In Alexandershemetev/alexandershemetev:

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(ExPanDaR)
library(knitr)
library(kableExtra)
library(devtools)
library(plm)# For datasets
#data("EmplUK", package="plm")
data("Produc", package="plm")
#data("Grunfeld", package="plm")
#data("Wages", package="plm")
install_github("Alexandershemetev/alexandershemetev")
library(alexandershemetev)

While the main purpose of the alexandershemetev package is to clarify working with R for people with very basic skills for the purposes of qualitative statistical analysis, this package also has additional useful functions and will be developed over time by adding new functions.

To see what alexandershemetev has to offer, let's take a quick tour. For more detailed guidance on how to use a specific function presented below, take a look at the respective function's help page.

Preparation

is designed for helping to newcomers in R with \the most basic functions to match the purposes of their statistical analysis. For this walk-through I will use the data set mtcars, which comes with the basic R environment. ``` {r variables}

alex_hello()

## Descriptive Statistics

```r
alex_dataframe_descriptive_table(mtcars, showdf = TRUE, showhtmlinR = TRUE)
t1_12_28_90_df

Clear environment but save data by pattern

The figure sizes have been customised so that you can easily put two images side-by-side.

a = 5
b = 6
c1 = a + b
df1 = c(1, 2, 3, 4)
gf1 = c(8,9,6,8)
ls()
alex_clean(except = c("a", "b", "c", "d"), pattern = "df")  
ls()
alex_clean()
ls()

Reveal NAs in the dataset

Sometimes you need to visualize: how many missing values you have in your data, especially in the panel-type data. Good visualization may always help you with this.

# using dataset Produc from plm package as an example 
data("Produc", package="plm")
Produc$test = ifelse(Produc$unemp > 5, Produc$unemp, NA) #Adding NAs occasionally to the dataset
alex_na_plot(Produc, ts_id = "year")

Conclusion

This is all there is (currently). All these functions are rather simple wrappers around established R functions. They can easily be modified to fit your needs and taste. Take look at the github repository of the alexandershemetev package for the codes. Have fun!

Further development

The full package is under construction now (this version is the very basic package). To install it use: - library(devtools) - install_github("Alexandershemetev/alexandershemetev") - library(alexandershemetev)

Alexandershemetev/alexandershemetev documentation built on Dec. 30, 2020, 9:47 p.m.