correlate: Compute the correlation coefficient between two numerical data

Description

The correlate() compute Pearson's the correlation coefficient of the numerical data.

Usage

correlate(.data, ...)
# S3 method for data.frame
correlate(.data, ..., method = c("pearson", "kendall", "spearman"))

Arguments

.data

a data.frame or a tbl_df.

...

one or more unquoted expressions separated by commas. You can treat variable names like they are positions. Positive values select variables; negative values to drop variables. If the first expression is negative, correlate() will automatically start with all variables. These arguments are automatically quoted and evaluated in a context where column names represent column positions. They support unquoting and splicing.

See vignette("EDA") for an introduction to these concepts.

method

a character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated.

Correlation coefficient information

The information derived from the numerical data compute is as follows.

var1 : names of numerical variable
var2 : name of the corresponding numeric variable
coef_corr : Pearson's correlation coefficient

Details

This function is useful when used with the group_by() function of the dplyr package. If you want to compute by level of the categorical data you are interested in, rather than the whole observation, you can use grouped_df as the group_by() function. This function is computed stats::cor() function by use = "pairwise.complete.obs" option.

Examples

Run this code

# NOT RUN {
# Correlation coefficients of all numerical variables
correlate(heartfailure)

# Select the variable to compute
correlate(heartfailure, creatinine, sodium)
correlate(heartfailure, -creatinine, -sodium)
correlate(heartfailure, "creatinine", "sodium")
correlate(heartfailure, 1)
# Non-parametric correlation coefficient by kendall method
correlate(heartfailure, creatinine, method = "kendall")
 
# Using dplyr::grouped_dt
library(dplyr)

gdata <- group_by(heartfailure, smoking, death_event)
correlate(gdata, "creatinine")
correlate(gdata)

# Using pipes ---------------------------------
# Correlation coefficients of all numerical variables
heartfailure %>%
 correlate()
# Positive values select variables
heartfailure %>%
 correlate(creatinine, sodium)
# Negative values to drop variables
heartfailure %>%
 correlate(-creatinine, -sodium)
# Positions values select variables
heartfailure %>%
 correlate(1)
# Positions values select variables
heartfailure %>%
 correlate(-1, -3, -5, -7)
# Non-parametric correlation coefficient by spearman method
heartfailure %>%
 correlate(creatinine, sodium, method = "spearman")
 
# ---------------------------------------------
# Correlation coefficient
# that eliminates redundant combination of variables
heartfailure %>%
 correlate() %>%
 filter(as.integer(var1) > as.integer(var2))

heartfailure %>%
 correlate(creatinine, sodium) %>%
 filter(as.integer(var1) > as.integer(var2))

# Using pipes & dplyr -------------------------
# Compute the correlation coefficient of Sales variable by 'smoking'
# and 'death_event' variables. And extract only those with absolute
# value of correlation coefficient is greater than 0.2
heartfailure %>%
 group_by(smoking, death_event) %>%
 correlate(creatinine) %>%
 filter(abs(coef_corr) >= 0.2)

# extract only those with 'smoking' variable level is "Yes",
# and compute the correlation coefficient of 'Sales' variable
# by 'hblood_pressure' and 'death_event' variables.
# And the correlation coefficient is negative and smaller than 0.5
heartfailure %>%
 filter(smoking == "Yes") %>%
 group_by(hblood_pressure, death_event) %>%
 correlate(creatinine) %>%
 filter(coef_corr < 0) %>%
 filter(abs(coef_corr) > 0.5)
# }