README.md

In Jofou/jofou.lib: Useful Personal Function

jofou.lib

The goal of jofou.lib is to regroup all the functions that are useful for me to work efficiently.

Installation

You can install the lastest version of jofou.lib with:

devtools::install_github("jofou/jofou.lib")

EDA Functions

Example

This are basics examples which shows you how use the my_inspect group function:

library(tidyverse)
library(jofou.lib)

iris %>% 
  my_inspect_cat()

Resume et comparaison des variables categoriques Nom de la variable Nombre Commun pourcentage Species 3 setosa 33

library(tidyverse)
library(jofou.lib)

iris %>% 
  my_inspect_cor()

Coeficient de correlation pour toutes les variables numeriques Variable 1 Variable 2 Correlation Valeur P N-NA (%) Petal.Width Petal.Length 0.96 0.000 100 Petal.Length Sepal.Length 0.87 0.000 100 Petal.Width Sepal.Length 0.82 0.000 100 Petal.Length Sepal.Width -0.43 0.000 100 Petal.Width Sepal.Width -0.37 0.000 100 Sepal.Width Sepal.Length -0.12 0.154 100

library(tidyverse)
library(jofou.lib)

iris %>% 
  my_inspect_imb()

Resume des categories les plus utilisees Nom de la variable Categorie Pourcentage Nombre Species setosa 33 50

library(tidyverse)
library(jofou.lib)

iris %>% 
  my_inspect_na()

Nombre de valeur manquante pour chaque variable Nom de la variable NA Pourcentage Sepal.Length 0 0 Sepal.Width 0 0 Petal.Length 0 0 Petal.Width 0 0 Species 0 0

library(tidyverse)
library(jofou.lib)

iris %>% 
  my_inspect_num()

Resume et comparaison des variables numeriques Nom de la variable min q1 median moyenne q3 max sd NA (%) Sepal.Length 4 5 6 6 6 8 1 0 Sepal.Width 2 3 3 3 3 4 0 0 Petal.Length 1 2 4 4 5 7 2 0 Petal.Width 0 0 1 1 2 2 1 0

library(tidyverse)
library(jofou.lib)

iris %>% 
  my_inspect_types()

Types de variables disponibles Type Nombre Pourcentage Nom des variables numeric 4 80 Sepal.Length, Sepal.Width , Petal.Length, Petal.Width factor 1 20 Species

I also have a couples of other functions to show distributions of numeric and categorical variables:

library(tidyverse)
library(jofou.lib)

iris %>% 
  my_num_dist()

library(tidyverse)
library(jofou.lib)

iris %>% 
  my_corr_num_graph()

#> NULL

library(tidyverse)
library(jofou.lib)

iris %>% 
  my_cat_dist()

Utilities Functions

Example

These are basic examples that show you how to use my utilities functions:

library(tidyverse)
library(jofou.lib)

iris %>%
  mutate(cat_Sepal.Length=round(Sepal.Length, digits = 0)) %>%
  group_by(cat_Sepal.Length) %>%
  summarise(mode_species=calculate_mode(Species))
#> # A tibble: 5 x 2
#>   cat_Sepal.Length mode_species
#> *            <dbl> <fct>       
#> 1                4 setosa      
#> 2                5 setosa      
#> 3                6 versicolor  
#> 4                7 virginica   
#> 5                8 virginica

library(tidyverse)
library(jofou.lib)

iris %>%
  filter(Species %ni% "setosa") %>%
  group_by(Species) %>%
  summarise(nb=dplyr::n())
#> # A tibble: 2 x 2
#>   Species       nb
#> * <fct>      <int>
#> 1 versicolor    50
#> 2 virginica     50

ML Functions

Example

These are basic examples that show you how to use my machine learning utilities functions:

library(tidyverse)
library(lubridate)
library(timetk)
library(parsnip)
library(rsample)
library(modeltime)

# Data
data_prepared_tbl <- m4_monthly %>% filter(id == "M750")

# Split Data 80/20
splits <- initial_time_split(data_prepared_tbl, prop = 0.9)

# Model: auto_arima
model_fit_arima <- arima_reg() %>%
   set_engine(engine = "auto_arima") %>%
   fit(value ~ date, data = training(splits))

# Calibrate and plot
calibrate_and_plot(model_fit_arima, type="testing")
#> # A tibble: 1 x 9
#>   .model_id .model_desc             .type   mae  mape  mase smape  rmse   rsq
#>       <int> <chr>                   <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         1 ARIMA(0,1,1)(0,1,1)[12] Test   151.  1.41 0.516  1.43  198. 0.930

Jofou/jofou.lib documentation built on May 22, 2022, 11:42 a.m.