mollietaylor

# importing dates:
  dates <- c("05/27/84", "07/07/05")
	betterDates <- as.Date(dates,
		format = "%m/%d/%y") # here you put the format your dates are currently in
	# it will output the ISO standard dates (%Y-%m-%d)

# or:

	dates <- c("May 27 1984", "July 7 2005")
	betterDates <- as.Date(dates,

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library(ggplot2)
library(scales)

# load data:
log <- data.frame(Date = c("2013/05/25","2013/05/28","2013/05/31","2013/06/01","2013/06/02","2013/06/05","2013/06/07"), 
  Quantity = c(9,1,15,4,5,17,18))
log
str(log)

# convert date variable from factor to date format:

mollietaylor

# load up area shape file:
library(maptools)
area <- readShapePoly("ne_10m_parks_and_protected_lands_area.shp")

# # or file.choose:
# area <- readShapePoly(file.choose())

library(RColorBrewer)
colors <- brewer.pal(9, "BuGn")

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gps <- read.csv("callan.csv", 
  header = TRUE)

# calculate moving average: (this section is optional)
library(TTR)
movingN <- 5 # define the n for the moving average calculations
gps$Altitude <- gps$Altitude * 3.281 # convert m to ft
gps$SMA <- SMA(gps$Altitude, 
  n = movingN)
gps <- gps[movingN:length(gps$SMA), ] # remove first n-1 points

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gps <- read.csv("elwyn.csv", 
  header = TRUE)

library(ggmap)
mapImageData <- get_map(location = c(lon = mean(gps$Longitude), 
  lat = 33.824),
  color = "color", # or bw
  source = "google",
  maptype = "satellite",
  # api_key = "your_api_key", # only needed for source = "cloudmade"

mollietaylor

library(RColorBrewer)
colors = brewer.pal(8, "Dark2")

library(ggplot2)

data <- as.data.frame(USPersonalExpenditure) # data from package datasets
data$Category <- as.character(rownames(USPersonalExpenditure)) # this makes things simpler later

ggplot(data,
  aes(x = Expenditure,

mollietaylor

# calling the functions
# this is the file where we will call the functions in fun.R and times.R
times <- dget("times.R")
times(-4:4, 2)

source("fun.R")
mult(-4:4, 2)

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# generate random data:
city <- c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J")
income <- sample(1:100000, 
  100, 
	replace = TRUE)
cities <- data.frame(city, income)

# graph our data:
library(ggplot2)
ggplot(cities,

mollietaylor

# cite: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/reshape.html
# cite: http://www.cs.grinnell.edu/~rebelsky/Courses/MAT115/2008S/R/stacked-bar-graphs.html
# cite: http://www.harding.edu/fmccown/r/#barcharts

library(RColorBrewer)
sequential <- brewer.pal(6, "BuGn")

Loblolly[1:10,]

wide <- reshape(Loblolly, 

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gps <- read.csv("out.csv", 
	header = TRUE)

library(ggmap)
mapImageData <- get_googlemap(center = c(lon = median(gps$Longitude), lat = median(gps$Latitude)),
	zoom = 11,
#	size = c(500, 500),
	maptype = c("terrain"))

ggmap(mapImageData,