Provides a pipe-friendly framework to perform correlation test
between paired samples, using Pearson, Kendall or Spearman method. Wrapper
around the function cor.test().
Can also performs multiple pairwise correlation analyses between more than two variables or between two different vectors of variables. Using this function, you can also compute, for example, the correlation between one variable vs many.
cor_test(
data,
...,
vars = NULL,
vars2 = NULL,
alternative = "two.sided",
method = "pearson",
conf.level = 0.95,
use = "pairwise.complete.obs"
)a data.frame containing the variables.
One or more unquoted expressions (or variable names) separated by
commas. Used to select a variable of interest. Alternative to the argument
vars.
optional character vector containing variable names for correlation analysis. Ignored when dot vars are specified.
If vars is NULL, multiple pairwise correlation tests is performed
between all variables in the data.
If vars contain only one
variable, a pairwise correlation analysis is performed between the specified
variable vs either all the remaining numeric variables in the data or
variables in vars2 (if specified).
If vars contain two
or more variables: i) if vars2 is not specified, a pairwise
correlation analysis is performed between all possible combinations of
variables. ii) if vars2 is specified, each element in vars is
tested against all elements in vars2
. Accept unquoted
variable names: c(var1, var2).
optional character vector. If specified, each element in
vars is tested against all elements in vars2. Accept unquoted
variable names: c(var1, var2).
indicates the alternative hypothesis and must be
one of "two.sided", "greater" or "less". You
can specify just the initial letter. "greater" corresponds
to positive association, "less" to negative association.
a character string indicating which correlation
coefficient is to be used for the test. One of "pearson",
"kendall", or "spearman", can be abbreviated.
confidence level for the returned confidence interval. Currently only used for the Pearson product moment correlation coefficient if there are at least 4 complete pairs of observations.
an optional character string giving a
method for computing covariances in the presence
of missing values. This must be (an abbreviation of) one of the strings
"everything", "all.obs", "complete.obs",
"na.or.complete", or "pairwise.complete.obs".
return a data frame with the following columns:
var1, var2: the variables used in the correlation test.
cor: the correlation coefficient.
statistic: Test
statistic used to compute the p-value.
p: p-value.
conf.low,conf.high: Lower and upper bounds on a confidence interval.
method: the method used to compute the statistic.
cor_test(): correlation test between two or more variables.
cor_mat(), as_cor_mat()
# Correlation between the specified variable vs
# the remaining numeric variables in the data
#:::::::::::::::::::::::::::::::::::::::::
mtcars %>% cor_test(mpg)
#> # A tibble: 10 × 8
#> var1 var2 cor statistic p conf.low conf.high method
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 mpg cyl -0.85 -8.92 6.11e-10 -0.926 -0.716 Pearson
#> 2 mpg disp -0.85 -8.75 9.38e-10 -0.923 -0.708 Pearson
#> 3 mpg hp -0.78 -6.74 1.79e- 7 -0.885 -0.586 Pearson
#> 4 mpg drat 0.68 5.10 1.78e- 5 0.436 0.832 Pearson
#> 5 mpg wt -0.87 -9.56 1.29e-10 -0.934 -0.744 Pearson
#> 6 mpg qsec 0.42 2.53 1.71e- 2 0.0820 0.670 Pearson
#> 7 mpg vs 0.66 4.86 3.42e- 5 0.410 0.822 Pearson
#> 8 mpg am 0.6 4.11 2.85e- 4 0.318 0.784 Pearson
#> 9 mpg gear 0.48 3.00 5.4 e- 3 0.158 0.710 Pearson
#> 10 mpg carb -0.55 -3.62 1.08e- 3 -0.755 -0.250 Pearson
# Correlation test between two variables
#:::::::::::::::::::::::::::::::::::::::::
mtcars %>% cor_test(wt, mpg)
#> # A tibble: 1 × 8
#> var1 var2 cor statistic p conf.low conf.high method
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 wt mpg -0.87 -9.56 1.29e-10 -0.934 -0.744 Pearson
# Pairwise correlation between multiple variables
#:::::::::::::::::::::::::::::::::::::::::
mtcars %>% cor_test(wt, mpg, disp)
#> # A tibble: 9 × 8
#> var1 var2 cor statistic p conf.low conf.high method
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 wt wt 1 367570386. 2.27e-236 1 1 Pearson
#> 2 wt mpg -0.87 -9.56 1.29e- 10 -0.934 -0.744 Pearson
#> 3 wt disp 0.89 10.6 1.22e- 11 0.781 0.944 Pearson
#> 4 mpg wt -0.87 -9.56 1.29e- 10 -0.934 -0.744 Pearson
#> 5 mpg mpg 1 Inf 0 1 1 Pearson
#> 6 mpg disp -0.85 -8.75 9.38e- 10 -0.923 -0.708 Pearson
#> 7 disp wt 0.89 10.6 1.22e- 11 0.781 0.944 Pearson
#> 8 disp mpg -0.85 -8.75 9.38e- 10 -0.923 -0.708 Pearson
#> 9 disp disp 1 Inf 0 1 1 Pearson
# Grouped data
#:::::::::::::::::::::::::::::::::::::::::
iris %>%
group_by(Species) %>%
cor_test(Sepal.Width, Sepal.Length)
#> # A tibble: 3 × 9
#> Species var1 var2 cor stati…¹ p conf.…² conf.…³ method
#> <fct> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 setosa Sepal.Width Sepal.Le… 0.74 7.68 6.71e-10 0.585 0.846 Pears…
#> 2 versicolor Sepal.Width Sepal.Le… 0.53 4.28 8.77e- 5 0.290 0.702 Pears…
#> 3 virginica Sepal.Width Sepal.Le… 0.46 3.56 8.43e- 4 0.205 0.653 Pears…
#> # … with abbreviated variable names ¹statistic, ²conf.low, ³conf.high
# Multiple correlation test
#:::::::::::::::::::::::::::::::::::::::::
# Correlation between one variable vs many
mtcars %>% cor_test(
vars = "mpg",
vars2 = c("disp", "hp", "drat")
)
#> # A tibble: 3 × 8
#> var1 var2 cor statistic p conf.low conf.high method
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 mpg disp -0.85 -8.75 9.38e-10 -0.923 -0.708 Pearson
#> 2 mpg hp -0.78 -6.74 1.79e- 7 -0.885 -0.586 Pearson
#> 3 mpg drat 0.68 5.10 1.78e- 5 0.436 0.832 Pearson
# Correlation between two vectors of variables
# Each element in vars is tested against all elements in vars2
mtcars %>% cor_test(
vars = c("mpg", "wt"),
vars2 = c("disp", "hp", "drat")
)
#> # A tibble: 6 × 8
#> var1 var2 cor statistic p conf.low conf.high method
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 mpg disp -0.85 -8.75 9.38e-10 -0.923 -0.708 Pearson
#> 2 mpg hp -0.78 -6.74 1.79e- 7 -0.885 -0.586 Pearson
#> 3 mpg drat 0.68 5.10 1.78e- 5 0.436 0.832 Pearson
#> 4 wt disp 0.89 10.6 1.22e-11 0.781 0.944 Pearson
#> 5 wt hp 0.66 4.80 4.15e- 5 0.403 0.819 Pearson
#> 6 wt drat -0.71 -5.56 4.78e- 6 -0.850 -0.484 Pearson