forked from nbremer's block: Linear Gradient - Traffic accidents per Day & Hour combination
James Curley jalapic
jalapic
/ gist:612036977d9f9c773107681bc4a46d58
Created
April 6, 2016 16:18
— forked from thomasp85/gist:c8e22be4628e4420d4f66bcc6c88ac87
ggraph + gganimate
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| #devtools::install_github('thomasp85/ggforce') | |
| #devtools::install_github("dgrtwo/gganimate") | |
| #devtools::install_github('thomasp85/ggraph') | |
| #devtools::install_github("hadley/ggplot2") | |
| library(ggraph) | |
| library(ggforce) | |
| library(gganimate) | |
| library(ggplot2) | |
| library(igraph) |
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| #devtools::install_github('thomasp85/ggforce') | |
| #devtools::install_github("dgrtwo/gganimate") | |
| #devtools::install_github('thomasp85/ggraph') | |
| #devtools::install_github("hadley/ggplot2") | |
| library(ggraph) | |
| library(ggforce) | |
| library(gganimate) | |
| library(ggplot2) | |
| library(igraph) |
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| library(randomNames) | |
| library(networkD3) | |
| library(igraph) | |
| set.seed(3) | |
| nl = 100 | |
| df = data.frame(source = randomNames(1000,which.names='both', name.order = 'first.last', name.sep=' '), target = '') | |
| df = df[rep(seq_len(nrow(df)), sample(1:10,nrow(df), replace=T)),] | |
| df = df[sample(nrow(df),nl),] | |
| df$target = sample(df$source,nrow(df), replace = T) |
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| license: mit |
I added a new attribute to the dataset which is region.
In the node-link diagram, states in the same region likely stick together as they have links in between.
States in region which has less number of links spread out in the diagram. For example: look at the light-green color (South Atlantic), Florida is far apart with those states (DC, Maryland, Virginia, West Viginia, Delaware).
Thus, although the dataset is non-spatial, users can explore the relationship of nodes by looking the distance or number of hops in between.
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forked from jalapic's block: network fade
forked from jalapic's block: network fade 2
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| height: 800 |
Use the mouse to click on a dot and see the word plotted. Click a label to hide it again.
Color indicates polarity based on simple word labeling from the AFINN wordlist. It may be that context in this dataset affects polarity :)
forked from arnicas's block: TSNE Plot of Yelp Reviews