funModeling quick-start

In funModeling: Exploratory Data Analysis and Data Preparation Tool-Box

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funModeling quick-start {#quick_start}

This package contains a set of functions related to exploratory data analysis, data preparation, and model performance. It is used by people coming from business, research, and teaching (professors and students).

funModeling is intimately related to the Data Science Live Book -Open Source- (2017) in the sense that most of its functionality is used to explain different topics addressed by the book.

Blog posts based on funModeling:

• Exploratory Data Analysis in R (introduction)
• Automatic data types checking in predictive models
• Fast data exploration for predictive modeling
• New discretization method: Recursive information gain ratio maximization

Opening the black-box

Some functions have in-line comments so the user can open the black-box and learn how it was developed, or to tune or improve any of them.

All the functions are well documented, explaining all the parameters with the help of many short examples. R documentation can be accessed by: help("name_of_the_function").

About this quick-start

This quick-start is focused only on the functions. All explanations around them, and the how and when to use them, can be accessed by following the "Read more here." links below each section, which redirect you to the book.

Below there are most of the funModeling functions divided by category.

Exploratory data analysis

status: Dataset health status (2nd version)

Similar to df_status, but it returns all percentages in the 0 to 1 range (not 1 to 100).

library(funModeling)

status(heart_disease)

Note: df_status will be deprecated, please use status instead.

data_integrity: Dataset health status (2nd version)

A handy function to return different vectors of variable names aimed to quickly filter NA, categorical (factor / character), numerical and other types (boolean, date, posix).

It also returns a vector of variables which have high cardinality.

It returns an 'integrity' object, which has: 'status_now' (comes from status function), and 'results' list, following elements can be found: vars_cat, vars_num, vars_num_with_NA, etc. Explore the object for more.

library(funModeling)

di=data_integrity(heart_disease)

# returns a summary
summary(di)

# print all the metadata information
print(di)

plot_num: Plotting distributions for numerical variables

Plots only numeric variables.

plot_num(heart_disease)

Notes:

• bins: Sets the number of bins (10 by default).
• path_out indicates the path directory; if it has a value, then the plot is exported in jpeg. To save in current directory path must be dot: "."

Read more here.

profiling_num: Calculating several statistics for numerical variables

Retrieves several statistics for numerical variables.

profiling_num(heart_disease)

Note:

• plot_num and profiling_num automatically exclude non-numeric variables

Read more here.

freq: Getting frequency distributions for categoric variables

library(dplyr)

# Select only two variables for this example
heart_disease_2=heart_disease %>% select(chest_pain, thal)

# Frequency distribution
freq(heart_disease_2)

Notes:

• freq only processes factor and character, excluding non-categorical variables.
• It returns the distribution table as a data frame.
• If input is empty, then it runs for all categorical variables.
• path_out indicates the path directory; if it has a value, then the plot is exported in jpeg. To save in current directory path must be dot: "."
• na.rm indicates if NA values should be excluded (FALSE by default).

Read more here.

Correlation

correlation_table: Calculates R statistic

Retrieves R metric (or Pearson coefficient) for all numeric variables, skipping the categoric ones.

correlation_table(heart_disease, "has_heart_disease")

Notes:

• Only numeric variables are analyzed. Target variable must be numeric.
• If target is categorical, then it will be converted to numeric.

Read more here.

var_rank_info: Correlation based on information theory

Calculates correlation based on several information theory metrics between all variables in a data frame and a target variable.

var_rank_info(heart_disease, "has_heart_disease")

Note: It analyzes numerical and categorical variables. It is also used with the numeric discretization method as before, just as discretize_df.

Read more here.

cross_plot: Distribution plot between input and target variable

Retrieves the relative and absolute distribution between an input and target variable. Useful to explain and report if a variable is important or not.

cross_plot(data=heart_disease, input=c("age", "oldpeak"), target="has_heart_disease")

Notes:

• auto_binning: TRUE by default, shows the numerical variable as categorical.
• path_out indicates the path directory; if it has a value, then the plot is exported in jpeg.
• input can be numeric or categoric, and target must be a binary (two-class) variable.
• If input is empty, then it runs for all variables.

Read more here.

plotar: Boxplot and density histogram between input and target variables

Useful to explain and report if a variable is important or not.

Boxplot:

plotar(data=heart_disease, input = c("age", "oldpeak"), target="has_heart_disease", plot_type="boxplot")

Read more here.

Density histograms:

plotar(data=mtcars, input = "gear", target="cyl", plot_type="histdens")

Read more here.

Notes:

• path_out indicates the path directory; if it has a value, then the plot is exported in jpeg.
• If input is empty, then it runs for all numeric variables (skipping the categorical ones).
• input must be numeric and target must be categoric.
• target can be multi-class (not only binary).

categ_analysis: Quantitative analysis for binary outcome

Profile a binary target based on a categorical input variable, the representativeness (perc_rows) and the accuracy (perc_target) for each value of the input variable; for example, the rate of flu infection per country.

df_ca=categ_analysis(data = data_country, input = "country", target = "has_flu")

head(df_ca)

Note:

• input variable must be categorical.
• target variable must be binary (two-value).

This function is used to analyze data when we need to reduce variable cardinality in predictive modeling.

Read more here.

Data preparation

Data discretization

discretize_get_bins + discretize_df: Convert numeric variables to categoric

We need two functions: discretize_get_bins, which returns the thresholds for each variable, and then discretize_df, which takes the result from the first function and converts the desired variables. The binning criterion is equal frequency.

Example converting only two variables from a dataset.

# Step 1: Getting the thresholds for the desired variables: "max_heart_rate" and "oldpeak"
d_bins=discretize_get_bins(data=heart_disease, input=c("max_heart_rate", "oldpeak"), n_bins=5)

# Step 2: Applying the threshold to get the final processed data frame
heart_disease_discretized=discretize_df(data=heart_disease, data_bins=d_bins, stringsAsFactors=T)

The following image illustrates the result. Please note that the variable name remains the same.

Notes:

• This two-step procedure is thought to be used in production with new data.
• Min and max values for each bin will be -Inf and Inf, respectively.
• A fix in the latest funModeling release (1.6.7) may change the output in certain scenarios. Please check the results if you were using version 1.6.6. More info about this change here.

Read more here.

equal_freq: Convert numeric variable to categoric

Converts numeric vector into a factor using the equal frequency criterion.

new_age=equal_freq(heart_disease$age, n_bins = 5)

# checking results
Hmisc::describe(new_age)

Read more here.

Notes:

• Unlike discretize_get_bins, this function doesn't insert -Inf and Inf as the min and max value respectively.

discretize_rgr: Variable discretization based on gain ratio maximization

This is a new method developed in funModeling developed improve the binning based on a binary target variable.

input=heart_disease$oldpeak
target=heart_disease$has_heart_disease

input2=discretize_rgr(input, target)

# checking:
summary(input2)

Adjust max number of bins with: max_n_bins; 5 as default. Control minimum sample size per bin with min_perc_bins; 0.1 (or 10%) as default)

range01: Scales variable into the 0 to 1 range

Convert a numeric vector into a scale from 0 to 1 with 0 as the minimum and 1 as the maximum.

age_scaled=range01(heart_disease$oldpeak)

# checking results
summary(age_scaled)

Outliers data preparation

hampel_outlier and tukey_outlier: Gets outliers threshold

Both functions retrieve a two-value vector that indicates the thresholds for which the values are considered as outliers. The functions tukey_outlier and hampel_outlier are used internally in prep_outliers.

Using Tukey's method:

tukey_outlier(heart_disease$resting_blood_pressure)

Read more here.

Using Hampel's method:

hampel_outlier(heart_disease$resting_blood_pressure)

Read more here.

prep_outliers: Prepare outliers in a data frame

Takes a data frame and returns the same data frame plus the transformations specified in the input parameter. It also works with a single vector.

Example considering two variables as input:

# Get threshold according to Hampel's method
hampel_outlier(heart_disease$max_heart_rate)

# Apply function to stop outliers at the threshold values
data_prep=prep_outliers(data = heart_disease, input = c('max_heart_rate','resting_blood_pressure'), method = "hampel", type='stop')

Checking the before and after for variable max_heart_rate:

# Checking max and min value for 'max_heart_rate' before the transformation
sprintf("Before transformation -> Min: %s; Max: %s", min(heart_disease$max_heart_rate), max(heart_disease$max_heart_rate))

# Apply function to stop outliers at the threshold values
data_prep=prep_outliers(data = heart_disease, input = c('max_heart_rate','resting_blood_pressure'), method = "hampel", type='stop')

# Checking the results, the maximum value is now 174.5 (the minimum remains the same)
# Checking max and min value for 'max_heart_rate' before the transformation
sprintf("After transformation -> Min: %s; Max: %s", min(data_prep$max_heart_rate), max(data_prep$max_heart_rate))

The min value changed from 71 to 86.23, while the max value remained the same at 202.

Notes:

• method can be: bottom_top, tukey or hampel.
• type can be: stop or set_na. If stop all values flagged as outliers will be set to the threshold. If set_na, then the flagged values will set to NA.

Read more here.

Predictive model performance

gain_lift: Gain and lift performance curve

After computing the scores or probabilities for the class we want to predict, we pass it to the gain_lift function, which returns a data frame with performance metrics.

# Create machine learning model and get its scores for positive case 
fit_glm=glm(has_heart_disease ~ age + oldpeak, data=heart_disease, family = binomial)
heart_disease$score=predict(fit_glm, newdata=heart_disease, type='response')

# Calculate performance metrics
gain_lift(data=heart_disease, score='score', target='has_heart_disease')

Read more here.

coord_plot: Coordinate plot (clustering models)

Useful when we want to profile cluster results in terms of its means.

Imagine cyl can be the cluster number.

coord_plot(data=mtcars, group_var="cyl", group_func=median, print_table=TRUE)

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• Github repository (report bugs or improvements): https://github.com/pablo14/funModeling
• Creator and maintainer: Pablo Casas

funModeling documentation built on May 29, 2024, 3:24 a.m.