New set of snippits.

Author: Chris R. Albon

.Rhistory

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+.Rproj.user
+.Rhistory
+.RData

.gitignore

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+# 2D Density Plot
+# Original source: r graphics cookbook
+
+# load the gcookbook package for the data
+library(gcookbook)
+
+# load the ggplot2 package
+library(ggplot2)
+
+# reset the graphing device
+dev.off()
+
+# create the ggplot2 data
+p <- ggplot(faithful, aes(x = eruptions, y = waiting)) + 
+  # add a layer with the points
+  geom_point() +
+  # and a layer for the density heatmap with the alpha and the color determined by density (the .. refers to the fact that density is a variable that was created inside the ggplot() function)
+  stat_density2d(aes(alpha=..density.., fill=..density..), geom="tile", contour=FALSE); p

2d-density-plot.r

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+# 2D Density Plot
+# Original source: r graphics cookbook
+
+# load the gcookbook package for the data
+library(gcookbook)
+
+# load the ggplot2 package
+library(ggplot2)
+
+# reset the graphing device
+dev.off()
+
+# create the ggplot2 data
+p <- ggplot(faithful, aes(x = eruptions, y = waiting))
+
+# Add Text
+
+# create the ggplot2 plot
+p + geom_point() + 
+  # add text
+  annotate("text", x = 3, y = 48, label="Group 1", family="serif", fontface="italic", colour="darkred", size=6) +
+  # add more text
+  annotate("text", x = 4.5, y = 66, label="Group 2", family="serif", fontface="italic", colour="darkred", size=6)
+
+# Add Mathematical Expressions
+
+# create the ggplot2 plot
+p + geom_point() +
+  # add the formula, parse=TRUE turns the next into a formula
+  annotate("text", x = 4.5, y = 66, parse = TRUE, label = "frac(1, sqrt(2 * pi)) * e ^ {-x^2 / 2}")
+
+# Add Lines
+
+# load the grid package to create the flat ends of the line seqment and arrow
+library(grid)
+
+# create the ggplot2 plot
+p + geom_point() + 
+  # add a horizontal line at y = 66
+  geom_hline(yintercept = 66) + 
+  # add a vertical line at 3 = 3
+  geom_vline(xintercept = 3) + 
+  # add an angled line
+  geom_abline(intercept = 37.4, slope = 9) +
+  # add a line segment
+  annotate("segment", x = 1, xend = 2.5, y = 75, yend = 75, arrow=arrow(ends="both", angle=90, length=unit(.2,"cm"))) + 
+  # add an arrow
+  annotate("segment", x = 4, xend = 5, y = 60, yend = 55, colour="blue", size=2, arrow=arrow())
+
+# Add A Shaded Rectangle
+
+# create the ggplot2 plot
+p + geom_point() +
+  # add a shaped blue rectangle
+  annotate("rect", xmin=1, xmax=3, ymin=40, ymax=100, alpha=.1, fill="blue")

ggplot2/add-labels-to-bar-graph.r add-labels-to-bar-graph.r

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+# Balloon Plot
+# Original source: r graphics cookbook
+
+# load the gcookbook package for the data
+library(gcookbook)
+
+# load the ggplot2 package
+library(ggplot2)
+
+# reset the graphing device
+dev.off()
+
+# create a subset of countries for year 2009 and only for a vector of countries names
+cdat <- subset(countries, Year==2009 & Name %in% c("Canada", "Ireland", "United Kingdom", "United States",
+"New Zealand", "Iceland", "Japan", "Luxembourg", "Netherlands", "Switzerland"))
+
+# create the ggplot data with the size of the balloons determined by GDP
+p <- ggplot(cdat, aes(x = healthexp, y = infmortality, size = GDP)) +
+  # select the select of the points and their colors
+  geom_point(shape=21, colour="#3333ff", fill="#0066CC") +
+  # change the scale of the point size to make them bigger
+  scale_size_area(max_size=15); p

ggplot2/aggregate-by-week-or-month.r aggregate-by-week-or-month.r

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+# Change The Barplot Bar Width# load the gcookbook package for the datalibrary(gcookbook)# load the ggplot2 packagelibrary(ggplot2)# plot a default barplotggplot(pg_mean, aes(x=group, y=weight)) + geom_bar(stat="identity")# plot a barplot with skinny barsggplot(pg_mean, aes(x=group, y=weight)) + geom_bar(stat="identity", width=0.5)# plot a barplot with thick barsggplot(pg_mean, aes(x=group, y=weight)) + geom_bar(stat="identity", width=1)

annotating-plots.r

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX

ggplot2/apply-function-to-every-row.r apply-function-to-every-row.r

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+# Density Curve Plot
+# Original source: r graphics cookbook
+
+# load the gcookbook package for the data
+library(gcookbook)
+
+# load the ggplot2 package
+library(ggplot2)
+
+# reset the graphing device
+dev.off()
+
+# create the ggplot2 data for the faithful$waiting variable
+ggplot(faithful, aes(x=waiting)) + 
+  # add a sensity line
+  geom_line(stat="density") +
+  # zoom out the plot a little bit
+  expand_limits(y=0)
+
+# create the ggplot2 data for three lines of different levels of smoothing
+ggplot(faithful, aes(x=waiting)) +
+  # "adjust" determines the level of smoothing, larger the number, the more smoothing
+  geom_line(stat="density", adjust=.25, colour="red") +
+  geom_line(stat="density") +
+  geom_line(stat="density", adjust=2, colour="blue")

balloon-plot.r

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+# Dot Plot (Wilkinson Plot)
+# Original source: r graphics cookbook
+
+# load the gcookbook package for the data
+library(gcookbook)
+
+# load the ggplot2 package
+library(ggplot2)
+
+# reset the graphing device
+dev.off()
+
+# create a subset of countries
+countries2009 <- subset(countries, Year==2009 & healthexp>2000)
+
+# create the ggplot2 data
+p <- ggplot(countries2009, aes(x=  infmortality))
+
+# create the dotplot layer
+p + geom_dotplot(binwidth=.25) + 
+  # rescale the y axis and remove tic marks
+  scale_y_continuous(breaks=NULL) + 
+  # remove the axis labels
+  theme(axis.title.y=element_blank())

change-barplot-bar-width.r

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+# Frequency Polygon Plot
+# Original source: r graphics cookbook
+
+# load the gcookbook package for the data
+library(gcookbook)
+
+# load the ggplot2 package
+library(ggplot2)
+
+# reset the graphing device
+dev.off()
+
+# divide the faithful$waiting data into 10 bins
+binsize <- diff(range(faithful$waiting))/10
+
+# create the ggplot2 data for the faithful$waiting variable
+ggplot(faithful, aes(x=waiting)) + 
+  # add a sensity line
+  geom_freqpoly(binwidth=binsize) +
+  # zoom out the plot a little bit
+  expand_limits(y=0)

ggplot2/circle-plot.r circle-plot.r

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+# ACLED Exploration
+
+library(foreign)
+
+df <- read.csv("earthquake.csv", header = TRUE, stringsAsFactors = FALSE)
+
+# load the ggplot2 package
+library(ggplot2)
+
+# load the ggmap package
+library(ggmap)
+
+# get the map from google maps, centered on the median long/lat. 
+mapImageData <- get_googlemap(
+  center = c(lon = median(df$Longitude), lat = median(df$Latitude)),
+  zoom = 4,
+  maptype = c("terrain")
+)
+
+# plot the points on the map in red (the aes has some problems inheriting from prevois models, so that is why we've FALSE'd the inherit)
+ggmap(mapImageData, extent = "device") +
+  geom_point(inherit.aes = FALSE, aes(x = df$Longitude, y = df$Latitude),
+             data = df,
+             color = "red",
+             size = df$Magnitude,
+             pch = 20)

ggplot2/cleveland-plot.r cleveland-plot.r

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+# Venn-Scatterplot
+# Original source: http://stackoverflow.com/questions/2397097/how-can-a-data-ellipse-be-superimposed-on-a-ggplot2-scatterplot
+
+# create a dataframe of simulated data
+x <- c(2,3,4, 10,12,10, 20,21,23)
+y <- c(50,54,49, 30,25,26, 5,6,5)
+group <- c("a", "a", "a", "b", "b", "b", "c", "c", "c")
+df <- data.frame(x,y, group)
+rm(x, y, group)
+
+# load ggplot2
+library(ggplot2)
+library(devtools)
+source_url("https://raw.github.com/JoFrhwld/FAAV/master/r/stat-ellipse.R")
+
+#calculating ellipses
+library(ellipse)
+df_ell <- data.frame()
+for(g in levels(df$group)){
+  df_ell <- rbind(df_ell, cbind(as.data.frame(with(df[df$group==g,], ellipse(cor(x, y), 
+                                                                             scale=c(sd(x),sd(y)), 
+                                                                             centre=c(mean(x),mean(y))))),group=g))
+}
+#drawing
+library(ggplot2)
+ggplot(data=df, aes(x=x, y=y,colour=group)) + 
+  geom_point(size=1.5, alpha=.6) +
+  geom_path(data=df_ell, aes(x=x, y=y,colour=group), size=1, linetype=2)

code_r.Rproj

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+# ACLED Exploration
+
+library(foreign)
+
+df <- read.csv("Kenya.csv", header = TRUE, stringsAsFactors = FALSE)
+
+# load the ggplot2 package
+library(ggplot2)
+
+# load the ggmap package
+library(ggmap)
+
+# get the map from google maps, centered on the median long/lat. 
+mapImageData <- get_googlemap(
+  center = c(lon = median(df$LONGITUDE), lat = median(df$LATITUDE)),
+  zoom = 7,
+  maptype = c("terrain")
+)
+
+# plot the points on the map in red (the aes has some problems inheriting from prevois models, so that is why we've FALSE'd the inherit)
+p <- ggmap(mapImageData, extent = "device") +
+  geom_point(inherit.aes = FALSE, aes(x = df$LONGITUDE, y = df$LATITUDE, colour = df$EVENT_TYPE),
+             data = df,
+             size = 4,
+             pch = 20)

ggplot2/color-boxplot-outliers.r color-boxplot-outliers.r

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+# Line Plot With Multiple Lines
+# Original source: r graphics cookbook
+
+# load the gcookbook package for the data
+library(gcookbook)
+
+# load the ggplot2 package
+library(ggplot2)
+
+# load plyr package
+library(plyr)
+
+# reset the graphing device
+dev.off()
+
+# summarize the ToothGrowth data
+tg <- ddply(ToothGrowth, c("supp", "dose"), summarise, length=mean(len))
+
+# create a ggplot with lines colored by the tg$supp variable
+ggplot(tg, aes(x=dose, y=length, colour=supp)) + 
+  geom_line()
+
+# create a ggplot with line-types determined by the tg$supp variable
+ggplot(tg, aes(x=dose, y=length, linetype=supp)) + 
+  geom_line()

density-plot.r

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+# Plot With Both Lines And Poiints
+
+# load the gcookbook package for the data
+library(gcookbook)
+
+# load the ggplot2 package
+library(ggplot2)
+
+# reset the graphing device
+dev.off()
+
+# create a plot with both lines and dots
+ggplot(BOD, aes(x=Time, y=demand)) + 
+  geom_line() + 
+  geom_point()
+

ggplot2/dodge-points.r dodge-points.r

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+# Rug Plot
+# Original source: r graphics cookbook
+
+# load the gcookbook package for the data
+library(gcookbook)
+
+# load the ggplot2 package
+library(ggplot2)
+
+# reset the graphing device
+dev.off()
+
+# create the scatterplot
+sps <- ggplot(heightweight, aes(x = ageYear, y = heightIn, colour = sex)) +
+  geom_point() +
+  scale_colour_brewer(palette = "Set1")
+
+# add the riug plot
+sps + geom_rug(position="jitter", size=.2)

dot-plot.r

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+# Rug Plot
+# Original source: r graphics cookbook
+
+# load the gcookbook package for the data
+library(gcookbook)
+
+# load the ggplot2 package
+library(ggplot2)
+
+# reset the graphing device
+dev.off()
+
+# create the scatterplot
+sps <- ggplot(heightweight, aes(x = ageYear, y = heightIn, colour = sex)) +
+  geom_point() +
+  scale_colour_brewer(palette = "Set1")
+
+# create a text annotation layer at location x=16.5 and y=52
+sps + annotate("text", label="r-score: .02", parse = TRUE, x=16.5, y=52)