Add correlations from the correlation package in easystats
Source: R/grid-pipeline.R
add_correlations.RdAdd correlations from the correlation package in easystats
Usage
add_correlations(
.df,
var_set,
variables,
focus_set = NULL,
method = "auto",
redundant = TRUE,
add_matrix = TRUE
)Arguments
- .df
the original
data.frame(e.g., base data set). If part of set of add_* decision functions in a pipeline, the base data will be passed along as an attribute.- var_set
character string. Should be a descriptive name of the correlation matrix.
- variables
the variables for which you would like to correlations. These variables will be passed to
link[correlation]{correlation}. You can also use tidyselect to select variables.- focus_set
variables to focus one in a table. This produces a table where rows are each focused variables and columns are all other variables
- method
a valid method of correlation supplied to
link[correlation]{correlation}(e.g., 'pearson' or 'kendall'). Defaults to'auto'. Seelink[correlation]{correlation}for more details.- redundant
logical, should the result include repeated correlations? Defaults to
TRUESeelink[correlation]{correlation}for details.- add_matrix
logical, add a traditional correlation matrix to the output. Defaults to
TRUE.
Value
a data.framewith three columns: type, group, and code. Type
indicates the decision type, group is a decision, and the code is the
actual code that will be executed. If part of a pipe, the current set of
decisions will be appended as new rows.
Examples
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr 1.1.4 ✔ readr 2.1.5
#> ✔ forcats 1.0.0 ✔ stringr 1.5.1
#> ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
#> ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
#> ✔ purrr 1.0.2
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(multitool)
the_data <-
data.frame(
id = 1:500,
iv1 = rnorm(500),
iv2 = rnorm(500),
iv3 = rnorm(500),
mod1 = rnorm(500),
mod2 = rnorm(500),
mod3 = rnorm(500),
cov1 = rnorm(500),
cov2 = rnorm(500),
dv1 = rnorm(500),
dv2 = rnorm(500),
include1 = rbinom(500, size = 1, prob = .1),
include2 = sample(1:3, size = 500, replace = TRUE),
include3 = rnorm(500)
)
the_data |>
add_filters(include1 == 0,include2 != 3,include2 != 2, include3 > -2.5) |>
add_variables("ivs", iv1, iv2, iv3) |>
add_variables("dvs", dv1, dv2) |>
add_variables("mods", starts_with("mod")) |>
add_correlations("predictors", matches("iv|mod|cov"), focus_set = c(cov1,cov2))
#> # A tibble: 18 × 3
#> type group code
#> <chr> <chr> <chr>
#> 1 filters include1 "include1 == 0"
#> 2 filters include1 "include1 %in% unique(include1)"
#> 3 filters include2 "include2 != 3"
#> 4 filters include2 "include2 != 2"
#> 5 filters include2 "include2 %in% unique(include2)"
#> 6 filters include3 "include3 > -2.5"
#> 7 filters include3 "include3 %in% unique(include3)"
#> 8 variables ivs "iv1"
#> 9 variables ivs "iv2"
#> 10 variables ivs "iv3"
#> 11 variables dvs "dv1"
#> 12 variables dvs "dv2"
#> 13 variables mods "mod1"
#> 14 variables mods "mod2"
#> 15 variables mods "mod3"
#> 16 corrs predictors_rs "select(matches(\"iv|mod|cov\")) |> correlation(…
#> 17 corrs predictors_matrix "select(matches(\"iv|mod|cov\")) |> correlation(…
#> 18 corrs predictors_focus "select(matches(\"iv|mod|cov\")) |> correlation(…