Since my freshman year, every time we stepped on the court, we thought we were the better team. “We all just wanted to have that competitive spirit. “I have to say it’s the players we had,” Tapley said. What Tapley has already seen in his four years on Montezuma Mesa is the transformation of a basketball program a program that had not made the NCAA Tournament since the 2005-06 season before he arrived in 2009, but has now gone to the Big Dance for three consecutive seasons and will likely be back after starting the season in the Top 25 in numerous preseason polls. “I had no dunks last year, so when I dunked it tonight, everybody was hyped and mauled me, so I hope to see that a lot this year.” “It felt good because teammates get on me everyday, telling me I don’t dunk the ball enough,” Tapley said with a smile. The play was followed by a timeout, as several players from the Aztec bench met Tapley at half-court for high fives and chest bumps. On a UCSD inbound pass, Tapley stole the ball near the free-throw line, drove to the basket and threw down a two-handed dunk. The Aztecs led by 28 points and there were few things left for San Diego State fans, players and coaches to cheer for with less than five minutes left in the game.īut in a matter of seconds, senior guard Chase Tapley changed all of that.
#MARK TAPLY HOW TO#
If you’d like to perform more advanced statistical analysis with this dataset, check out this tutorial that explains how to fit linear regression models and generalized linear models using the mtcars dataset.The exhibition game was all but over against a lowly Division II University of California, San Diego team. wtīy using these built-in functions in R, we can learn a great deal about the mtcars dataset. We can also use the plot() function to create a scatterplot of any pairwise combination of variables: #create scatterplot of mpg vs. We could also use the boxplot() function to create a boxplot to visualize the distribution of values for a certain variable: #create boxplot of values for mpg We can also create some plots to visualize the values in the dataset.įor example, we can use the hist() function to create a histogram of the values for a certain variable: #create histogram of values for mpg We can also use the names() function to display the column names of the data frame: #display column names We can see that the dataset has 32 rows and 11 columns. We can use the dim() function to get the dimensions of the dataset in terms of number of rows and number of columns: #display rows and columns
We can use the summary() function to quickly summarize each variable in the dataset: #summarize mtcars dataset Mpg cyl disp hp drat wt qsec vs am gear carb We can take a look at the first six rows of the dataset by using the head() function: #view first six rows of mtcars dataset Since the mtcars dataset is a built-in dataset in R, we can load it by using the following command: data(mtcars) Related: A Complete Guide to the Iris Dataset in R Load the mtcars Dataset This tutorial explains how to explore, summarize, and visualize the mtcars dataset in R. The mtcars dataset is a built-in dataset in R that contains measurements on 11 different attributes for 32 different cars.
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