Sports Analytics in Practice with R. Ted KwartlerЧитать онлайн книгу.

      Figure 1.8 As expected the more minutes a player averages the higher the average points.

      qplot(x = MPG_min_per_game, y = POINTS_PER_GAME, size = 5, data = nbaData) + theme_hc()

      Let’s add a bit more complexity to the visualization by creating a heatmap. The heatmap chart has x and y axes but represents data amounts as color intensity. A heatmap allows the audience to comprehend complex data quickly and concisely. To begin, let’s use the `data.frame` function to create a smaller data set. Here, the column names are being renamed and each individual column from the `nbaData` object is explicitly selected. The new object has the same number of rows but a subset of the columns. There are additional functions to perform this operation but this is straightforward. As the book continues, more concise though complex examples will perform the same operation.

smallerStats <- data.frame(player = nbaData$ï.PLAYER, FTA = nbaData$FTA_free_throws_attempted, TWO_PA = nbaData$TWO_PA, THREE_PA = nbaData$THREE_PA)

      In order to construct a heatmap with `ggplot2`, the `smallerStats` data frame must be rearranged into a “tidy” format. This type of data organization can be difficult to comprehend for novice R programmers, but the main point is that the data is not being changed, merely rearranged. The `tidyr` library function `pivot_longer` accepts the data frame first. Next, the `cols` parameter is defined. In this case, the column to pivot upon is the `player` column. This will result in each player’s name being repeated and two new columns being created. These columns are defined in the function as `names_to` and `values_to`, respectively. In the end, each player and corresponding statistic name and value are captured as a row. Whereas the `smallerStats` data frame had 19 observations with 4 columns, now the `nbaDataLong` object which has been pivoted by the `player` column has 57 rows and 3 columns. After the pivot the `head` function is executed to demonstrate the difference.

      nbaDataLong <- pivot_longer(data = smallerStats, cols = -c(player), names_to = "stat", values_to = "value") head(nbaDataLong)

      Now that the data has been modified, it will be readily accepted by the `ggplot` function. Instead of the previous `qplot` function, now the more expansive `ggplot` function is called. The first parameter is the `data` object. The next parameter is the `mapping` aesthetics. This is a multi-part input declared with yet another function `aes`. Within the `aes` function, the column names to be plotted are defined. Specifically, the x-axis column name, `stat`, followed by the y-axis column name `player`, and finally the fill value which corresponds to the `value` column. Thus, the visual is set up so that player statistics are arranged on the x-axis, individual players will be a single row along the y-axis, and the color intensity will be scaled by the players corresponding statistical value. Once the base layer plot has been defined, another layer is added with the `+` sign to declare the type of plot needed. In this case, the heatmap is called using `geom_tile`. In subsequent chapters, additional visuals are illustrated including ggplot2 and more dynamic interactive graphics. Since this text requires gray-scale graphics, another layer is added to define the color intensity between `lightgrey` and `black`. Finally, another layer is added to retitle the x-axis label as “Scoring Statistics” encased in quotes because it is a label not an object or column name. For simplicity, this is captured in an object called `heatPlot`.

heatPlot <- ggplot(data = nbaDataLong, mapping = aes(x = stat, y = player, fill = value)) + geom_tile() + scale_fill_gradient(low="lightgrey", high="black") + xlab(label = "Scoring Statistics")

Although calling `heatPlot` now in the console will create the visual, some additional layers can be added. First, a predefined theme for Highcharts is added, just as before using `theme_hc`. Next, a chart title is declared with `ggtitle` along with the quoted “Dallas Team Offensive Stats.” Lastly, a `theme` is appended as the final layer that simply removed the legend altogether. Now when the `heatPlot` object is called, a clean, visually compelling plot is created that clearly shows the most offensively productive player for the three statistics on the team. Additionally, other player’s strengths in these statistics are easily understood because their sections are darker compared to teammates. Conversely weaker players in these stats have a lighter color. These facts are more quickly understood in a visual compared to reviewing a table of player data. The result of the `heatPlot` object is shown in Figure 1.9.