| corr_cross | R Documentation | 
This function creates a correlation full study and returns a rank of the highest correlation variables obtained in a cross-table.
corr_cross(
  df,
  plot = TRUE,
  pvalue = TRUE,
  max_pvalue = 1,
  type = 1,
  max = 1,
  top = 20,
  local = 1,
  ignore = NULL,
  contains = NA,
  grid = TRUE,
  rm.na = FALSE,
  quiet = FALSE,
  ...
)
| df | Dataframe. It doesn't matter if it's got non-numerical columns: they will be filtered. | 
| plot | Boolean. Show and return a plot? | 
| pvalue | Boolean. Returns a list, with correlations and statistical significance (p-value) for each value. | 
| max_pvalue | Numeric. Filter non-significant variables. Range (0, 1] | 
| type | Integer. Plot type. 1 is for overall rank. 2 is for local rank. | 
| max | Numeric. Maximum correlation permitted (from 0 to 1) | 
| top | Integer. Return top n results only. Only valid when type = 1. Set value to NA to use all cross-correlations | 
| local | Integer. Label top n local correlations. Only valid when type = 2 | 
| ignore | Vector or character. Which column should be ignored? | 
| contains | Character vector. Filter cross-correlations with variables that contains certain strings (using any value if vector used). | 
| grid | Boolean. Separate into grids? | 
| rm.na | Boolean. Remove NAs? | 
| quiet | Boolean. Keep quiet? If not, informative messages will be shown. | 
| ... | Additional parameters passed to  | 
DataScience+ Post: Find Insights with Ranked Cross-Correlations
Depending on input plot, we get correlation and p-value results for
every combination of features, arranged by descending absolute correlation value,
with a data.frame plot = FALSE or plot plot = TRUE.
Other Correlations: 
corr(),
corr_var()
Other Exploratory: 
corr_var(),
crosstab(),
df_str(),
distr(),
freqs(),
freqs_df(),
freqs_list(),
freqs_plot(),
lasso_vars(),
missingness(),
plot_cats(),
plot_df(),
plot_nums(),
tree_var()
Sys.unsetenv("LARES_FONT") # Temporal
data(dft) # Titanic dataset
# Only data with no plot
corr_cross(dft, plot = FALSE, top = 10)
# Show only most relevant results filtered by pvalue
corr_cross(dft, rm.na = TRUE, max_pvalue = 0.05, top = 15)
# Cross-Correlation for certain variables
corr_cross(dft, contains = c("Survived", "Fare"))
# Cross-Correlation max values per category
corr_cross(dft, type = 2, top = NA)
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