Treemaps Heatmaps Streamgraphs and Alluvials - HW Assignment

Author

Ibrahim Pinzon Perez

Published

February 26, 2023

So many ways to visualize data

Load the packages and the data from flowingdata.com website

# install.packages("treemap")
# install.packages("RColorBrewer")
library(treemap)
library(tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0     ✔ purrr   1.0.1
✔ tibble  3.1.8     ✔ dplyr   1.1.0
✔ tidyr   1.3.0     ✔ stringr 1.5.0
✔ readr   2.1.3     ✔ forcats 1.0.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(RColorBrewer)

Heatmaps

Load the nba data from Yau’s website

nba <- read.csv("http://datasets.flowingdata.com/ppg2008.csv")  # read.csv not read_csv
head(nba)

            Name  G  MIN  PTS  FGM  FGA   FGP FTM FTA   FTP X3PM X3PA  X3PP ORB
1   Dwyane Wade  79 38.6 30.2 10.8 22.0 0.491 7.5 9.8 0.765  1.1  3.5 0.317 1.1
2  LeBron James  81 37.7 28.4  9.7 19.9 0.489 7.3 9.4 0.780  1.6  4.7 0.344 1.3
3   Kobe Bryant  82 36.2 26.8  9.8 20.9 0.467 5.9 6.9 0.856  1.4  4.1 0.351 1.1
4 Dirk Nowitzki  81 37.7 25.9  9.6 20.0 0.479 6.0 6.7 0.890  0.8  2.1 0.359 1.1
5 Danny Granger  67 36.2 25.8  8.5 19.1 0.447 6.0 6.9 0.878  2.7  6.7 0.404 0.7
6  Kevin Durant  74 39.0 25.3  8.9 18.8 0.476 6.1 7.1 0.863  1.3  3.1 0.422 1.0
  DRB TRB AST STL BLK  TO  PF
1 3.9 5.0 7.5 2.2 1.3 3.4 2.3
2 6.3 7.6 7.2 1.7 1.1 3.0 1.7
3 4.1 5.2 4.9 1.5 0.5 2.6 2.3
4 7.3 8.4 2.4 0.8 0.8 1.9 2.2
5 4.4 5.1 2.7 1.0 1.4 2.5 3.1
6 5.5 6.5 2.8 1.3 0.7 3.0 1.8

Create a cool-color heatmap

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 Player Stats in 2008")

Change to warm color palette

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 Player Stats in 2008")

# install.packages("viridis")

Use the viridis color palette

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

Create a treemap which explores categories of views

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 Sources

Use RColorBrewer to change the palette to RdYlBu

treemap(data, index="category", vSize="views", 
        vColor="comments", type="manual",    
        palette="RdYlBu")

Use the dataset NYCFlights13 to create a heatmap that explores Late Arrivals

# install.packages("nycflights13")
library(nycflights13)
library(RColorBrewer)
data(flights)

Create an initial scatterplot with loess smoother for distance to delays

Remove observations with NA values from distand and arr_delay variables - notice number of rows changed from 336,776 to 327,346

flights_nona <- flights %>%
  filter(!is.na(distance) & !is.na(arr_delay))  # remove na's for distance and arr_delay

Use group_by and summarise to create a summary table

The table includes, counts for each tail number, mean distance traveled, and mean arrival delay

by_tailnum <- flights_nona %>%
  group_by(tailnum) %>%  # group all tailnumbers together
  summarise(count = n(),   # counts totals for each tailnumber
        dist = mean(distance), # calculates the mean distance traveled
        delay = mean(arr_delay)) # calculates the mean arrival delay
delay <- filter(by_tailnum, count > 20, dist < 2000) # only include counts > 20 and distance < 2000 mi

Average delay is only slightly related to average distance flown by a plane.

ggplot(delay, aes(dist, delay)) +
  geom_point(aes(size = count), alpha = 1/2) +
  geom_smooth() +
  scale_size_area()

`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

Late Arrivals Affect the Usage Cost of Airports

Creating a dataframe that is composed of summary statistics

delays <- flights_nona %>%    # create a delays dataframe by:
  group_by (dest) %>%         # grouping by point of destination
  summarize (count = n(),     # creating variables: number of flights to each destination,
             dist = mean (distance),   # the mean distance flown to each destination,
             delay = mean (arr_delay), # the mean delay of arrival to each destination,
             delaycost = mean(count*delay/dist)) # delay cost index defined as:
                                       #  [(number of flights)*delay/distance] for a destination
delays <- arrange(delays, desc(delaycost))    # sort the rows by delay cost
head(delays)                            # look at the data

# A tibble: 6 × 5
  dest  count  dist delay delaycost
  <chr> <int> <dbl> <dbl>     <dbl>
1 DCA    9111  211.  9.07      391.
2 IAD    5383  225. 13.9       332.
3 ATL   16837  757. 11.3       251.
4 BOS   15022  191.  2.91      230.
5 CLT   13674  538.  7.36      187.
6 RDU    7770  427. 10.1       183.

This shows Reagan National (DCA) with the highest delay cost, and Dulles

Here is another way to display all destinations in the table using the knitr package with the function, kable

# install.packages("knitr")
library(knitr)
kable(delays, 
      caption = "Table of Mean Distance, Mean Arrival Delay, and Highest Delay Costs",
      digits = 2)  # round values to 2 decimal places

Table of Mean Distance, Mean Arrival Delay, and Highest Delay Costs
dest	count	dist	delay	delaycost
DCA	9111	211.08	9.07	391.36
IAD	5383	224.74	13.86	332.08
ATL	16837	757.14	11.30	251.29
BOS	15022	190.74	2.91	229.53
CLT	13674	538.01	7.36	187.07
RDU	7770	426.73	10.05	183.04
RIC	2346	281.27	20.11	167.74
PHL	1541	94.34	10.13	165.42
BUF	4570	296.87	8.95	137.71
ORD	16566	729.02	5.88	133.54
ROC	2358	259.36	11.56	105.11
BWI	1687	179.35	10.73	100.90
CVG	3725	575.23	15.36	99.50
DTW	9031	498.20	5.43	98.43
CLE	4394	414.00	9.18	97.45
PWM	2288	276.03	11.66	96.65
BNA	6084	758.22	11.81	94.78
FLL	11897	1070.06	8.08	89.86
BTV	2510	265.12	8.95	84.74
MCO	13967	943.11	5.45	80.78
CMH	3326	476.55	10.60	73.99
SYR	1707	206.07	8.90	73.76
MDW	4025	718.09	12.36	69.30
MHT	932	207.38	14.79	66.46
PIT	2746	334.10	7.68	63.13
TPA	7390	1003.93	7.41	54.53
ORF	1434	288.55	10.95	54.41
PBI	6487	1028.82	8.56	53.99
MKE	2709	733.37	14.17	52.33
STL	4142	878.83	11.08	52.21
MSP	6929	1017.46	7.27	49.51
GSO	1492	449.79	14.11	46.81
CHS	2759	632.96	10.59	46.17
ALB	418	143.00	14.40	42.08
CAK	842	397.00	19.70	41.78
DEN	7169	1614.69	8.61	38.21
JAX	2623	824.71	11.84	37.67
PVD	358	160.00	16.23	36.32
DAY	1399	536.91	12.68	33.04
IND	1981	652.26	9.94	30.19
BDL	412	116.00	7.05	25.03
MCI	1885	1097.65	14.51	24.92
GRR	728	605.71	18.19	21.86
TYS	578	638.34	24.07	21.79
SDF	1104	645.96	12.67	21.65
IAH	7085	1407.18	4.24	21.35
GSP	790	595.98	15.94	21.12
MSY	3715	1177.73	6.49	20.47
MEM	1686	954.48	10.65	18.80
SAV	749	709.27	15.13	15.98
MSN	556	803.93	20.20	13.97
SFO	13173	2577.93	2.67	13.66
OMA	817	1135.56	14.70	10.58
RSW	3502	1072.85	3.24	10.57
HOU	2083	1420.26	7.18	10.52
DSM	523	1020.56	19.01	9.74
AUS	2411	1514.25	6.02	9.58
SJU	5773	1599.84	2.52	9.10
TUL	294	1215.00	33.66	8.14
BGR	358	378.00	8.03	7.60
CAE	106	603.70	41.76	7.33
OKC	315	1325.00	30.62	7.28
XNA	992	1142.44	7.47	6.48
ACK	264	199.00	4.85	6.44
BHM	269	866.00	16.88	5.24
BQN	888	1578.99	8.25	4.64
PHX	4606	2141.34	2.10	4.51
CRW	134	444.00	14.67	4.43
AVL	261	583.61	8.00	3.58
LAX	16026	2468.62	0.55	3.55
SRQ	1201	1044.64	3.08	3.54
SAN	2709	2437.28	3.14	3.49
MIA	11593	1091.54	0.30	3.18
SAT	659	1578.18	6.95	2.90
PDX	1342	2445.61	5.14	2.82
DFW	8388	1383.06	0.32	1.95
TVC	95	652.45	12.97	1.89
PSE	358	1617.00	7.87	1.74
CHO	46	305.00	9.50	1.43
SMF	282	2521.00	12.11	1.35
BUR	370	2465.00	8.18	1.23
ILM	107	500.00	4.64	0.99
EGE	207	1735.80	6.30	0.75
LAS	5952	2240.98	0.26	0.68
ABQ	254	1826.00	4.38	0.61
MYR	58	550.67	4.60	0.48
SJC	328	2569.00	3.45	0.44
OAK	309	2576.00	3.08	0.37
JAC	21	1875.90	28.10	0.31
SLC	2451	1986.99	0.18	0.22
BZN	35	1882.00	7.60	0.14
SBN	10	645.40	6.50	0.10
EYW	17	1207.00	6.35	0.09
HDN	14	1728.00	2.14	0.02
MTJ	14	1795.00	1.79	0.01
ANC	8	3370.00	-2.50	-0.01
LGB	661	2465.00	-0.06	-0.02
LEX	1	604.00	-22.00	-0.04
PSP	18	2378.00	-12.72	-0.10
HNL	701	4972.76	-1.37	-0.19
MVY	210	173.00	-0.29	-0.35
STT	518	1626.99	-3.84	-1.22
SEA	3885	2412.68	-1.10	-1.77
SNA	812	2434.00	-7.87	-2.62

Now get the top 100 delay costs to create a heatmap of those flights.

top100 <- delays %>%                  # select the 100 largest delay costs
  head(100) %>%
  arrange(delaycost)                  # sort ascending so the heatmap displays descending costs
row.names(top100) <- top100$dest      # rename the rows according to destination airport codes

Warning: Setting row names on a tibble is deprecated.

In order to make a heatmap, convert the dataframe to matrix form

delays_mat <- data.matrix(top100)  # convert delays dataframe to a matrix  (required by heatmap)
delays_mat2 <- delays_mat[,2:5]    # remove the redundant column of destination airport codes

Create a heatmap using colorBrewer

color set, margins=c(7,10) for aspect ratio, titles of graph, x and y labels,font size of x and y labels, and set up a RowSideColors bar

Flights Plot

heatmap(delays_mat2, 
        Rowv = NA, Colv = NA, 
        col= viridis(25),
        s=0.6, v=1, scale="column", 
        margins=c(7,10), 
        main = "Cost of Late Arrivals", 
        xlab = "Flight Characteristics", 
        ylab="Arrival Airport", labCol =      c("Flights","Distance","Delay","Cost Index"),
        cexCol=1, cexRow =1)

layout: widths =  0.05 4 , heights =  0.25 4 ; lmat=
     [,1] [,2]
[1,]    0    3
[2,]    2    1

Streamgraph code

Load devtools and libraries to create the following streamgraphs

# install.packages("devtools")
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 installation

devtools::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.2 (2022-10-31)
 os       macOS Monterey 12.0.1
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2023-02-28
 pandoc   2.19.2 @ /private/var/folders/fl/j75y2r0x3n9cxlrjwg42p23h0000gn/T/AppTranslocation/39D08012-B4FF-48EF-870F-44AE53A0623C/d/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version date (UTC) lib source
 assertthat      0.2.1   2019-03-21 [1] CRAN (R 4.2.0)
 backports       1.4.1   2021-12-13 [1] CRAN (R 4.2.0)
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 magrittr        2.0.3   2022-03-30 [1] CRAN (R 4.2.0)
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 [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library

──────────────────────────────────────────────────────────────────────────────

library(dplyr)
library(streamgraph)
library(babynames)
babynames <- babynames

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, package, id = x$id, style = css(width =
validateCssUnit(sizeInfo$width), : streamgraph_html returned an object of class
`list` instead of a `shiny.tag`.

Warning: `bindFillRole()` only works on htmltools::tag() objects (e.g., div(),
p(), etc.), not objects of type 'list'.

Now look at the babynames dataset

ncol(babynames)

[1] 5

head(babynames)

# A tibble: 6 × 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 0.0162

str(babynames)

tibble [1,924,665 × 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

Mouse over the colors and years to look at the pattern of various names

babynames %>%
  filter(grepl("^Ibra", name)) %>%
  group_by(year, name) %>%
  tally(wt=n) %>%
  streamgraph("name", "n", "year")

Warning in widget_html(name, package, id = x$id, style = css(width =
validateCssUnit(sizeInfo$width), : streamgraph_html returned an object of class
`list` instead of a `shiny.tag`.

Warning: `bindFillRole()` only works on htmltools::tag() objects (e.g., div(),
p(), etc.), not objects of type 'list'.

Alluvials

Refugees is a prebuilt dataset in the alluvial package

library(alluvial)
library(ggalluvial)
data(Refugees)

Show UNHCR-recognised refugees

Top 10 most affected countries causing refugees from 2003-2013

ggalluv <- ggplot(Refugees,
             aes(x = year, y = refugees, alluvium = country)) + # time series bump chart (quintic flows)
  theme_bw() +
  geom_alluvium(aes(fill = country), 
                color = "white",
                width = .1, 
                alpha = .8,
                decreasing = FALSE) +
  scale_fill_brewer(palette = "Spectral") + # Spectral has enough colors for all countries listed
  scale_x_continuous(lim = c(2002, 2013))+
  ggtitle("UNHCR-Recognised Refugees \n Top 10 Countries(2003-2013)\n")+ # \n breaks the long title
  ylab("Number of Refugees")

Refugee Plot

ggalluv

A final touch to fix the y-axis scale

Notice the y-values are in scientific notation. We can convert them to standard notation with options scipen function

options(scipen = 999)
ggalluv