keithmcnulty

import pandas as pd
from scipy import stats
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import RandomizedSearchCV, KFold
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from xgboost import XGBClassifier

# spit data into train test
def split_data(df: pd.DataFrame, parameters: dict) -> dict:

keithmcnulty

library(patchwork)

# generate scatter for entire dataset
p_all <- scatter_fn(penguins, bill_length_mm, bill_depth_mm, "All Species") 

# get species scatters from penguin_scatters dataframe
for (i in 1:3) {
 assign(paste("p", i, sep = "_"),
        penguin_scatters$plot[i][[1]]) 
}

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# generic function for generating a simple scatter plot in ggplot2
scatter_fn <- function(df, col1, col2, title) {
  df %>% 
    ggplot2::ggplot(aes(x = {{col1}}, y = {{col2}})) +
    ggplot2::geom_point() +
    ggplot2::geom_smooth() +
    ggplot2::labs(title = title)
}

# run function across species and store plots in a list column

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penguins %>% 
  nest_by(species)
  
# A tibble: 3 x 2
# Rowwise:  species
  species                 data
  <fct>     <list<tbl_df[,7]>>
1 Adelie             [152 × 7]
2 Chinstrap           [68 × 7]
3 Gentoo             [124 × 7]

keithmcnulty

penguins %>% 
  dplyr::group_by(species) %>% 
  tidyr::nest() %>% 
  dplyr::rowwise()
  
# A tibble: 3 x 2
# Rowwise:  species
  species   data              
  <fct>     <list>            
1 Adelie    <tibble [152 × 7]>

keithmcnulty

library(broom)

penguin_models <- penguins %>% 
  dplyr::group_by(species) %>% 
  dplyr::summarise(broom::glance(lm(body_mass_g ~ flipper_length_mm + bill_length_mm + bill_depth_mm))) # summarise model stats

penguin_models

# A tibble: 3 x 13
  species   r.squared adj.r.squared sigma statistic  p.value    df logLik   AIC   BIC  deviance df.residual  nobs

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penguin_models <- penguins %>% 
  dplyr::group_by(species) %>% 
  dplyr::summarise(model = list(lm(body_mass_g ~ flipper_length_mm + bill_length_mm + bill_depth_mm)))  # store models in a list column

penguin_models

# A tibble: 3 x 2
  species   model 
  <fct>     <list>
1 Adelie    <lm>  

keithmcnulty

penguin_stats <- penguins %>% 
  dplyr::group_by(species) %>% 
  dplyr::summarise(across(ends_with("mm"), 
                          list(mean = ~mean(.x, na.rm = TRUE), sd = ~sd(.x, na.rm = TRUE)), # name summary functions
                          .names = "{gsub('_|_mm', '', col)}_{fn}")) # structure for summarised column names

penguin_stats

# A tibble: 3 x 7
  species   billlength_mean billlength_sd billdepth_mean billdepth_sd flipperlength_mean flipperlength_sd

keithmcnulty

penguin_stats <- penguins %>% 
  dplyr::group_by(species) %>% 
  dplyr::summarise(across(ends_with("mm"), # do this for any column ending in mm
                          list(~mean(.x, na.rm = TRUE), ~sd(.x, na.rm = TRUE)))) # calculate a mean and sd

penguin_stats

# A tibble: 3 x 7
  species   bill_length_mm_1 bill_length_mm_2 bill_depth_mm_1 bill_depth_mm_2 flipper_length_mm_1 flipper_length_mm_2
  <fct>                <dbl>            <dbl>           <dbl>           <dbl>               <dbl>               <dbl>

keithmcnulty

penguins_wide <- penguins_long %>% 
  tidyr::pivot_wider(names_from = c("part", "measure", "unit"), # pivot these columns
                     values_from = "value", # take the values from here
                     names_sep = "_") # combine col names using an underscore

penguins_wide

# A tibble: 344 x 9
# Groups:   species, island, sex, year [35]
   species island    sex     year penguinid bill_length_mm bill_depth_mm flipper_length_mm body_mass_g