ramnathv

# Find winner and percent of votes secured
candidates = names(df)[4:9];                                
df$winner  = candidates[apply(df[, candidates], 1, which.max)];    
df$winpct  = apply(df[, candidates], 1, max);

# Assign palettes to winners
df$colpal = sapply(df$winner, switch, 
  'braun'    = 'PuRd', 'chico'    = 'Greens',
  'delvalle' = 'Oranges', 'emanuel'  = 'Purples');

ramnathv

# London Sport Participation
london <- readShapeSpatial('1_data/maps/london_sport.shp');
choro4 = plotChoropleth(.poly = london, 
  id1 = 'name', field = 'Partic_Per',
  title    = 'London Sport Participation'
  legtitle = 'Partic_Per',
  colpal   = 'Blues', fixed = T,
  breaks   = seq(0, 30, by = 5)) +
  opts(legend.position = c(0.90, 0.25);

ramnathv

# Animated Choropleth of Population Density Change

# read data with population
df        = read.csv('http://goo.gl/yJQs8');
names(df) = sub('Persons.', '', names(df));

# read data with areas
df_area   = read.xls('http://goo.gl/UJbKE', 
              sheet = 2, pattern = 'Code')[,c('Code', 'Total')];
df_area$Total = with(df_area, sapply(as.character(Total), sub, pa = ",", r = ''))

ramnathv

# FUNCTION TO CREATE A CHOROPLETH MAP USING SPPLOT
choropleth_sp <- function(.poly, id1, .df = .poly@data, id2 = id1, field, 
                          .title = "", .legtitle = "Values", colpal = 'PuRd', ...){
                            
     # load required libraries
     library(maptools); library(spatial); library(RColorBrewer);
     library(classInt); library(ggplot2); gpclibPermit();
                            
     # create id to match shapefile with data
     id1 = llply(id1, as.name); 

ramnathv

require(ggplot2)
require(lubridate)
# df is the data frame on the page http://goo.gl/MvG02 in the answer by Andrie
# this solution modifies his approach to allow multiple time series to be 
# incorporated into the month plot

df = transform(df, values2 = values + 100, values3 = values + 200)
dfm = melt(df, id = 'dates')
dfm$month <- factor(month(dfm$dates), levels=1:12, labels=month.abb, ordered=TRUE)
dfm$year  <- year(dfm$dates)

ramnathv

# LOAD LIBRARIES ---------------------------------------------------------------
library(ggplot2)
library(maps)

# LOAD DATA --------------------------------------------------------------------
# read data and add column names
auto_plants = read.csv('Data.csv', as.is = TRUE,
    col.names = c('location', 'firm', 'lat', 'long', 'Production', 'temparature'))

# classify firm as others if total plants < 5

ramnathv

# REFACTORED VERSION OF CODE IN GIST: git://gist.github.com/1362541.git

# FUNCTION TO GET TABLE BASED ON COLLEGE TYPE
get_table <- function(college_type){
  require(XML)
  url_base <- "http://www.kiplinger.com/tools/privatecolleges/index.php?table"
  url_1 <- paste(url_base, college_type, sep = "=")
  tab   <- readHTMLTable(url_1, header = TRUE, stringsAsFactors = FALSE)
  return(tab[[1]])
}

ramnathv

% DEFINE COLORS FOR SECTION HEADINGS ----
\titleformat*{\section}{\sffamily\Large\bfseries\color{DarkRed}}
\titleformat*{\subsection}{\sffamily\large\bfseries\color{DarkBlue}}
\titleformat*{\subsubsection}{\sffamily\normalsize\bfseries\color{DarkGreen}}

ramnathv

# LOAD LIBRARIES
require(quantmod)
require(ggplot2)

# GET DATA, CONVERT TO DATA FRAMES
getSymbols("VBMFX", from = "1990-01-01", to = Sys.Date(), adjust = TRUE)
getSymbols("VFINX", from = "1990-01-01", to = Sys.Date(), adjust = TRUE)
vbfmx <- data.frame(VBMFX, date = index(VBMFX))
vfinx <- data.frame(VFINX, date = index(VFINX))

ramnathv

% Full Screen Environment for Beamer Slides
\newenvironment{changemargin}[2]{%
\begin{list}{}{%
  \setlength{\topsep}{0pt}%
  \setlength{\leftmargin}{#1}%
  \setlength{\rightmargin}{#2}%
  \setlength{\listparindent}{\parindent}%
  \setlength{\itemindent}{\parindent}%
  \setlength{\parsep}{\parskip}%
}%