In this post I will analyze OECD Social spending data using R.
OECD (2022), Social spending (indicator). doi: 10.1787/7497563b-en (Accessed on 26 November 2022)
This indicator is measured as a percentage of GDP or USD per capita.
From the web site, I downloaded CSV file as below.
Before loading the CSV file, I load some packages such as tidyverse and so on.
I loaded tidyvese, moderndive, infer and gridExtra packages. tidyverse is de-facto standard package in R for data science. I load moderndive package and infer package because I am learning Statistical Inference via Data Science (moderndive.com)
I loaded gridExtra package to display multiple plots at one screen.
Now, let's load the CSV file data with read_csv() function.
Then, using glimpse() function, let's check if data is correctly loaded.
Nice! The CSV file data is loaded succesfully into R.
There are 7 variables in the dataframe, let's see each variables one by one.
LOCATION is ISO country code, I see USA(United States of America) has the most observations.
INDICATOR has no variation, has just one value, SOCEXP. So, I will remove INDICATOR.
According to OECD web site below, PUB means Public, PRIV means Privae, PUBNET means Public net and TOTNET means Total net. We see PUB has the most observations.
PC_GDP means a percentage of GDP, USD_CAP means ISD per capita.
There is no variation in FREQUENCY, then I remove it.
TIME means year, I see 2017 has the most observations, 190.
There is no NA in Value.
That's it. Thank you!
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