In paul4forest/tradeflows: Process forest products trade flow data from COMTRADE and COMEXT

library(ggplot2)
library(dplyr)
library(reshape2)

Explore some of the ggplot2 functions for use in the project

Sample plot

ggplot(iris, 
       aes(x=Petal.Length, y=Petal.Width, color=Species)) + 
    geom_point()

Alpha

Alpha to avoid overplotting

Use aesthetic to make points more transparent on a coutry * contry grid display trade volume as alpha level.

df <- data.frame(x = rnorm(5000), y = rnorm(5000))
ggplot(df, aes(x,y)) + geom_point(alpha = 1/10)

Alpha to add shading

b <- ggplot(economics, aes(x = date, y = unemploy))
j <- b + geom_line()
# j # uncomment to display basic plot
yrng <- range(economics$unemploy)
j <- j + geom_rect(aes(NULL, NULL, xmin = start, xmax = end, fill = party),
ymin = yrng[1], ymax = yrng[2], data = presidential)
# j
library(scales) # to access the alpha function
j + scale_fill_manual(values = alpha(c("blue", "red"), .3))

Scales for color and linetype

For some countries, the completeness table contained only true values. WHIch were rendered as red lines, for other countries true values were rendered as blue lines. Changed this behaviour by using manual color palettes for colours scale_colour_manual() and line palette for lines scale_linetype_manual().

# plot completeness
rawdata <- tradeflows::sawnwoodexample
r <- "France"
productcodeinreport <- 4407
completeness <- rawdata %>%
    filter(productcode == 4407) %>%
    select(reporter, classification, year, quantity, weight, tradevalue) %>%
    melt(id=c("reporter", "classification", "year"))  %>%
    mutate(value = !is.na(value)) %>%
    unique %>% 
    arrange(classification, reporter, year)

    p <- ggplot(data=filter(completeness, reporter == r), 
                aes(x=year, y=variable, 
                    xend=year+1, yend=variable, 
                    color=value, linetype=value)) +
        scale_colour_manual(values = c("TRUE" = "blue", "FALSE" = "red")) +
        scale_linetype_manual(values = c("TRUE" = 2, "FALSE" = 1)) +
    #"solid", "dashed"
        geom_segment(size=3) + 
        facet_grid(classification~.) +
        ggtitle(paste("Data completeness of", productcodeinreport,
                      "in", r))
    plot(p)

Shapes

ggplot(data.frame(x = seq(1,6)), aes(x = x,y =x,shape=as.factor(x),color=as.factor(x))) + geom_point(size=10)

Two plots with grid.arrange() sharing one legend

Seen in Hadley's wiki and in this Stackoverflow question

library(ggplot2)
library(grid) # to use unit.c
library(gridExtra)

grid_arrange_shared_legend <- function(...) {
    plots <- list(...)
    g <- ggplotGrob(plots[[1]] + theme(legend.position="bottom"))$grobs
    legend <- g[[which(sapply(g, function(x) x$name) == "guide-box")]]
    lheight <- sum(legend$height)
    grid.arrange(
        do.call(arrangeGrob, lapply(plots, function(x)
            x + theme(legend.position="none"))),
        legend,
        ncol = 1,
        heights = unit.c(unit(1, "npc") - lheight, lheight))
}

dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
p1 <- qplot(carat, price, data=dsamp, colour=clarity)
p2 <- qplot(cut, price, data=dsamp, colour=clarity)
p3 <- qplot(color, price, data=dsamp, colour=clarity)
p4 <- qplot(depth, price, data=dsamp, colour=clarity)
grid_arrange_shared_legend(p1, p2, p3, p4)

paul4forest/tradeflows documentation built on Oct. 8, 2019, 10:35 a.m.