baseballr VS ggplot2

A basic understanding of R should be enough if you install the baseball r package. From there you can scrape off of Baseball Reference or Fangraphs for custom date ranges to get stats on whatever time frame basis you would like. Then you can export/copy/whatever to excel if you want, or do the analysis right in R.

Source: MLB Stats API via baseballr.

I like this idea, too! I use baseballr all the time. It is a godsend.

In order to make it, I need to get every lineup from every game in the season. I am using the baseballr package to get the game_pk number. Each game has a game_pk number, and each lineup is tied to that game_pk. So I need to create a dataframe (all_games_list) for each game with its game_pk number in it, and then use the game_pk numbers to create a new dataframe (lineup_all) that contains the lineup for said game_pk.

I just remembered there is also this R package: Acquiring and Analyzing Baseball Data • baseballr.

To get all the data, I would suggest checking out baseballr if you are familiar with R. https://billpetti.github.io/baseballr/

Visualization originally posted on my blog - I built the boxplot using R and ggplot2, and was fortunate to be able to use the excellent baseballr package to query MLB game information for the runtime source data!

baseballR (baseball)

most folks without direct access to mlb's api scrape baseball savant's data api. packages like baseballr or pybaseball can help with this. remember, this is in the open on a trust model: no commercial use, and don't hammer the api.

If you're using R, this is the gold standard package to use for getting baseball data. This helps you scrape data.

ggplot2

I got the list of five-letter words from the words package in R, created the QWERTY keyboard grid with base R and tibble, and visualized the data with geom_tile in the ggplot2 package.

Thanks, it's an interesting idea! I definitely could implement this with scale_fill_gradientn) in ggplot2.

I used the ggplot2 package in R to create these figures.

This might not be at the top of your list, but science fiction often presents advanced data analysis and visualization technologies. Open source data analysis tools such as Python's Pandas and R's ggplot2 have revolutionized the field, making complex data manipulation and visualization accessible to all. In the science fiction novel The Martian, astronaut Mark Watney uses a variety of data analysis and visualization tools to survive on Mars. He uses Python's Pandas to clean and organize data, and he uses R's ggplot2 to create visualizations of his data. These tools allow him to make sense of the vast amounts of data and help him to make critical decisions about his survival.

Sorry about the slow reply, but the auto-moderator seems to be deleting my comments (for some unknown reason). I will try once more: the geom_tile function in ggplot2.

Visual cues are a much better idea, thanks! Unfortunately, I don't know how to do that in ggplot2, either (I created these figures in R).