Visualizing the Average Life Expectancy by Country from 1950 – 1975

Charts & Graphs, Lab Reports, Visualization


Life expectancy is the average number of years in which a person is expected to live. The expectancy can be measured based on multiple significant factors that play roles in a human’s wellbeing; for instance, gender, genetics, access to health care, hygiene, diet and nutrition, exercise, lifestyle, and crime rates.

Why is measuring a country’s life expectancy important? Measuring people’s life expectancy in a country can allow us to compare it by generations — what has changed and why it changed, and also, allows us to analyze trends throughout the history and to be able to predict how populations will age, resulting in information and preparation on provisioning services and support for the people. Moreover, by having a visual record of life expectancy from back in the days can allow people to learn and be reminded of significant events that lead to the result of life expectancy at that time period.

Data Collection

As mentioned above, analyzing life expectancy from the past, present, and future gives you insights on how life quality and conditions had changed comparing to today and what can be expected in the future. Personally, I found this fascinating as life expectancy is tied to multiple historical events. For example, when I was looking through multiple life expectancy datas from different sites, I found that the life expectancy of China shows a significant drop from 1958 to 1963; therefore, I did a research to find out what happened between those years; as a result, I found that during 1959 to 1961, China was in the middle of the world’s largest famine where 30 million Chinese starved to death and about the same number of births were lost or postponed. (Smil, 1999) For that reason, the life expectancy during those years dropped significantly.

I chose to create a life expectancy visualization from 1950 to 1975 as I want to include Vietnam war, which happened in between the years of 1955 to 1975. I got the dataset of life expectancy by countries from Kaggle. This dataset includes the life expectancy record from 1800 to 2016 of 15 countries — Australia, Brazil, Canada, China, France, Germany, India, Italy, Japan, Mexico, Russia, Spain, Switzerland, United Kingdom, and United States. However, this dataset does not include Vietnam and as mentioned before, I wanted to add Vietnam from the years of 1955 to 1975 to create a visualization that shows a significant drop during those years. After researching, I could not find any dataset of the life expectancy of Vietnam before 1950. I found the life expectancy of Vietnam from 1950 to 2023 dataset on so I decided to focus on the life expectancy of all 16 countries from 1950 to 1975. I added the data of Vietnam into the original dataset from Kaggle and keeping only the record from 1950 to 1975.

Tools and Materials

I downloaded the csv file from Kaggle and uploaded it to OpenRefine. OpenRefine filtered the elements that had to be changed and eliminated some blank cells, rows, or columns. I also used OpenRefine to select the years I want to focus on while deleting the others. After having an efficient dataset, I went ahead and downloaded it as an excel and added the Vietnam life expectancy data into it. After making sure the dataset of the life expectancy of 16 countries is completed, I then uploaded it to Tableau Public. Tableau Public allows me to turn the dataset into visualizations, such as line graph, map, bar chart, and more.

Visualization and Findings

After uploading the dataset to Tableau Public, I decided to create 4 different visualizations, each has its own purpose and highlights different elements of the dataset. First, I created a line graph where all 16 lines that represent the trend of the countries’ life expectancy are visible in one plane. This type of visualization allows viewers to compare the trends with each other; for example, seeing China with a generally higher life expectancy compared to India; however, drops dramatically at one point which resulted in a much lower life expectancy than India from 1959 (38.40) to 1962 (44.50). The look of these lines informs viewers that while India’s life expectancy increases over time, China’s life expectancy somehow fluctuates and then dramatically dropped from 1958 to 1961. I was curious of what was the cause of this dramatic drop in China’s life expectancy; therefore, I did a research and found out that it was the years of The Great Chinese Famine in 1959 to 1961, which is “the deadliest famine and one of the greatest man-made disasters in human history,” (Wikipedia) The death toll due to starvation ranges in tens of millions (15 – 55 million). (Wikipedia).

China (orange line) drops dramatically from 1959 to 1962 while India (green line) increases continuously over time.
The line graph where the year is on the x-axis and the average life expectancy in years is on the y-axis. Each country has its own color and are all visible in the same plane for comparison.

Second, I created another line graph where each country’s life expectancy represented with line is in its own separated plane. This type of visualization allows viewers to see clearly how each country’s life expectancy trend looks like on its own. I also added the gradient feature where the lower the life expectancy, the lighter of red the line is in which it gradually gets darker as the life expectancy increases. From this visualization, one could look at Vietnam’s life expectancy line and see a dramatic drop in 1968 to 1973, that decrease was caused by the Vietnam War back from 1955 to 1975.
The line graph where the x-axis represents country and y-axis represents the average life expectancy in years. The red color on the lines fade as the life expectancy number decrease.

The third visualization I created was the bar chart where it shows the average number of each country’s life expectancy. This visualization shows viewers which country has the highest and which has the lowest life expectancy as well as comparing the average life expectancy among the 16 countries. The visualization indicated that Switzerland has the highest average life expectancy of 71.73, followed by Canada with an average life expectancy of 71.21. India has the lowest average life expectancy of 42.08, followed by China with the average life expectancy of 51.56.
The bar chart where the x-axis represents the country and y-axis represents the average life expectancy in year.

Finally, the fourth visualization is a geographic map which shows the 16 countries filled with blue color. The higher the total of life expectancy years, the darker the color and vice versa. This visualization also has the countries labeled on the map along with the average life expectancy years.
The geographic map filled the countries with the shade of blue based on the number of total life expectancy number.

Peer Critique

Before finalizing my visualization, I created my bar chart in which each country has its own color. I was told by my critique that having multiple colors in a chart can be overwhelming and is not necessary since the country is clearly labeled on the x-axis. I took my critique’s advice and made all the bars in green. I also struggled to pick the dates in which I would like to focus on since the dataset contains life expectancy from 1800 to 2016. I originally wanted to include the years in which these significant events happened — World War I, World War II, The Cold War, and Vietnam war; however, I was not able to find the dataset of life expectancy of Vietnam before 1950 and I wanted to have Vietnam in my data; thus, I decided to focus on life expectancy between the years of 1950 to 1975 and combine two datasets together.


I found this project enjoyable and useful in both visualizing skills and also the knowledge on life expectancy of countries. After analyzing and visualizing the datasets of life expectancy by country, I have learned why it is important to analyze and study the life expectancy trends from the past, present, and future — it is one of the ways to catch on the factors that lead to the low life expectancy as well as the past patterns; therefore, being able to prevent them from happening and also, future predictions will allow people to prepare the services and support for the people. Moreover, what I found fascinating was how the dataset of life expectancy leads me into learning more about significant historical events. I did not know about the deadly famine in China during the years of 1959 to 1961. I saw that the trend dropped dramatically for China; for that reason, I was curious and did my research. Most importantly, analyzing these datasets made me realize the importance of visualizations as it holds so much historical information and future predictions. Data visualization is significant for almost every career from computer scientists to business owners. I believe everyone can benefit from data visualization in which we come across them everyday whether we realize it or not.