What is R?
R is a language and environment for statistical computing and graphics (https://www.r-project.org/l)

What is R used for?

• Basic and Advanced statistical analysis
  
• Machine learning algorithm
  
• Data Wrangling
  
• Data scraping
  
• Data visualization and Dashboard
  
• Make a Report or Presentation (pdf, word, ppt)

Why use R ?

• Most comprehensive statistical analysis tools
• Open source
• Supports various data types
• Powerful graphics
• Continuously growing

Disadvantages of R

• Need time for coding
  
• Some Packages may be of Poor Quality

R vs RStudio vs RPackage

• R : A programming language.
  
• RStudio : An IDE for using R.
  
• R-Package : A collection of script (function, dataset) used to do a specific task.

Installing R

• Download R via https://cran.r-project.org/bin/windows/base/
• Download RStudio via https://www.rstudio.com/products/rstudio/download/

RStudio

• 01 : Script file, contains our code.
  
• 02 : All objects we have defined in the environment.
  
• 03 : Help files and plots.
  
• 04 : The console, to type commands and display results.

R-Package

• Can be accessed and also contribute on https://cran.r-project.org/
  
• Now, there are 17525 available packages.
  
• Install package and load package

# install.packages("survey")
library(survey)

Most common package on R:

• tidyverse : data exploration, data wrangling
• ggplot2 : data visualization
• shiny : make a dashboard
• survey : analysis of complex survey samples

Variables and Data Structure

• Variable

number = 1
name <- "Novia Permatasari"

• Vector

num_vect <- c(1,2,3,4)
num_vect

## [1] 1 2 3 4

• DataFrame

BMI <-  data.frame(
   gender = c("Male", "Male","Female"), 
   height = c(152, 171.5, 165), 
   weight = c(81,93, 78),
   Age = c(42,38,26)
)
BMI

##   gender height weight Age
## 1   Male  152.0     81  42
## 2   Male  171.5     93  38
## 3 Female  165.0     78  26

• Another data structure: matrix, list, array

Tabulation

## [1] "Banyaknya Penduduk Berumur 5 Tahun ke Atas menurut Karakteristik dan Status Pendidikan, 2020"

##   Jenis Kelamin 02 Tidak / belums ekolah     03 SD   04 SMP 05 SMA keatas
## 1             1                 3270.072 10384.928 6146.938      5735.927
## 2             2                 3618.676  8769.409 4430.642      8604.462
##   06 Tidak bersekolah lagi
## 1                 72952.29
## 2                 69074.83

## [1] "Persentase Penduduk Berumur 5 Tahun ke Atas menurut Karakteristik dan Status Pendidikan, 2020"

##   Jenis Kelamin 02 Tidak / belums ekolah     03 SD   04 SMP 05 SMA keatas
## 1             1                 3.320202 10.544128 6.241170      5.823858
## 2             2                 3.829367  9.279993 4.688609      9.105442
##   06 Tidak bersekolah lagi
## 1                 74.07064
## 2                 73.09659

Data Visualization

Another visualization : https://www.r-graph-gallery.com/

Dashboard

https://gallery.shinyapps.io/nz-trade-dash/?_ga=2.132804878.1015659636.1620275477-171881207.1614696815

Direct Estimation - Complex Sampling

##   Jenis Kelamin Tidak Sekolah (Proporsi)          SE   CI LOWER   CI UPPER
## 1             1               0.03320202 0.006364916 0.02072702 0.04567703
## 2             2               0.03829367 0.005958246 0.02661572 0.04997162
##         RSE  RSE (%)          VAR      DEFF
## 1 0.1917027 19.17027 4.051215e-05 1.2415514
## 2 0.1555935 15.55935 3.550069e-05 0.9309919

Statistical Method for Analysis

• Statistical Analysis
• Forecasting
  
• Small Area Estimation

Persentase Penduduk Berumur 5 Tahun ke Atas menurut Karakteristik dan Status Pendidikan, 2020

Preparation

• R and R Studio
  
• Package :
  • tidyverse (data preparation)
    
  • ggplot2 (visualization)
    
  • survey (direct estimates)

# install.packages("tidyverse")
# install.packages("ggplot2")
# install.packages("survey")

base function

• sum(), mean(), max(), min() : basic statistics
  
• plot(), points(), lines(), hist() : visualization
  
• xtabs(), proportions() : make a contigency table

tidyverse

• ‘%>%’ : to show sequence of operators
  
• select() : select variables
  
• filter() : filter records / rows
  
• mutate() : compute and add new variable
  
• group_by() & summarise() : aggregate data
  
• left_join(), right_join(), inner_join() : join table

ggplot2

• ggplot() : define data and variables
  
• geom_bar() : make a bar chart
  
• geom_line() : make a line chart
  
• geom_scatter() : make a scatter plot

survey

• svydesign() : define survey design (psu, ssu, strata, weight, data)
  
• svyby() : get direct estimates