First upload of files

00-1-age-and-year-of-deathofharold-shipmans-victims/00-1-age-year-shipman victims-x.Rmd

@@ -0,0 +1,38 @@
+---
+  title: "Art of Statistics: 0-1 Age and Year of Shipman Victims"
+  #output: md_document
+  output: html_document
+---
+
+***************************************************************************
+
+Data are contained in [00-1-shipman-confirmed-victims-x.csv](00-1-shipman-confirmed-victims-x.csv), and taken from [Chronological List of Decided Cases in the Shipman Inquiry](https://webarchive.nationalarchives.gov.uk/20090808221518/http://www.the-shipman-inquiry.org.uk/fr_casesbyyear.asp?year=74&from=r).
+
+```{r 0-1 Scatter and bars, fig.height=6, fig.width=6, fig.align='left'}
+
+library(magrittr)
+library(ggplot2)
+library(ggpubr)
+library(ggExtra)
+
+# read data to dataframe
+df<-read.csv("00-1-shipman-confirmed-victims-x.csv",header=TRUE) 
+
+# scatter-plot
+s <- ggplot(df, aes(x=fractionalDeathYear, y=Age, colour=reorder(gender2,gender))) # initialise plot for the scatter-chart
+s <- s + geom_point(size=1) # assign scatter chart-type with size 1 points
+s <- s +  labs(x ="Year", y="Age of victim") # Adds axis labels  
+s <- s + scale_x_continuous(breaks=seq(1975, 1995, 5), limits = c(1974,1998)) #x-axis labels every 5 years and between 74 and 98 
+s <- s + scale_y_continuous(breaks=seq(40, 90, 10), limits = c(39,95)) # y-axis every 10 years and between 39 and 95
+#s <- s  + scale_size_continuous(name = "Size", guide = FALSE)  # turns off size legend
+s <- s + theme(legend.position=c(0.125,1.12 ), legend.background = element_rect(colour = "black"), legend.title = element_blank()) # positions. borders, and un-titles the legend
+
+# with marginal histogram
+ggMarginal(s, type="histogram")
+
+
+
+```
+
+Figure 0.1  A scatter-plot showing the age and the year of death of Harold Shipman's 215 confirmed victims. Bar-charts have been added on the axes to reveal the pattern of ages and the pattern of years in which he committed murders.
+

00-2-shipman-times/0-2-shipman-times-x.Rmd → 00-2-shipman-times/00-2-shipman-times-x.Rmd

@@ -5,11 +5,11 @@
 ---
 ### At what time of day did Harold Shipman's victims die?
 
-Data are contained in [0-2-shipman-times-x.csv](0-2-shipman-times-x.csv), and taken from [Harold Shipman's Clinical Practice 1974-1998: A Clinical Audit Commissioned by the Chief Medical Officer, by Richard Baker, page 40, Figure 5.2](https://webarchive.nationalarchives.gov.uk/20090808160000/http://www.the-shipman-inquiry.org.uk/ge_doclist.asp?ID=5), derived from the cremation forms of Shipman's victims.
+Data are contained in [00-2-shipman-times-x.csv](00-2-shipman-times-x.csv), and taken from [Harold Shipman's Clinical Practice 1974-1998: A Clinical Audit Commissioned by the Chief Medical Officer, by Richard Baker, page 40, Figure 5.2](https://webarchive.nationalarchives.gov.uk/20090808160000/http://www.the-shipman-inquiry.org.uk/ge_doclist.asp?ID=5), derived from the cremation forms of Shipman's victims.
 ```{r}
 
 library(ggplot2)
-shipmantimes<-read.csv("0-2-shipman-times-x.csv", header=TRUE) # reads data into shipmantimes data frame
+shipmantimes<-read.csv("00-2-shipman-times-x.csv", header=TRUE) # reads data into shipmantimes data frame
 p <- ggplot(shipmantimes, aes(x=Hour, y)) + ylim(0,15) # constructs initial plot object, p
 p <- p + geom_line(aes(y = Comparison, col = "Comparison GPs"), size=1.5) # adds a y-series
 p <- p + geom_line(aes(y = Shipman, col = "Shipman"), size=1.5) # adds a y-series

00-2-shipman-times/0-2-shipman-times-x.html → 00-2-shipman-times/00-2-shipman-times-x.html

@@ -375,9 +375,9 @@ <h1 class="title toc-ignore">Art of Statistics: Figure 0.2, page 5.</h1>
 
 <div id="at-what-time-of-day-did-harold-shipmans-victims-die" class="section level3">
 <h3>At what time of day did Harold Shipman’s victims die?</h3>
-<p>Data are contained in <a href="0-2-shipman-times-x.csv" class="uri">0-2-shipman-times-x.csv</a>, and taken from <a href="https://webarchive.nationalarchives.gov.uk/20090808160000/http://www.the-shipman-inquiry.org.uk/ge_doclist.asp?ID=5">Harold Shipman’s Clinical Practice 1974-1998: A Clinical Audit Commissioned by the Chief Medical Officer, by Richard Baker, page 40, Figure 5.2</a>, derived from the cremation forms of Shipman’s victims.</p>
+<p>Data are contained in <a href="00-2-shipman-times-x.csv" class="uri">00-2-shipman-times-x.csv</a>, and taken from <a href="https://webarchive.nationalarchives.gov.uk/20090808160000/http://www.the-shipman-inquiry.org.uk/ge_doclist.asp?ID=5">Harold Shipman’s Clinical Practice 1974-1998: A Clinical Audit Commissioned by the Chief Medical Officer, by Richard Baker, page 40, Figure 5.2</a>, derived from the cremation forms of Shipman’s victims.</p>
 <pre class="r"><code>library(ggplot2)
-shipmantimes&lt;-read.csv(&quot;0-2-shipman-times-x.csv&quot;, header=TRUE) # reads data into shipmantimes data frame
+shipmantimes&lt;-read.csv(&quot;00-2-shipman-times-x.csv&quot;, header=TRUE) # reads data into shipmantimes data frame
 p &lt;- ggplot(shipmantimes, aes(x=Hour, y)) + ylim(0,15) # constructs initial plot object, p
 p &lt;- p + geom_line(aes(y = Comparison, col = &quot;Comparison GPs&quot;), size=1.5) # adds a y-series
 p &lt;- p + geom_line(aes(y = Shipman, col = &quot;Shipman&quot;), size=1.5) # adds a y-series

01-1-2-3-child-heart-survival-times/01-1-child-heart-survival-x.Rmd

@@ -0,0 +1,29 @@
+---
+  title: "Art of Statistics: Figure 1.1 (page 26)"
+  #output: md_document
+  output: html_document
+---
+### Figure 1.1: Survival rates following child heart surgery in thirteen hospitals from 2012-2015
+
+Data are shown in Table 1.1 (page 23) and are contained in [01-1-child-heart-survival-x.csv](01-1-child-heart-survival-x.csv). The data were originally presented in the [NCHDA 2012-15 report](https://nicor4.nicor.org.uk/chd/an_paeds.nsf/vwContent/Analysis%20Documents?Opendocument), but are best seen on  [childrensheartsurgery.info](http://childrensheartsurgery.info/).
+
+```{r figure 1-1}
+
+library(ggplot2)
+
+ThirtyDaySurv <-read.csv("01-1-child-heart-survival-x.csv", header=TRUE) # reads data into ThirtyDaySurv data frame
+nhosp=length(ThirtyDaySurv$Hospital)
+p <- ggplot(ThirtyDaySurv, aes(x=reorder(Hospital,nhosp:1), y= ThirtyDaySurvival, fill=Hospital)) # constructs initial plot object, , starting with top row
+p <- p + geom_bar(stat = "identity") # assigns bar chart-type
+p <- p + coord_flip(ylim = c(86,100)) # flips to horizontal bars and limits y-axis
+p <- p + scale_y_continuous(breaks=seq(86, 100, 2)) # assigns breaks every 2 percent
+p <- p + scale_colour_brewer(palette = "Accent") # sets the colour palette 
+p <- p + theme(legend.position="none") # removes the legend
+p <- p +  labs(x="", y="% surviving 30 days") # Adds y-axis label  
+p # draws the plot
+
+```
+
+_Figure 1.1 Bar-chart of 30-day survival rates for thirteen hospitals. The choice of the start of the horizontal axis, here 86%, can have a crucial effect on the impression given by the graphic. If the axis starts at 0%, all the hospitals will look indistinguishable, whereas if we started at 95% the differences would look misleadingly dramatic._
+
+For other ways of displaying and explaining this data, and more recent results, see [childrensheartsurgery.info](http://childrensheartsurgery.info/).

01-1-2-3-child-heart-survival-times/01-1-child-heart-survival-x.csv

@@ -0,0 +1,14 @@
+Hospital,Operations,Survivors,Deaths,ThirtyDaySurvival,PercentageDying
+London - Harley Street ,418,413,5,98.8,1.2
+Leicester,607,593,14,97.7,2.3
+Newcastle,668,653,15,97.8,2.2
+Glasgow,760,733,27,96.3,3.7
+Southampton,829,815,14,98.3,1.7
+Bristol,835,821,14,98.3,1.7
+Dublin,983,960,23,97.7,2.3
+Leeds,1038,1016,22,97.9,2.1
+London - Brompton,1094,1075,19,98.3,1.7
+Liverpool,1132,1112,20,98.2,1.8
+London - Evelina,1220,1185,35,97.1,2.9
+Birmingham,1457,1421,36,97.5,2.5
+London - Great Ormond Street,1892,1873,19,99,1

01-1-2-3-child-heart-survival-times/01-3-child-heart-proportions-x.Rmd

@@ -0,0 +1,29 @@
+---
+  title: "Art of Statistics: Figure 1.3 (page 30)"
+  #output: md_document
+  output: html_document
+---
+
+### Figure 1.3: Percentage of all child heart surgery being carried out in each of thirteen hospitals
+
+Data are shown in Table 1.1 (page 23) and are contained in [01-1-child-heart-survival-x.csv](01-1-child-heart-survival-x.csv). The data were originally presented in the [NCHDA 2012-15 report](https://nicor4.nicor.org.uk/chd/an_paeds.nsf/vwContent/Analysis%20Documents?Opendocument), but are best seen on  [childrensheartsurgery.info](http://childrensheartsurgery.info/).
+
+
+```{r figure 1-3}
+
+library(ggplot2)
+
+df <- read.csv("01-1-child-heart-survival-x.csv", header=TRUE) # reads csv into dataframe, df
+df$Percentage = 100*df$Operations/sum(df$Operations)
+df$Pos= rank(df$Percentage)
+bp <- ggplot(df, aes(x=reorder(Hospital,-Pos), y=Percentage, fill=Hospital)) #sets initial plot object from the dataframe for Hospitals, reordered by Percentage (descending) as the y-values, colour-filled by Hospital
+bp <- bp + geom_bar(stat = "identity") + labs(x="Hospital") # makes the plot a bar-chart
+bp <- bp + coord_flip() # makes it an horizontal bar chart
+bp <- bp + scale_y_continuous(breaks=seq(2,16,2)) # breaks every two-count
+bp <- bp + theme(legend.position="none") # removes the legend
+bp <- bp +  labs(y="Percentage of all operations in 2012-15 \nthat are carried out in each hospital", x="") # Adds labels
+bp # draws the plot
+
+```
+
+_Figure 1.3 Percentage of all operations in 2012-15 that are carried out in each hospital: a clearer representation using a horizontal bar chart._