This week we tried something new yet essential: analyzing and visualizing cold data. During a short lecture done by Dr.Winton, we gained valuable insight on how to present data in an efficient and powerful way, and thus I had a first try on data analysis of popular New Zealand baby names, based on the website RAWGraphs.
Basic parts of the visualization
In my visualization, I created the sum of the counts of all names present, in a ten-year period. The names are then put into a bar graph, ordered in total number of name counts in the ten years. A bar graph is appropriate because it graphically shows the frequency a name is given. It seems that, the most popular name is the boy’s name Jack (which totaled near 4500 counts), and the least popular name is the female name Amelia (which totaled about 200 hundred counts, but still is the top 20 popular name). What we can learn from this analysis is that popular male names are “more popular” than female names, which probably means that the standard deviation of total counts of female names are much smaller than that of males. Those observations may seem insignificant, but such conclusions could not be reached without such a method. My graph also points out that New Zealand is dominated by English descendants (since its newborn babies have mostly English names) and has a relatively tiny population (since the most popular name only numbered 4500 in a ten-year period).
Improvements I made about the visualization style
First, I ordered the names in a descending order of total counts. Besides, I used 2 different colors to showcase different genders of those names, males in orange and females in blue (which is a bit obvious, honestly).
Reflection on the lecture
I feel that while data visualization is a great tool, it could significantly amplify readers’ emotions or even distort data to a certain level. So, the problem is, when analyzing data, what is the boundary between making things clearer or distorting the truth? Do we need trained professionals to tackle this issue?
4 thoughts on “Analyzing data using software”
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This is a neat way to visualize this data! Bringing attention to the overall sum rather than breaking it down by year truly underscores the popularity of the name Jack. Interestingly though, at first glance, it also makes it appear as though there’s a huge imbalance in the gender ratio of babies born over this ten-year period.
I liked looking at your data visualization because it is interesting to me how your graph presents such different ideas than my line graph did. I think the bar graph truly emphasizes the sums rather than the patterns, which is an important perspective to understand. I also thought the way you focus on gender is interesting, especially with the way it seems to show so many more males with popular names. This brings up a lot of interesting questions that can be researched based on this data, and I think that is an important trait of data visualization.
This is a creative visualization! I chose to represent the “stories” in the data differently, as I evaluated the popularity of names over time, rather than as a sum. I wonder if you were to split the male and female names into two different graphs, it might control for the difference in the number of female vs male babies born over the 10 years we looked at, so it would be easier to see trends in each separate set of data. I think it’s a matter of preference, it also adds something to see everything on one graph.
I really like the bar graph you made that shows the total count of popular baby names in New Zealand. It is very easy to look at which is a huge plus for data visualizations in my book. I also really like the questions you brought up in your reflection. I thought about some similar topics in my reflection so it was nice to see someone else had similar questions. Although I won’t claim to have a definite answer to either of the questions, I think having trained professionals to tackle the issue would be very beneficial.