First of all we load the mtcars data set. Then we want to see what is contained within the data set and also view what type of data each variable is.

The raw code is also available from my github, see:

https://github.com/granger89/SCALEITUP/blob/master/graphs%20in%20ggplot2

library(ggplot2)
cars <- mtcars
cars
str(cars)

We can see there is no categorical variable. We are going to create one which we will use for plotting we are going to create two; one for the make of car and another for its weight class.

cars$car_type <- rownames(cars)

Next we use summary to help us when deciding upon the thresholds.

summary(cars$wt)
cars$wt_class <- NA 
indx <- cars$wt<2.5 
cars[indx, "wt_class"] <- "light"
indx <- cars$wt>=2.5 & cars$wt<3.4
cars[indx, "wt_class"] <- "medium"
indx <- cars$wt>=3.4
cars[indx, "wt_class"] <- "heavy"

We now have the data frame in the correct format we want for plotting

str(cars)

The following code gives us a simple plot of two variables mpg (x-axis) and hp (y-axis) using the dataset cars.

ggplot(cars) +
geom_point(aes(mpg, hp))

We now introduce a third variable. We can change the point shapes using a categorical variable. For this we use the weight class variable we created earlier.

ggplot(cars, aes(x=mpg, y=hp, group=wt_class))+
geom_point(aes(shape=wt_class), size=2, alpha=1)

Now we have the graph we next steps involved editing the axes, legend, title and background so that we get a cleaner graph.

Editing the axes and background

Firstly we will import new fonts.

library(gridExtra)
library(extrafont)
library(ggthemes) # Load
fonts()
windowsFonts("Arial" = windowsFont("Arial"))
windowsFonts("Times New Roman" = windowsFont("Times New Roman"))
windowsFonts("Goudy Stout" = windowsFont("Goudy Stout"))

Give the plot a white background and change the fonts

ggplot(cars, aes(x=mpg, y=hp, group=wt_class))+
geom_point(aes(shape=wt_class), size=2, alpha=1)+
theme_bw()+
theme(panel.background = element_rect(fill = "white", colour = "white"))+
theme(axis.text=element_text(size=11, family="Arial"),axis.title=element_text(size=12,face="bold", family="Arial"))+
theme(panel.grid=element_line(color = "grey80"))

Change the x and y axis names and also remove spacing around the origin. We can also change the limits of the x and y axes.

ggplot(cars, aes(x=mpg, y=hp, group=wt_class))+
geom_point(aes(shape=wt_class), size=2, alpha=1)+
theme_bw()+
theme(panel.background = element_rect(fill = "white", colour = "white"))+
theme(axis.text=element_text(size=11, family="Arial"),axis.title=element_text(size=12,face="bold", family="Arial"))+
theme(panel.grid=element_line(color = "grey80")) +
labs(x = "Miles per Gallon (mpg)", y = "Horse Power (HP)" )+
labs(title = "Cars power, weight and fuel economy")+
scale_x_continuous(limits = c(0,35), expand = c(0, 0)) +
scale_y_continuous(limits = c(0,350), expand = c(0, 0))

Fixing the legend

One thing I would like to fix is the legend as it does not look that pleasant. I would like the first letters to be capitals and ‘wt_class’ does not explain what the values show.

scale_shape_manual(values=c(1, 2, 3), name = "Weight Group", breaks=c("heavy","medium","light"), labels=c("Heavy >3.4 Tons", "Medium 2.5-3.4 Tons", "Light <2.5 Tons"))

To explain this code, the values are the type of shapes used to represent the different classes. See the link for what the different values mean.

Next ‘name’ is the title of the legend.
‘breaks’ are the different classes. Once we know the values we can then put them in whatever order we want. (“heavy”,”medium”,”light”) or (“light”,”medium”,”heavy”) . That is the order they appear in the legend.
Next we use labels to replace the raw data values with whatever value we want.
http://www.sthda.com/english/wiki/ggplot2-point-shapes

ggplot(cars, aes(x=mpg, y=hp, group=wt_class))+
   geom_point(aes(shape=wt_class), size=2, alpha=1)+
   theme_bw()+
   theme(panel.background = element_rect(fill = "white", colour = "white"))+
   theme(axis.text=element_text(size=11, family="Arial"),axis.title=element_text(size=12,face="bold", family="Arial"))+
   theme(panel.grid=element_line(color = "grey80")) +
   labs(x = "Miles per Gallon (mpg)", y = "Horse Power (HP)" )+
   labs(title = "Cars power, weight and fuel economy")+
   scale_x_continuous(limits = c(0,35), expand = c(0, 0)) +
   scale_y_continuous(limits = c(0,350), expand = c(0, 0))+
   scale_shape_manual(values=c(1, 2, 3), name = "Weight Group",
                      breaks=c("heavy","medium","light"),
                      labels=c("Heavy >3.4 Tons", "Medium 2.5-3.4 Tons", "Light <2.5 Tons"))

Finally we can position the legend. Currently it is squeezing the size of the plot area. We are likeyl to have some empty space where we can place it. The position varies on the x and y axes from 0 to 1. So how about we place it at 0.2 on the x-axis and 0.2 on the y-axis. I’ve also given it a border and made the background white.

ggplot(cars, aes(x=mpg, y=hp, group=wt_class))+
   geom_point(aes(shape=wt_class), size=2, alpha=1)+
   theme_bw()+
   theme(panel.background = element_rect(fill = "white", colour = "white"))+
   theme(axis.text=element_text(size=11, family="Arial"),axis.title=element_text(size=12,face="bold", family="Arial"))+
   theme(panel.grid=element_line(color = "grey80")) +
   labs(x = "Miles per Gallon (mpg)", y = "Horse Power (HP)" )+
   labs(title = "Cars power, weight and fuel economy")+
   scale_x_continuous(limits = c(0,35), expand = c(0, 0)) +
   scale_y_continuous(limits = c(0,350), expand = c(0, 0))+
   scale_shape_manual(values=c(1, 2, 3), name = "Weight Group",
                      breaks=c("heavy","medium","light"),
                      labels=c("Heavy >3.4 Tons", "Medium 2.5-3.4 Tons", "Light <2.5 Tons"))+
   theme(legend.position=c(0.2,0.2))+
   theme(legend.text.align = 0)+
   theme(legend.title=element_text(size=10))+
   theme(legend.text=element_text(size=10))+
   theme(legend.background = element_rect(colour = 'black', fill = 'white', size = 1, linetype='solid'))

Vary size by value of variable

Finally lets introduce a fourth variable. Instead of all points having a fixed size let us vary the size based on the number of gears.

ggplot(cars, aes(x=mpg, y=hp, group=wt_class, size=gear))+
   geom_point(aes(shape=wt_class), alpha=1)+
   theme_bw()+
   theme(panel.background = element_rect(fill = "white", colour = "white"))+
   theme(axis.text=element_text(size=11, family="Arial"),axis.title=element_text(size=12,face="bold", family="Arial"))+
   theme(panel.grid=element_line(color = "grey80")) +
   labs(x = "Miles per Gallon (mpg)", y = "Horse Power (HP)" )+
   labs(title = "Cars power, weight and fuel economy")+
   scale_x_continuous(limits = c(0,35), expand = c(0, 0)) +
   scale_y_continuous(limits = c(0,350), expand = c(0, 0))+
   scale_shape_manual(values=c(1, 2, 3), name = "Weight Group",
                      breaks=c("heavy","medium","light"),
                      labels=c("Heavy >3.4 Tons", "Medium 2.5-3.4 Tons", "Light <2.5 Tons"))+
   theme(legend.position=c(0.2,0.3))+
   theme(legend.text.align = 0)+
   theme(legend.title=element_text(size=10))+
   theme(legend.text=element_text(size=10))+
   theme(legend.background = element_rect(colour = 'black', fill = 'white', size = 1, linetype='solid'))+
   scale_size_continuous(range = c(3,5),  
                         breaks= c(3,4,5), name="Number of \nGears")

Label specific points

Also I would like to label the outliers at the upper and lower end. For this we use ggrepel

library(ggrepel)

ggplot(cars, aes(x=mpg, y=hp, group=wt_class, size=gear))+
   geom_point(aes(shape=wt_class), alpha=1)+
   theme_bw()+
   theme(panel.background = element_rect(fill = "white", colour = "white"))+
   theme(axis.text=element_text(size=11, family="Arial"),axis.title=element_text(size=12,face="bold", family="Arial"))+
   theme(panel.grid=element_line(color = "grey80")) +
   labs(x = "Miles per Gallon (mpg)", y = "Horse Power (HP)" )+
   labs(title = "Cars power, weight and fuel economy")+
   scale_x_continuous(limits = c(0,35), expand = c(0, 0)) +
   scale_y_continuous(limits = c(0,350), expand = c(0, 0))+
   scale_shape_manual(values=c(1, 2, 3), name = "Weight Group",
                      breaks=c("heavy","medium","light"),
                      labels=c("Heavy >3.4 Tons", "Medium 2.5-3.4 Tons", "Light <2.5 Tons"))+   theme(legend.position=c(0.2,0.3))+   theme(legend.text.align = 0)+   theme(legend.title=element_text(size=10))+   theme(legend.text=element_text(size=10))+   theme(legend.background = element_rect(colour = 'black', fill = 'white', size = 1, linetype='solid'))+   scale_size_continuous(range = c(3,5),                           breaks= c(3,4,5), name="Number of \nGears")+   labs(size="Number of Gears")+   geom_text_repel(data = subset(cars, mpg > 25 | mpg <16), size=3,aes(label = car_type))

For other customisable options please refer to the ggplot2 documentation which is excellent at explaining how to add further options.

https://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf

The slides from my presentation “Change in Artificial Land Use over time across European Cities: A rescaled radial perspective” can be found here.

Workshops

The first workshop was given by Prof. Nick Holliman of Newcastle University looking at data visualisation and exploring the use of Microsoft Power Bi for visualising data. He also presented some of the data currently being collected in the Newcastle Urban Observatory using censors.

The second workshop explored the use of API’s, what they are and how to use them. This workshop was run by researchers from the Newcastle Urban Observatory. Materials from the API workshop can be found here.

The workshop on Wednesday morning was run by Dr. Robin Lovelace and Dr. Nick Bearman and explored mapping in R. Robin presented some materials from his latest book ‘Geocomputation with R’. More details of the book here. Documents and reproducible code from the GIS mapping in R workshop can be found here.

Finally the last workshop was given by Dr. Laura Hanson along with a number of other researchers and spatial analysts from both the private and public sector.

Conference

One of the main trends I picked up from this conference was the use of visual aids and GIF’s created in R. Any of the presentations which used these presented their research in a particularly powerful way. As researchers one of the biggest challenges is presenting our research in a format which is quick and easy to understand. This can be difficult and sometimes require many graphs, figures and tables to get a point across. Such animations present research in a dynamic way.

R code

From an organisational viewpoint the name badges and reusable coffee cup given out at the start of the conference were two welcome features. The name badges made the first name particularly easy to read without a need to strain your eyes while the coffee cups meant a substantial reduction in disposable coffee cup usage given the approximate 200 attendance.

Twitter

An analysis of the tweets from the conferences reveals some interesting trends. It was not surprising to see the term ‘reproducible’ so high up the list. There is a growing number of publications and producing reproducible research along with the code is fast becoming the norm. Representing the code from R or PyQGIS on GitHub alongside the results that appear in the academic article or conference presentation. It is heartening to see such sharing of knowledge in the geocomputation community and will surely only lead to better outcomes in terms of improved research and evidence for policymakers.Code for carrying this out in R can be found in my GitHub here.

Newcastle

One of the most famous features of Newcastle are the seven bridges which cross the River Tyne ranging in both size and age.

There is also the Newcastle castle and gate house. There is a good mixture in this city of both the new with the historic. Nearby Newcastle is situated Durham Cathedral. Construction started in 1093. The cathedral houses the relics of Saint Cuthbert.

The compactness of the city was evident throughput. Public transport also made it very easy to get around with intercity trains, buses and a metro. The city appears to be going from strength to strength.

The conference dinner took place at St. James’s Park home to Newcastle United FC. The pride of the north east (although I am not sure Sunderland or Middlesbrough fans will agree!!) it has been a trophy less period for the magpies over the last couple of decades despite coming close to breaking Manchester United’s dominance in the mid-nineties under Kevin Keegan. An impressive stadium it has a certain character that a lot of modern stadia lack.