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fastStat (version 1.4)

Faster for Statistic Work

Description

When we do statistic work, we need to see the structure of the data. list.str() function will help you see the structure of the data quickly. list.plot() function can help you check every variable in your dataframe. table_one() function will make it easy to make a baseline table including difference tests. uv_linear(), uv_logit(), uv_cox(), uv_logrank() will give you a hand to do univariable regression analysis, while mv_linear(), mv_logit() and mv_cox() will carry out multivariable regression analysis.

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Version

Install

install.packages('fastStat')

Version

1.4

License

GPL-3

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Maintainer

Last Published

September 17th, 2020

Functions in fastStat (1.4)

cor_sig_star

Correlation Analysis with Sinificant and Correlation Value
digital

Set Digital Number
list.NA

Return Na Count and Percentage
cor_sig

Correlation Analysis with Signicant Values
cor_star

Correlation Analysis
list.factor

Return All Factor Variables
list.numeric

Return All Numeric Variables in A Dataframe
cor2

Correlation Analysis
list.plot

Scatter Plot for Single Value
survdiff_p.value

Extract P Value after survdiff() function
table_one

Get Summary Table
to.factor<-

Set Factor Class
to.factor

Set Factor Class
to.labels<-

Give Labels to Factor
round<-

Change the Digital for Double
mv_cox

Multivariable Logistic Regression
survSum

Calculate Survival Rate and Time
mv_linear

Multivariable Linear Regression
to.refer<-

Set Refer for Factor
to.labels

Give Labels to Factor
to.refer

Set Refer for Factor
list.str

Structure for Data
list.summary

Summary for Data
to.numeric<-

Change to Numeric Form
to.numeric

Change to Numeric Form
mv_logit

Multivariable Logistic Regression
uv_cox

Looping for Univariable Cox Regression
uv_logit

Looping for Univariable Logistic Regression
uv_linear

Looping for Univariable Logistic Regression
normal

Normal Distribution Test
idi

Perform IDI for logistic and cox regression
uv_logrank

Looping for logrank Regression