Penguin data analysis. - A ShareGPT conversation

Model: GPT-4

I want you to act as a data scientist using R programming language and code for me. I have a dataset of penguins as follows. If you understand what I instructed. print "OK" and wait for another instruction. species,island,culmen_length_mm,culmen_depth_mm,flipper_length_mm,body_mass_g,sex Adelie,Torgersen,39.1,18.7,181,3750,MALE Adelie,Torgersen,39.5,17.4,186,3800,FEMALE Adelie,Torgersen,40.3,18,195,3250,FEMALE Adelie,Torgersen,NA,NA,NA,NA,NA Adelie,Torgersen,36.7,19.3,193,3450,FEMALE Adelie,Torgersen,39.3,20.6,190,3650,MALE Adelie,Torgersen,38.9,17.8,181,3625,FEMALE Adelie,Torgersen,39.2,19.6,195,4675,MALE Adelie,Torgersen,34.1,18.1,193,3475,NA Adelie,Torgersen,42,20.2,190,4250,NA Adelie,Torgersen,37.8,17.1,186,3300,NA Adelie,Torgersen,37.8,17.3,180,3700,NA Adelie,Torgersen,41.1,17.6,182,3200,FEMALE Adelie,Torgersen,38.6,21.2,191,3800,MALE Adelie,Torgersen,34.6,21.1,198,4400,MALE Adelie,Torgersen,36.6,17.8,185,3700,FEMALE Adelie,Torgersen,38.7,19,195,3450,FEMALE Adelie,Torgersen,42.5,20.7,197,4500,MALE Adelie,Torgersen,34.4,18.4,184,3325,FEMALE Adelie,Torgersen,46,21.5,194,4200,MALE Adelie,Biscoe,37.8,18.3,174,3400,FEMALE Adelie,Biscoe,37.7,18.7,180,3600,MALE Adelie,Biscoe,35.9,19.2,189,3800,FEMALE Adelie,Biscoe,38.2,18.1,185,3950,MALE Adelie,Biscoe,38.8,17.2,180,3800,MALE Adelie,Biscoe,35.3,18.9,187,3800,FEMALE Adelie,Biscoe,40.6,18.6,183,3550,MALE Adelie,Biscoe,40.5,17.9,187,3200,FEMALE Adelie,Biscoe,37.9,18.6,172,3150,FEMALE Adelie,Biscoe,40.5,18.9,180,3950,MALE Adelie,Dream,39.5,16.7,178,3250,FEMALE Adelie,Dream,37.2,18.1,178,3900,MALE Adelie,Dream,39.5,17.8,188,3300,FEMALE Adelie,Dream,40.9,18.9,184,3900,MALE Adelie,Dream,36.4,17,195,3325,FEMALE Adelie,Dream,39.2,21.1,196,4150,MALE Adelie,Dream,38.8,20,190,3950,MALE Adelie,Dream,42.2,18.5,180,3550,FEMALE Adelie,Dream,37.6,19.3,181,3300,FEMALE Adelie,Dream,39.8,19.1,184,4650,MALE Adelie,Dream,36.5,18,182,3150,FEMALE Adelie,Dream,40.8,18.4,195,3900,MALE Adelie,Dream,36,18.5,186,3100,FEMALE Adelie,Dream,44.1,19.7,196,4400,MALE Adelie,Dream,37,16.9,185,3000,FEMALE Adelie,Dream,39.6,18.8,190,4600,MALE Adelie,Dream,41.1,19,182,3425,MALE Adelie,Dream,37.5,18.9,179,2975,NA Adelie,Dream,36,17.9,190,3450,FEMALE Adelie,Dream,42.3,21.2,191,4150,MALE Adelie,Biscoe,39.6,17.7,186,3500,FEMALE Adelie,Biscoe,40.1,18.9,188,4300,MALE Adelie,Biscoe,35,17.9,190,3450,FEMALE Adelie,Biscoe,42,19.5,200,4050,MALE Adelie,Biscoe,34.5,18.1,187,2900,FEMALE Adelie,Biscoe,41.4,18.6,191,3700,MALE Adelie,Biscoe,39,17.5,186,3550,FEMALE Adelie,Biscoe,40.6,18.8,193,3800,MALE Adelie,Biscoe,36.5,16.6,181,2850,FEMALE Adelie,Biscoe,37.6,19.1,194,3750,MALE Adelie,Biscoe,35.7,16.9,185,3150,FEMALE Adelie,Biscoe,41.3,21.1,195,4400,MALE Adelie,Biscoe,37.6,17,185,3600,FEMALE Adelie,Biscoe,41.1,18.2,192,4050,MALE Adelie,Biscoe,36.4,17.1,184,2850,FEMALE Adelie,Biscoe,41.6,18,192,3950,MALE Adelie,Biscoe,35.5,16.2,195,3350,FEMALE Adelie,Biscoe,41.1,19.1,188,4100,MALE Adelie,Torgersen,35.9,16.6,190,3050,FEMALE Adelie,Torgersen,41.8,19.4,198,4450,MALE Adelie,Torgersen,33.5,19,190,3600,FEMALE Adelie,Torgersen,39.7,18.4,190,3900,MALE Adelie,Torgersen,39.6,17.2,196,3550,FEMALE Adelie,Torgersen,45.8,18.9,197,4150,MALE Adelie,Torgersen,35.5,17.5,190,3700,FEMALE Adelie,Torgersen,42.8,18.5,195,4250,MALE Adelie,Torgersen,40.9,16.8,191,3700,FEMALE Adelie,Torgersen,37.2,19.4,184,3900,MALE Adelie,Torgersen,36.2,16.1,187,3550,FEMALE Adelie,Torgersen,42.1,19.1,195,4000,MALE Adelie,Torgersen,34.6,17.2,189,3200,FEMALE Adelie,Torgersen,42.9,17.6,196,4700,MALE Adelie,Torgersen,36.7,18.8,187,3800,FEMALE Adelie,Torgersen,35.1,19.4,193,4200,MALE Adelie,Dream,37.3,17.8,191,3350,FEMALE Adelie,Dream,41.3,20.3,194,3550,MALE Adelie,Dream,36.3,19.5,190,3800,MALE Adelie,Dream,36.9,18.6,189,3500,FEMALE Adelie,Dream,38.3,19.2,189,3950,MALE Adelie,Dream,38.9,18.8,190,3600,FEMALE Adelie,Dream,35.7,18,202,3550,FEMALE Adelie,Dream,41.1,18.1,205,4300,MALE Adelie,Dream,34,17.1,185,3400,FEMALE Adelie,Dream,39.6,18.1,186,4450,MALE Adelie,Dream,36.2,17.3,187,3300,FEMALE Adelie,Dream,40.8,18.9,208,4300,MALE Adelie,Dream,38.1,18.6,190,3700,FEMALE Adelie,Dream,40.3,18.5,196,4350,MALE Adelie,Dream,33.1,16.1,178,2900,FEMALE Adelie,Dream,43.2,18.5,192,4100,MALE Adelie,Biscoe,35,17.9,192,3725,FEMALE Adelie,Biscoe,41,20,203,4725,MALE Adelie,Biscoe,37.7,16,183,3075,FEMALE Adelie,Biscoe,37.8,20,190,4250,MALE Adelie,Biscoe,37.9,18.6,193,2925,FEMALE Adelie,Biscoe,39.7,18.9,184,3550,MALE Adelie,Biscoe,38.6,17.2,199,3750,FEMALE Adelie,Biscoe,38.2,20,190,3900,MALE Adelie,Biscoe,38.1,17,181,3175,FEMALE Adelie,Biscoe,43.2,19,197,4775,MALE Adelie,Biscoe,38.1,16.5,198,3825,FEMALE Adelie,Biscoe,45.6,20.3,191,4600,MALE Adelie,Biscoe,39.7,17.7,193,3200,FEMALE Adelie,Biscoe,42.2,19.5,197,4275,MALE Adelie,Biscoe,39.6,20.7,191,3900,FEMALE Adelie,Biscoe,42.7,18.3,196,4075,MALE Adelie,Torgersen,38.6,17,188,2900,FEMALE Adelie,Torgersen,37.3,20.5,199,3775,MALE Adelie,Torgersen,35.7,17,189,3350,FEMALE Adelie,Torgersen,41.1,18.6,189,3325,MALE Adelie,Torgersen,36.2,17.2,187,3150,FEMALE Adelie,Torgersen,37.7,19.8,198,3500,MALE Adelie,Torgersen,40.2,17,176,3450,FEMALE Adelie,Torgersen,41.4,18.5,202,3875,MALE Adelie,Torgersen,35.2,15.9,186,3050,FEMALE Adelie,Torgersen,40.6,19,199,4000,MALE Adelie,Torgersen,38.8,17.6,191,3275,FEMALE Adelie,Torgersen,41.5,18.3,195,4300,MALE Adelie,Torgersen,39,17.1,191,3050,FEMALE Adelie,Torgersen,44.1,18,210,4000,MALE Adelie,Torgersen,38.5,17.9,190,3325,FEMALE Adelie,Torgersen,43.1,19.2,197,3500,MALE Adelie,Dream,36.8,18.5,193,3500,FEMALE Adelie,Dream,37.5,18.5,199,4475,MALE Adelie,Dream,38.1,17.6,187,3425,FEMALE Adelie,Dream,41.1,17.5,190,3900,MALE Adelie,Dream,35.6,17.5,191,3175,FEMALE Adelie,Dream,40.2,20.1,200,3975,MALE Adelie,Dream,37,16.5,185,3400,FEMALE Adelie,Dream,39.7,17.9,193,4250,MALE Adelie,Dream,40.2,17.1,193,3400,FEMALE Adelie,Dream,40.6,17.2,187,3475,MALE Adelie,Dream,32.1,15.5,188,3050,FEMALE Adelie,Dream,40.7,17,190,3725,MALE Adelie,Dream,37.3,16.8,192,3000,FEMALE Adelie,Dream,39,18.7,185,3650,MALE Adelie,Dream,39.2,18.6,190,4250,MALE Adelie,Dream,36.6,18.4,184,3475,FEMALE Adelie,Dream,36,17.8,195,3450,FEMALE Adelie,Dream,37.8,18.1,193,3750,MALE Adelie,Dream,36,17.1,187,3700,FEMALE Adelie,Dream,41.5,18.5,201,4000,MALE Chinstrap,Dream,46.5,17.9,192,3500,FEMALE Chinstrap,Dream,50,19.5,196,3900,MALE Chinstrap,Dream,51.3,19.2,193,3650,MALE Chinstrap,Dream,45.4,18.7,188,3525,FEMALE Chinstrap,Dream,52.7,19.8,197,3725,MALE Chinstrap,Dream,45.2,17.8,198,3950,FEMALE Chinstrap,Dream,46.1,18.2,178,3250,FEMALE Chinstrap,Dream,51.3,18.2,197,3750,MALE Chinstrap,Dream,46,18.9,195,4150,FEMALE Chinstrap,Dream,51.3,19.9,198,3700,MALE Chinstrap,Dream,46.6,17.8,193,3800,FEMALE Chinstrap,Dream,51.7,20.3,194,3775,MALE Chinstrap,Dream,47,17.3,185,3700,FEMALE Chinstrap,Dream,52,18.1,201,4050,MALE Chinstrap,Dream,45.9,17.1,190,3575,FEMALE Chinstrap,Dream,50.5,19.6,201,4050,MALE Chinstrap,Dream,50.3,20,197,3300,MALE Chinstrap,Dream,58,17.8,181,3700,FEMALE Chinstrap,Dream,46.4,18.6,190,3450,FEMALE Chinstrap,Dream,49.2,18.2,195,4400,MALE Chinstrap,Dream,42.4,17.3,181,3600,FEMALE Chinstrap,Dream,48.5,17.5,191,3400,MALE Chinstrap,Dream,43.2,16.6,187,2900,FEMALE Chinstrap,Dream,50.6,19.4,193,3800,MALE Chinstrap,Dream,46.7,17.9,195,3300,FEMALE Chinstrap,Dream,52,19,197,4150,MALE Chinstrap,Dream,50.5,18.4,200,3400,FEMALE Chinstrap,Dream,49.5,19,200,3800,MALE Chinstrap,Dream,46.4,17.8,191,3700,FEMALE Chinstrap,Dream,52.8,20,205,4550,MALE Chinstrap,Dream,40.9,16.6,187,3200,FEMALE Chinstrap,Dream,54.2,20.8,201,4300,MALE Chinstrap,Dream,42.5,16.7,187,3350,FEMALE Chinstrap,Dream,51,18.8,203,4100,MALE Chinstrap,Dream,49.7,18.6,195,3600,MALE Chinstrap,Dream,47.5,16.8,199,3900,FEMALE Chinstrap,Dream,47.6,18.3,195,3850,FEMALE Chinstrap,Dream,52,20.7,210,4800,MALE Chinstrap,Dream,46.9,16.6,192,2700,FEMALE Chinstrap,Dream,53.5,19.9,205,4500,MALE Chinstrap,Dream,49,19.5,210,3950,MALE Chinstrap,Dream,46.2,17.5,187,3650,FEMALE Chinstrap,Dream,50.9,19.1,196,3550,MALE Chinstrap,Dream,45.5,17,196,3500,FEMALE Chinstrap,Dream,50.9,17.9,196,3675,FEMALE Chinstrap,Dream,50.8,18.5,201,4450,MALE Chinstrap,Dream,50.1,17.9,190,3400,FEMALE Chinstrap,Dream,49,19.6,212,4300,MALE Chinstrap,Dream,51.5,18.7,187,3250,MALE Chinstrap,Dream,49.8,17.3,198,3675,FEMALE Chinstrap,Dream,48.1,16.4,199,3325,FEMALE Chinstrap,Dream,51.4,19,201,3950,MALE Chinstrap,Dream,45.7,17.3,193,3600,FEMALE Chinstrap,Dream,50.7,19.7,203,4050,MALE Chinstrap,Dream,42.5,17.3,187,3350,FEMALE Chinstrap,Dream,52.2,18.8,197,3450,MALE Chinstrap,Dream,45.2,16.6,191,3250,FEMALE Chinstrap,Dream,49.3,19.9,203,4050,MALE Chinstrap,Dream,50.2,18.8,202,3800,MALE Chinstrap,Dream,45.6,19.4,194,3525,FEMALE Chinstrap,Dream,51.9,19.5,206,3950,MALE Chinstrap,Dream,46.8,16.5,189,3650,FEMALE Chinstrap,Dream,45.7,17,195,3650,FEMALE Chinstrap,Dream,55.8,19.8,207,4000,MALE Chinstrap,Dream,43.5,18.1,202,3400,FEMALE Chinstrap,Dream,49.6,18.2,193,3775,MALE Chinstrap,Dream,50.8,19,210,4100,MALE Chinstrap,Dream,50.2,18.7,198,3775,FEMALE Gentoo,Biscoe,46.1,13.2,211,4500,FEMALE Gentoo,Biscoe,50,16.3,230,5700,MALE Gentoo,Biscoe,48.7,14.1,210,4450,FEMALE Gentoo,Biscoe,50,15.2,218,5700,MALE Gentoo,Biscoe,47.6,14.5,215,5400,MALE Gentoo,Biscoe,46.5,13.5,210,4550,FEMALE Gentoo,Biscoe,45.4,14.6,211,4800,FEMALE Gentoo,Biscoe,46.7,15.3,219,5200,MALE Gentoo,Biscoe,43.3,13.4,209,4400,FEMALE Gentoo,Biscoe,46.8,15.4,215,5150,MALE Gentoo,Biscoe,40.9,13.7,214,4650,FEMALE Gentoo,Biscoe,49,16.1,216,5550,MALE Gentoo,Biscoe,45.5,13.7,214,4650,FEMALE Gentoo,Biscoe,48.4,14.6,213,5850,MALE Gentoo,Biscoe,45.8,14.6,210,4200,FEMALE Gentoo,Biscoe,49.3,15.7,217,5850,MALE Gentoo,Biscoe,42,13.5,210,4150,FEMALE Gentoo,Biscoe,49.2,15.2,221,6300,MALE Gentoo,Biscoe,46.2,14.5,209,4800,FEMALE Gentoo,Biscoe,48.7,15.1,222,5350,MALE Gentoo,Biscoe,50.2,14.3,218,5700,MALE Gentoo,Biscoe,45.1,14.5,215,5000,FEMALE Gentoo,Biscoe,46.5,14.5,213,4400,FEMALE Gentoo,Biscoe,46.3,15.8,215,5050,MALE Gentoo,Biscoe,42.9,13.1,215,5000,FEMALE Gentoo,Biscoe,46.1,15.1,215,5100,MALE Gentoo,Biscoe,44.5,14.3,216,4100,NA Gentoo,Biscoe,47.8,15,215,5650,MALE Gentoo,Biscoe,48.2,14.3,210,4600,FEMALE Gentoo,Biscoe,50,15.3,220,5550,MALE Gentoo,Biscoe,47.3,15.3,222,5250,MALE Gentoo,Biscoe,42.8,14.2,209,4700,FEMALE Gentoo,Biscoe,45.1,14.5,207,5050,FEMALE Gentoo,Biscoe,59.6,17,230,6050,MALE Gentoo,Biscoe,49.1,14.8,220,5150,FEMALE Gentoo,Biscoe,48.4,16.3,220,5400,MALE Gentoo,Biscoe,42.6,13.7,213,4950,FEMALE Gentoo,Biscoe,44.4,17.3,219,5250,MALE Gentoo,Biscoe,44,13.6,208,4350,FEMALE Gentoo,Biscoe,48.7,15.7,208,5350,MALE Gentoo,Biscoe,42.7,13.7,208,3950,FEMALE Gentoo,Biscoe,49.6,16,225,5700,MALE Gentoo,Biscoe,45.3,13.7,210,4300,FEMALE Gentoo,Biscoe,49.6,15,216,4750,MALE Gentoo,Biscoe,50.5,15.9,222,5550,MALE Gentoo,Biscoe,43.6,13.9,217,4900,FEMALE Gentoo,Biscoe,45.5,13.9,210,4200,FEMALE Gentoo,Biscoe,50.5,15.9,225,5400,MALE Gentoo,Biscoe,44.9,13.3,213,5100,FEMALE Gentoo,Biscoe,45.2,15.8,215,5300,MALE Gentoo,Biscoe,46.6,14.2,210,4850,FEMALE Gentoo,Biscoe,48.5,14.1,220,5300,MALE Gentoo,Biscoe,45.1,14.4,210,4400,FEMALE Gentoo,Biscoe,50.1,15,225,5000,MALE Gentoo,Biscoe,46.5,14.4,217,4900,FEMALE Gentoo,Biscoe,45,15.4,220,5050,MALE Gentoo,Biscoe,43.8,13.9,208,4300,FEMALE Gentoo,Biscoe,45.5,15,220,5000,MALE Gentoo,Biscoe,43.2,14.5,208,4450,FEMALE Gentoo,Biscoe,50.4,15.3,224,5550,MALE Gentoo,Biscoe,45.3,13.8,208,4200,FEMALE Gentoo,Biscoe,46.2,14.9,221,5300,MALE Gentoo,Biscoe,45.7,13.9,214,4400,FEMALE Gentoo,Biscoe,54.3,15.7,231,5650,MALE Gentoo,Biscoe,45.8,14.2,219,4700,FEMALE Gentoo,Biscoe,49.8,16.8,230,5700,MALE Gentoo,Biscoe,46.2,14.4,214,4650,NA Gentoo,Biscoe,49.5,16.2,229,5800,MALE Gentoo,Biscoe,43.5,14.2,220,4700,FEMALE Gentoo,Biscoe,50.7,15,223,5550,MALE Gentoo,Biscoe,47.7,15,216,4750,FEMALE Gentoo,Biscoe,46.4,15.6,221,5000,MALE Gentoo,Biscoe,48.2,15.6,221,5100,MALE Gentoo,Biscoe,46.5,14.8,217,5200,FEMALE Gentoo,Biscoe,46.4,15,216,4700,FEMALE Gentoo,Biscoe,48.6,16,230,5800,MALE Gentoo,Biscoe,47.5,14.2,209,4600,FEMALE Gentoo,Biscoe,51.1,16.3,220,6000,MALE Gentoo,Biscoe,45.2,13.8,215,4750,FEMALE Gentoo,Biscoe,45.2,16.4,223,5950,MALE Gentoo,Biscoe,49.1,14.5,212,4625,FEMALE Gentoo,Biscoe,52.5,15.6,221,5450,MALE Gentoo,Biscoe,47.4,14.6,212,4725,FEMALE Gentoo,Biscoe,50,15.9,224,5350,MALE Gentoo,Biscoe,44.9,13.8,212,4750,FEMALE Gentoo,Biscoe,50.8,17.3,228,5600,MALE Gentoo,Biscoe,43.4,14.4,218,4600,FEMALE Gentoo,Biscoe,51.3,14.2,218,5300,MALE Gentoo,Biscoe,47.5,14,212,4875,FEMALE Gentoo,Biscoe,52.1,17,230,5550,MALE Gentoo,Biscoe,47.5,15,218,4950,FEMALE Gentoo,Biscoe,52.2,17.1,228,5400,MALE Gentoo,Biscoe,45.5,14.5,212,4750,FEMALE Gentoo,Biscoe,49.5,16.1,224,5650,MALE Gentoo,Biscoe,44.5,14.7,214,4850,FEMALE Gentoo,Biscoe,50.8,15.7,226,5200,MALE Gentoo,Biscoe,49.4,15.8,216,4925,MALE Gentoo,Biscoe,46.9,14.6,222,4875,FEMALE Gentoo,Biscoe,48.4,14.4,203,4625,FEMALE Gentoo,Biscoe,51.1,16.5,225,5250,MALE Gentoo,Biscoe,48.5,15,219,4850,FEMALE Gentoo,Biscoe,55.9,17,228,5600,MALE Gentoo,Biscoe,47.2,15.5,215,4975,FEMALE Gentoo,Biscoe,49.1,15,228,5500,MALE Gentoo,Biscoe,47.3,13.8,216,4725,NA Gentoo,Biscoe,46.8,16.1,215,5500,MALE Gentoo,Biscoe,41.7,14.7,210,4700,FEMALE Gentoo,Biscoe,53.4,15.8,219,5500,MALE Gentoo,Biscoe,43.3,14,208,4575,FEMALE Gentoo,Biscoe,48.1,15.1,209,5500,MALE Gentoo,Biscoe,50.5,15.2,216,5000,FEMALE Gentoo,Biscoe,49.8,15.9,229,5950,MALE Gentoo,Biscoe,43.5,15.2,213,4650,FEMALE Gentoo,Biscoe,51.5,16.3,230,5500,MALE Gentoo,Biscoe,46.2,14.1,217,4375,FEMALE Gentoo,Biscoe,55.1,16,230,5850,MALE Gentoo,Biscoe,44.5,15.7,217,4875,. Gentoo,Biscoe,48.8,16.2,222,6000,MALE Gentoo,Biscoe,47.2,13.7,214,4925,FEMALE Gentoo,Biscoe,NA,NA,NA,NA,NA Gentoo,Biscoe,46.8,14.3,215,4850,FEMALE Gentoo,Biscoe,50.4,15.7,222,5750,MALE Gentoo,Biscoe,45.2,14.8,212,5200,FEMALE Gentoo,Biscoe,49.9,16.1,213,5400,MALE

Please build a machine learning classification model that predict sex using tidymodels.

As an AI language model, I'm unable to run code directly, but I can provide you with a step-by-step guide on how to build a machine learning classification model using R and the tidymodels package. Here's an outline of the process:

Load libraries
Import dataset
Data preprocessing and cleaning
Split data into training and testing sets
Create a model recipe
Create a model specification
Create a workflow
Train the model
Evaluate the model performance

Here's an example R code implementing these steps:

RCopy code
# 1. Load libraries
library(tidymodels)
library(readr)

# 2. Import dataset
data <- read_csv("penguins.csv")

# 3. Data preprocessing and cleaning
data_clean <- data %>%
  drop_na() %>%
  mutate(sex = as.factor(sex))

# 4. Split data into training and testing sets
set.seed(123)
data_split <- initial_split(data_clean, prop = 0.8)
train_data <- training(data_split)
test_data <- testing(data_split)

# 5. Create a model recipe
recipe <- recipe(sex ~ ., data = train_data)

# 6. Create a model specification
model_spec <- logistic_reg() %>%
  set_engine("glm") %>%
  set_mode("classification")

# 7. Create a workflow
workflow <- workflow() %>%
  add_recipe(recipe) %>%
  add_model(model_spec)

# 8. Train the model
model_fit <- workflow %>%
  fit(data = train_data)

# 9. Evaluate the model performance
model_performance <- model_fit %>%
  predict(test_data) %>%
  bind_cols(test_data) %>%
  metrics(truth = sex, estimate = .pred_class)

print(model_performance)

This script assumes you have a CSV file named "penguins.csv" containing the data you provided. Replace the filename if necessary. The script creates a logistic regression model to predict the sex of penguins based on the given features.

Remember to adapt the code to your specific needs and adjust any parameters as required.

Please write code for the exploratory data analysis (EDA) task and target variable is sex.

Here's an example of an R script that performs exploratory data analysis (EDA) on a dataset, with the target variable being 'sex'. The code uses the same dataset ('penguins.csv') as the previous answer.

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# Load libraries
library(tidyverse)
library(tidymodels)
library(ggplot2)
library(readr)

# Import dataset
data <- read_csv("penguins.csv")

# Data preprocessing and cleaning
data_clean <- data %>%
  drop_na() %>%
  mutate(sex = as.factor(sex))

# Examine the structure of the dataset
print(str(data_clean))

# Summary statistics
print(summary(data_clean))

# Target variable distribution
print(table(data_clean$sex))

# Visualize target variable distribution
ggplot(data_clean, aes(x = sex)) +
  geom_bar() +
  ggtitle("Sex Distribution")

# Pairwise scatterplot of continuous variables
pairs_plot <- data_clean %>%
  select_if(is.numeric) %>%
  ggpairs()

print(pairs_plot)

# Boxplots for continuous variables by sex
data_clean %>%
  gather("variable", "value", -sex) %>%
  ggplot(aes(x = sex, y = value)) +
  geom_boxplot() +
  facet_wrap(~ variable, scales = "free") +
  ggtitle("Boxplots of Continuous Variables by Sex")

# Visualize categorical variables by sex
data_clean %>%
  ggplot(aes(x = island, fill = sex)) +
  geom_bar(position = "dodge") +
  ggtitle("Island Distribution by Sex")

# Correlation matrix
cor_matrix <- cor(data_clean %>% select_if(is.numeric))
print(cor_matrix)

This script includes several EDA techniques such as examining the dataset's structure, summarizing the dataset, visualizing the target variable distribution, creating pairwise scatterplots, boxplots, and bar charts, and calculating the correlation matrix for the continuous variables.

Make sure to replace the filename and adapt the code to your specific needs.

use pivot_longer instead of gather function

Sure, here's the updated code using pivot_longer instead of the deprecated gather function:

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# Load libraries
library(tidyverse)
library(tidymodels)
library(ggplot2)
library(readr)

# Import dataset
data <- read_csv("penguins.csv")

# Data preprocessing and cleaning
data_clean <- data %>%
  drop_na() %>%
  mutate(sex = as.factor(sex))

# Examine the structure of the dataset
print(str(data_clean))

# Summary statistics
print(summary(data_clean))

# Target variable distribution
print(table(data_clean$sex))

# Visualize target variable distribution
ggplot(data_clean, aes(x = sex)) +
  geom_bar() +
  ggtitle("Sex Distribution")

# Pairwise scatterplot of continuous variables
pairs_plot <- data_clean %>%
  select_if(is.numeric) %>%
  ggpairs()

print(pairs_plot)

# Boxplots for continuous variables by sex
data_clean %>%
  pivot_longer(cols = -sex, names_to = "variable", values_to = "value") %>%
  ggplot(aes(x = sex, y = value)) +
  geom_boxplot() +
  facet_wrap(~ variable, scales = "free") +
  ggtitle("Boxplots of Continuous Variables by Sex")

# Visualize categorical variables by sex
data_clean %>%
  ggplot(aes(x = island, fill = sex)) +
  geom_bar(position = "dodge") +
  ggtitle("Island Distribution by Sex")

# Correlation matrix
cor_matrix <- cor(data_clean %>% select_if(is.numeric))
print(cor_matrix)

In this version of the script, I replaced the gather function with the pivot_longer function to reshape the data for creating boxplots of continuous variables by sex.