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library(tidyverse) 
library(ggraph) 
sw<-starwars%>%
  unnest(films)%>%
  mutate(movie=films)%>%
  select(c(1,11,12))%>%
  rename(from=name,to=films)%>%
  igraph::graph_from_data_frame() 

ggraph(sw,layout='fr')+ 

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image_tile <- formattable::formatter("img",
                        src=x~x,
                        height=25,
                        width=25,
                        NA)

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data(mtcars)

col_compare<-function(df,cols,fun=which.max){
  x<-data.frame(df)
  temp<-x[,c(cols)]
  wins<-colnames(temp)[apply(temp,1,fun)]
  return(wins)
}

mtcars$max<-col_compare(mtcars,3:5)

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# data from http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat
# Originally seen at http://spatial.ly/2014/08/population-lines/

# So, this blew up on both Reddit and Twitter. Two bugs fixed (southern Spain was a mess,
# and some countries where missing -- measure twice, submit once, damnit), and two silly superflous lines removed after
# @hadleywickham pointed that out. Also, switched from geom_segment to geom_line.

# The result of the code below can be seen at http://imgur.com/ob8c8ph

library(tidyverse)

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library(acs)
library(ggplot2)
library(plyr)
library(tidyr)
library(maps)
library(viridis)
library(hrbrthemes)

#key
api.key.install(key="pasteyourkeyhere")

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library(rvest)
library(tidytext)
library(dplyr)
library(stringr)
library(tidyr)
library(ggplot2)
library(viridis)
library(ggthemes)
library(syuzhet)

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test<-get_nrc_sentiment(spells$Concordance)
summary.spells.e<-aggregate(test ,by=list(spells$Spell),mean)
try<-melt(summary.spells.e,id.vars = c("Group.1"))
#emotion test plot
em<-ggplot(try,aes(x=Group.1,y=value,fill=variable))+geom_bar(stat='identity')+coord_flip()
em+ facet_wrap(~ variable)
#waffle
ggplot(try, aes(x = variable, y = Group.1, fill = value)) +
  scale_fill_viridis(name="Severity",option = "D", direction=-1,limits = c(0, max(try$value))) + 
  geom_tile(color = "white", size = 0.4) +  theme_tufte()+labs(x='Emotion',y='Spell')

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#spell sentiment
sent<-get_sentiment(spells$Concordance,method=c('bing'))
sent<-t(sent)
sent<-t(sent)
spells$sentiment<-sent
summary.spells<-aggregate(spells$sentiment ,by=list(spells$Spell),mean)
summary.spells$fill<-ifelse(summary.spells$V1 > 0, 'positive', ifelse(summary.spells$V1< 0, 'negative', 'neutral'))

#sentiment plot
Sentp<- c('#F8766D','white','#619Cff')

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library(dplyr)
library(ggplot2)
library(ggthemes)
library(gridExtra)
library(syuzhet)
library(grid)
library(reshape2)
library(viridis)

spells <- read.csv("pathtospells.csv", stringsAsFactors=FALSE)

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library(acs)
library(tigris)
library(ggplot2)
library(dplyr)
library(maps)
library(ggthemes)
library(viridis)
library(extrafont)
#only need to do this once
api.key.install(key="YOUR API KEY HERE")