That’s what we want.īut we also want to name the rows by player name instead of row number, so type this in the window: row.names(nba) <- nba$Name Prepare dataĪs is, the column names match the CSV file’s header. We could just as easily chosen to order by assists, blocks, etc. Let’s make it the other way around so that it’s least to greatest. The data is sorted by points per game, greatest to least. What the data looks like when you load it into R Step 2. Type nba in the window, and you can see the data. We’ve read a CSV file from a URL and specified the field separator as a comma. Now we’ll load the data using read.csv(). I’ve made it available here as a CSV file. No data? No visualization for you.įor this tutorial, we’ll use NBA basketball statistics from last season that I downloaded from databaseBasketball. Like all visualization, you should start with the data. I’ve never tried Linux.ĭid you download and install R? Okay, let’s move on. It’s a simple one-click install for Windows and Mac. It’s a statistical computing language and environment, and it’s free. It’s useful for finding highs and lows and sometimes, patterns. Each column can be a different metric like above, or it can be all the same like this one. Colors correspond to the level of the measurement. A heatmap is basically a table that has colors in place of numbers.
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