Felix_plot.Rmd

---
title: "Felix_plot"
author: 'Fei Ren (ID: 704456634)'
date: "April 28, 2018"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Plot `clicks` againt `jobAgeDays`
```{r}
library(arules)

# load cleaned sample data
setwd(dir = "E:/Programs and Activities/Datafest 2018/Data")
load(file = "IndeedSampleclean")
indeedClean <- IndeedSample
rm(IndeedSample)

# subset US data
usIndeed <- filter(.data = indeedClean, country == "US")

min <- min(usIndeed$jobAgeDays)
max <- max(usIndeed$jobAgeDays)
usIndeed$jobAgeDays_byWeek <- cut(x = usIndeed$jobAgeDays, breaks = c( seq(min-1,max,7),max))

click_week_avg<- usIndeed %>% group_by(jobAgeDays_byWeek) %>% summarise(avg_click = mean(clicks))
click_week_avg$jobAgeDays_byWeek <- 1:length(click_week_avg$jobAgeDays_byWeek)

```

```{r ggplot}
library(ggplot2)

p <- ggplot(data = click_week_avg, mapping = aes(x = jobAgeDays_byWeek, y = avg_click)) +
  geom_point() +
  geom_smooth(se = F, colour = "blue") +
  scale_x_continuous(breaks = 1:length(click_week_avg$jobAgeDays_byWeek)) +
  ylab("Weekly Click Average") +
  xlab("Week") +
  labs(title = "US Weekly Clicks") 

ggsave(filename = "p.png", width = 5, height = 3)
  # geom_boxplot(outlier.alpha = 0.1) +
  # geom_ribbon()
  # stat_smooth(method = "loess", level = 0.5)
  # geom_line(stat = "mean")

# indeedClean$month <- months(indeedClean$date)
# clickMoAvg <- indeedClean %>% group_by(month) %>% summarise(mon_avg = mean(clicks))

# qplot(x = month, y = mon_avg, data = clickMoAvg)
```

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.