From cbe22da02b05863f9956a935ee24d3d9a4fd0dbd Mon Sep 17 00:00:00 2001 From: Holtz Yan Date: Fri, 27 Sep 2024 08:49:10 +0200 Subject: [PATCH] fix Guy --- Example_dataset/Readme.md | 76 +- graph/chord.Rmd | 4 +- graph/chord.html | 1000 ++-- story/AdjacencyMatrix.Rmd | 4 +- story/AdjacencyMatrix.html | 9837 ++++++++++++++++++++++++++++++++++-- 5 files changed, 9975 insertions(+), 946 deletions(-) diff --git a/Example_dataset/Readme.md b/Example_dataset/Readme.md index e66c75a..808b689 100644 --- a/Example_dataset/Readme.md +++ b/Example_dataset/Readme.md @@ -1,56 +1,24 @@ This folder lists the datasets used in the website [data-to-viz.com](https://www.data-to-viz.com). -| id | Name | Source | Description | Analysis | -|----|------------- |---------------| ------------| ---------| -|1 | OneNum | [AirBnb](https://s3.amazonaws.com/tomslee-airbnb-data-2/alpes_maritime.zip) | Night price for 10000 appartments on the french riviera | [link](https://www.data-to-viz.com/story/OneNum.html) | -|2 | TwoNum | [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data) | Apartment price and ground living area | [link](https://www.data-to-viz.com/story/TwoNum.html) | -|3 | TwoNumOrdered | [CoinMarketCap](https://github.com/JesseVent/crypto) | Daily bitcoin price between 2014 and 2018 | [link](https://www.data-to-viz.com/story/TwoNum.html) | -|4 | ThreeNum | [Gapminder](https://github.com/jennybc/gapminder) | Gapminder: life Expectancy, pop and per-capita GDP for several countries | [link](https://www.data-to-viz.com/story/ThreeNum.html) | -|4 | ThreeNum | [R & Ross Ihaka](https://vincentarelbundock.github.io/Rdatasets/doc/datasets/volcano.html) | Topographic information for Maunga Whau volcano | [link](https://www.data-to-viz.com/story/OneCatSevOrderedNum.html) | -|5 | OneCatSevNumOrdered | [Babynames R library](https://github.com/hadley/babynames) | Evolution of first name popularity in the US | [link](https://www.data-to-viz.com/story/OneCatSevOrderedNum.html) | -|6 | SeveralNum | [Cars](https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/mtcars.html) | Features of 32 cars | [link](https://www.data-to-viz.com/story/SeveralNum.html) | -|7 | OneNumOneCat | [Peace Research Inst.](http://armstrade.sipri.org/armstrade/page/toplist.php) | Quantity of weapons exported by the top 50 largest exporters | [link](https://www.data-to-viz.com/story/OneNumOneCat.html) | -|8 | OneNumOneCatSevObs | [Reddit](https://github.com/zonination/perceptions) | Perception of probability | [link]() | -|9 | OneNumSevCatSubgroupOneObs | [OECD](https://stats.oecd.org/index.aspx?queryid=54751) | The Gender Wage Gap | [link](https://www.data-to-viz.com/story/OneNumSevCatSubgroupOneObsPerGroup.html) | -|10 | OneNumSevCatSubgroupSevObsPerGroup | [Seaborn Python library](https://www.data-to-viz.com/story/OneNumSevCatSubgroupSevObsPerGroup.html) | How much do people tip? | [link]() | -|11 | SevCatOneNumNestedOneObsPerGroup | [Wikipedia](https://github.com/mledoze/countries) | World population for 250 countries | [link](https://www.data-to-viz.com/story/SevCatOneNumNestedOneObsPerGroup.html) | -|13 | AdjacencyMatrix | [Goggle Scholar](https://github.com/holtzy/Google-Scholar-Network) | Co-autorship network of a researcher | [link](https://www.data-to-viz.com/story/AdjacencyMatrix.html) | -|13 | AdjacencyMatrix | [Gui J. Abel Publication](https://onlinelibrary.wiley.com/doi/abs/10.1111/imre.12327) | Migration of people at world scale | [link](https://www.data-to-viz.com/story/AdjacencyMatrix.html) | -|14 | SeveralIndepLists | [Paroles.net](https://www.paroles.net/) | Lyrics of french rapers | [link](https://www.data-to-viz.com/story/SeveralIndepLists.html) | -|15 | NestedLinksValue | [-](https://www.paroles.net/) | - | [link]() | -|16 | NestedAndConnection | [Flare](https://www.paroles.net/) | Connection between informatic components | [link]() | -|17 | ListGPSCoordinates | [Surfer project](https://github.com/holtzy/About-Surfers-On-Twitter) | Where do people twitting #surf live? | [link]() | -|18 | ListGPSCoordinatesWithValue | [Maps R library](https://github.com/adeckmyn/maps) | Population of 925 cities in the UK | [link]() | -|19 | MapConnection | [Surfer Project](https://github.com/holtzy/About-Surfers-On-Twitter) | Where do surfers travel | [link]() | - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +| id | Name | Source | Description | Analysis | +| --- | ---------------------------------- | --------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------ | --------------------------------------------------------------------------------- | +| 1 | OneNum | [AirBnb](https://s3.amazonaws.com/tomslee-airbnb-data-2/alpes_maritime.zip) | Night price for 10000 appartments on the french riviera | [link](https://www.data-to-viz.com/story/OneNum.html) | +| 2 | TwoNum | [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data) | Apartment price and ground living area | [link](https://www.data-to-viz.com/story/TwoNum.html) | +| 3 | TwoNumOrdered | [CoinMarketCap](https://github.com/JesseVent/crypto) | Daily bitcoin price between 2014 and 2018 | [link](https://www.data-to-viz.com/story/TwoNum.html) | +| 4 | ThreeNum | [Gapminder](https://github.com/jennybc/gapminder) | Gapminder: life Expectancy, pop and per-capita GDP for several countries | [link](https://www.data-to-viz.com/story/ThreeNum.html) | +| 4 | ThreeNum | [R & Ross Ihaka](https://vincentarelbundock.github.io/Rdatasets/doc/datasets/volcano.html) | Topographic information for Maunga Whau volcano | [link](https://www.data-to-viz.com/story/OneCatSevOrderedNum.html) | +| 5 | OneCatSevNumOrdered | [Babynames R library](https://github.com/hadley/babynames) | Evolution of first name popularity in the US | [link](https://www.data-to-viz.com/story/OneCatSevOrderedNum.html) | +| 6 | SeveralNum | [Cars](https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/mtcars.html) | Features of 32 cars | [link](https://www.data-to-viz.com/story/SeveralNum.html) | +| 7 | OneNumOneCat | [Peace Research Inst.](http://armstrade.sipri.org/armstrade/page/toplist.php) | Quantity of weapons exported by the top 50 largest exporters | [link](https://www.data-to-viz.com/story/OneNumOneCat.html) | +| 8 | OneNumOneCatSevObs | [Reddit](https://github.com/zonination/perceptions) | Perception of probability | [link]() | +| 9 | OneNumSevCatSubgroupOneObs | [OECD](https://stats.oecd.org/index.aspx?queryid=54751) | The Gender Wage Gap | [link](https://www.data-to-viz.com/story/OneNumSevCatSubgroupOneObsPerGroup.html) | +| 10 | OneNumSevCatSubgroupSevObsPerGroup | [Seaborn Python library](https://www.data-to-viz.com/story/OneNumSevCatSubgroupSevObsPerGroup.html) | How much do people tip? | [link]() | +| 11 | SevCatOneNumNestedOneObsPerGroup | [Wikipedia](https://github.com/mledoze/countries) | World population for 250 countries | [link](https://www.data-to-viz.com/story/SevCatOneNumNestedOneObsPerGroup.html) | +| 13 | AdjacencyMatrix | [Goggle Scholar](https://github.com/holtzy/Google-Scholar-Network) | Co-autorship network of a researcher | [link](https://www.data-to-viz.com/story/AdjacencyMatrix.html) | +| 13 | AdjacencyMatrix | [Guy J. Abel Publication](https://onlinelibrary.wiley.com/doi/abs/10.1111/imre.12327) | Migration of people at world scale | [link](https://www.data-to-viz.com/story/AdjacencyMatrix.html) | +| 14 | SeveralIndepLists | [Paroles.net](https://www.paroles.net/) | Lyrics of french rapers | [link](https://www.data-to-viz.com/story/SeveralIndepLists.html) | +| 15 | NestedLinksValue | [-](https://www.paroles.net/) | - | [link]() | +| 16 | NestedAndConnection | [Flare](https://www.paroles.net/) | Connection between informatic components | [link]() | +| 17 | ListGPSCoordinates | [Surfer project](https://github.com/holtzy/About-Surfers-On-Twitter) | Where do people twitting #surf live? | [link]() | +| 18 | ListGPSCoordinatesWithValue | [Maps R library](https://github.com/adeckmyn/maps) | Population of 925 cities in the UK | [link]() | +| 19 | MapConnection | [Surfer Project](https://github.com/holtzy/About-Surfers-On-Twitter) | Where do surfers travel | [link]() | diff --git a/graph/chord.Rmd b/graph/chord.Rmd index 9053bea..478550d 100644 --- a/graph/chord.Rmd +++ b/graph/chord.Rmd @@ -32,7 +32,7 @@ A `chord diagram` represents flows or connections between several entities (call -Here is an example displaying the number of people migrating from one country to another. Data used comes from this [scientific publication](https://onlinelibrary.wiley.com/doi/abs/10.1111/imre.12327) from [Gui J. Abel](http://guyabel.com). +Here is an example displaying the number of people migrating from one country to another. Data used comes from this [scientific publication](https://onlinelibrary.wiley.com/doi/abs/10.1111/imre.12327) from [Guy J. Abel](http://guyabel.com). ```{r, warning=FALSE, message=FALSE, fig.align="center", fig.height=7, fig.width=7} # Libraries @@ -108,7 +108,7 @@ circos.trackPlotRegion( ``` -*Note*: this plot is made using the circlize library, and very strongly inspired from the [Migest package](https://github.com/cran/migest) from [Gui J. Abel](http://guyabel.com). Read more about this story [here](https://www.data-to-viz.com/story/AdjacencyMatrix.html). +*Note*: this plot is made using the circlize library, and very strongly inspired from the [Migest package](https://github.com/cran/migest) from [Guy J. Abel](http://guyabel.com). Read more about this story [here](https://www.data-to-viz.com/story/AdjacencyMatrix.html). diff --git a/graph/chord.html b/graph/chord.html index 0e90792..60928a4 100644 --- a/graph/chord.html +++ b/graph/chord.html @@ -1,8 +1,6 @@ - + Chord diagram – from Data to Viz @@ -36,10 +34,7 @@ content="An extensive description of Chord diagram. Definition, examples, input data, common caveats, tool to build it and potential alternatives." /> - + - - - - - - - - - - + + + + + + + + + + + - + - - - + + + - -
- - - + -
-
-

Definition

-
-

A chord diagram represents flows or connections between -several entities (called nodes). Each entity is represented -by a fragment on the outer part of the circular layout. -Then, arcs are drawn between each entities. The size of the -arc is proportional to the importance of the flow.

-

Here is an example displaying the number of people migrating from one -country to another. Data used comes from this scientific -publication from Gui J. Abel.

-
# Libraries
+      

+        

+          
Definition

+          

+          

+            A chord diagram represents flows or connections between
+            several entities (called nodes). Each entity is
+            represented by a fragment on the outer part of the
+            circular layout. Then, arcs are drawn
+            between each entities. The size of the arc is proportional to the
+            importance of the flow.
+          

+          

+            Here is an example displaying the number of people migrating from
+            one country to another. Data used comes from this
+            scientific publication
+            from Guy J. Abel.
+          

+          

+            
# Libraries
 library(tidyverse)
 library(viridis)
 library(patchwork)
@@ -371,44 +505,74 @@ 
Definition

       major.tick.percentage = 0.5,
       labels.niceFacing = FALSE)
   }
-)

-

-
Note: this plot is made using the circlize library, and very
-strongly inspired from the Migest package from Gui J. Abel. Read more about this story here.

-

-

-
What for

-

-
Chord diagrams are eye catching and quite popular in data
-visualization. They allow to visualize weigthed
-relationships between several entities. They are adapted for several
-specific situations that slightly modify the output and the way to read
-them:

-
    
-
  • Flow. This is the example decribed in the chord diagram above.
-But two ways to represent it:
    
-
      
-
    • One asymetric arc per pair
      • 
-
    • Two arcs per pair
      • 
-
    • 
-
  • Bipartite: nodes are grouped in a few categories. Connections go
-between categories but not within categories. In my
-opinion sankey
-diagrams are more adapted in this situation.
    • 
-

-
Note: this section is under construction.

-

-

-
Variation

-

-
Interactivity is a real plus to make the chord diagram
-understandable. In the example below, you can hover a specific group to
-highlight all its connections.

-

-
library(chorddiag)
+)

+          

+          

+            
+          

+          

+            Note: this plot is made using the circlize library, and
+            very strongly inspired from the
+            Migest package from
+            Guy J. Abel. Read more about this
+            story
+            here.
+          

+        

+        

+          
What for

+          

+          

+            Chord diagrams are eye catching and quite popular in data
+            visualization. They allow to visualize
+            weigthed relationships between several entities. They
+            are adapted for several specific situations that slightly modify the
+            output and the way to read them:
+          

+          
    
+            
  • 
+              
    
+                Flow. This is the example decribed in the chord diagram above.
+                But two ways to represent it:
+              
    
+              
      
+                
    • One asymetric arc per pair
      • 
+                
    • Two arcs per pair
      • 
+              
    
+            
    • 
+            
  • 
+              
    
+                Bipartite: nodes are grouped in a few categories. Connections go
+                between categories but not within categories.
+                In my opinion
+                sankey diagrams
+                are more adapted in this situation.
+              
    
+            
    • 
+          

+          
Note: this section is under construction.

+        

+        

+          
Variation

+          

+          

+            Interactivity is a real plus to make the chord diagram
+            understandable. In the example below, you can hover a specific group
+            to highlight all its connections.
+          

+          

+            

+              
library(chorddiag)
 m <- matrix(c(11975,  5871, 8916, 2868,
               1951, 10048, 2060, 6171,
               8010, 16145, 8090, 8045,
@@ -420,207 +584,315 @@ 
Variation

                     prefer = haircolors)
 
 groupColors <- c("#000000", "#FFDD89", "#957244", "#F26223")
-chorddiag(m, groupColors = groupColors, groupnamePadding = 20)

-

-
-

-
Note: this example comes from the chorddiag package
-documentation.

-

-

-
Common mistakes

-

-
    
-
  • The group order around the circle is important. Try to minimize the
-number of arc crossing.
    • 
-
  • Mind over-cluttering that makes the figure unreadable. It is advised
-to dismiss weak connections.
    • 
-
  • Chord diagrams are not straightforward to understand at all. Give
-plenty of explanation to your audience when you present it. It is
-advised to break down the graphic, presenting its components
-progressively. See this awesome example of storytelling from Nadieh
-Bremer.
    • 
-

-

-
-

-
Build your own

-

-
The R,
-Python, React and
-D3 graph galleries
-are 4 websites providing hundreds of chart example, always providing the
-reproducible code. Click the button below to see how to build the chart
-you need with your favorite programing language.

-

-R
-graph gallery
-Python
-gallery
-React
-gallery
-D3
-gallery
-

-

-

-
-          

-            

-              
chorddiag(m, groupColors = groupColors, groupnamePadding = 20)
+
+
+ + +

+ Note: this example comes from the + chorddiag + package documentation. +

+
+
+

Common mistakes

+
+
    +
  • + The group order around the circle is important. Try to minimize + the number of arc crossing. +
    • +
  • + Mind over-cluttering that makes the figure unreadable. It is + advised to dismiss weak connections. +
    • +
  • + Chord diagrams are not straightforward to understand at all. Give + plenty of explanation to your audience when you present it. It is + advised to break down the graphic, presenting its components + progressively. See this awesome example of storytelling from + Nadieh Bremer. +
    • +
+
+ +
+
+

Build your own

+
+

+ The R, + Python, + React + and D3 graph + galleries are 4 websites providing hundreds of chart example, always + providing the reproducible code. Click the button below to see how + to build the chart you need with your favorite programing language. +

+

+ R graph gallery + Python gallery + React gallery + D3 gallery +

+
+
+ +
+
+
+

+ Dataviz decision tree +

+

+ Data To Viz is a + comprehensive classification of chart types organized by + data input format. Get a high-resolution version of our decision + tree delivered to your inbox now! +

+
+ +
+
+
+ High Resolution Poster +
+
+
+
-   -
-

A work by Yan Holtz for data-to-viz.com

+   +
+

+ A work by Yan Holtz for + data-to-viz.com +

- - + + - -

- - - -

+ +

+ + + +

- - - + - + gtag("config", "UA-79254642-3"); + + -  +   + - + + diff --git a/story/AdjacencyMatrix.Rmd b/story/AdjacencyMatrix.Rmd index db858a2..98336fd 100644 --- a/story/AdjacencyMatrix.Rmd +++ b/story/AdjacencyMatrix.Rmd @@ -33,7 +33,7 @@ output: Adjacency and incidence matrices provide relationship between several nodes. The information they contain can have different nature, thus this document will consider several examples: -- Relationships can be `directed` and `weighted`. Like the number of people migrating from one country to another. Data used comes from this [scientific publication](https://onlinelibrary.wiley.com/doi/abs/10.1111/imre.12327) from [Gui J. Abel](http://guyabel.com). +- Relationships can be `directed` and `weighted`. Like the number of people migrating from one country to another. Data used comes from this [scientific publication](https://onlinelibrary.wiley.com/doi/abs/10.1111/imre.12327) from [Guy J. Abel](http://guyabel.com). ```{r, warning=FALSE, message=FALSE} # Libraries library(tidyverse) @@ -80,7 +80,7 @@ dataUU %>% head(3) %>% select(1:4) %>% kable() %>% A chord diagram is a good way to represent migration flows. It works well if your data are directed and weighted like for migration flows between country. -Disclaimer: this plot is made using the circlize library, and very strongly inspired from the [Migest package](https://github.com/cran/migest) from [Gui J. Abel](http://guyabel.com), who is also the author of the migration [dataset](https://www.oeaw.ac.at/fileadmin/subsites/Institute/VID/PDF/Publications/Working_Papers/WP2016_02.pdf) used here. +Disclaimer: this plot is made using the circlize library, and very strongly inspired from the [Migest package](https://github.com/cran/migest) from [Guy J. Abel](http://guyabel.com), who is also the author of the migration [dataset](https://www.oeaw.ac.at/fileadmin/subsites/Institute/VID/PDF/Publications/Working_Papers/WP2016_02.pdf) used here. Since this kind of graphic is used to display flows, it can be applied only on networks in which connections are `weighted`. It does not work for the other example on authors connections. diff --git a/story/AdjacencyMatrix.html b/story/AdjacencyMatrix.html index 08bb265..98b7ee5 100644 --- a/story/AdjacencyMatrix.html +++ b/story/AdjacencyMatrix.html @@ -1,8 +1,6 @@ - + @@ -54,142 +52,280 @@

- + - + - + - + - + - + - AdjacencyMatrix.knit - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - + - - - + + + - -
- - - -

-
-

Adjacency and incidence matrices provide relationship between several -nodes. The information they contain can have different nature, thus this -document will consider several examples:

-
    -
  • Relationships can be directed and -weighted. Like the number of people migrating from one -country to another. Data used comes from this scientific -publication from Gui J. Abel.
    • -
-
# Libraries
+      
+      


+      

+        

+          Adjacency and incidence matrices provide relationship between several
+          nodes. The information they contain can have different nature, thus
+          this document will consider several examples:
+        

+        
    
+          
  • 
+            Relationships can be directed and
+            weighted. Like the number of people migrating from one
+            country to another. Data used comes from this
+            scientific publication
+            from Guy J. Abel.
+          
    • 
+        

+        

+          
# Libraries
 library(tidyverse)
 library(hrbrthemes)
 library(circlize)
@@ -313,171 +451,142 @@
 
 # show data
 data %>% head(3) %>% select(1:3) %>% kable() %>%
-  kable_styling(bootstrap_options = "striped", full_width = F)

-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-

-
-Africa
-
-East.Asia
-
-Europe
-

-Africa
-
-3.142471
-
-0.000000
-
-2.107883
-

-East Asia
-
-0.000000
-
-1.630997
-
-0.601265
-

-Europe
-
-0.000000
-
-0.000000
-
-2.401476
-

-
    
-
  • Relationships can be undirected and
-unweighted. I will consider all the co-authors of a
-researcher and study who is connected through a common publication. Data
-have been retrieved using the scholar package, the
-pipeline is describe in this github
-repository. The result is an adjacency matrix with about 100
-researchers, filled with 1 if they have published a paper together, 0
-otherwise.
    • 
-

-
# Load data
+  kable_styling(bootstrap_options = "striped", full_width = F)

+        

+        
+          
+            
+              
+              
+              
+              
+            
+          
+          
+            
+              
+              
+              
+              
+            
+            
+              
+              
+              
+              
+            
+            
+              
+              
+              
+              
+            
+          
+        
AfricaEast.AsiaEurope
Africa3.1424710.0000002.107883
East Asia0.0000001.6309970.601265
Europe0.0000000.0000002.401476

+        
    
+          
  • 
+            Relationships can be undirected and
+            unweighted. I will consider all the co-authors of a
+            researcher and study who is connected through a common publication.
+            Data have been retrieved using the
+            scholar package,
+            the pipeline is describe in this
+            github repository. The result is an adjacency matrix with about 100 researchers,
+            filled with 1 if they have published a paper together, 0 otherwise.
+          
    • 
+        

+        

+          
# Load data
 dataUU <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/13_AdjacencyUndirectedUnweighted.csv", header=TRUE)
 
 # show data
 dataUU %>% head(3) %>% select(1:4) %>% kable() %>%
-  kable_styling(bootstrap_options = "striped", full_width = F)

-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-

-from
-
-A.Bateman
-
-A.Besnard
-
-A.Breil
-

-A Armero
-
-NA
-
-NA
-
-1
-

-A Bateman
-
-NA
-
-NA
-
-NA
-

-A Besnard
-
-NA
-
-NA
-
-NA
-

-
    
-
  • Relationships can also be undirected and
-weighted
    • 
-
  • Relationships can also be directed and
-unweighted
    • 
-

-

-
Chord diagram

-

-
A chord diagram is a good way to represent migration flows. It works
-well if your data are directed and weighted like for migration flows
-between country.

-
Disclaimer: this plot is made using the circlize library, and very
-strongly inspired from the Migest package from Gui J. Abel, who is also the author of the
-migration dataset
-used here.

-
Since this kind of graphic is used to display flows, it can be
-applied only on networks in which connections are weighted.
-It does not work for the other example on authors connections.

-
# short names
+  kable_styling(bootstrap_options = "striped", full_width = F)

+        

+        
+          
+            
+              
+              
+              
+              
+            
+          
+          
+            
+              
+              
+              
+              
+            
+            
+              
+              
+              
+              
+            
+            
+              
+              
+              
+              
+            
+          
+        
fromA.BatemanA.BesnardA.Breil
A ArmeroNANA1
A BatemanNANANA
A BesnardNANANA

+        
    
+          
  • 
+            
    
+              Relationships can also be undirected and
+              weighted
+            
    
+          
    • 
+          
  • 
+            
    
+              Relationships can also be directed and
+              unweighted
+            
    
+          
    • 
+        

+        

+          
Chord diagram

+          

+          

+            A chord diagram is a good way to represent migration flows. It works
+            well if your data are directed and weighted like for migration flows
+            between country.
+          

+          

+            Disclaimer: this plot is made using the circlize library, and very
+            strongly inspired from the
+            Migest package from
+            Guy J. Abel, who is also the author
+            of the migration
+            dataset
+            used here.
+          

+          

+            Since this kind of graphic is used to display flows, it can be
+            applied only on networks in which connections are
+            weighted. It does not work for the other example on
+            authors connections.
+          

+          

+            
# short names
 colnames(data) <- c("Africa", "East Asia", "Europe", "Latin Ame.",   "North Ame.",   "Oceania", "South Asia", "South East Asia", "Soviet Union", "West.Asia")
 rownames(data) <- colnames(data)
 
@@ -535,8 +644,9 @@ 
Chord diagram

       major.tick.percentage = 0.5,
       labels.niceFacing = FALSE)
   }
-)

-
## `major.tick.percentage` is not used any more, please directly use argument `major.tick.length`.
+)

+          

+          
## `major.tick.percentage` is not used any more, please directly use argument `major.tick.length`.
 ## `major.tick.percentage` is not used any more, please directly use argument `major.tick.length`.
 ## `major.tick.percentage` is not used any more, please directly use argument `major.tick.length`.
 ## `major.tick.percentage` is not used any more, please directly use argument `major.tick.length`.
@@ -546,27 +656,44 @@ 
Chord diagram

 ## `major.tick.percentage` is not used any more, please directly use argument `major.tick.length`.
 ## `major.tick.percentage` is not used any more, please directly use argument `major.tick.length`.
 ## `major.tick.percentage` is not used any more, please directly use argument `major.tick.length`.

-

-In my opinion this is a powerful way to display information. Major flows
-are easy to detect, like the migration from South Asia towards Westa
-Asia, or Africa to Europe. Moreover, for each continent it is quite easy
-to quantify the proportion of people leaving and arriving.

-
However chord diagram is not an usual way of displaying information.
-Thus, it is advised to give a good amount of explanation to educate your
-audience. A good way to do so is to draw just a few connections in a
-first step, before displaying the whole graphic. See this blog post by
-Nadieh
-Bremer for more ideas on this topic.

-

-

-
Sankey diagram

-

-
Sankey diagram is another option
-to display weighted connection. Intead of displaying regions on a
-circle, they are duplicated and represented on both sides of the
-graphic. Origin is usually on the left, destination on the right.

-
# Package
+          

+            
+            In my opinion this is a powerful way to display information. Major
+            flows are easy to detect, like the migration from South Asia towards
+            Westa Asia, or Africa to Europe. Moreover, for each continent it is
+            quite easy to quantify the proportion of people leaving and
+            arriving.
+          

+          

+            However chord diagram is not an usual way of displaying information.
+            Thus, it is advised to give a good amount of explanation to educate
+            your audience. A good way to do so is to draw just a few connections
+            in a first step, before displaying the whole graphic. See this blog
+            post by
+            Nadieh Bremer
+            for more ideas on this topic.
+          

+        

+        

+          
Sankey diagram

+          

+          

+            Sankey diagram is another option
+            to display weighted connection. Intead of displaying regions on a
+            circle, they are duplicated and represented on both sides of the
+            graphic. Origin is usually on the left, destination on the right.
+          

+          

+            
# Package
 library(networkD3)
 
 # I need a long format
@@ -591,20 +718,125 @@ 
Sankey diagram

 sankeyNetwork(Links = data_long, Nodes = nodes,
                      Source = "IDsource", Target = "IDtarget",
                      Value = "value", NodeID = "name",
-                     sinksRight=FALSE, colourScale=ColourScal, nodeWidth=40, fontSize=13, nodePadding=20)

-

-
-

-

-
Heatmap

-

-
The heatmap is
-another great alternative to represent an adjacency matrix. Here, all
-the origin countries are represented as row, and all the destination as
-columns. The diagonal pops out with a lot of yellow squares, which means
-that most of the migrations are intra continental.

-
library(heatmaply)
+                     sinksRight=FALSE, colourScale=ColourScal, nodeWidth=40, fontSize=13, nodePadding=20)

+          

+          

+          
+        

+        

+          
Heatmap

+          

+          

+            The
+            heatmap
+            is another great alternative to represent an adjacency matrix. Here,
+            all the origin countries are represented as row, and all the
+            destination as columns. The diagonal pops out with a lot of yellow
+            squares, which means that most of the migrations are intra
+            continental.
+          

+          

+            
library(heatmaply)
 p <- heatmaply(data,
         dendrogram = "none",
         xlab = "", ylab = "",
@@ -621,20 +853,1016 @@ 
Heatmap

         labCol = colnames(data),
         labRow = rownames(data),
         heatmap_layers = theme(axis.line=element_blank())
-        )

-

-

-
-

-



-
Note that if the matrix is unweighted, each connection
-can have only 2 values: 1 if there is a connection, 0 otherwise. It is
-the case for the co-authorship network example, where researchers are
-connected if they have already published a paper together. The heatmap
-below shows these connections and also applies a clustering algorithm to
-the data: researchers that tend to be involved in the same papers are
-grouped together.

-
# Format data
+        )

+          

+          

+            

+            
+          

+          



+          

+            Note that if the matrix is unweighted, each connection
+            can have only 2 values: 1 if there is a connection, 0 otherwise. It
+            is the case for the co-authorship network example, where researchers
+            are connected if they have already published a paper together. The
+            heatmap below shows these connections and also applies a clustering
+            algorithm to the data: researchers that tend to be involved in the
+            same papers are grouped together.
+          

+          

+            
# Format data
 tmp <- dataUU
 rownames(tmp) <- tmp$from
 tmp <- tmp %>% select(-from)
@@ -660,33 +1888,7560 @@ 
Heatmap

         labCol = colnames(tmp),
         labRow = rownames(tmp),
         heatmap_layers = theme(axis.line=element_blank())
-        )

-

-

-
-

-

-

-
Network

-

-
Since an adjacency matrix is a network structure, it is
-possible to build a network graph.
-In a network graph, each entity is represented as a node,
-and each connection as an edge.

-
In my opinion, this type of representation makes more sense when the
-connections are unweighted, since drawing edges with
-different sizes tends to clutter the figure and make it unreadable.

-
Thus, here is an application of this chart type to the coauthor
-network. Researchers are the nodes, represented as dots. If 2
-researchers have published at least one scientific paper together, they
-are connected. The node size is proportionnal to the number of
-coauthors.

-

-

-

-
# Transform the adjacency matrix in a long format
+        )

+          

+          

+            

+            
+          

+        

+        

+          
Network

+          

+          

+            Since an adjacency matrix is a network structure, it is
+            possible to build a
+            network graph. In a network graph, each entity is represented as a
+            node, and each connection as an edge.
+          

+          

+            In my opinion, this type of representation makes more sense when the
+            connections are unweighted, since drawing edges with
+            different sizes tends to clutter the figure and make it unreadable.
+          

+          

+            Thus, here is an application of this chart type to the coauthor
+            network. Researchers are the nodes, represented as dots. If 2
+            researchers have published at least one scientific paper together,
+            they are connected. The node size is proportionnal to the number of
+            coauthors.
+          

+        

+      

+      

+        

+          
# Transform the adjacency matrix in a long format
 connect <- dataUU %>%
   gather(key="to", value="value", -1) %>%
   mutate(to = gsub("\\.", " ",to)) %>%
@@ -740,31 +9495,49 @@ 
Network

     plot.margin=unit(c(0,0,0,0), "null"),
     panel.spacing=unit(c(0,0,0,0), "null")
   ) +
-  expand_limits(x = c(-1.2, 1.2), y = c(-1.2, 1.2))

-

-

-

-
Network
-graphs are very powerful to study the global structure of the
-network. Here, a few groups of researchers are isolated. Each actually
-represents one single paper where Vincent Ranwez was involved. In the
-middle a massive network of researchers appear: these are the people who
-Vincent published with most often, and are therefore all linked
-together.

-


-
However, network charts are very bad at annotating every single
-points: names tend to overlap edges making the figure unreadable. The
-arc diagram described below is a good alternative if you want to show
-labels.

-

-
Chord diagram (again)

-

-
Instead of using a custom algorithm to position each nodes, it is
-possible to place them all around a circle, making a chord diagram. But
-this kind of chart makes sense only if the order of nodes around the
-circle is carefully chosen, to avoid having a cluttered and unreadable
-figure.

-
# Transform the adjacency matrix in a long format
+  expand_limits(x = c(-1.2, 1.2), y = c(-1.2, 1.2))

+        

+        

+          
+        

+      

+      

+        

+          Network graphs
+          are very powerful to study the global structure of the network. Here,
+          a few groups of researchers are isolated. Each actually represents one
+          single paper where Vincent Ranwez was involved. In the middle a
+          massive network of researchers appear: these are the people who
+          Vincent published with most often, and are therefore all linked
+          together.
+        

+        


+        

+          However, network charts are very bad at annotating every single
+          points: names tend to overlap edges making the figure unreadable. The
+          arc diagram described below is a good alternative if you want to show
+          labels.
+        

+        

+          
Chord diagram (again)

+          

+          

+            Instead of using a custom algorithm to position each nodes, it is
+            possible to place them all around a circle, making a chord diagram.
+            But this kind of chart makes sense only if the order of nodes around
+            the circle is carefully chosen, to avoid having a cluttered and
+            unreadable figure.
+          

+          

+            
# Transform the adjacency matrix in a long format
 connect <- dataUU %>%
   gather(key="to", value="value", -1) %>%
   mutate(to = gsub("\\.", " ",to)) %>%
@@ -825,20 +9598,39 @@ 
Chord diagram (again)

     plot.margin=unit(c(0,0,0,0), "null"),
     panel.spacing=unit(c(0,0,0,0), "null")
   ) +
-  expand_limits(x = c(-1.2, 1.2), y = c(-1.2, 1.2))

-

-

-

-
Arc diagram

-

-
An arc diagram follows the same concept, but displays nodes along a
-single axis and links with arcs. The main advantage is that it allows to
-make the labels easy to read.

-

-

-

-
# Make the graph
+  expand_limits(x = c(-1.2, 1.2), y = c(-1.2, 1.2))

+          

+          

+            
+          

+        

+        

+          
Arc diagram

+          

+          

+            An arc diagram follows the same concept, but displays nodes along a
+            single axis and links with arcs. The main advantage is that it
+            allows to make the labels easy to read.
+          

+        

+      

+      

+        

+          
# Make the graph
 ggraph(mygraph, layout="linear") +
   geom_edge_arc(edge_colour="black", edge_alpha=0.2, edge_width=0.3, fold=TRUE) +
   geom_node_point(aes(size=n, color=as.factor(grp), fill=grp), alpha=0.5) +
@@ -851,68 +9643,45 @@ 
Arc diagram

     plot.margin=unit(c(0,0,0.4,0), "null"),
     panel.spacing=unit(c(0,0,3.4,0), "null")
   ) +
-  expand_limits(x = c(-1.2, 1.2), y = c(-5.6, 1.2))

-

-

-

-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-          
-          
Going further

-          

-          

-            You can learn more about each type of graphic presented in this
-            story in the dedicated sections. Click the icon below:
-          

-                    
-            
-                     
-            
-                     
-            
-                     
-            
-                     
-            
-                     

+  expand_limits(x = c(-1.2, 1.2), y = c(-5.6, 1.2))
+
+

+ +

+
+
+ +

Going further

+
+

+ You can learn more about each type of graphic presented in this story + in the dedicated sections. Click the icon below: +

+ + + + + + + + + + +
-   -
-

A work by Yan Holtz for data-to-viz.com

- - - +   +
+

+ A work by Yan Holtz for + data-to-viz.com +

- -

- - - -

+ + - - - - + + + +   + - + +