#Preliminaries:
knitr::opts_chunk$set( message=FALSE, warning=FALSE) #echo = FALSE,

rm(list=ls())
library(readxl)
library(tidyverse)
library(viridis)

Introduction

In our rapidly evolving digital age, Mark Twain’s famous quip, “I only believe in statistics that I doctored myself”, takes on new significance. As the amount and accessibility of data continue to expand, the challenge of transforming raw data into actionable insights becomes increasingly vital. This informational revolution is equally relevant to business decisions, policy making, and empowering people to navigate an information-saturated world. The good news is that we can now make more evidence-based decisions, thanks to the decreasing barriers of data availability (e.g. this blog post or ICPSR), advancements in software and hardware, and the democratization of data analysis tools supported by the open source movement. However, the challenge remains: How do we separate valuable insights from the noise? The answer lies, in part, in fostering data literacy and transparency.

Data Exploration

Let’s take the first step together by demonstrating how you can turn data into valuable insights using just a few lines of R code, to uncover intriguing patterns in global happiness. We’ll use data from the World Happiness Report 2023, which compiles self-reported happiness data from various countries. We first download and read the data

if (file.exists("DataForTable2.1WHR2023.xls")) {
  
  data_in <- readxl::read_excel("DataForTable2.1WHR2023.xls")
  
} else {
    
  download.file("https://happiness-report.s3.amazonaws.com/2023/DataForTable2.1WHR2023.xls", "DataForTable2.1WHR2023.xls", mode = 'wb') #adjust 'mode' if not running on windows machine
  
  data_in <- read_excel("DataForTable2.1WHR2023.xls")
  
}

#show data: 
head(data_in)

and with the aid of the popular R-tidyverse framework, we’ll manipulate the data to explore regional and developmental trends in average perceived happiness across the globe. Our analysis focuses on information from year 2010 onward and we order the 163 countries by their “happiness index”, averaged over time. For data visualization we use the library ggplot2.

# prepare data:
data_plot <- data_in %>%
  filter(year >= 2010) %>%
  group_by(`Country name`) %>%
  mutate(happiness_mean=mean(`Life Ladder`), n=n()) %>% #mean happiness by country
  ungroup() %>%
  mutate(rank=rank(happiness_mean))  %>% #create happiness rank across countries
  mutate(year=as.factor(year), `Country name`=fct_reorder(factor(`Country name`), rank)) #order countries by average happiness

# create plot:
data_plot %>%
  ggplot(aes(y=`Country name`, x=year,  fill=`Life Ladder`)) + # graph
  scale_fill_viridis(option="inferno") + #colour
  geom_tile(colour="white") +            #background colors
  theme_minimal(base_size = 7) +
  theme(axis.text.x = element_text(angle = 45, hjust=1)) +
  labs(title="Hierarchy and Development of Happiness", fill="Happiness Score",
       y="Country", x="Year")  

The resulting heatmap, inspired by Healy (2018), is showing the world’s happiness across countries and over time. Nordic countries consistently rank high, while many African and Oriental countries trend toward the bottom. We also see that from 2020 there are more white spaces across the world. Is this phenomenon linked to the COVID-19 pandemic? Examining differences, changes, extreme values, and missing values can be a rich source of insights when exploring a data set.

Data Science in Action

The data journey doesn’t stop at mere observation: Knowledge of programming, modeling, and statistical communication provides us with a range of skills from data wrangling and visualization to predicting outcomes for new instances (eg. Kuhn and Johnson (2013)), and assessing causal relationships (eg. Pearl and Mackenzie (2018)). These empower us to enhance business decision-making, create new data-based products, tell stories with data, and much more.

In the spirit of this discussion, this website gives some examples of reproducible data analytics using various real-world data, which may be interesting to you, whether you are new to data science or a seasoned professional. Your input helps to improve the content. Explore the case studies on this webpage, and share your feedback via email, or connect with me on LinkedIn.

References

Franconeri, Steven L, Lace M Padilla, Priti Shah, Jeffrey M Zacks, and Jessica Hullman. 2021. “The Science of Visual Data Communication: What Works.” Psychological Science in the Public Interest 22 (3): 110–61.
Garnier, Simon, Ross, Noam, Rudis, Robert, Camargo, et al. 2021. viridis - Colorblind-Friendly Color Maps for r. https://doi.org/10.5281/zenodo.4679424.
Gelman, Andrew, Jennifer Hill, and Aki Vehtari. 2021. Regression and Other Stories. Cambridge University Press.
Healy, Kieran. 2018. “Visualizing the Baby Boom.” Socius 4: 2378023118777324.
Kahneman, Daniel. 2011. Thinking, Fast and Slow. Macmillan.
Kuhn, Max, and Kjell Johnson. 2013. Applied Predictive Modeling. Vol. 26. Springer.
Mousavi, Shabnam, and Gerd Gigerenzer. 2014. “Risk, Uncertainty, and Heuristics.” Journal of Business Research 67 (8): 1671–78.
Pearl, Judea, and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect. Basic books.
R Core Team. 2021. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Spiegelhalter, David. 2019. The Art of Statistics: Learning from Data. Penguin UK.
Watzlawick, Paul. 2018. Wie Wirklich Ist Die Wirklichkeit?: Wahn, Täuschung, Verstehen. Piper ebooks.
Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.
Wickham, Hadley, and Jennifer Bryan. 2019. Readxl: Read Excel Files. https://CRAN.R-project.org/package=readxl.