Codes for generating correlation analysis.^[See childRmd/_20correlation.Rmd
file for other codes]
# https://easystats.github.io/correlation/ # install.packages("devtools") # devtools::install_github("easystats/correlation") library("correlation")
correlation::correlation(iris)
library(dplyr) iris %>% select(Species, starts_with("Sepal")) %>% group_by(Species) %>% correlation::correlation() %>% filter(r < 0.9)
correlation::correlation(select(iris, Species, starts_with("Sepal")), select(iris, Species, starts_with("Petal")), partial=TRUE)
correlation(iris, bayesian=TRUE)
library(report) iris %>% select(starts_with("Sepal")) %>% correlation::correlation(bayesian=TRUE) %>% report()
report::report(cor.test(iris$Sepal.Length, iris$Petal.Length))
https://stat.ethz.ch/R-manual/R-patched/library/stats/html/cor.test.html
iris %>% group_by(Species) %>% correlation() %>% report() %>% to_table()
iris %>% explore(Sepal.Length, Petal.Length) iris$is_versicolor <- ifelse(iris$Species == "versicolor", 1, 0) iris %>% explore(Sepal.Length, Petal.Length, target = is_versicolor)
dlookr::correlate(carseats) dlookr::correlate(carseats, Sales, CompPrice, Income) dlookr::correlate(carseats, Sales:Income) dlookr::correlate(carseats, -(Sales:Income)) carseats %>% dlookr::correlate(Sales:Income) %>% dplyr::filter(as.integer(var1) > as.integer(var2)) carseats %>% dplyr::filter(ShelveLoc == "Good") %>% group_by(Urban, US) %>% dlookr::correlate(Sales) %>% dplyr::filter(abs(coef_corr) > 0.5)
dlookr::plot_correlate(carseats) dlookr::plot_correlate(carseats, Sales, Price) carseats %>% dplyr::filter(ShelveLoc == "Good") %>% dplyr::group_by(Urban, US) %>% dlookr::plot_correlate(Sales)
## Summary statistics by – overall with correlation SmartEDA::ExpNumStat( Carseats, by = "A", gp = "Price", Qnt = seq(0, 1, 0.1), MesofShape = 1, Outlier = TRUE, round = 2 )
# https://alastairrushworth.github.io/inspectdf/articles/pkgdown/inspect_cor_exampes.html inspectdf::inspect_cor(storms) inspectdf::inspect_cor(storms) %>% inspectdf::show_plot() inspectdf::inspect_cor(storms, storms[-c(1:200), ]) inspectdf::inspect_cor(storms, storms[-c(1:200), ]) %>% slice(1:20) %>% inspectdf::show_plot()
https://neuropsychology.github.io/psycho.R/2018/05/20/correlation.html devtools::install_github("neuropsychology/psycho.R") # Install the newest version remove.packages("psycho") renv::install("neuropsychology/psycho.R@0.4.0") # devtools::install_github("neuropsychology/psycho.R@0.4.0") library(psycho) <!-- library(tidyverse) --> cor <- psycho::affective %>% correlation() summary(cor) plot(cor) print(cor)
cor %>% report::to_values()
summary(cor) %>% knitr::kable(format = "latex") %>% kableExtra::kable_styling(latex_options="scale_down") ggplot(mydata, aes(x = tx_zamani_verici_yasi, y = trombosit)) + geom_point() + geom_smooth(method = lm, size = 1)
mydata %>% select(continiousVariables, -dateVariables) %>% visdat::vis_cor()
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