Treemaps Heatmaps Streamgraphs and Alluvials
Joey Cartwright
10/05/2021
So many ways to visualize data
Treemaps
Treemaps display hierarchical (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”.
Create a treemap which explores categories of views
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)## Warning: package 'treemap' was built under R version 4.0.4library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.0.3## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.0.6 v dplyr 1.0.4
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0## Warning: package 'ggplot2' was built under R version 4.0.3## Warning: package 'tibble' was built under R version 4.0.3## Warning: package 'tidyr' was built under R version 4.0.2## Warning: package 'readr' was built under R version 4.0.3## Warning: package 'dplyr' was built under R version 4.0.3## Warning: package 'forcats' was built under R version 4.0.2## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(RColorBrewer)## Warning: package 'RColorBrewer' was built under R version 4.0.3data <- 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 SourcesHeatmaps
A heatmap is a literal way of visualizing a table of numbers, where you substitute the numbers with colored cells. There are two fundamentally different categories of heat maps: the cluster heat map and the spatial heat map. In a cluster heat map, magnitudes are laid out into a matrix of fixed cell size whose rows and columns are discrete categories, and the sorting of rows and columns is intentional. The size of the cell is arbitrary but large enough to be clearly visible. By contrast, the position of a magnitude in a spatial heat map is forced by the location of the magnitude in that space, and there is no notion of cells; the phenomenon is considered to vary continuously. (Wikipedia)
Load the nba data from 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 2.8Create 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 Player Stats in 2008")
Change the heatmap colors to heat colors
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")
Use the viridis colors
Try using direction = -1 and then try removing it; you will see that it reverses the heatmap levels
library(viridis)## Warning: package 'viridis' was built under R version 4.0.4## Loading required package: viridisLitenba_heatmap <- heatmap(nba_matrix, Rowv=NA, Colv = NA, col = viridis(25, direction = -1),
scale="column", margins=c(5,10),
xlab = "NBA Player Stats",
ylab = "NBA Players",
main = "NBA Player Stats in 2008")
Code for creating a treemap
treemap(data, index="category", vSize="views",
vColor="comments", type="value",
palette="RdYlBu")
- Notice how the treemap includes a legend for number of comments *
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
library(nycflights13)## Warning: package 'nycflights13' was built under R version 4.0.4library(RColorBrewer)
flights <- flights
#view(flights)Late Arrivals Affect the Usage Cost of Airports
This was modified from Raul Miranda’s work
flights_nona <- flights %>%
filter(!is.na(distance) & !is.na(arr_delay)) # remove observations with NA values - notice number of rows changed from 336,776 to 327,346delays <- 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 x 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 gives Reagan National (DCA) with the highest delay cost. Now get the top 100 and create the heatmap.
top100 <- delays %>% # select the 100 largest delay costs
head(100) %>%
arrange(delaycost) # sort ascending so the heatmap displays descending costs
row.names(top100) <- top100$dest # rename the rows according to destination airport codes## Warning: Setting row names on a tibble is deprecated.Create a matrix from the dataframe
row.names(top100) <- top100$dest # rename the rows according to destination airport codes## Warning: Setting row names on a tibble is deprecated.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 codesCall heatmap using a 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
varcols = setNames(colorRampPalette(brewer.pal(nrow(delays_mat2), "YlGnBu"))(nrow(delays_mat2)),
rownames(delays_mat2)) # parameter for RowSideColors## Warning in brewer.pal(nrow(delays_mat2), "YlGnBu"): n too large, allowed maximum for palette YlGnBu is 9
## Returning the palette you asked for with that many colorsheatmap(delays_mat2,
Rowv = NA, Colv = NA,
col= colorRampPalette(brewer.pal(nrow(delays_mat2), "YlGnBu"))(nrow(delays_mat2)),
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, RowSideColors = varcols)## layout: widths = 0.05 0.2 4 , heights = 0.25 4 ; lmat=
## [,1] [,2] [,3]
## [1,] 0 0 4
## [2,] 3 1 2## Warning in brewer.pal(nrow(delays_mat2), "YlGnBu"): n too large, allowed maximum for palette YlGnBu is 9
## Returning the palette you asked for with that many colors
What did this heatmap show?
“Cost index” is defined as a measure of how arrival delays impact the cost of flying into each airport and is calculated as number of flights * mean delay / mean flight distance. For airlines it is a measure of how much the cost to fly to an airport increases due to frequent delays of arrival. Cost index is inversely proportional to distance because delays affect short flights more than long flights and because the profit per seat increases with distance due to the larger and more efficient planes used for longer distances.
The variance in delays across airports is mainly due to (a) airline traffic congestion relative to the airport size; and (b)regional climate and weather events. It is not strongly dependent upon airline carrier or tailnumber.
Therefore, airports such as ORD and BOS have high cost index because they are highly congested and are frequently delayed due to weather. Airports like IAD, PHL, DTW, etc., are very congested despite their large size and also show high cost index. Smaller airports such as HDN, SNA, HNL, LEX, etc., have null to slightly negative cost index because they are not congested and keep flights on time.
Streamgraphs
This type of visualisation is a variation of a stacked area graph, but instead of plotting values against a fixed, straight axis, a streamgraph has values displaced around a varying central baseline. Streamgraphs display the changes in data over time of different categories through the use of flowing, organic shapes that somewhat resemble a river-like stream. This makes streamgraphs aesthetically pleasing and more engaging to look at.
The size of each individual stream shape is proportional to the values in each category. The axis that a streamgraph flows parallel to is used for the timescale. Color can be used to either distinguish each category or to visualize each category’s additional quantitative values through varying the color shade.
Streamgraphs are ideal for displaying high-volume datasets, in order to discover trends and patterns over time across a wide range of categories. For example, seasonal peaks and troughs in the stream shape can suggest a periodic pattern. A streamgraph could also be used to visualize the volatility for a large group of assets over a certain period of time.
The downside to a streamgraph is that they suffer from legibility issues, as they are often very cluttered. The categories with smaller values are often drowned out to make way for categories with much larger values, making it impossible to see all the data. Also, it’s impossible to read the exact values visualized, as there is no axis to use as a reference.
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.
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 4.0.0 (2020-04-24)
## 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 2021-10-05
##
## - Packages -------------------------------------------------------------------
## package * version date lib source
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.0)
## backports 1.2.1 2020-12-09 [1] CRAN (R 4.0.3)
## broom 0.7.2 2020-10-20 [1] CRAN (R 4.0.3)
## bslib 0.2.4 2021-01-25 [1] CRAN (R 4.0.4)
## cachem 1.0.4 2021-02-13 [1] CRAN (R 4.0.4)
## callr 3.5.1 2020-10-13 [1] CRAN (R 4.0.3)
## cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.0.2)
## cli 2.2.0 2020-11-20 [1] CRAN (R 4.0.3)
## colorspace 1.4-1 2019-03-18 [1] CRAN (R 4.0.0)
## crayon 1.3.4 2017-09-16 [1] CRAN (R 4.0.0)
## curl 4.3 2019-12-02 [1] CRAN (R 4.0.0)
## data.table 1.13.2 2020-10-19 [1] CRAN (R 4.0.3)
## DBI 1.1.0 2019-12-15 [1] CRAN (R 4.0.2)
## dbplyr 2.0.0 2020-11-03 [1] CRAN (R 4.0.3)
## desc 1.2.0 2018-05-01 [1] CRAN (R 4.0.0)
## devtools 2.3.2 2020-09-18 [1] CRAN (R 4.0.4)
## digest 0.6.27 2020-10-24 [1] CRAN (R 4.0.3)
## dplyr * 1.0.4 2021-02-02 [1] CRAN (R 4.0.3)
## ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.4)
## evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.0)
## fansi 0.4.1 2020-01-08 [1] CRAN (R 4.0.0)
## fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.0.4)
## forcats * 0.5.0 2020-03-01 [1] CRAN (R 4.0.2)
## fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2)
## generics 0.1.0 2020-10-31 [1] CRAN (R 4.0.3)
## ggplot2 * 3.3.3 2020-12-30 [1] CRAN (R 4.0.3)
## glue 1.4.0 2020-04-03 [1] CRAN (R 4.0.0)
## gridBase 0.4-7 2014-02-24 [1] CRAN (R 4.0.4)
## gridExtra 2.3 2017-09-09 [1] CRAN (R 4.0.4)
## gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.0)
## haven 2.3.1 2020-06-01 [1] CRAN (R 4.0.2)
## hms 0.5.3 2020-01-08 [1] CRAN (R 4.0.0)
## htmltools 0.5.1.1 2021-01-22 [1] CRAN (R 4.0.4)
## httpuv 1.5.4 2020-06-06 [1] CRAN (R 4.0.3)
## httr 1.4.2 2020-07-20 [1] CRAN (R 4.0.3)
## igraph 1.2.6 2020-10-06 [1] CRAN (R 4.0.4)
## jquerylib 0.1.3 2020-12-17 [1] CRAN (R 4.0.4)
## jsonlite 1.7.2 2020-12-09 [1] CRAN (R 4.0.0)
## knitr 1.30 2020-09-22 [1] CRAN (R 4.0.3)
## later 1.1.0.1 2020-06-05 [1] CRAN (R 4.0.2)
## lifecycle 0.2.0 2020-03-06 [1] CRAN (R 4.0.0)
## lubridate 1.7.9.2 2020-11-13 [1] CRAN (R 4.0.3)
## magrittr 2.0.1 2020-11-17 [1] CRAN (R 4.0.3)
## memoise 2.0.0 2021-01-26 [1] CRAN (R 4.0.4)
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##
## [1] C:/Users/Joeyc/Documents/R/win-library/4.0
## [2] C:/Program Files/R/R-4.0.0/librarylibrary(dplyr)
library(streamgraph)
library(babynames)## Warning: package 'babynames' was built under R version 4.0.5Here 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")Now look at the babynames dataset
ncol(babynames)## [1] 5head(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
Mouse over the colors and years to look at the pattern of various names
babynames %>%
filter(grepl("^Kr", name)) %>%
group_by(year, name) %>%
tally(wt=n) %>%
streamgraph("name", "n", "year")# Streamgraphing Commercial Real Estate Transaction Volume by Asset Class Since 2001
dat <- read.csv("http://asbcllc.com/blog/2015/february/cre_stream_graph_test/data/cre_transaction-data.csv")
dat %>%
streamgraph("asset_class", "volume_billions", "year", interpolate="cardinal") %>%
sg_axis_x(1, "year", "%Y") %>%
sg_fill_brewer("PuOr") Alluvials
Load the alluvial package
#install.packages("alluvial")
library(alluvial)## Warning: package 'alluvial' was built under R version 4.0.4Refugees is a prebuilt dataset in the alluvial package
If you want to save the prebuilt dataset to your folder, use the write_csv function
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 refugess in the top 10 countries from 2003-2013
Alluvials need the variables: category, 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() +
scale_fill_brewer(palette = "Accent") +
ggtitle("UNHCR-recognised refugees\nTop 10 countries (2003-2013)\n") +
ylab("Number of Refugees")
ggalluv