_DescriptiveStatistics.Rmd

# My R Codes For Data Analysis 


## Descriptive Statistics


```
{r eval=FALSE, include=FALSE, echo=TRUE}
Epi::stat.table(gender,mean(age), data = scabies)

```

```
{r eval=FALSE, include=FALSE, echo=TRUE}
table <- Epi::stat.table(gender,mean(age), data = scabies)

pander::pander(table)


```

```
{r eval=FALSE, include=FALSE, echo=TRUE}
#Tabulate, by gender, the mean age from the scabies dataset

Epi::stat.table(gender,list(mean(age),median(age)), data = scabies)
```

```
{r eval=FALSE, include=FALSE, echo=TRUE}
summary_data <- arsenal::tableby(gender~age+scabies_infestation,data=scabies)
summary(summary_data)

```


## skimr

https://cran.r-project.org/web/packages/skimr/vignettes/Using_skimr.html

```
{r eval=FALSE, include=FALSE, echo=TRUE}
require(skimr)
```

```
{r eval=FALSE, include=FALSE, echo=TRUE}
summary(iris)
```

```
{r eval=FALSE, include=FALSE, echo=TRUE}
summary(iris$Sepal.Length)
```


```
{r eval=FALSE, include=FALSE, echo=TRUE}
fivenum(iris$Sepal.Length)
```


```
{r eval=FALSE, include=FALSE, echo=TRUE}
summary(iris$Species)
```


```
{r eval=FALSE, include=FALSE, echo=TRUE}
skim(iris)
```


```
{r eval=FALSE, include=FALSE, echo=TRUE}
iris_results <- skim(iris)
str(iris_results)
iris_results$variable
iris_results$type
```


```
{r eval=FALSE, include=FALSE, echo=TRUE}
skimr::skim(iris) %>%
  dplyr::filter(stat ==  mean )
```

```
{r eval=FALSE, include=FALSE, echo=TRUE}
head(iris_results, n=15)
```



```
{r eval=FALSE, include=FALSE, echo=TRUE}
mtcars %>%
  dplyr::group_by(gear) %>%
  skim()
```



```
{r eval=FALSE, include=FALSE, echo=TRUE}
skim(iris, Sepal.Length, Species)
```


```
{r eval=FALSE, include=FALSE, echo=TRUE}
skim(iris, starts_with( Sepal ))
```



```
{r eval=FALSE, include=FALSE, echo=TRUE}
skim(datasets::lynx)
```


- Exploratory Data Analysis in R (introduction)


https://blog.datascienceheroes.com/exploratory-data-analysis-in-r-intro/


```
{r eval=FALSE, include=FALSE, echo=TRUE}
library(tidyverse)
library(summarytools)
# library(funModeling) 
library(tidyverse) 
library(Hmisc)

basic_eda <- function(data)
{
  glimpse(data)
 # df_status(data)
 # freq(data) 
 # profiling_num(data)
 # plot_num(data)
  describe(data)
}

basic_eda(irisdata)
```



- **What's so hard about histograms?**

http://tinlizzie.org/~aran/histograms/






# DataExplorer




# Webinar: Tidyverse Exploratory Analysis (Emily Robinson)


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https://hookedondata.org/the-lesser-known-stars-of-the-tidyverse/

https://www.rstudio.com/resources/videos/the-lesser-known-stars-of-the-tidyverse/

https://github.com/robinsones/robinsones_blog/blob/master/content/post/multipleChoiceResponses.csv

https://github.com/robinsones/robinsones_blog/blob/master/content/post/2018-11-16-the-lesser-known-stars-of-the-tidyverse.Rmd



# I “only” use R for descriptive stats — and that’s OK

https://rforeval.com/descriptive-stats-r/


# histograms

http://tinlizzie.org/histograms/