# funModeling 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.

## 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").

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)
##                  variable q_zeros   p_zeros q_na       p_na q_inf p_inf    type
## 1                     age       0 0.0000000    0 0.00000000     0     0 integer
## 2                  gender       0 0.0000000    0 0.00000000     0     0  factor
## 3              chest_pain       0 0.0000000    0 0.00000000     0     0  factor
## 4  resting_blood_pressure       0 0.0000000    0 0.00000000     0     0 integer
## 5       serum_cholestoral       0 0.0000000    0 0.00000000     0     0 integer
## 6     fasting_blood_sugar     258 0.8514851    0 0.00000000     0     0  factor
## 7         resting_electro     151 0.4983498    0 0.00000000     0     0  factor
## 8          max_heart_rate       0 0.0000000    0 0.00000000     0     0 integer
## 9             exer_angina     204 0.6732673    0 0.00000000     0     0 integer
## 10                oldpeak      99 0.3267327    0 0.00000000     0     0 numeric
## 11                  slope       0 0.0000000    0 0.00000000     0     0 integer
## 12      num_vessels_flour     176 0.5808581    4 0.01320132     0     0 integer
## 13                   thal       0 0.0000000    2 0.00660066     0     0  factor
## 14 heart_disease_severity     164 0.5412541    0 0.00000000     0     0 integer
## 15           exter_angina     204 0.6732673    0 0.00000000     0     0  factor
## 16      has_heart_disease       0 0.0000000    0 0.00000000     0     0  factor
##    unique
## 1      41
## 2       2
## 3       4
## 4      50
## 5     152
## 6       2
## 7       3
## 8      91
## 9       2
## 10     40
## 11      3
## 12      4
## 13      3
## 14      5
## 15      2
## 16      2

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)
##
## ◌ {Numerical with NA} num_vessels_flour
## ◌ {Categorical with NA} thal
# print all the metadata information
print(di)
## $vars_num_with_NA ## variable q_na p_na ## 1 num_vessels_flour 4 0.01320132 ## ##$vars_cat_with_NA
##   variable q_na       p_na
## 1     thal    2 0.00660066
##
## $vars_cat_high_card ## [1] variable unique ## <0 rows> (or 0-length row.names) ## ##$MAX_UNIQUE
## [1] 35
##
## $vars_one_value ## character(0) ## ##$vars_cat
## [1] "gender"              "chest_pain"          "fasting_blood_sugar"
## [4] "resting_electro"     "thal"                "exter_angina"
## [7] "has_heart_disease"
##
## $vars_num ## [1] "age" "resting_blood_pressure" "serum_cholestoral" ## [4] "max_heart_rate" "exer_angina" "oldpeak" ## [7] "slope" "num_vessels_flour" "heart_disease_severity" ## ##$vars_char
## character(0)
##
## $vars_factor ## [1] "gender" "chest_pain" "fasting_blood_sugar" ## [4] "resting_electro" "thal" "exter_angina" ## [7] "has_heart_disease" ## ##$vars_other
## character(0)

### 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: “.”

### profiling_num: Calculating several statistics for numerical variables

Retrieves several statistics for numerical variables.

profiling_num(heart_disease)
##                 variable        mean    std_dev variation_coef   p_01  p_05
## 1                    age  54.4389439  9.0386624      0.1660330  35.00  40.0
## 2 resting_blood_pressure 131.6897690 17.5997477      0.1336455 100.00 108.0
## 3      serum_cholestoral 246.6930693 51.7769175      0.2098840 149.00 175.1
## 4         max_heart_rate 149.6072607 22.8750033      0.1529004  95.02 108.1
## 5            exer_angina   0.3267327  0.4697945      1.4378558   0.00   0.0
## 6                oldpeak   1.0396040  1.1610750      1.1168436   0.00   0.0
## 7                  slope   1.6006601  0.6162261      0.3849825   1.00   1.0
## 8      num_vessels_flour   0.6722408  0.9374383      1.3944978   0.00   0.0
## 9 heart_disease_severity   0.9372937  1.2285357      1.3107265   0.00   0.0
##    p_25  p_50  p_75  p_95   p_99   skewness kurtosis  iqr        range_98
## 1  48.0  56.0  61.0  68.0  71.00 -0.2080241 2.465477 13.0        [35, 71]
## 2 120.0 130.0 140.0 160.0 180.00  0.7025346 3.845881 20.0      [100, 180]
## 3 211.0 241.0 275.0 326.9 406.74  1.1298741 7.398208 64.0   [149, 406.74]
## 4 133.5 153.0 166.0 181.9 191.96 -0.5347844 2.927602 32.5 [95.02, 191.96]
## 5   0.0   0.0   1.0   1.0   1.00  0.7388506 1.545900  1.0          [0, 1]
## 6   0.0   0.8   1.6   3.4   4.20  1.2634255 4.530193  1.6        [0, 4.2]
## 7   1.0   2.0   2.0   3.0   3.00  0.5057957 2.363050  1.0          [1, 3]
## 8   0.0   0.0   1.0   3.0   3.00  1.1833771 3.234941  1.0          [0, 3]
## 9   0.0   0.0   2.0   3.0   4.00  1.0532483 2.843788  2.0          [0, 4]
##         range_80
## 1       [42, 66]
## 2     [110, 152]
## 3 [188.8, 308.8]
## 4   [116, 176.6]
## 5         [0, 1]
## 6       [0, 2.8]
## 7         [1, 2]
## 8         [0, 2]
## 9         [0, 3]

Note:

• plot_num and profiling_num automatically exclude non-numeric variables

### 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)

##   chest_pain frequency percentage cumulative_perc
## 1          4       144      47.52           47.52
## 2          3        86      28.38           75.90
## 3          2        50      16.50           92.40
## 4          1        23       7.59          100.00

##   thal frequency percentage cumulative_perc
## 1    3       166      54.79           54.79
## 2    7       117      38.61           93.40
## 3    6        18       5.94           99.34
## 4 <NA>         2       0.66          100.00
## [1] "Variables processed: chest_pain, thal"

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

### 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")
##                 Variable has_heart_disease
## 1      has_heart_disease              1.00
## 2 heart_disease_severity              0.83
## 3      num_vessels_flour              0.46
## 4                oldpeak              0.42
## 5                  slope              0.34
## 6                    age              0.23
## 7 resting_blood_pressure              0.15
## 8      serum_cholestoral              0.08
## 9         max_heart_rate             -0.42

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 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")
##                       var    en    mi           ig           gr
## 1  heart_disease_severity 1.846 0.995 0.9950837595 0.5390655068
## 2                    thal 2.032 0.209 0.2094550580 0.1680456709
## 3             exer_angina 1.767 0.139 0.1391389302 0.1526393841
## 4            exter_angina 1.767 0.139 0.1391389302 0.1526393841
## 5              chest_pain 2.527 0.205 0.2050188327 0.1180286190
## 6       num_vessels_flour 2.381 0.182 0.1815217813 0.1157736478
## 7                   slope 2.177 0.112 0.1124219069 0.0868799615
## 8       serum_cholestoral 7.481 0.561 0.5605556771 0.0795557228
## 9                  gender 1.842 0.057 0.0572537665 0.0632970555
## 10                oldpeak 4.874 0.249 0.2491668741 0.0603576874
## 11         max_heart_rate 6.832 0.334 0.3336174096 0.0540697329
## 12 resting_blood_pressure 5.567 0.143 0.1425548155 0.0302394591
## 13                    age 5.928 0.137 0.1371752885 0.0270548944
## 14        resting_electro 2.059 0.024 0.0241482908 0.0221938072
## 15    fasting_blood_sugar 1.601 0.000 0.0004593775 0.0007579095

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 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")
## Plotting transformed variable 'age' with 'equal_freq', (too many values). Disable with 'auto_binning=FALSE'
## Plotting transformed variable 'oldpeak' with 'equal_freq', (too many values). Disable with 'auto_binning=FALSE'

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 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")
## Warning: fun.y is deprecated. Use fun instead.

## Warning: fun.y is deprecated. Use fun instead.

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 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)
##          country mean_target sum_target perc_target q_rows perc_rows
## 1       Malaysia       1.000          1       0.012      1     0.001
## 2         Mexico       0.667          2       0.024      3     0.003
## 3       Portugal       0.200          1       0.012      5     0.005
## 4 United Kingdom       0.178          8       0.096     45     0.049
## 5        Uruguay       0.175         11       0.133     63     0.069
## 6         Israel       0.167          1       0.012      6     0.007

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.

## 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)
## Variables processed: max_heart_rate, oldpeak
# 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)
## Variables processed: max_heart_rate, oldpeak

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 categoric

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

target=heart_disease$has_heart_disease input2=discretize_rgr(input, target) # checking: summary(input2) ## [0.0,0.6) [0.6,1.0) [1.0,1.4) [1.4,1.9) [1.9,6.2] ## 135 31 34 39 64 Adjust max number of bins with: max_n_bins; 5 as default. Control minimum sample size per bin with min_perc_bins; 0.1 (or 10%) as default) ### range01: Scales variable into the 0 to 1 range Convert a numeric vector into a scale from 0 to 1 with 0 as the minimum and 1 as the maximum. age_scaled=range01(heart_disease$oldpeak)

# checking results
summary(age_scaled)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##  0.0000  0.0000  0.1290  0.1677  0.2581  1.0000

## 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$resting_blood_pressure) ## bottom_threshold top_threshold ## 60 200 Read more here. Using Hampel’s method: hampel_outlier(heart_disease$resting_blood_pressure)
## bottom_threshold    top_threshold
##           85.522          174.478

### 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$max_heart_rate) ## bottom_threshold top_threshold ## 86.283 219.717 # Apply function to stop outliers at the threshold values data_prep=prep_outliers(data = heart_disease, input = c('max_heart_rate','resting_blood_pressure'), method = "hampel", type='stop') Checking the before and after for variable max_heart_rate: ## [1] "Before transformation -> Min: 71; Max: 202" ## [1] "After transformation -> Min: 71; Max: 202" The min value changed from 71 to 86.23, while the max value remained the same at 202. Notes: • method can be: bottom_top, tukey or hampel. • type can be: stop or set_na. If stop all values flagged as outliers will be set to the threshold. If set_na, then the flagged values will set to NA. Read more here. ## Predictive model performance ### gain_lift: Gain and lift performance curve After computing the scores or probabilities for the class we want to predict, we pass it to the gain_lift function, which returns a data frame with performance metrics. # Create machine learning model and get its scores for positive case fit_glm=glm(has_heart_disease ~ age + oldpeak, data=heart_disease, family = binomial) heart_disease$score=predict(fit_glm, newdata=heart_disease, type='response')

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

##    Population   Gain Lift Score.Point
## 1          10  20.86 2.09   0.8185793
## 2          20  35.97 1.80   0.6967124
## 3          30  48.92 1.63   0.5657817
## 4          40  61.15 1.53   0.4901940
## 5          50  69.06 1.38   0.4033640
## 6          60  78.42 1.31   0.3344170
## 7          70  87.77 1.25   0.2939878
## 8          80  92.09 1.15   0.2473671
## 9          90  96.40 1.07   0.1980453
## 10        100 100.00 1.00   0.1195511

### 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)

##        cyl  mpg  disp    hp  drat    wt  qsec vs am gear carb
## 1        4 26.0 108.0  91.0 4.080 2.200 18.90  1  1    4  2.0
## 2        6 19.7 167.6 110.0 3.900 3.210 18.30  1  0    4  4.0
## 3        8 15.2 350.5 192.5 3.120 3.750 17.18  0  0    3  3.5
## 4 All_Data 19.2 196.3 123.0 3.695 3.325 17.71  0  0    4  2.0