That's awesome!
After reading this I downloaded the data and did some processing of my own. I focused a bit on the names given to colors.
Starting off similar to you (including the code just so that any small differences won't cause confusion) I import the data and get the names:
fullData=Import["http://xkcd.com/color/rgb.txt","Data"][[All,1;;2]];
names = fullData[[All, 1]];
We can make a histogram showing the distribution of the number of words used in a description:
Histogram[Length/@StringSplit[names]]
It's not very interesting because not very many words are typically used. Instead we can calculate the average number of accompanying words to get a more interesting plot:
Histogram@Sort[Merge[Flatten[Function[words,#->Length[words]-1&/@words]/@StringSplit[names]],Mean]]
Next we can look at which words are used the most often. This is a pie chart shows the average color of all colors that share a certain word in their names:
nameToColors=SortBy[Merge[Flatten[Function[color,#->color[[2]]&/@StringSplit[color[[1]]]]/@data],Flatten],Length];
PieChart[Length/@nameToColors,ChartStyle->(LUVColor@@Mean[List@@@#]&/@Values[nameToColors])]
And another (uglier) version which labels each slice:
We can immediately see what words are popular for describing colors.
This will tell us what words are the most vague, this is being calculated by the mean deviation of
Sort[Function[color,Norm[MeanDeviation[List@@@color]]]/@nameToColors]
Here are the last few:
<| "green" -> 0.301527, "yellowish" -> 0.301806,
"orangish" -> 0.309022, "greeny" -> 0.310186, "magenta" -> 0.310729,
"red" -> 0.326567, "fuchsia" -> 0.331209, "greenish" -> 0.331261,
"cool" -> 0.340017, "purply" -> 0.344614, "yellowy" -> 0.349218,
"powder" -> 0.358974, "blood" -> 0.362786, "browny" -> 0.375569,
"pink" -> 0.377552, "purple" -> 0.39065, "sea" -> 0.392243,
"reddish" -> 0.407342, "pinkish" -> 0.409191, "off" -> 0.414668,
"purpley" -> 0.415278, "blue" -> 0.417053, "orangey" -> 0.417463,
"purpleish" -> 0.418713, "pale" -> 0.421891, "flat" -> 0.422791,
"dull" -> 0.422931, "dusty" -> 0.429657, "muted" -> 0.430807,
"dirty" -> 0.431016, "purplish" -> 0.43483, "baby" -> 0.441156,
"marine" -> 0.444411, "dark" -> 0.450202, "very" -> 0.458118,
"pinky" -> 0.46324, "ugly" -> 0.478061, "bluey" -> 0.489151,
"violet" -> 0.490251, "light" -> 0.502694, "faded" -> 0.505302,
"soft" -> 0.531751, "mid" -> 0.538431, "deep" -> 0.544057,
"warm" -> 0.548372, "bluish" -> 0.549277, "medium" -> 0.553847,
"rich" -> 0.571403, "pastel" -> 0.580348, "darkish" -> 0.612625,
"true" -> 0.777189, "hot" -> 0.83747, "easter" -> 0.879319,
"strong" -> 0.93553, "vibrant" -> 0.938836, "bright" -> 0.939123,
"lightish" -> 0.957704, "lighter" -> 0.967594, "vivid" -> 0.998611,
"electric" -> 1.07712, "neon" -> 1.08666|>
It makes sense that words like vivid and neon are at the top but i tis amusing to see anyway. Now it's time to do something really cool, we are going to make a graph with words at each vertex, and where vertexes are connected if there is a color that uses both words. Example: "bright red" would connect the bright and red vertexes. Here is the graph:
wordRelations=DeleteDuplicates[Flatten[Function[{fullName,color},Thread[{UndirectedEdge@@@Permutations[StringSplit[fullName],{2}],color}]]@@@data,1],#1[[1]]===Reverse[#2[[1]]]&];
namesGraph=Graph[DeleteDuplicates[Flatten[List@@@wordRelations[[All,1]]]],wordRelations[[All,1]],EdgeStyle->Rule@@@wordRelations,VertexSize->1,VertexStyle->name_:>LUVColor@@Mean[List@@@nameToColors[name]]]
Here edges are colored by the color that both words are mentioned in, and vertexes colored with the mean color of connecting edges. Next we can find communities in this graph:
communities = FindGraphCommunities[namesGraph];
CommunityGraphPlot[namesGraph, communities]
Finally we can do some coloring to see what colors are in these groups:
CommunityGraphPlot[namesGraph,communities,CommunityRegionStyle->Function[names,LUVColor@@Mean[List@@@Flatten[nameToColors/@names]]]/@communities]
I may do some more with this data, if I do I'll post it here.
Random note: CommunityGraphPlot returns particularly pretty result when run over your final graph:
Attachments:
