OECD Social spending data analysis 3 - Data Visualization with 5 Named Graphs (5NG) using R

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This post is following of above post.
In the previous post, I made a dataframe for data analysis, named 'df2'.
Now, let's start data analysis with data visualization.
I will make 5 Named Graphs (5NG) using R.
My text book is

Chapter 2 Data Visualization | Statistical Inference via Data Science (moderndive.com)

Let's start with 5NG #1, Scatter Plot.

geom_point() makes scatter plot. I see relatively positive relationship between priv_pc_gdp and pub_pc_gdp.

I see relatively positive relationship between priv_pc_gdp and pub_usd_cap too.

I see there is strong correlation between pub_pc_gdp and pub_usd_cap.

Next, Line Graph.

geom_line() makes line graphs. prive_pc_gdp are increasing in general.

pub_pc_gdp has more variation than priv_pc_gdp.

pub_usd_cap are increasing for almost all countires.


#3 5NG is histogram.

geom_histogram() makes histograms. I use facet_wrap(~ continent) to make histograms for each continent.

I see Europe has higher percentage than other continents about pub_pc_gdp.

Sounth America has the lowest pub_usd_cap distribution.

The 4th 5NG is Boxplot.

geom_boxplot() makes a boxplot. I use mutate() and reorder() to reorder continent by average value. So, I can easily see Notrh America has the highest average priv_pc_gdp.

I see Europe has the higest average pub_pc_gdp and the highest medain too.

Europe has the highest average and median value for pub_usd_cap.

The last 5NG is Barplot.
To make a barplot, we can use geom_bar() and geom_col() function.

geom_bar() automatically count number of observations and make barplot.

Before using geom_col() function, we need to calculate summary statistics data such as number of observations.

That's it. Thank you!

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