charpter_9_supplymentary.Rmd

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## install.packages
```{r cars}
install.packages("easystats")
#加载easystats运行会需要到的辅助文件，确保运行不会出问题
easystats::install_suggested()
library(easystats)
```

##datawizard
```{r}
###Data manipulation
##选择、过滤、筛选等操作
mtcars
data_match(mtcars, data.frame(vs = 0, am = 1))
data_filter(mtcars, vs == 0 & am == 1)
data_extract(mtcars, "gear")

###Statistical transformations数据转换
##标准化数据
summary(swiss)
summary(standardize(swiss))
##数据旋转或转置
data_rotate(swiss)

##各个值与均值的差，离均差
center(anscombe)
##描述性摘要
data(iris)
describe_distribution(iris)

```


##correlation
```{r pressure, echo=FALSE}
results <- correlation(iris)
results
summary(results)
#可通过see包来进行可视化
library(tidyverse)
results %>%
  summary(redundant = TRUE) %>%
  plot()

#计算其它的相关
iris %>%
  correlation(partial = TRUE) %>%
  summary()

```

##parameters
```{r}
#Classical Regression Models
model <- lm(Sepal.Width ~ Petal.Length * Species + Petal.Width, data = iris)
model_parameters(model)

```

##performance
```{r}
#Linear model
m1 <- lm(mpg ~ wt + cyl, data = mtcars)
model_performance(m1)
#Logistic regression
m2 <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
model_performance(m2)
#Linear mixed model
library(lme4)
m3 <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
model_performance(m3)
#Models comparison
compare_performance(m1, m2, m3, rank = TRUE, verbose = FALSE)
```

## report

```{r pressure, echo=FALSE}
model <- glm(vs ~ mpg * drat, data = mtcars, family = "binomial")
report(model)
```

##Citation
```{r}
citation("easystats")
citation("datawizard")
```
```

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.