Introduction

The goal of the project is to create a web page presentation using R Markdown that features a plot created with Plotly, and to host the resulting web page on either GitHub Pages, RPubs, or NeoCities.

The interactive plot on the next slide represents the number of road accidents in Great Britain from 2005 to 2015, grouped by severity (slight, serious, or fatal).

rm(list=ls())
library(plotly)
## Warning: package 'plotly' was built under R version 3.5.3
## Loading required package: ggplot2
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
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##     last_plot
## The following object is masked from 'package:stats':
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##     filter
## The following object is masked from 'package:graphics':
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##     layout
library(data.table)
## Warning: package 'data.table' was built under R version 3.5.3
library(tidyr)
## Warning: package 'tidyr' was built under R version 3.5.3
library(lubridate)
## Warning: package 'lubridate' was built under R version 3.5.3
## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:data.table':
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##     hour, isoweek, mday, minute, month, quarter, second, wday,
##     week, yday, year
## The following object is masked from 'package:base':
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##     date
library(zoo)
## Warning: package 'zoo' was built under R version 3.5.3
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## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
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##     as.Date, as.Date.numeric
# The source data sets are not included in this repository.
# To reproduce this presentation, you will first need to download the two
# following zipped data sets:
# - All STATS19 data (accident, casualties and vehicle tables) for 2005 to
#   2014", from
#   https://data.gov.uk/dataset/road-accidents-safety-data/resource/8ecee6ac-33fd-4f5b-8973-e900cc65d24a)
# - Road Safety - Accidents 2015, from
#   https://data.gov.uk/dataset/road-accidents-safety-data/resource/ceb00cff-443d-4d43-b17a-ee13437e9564)
# Then extract the `Accidents0514.csv` and `Accidents_2015.csv` files from
# the zip files in a subdirectory named `data`.

# read data for 2005-2014 and 2015 as data tables and keep only severity and
# date columns
accidents0514 <- fread("Accidents0514.csv", header = TRUE, sep = ",")
accidents0514 <- accidents0514 %>%
    select(Accident_Severity, Date)

accidents15 <- fread("Accidents0514.csv", header = TRUE, sep = ",")
accidents15 <- accidents15 %>%
    select(Accident_Severity, Date)

# concatenate data tables and free up environment
accidents <- rbind(accidents0514, accidents15)
rm(list = c("accidents0514", "accidents15"))
# convert severity to factor and add labels
accidents$Accident_Severity <- factor(accidents$Accident_Severity, levels = 1:3, labels = c("Fatal", "Serious", "Slight"))

# convert date strings to Date objects
accidents$Date <- dmy(accidents$Date)

# group data by date and severity, get count, one row per date
accident_count <- accidents %>%
    group_by(Date, Accident_Severity) %>%
    summarise(count = n()) %>%
    spread(key = Accident_Severity, value = count) %>% 
    as.data.frame()
## Warning: Calling `n()` without importing or prefixing it is deprecated, use `dplyr::n()`.
## This warning is displayed once per session.
# create a smoother for each severity to visualise general trends
loess_slight <- loess(Slight ~ as.numeric(Date), data = accident_count)
loess_serious <- loess(Serious ~ as.numeric(Date), data = accident_count)
loess_fatal <- loess(Fatal ~ as.numeric(Date), data = accident_count)

Road accidents in Great Britain (2005-2015)

# plot data
plot_ly(accident_count) %>%
    add_trace(x = ~Date, y = ~Slight, type="scatter", mode = "markers", 
              name = "slight", legendgroup = "slight", 
              marker = list(color = "#52A9BD")) %>%
    add_trace(x = ~Date, y = ~Serious, type="scatter", mode = "markers",
              name = "serious", legendgroup = "serious", 
              marker = list(color = "#FFF16B")) %>%
    add_trace(x = ~Date, y = ~Fatal, type="scatter", mode = "markers",
              name = "fatal", legendgroup = "fatal", 
              marker = list(color = "#F5677D")) %>%
    add_trace(x = as.Date(loess_slight$x), y = fitted(loess_slight),
              type="scatter", mode = "lines",
              line = list(color = '#1A7A90'), 
              name = "slight Loess smoother", legendgroup = "slight", 
              hoverinfo = 'none', showlegend = FALSE) %>%
    add_trace(x = as.Date(loess_serious$x), y = fitted(loess_serious),
              type="scatter", mode = "lines",
              line = list(color = '#E9D625'),
              name = "serious Loess smoother", legendgroup = "serious",
              hoverinfo = 'none', showlegend = FALSE) %>%
    add_lines(x = as.Date(loess_fatal$x), y = fitted(loess_fatal),
              type="scatter", mode = "lines",
              line = list(color = '#DC2340'),
              name = "fatal Loess smoother", legendgroup = "fatal",
              hoverinfo = 'none', showlegend = FALSE) %>%
    layout(xaxis = list(title = "Year"),
           yaxis = list(title = "Number of Accidents")
    )
## Warning: Ignoring 9 observations