#googlevis

[fichamentos01]

Exercício 08 (Gerando um mapa) | Por Émerson Rodrigues

ELEIÇÕES 2018 - Partidos eleitos por Estado*

LINK INTERATIVO:  http://127.0.0.1:14497/custom/googleVis/GeoChartID191062a1de2.html

LEGENDA:

Preto: 2º turno

Azul: PSDB

Lilás: PP 

Marrom: MDB

Verde: PSB

Cinza: PSD

Rosa: DEM 

Vermelho: PT

Amarelo: PHS

CÓDIGO:

# fazendo um mapa dados <- read.csv("estados.csv")

est<data.frame(estado=c("Acre","Alagoas","Amapa","Amazonas","Bahia","Ceara","Distrito Federal","Espirito Santo","Goias","Maranhao","Mato Grosso","Mato Grosso do Sul","Minas Gerais","Para","Paraiba","Parana","Pernambuco","Piaui","Rio de Janeiro","Rio Grande do Norte","Rio Grande do Sul","Rondonia","Roraima","Santa Catarina","Sao Paulo","Sergipe","Tocantins"),partido=c("PP","MDB","2ºturno","PT","PSB","DEM","PCdoB","PSD,,"PHS"))

geo<gvisGeoChart(est,"estado","partido",options=list(region="BR",displayMode="region",resolution="provinces",colors['black','blue','brown','green','grey','pink','purple','red','yellow']"))

plot(geo)

*dados aleatórios

[dataandme]

ICYMI #dataviz tutorial: "Bring Your Data to Life with #googleVis and #Rstats" https://t.co/OGqTecTQ6Q via @DataCamp https://t.co/xQFM90jN5R

[dataandme]

Nice #dataviz tutorial: "Bring Your Data to Life with #googleVis and #Rstats" https://t.co/7tO1WyH7Rj via @DataCamp https://t.co/CEIgt4FPaW

[simplystatistics]

Sunday Data/Statistics Link Roundup (9/9/12)

Not necessarily statistics related, but pretty appropriate now that the school year is starting. Here is a little introduction to "how to google" (via Andrew J.). Being able to "just google it" and find answers for oneself without having to resort to asking folks is maybe the #1 most useful skill as a statistician. 

A really nice presentation on interactive graphics with the googleVis package. I think one of the most interesting things about the presentation is that it was built with markdown/knitr/slidy (see slide 53). I am seeing more and more of these web-based presentations. I like them for a lot of reasons (ability to incorporate interactive graphics, easy sharing, etc.), although it is still harder than building a Powerpoint. I also wonder, what happens when you are trying to present somewhere that doesn't have a good internet connection?

We talked a lot about the ENCODE project this week. We had an interview with Steven Salzberg, then Rafa followed it up with a discussion of top-down vs. bottom-up science. Tons of data from the ENCODE project is now available, there is even a virtual machine with all the software used in the main analysis of the data that was just published. But my favorite quote/tweet/comment this week came from Leonid K. about a flawed/over the top piece trying to make a little too much of the ENCODE discoveries: "that's a clown post, bro".

Another breathless post from the Chronicle about how there are "dozens of plagiarism cases being reported on Coursera". Given that tens of thousands of people are taking the course, it would be shocking if there wasn't plagiarism, but my guess is it is about the same rate you see in in-person classes. I will be using peer grading in my course, hopefully plagiarism software will be in place by then. 

A New York Times article about a new book on visualizing data for scientists/engineers. I love all the attention data visualization is getting. I'll take a look at the book for sure. I bet it says a lot of the same things Tufte said and a lot of the things Nathan Yau says in his book. This one may just be targeted at scientists/engineers. (link via Dan S.)

Edo and co. are putting together a workshop on the analysis of social network data for NIPS in December. If you do this kind of stuff, it should be a pretty awesome crowd, so get your paper in by the Oct. 15th deadline!