#install.packages("readxl")
library("readxl")
TUIK <- read_excel("C:\\Users\\ryigi\\Desktop\\Data-Analysis-Project\\TUIK-Weighted-Expenditure.xlsx")
#Then to save it as .RData
save(TUIK, file = "TUIK.RData")
load("TUIK.RData")
#And for US Expenditure Groups dataset
US_Data <- read_excel("C:\\Users\\ryigi\\Desktop\\Data-Analysis-Project\\US-Weighted-Expenditure.xlsx")
save(US_Data, file = "US_Data.RData")
load("US_Data.RData")Data
Our Objective
In this project, our primary focus will be on analyzing the Consumer Price Index (CPI) data sourced from the TUIK page. The CPI basket, which constitutes the basis of our study, is a representation of the consumption patterns of an average household. This basket encompasses various main expenditure groups, such as food, clothing, and health, each assigned a specific weight percentage. The measurement of inflation, or CPI, is derived from the price changes within these main groups, and our goal is to study into the shifts in the main expenditure groups’ weights in the CPI basket in Turkey over the years, incorporating both economic and sociological perspectives.
We have chosen to base our sociological analysis on the US CPI data, considering the United States as a developed country. To make our sociological assessments more understandable and consistent, we will use the US CPI as a benchmark. The data for analysis will be taken from the TUIK’s annual updates to the basket weights, specifically focusing on the December data of each year.
Our objectives in this project involve creating comprehensive tables and analytical tools based on the December data, considering its annual update. These tables and tools will be essential for both data visualization and the exploration of topics in other related courses. Through the use of weight data from the main expenditure groups in the CPI basket, we want to do sociological and economic analyses, comparing Turkey and the US.
With the help of this research, we hope to get an in-depth view of the variables affecting inflation and provide knowledge to the discussion on social and economic dynamics in Turkey. We will do this by interpreting and comparing the US CPI information.
Data Import Process
We started editing our data set by downloading the weighted expenditure group analysis from TUIK’s website. Then, we rearranged the relevant Excel sheet according to the data of December 2011-2022 to make it more understandable and sensitive.
The first and most important step to star our analysis process is to transfer our data set to R. To do this, we first needed to download the readxl package on R. After determining the file path of our document, we transferred our Excel document to the R environment with the read_excel function. Lastly, to convert our file format .xlsx to .RData, we used save function, and loaded our TUIK.RData document.
You can reach our TUIK dataset as .RData from here, and also you can reach it as .xlsx from here. And for US Dataset, you can reach it as RData from here, and as .xlsx from here
Exploratory Data Analysis
#To learn how many column and row our dataset have:
load("TUIK.RData")
ncol(TUIK)[1] 3nrow(TUIK)[1] 144load("US_Data.RData")
ncol(US_Data)[1] 3nrow(US_Data)[1] 120When we review TUIKdata set, we can observe that it contains 3 columns and 144 rows. While the columns of our dataset contain date, category, and weight percentage. The dates are between 2011-2022, and the categories represent expenditure groups. If we examine the expenditure groups in our data set: Food and non-alcoholic beverages, Alcoholic beverages and tobacco, Clothing and footwear, Housing water electricity gas and fuels, Furnishing household equipment routine maintenance of the house, health, transportation, communucation, Recreation and culture, education, Hotels cafes and restaurants, and lastly Miscellaneous goods and services.
When we review US data set, we can observe that it contains 3 columns and 120rows. While the columns of our dataset contain date, category, and weight percentage. The dates are between 2011-2022, and the categories represent expenditure groups. If we examine the expenditure groups in our data set: Food and non-alcoholic beverages, Alcoholic beverages and tobacco, Clothing and footwear, Housing water electricity gas and fuels, health, transportation, communucation, Recreation and culture, education, and lastly Miscellaneous goods and services.
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