Summarise and compare Pearson, Kendall and Spearman correlations for numeric columns in one, two or grouped dataframes.
inspect_cor(df1, df2 = NULL, method = "pearson", with_col = NULL, alpha = 0.05)A data frame.
An optional second data frame for comparing correlation
coefficients. Defaults to NULL.
a character string indicating which type of correlation coefficient to use, one
of "pearson", "kendall", or "spearman", which can be abbreviated.
Character vector of column names to calculate correlations with all other numeric
features. The default with_col = NULL returns all pairs of correlations.
Alpha level for correlation confidence intervals. Defaults to 0.05.
A tibble summarising and comparing the correlations for each numeric column in one or a pair of data frames.
When df2 = NULL, a tibble containing correlation coefficients for df1 is
returned:
col_1, co1_2 character vectors containing names of numeric
columns in df1.
corr the calculated correlation coefficient.
p_value p-value associated with a test where the null hypothesis is that
the numeric pair have 0 correlation.
lower, upper lower and upper values of the confidence interval
for the correlations.
pcnt_nna the number of pairs of observations that were non missing for each
pair of columns. The correlation calculation used by inspect_cor() uses only
pairwise complete observations.
If df1 has class grouped_df, then correlations will be calculated within the grouping levels
and the tibble returned will have an additional column corresponding to the group labels.
When both df1 and df2 are specified, the tibble returned contains
a comparison of the correlation coefficients across pairs of columns common to both
dataframes.
col_1, co1_2 character vectors containing names of numeric columns
in either df1 or df2.
corr_1, corr_2 numeric columns containing correlation coefficients from
df1 and df2, respectively.
p_value p-value associated with the null hypothesis that the two correlation
coefficients are the same. Small values indicate that the true correlation coefficients
differ between the two dataframes.
Note that confidence intervals for kendall and spearman assume a normal sampling
distribution for the Fisher z-transform of the correlation.
# Load dplyr for starwars data & pipe
library(dplyr)
# Single dataframe summary
inspect_cor(starwars)
#> # A tibble: 3 × 7
#> col_1 col_2 corr p_value lower upper pcnt_nna
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 birth_year mass 0.478 0.00602 0.177 0.697 41.4
#> 2 birth_year height -0.400 0.0114 -0.625 -0.113 49.4
#> 3 mass height 0.134 0.316 -0.127 0.377 67.8
# Only show correlations with 'mass' column
inspect_cor(starwars, with_col = "mass")
#> # A tibble: 2 × 7
#> col_1 col_2 corr p_value lower upper pcnt_nna
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mass birth_year 0.478 0.00602 0.177 0.697 41.4
#> 2 mass height 0.134 0.316 -0.127 0.377 67.8
# Paired dataframe summary
inspect_cor(starwars, starwars[1:10, ])
#> # A tibble: 3 × 5
#> col_1 col_2 corr_1 corr_2 p_value
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 birth_year mass 0.478 0.150 0.348
#> 2 birth_year height -0.400 0.141 0.151
#> 3 mass height 0.134 0.866 0.00264
# NOT RUN - change in correlation over time
# library(dplyr)
# tech_grp <- tech %>%
# group_by(year) %>%
# inspect_cor()
# tech_grp %>% show_plot()