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, ...)

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.

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 {
# Generate data for the example
carseats <- ISLR::Carseats
carseats[sample(seq(NROW(carseats)), 20), "Income"] <- NA
carseats[sample(seq(NROW(carseats)), 5), "Urban"] <- NA

# Correlation coefficients of all numerical variables
correlate(carseats)

# Select the variable to compute
correlate(carseats, Sales, Price)
correlate(carseats, -Sales, -Price)
correlate(carseats, "Sales", "Price")
correlate(carseats, 1)

# Using dplyr::grouped_dt
library(dplyr)

gdata <- group_by(carseats, ShelveLoc, US)
correlate(gdata, "Sales")
correlate(gdata)

# Using pipes ---------------------------------
# Correlation coefficients of all numerical variables
carseats %>%
 correlate()
# Positive values select variables
carseats %>%
 correlate(Sales, Price)
# Negative values to drop variables
carseats %>%
 correlate(-Sales, -Price)
# Positions values select variables
carseats %>%
 correlate(1)
# Positions values select variables
carseats %>%
 correlate(-1, -2, -3, -5, -6)
# ---------------------------------------------
# Correlation coefficient
# that eliminates redundant combination of variables
carseats %>%
 correlate() %>%
 filter(as.integer(var1) > as.integer(var2))

carseats %>%
 correlate(Sales, Price) %>%
 filter(as.integer(var1) > as.integer(var2))

# Using pipes & dplyr -------------------------
# Compute the correlation coefficient of Sales variable by 'ShelveLoc'
# and 'US' variables. And extract only those with absolute
# value of correlation coefficient is greater than 0.5
carseats %>%
 group_by(ShelveLoc, US) %>%
 correlate(Sales) %>%
 filter(abs(coef_corr) >= 0.5)

# extract only those with 'ShelveLoc' variable level is "Good",
# and compute the correlation coefficient of 'Sales' variable
# by 'Urban' and 'US' variables.
# And the correlation coefficient is negative and smaller than 0.5
carseats %>%
 filter(ShelveLoc == "Good") %>%
 group_by(Urban, US) %>%
 correlate(Sales) %>%
 filter(coef_corr < 0) %>%
 filter(abs(coef_corr) > 0.5)
# }