Exploratory Data Analysis and Data Preparation Tool-Box Book


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Documentation for package ‘funModeling’ version 1.6.6

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auto_grouping Reduce cardinality in categorical variable by automatic grouping
categ_analysis Profiling analysis of categorical vs. target variable
compare_df Compare two data frames by keys
concatenate_n_vars Concatenate 'N' variables
convert_df_to_categoric Convert every column in a data frame to character
coord_plot Coordinate plot
correlation_table Get correlation against target variable
cross_plot Cross-plotting input variable vs. target variable
data_country People with flu data
data_golf Play golf
desc_groups Profiling categorical variable
desc_groups_rank Profiling categorical variable (rank)
df_status Get a summary for the given data frame (o vector).
discretize_df Discretize a data frame
discretize_get_bins Get the data frame thresholds for discretization
entropy_2 Computes the entropy between two variables
equal_freq Equal frequency binning
fibonacci Fibonacci series
filter_vars Filtering variables by string name
freq Frequency table for categorical variables
gain_lift Generates lift and cumulative gain performance table and plot
gain_ratio Gain ratio
get_sample Sampling training and test data
hampel_outlier Hampel Outlier Threshold
heart_disease Heart Disease Data
information_gain Information gain
infor_magic Computes several information theory metrics between two vectors
model_performance Get model perfomance metrics (KS, AUC and ROC)
plotar Correlation plots
plot_num Plotting numerical data
prep_outliers Outliers Data Preparation
profiling_num Profiling numerical data
range01 Transform a variable into the [0-1] range
tukey_outlier Tukey Outlier Threshold
var_rank_info Importance variable ranking based on information theory
v_compare Compare two vectors