reflection4-mcaten-maddison-caten

week1.md

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+![users since launch](img/socialmedia.png)
+
+link: https://www.reddit.com/r/dataisbeautiful/comments/ssbk49/number_of_social_media_users_since_launch_oc/
+
+As an avid social media user myself, I found this data visualization very intriguing. This visual was created using user growth of popular social media platforms, with the zero point on the X-axis being the moment they launched. It is also based on monthly active users (meaning people that use each service at least once a month). This data was gathered from each platform's respective earnings reports. It is strange to see that Twitter is one of the oldest platforms of those in the visual, yet has the least amount of growth in terms of users because I feel like I see tweets the most online in the news and whatnot. I'm also not surprised that Tiktok has the fastest user growth out of all the platforms in the shortest amount of time (that app is so addictive). I feel like a lot of the younger population is on Tiktok now, and because it's newer it completely exploded over the past couple of years. I would think it would be cool to see the age breakdown on each of these platforms, since I feel like a lot of older people are using Facebook versus Instagram and Snapchat. I'm excited to see the growth that Tiktok will have in the future and if it will continue to grow at a huge rate. I would also think it would be cool to see the ratio of inactive accounts to active accounts on each of the respective platforms since platforms with more accounts might have more active users. I would also like to see Tiktoks growth more accurately since it rebranded from Musical.ly in 2017, therefore not starting with 0 users 4 years ago.

week2.md

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+![tooth fairy money by state](img/tooth-fairy.png)
+
+link: https://www.reddit.com/r/dataisbeautiful/comments/suoei2/how_much_the_tooth_fairy_pays_per_tooth_in_every/
+
+As a now-adult, the concept of the tooth fairy is really fascinating to me. How did it start? This data visualization represents the average amount of money children recieve from the tooth fairy in each state. The data for this visual was from a study conducted by the Dental Care Alliance, where they asked 1,218 parents across the United States what state they live in and how much money their children get for losing a tooth. The larger the tooth in each state, the more money children get from the tooth fairy. I was shocked to see that in Delaware the average is almost $9! As a kid I only got around $5 from what I remember, and it was interesting to see that Massachusetts is one of the states with the lowest amount the tooth fairy gives out. I think it would be even more interesting if the study asked for even more data, such as what year their children were born to see how the amount has changed over the years (if it has). I know some people born in the 70s and 80s got less than a dollar from their tooth fairy, so I'd like to see how much it changed. It would also be helpful if the study included how many respondants it had from each state. It claims to have more than 20 for each state and values must have been between $0.01 and $50 so I would like to see the range of answers people gave to the questionnaire. Perhaps it would also be worthwhile to ask how many children are in the household to see if the amount changes if there are more/less children being visited by the tooth fairy in the home. 

week3.md

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+![bob ross color frequency](img/bobross.png)
+
+link: https://www.reddit.com/r/dataisbeautiful/comments/st7035/oc_color_frequency_in_the_joy_of_painting_by_bob/
+
+As an avid Bob Ross fan, this data visualization piqued my interest. This visual was created using the colors used by Bob Ross in his 'Joy of Painting' television series. The graph shows the frequency of color used in his paintings. The purple line represents the number of colors used in each episode of the television series and each dot represents each specific combination of colors used. The size of the dot represents the number of paintings this combination of colors can be used for, and the color of each dot represents number of colors they contain. Overall I found this visual quite difficult for me to understand. I looked at it for quite a while in an attempt to understand what the number of dots under each number of colors means. I think that this visual just has too much information trying to be displayed in such limited space. There are so many different colors, sizes, and lines which makes it confusing. I think that this could have been simpler if the author separated some of the data into different charts and graphs. It might be difficult to understand because both the line and the dots are vertically stacked, which makes it very overwhelming. I also find it odd to believe that 15 colors can be used to make 300 different paintings but I might be reading this graph incorrectly due to its confusing-ness.

week4.md

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+![average height and weight by sport](img/sports.png)
+
+link: https://www.reddit.com/r/dataisbeautiful/comments/sm20ir/relation_between_height_and_weight_per_olympic/
+
+With the Winter Olympics happening right now, I found this data visualization to be really interesting. This graph depicts the average height (in cm) and weight (in kg) of Olympians from the Albertville 1992 to Rio 2016 games. This data is also broken down by Olympic sport and by whether the event is a winter or summer event. Also in this visual, the size of a dot represents the variance in weight and height within a sport, meaning a bigger dot the bigger the differences in height and weight per athlete are. I found it particularly interesting how there were a few sports, such as rhythimic gymnastics and synchronized swimming, that there is very little variation between the height and weight of its athletes. Additionally, I was surprised that basketball had the tallest and heaviest players, compared to sports such as wrestling and boxing (I would think these athletes would be the heaviest). It seems that majority of Olympians are between 60kg and 80kg and between 170cm and 180 cm tall. To improve this visualization, it would have been more helpful if the creator included units for each measurement because it might not be clear to people who aren't used to the metric system. Also, some of the sport names are really clustered together so it is difficult to determine which dot corresponds to which sport. I would have also liked to see the chart broken up by sex, since males tend to be taller and in turn more heavy than females. It also could be cool to see this further broken up by the athlete's home country. After looking at the data set that was used to create this data visualization, I would also be curious about old Olympic sports that might not be competed anymore such as Tug-of-War (data permitting). 

week5.md

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+![best starting words in wordle](img/wordle.png)
+
+link: https://www.reddit.com/r/dataisbeautiful/comments/sgc5g6/oc_how_good_is_your_favorite_wordle_starter/
+
+As a recently avid wordle player, I am constantly looking for ways to improve my strategy and beat all my friends. This visualization depicts the best 12 starting words in the game wordle, based on the word's match score (average score of first guess vs. all 2,315 possible solutions, assuming each green is 2 points, yellow is 2 points, and black is 0 points), average remaining (average number of possible solutions after feedack from the first guess), worst case (worst case for number of possible solutions remaining based on feedback from the game), match pattern (total number of unique match patterns). To accomplish this, the author used a decision tree with optimizations to prune branches that were extremely unlikely to be optimal. The ranking is based on the least number of total guesses required to solve all 2,315 valid solutions. I found this visual particularly interesting because in the past I have based my first guess after the most common letters that appear in 5 letter words (I use 'tears'). I took an artificial intelligence course at WPI last year and am familiar with the minimax pruning algorithm, so this approach seems much more doable programmatically. I'm curious to see how the top word 'salet" works as a first guess. I'm somewhat skeptical how well this will work (since none of the letters would be a match for today's wordle # 239). It might be interesting to collect data on this from a human standpoint guessing, since the pruning algorithm is very different from how a human brain would think in the moment. A lot of people have commented that they try to guess most of the vowels first and going from there. I'm curious to see if there is correlation between how good computer guesses are and human guesses. Perhaps there could be some type of study similar to my data visualization MQP for guessing words? I've also seen that cleat followed by irons is a good first couple guesses to get the most commonly used letters in. 

week6.md

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+![US inflation over time](img/wages.PNG)
+
+link: https://www.reddit.com/r/dataisbeautiful/comments/suqj9n/oc_us_wages_are_now_falling_in_real_terms/
+
+Recently I have noticed the cost of everyday items have been increasing at an alarming rate, which is why I found this data visualization particularly interesting. Using wage data from the Federal Reserve of Atlanta and the US Bureau of Labor Statistics, this visual shows the change in wage vs. inflation from January 2015 to December 2021. For most of this time period, the wage increase is higher than the rate of inflation. Mid 2021 the rate of inflation surpasses the rate of wage change, which is frightening for me to think about considering I will (hopefully) be entering the work force soon when I graduate. I think it would be interesting to see this data further broken down by other demographics, such as race, age, income ranges, etc. which could give us more information behind the trendlines we see in the visual. It is also fascinating to see how inflation played out at the start of the pandemic to now. Perhaps the change of inflation surpassing wage in mid-2021 could be attributed to the pandemic and the closure of many businesses and services. I'm curious to see if the spike in inflation improves now that covid-19 cases are starting to drop and if the economy just needs time to recover. Someone made an interesting comment that "you have to account for the fact that these are using average wage. When we entered the Covid recession, this statistic skyrocketed. Why? Because all the people laid off by Covid were lower paid. So the average hourly wage went up a ton. Those people getting rehired then brings the average back down. It's a hidden source of error that makes tracking month to month of this stat misleading." This also could be reasoning behind the increase of inflation since businesses such as restaurants are starting to open back up and servers tend to have lower wages due to tips. I think that this data could be really interesting to examine by different demographics and populations!

week7.md

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+![animal crossing character personality breakdown](img/animal.PNG)
+
+link: https://www.reddit.com/r/dataisbeautiful/comments/qmnrte/oc_animal_crossing_v20_update_personality_and/
+
+As an avid animal crossing player, I found this data visualization particularly fun and interesting. It is a chord diagram created in Python with the PlotAPI. It shows the different types of animal crossing villagers (dog, bird, frog, etc.) and the different types of personalities (snooty, normal, peppy) and their relationships. In the interactive version, you can click the different types of personalities and see what types of animals those villagers are, or you can hover over individual lines to see villagers that are a certain personality and a certain type of animal. I've seen pictures of these types of graphs/visualizations before and didn't understand what they meant/how they worked, but the video on this reddit page makes it very simple for me to understand. I also like the use of different colors that make this visualization appealing to the eye, but all the different lines unless a user hovers over one is a tiny bit overwhelming. It is unclear where the person who made this got their data from, but I think it might be interesting to see the different villagers that are for different games. Perhaps they could add a drop down setting where the user can select an animal crossing game such as new leaf or new horizons and the graph would display the types of villagers that are included in that game. I think it would also be helpful to highlight the type of animal that has the most characters for a given personality so if someone was trying to look for a villager with a certain personality they could see which type of animal they should look into. I would be interested to see how to make a graph such as this one code-wise in Python and hope to make one in the future.