Lab Assignment Unit 1
Honore Nguessan
7/16/2019
Introduction
In our Lab Unit 1, we were assigned to create a heatmap for Basketball 2008 Data and a treemap for posting data collected on the Flowing Data Website. Then, we will follow the steps of the Flowing Data Tutorial.
1- create a heatmap
We are going to create a heatmap in 6 steps.
Step 1: Load and read the data
Now, we’ll load the data using read.csv().
nba <- read.csv("http://datasets.flowingdata.com/ppg2008.csv", sep=",")
nba## Name G MIN PTS FGM FGA FGP FTM FTA FTP X3PM
## 1 Dwyane Wade 79 38.6 30.2 10.8 22.0 0.491 7.5 9.8 0.765 1.1
## 2 LeBron James 81 37.7 28.4 9.7 19.9 0.489 7.3 9.4 0.780 1.6
## 3 Kobe Bryant 82 36.2 26.8 9.8 20.9 0.467 5.9 6.9 0.856 1.4
## 4 Dirk Nowitzki 81 37.7 25.9 9.6 20.0 0.479 6.0 6.7 0.890 0.8
## 5 Danny Granger 67 36.2 25.8 8.5 19.1 0.447 6.0 6.9 0.878 2.7
## 6 Kevin Durant 74 39.0 25.3 8.9 18.8 0.476 6.1 7.1 0.863 1.3
## 7 Kevin Martin 51 38.2 24.6 6.7 15.9 0.420 9.0 10.3 0.867 2.3
## 8 Al Jefferson 50 36.6 23.1 9.7 19.5 0.497 3.7 5.0 0.738 0.0
## 9 Chris Paul 78 38.5 22.8 8.1 16.1 0.503 5.8 6.7 0.868 0.8
## 10 Carmelo Anthony 66 34.5 22.8 8.1 18.3 0.443 5.6 7.1 0.793 1.0
## 11 Chris Bosh 77 38.1 22.7 8.0 16.4 0.487 6.5 8.0 0.817 0.2
## 12 Brandon Roy 78 37.2 22.6 8.1 16.9 0.480 5.3 6.5 0.824 1.1
## 13 Antawn Jamison 81 38.2 22.2 8.3 17.8 0.468 4.2 5.6 0.754 1.4
## 14 Tony Parker 72 34.1 22.0 8.9 17.5 0.506 3.9 5.0 0.782 0.3
## 15 Amare Stoudemire 53 36.8 21.4 7.6 14.1 0.539 6.1 7.3 0.835 0.1
## 16 Joe Johnson 79 39.5 21.4 7.8 18.0 0.437 3.8 4.6 0.826 1.9
## 17 Devin Harris 69 36.1 21.3 6.6 15.1 0.438 7.2 8.8 0.820 0.9
## 18 Michael Redd 33 36.4 21.2 7.5 16.6 0.455 4.0 4.9 0.814 2.1
## 19 David West 76 39.3 21.0 8.0 17.0 0.472 4.8 5.5 0.884 0.1
## 20 Zachary Randolph 50 35.1 20.8 8.3 17.5 0.475 3.6 4.9 0.734 0.6
## 21 Caron Butler 67 38.6 20.8 7.3 16.2 0.453 5.1 6.0 0.858 1.0
## 22 Vince Carter 80 36.8 20.8 7.4 16.8 0.437 4.2 5.1 0.817 1.9
## 23 Stephen Jackson 59 39.7 20.7 7.0 16.9 0.414 5.0 6.0 0.826 1.7
## 24 Ben Gordon 82 36.6 20.7 7.3 16.0 0.455 4.0 4.7 0.864 2.1
## 25 Dwight Howard 79 35.7 20.6 7.1 12.4 0.572 6.4 10.7 0.594 0.0
## 26 Paul Pierce 81 37.4 20.5 6.7 14.6 0.457 5.7 6.8 0.830 1.5
## 27 Al Harrington 73 34.9 20.1 7.3 16.6 0.439 3.2 4.0 0.793 2.3
## 28 Jamal Crawford 65 38.1 19.7 6.4 15.7 0.410 4.6 5.3 0.872 2.2
## 29 Yao Ming 77 33.6 19.7 7.4 13.4 0.548 4.9 5.7 0.866 0.0
## 30 Richard Jefferson 82 35.9 19.6 6.5 14.9 0.439 5.1 6.3 0.805 1.4
## 31 Jason Terry 74 33.6 19.6 7.3 15.8 0.463 2.7 3.0 0.880 2.3
## 32 Deron Williams 68 36.9 19.4 6.8 14.5 0.471 4.8 5.6 0.849 1.0
## 33 Tim Duncan 75 33.7 19.3 7.4 14.8 0.504 4.5 6.4 0.692 0.0
## 34 Monta Ellis 25 35.6 19.0 7.8 17.2 0.451 3.1 3.8 0.830 0.3
## 35 Rudy Gay 79 37.3 18.9 7.2 16.0 0.453 3.3 4.4 0.767 1.1
## 36 Pau Gasol 81 37.1 18.9 7.3 12.9 0.567 4.2 5.4 0.781 0.0
## 37 Andre Iguodala 82 39.8 18.8 6.6 14.0 0.473 4.6 6.4 0.724 1.0
## 38 Corey Maggette 51 31.1 18.6 5.7 12.4 0.461 6.7 8.1 0.824 0.5
## 39 O.J. Mayo 82 38.0 18.5 6.9 15.6 0.438 3.0 3.4 0.879 1.8
## 40 John Salmons 79 37.5 18.3 6.5 13.8 0.472 3.6 4.4 0.830 1.6
## 41 Richard Hamilton 67 34.0 18.3 7.0 15.6 0.447 3.3 3.9 0.848 1.0
## 42 Ray Allen 79 36.3 18.2 6.3 13.2 0.480 3.0 3.2 0.952 2.5
## 43 LaMarcus Aldridge 81 37.1 18.1 7.4 15.3 0.484 3.2 4.1 0.781 0.1
## 44 Josh Howard 52 31.9 18.0 6.8 15.1 0.451 3.3 4.2 0.782 1.1
## 45 Maurice Williams 81 35.0 17.8 6.5 13.9 0.467 2.6 2.8 0.912 2.3
## 46 Shaquille O'neal 75 30.1 17.8 6.8 11.2 0.609 4.1 6.9 0.595 0.0
## 47 Rashard Lewis 79 36.2 17.7 6.1 13.8 0.439 2.8 3.4 0.836 2.8
## 48 Chauncey Billups 79 35.3 17.7 5.2 12.4 0.418 5.3 5.8 0.913 2.1
## 49 Allen Iverson 57 36.7 17.5 6.1 14.6 0.417 4.8 6.1 0.781 0.5
## 50 Nate Robinson 74 29.9 17.2 6.1 13.9 0.437 3.4 4.0 0.841 1.7
## X3PA X3PP ORB DRB TRB AST STL BLK TO PF
## 1 3.5 0.317 1.1 3.9 5.0 7.5 2.2 1.3 3.4 2.3
## 2 4.7 0.344 1.3 6.3 7.6 7.2 1.7 1.1 3.0 1.7
## 3 4.1 0.351 1.1 4.1 5.2 4.9 1.5 0.5 2.6 2.3
## 4 2.1 0.359 1.1 7.3 8.4 2.4 0.8 0.8 1.9 2.2
## 5 6.7 0.404 0.7 4.4 5.1 2.7 1.0 1.4 2.5 3.1
## 6 3.1 0.422 1.0 5.5 6.5 2.8 1.3 0.7 3.0 1.8
## 7 5.4 0.415 0.6 3.0 3.6 2.7 1.2 0.2 2.9 2.3
## 8 0.1 0.000 3.4 7.5 11.0 1.6 0.8 1.7 1.8 2.8
## 9 2.3 0.364 0.9 4.7 5.5 11.0 2.8 0.1 3.0 2.7
## 10 2.6 0.371 1.6 5.2 6.8 3.4 1.1 0.4 3.0 3.0
## 11 0.6 0.245 2.8 7.2 10.0 2.5 0.9 1.0 2.3 2.5
## 12 2.8 0.377 1.3 3.4 4.7 5.1 1.1 0.3 1.9 1.6
## 13 3.9 0.351 2.4 6.5 8.9 1.9 1.2 0.3 1.5 2.7
## 14 0.9 0.292 0.4 2.7 3.1 6.9 0.9 0.1 2.6 1.5
## 15 0.1 0.429 2.2 5.9 8.1 2.0 0.9 1.1 2.8 3.1
## 16 5.2 0.360 0.8 3.6 4.4 5.8 1.1 0.2 2.5 2.2
## 17 3.2 0.291 0.4 2.9 3.3 6.9 1.7 0.2 3.1 2.4
## 18 5.8 0.366 0.7 2.5 3.2 2.7 1.1 0.1 1.6 1.4
## 19 0.3 0.240 2.1 6.4 8.5 2.3 0.6 0.9 2.1 2.7
## 20 1.9 0.330 3.1 6.9 10.1 2.1 0.9 0.3 2.3 2.7
## 21 3.1 0.310 1.8 4.4 6.2 4.3 1.6 0.3 3.1 2.5
## 22 4.9 0.385 0.9 4.2 5.1 4.7 1.0 0.5 2.1 2.9
## 23 5.2 0.338 1.2 3.9 5.1 6.5 1.5 0.5 3.9 2.6
## 24 5.1 0.410 0.6 2.8 3.5 3.4 0.9 0.3 2.4 2.2
## 25 0.0 0.000 4.3 9.6 13.8 1.4 1.0 2.9 3.0 3.4
## 26 3.8 0.391 0.7 5.0 5.6 3.6 1.0 0.3 2.8 2.7
## 27 6.4 0.364 1.4 4.9 6.2 1.4 1.2 0.3 2.2 3.1
## 28 6.1 0.360 0.4 2.6 3.0 4.4 0.9 0.2 2.3 1.4
## 29 0.0 1.000 2.6 7.2 9.9 1.8 0.4 1.9 3.0 3.3
## 30 3.6 0.397 0.7 3.9 4.6 2.4 0.8 0.2 2.0 3.1
## 31 6.2 0.366 0.5 1.9 2.4 3.4 1.3 0.3 1.6 1.9
## 32 3.3 0.310 0.4 2.5 2.9 10.7 1.1 0.3 3.4 2.0
## 33 0.0 0.000 2.7 8.0 10.7 3.5 0.5 1.7 2.2 2.3
## 34 1.0 0.308 0.6 3.8 4.3 3.7 1.6 0.3 2.7 2.7
## 35 3.1 0.351 1.4 4.2 5.5 1.7 1.2 0.7 2.6 2.8
## 36 0.0 0.500 3.2 6.4 9.6 3.5 0.6 1.0 1.9 2.1
## 37 3.2 0.307 1.1 4.6 5.7 5.3 1.6 0.4 2.7 1.9
## 38 1.9 0.253 1.0 4.6 5.5 1.8 0.9 0.2 2.4 3.8
## 39 4.6 0.384 0.7 3.1 3.8 3.2 1.1 0.2 2.8 2.5
## 40 3.8 0.417 0.7 3.5 4.2 3.2 1.1 0.3 2.1 2.3
## 41 2.8 0.368 0.7 2.4 3.1 4.4 0.6 0.1 2.0 2.6
## 42 6.2 0.409 0.8 2.7 3.5 2.8 0.9 0.2 1.7 2.0
## 43 0.3 0.250 2.9 4.6 7.5 1.9 1.0 1.0 1.5 2.6
## 44 3.2 0.345 1.1 3.9 5.1 1.6 1.1 0.6 1.7 2.6
## 45 5.2 0.436 0.6 2.9 3.4 4.1 0.9 0.1 2.2 2.7
## 46 0.0 0.000 2.5 5.9 8.4 1.7 0.7 1.4 2.2 3.4
## 47 7.0 0.397 1.2 4.6 5.7 2.6 1.0 0.6 2.0 2.5
## 48 5.0 0.408 0.4 2.6 3.0 6.4 1.2 0.2 2.2 2.0
## 49 1.7 0.283 0.5 2.5 3.0 5.0 1.5 0.1 2.6 1.5
## 50 5.2 0.325 1.3 2.6 3.9 4.1 1.3 0.1 1.9 2.8Step 2: Sort data
We are going to order the data by least to greatest.
nba <- nba[order(nba$PTS),]Step 3: Prepare data
AT this step, we want to name the rows by player name instead of row number.
row.names(nba) <- nba$NameStep 4: Delete column
Now the rows are named by player, and we don’t need the first column anymore so we’ll get rid of it
nba <- nba[,2:20]Step 5: Prepare data, again
At this moment of our work, we are going to get a data matrix to make our heatmap.
nba_matrix <- data.matrix(nba)Step 6: Make a heatmap
nba_heatmap <- heatmap(nba_matrix, Rowv=NA, Colv=NA, col = cm.colors(256), scale="column", margins=c(5,10))
Color Selection
Now, we are going to get a different color scheme by using heat.colors(256) instead of cm.colors(256).
nba_heatmap <- heatmap(nba_matrix, Rowv=NA, Colv=NA, col = heat.colors(256), scale="column", margins=c(5,10))
2- create a treemap
Now, we are going to create a treemap in 3 steps.
Step 1: Read the data
data <- read.csv("http://datasets.flowingdata.com/post-data.txt")Step 2: Load the Portfolio package
library(portfolio)## Warning: package 'portfolio' was built under R version 3.6.1## Loading required package: grid## Loading required package: lattice## Loading required package: nlmeStep 3: Make the Treemap
Now, it is time to make the treemap with map.market().
map.market(id=data$id, area=data$views, group=data$category, color=data$comments, main="FlowingData Map")
Conclusion
By following the steps of the Flowing Data Tutorial, we created a heatmap for Basketball 2008 Data and a treemap.