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How to use snapedautilityR

Kyle Ahn, Dongxiao Li, Harry Chan

my-vignette.Rmd

snapedautilityR an open-source library that generate useful function to kickstart EDA (Exploratory Data Analysis) with just a few lines of code. The system is built around quickly analyzing the whole dataset and providing a detailed report with visualization. Its goal is to help quick analysis of feature characteristics, detecting outliers from the observations and other such data characterization tasks.

Import library

Here we will demonstrate how to use snapedautilityR package for your data science project.

library(snapedautilityR)

Sample data

We will be using the penguins data and iris data here.

library(palmerpenguins)
head(penguins)
#> # A tibble: 6 × 8
#>   species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex  
#>   <fct>   <fct>           <dbl>         <dbl>            <int>       <int> <fct>
#> 1 Adelie  Torge…           39.1          18.7              181        3750 male 
#> 2 Adelie  Torge…           39.5          17.4              186        3800 fema…
#> 3 Adelie  Torge…           40.3          18                195        3250 fema…
#> 4 Adelie  Torge…           NA            NA                 NA          NA NA   
#> 5 Adelie  Torge…           36.7          19.3              193        3450 fema…
#> 6 Adelie  Torge…           39.3          20.6              190        3650 male 
#> # … with 1 more variable: year <int>
head(iris)
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          5.1         3.5          1.4         0.2  setosa
#> 2          4.9         3.0          1.4         0.2  setosa
#> 3          4.7         3.2          1.3         0.2  setosa
#> 4          4.6         3.1          1.5         0.2  setosa
#> 5          5.0         3.6          1.4         0.2  setosa
#> 6          5.4         3.9          1.7         0.4  setosa

Plot Histograms

Functionality

This function detects outliers in the given list, plots histogram given numeric features of the input data frame, and plots bar charts for categorical features of the input data frame.

Function inputs

  • df: dfFrame that to be used for the plot
  • features: List of string feature names
  • facet_column: Number of columns in Integer type for facet options
plot_histograms(penguins, c("species", "flipper_length_mm", "bill_length_mm", "body_mass_g", "island"), 3)
#> Warning: Removed 6 rows containing non-finite values (stat_bin).

Plot Correlation

Functionality

This function generates a pearson correlation plot for a list of numeric features in a given data frame.

Function inputs

  • df: dfFrame that to be used for the plot
  • features: List of string feature names
numerical_features <- c('Sepal.Length','Sepal.Width','Petal.Width')
plot_corr(iris, features = numerical_features)
#> Registered S3 method overwritten by 'GGally':
#>   method from   
#>   +.gg   ggplot2

Detect Outliers

Functionality

This function detect outliers in the given list Returns a threshold value for the lower bound and upper bound of the outliers and Plot a violin plot of the observations.

Function inputs

  • s: list of double that contains the data
  • width: The width of ggplot. Default set to 150
  • height: The height of ggplot. Default set to 150
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
detect_outliers(pull(iris["Sepal.Length"]), 150, 150)
#> [[1]]
#> [1] 3.15
#> 
#> [[2]]
#> [1] 8.35
#> 
#> [[3]]