Homework5a
Dong-Min Kim
9/29/2021
I have loaded the NBA dataset from the Yau’s website.
This data appears to contain data about 2008 NBA player stats.
# How to make a heatmap
nba <- read.csv("http://datasets.flowingdata.com/ppg2008.csv")
nba## Name G MIN PTS FGM FGA FGP FTM FTA FTP X3PM X3PA
## 1 Dwyane Wade 79 38.6 30.2 10.8 22.0 0.491 7.5 9.8 0.765 1.1 3.5
## 2 LeBron James 81 37.7 28.4 9.7 19.9 0.489 7.3 9.4 0.780 1.6 4.7
## 3 Kobe Bryant 82 36.2 26.8 9.8 20.9 0.467 5.9 6.9 0.856 1.4 4.1
## 4 Dirk Nowitzki 81 37.7 25.9 9.6 20.0 0.479 6.0 6.7 0.890 0.8 2.1
## 5 Danny Granger 67 36.2 25.8 8.5 19.1 0.447 6.0 6.9 0.878 2.7 6.7
## 6 Kevin Durant 74 39.0 25.3 8.9 18.8 0.476 6.1 7.1 0.863 1.3 3.1
## 7 Kevin Martin 51 38.2 24.6 6.7 15.9 0.420 9.0 10.3 0.867 2.3 5.4
## 8 Al Jefferson 50 36.6 23.1 9.7 19.5 0.497 3.7 5.0 0.738 0.0 0.1
## 9 Chris Paul 78 38.5 22.8 8.1 16.1 0.503 5.8 6.7 0.868 0.8 2.3
## 10 Carmelo Anthony 66 34.5 22.8 8.1 18.3 0.443 5.6 7.1 0.793 1.0 2.6
## 11 Chris Bosh 77 38.1 22.7 8.0 16.4 0.487 6.5 8.0 0.817 0.2 0.6
## 12 Brandon Roy 78 37.2 22.6 8.1 16.9 0.480 5.3 6.5 0.824 1.1 2.8
## 13 Antawn Jamison 81 38.2 22.2 8.3 17.8 0.468 4.2 5.6 0.754 1.4 3.9
## 14 Tony Parker 72 34.1 22.0 8.9 17.5 0.506 3.9 5.0 0.782 0.3 0.9
## 15 Amare Stoudemire 53 36.8 21.4 7.6 14.1 0.539 6.1 7.3 0.835 0.1 0.1
## 16 Joe Johnson 79 39.5 21.4 7.8 18.0 0.437 3.8 4.6 0.826 1.9 5.2
## 17 Devin Harris 69 36.1 21.3 6.6 15.1 0.438 7.2 8.8 0.820 0.9 3.2
## 18 Michael Redd 33 36.4 21.2 7.5 16.6 0.455 4.0 4.9 0.814 2.1 5.8
## 19 David West 76 39.3 21.0 8.0 17.0 0.472 4.8 5.5 0.884 0.1 0.3
## 20 Zachary Randolph 50 35.1 20.8 8.3 17.5 0.475 3.6 4.9 0.734 0.6 1.9
## 21 Caron Butler 67 38.6 20.8 7.3 16.2 0.453 5.1 6.0 0.858 1.0 3.1
## 22 Vince Carter 80 36.8 20.8 7.4 16.8 0.437 4.2 5.1 0.817 1.9 4.9
## 23 Stephen Jackson 59 39.7 20.7 7.0 16.9 0.414 5.0 6.0 0.826 1.7 5.2
## 24 Ben Gordon 82 36.6 20.7 7.3 16.0 0.455 4.0 4.7 0.864 2.1 5.1
## 25 Dwight Howard 79 35.7 20.6 7.1 12.4 0.572 6.4 10.7 0.594 0.0 0.0
## 26 Paul Pierce 81 37.4 20.5 6.7 14.6 0.457 5.7 6.8 0.830 1.5 3.8
## 27 Al Harrington 73 34.9 20.1 7.3 16.6 0.439 3.2 4.0 0.793 2.3 6.4
## 28 Jamal Crawford 65 38.1 19.7 6.4 15.7 0.410 4.6 5.3 0.872 2.2 6.1
## 29 Yao Ming 77 33.6 19.7 7.4 13.4 0.548 4.9 5.7 0.866 0.0 0.0
## 30 Richard Jefferson 82 35.9 19.6 6.5 14.9 0.439 5.1 6.3 0.805 1.4 3.6
## 31 Jason Terry 74 33.6 19.6 7.3 15.8 0.463 2.7 3.0 0.880 2.3 6.2
## 32 Deron Williams 68 36.9 19.4 6.8 14.5 0.471 4.8 5.6 0.849 1.0 3.3
## 33 Tim Duncan 75 33.7 19.3 7.4 14.8 0.504 4.5 6.4 0.692 0.0 0.0
## 34 Monta Ellis 25 35.6 19.0 7.8 17.2 0.451 3.1 3.8 0.830 0.3 1.0
## 35 Rudy Gay 79 37.3 18.9 7.2 16.0 0.453 3.3 4.4 0.767 1.1 3.1
## 36 Pau Gasol 81 37.1 18.9 7.3 12.9 0.567 4.2 5.4 0.781 0.0 0.0
## 37 Andre Iguodala 82 39.8 18.8 6.6 14.0 0.473 4.6 6.4 0.724 1.0 3.2
## 38 Corey Maggette 51 31.1 18.6 5.7 12.4 0.461 6.7 8.1 0.824 0.5 1.9
## 39 O.J. Mayo 82 38.0 18.5 6.9 15.6 0.438 3.0 3.4 0.879 1.8 4.6
## 40 John Salmons 79 37.5 18.3 6.5 13.8 0.472 3.6 4.4 0.830 1.6 3.8
## 41 Richard Hamilton 67 34.0 18.3 7.0 15.6 0.447 3.3 3.9 0.848 1.0 2.8
## 42 Ray Allen 79 36.3 18.2 6.3 13.2 0.480 3.0 3.2 0.952 2.5 6.2
## 43 LaMarcus Aldridge 81 37.1 18.1 7.4 15.3 0.484 3.2 4.1 0.781 0.1 0.3
## 44 Josh Howard 52 31.9 18.0 6.8 15.1 0.451 3.3 4.2 0.782 1.1 3.2
## 45 Maurice Williams 81 35.0 17.8 6.5 13.9 0.467 2.6 2.8 0.912 2.3 5.2
## 46 Shaquille O'neal 75 30.1 17.8 6.8 11.2 0.609 4.1 6.9 0.595 0.0 0.0
## 47 Rashard Lewis 79 36.2 17.7 6.1 13.8 0.439 2.8 3.4 0.836 2.8 7.0
## 48 Chauncey Billups 79 35.3 17.7 5.2 12.4 0.418 5.3 5.8 0.913 2.1 5.0
## 49 Allen Iverson 57 36.7 17.5 6.1 14.6 0.417 4.8 6.1 0.781 0.5 1.7
## 50 Nate Robinson 74 29.9 17.2 6.1 13.9 0.437 3.4 4.0 0.841 1.7 5.2
## X3PP ORB DRB TRB AST STL BLK TO PF
## 1 0.317 1.1 3.9 5.0 7.5 2.2 1.3 3.4 2.3
## 2 0.344 1.3 6.3 7.6 7.2 1.7 1.1 3.0 1.7
## 3 0.351 1.1 4.1 5.2 4.9 1.5 0.5 2.6 2.3
## 4 0.359 1.1 7.3 8.4 2.4 0.8 0.8 1.9 2.2
## 5 0.404 0.7 4.4 5.1 2.7 1.0 1.4 2.5 3.1
## 6 0.422 1.0 5.5 6.5 2.8 1.3 0.7 3.0 1.8
## 7 0.415 0.6 3.0 3.6 2.7 1.2 0.2 2.9 2.3
## 8 0.000 3.4 7.5 11.0 1.6 0.8 1.7 1.8 2.8
## 9 0.364 0.9 4.7 5.5 11.0 2.8 0.1 3.0 2.7
## 10 0.371 1.6 5.2 6.8 3.4 1.1 0.4 3.0 3.0
## 11 0.245 2.8 7.2 10.0 2.5 0.9 1.0 2.3 2.5
## 12 0.377 1.3 3.4 4.7 5.1 1.1 0.3 1.9 1.6
## 13 0.351 2.4 6.5 8.9 1.9 1.2 0.3 1.5 2.7
## 14 0.292 0.4 2.7 3.1 6.9 0.9 0.1 2.6 1.5
## 15 0.429 2.2 5.9 8.1 2.0 0.9 1.1 2.8 3.1
## 16 0.360 0.8 3.6 4.4 5.8 1.1 0.2 2.5 2.2
## 17 0.291 0.4 2.9 3.3 6.9 1.7 0.2 3.1 2.4
## 18 0.366 0.7 2.5 3.2 2.7 1.1 0.1 1.6 1.4
## 19 0.240 2.1 6.4 8.5 2.3 0.6 0.9 2.1 2.7
## 20 0.330 3.1 6.9 10.1 2.1 0.9 0.3 2.3 2.7
## 21 0.310 1.8 4.4 6.2 4.3 1.6 0.3 3.1 2.5
## 22 0.385 0.9 4.2 5.1 4.7 1.0 0.5 2.1 2.9
## 23 0.338 1.2 3.9 5.1 6.5 1.5 0.5 3.9 2.6
## 24 0.410 0.6 2.8 3.5 3.4 0.9 0.3 2.4 2.2
## 25 0.000 4.3 9.6 13.8 1.4 1.0 2.9 3.0 3.4
## 26 0.391 0.7 5.0 5.6 3.6 1.0 0.3 2.8 2.7
## 27 0.364 1.4 4.9 6.2 1.4 1.2 0.3 2.2 3.1
## 28 0.360 0.4 2.6 3.0 4.4 0.9 0.2 2.3 1.4
## 29 1.000 2.6 7.2 9.9 1.8 0.4 1.9 3.0 3.3
## 30 0.397 0.7 3.9 4.6 2.4 0.8 0.2 2.0 3.1
## 31 0.366 0.5 1.9 2.4 3.4 1.3 0.3 1.6 1.9
## 32 0.310 0.4 2.5 2.9 10.7 1.1 0.3 3.4 2.0
## 33 0.000 2.7 8.0 10.7 3.5 0.5 1.7 2.2 2.3
## 34 0.308 0.6 3.8 4.3 3.7 1.6 0.3 2.7 2.7
## 35 0.351 1.4 4.2 5.5 1.7 1.2 0.7 2.6 2.8
## 36 0.500 3.2 6.4 9.6 3.5 0.6 1.0 1.9 2.1
## 37 0.307 1.1 4.6 5.7 5.3 1.6 0.4 2.7 1.9
## 38 0.253 1.0 4.6 5.5 1.8 0.9 0.2 2.4 3.8
## 39 0.384 0.7 3.1 3.8 3.2 1.1 0.2 2.8 2.5
## 40 0.417 0.7 3.5 4.2 3.2 1.1 0.3 2.1 2.3
## 41 0.368 0.7 2.4 3.1 4.4 0.6 0.1 2.0 2.6
## 42 0.409 0.8 2.7 3.5 2.8 0.9 0.2 1.7 2.0
## 43 0.250 2.9 4.6 7.5 1.9 1.0 1.0 1.5 2.6
## 44 0.345 1.1 3.9 5.1 1.6 1.1 0.6 1.7 2.6
## 45 0.436 0.6 2.9 3.4 4.1 0.9 0.1 2.2 2.7
## 46 0.000 2.5 5.9 8.4 1.7 0.7 1.4 2.2 3.4
## 47 0.397 1.2 4.6 5.7 2.6 1.0 0.6 2.0 2.5
## 48 0.408 0.4 2.6 3.0 6.4 1.2 0.2 2.2 2.0
## 49 0.283 0.5 2.5 3.0 5.0 1.5 0.1 2.6 1.5
## 50 0.325 1.3 2.6 3.9 4.1 1.3 0.1 1.9 2.8I have created a cool-color heatmap of the NBA 2008 data.
nba <- nba[order(nba$PTS),]
row.names(nba) <- nba$Name
nba <- nba[,2:19]
nba_matrix <- data.matrix(nba)
nba_heatmap <- heatmap(nba_matrix, Rowv=NA, Colv=NA,
col = cm.colors(256), scale="column", margins=c(5,10),
xlab = "NBA Player Stats",
ylab = "NBA Players",
main = "NBA Payer Stats in 2008")
# I have changed the heatmap colors to heat color.
nba_heatmap <- heatmap(nba_matrix, Rowv=NA, Colv=NA, col = heat.colors(256),
scale="column", margins=c(5,10),
xlab = "NBA Player Stats",
ylab = "NBA Players",
main = "NBA Payer Stats in 2008")
I have used the viridis colors.
I have tried to use direction= -1 and then try to remove it.
library(viridis)## Loading required package: viridisLite## Loading required package: viridisLite
nba_heatmap <- heatmap(nba_matrix, Rowv=NA, col = viridis(25, direction = -1),
scale="column", margins=c(5,10),
xlab = "NBA Player Stats",
ylab = "NBA Players",
main = "NBA Payer Stats in 2008")
library(viridis)
## Loading required package: viridisLite
nba_heatmap <- heatmap(nba_matrix, Rowv=NA, col = viridis(25),
scale="column", margins=c(5,10),
xlab = "NBA Player Stats",
ylab = "NBA Players",
main = "NBA Payer Stats in 2008")
# Treemaps ### Treemaps display hierarchial (tree-structured) data as a set of nested rectangles. Each branch of the tree is given a rectangle, which is then tiled with smaller rectangles representing sub-branches. A leaf node’s rectangle has an area proportional to a specified dimension of the data.[1] Often the leaf nodes are colored to show a separate dimension of the data. ### When the color and size dimensions are correlated in some way with the tree structure, one can often easily see patterns that would be difficult to spot in other ways, such as whether a certain color is particularly relevant. A second advantage of treemaps is that, by construction, they make efficient use of space. As a result, they can legibly display thousands of items on the screen simultaneously. ### The downside of treemaps is that as the aspect ratio is optimized, the order of placement becomes less predictable. As the order becomes more stable, the aspect ratio is degraded. (Wikipedia) ### Use Nathan Yau’s dataset from the flowingdata website: http://datasets.flowingdata.com/post-data.txt You will need the package “treemap” and the package “RColorBrewer”.
I have created a treemap which explores categories of view.
Load the packages and the data from flowingdata.com website.
The data is a csv file that compares number of views, number of comments to various categories of Yau’s visualization creations
library(treemap)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.4 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 2.0.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(RColorBrewer)
data<-read.csv("http://datasets.flowingdata.com/post-data.txt")
head(data)## id views comments category
## 1 5019 148896 28 Artistic Visualization
## 2 1416 81374 26 Visualization
## 3 1416 81374 26 Featured
## 4 3485 80819 37 Featured
## 5 3485 80819 37 Mapping
## 6 3485 80819 37 Data SourcesCode for creating a treemap
treemap(data, index="category", vSize="views",
vColor="comments", type="value",
palette="RdYlBu")
## Use RColorBrewer to change the palette to RdYlBu
treemap(data, index="category", vSize="views",
vColor="comments", type="manual",
palette="RdYlBu")
I have created Streamgraph code.
The code for making streamgraphs has changed with new updates to R. You have to download and install Rtools40 from the link, https://cran.rstudio.com/bin/windows/Rtools/. and then used the code provided below.
I have load devtools and libraries to create the following streamgraphs.
devtools::install_github("hrbrmstr/streamgraph")## Skipping install of 'streamgraph' from a github remote, the SHA1 (76f7173e) has not changed since last install.
## Use `force = TRUE` to force installationdevtools::session_info()## - Session info ---------------------------------------------------------------
## setting value
## version R version 4.1.1 (2021-08-10)
## os Windows 10 x64
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate English_United States.1252
## ctype English_United States.1252
## tz America/New_York
## date 2021-10-02
##
## - Packages -------------------------------------------------------------------
## package * version date lib source
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.1.1)
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## DBI 1.1.1 2021-01-15 [1] CRAN (R 4.1.1)
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##
## [1] C:/Users/dkim174/Documents/R/win-library/4.1
## [2] C:/Program Files/R/R-4.1.1/librarylibrary(dplyr)
library(streamgraph)
library(babynames)Here is a trivial streamgraph using simulated names over time.
# Create data:
year=rep(seq(1990,2016),each=10)
name=rep(letters[1:10],27)
value=sample( seq(0,1,0.0001) , length(year))
data=data.frame(year, name, value)
# Basic stream graph: just give the 3 arguments
streamgraph(data, key="name", value="value", date="year")## Warning in widget_html(name = class(x)[1], package = attr(x, "package"), :
## streamgraph_html returned an object of class `list` instead of a `shiny.tag`.Now look at the babynames dataset
ncol(babynames)## [1] 5[1] 5
head(babynames)## # A tibble: 6 x 5
## year sex name n prop
## <dbl> <chr> <chr> <int> <dbl>
## 1 1880 F Mary 7065 0.0724
## 2 1880 F Anna 2604 0.0267
## 3 1880 F Emma 2003 0.0205
## 4 1880 F Elizabeth 1939 0.0199
## 5 1880 F Minnie 1746 0.0179
## 6 1880 F Margaret 1578 0.0162str(babynames)## tibble [1,924,665 x 5] (S3: tbl_df/tbl/data.frame)
## $ year: num [1:1924665] 1880 1880 1880 1880 1880 1880 1880 1880 1880 1880 ...
## $ sex : chr [1:1924665] "F" "F" "F" "F" ...
## $ name: chr [1:1924665] "Mary" "Anna" "Emma" "Elizabeth" ...
## $ n : int [1:1924665] 7065 2604 2003 1939 1746 1578 1472 1414 1320 1288 ...
## $ prop: num [1:1924665] 0.0724 0.0267 0.0205 0.0199 0.0179 ...Babynames streamgraph
babynames %>%
filter(grepl("^Kr", name)) %>%
group_by(year, name) %>%
tally(wt=n) %>%
streamgraph("name", "n", "year")## Warning in widget_html(name = class(x)[1], package = attr(x, "package"), :
## streamgraph_html returned an object of class `list` instead of a `shiny.tag`.#Alluvials
Load the Alluvial package.
#install.packages("alluvial")
library(alluvial)Rufugees is a prebuilt dataset in the alluvial package.
use the prebuilt dataset and the write .csv function.
library(ggalluvial)
alluvial::Refugees## country year refugees
## 1 Afghanistan 2003 2136043
## 2 Burundi 2003 531637
## 3 Congo DRC 2003 453465
## 4 Iraq 2003 368580
## 5 Myanmar 2003 151384
## 6 Palestine 2003 350568
## 7 Somalia 2003 402336
## 8 Sudan 2003 606242
## 9 Syria 2003 20819
## 10 Vietnam 2003 363179
## 11 Afghanistan 2004 2084109
## 12 Burundi 2004 485454
## 13 Congo DRC 2004 461042
## 14 Iraq 2004 311905
## 15 Myanmar 2004 161013
## 16 Palestine 2004 350617
## 17 Somalia 2004 389304
## 18 Sudan 2004 730647
## 19 Syria 2004 21440
## 20 Vietnam 2004 349809
## 21 Afghanistan 2005 2166149
## 22 Burundi 2005 438706
## 23 Congo DRC 2005 430929
## 24 Iraq 2005 262299
## 25 Myanmar 2005 164864
## 26 Palestine 2005 349673
## 27 Somalia 2005 395553
## 28 Sudan 2005 693632
## 29 Syria 2005 16401
## 30 Vietnam 2005 358268
## 31 Afghanistan 2006 2107519
## 32 Burundi 2006 396541
## 33 Congo DRC 2006 401914
## 34 Iraq 2006 1450905
## 35 Myanmar 2006 202826
## 36 Palestine 2006 334142
## 37 Somalia 2006 464252
## 38 Sudan 2006 686311
## 39 Syria 2006 12338
## 40 Vietnam 2006 374279
## 41 Afghanistan 2007 1909911
## 42 Burundi 2007 375715
## 43 Congo DRC 2007 370386
## 44 Iraq 2007 2279245
## 45 Myanmar 2007 191256
## 46 Palestine 2007 335219
## 47 Somalia 2007 455356
## 48 Sudan 2007 523032
## 49 Syria 2007 13671
## 50 Vietnam 2007 327776
## 51 Afghanistan 2008 1817913
## 52 Burundi 2008 281592
## 53 Congo DRC 2008 367995
## 54 Iraq 2008 1873519
## 55 Myanmar 2008 184347
## 56 Palestine 2008 333990
## 57 Somalia 2008 559153
## 58 Sudan 2008 397013
## 59 Syria 2008 15186
## 60 Vietnam 2008 328183
## 61 Afghanistan 2009 1905804
## 62 Burundi 2009 94239
## 63 Congo DRC 2009 455852
## 64 Iraq 2009 1785212
## 65 Myanmar 2009 206650
## 66 Palestine 2009 95177
## 67 Somalia 2009 678308
## 68 Sudan 2009 348500
## 69 Syria 2009 17884
## 70 Vietnam 2009 339289
## 71 Afghanistan 2010 3054709
## 72 Burundi 2010 84064
## 73 Congo DRC 2010 476693
## 74 Iraq 2010 1683575
## 75 Myanmar 2010 215644
## 76 Palestine 2010 93299
## 77 Somalia 2010 770148
## 78 Sudan 2010 379067
## 79 Syria 2010 18428
## 80 Vietnam 2010 338698
## 81 Afghanistan 2011 2664436
## 82 Burundi 2011 101288
## 83 Congo DRC 2011 491481
## 84 Iraq 2011 1428308
## 85 Myanmar 2011 214594
## 86 Palestine 2011 94121
## 87 Somalia 2011 1075148
## 88 Sudan 2011 491013
## 89 Syria 2011 19900
## 90 Vietnam 2011 337829
## 91 Afghanistan 2012 2586034
## 92 Burundi 2012 73362
## 93 Congo DRC 2012 509082
## 94 Iraq 2012 746181
## 95 Myanmar 2012 215338
## 96 Palestine 2012 94820
## 97 Somalia 2012 1136713
## 98 Sudan 2012 558195
## 99 Syria 2012 728603
## 100 Vietnam 2012 336939
## 101 Afghanistan 2013 2556507
## 102 Burundi 2013 72652
## 103 Congo DRC 2013 499320
## 104 Iraq 2013 401384
## 105 Myanmar 2013 222053
## 106 Palestine 2013 96044
## 107 Somalia 2013 1121772
## 108 Sudan 2013 636400
## 109 Syria 2013 2457255
## 110 Vietnam 2013 314105Refugees <- Refugees
write_csv(Refugees, "refugees.csv")Create the alluvial to show UNHCR-recognised refugees in the top 10 countries from 2013-2013
Alluvials need the variables: categories, time-variable, value
options(scipen = 999) # this code eliminates scientific notation for the refugee values
ggalluv <- ggplot(alluvial::Refugees,
aes(y = refugees, x = year, alluvium = country)) + # time series bump chart (quintic flows)
theme_bw() +
geom_alluvium(aes(fill = country, color = country),
width = .1, alpha = .5, decreasing = FALSE,
curve_type = "sigmoid") +
scale_fill_brewer(palette = "Accent") +
ggtitle("UNHCR-recognised refugees\nTop 10 countries (2003-2013)\n") +
ylab("Number of Refugees")
ggalluv## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Accent is 8
## Returning the palette you asked for with that many colors