dplyr start

start_dplyr.R

require('dplyr')
require("ggplot2")
require('reshape2')

data <- read.table("~/Downloads/pp_3_body_bytes.txt")
names(data) <- c('bytes')
big <- filter(data, bytes > 20000)
sub <- filter(data, bytes < 20000)
p <- ggplot(sub, aes(x = bytes))
p + geom_histogram()

data <- read.table("~/Downloads/all_dates.txt")
names(data) <- c('date', 'rec', 'count')
#big <- filter(data, bytes > 20000)
#sub <- filter(data, bytes < 20000)
p <- ggplot(data, aes(x = factor(date, levels = data$date), y = count, fill= rec))
p + geom_bar(stat="identity", position = "dodge")

data <- read.table("~/Downloads/placement_counts.txt")
names(data) <- c('date', 'placement', 'count')

d_melt <- melt(data, c("date", "count"))

pp_3 <- filter(data, placement == "placement%5D=PP_3")
pp_4 <- filter(data, placement == "placement%5D=PP_4")
alongside <- merge(pp_3, pp_4, by = "date", all = TRUE, sort = FALSE, suffixes = c(".pp_3", ".pp_4"))

alongside$pp4_ratio <- alongside[,"count.pp_4"] / alongside[,"count.pp_3"]

alongside <- filter(alongside, count.pp_4 > 60000)

by_placement = data %>% group_by('placement')

p <- ggplot(data, aes(x = factor(date, levels = data$date), y = count, fill= placement))
p + geom_bar(stat="identity", position = "dodge")

p <- ggplot(alongside, aes(x = factor(date, levels = data$date), y = pp4_ratio))
p + geom_bar(stat="identity", position = "dodge")