]> <NULL>      
##  3 <split [250/90]> Bootstrap03 <NULL>   <tibble [1 x 1]> <NULL>      
##  4 <split [250/92]> Bootstrap04 <NULL>   <tibble [1 x 1]> <NULL>      
##  5 <split [250/86]> Bootstrap05 <NULL>   <tibble [1 x 1]> <NULL>      
##  6 <split [250/88]> Bootstrap06 <NULL>   <tibble [1 x 1]> <NULL>      
##  7 <split [250/96]> Bootstrap07 <NULL>   <tibble [1 x 1]> <NULL>      
##  8 <split [250/89]> Bootstrap08 <NULL>   <tibble [1 x 1]> <NULL>      
##  9 <split [250/96]> Bootstrap09 <NULL>   <tibble [1 x 1]> <NULL>      
## 10 <split [250/90]> Bootstrap10 <NULL>   <tibble [1 x 1]> <NULL>      
## # ... with 15 more rows