corrly

Corrly is a Data Visualization Package For Manufacturing Process.

Install Corrly

# install.packages("devtools")
# devtools::install_github("kmaheshKulkarni/corrly")

The package tests the use of htmlwidgets in pkgdown:

• Home page

correlation Coeficient using kendall Method

library(corrly)
corr_coef_kendall(variable1= mtcars$cyl, variable2=mtcars$carb, decimal = 2) 
#> [1] 0.47

correlation Coeficient using pearson Method

library(corrly)
corr_coef_pearson(variable1= mtcars$disp, variable2=mtcars$hp, decimal = 2)
#> [1] 0.79

correlation Coeficient using spearman Method

library(corrly)
corr_coef_spearman(variable1= cars$speed, variable2=cars$dist, decimal = 2)
#> [1] 0.83

Correlation Scatter Plot

library(plotly)
library(corrly)
spearman<- corr_coef_spearman(variable1= cars$speed, variable2=cars$dist, decimal = 2)
corr_scatterly(data=cars,x=cars$speed,y=cars$dist,corr_coef=spearman,xname="speed",yname="dist")

Correlation Matrix Plot

library(plotly)
library(corrly)
matrixly(data = mtcars)

Autocorrelation

library(plotly)
library(corrly)
series<- c(mtcars$gear, mtcars$carb)
acf_ly(series)

Partial Autocorrelation

library(plotly)
library(corrly)
series<- c(mtcars$mpg, mtcars$hp)
pacf_ly(series)

Cross Correlation

library(plotly)
library(corrly)
ccfq<- c(mtcars$mpg, mtcars$disp)
ccfa<- c(mtcars$gear, mtcars$carb)
ccf_ly(series1=ccfq, series2=ccfa)

I Chart

library(plotly)
library(corrly)
cars<- cars
ichart_ly(cars, x = cars$speed, y = cars$dist, xname = "mpg", yname = "hp")

Normal Distribution Chart

library(plotly)
library(corrly)
normal_distribution(data = mtcars, parameter = mtcars$mpg, xname = "MPG")