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,

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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"

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# 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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# 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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png("beaverhistextra.png")
layout(matrix(c(1:2), 2, 1, 
	byrow = TRUE))
hist(beaver1$temp, # histogram
	col = "peachpuff", # column color
	border = "black", 
	prob = TRUE, # show densities instead of frequencies
	xlim = c(36,38.5),
	ylim = c(0,3),
	xlab = "Temperature",

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str(ChickWeight$Diet)
table(ChickWeight$Diet)

ols <- lm(weight ~ Time + Diet,
  data = ChickWeight)
summary(ols)

ChickWeight$Diet <- relevel(ChickWeight$Diet,
  ref = 4)

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library(OIdata)
data(birds)
library(ggplot2)

levels(birds$effect) <- gsub(" ", "\n", levels(birds$effect))
ggplot(birds,
  aes(x = effect,
    y = speed)) +
geom_boxplot() + 
coord_flip()

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ols <- lm(Temp ~ Solar.R,
  data = airquality)

summary(ols)

str(ols)

plot(Temp ~ Solar.R,
  data = airquality)
abline(ols)

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library(OIdata)
library(RColorBrewer)
library(classInt)

# load state data from OIdata package:
data(state)

# set constants:
nclr <- 8 # number of bins
min <- 0 # theoretical minimum