Statistik-Saurus

Greek Debt Dramas Before the Global Financial Crisis (GFC) in 2008, the Greek had positive economic growth and it was considered high among countries in eurozone. Average economic growth reached almost four per cent between 1999 and 2007. Then the crisis hit in 2007 where housing bubble burst and made the subprime mortgage market in the United State collapsed. The crisis in the U.S. created a chain reaction which causing global banking crisis and credit crunch that lasts through 2009. The crisis made Lehman Brothers, big financial company, collapsed and the government in the United States and Europe prepared to bail out their banks. Greece failed to pay their huge debt since borrowing costs rose and financing dried up.  The financial crisis affected the Greek economy by reducing financial liquidity and business activity. Greece had been fortunate enough to face the crisis with the euro instead of its national currency, if they were using their national currency the crisis would

Nowadays, information and communication technology and the lifestyle of the citizen, especially in big cities, seem inseparable, including in Indonesia. Unlike countries in Europe, Indonesia is still in the process of going to digital oriented societies. Based on the latest research of Center of Innovation Policy and Governance (CIPG), Indonesia’s internet penetration growth is the highest in Asia which reaches 51 percent. Indonesia’s National Statistics Office also reports that Information and Communications Industry has been growing rapidly (more than 8% per year) for the last five years. We can see many Indonesian youths try to grasp this momentum by working or creating technology-based start-up companies. One of these Indonesia’s companies that has significant impact in the societies is called Gojek. Gojek, established in 2009, is Uber-like company that provides network services for hiring vehicle and delivering goods and services. This company quickly gains popularity sin

Developed countries are viewed as having high contribution of the Manufacturing Industry Sector (Bahasa: Sektor/Kategori Industri Pengolahan) in the economic structure (Gross Domestic Product). Most economists agree that a country can be viewed as developed country if the contribution of the Manufacturing Industry Sector reaches a total of 30 percent of the total Gross Domestic Product (GDP). What about Indonesia's Economy? BPS-Statistics of Indonesia (National Statistics Office), in January 2018, reported that Indonesia's economy in 2017 measured by Gross Domestic Product (GDP) at current prices reached Rp 13,588.8 trillion and GDP per capita reaches Rp 51.89 million or US$ 3,876.8. Indonesia's economy grew (c to c) 5.07 percent higher than the economic growth in 2016 (5.03 %). The highest growth was achieved by the Information and Communication Sector which was 9.81 percent. The structure of Indonesia's economy was dominated by three main sectors: Manufacturin

Shiny is an R package that makes it easy to build interactive web apps straight from R. You can host standalone apps on a webpage or embed them in R Markdown documents or build dashboards . (R Studio) This one below is an example of how useful Shiny for data visualisation: *Sudden changes starting in year 2010 caused by the implementation of new HDI method Codes for the above visualisation: library(shiny) library(googleCharts) library(dplyr) library(readxl) head(data) ## # A tibble: 6 x 6 ## woe_label Island year Grdpc HDI Population ## <fct> <fct> <dbl> <dbl> <dbl> <dbl> ## 1 Aceh Sumatera 2000 4995. 65.3 3930905 ## 2 North Sumatra Sumatera 2000 5848. 66.6 11649655 ## 3 West Sumatra Sumatera 2000 5388. 65.8 4248931 ## 4 Riau Sumatera 2000 5746. 67.3 4957627 ## 5 Jambi Sumatera 2000 3503. 65.4 2413846 ## 6 South Sumatra Sumatera 2000 4506.

Creating the Map In this article, I will try to explain how to make Indonesia Map in R. I will assume that you are already familiar with the basic codes in R. First, we need the required libraries : require (maps) #loading maps package require (mapdata) #loading mapdata package library(ggplot2) #ggplot2 package library(readxl) #package for read .xlsx file library(ggthemes) #package for ggplot2 theme library(ggrepel) #extendig the plotting package ggplot2 for maps Then, we prepare the data that contains the information of provinces name, latitude, and longitude of every province in Indonesia, e.g. : You can download the data in here:  Data Now open the file and create the polygon: setwd( "your file's path" ) #set your own directory mydata<- read _xlsx( "dummy.xlsx" ) #assign the data to "mydata" View(mydata) #view the data, notice the column of "latitude","longitude", "woe_label" glo

Grant Data : 500 Households that get Social Grant in a certain region, the data comes from Social and Economic Survey. Download the dummy data:  DATA grant <- read.csv( "grant.csv" ) #PFOODEXP: Proportion of Food Expenditure to Total Expenditure #HH_Income: Household Income ($) #HH_FOOD: Household Food Expenditure ($) #HH_Loc: Household Location (0: Rural, 1: Urban) #Educ_H: Household Head Education (Year) #HH_Size: Household Family Member #Gender_H: Household Head Gender (0: Female, 1: Male ) #Age_H: Household Head Age #Remove 1st column grant[ 1 ] <- NULL #change variable type to factor grant$Gender_H <- as.factor(grant$Gender_H) grant$HH_Loc <- as.factor(grant$HH_Loc) head(grant) ## PFOODEXP HH_Food HH_Loc Gender_H Age_H Educ_H HH_Size HH_Income ## 1 73.16933 675.1766 1 0 32 22 4 922.7590 ## 2 69.77417 596.2286 1 1 27 22 13 854.5119 ## 3 63.31992 585.4946 0 1 42 15