knitr::opts_chunk$set( out.width = "700px" )

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.

`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

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")`

.

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.

`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 variablesPlots 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: "."

`profiling_num`

: Calculating several statistics for numerical variablesRetrieves several statistics for numerical variables.

```
profiling_num(heart_disease)
```

Note:

`plot_num`

and`profiling_num`

automatically exclude non-numeric variables

`freq`

: Getting frequency distributions for categoric variableslibrary(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).

`correlation_table`

: Calculates R statisticRetrieves 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.

`var_rank_info`

: Correlation based on information theoryCalculates 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`

.

`cross_plot`

: Distribution plot between input and target variableRetrieves 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.

`plotar`

: Boxplot and density histogram between input and target variablesUseful 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")

**Density histograms:**

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

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 outcomeProfile 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.

`discretize_get_bins`

+ `discretize_df`

: Convert numeric variables to categoricWe 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.

`equal_freq`

: Convert numeric variable to categoricConverts 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)

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 maximizationThis 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 rangeConvert 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)

`hampel_outlier`

and `tukey_outlier`

: Gets outliers thresholdBoth 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)

**Using Hampel's method:**

hampel_outlier(heart_disease$resting_blood_pressure)

`prep_outliers`

: Prepare outliers in a data frameTakes 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`

.

`gain_lift`

: Gain and lift performance curveAfter 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')

`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)

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

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