R: Tidy correlation coefficients for numeric dataframe columns

inspect_cor {inspectdf} | R Documentation

## Tidy correlation coefficients for numeric dataframe columns

### Description

Summarise and compare Pearson, Kendall and Spearman correlations for numeric columns in one, two or grouped dataframes.

### Usage

    inspect_cor(df1, df2 = NULL, method = "pearson", with_col = NULL, alpha = 0.05)

### Arguments

`df1` | A data frame.
`df2` | An optional second data frame for comparing correlation coefficients. Defaults to `NULL`.
`method` | a character string indicating which type of correlation coefficient to use, one of `"pearson"`, `"kendall"`, or `"spearman"`, which can be abbreviated.
`with_col` | Character vector of column names to calculate correlations with all other numeric features. The default `with_col = NULL` returns all pairs of correlations.
`alpha` | Alpha level for correlation confidence intervals. Defaults to 0.05.

### Details

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.

### Value

A tibble summarising and comparing the correlations for each numeric column in one or a pair of data frames.

### Examples

    # Load dplyr for starwars data & pipe
    library(dplyr)

    # Single dataframe summary
    inspect_cor(starwars)
    # Only show correlations with 'mass' column
    inspect_cor(starwars, with_col = "mass")

    # Paired dataframe summary
    inspect_cor(starwars, starwars[1:10, ])

    # NOT RUN - change in correlation over time
    # library(dplyr)
    # tech_grp <- tech %>%
    #         group_by(year) %>%
    #         inspect_cor()
    # tech_grp %>% show_plot()

[Package inspectdf version 0.0.11 Index]