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Making the links between computing and mathematics
Jan 07, 2016
There are some great ways to link computing and all the other subjects on the school curriculum, but there’s a particularly strong argument for making connections with mathematics.
As a discipline, computing owes its origins to mathematics. Babbage’s analytical engine was designed to compute mathematical tables. The word ‘computer’ was originally applied to a person who performed calculations. Turing’s paper ‘On computable numbers with an application to the Entscheidungsproblem’ was more about proving a mathematical theorem than founding a new science. Cambridge’s computer lab started out as ‘the mathematical laboratory‘.
Let’s look at the connections from both perspectives: firstly, bringing maths into computing.
Given our current focus on thinking rather than coding, there are strong parallels between mathematical reasoning and computational thinking: logical reasoning, algorithms, abstraction, generalisation, decomposition and evaluation play a central role in both CS and mathematical problem solving. Conrad Wolfram’s four steps for doing maths parallel stages of traditional software development:
- define
- translate
- compute
- interpret
There’s a fair amount of mathematical content in computer science as taught in schools. In primary, Bee Bots and similar are used early on to practise positional language and emerging measure and children are now more likely to meet angles and coordinates in Scratch before these get covered in maths lessons. Topics such as binary conversion, binary arithmetic, Boolean logic and functions all make demands on pupils’ mathematical understanding. There’s some potentially gritty maths in computing at GCSE and A level: graph theory, modular arithmetic, formal logic and measures of algorithmic efficiency. It’s perhaps no surprise that grade B in GCSE maths is seen as a prerequisite for A Level computer science, or that A level maths / further maths, is often a requirement for studying CS at Russell Group universities.
Going in the other direction, there are very many opportunities to enrich school mathematics through computing, but more than that, computer science provides a means through which mathematical ideas become “incarnate, tangible and executable”.
Wolfram makes a strong case for ‘computer based mathematics’ being taught in schools – arguing that this reflects how real maths is done, making the subject far more relevant than when calculations are done by hand. It’s the third of Conrad’s steps (‘compute’) that the machines are unsurprisingly much better at: perhaps a pupil’s mathematical understanding would be enhanced by spending more of her time on steps 1, 2 and 4 and proportionately less on the easily automatable step 3. ‘Show your working’ could be at least as much about providing the source code for your program as making marks on paper.
In much of maths education working with software can be powerful: turtle graphics as a tool to think geometrically; spreadsheets as a visual introduction to functional programming; the classes, objects and methods of dynamic geometry software; data visualisation without the faff through Excel, Raw or R.
There’s a place for introducing pupils to the tools (some) professionals use, such as Mathematica or its slimmed down, online sibling, Wolfram Alpha. Mathematica is programmed using the Wolfram Language, which is very high level, with built-in functions for finite state machines, graph traversals, Turing machines and machine learning classifiers, amongst other things… Note Mathematica is bundled as part of NOOBS for the Raspberry Pi.
Programming has many applications in maths education: writing functions for fractions arithmetic, solving equations numerically, simulating probability experiments etc. The list is pretty inexhaustible, and provides rich territory to develop understanding in mathematics and computer science side by side.
The links between computer science and mathematics provide both challenge and opportunity for curriculum development in schools. We draw on mathematics in computing; we use computation (automated or otherwise) in mathematics. At the recent Computer Based Mathematics summit, CAS chair Simon Peyton Jones and Andrew Fitzgibbon made a strong case for teaching the two subjects together – with the current curricula and exam specs for the two subjects. What they suggest isn’t maths with a nod to CS or CS with a nod to maths, but rather CS bringing to life the abstractions of maths, and maths providing the foundational abstractions that underpin CS. Implementing such a vision at national scale isn’t trivial, but perhaps a few teachers in some forward thinking schools or academies might be up for trying this out? It would be phenomenally interesting experiment!
Originally published in the Spring 2016 edition of Switched On, the Computing at School newsletter, as an introduction to a section on computing and mathematics.
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Color Game - a really fun project showing possibilities created by basic JavaScript.
I am a quick learning student aspiring to be a developer. Constant improvement is a key to success. I have included some of my projects below, you can also get my CV here
Color Game - a really fun project showing possibilities created by basic JavaScript.
A single page image gallery made using Bootstrap, fully responsive and good looking.
Todo list - simple JavaScript app, first steps with jQuery library.
HTML canvas made awesome with howler.js and paper.js libraries.
© Tomasz Chruściński 2018


The unit class manages a wide range of different units for HealthKit.
The unit class supports most standard SI units (meters, seconds, and grams), SI units with prefixes (centimeters, milliseconds and kilograms) and equivalent non-SI units (feet, minutes, and pounds). HealthKit also supports creating complex units by mathematically combining existing units.
Units are used when working with HealthKit quantities. Quantities store both a double value and its corresponding unit. You can then request the value from the quantity in any compatible units.
You can use the string representation of a unit whenever a Ti.Healthkit.Unit object is required as parameter -- the module will automatically create the Unit object from the string, as long as the string represents a valid unit. See the native documentation for the rules of creating a valid unit string.
Returns the string representation of the unit.
Returns a Boolean value indicating whether the unit is null. Null units occur only when creating compound units where all the units cancel out. For example, if you created a dL/L unit, you would have a null unit.
Creates a complex unit by multiplying the receiving unit with another unit. You can create a complex unit by multiplying two units together. For example, you could create a foot-pound unit by multiplying a foot unit by a pound unit.
Creates a complex unit by dividing the receiving unit with another unit. You can create a complex unit by multiplying two units together. For example, you can create a meters-per-second unit by dividing a meters unit by a seconds unit.
This method creates a new, complex unit by raising the receiving unit by the given power. This task is often only one step in a series of operations. For example, you can use this method to create a meters-per-second-squared unit.
This method creates a new, complex unit by dividing 1 by the receiving unit.


nsss is an implementation of a subset of the pwd.h, group.h and shadow.h family of functions, performing user database access on Unix systems.
Usually, user database access via getpwnam() and similar function is provided by the system's libc. However, not all libcs implement a configurable backend for the user/group database. For instance the musl libc, on Linux, only supports the standard /etc/passwd mechanism; it also supports the nscd protocol but this is not quite enough:
The mechanism used by glibc, called Name Service Switch (abbreviated to nsswitch or NSS), has its own set of issues that makes it unsuitable in certain situations.
nsss is a secure implementation of a "name service switch": configurable user/group/shadow database access, providing getpwnam() et al. functionality by communicating over a Unix domain socket with a daemon; the daemon can perform lookups in any database it chooses.
nsss does not perform dynamic module loading, only adds a small footprint to the application's binary, and does not add any complex decision engine into the client's address space. Applications can be statically linked against the nsss library, and still benefit from configurable user database access functions.
nsss is free software. It is available under the ISC license.
git clone git://git.skarnet.org/nsss
The following commands are not meant to be directly invoked on the command-line. They are meant to be used behind a Unix domain socket super-server such as s6-ipcserver in order to provide a local service. Depending on the chosen command, the service will provide a different backend to the name service.
Future versions of nsss will come with more backends.


I stumbled into CS. My first semester of college I was kinda lost but
soon realized that I liked coding in the introductory programming course
and wanted to continue to build cool things through code. A wider theme
I see with my field is the abstraction that technology adds which makes interacting with it effortless through an
user perspective (until it breaks). Lastly CS is all about reuse and not
reinventing the wheel which is something I'm all about because it makes
my life easier.
Family and Friends
My family rocks and have always supported me in (mostly) whatever I do. My mom and dad have always been outdoorsy and active
and it eventually rubbed off me after I spent a lot of time indoors
gaming. My sister Stephanie has always been an inspiration / positive
influence as she always has better grades. Pictured on the right are my
homies who I jive with.
Entertainment
I spend a good chunk of time perusing Youtube and watching home made
movies. The one linked above really got me psyched last year to start
getting after it outdoors and to some day thru hike (or run) the PCT -
probably after I graduate. Mostly though I use media whether anime or
Youtube as a form of distraction and to just relax after a long day of
work. Music is also something I interact often with and I love
discovering new artists that use many genres.
Community
I think I have both an online and actual community. Playing videogames
for a good portion of my life has let me meet some pretty awesome people
who I know I can count on. I consider my actual community to include the
clubs I am in and the friends I have made along the way.
School
I never took school very seriously (to my parent's dismay) until I
reached college and realized I needed to get my stuff together. I still
got good grades in High School and on AP Exams but I question if I was
actually learning the material or overfitting and was just good at
taking the tests. I guess my current schooling at Pitt is preparing me
for a job in Software Engineering or a related field. But who knows as
long as I am working with technology in some way I'm sure I can adapt.


A representative from the IEEE-ISTO Printer Working Group (PWG) attended the World Wide Web Consortium (W3C) "Publishing and the Open Web Platform" workshop in September 2013 and participated in the "Standards Bodies: Who does what?" panel.
This W3C Workshop on publishing workflow was the third in a series of industry consultation events on publishing using the Open Web Platform. The specific goal of this workshop was to identify difficulties faced by existing professional publishing organizations in using tools based on the Open Web platform, including the production of printed books, and to find ways to eliminate or ameliorate those difficulties. Some of the topics listed were print-on-demand, color management, ink control, specifying media, binding, trimming, and finishing, all areas of interest to the PWG. The conclusions of these workshops will be communicated to the applicable W3C workgroups, particularly the Cascading Style Sheets (CSS) working group to address limitations in CSS design and implementation.
The PWG initially submitted a position paper prepared by Paul Tykodi. Once accepted, PWG representative Bill Wagner gave a presentation outlining PWG credentials as a printing standards body and the PWG position with respect to including PWG Print Job Ticket production specification elements along with content and format information in the Open Web publishing workflow.
The workshop identified areas needing attention that were within the purview of the W3C, possibly in conjunction with other organizations. However, it was observed that the schedule and plans of the CSS working group are very full and that their priorities are dictated by requirements identified by major user communities. Although no specific conclusions were drawn with respect to print production aspects, such as the PWG proposal, one resolution of the workshop was that:
"Liaisons should be established where needed with other standards bodies and industry organizations, and contact maintained in order to avoid duplication of effort or needless competition."
The PWG will support such contact with Digital Publishing Interest Group within the Digital Publishing Activity at W3C and the CSS working group.


Tom Scott Memorial 10 mile
Law => Motherwell SCO
Website: www.scottishdistancerunninghistory.scot/tom-scott-10-miles
Race Winners
(53 runnings)
08 Apr
2007
25 Mar
2006
03 Apr
2005
12 Apr
2004
06 Apr
2003
07 Apr
2002
07 Apr
2001
09 Apr
2000
10 Apr
1999
05 Apr
1998
13 Apr
1997
14 Apr
1996
09 Apr
1995
10 Apr
1994
11 Apr
1993
05 Apr
1992
06 Apr
1991
13 Apr
1990
1989
09 Apr
1988
04 Apr 1987
05 Apr
1986
13 Apr
1985
07 Apr
1984
09 Apr
1983
03 Apr
1982
04 Apr
1981
12 Apr
1980
07 Apr
1979
01 Apr
1978
02 Apr
1977
03 Apr
1976
05 Apr
1975
06 Apr 1974
07 Apr
1973
08 Apr 1972
03 Apr 1971
04 Apr
1970
05 Apr 1969
06 Apr 1968
01 Apr 1967
02 Apr 1966
03 Apr 1965
04 Apr 1964
20 Apr
1963
07 Apr 1962
Notes: 1962 is the inaugural year. This course is point-to-point
from roughly 167m to 87m, net drop is 5.0 m/km.
05 Apr
2015
49:37
Craig Ruddy (SCO)
(nc)
56:48
Megan Crawford (SCO)
(nc)
13 Apr
2014
53:21
Robert Gilroy (SCO)
(nc)
1:02:43
Lisa Finlay (SCO)
(nc)
07 Apr
2013
50:25
Robbie Simpson (SCO)
(nc)
58:08
Fiona Matheson-2
(nc)
08 Apr
2012
49:55
Tewolderhan Mengisteab-4
(nc)
57:45
Gemma Rankin-2
(nc)
10 Apr
2011
50:08
Tsegay Tewelde (ERI)
(nc)
1:00:08
Gemma Rankin (SCO)
(nc)
11 Apr
2010
48:36
Tsegezab Woldemichael-3
59:24
Fiona Matheson (SCO)
12 Apr
2009
49:24
Tsegezab Woldemichael-2
57:15
Toni McIntosh-2
13 Apr
2008
50:20
Tsegezab Woldemichael (ERI)
1:03:28
Michelle Hetherington (SCO)
51:46
David Millar (SCO)
59:10
Annabel Granger (ENG)
51:29
Glen Stewart-2
1:05:02
Claire Couper (SCO)
49:24
Glen Stewart (SCO)
1:00:39
Toni McIntosh (SCO)
52:12
Ben Hukins (SCO)
59:11
Jill Shannon (IRL)
48:25
Stephen Wylie (SCO
56:11
Trudi Thomson-2
49:46
Jamie Reid-2
58:16
Trudi Thomson (SCO)
50:24
Jamie Reid (SCO)
59:25
Addy Gerrard (SCO)
48:28
Allan Adams-2
56:16
Sandra Branney-3
49:18
Thomas Murray-3
59:23
Marlene Gemmell (SCO)
48:38
Thomas Murray-2
57:27
Tracey Brindley (SCO)
49:35
William Coyle (SCO)
53:56
Yvonne Murray (SCO)
47:54
Allan Adams (SCO)
1:03:16
Kim Fisher (SCO)
47:35
Adrian Callan
(SCO)
53:57
Vicki McPherson (SCO)
48:15
Thomas Murray (SCO)
58:16
Elaine McBrinn (SCO)
47:29
Stephen Wylie (SCO)
53:42
Karen Macleod (SCO)
48:05
Michael Carroll (SCO)
56:12
Sandra Branney-2
47:31
Nathaniel Muir (SCO)
54:05
Sandra Branney (SCO)
47:20
George Braidwood (SCO)
57:54
Penny Rother (SCO
---
not held due to lack of
sponsorship
---
not held due to lack of sponsorship
47:38
Alex Gilmour
(SCO)
54:30
Sharon Sinclair-3
47:38
Lawrence Spence-2
54:50
Sharon Sinclair-2
47:09
Lawrence Spence
(SCO)
55:30
Sharon Sinclair (SCO)
49:24
Alan Puckrin
(SCO)
1:06:42
Palm Gunstone (SCO)
47:09
John Graham (SCO)
54:49
Yvonne Murray (SCO)
49:12
Lindsay Robertson (SCO)
46:05
Allister Hutton (SCO)
47:39
Yasunori Hamada (JPN)
46:34
James Brown-3
48:04
James Brown-2
46:39
James Dingwall-4
49:40
James Dingwall-3
47:10
Andrew McKean-2
46:33
James Brown (SCO)
48:45
James Dingwall-2
48:08
James Dingwall (SCO)
47:45
Sam Downie (SCO)
47:36
Andrew McKean (SCO)
47:47
Lachlan Stewart-2
49:14
Ian
McCafferty-2
48:39
Ian McCafferty (SCO)
46:41
Lachlan Stewart (SCO)
48:06
Andrew Brown-2
47:34
James Alder (SCO)
49:27
Alastair Wood (SCO)
47:59
John Linaker (SCO)
50:33
Andrew Brown (SCO)
(nc) =>.
Scottish championship
2012 Ross Houston was the Scottish champion (2nd place, 50:08)
2011 Ross Houston was the Scottish champion (3rd place, 50:31)
Most Wins
(1962-date)
Men
|
4 |
James Dingwall (SCO) | 1973-84,1977-78 |
| Tsegezab Woldemichael (ERI) | 2008-10,2012 |
Women
|
3 |
Sharon Sinclair (SCO) | 1986-88 |
| . | Sandra Branney (SCO) | 1991-92,2000 |
Prize Money
(ADR qualified runners)
No information on prize money has been received for this race
Race Time Bias
(sec/km)
Insufficient data to calculate the race time bias.
Competitive Level
(1990-date)
Insufficient data to calculate the competitive level.
Quality Performances
Men<47:20 1 <47:40 2 <48:00 4 <48:20 7 <48:40 8 <49:00 13 <49:20 17 <49:40 29 Women <54:00 1 <54:30 1 <55:00 2 <55:30 2 <56:00 2 <56:30 2 <57:00 3 <57:30 4 <58:00 5
Last Updated on
16 Jan 2016Contributors: Mick McGeoch (50), Colin Youngson (50)


All routines in the Stream unit are exported qualified (and thus must be prefaced with "Stream."), with the exception of eof which is part of the language but conceptually part of this unit and is considered to be exported unqualified.
| eof* | Determines if the end of file has been reached. | |
| Flush | Flushes a specified stream. | |
| FlushAll | Flushes all open output streams. |
* Part of the language, conceptually part of the Stream unit.


MAIL SERVICES
The link will get you to the mail login page of ETH
PRINT SERVICES
To use the print service, you have to be connected to the ETH WiFi or use a VPN.
ACADEMIC CALENDAR
Here you'll find all the important dates of your semester and year.
COURSE CATALOGUE
This is where you'll find all your classes and links to the classes websites
ZÜRI MENSA
This is actually an App for your phones. It's super neat and easy to use, you'll have all menus in all cateens of ETH, daily. Download it, really.
ACCOMMODATION
For those searching for a place to stay...


--- title: Analyzing My Sleep Data author: Sean Nguyen date: '2018-04-06' slug: analyzing-my-sleep-data categories: - R - Visualization tags: - Sleep - Health - iOS subtitle: '' summary: '' authors: [] featured: yes image: caption: "Bedtime and Sleep Duration (2014-2018)" focal_point: 'Center' preview_only: false projects: [] ---
I’ve been using the iOS Sleep Cycle app to track my sleeping since late 2014 and have accumulated quite a bit of information about my sleeping habits. I wanted to see if I could do some exploratory data analysis and try out some different packages to clean up and visualize my data. The data from the app comes in a csv file and contains information like date, sleep quality, sleep duration, and more recently it can integrate with the pedometer so you can get the number of steps in a given day. The tidyverse is my go to package for a ton of my data analysis, it works so well with cleaning up and exploring data.
For this analysis I wanted to utilize four packages to learn more about them and apply them to this datatset:
library(tidyverse)
library(lubridate)
library(ggridges)
library(viridis)
library(here)
First I used the here package to tell R that I wanted to read in my file that was located in the ‘data’ folder. The nice thing about here() is that is automatically defaults to the root R project folder so as long as you have the project folder it doesn’t matter where you place the folder on your computer, the file will still be accessible since here() takes care of it for you.
The next step was reading in the file with read_delim() with the delim = “;”. I then used rename to change column names and used mutate() extensively to create new columns from existing data. I used a couple tricks to get the columns that I wanted like str_replace() to remove “%” from the sleep_quality column. I also used ifelse() and grepl() to create a column of Weekend Weekday to tease apart differences in days of the week. After mutate I used select() to pick the columns I wanted. mutate_at() is one of my favorite functions to convert multiple columns to a different data type in R. After that I was able to have a clean dataset that was ready for exploratory data analysis!
file <- here("data_files/sleepdata.csv")
df <- read_delim(file, delim = ";") %>%
rename(sleep_quality = `Sleep quality`,
time_in_bed = `Time in bed`,
steps = `Activity (steps)`) %>%
mutate(sleep_quality = str_replace(sleep_quality, "\\%", "") %>% as.numeric,
day_of_week = as.Date(Start) %>% wday(label = TRUE),
what_day = ifelse(grepl("Sat|Sun", day_of_week),"Weekend","Weekday") %>%
as.factor(),
sleep_hour = ymd_hms(Start) %>% hour(),
sleep_min = ymd_hms(Start) %>% minute()/60,
sleep_time = sleep_hour + sleep_min,
time_hr = period_to_seconds(hms(time_in_bed))/3600,
date = ymd_hms(Start),
day = day(date),
month = month(date),
year = year(date)) %>%
select(year, month, day, day_of_week,what_day,sleep_time, Start:time_in_bed, time_hr,steps) %>%
mutate_at(vars(year:day),as.factor)
df
## # A tibble: 1,270 x 12
## year month day day_of_week what_day sleep_time Start
## <fct> <fct> <fct> <ord> <fct> <dbl> <dttm>
## 1 2014 9 28 Sun Weekend 3.57 2014-09-28 03:34:30
## 2 2014 9 30 Tue Weekday 1 2014-09-30 01:00:51
## 3 2014 10 1 Wed Weekday 2.12 2014-10-01 02:07:23
## 4 2014 10 2 Thu Weekday 1.4 2014-10-02 01:24:34
## 5 2014 10 3 Fri Weekday 0.4 2014-10-03 00:24:24
## 6 2014 10 5 Sun Weekend 1.65 2014-10-05 01:39:23
## 7 2014 10 6 Mon Weekday 23.6 2014-10-06 23:37:14
## 8 2014 10 8 Wed Weekday 0.7 2014-10-08 00:42:22
## 9 2014 10 8 Wed Weekday 23.6 2014-10-08 23:37:56
## 10 2014 10 10 Fri Weekday 0.433 2014-10-10 00:26:28
## # … with 1,260 more rows, and 5 more variables: End <dttm>,
## # sleep_quality <dbl>, time_in_bed <time>, time_hr <dbl>, steps <dbl>
I plotted all the data points as a function of time to see if there was anything general trend with sleep duration and sleep quality these past couple years. Notice how I used scale_color_viridis() to have a nice diverging color palette for geom_point(). We can see that it seems like I get around 7-7.5 hours a sleep per night on average.
df %>%
ggplot(aes(x = Start, y = time_hr, color = sleep_quality)) +
geom_point(alpha = 0.6) +
geom_smooth() +
scale_color_viridis() +
labs(title = "Hours of Sleep",
subtitle = "Source: iPhone sleep cycle data (2014-2018)",
x = "Date",
y = "Hours of Sleep",
color = "Sleep Quality\n") +
theme_bw()
Next I wanted to see what time I went to sleep and look at the sleep quality. We can see that the quality appears to go up as I go to bed earlier.
df %>%
ggplot(aes(x = Start, y = sleep_time, color = sleep_quality)) +
geom_point() +
scale_color_viridis() +
scale_y_continuous(expand = c(0,0),
breaks = seq(0,24,4),
limits = c(0,24.1),
labels = c("12 AM","4 AM","8 AM", "12 PM", "4 PM","8 PM","11:59 PM")) +
labs(title = "Bedtime and Sleep Quality",
subtitle = "Source: iPhone sleep cycle data (2014-2018)",
x = "Date",
y = "Bedtime",
color = "Sleep Quality\n") +
theme_bw()
Then I wondered if there might be a difference in bedtime between weekdays and weekends. It looks like there’s not a huge difference between bedtime between the two.
df %>%
ggplot(aes(x = Start, y = sleep_time, color = what_day)) +
geom_point(alpha = 0.6) +
scale_y_continuous(expand = c(0,0),
breaks = seq(0,24,4),
limits = c(0,24.1),
labels = c("12 AM","4 AM","8 AM", "12 PM", "4 PM","8 PM","11:59 PM")) +
labs(title = "Bedtime and Sleep Quality",
subtitle = "Source: iPhone sleep cycle data (2014-2018)",
x = "Date",
y = "Bedtime",
color = "") +
theme_bw()
We can then look at the comparison between Sleep Quality and Hours of Sleep. Here it’s readily apparent the more you sleep the better the quality. This is isn’t ground breaking insight but it is pretty neat to be able to see my own data verifying what’s known in the literature. We can see that sleep quality taper off after 8+ hours of sleep.
df %>%
ggplot(aes(x = time_hr, y = sleep_quality, color = what_day)) +
geom_point(size = 2, alpha = 0.3) +
geom_smooth(aes(group = 1)) +
scale_x_continuous(breaks = seq(0,12,2)) +
labs(title = "Sleep Quality vs. Hours of Sleep",
subtitle = "Source: iPhone sleep cycle data (2014-2018)",
x = "Hours of Sleep",
y = "Sleep Quality",
color = "") +
theme_bw()
Sleep cycle also allows you to integrate your sleep data with the pedometer so I plotted sleep duration vs the number of steps per day to see if there was a trend. There doesn’t appear to be too much of a trend between the two. The only thing we really see is that 8+ hours of sleep is a good predictor of good sleep quality. Side note: all the days I went over 10,000 steps were days I where I was traveling since I tend to walk everywhere when traveling to different places.
df %>%
filter(steps > 0,
steps < 15000) %>%
ggplot(aes(x = steps, y = time_hr, color = sleep_quality)) +
geom_point(alpha = 0.9) +
geom_smooth(color = "Purple", fill = "White") +
scale_color_viridis() +
scale_y_continuous(breaks = seq(0,12,2)) +
labs(title = "Sleep Duration vs Daily Steps",
subtitle = "Source: iPhone sleep cycle data (2014-2018)",
x = "Number of Steps",
y = "Sleep Duration (hr)",
color = "Sleep Quality\n") +
theme_dark()
The next visualization we can perform is a ridgeline plot which is this nice way of visualizing changes over time. I like to think of them is layered/stacked histograms. We can make a ridgeline plot of steps and compare them throughout the days of the week. We see that there’s not too much of a difference in the number of steps but there tends to be more step activity as the week progresses.
df %>%
ggplot(aes(x = steps, y = day_of_week, fill = ..x..)) +
geom_density_ridges_gradient(scale = 3) +
scale_x_continuous(expand = c(0.01, 0)) +
scale_y_discrete(expand = c(0.01, 0)) +
scale_fill_viridis(name = "Steps\n", option = "C") +
labs(title = "Number of Steps Each Day of the Week",
subtitle = "Source: iPhone sleep cycle data (2014-2018)",
x = "Steps") +
theme_ridges(font_size = 13, grid = TRUE) + theme(axis.title.y = element_blank())
Next thing to do is look at sleep duration across the days of the week. Here we can see that I definitely catch up on my sleep on Saturdays!
df %>%
ggplot(aes(x = time_hr, y = day_of_week, fill = ..x..)) +
geom_density_ridges_gradient(scale = 3) +
scale_x_continuous(expand = c(0.01, 0),
breaks= seq(0,12,4)) +
scale_y_discrete(expand = c(0.01, 0)) +
scale_fill_viridis(name = "Hours", option = "C") +
labs(title = "Duration of Sleep",
subtitle = "Source: iPhone sleep cycle data (2014-2018)",
x = "Time spent sleeping (hr)") +
theme_ridges(font_size = 13, grid = TRUE) + theme(axis.title.y = element_blank())
I then wanted to look at quality of sleep across the week but didn’t see an obvious trend.
df %>%
ggplot(aes(x = sleep_quality, y = day_of_week, fill = ..x..)) +
geom_density_ridges_gradient(scale = 3) +
scale_x_continuous(expand = c(0.01, 0)) +
scale_y_discrete(expand = c(0.01, 0)) +
scale_fill_viridis(name = "Quality of Sleep", option = "C") +
labs(title = 'Quality of Sleep Each Day of the Week',
subtitle = "Source: iPhone sleep cycle data (2014-2018)") +
theme_ridges(font_size = 13, grid = TRUE) + theme(axis.title.y = element_blank())
One of the most interesting things that I saw was when I graphed my quality of sleep by year. 2014 was a rough time during my graduate school education and you can clearly see an improvement in my sleep quality after I joined a new lab in 2015. The crazy thing is that this is only sleep data and you can get a ton of interesting information from a phone app.
df %>%
ggplot(aes(x = sleep_quality, y = year, fill = ..x..)) +
geom_density_ridges_gradient(scale = 3) +
scale_x_continuous(expand = c(0.01, 0)) +
scale_y_discrete(expand = c(0.01, 0)) +
scale_fill_viridis(name = "Quality of Sleep", option = "C") +
labs(title = "Quality of Sleep by Year",
subtitle = "Source: iPhone sleep cycle data (2014-2018)",
x = "Sleep Quality (%)") +
theme_ridges(font_size = 13, grid = TRUE) + theme(axis.title.y = element_blank())
Here’s how I created the header of this blog post. I graphed bedtime across time and mapped the color to sleep duration and removed the axis to make it look more artistic.
p <- df %>%
ggplot(aes(x = Start, y = sleep_time, color = time_hr)) +
geom_point(size = 3, alpha = 0.8) +
scale_color_viridis(option="plasma") +
guides(color = FALSE) +
scale_y_continuous(expand = c(0,0))+
theme_bw() +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
p
ggsave(here("static/img/headers", "sleep.png"),
height = 4, width = 7, units = "in", dpi = 600)
Notice how I use here within the ggsave function to specify which folder within the R project directory to save the file. This makes sure that the code will work on ANYONE’S computer regardless of where they put their folder!
So I hope you were able to learn something new from this blog post or hopefully be inspired to analyze an interesting dataset of your own. Feel free to comment or reach out if you have any questions or suggestions!
Be sure to get enough sleep!


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.FrontendAppConfig @import controllers.register.routes._ @import views.html.components.{Heading, Link, SubmitButton} @this( mainTemplate: MainTemplate, heading: Heading, link: Link, formHelper: FormWithCSRF, submitButton: SubmitButton, config: FrontendAppConfig ) @(isAgent: Boolean)(implicit request: Request[_], messages: Messages) @mainTemplate( title = messages("matchingDown.title"), showBackLink = true ) { @heading("matchingDown.heading")
@messages("matchingDown.p1")
@messages("matchingDown.p2.beforeLink") @messages("matchingDown.p2.link") @messages("matchingDown.p2.afterLink").
@if(isAgent) {@messages("matchingDown.p3") @link(controllers.register.agents.routes.AgentOverviewController.onPageLoad().url, "agent-overview", "matchingDown.p3.link", false).
} @formHelper(action = MatchingDownController.onSubmit(), Symbol("autoComplete") -> "off") { @submitButton(overrideMessage = Some(messages("site.sign_out")), id = "sign-out") } }


All Packages Class Hierarchy This Package Previous Next Index
Class com.jclark.xml.apps.Time
java.lang.Object
|
+----com.jclark.xml.apps.Time
- public class Time
- extends Object
-
Time()
-
-
main(String[])
- Each of the specified argument is treated as the name
of a file containing an XML document to be parsed.
Time
public Time()
main
public static void main(String args[]) throws IOException
- Each of the specified argument is treated as the name
of a file containing an XML document to be parsed.
If no arguments are specified, the standard input is read.
The total time in seconds for the parse is reported
on the standard output.
For each document that is not well-formed, a message
is written in the standard error.
All Packages Class Hierarchy This Package Previous Next Index


apache_beam.coders.slow_stream module
A pure Python implementation of stream.pyx.
For internal use only; no backwards-compatibility guarantees.
-
class
apache_beam.coders.slow_stream.
OutputStream
[source]
Bases: object
For internal use only; no backwards-compatibility guarantees.
A pure Python implementation of stream.OutputStream.
-
write
(b, nested=False)[source]
-
write_byte
(val)[source]
-
write_var_int64
(v)[source]
-
write_bigendian_int64
(v)[source]
-
write_bigendian_uint64
(v)[source]
-
write_bigendian_int32
(v)[source]
-
write_bigendian_double
(v)[source]
-
get
()[source]
-
size
()[source]
-
class
apache_beam.coders.slow_stream.
ByteCountingOutputStream
[source]
Bases: apache_beam.coders.slow_stream.OutputStream
For internal use only; no backwards-compatibility guarantees.
A pure Python implementation of stream.ByteCountingOutputStream.
-
write
(byte_array, nested=False)[source]
-
write_byte
(_)[source]
-
get_count
()[source]
-
get
()[source]
-
class
apache_beam.coders.slow_stream.
InputStream
(data)[source]
Bases: object
For internal use only; no backwards-compatibility guarantees.
A pure Python implementation of stream.InputStream.
-
size
()[source]
-
read
(size)[source]
-
read_all
(nested)[source]
-
read_byte
()[source]
-
read_var_int64
()[source]
-
read_bigendian_int64
()[source]
-
read_bigendian_uint64
()[source]
-
read_bigendian_int32
()[source]
-
read_bigendian_double
()[source]
-
apache_beam.coders.slow_stream.
get_varint_size
(v)[source]
For internal use only; no backwards-compatibility guarantees.
Returns the size of the given integer value when encode as a VarInt.


Cache for async operations
Context of applying for all async handlers
The lock status. If true, then all new tasks won't be registered.
Cache for initialized workers
Map of namespaces for async operations
Link to Async.namespaces
Cancels all async tasks from the specified namespace
operation options
Cancels the specified promise
Cancels the specified promise or a group of promises
options for the operation
Cancels the specified proxy function
Cancels the specified proxy function or a group of functions
options for the operation
Cancels the specified request
Cancels the specified request or a group of requests
options for the operation
Cancels a task (or a group of tasks) from the specified namespace
operation options or a task link
Clears all async tasks
Cancels the specified promise
Cancels the specified promise or a group of promises
options for the operation
Cancels the specified proxy function
Cancels the specified proxy function or a group of functions
options for the operation
Cancels the specified request
Cancels the specified request or a group of requests
options for the operation
Terminates the specified worker
Terminates the specified worker or a group of workers
options for the operation
Returns a cache object by the specified name
Marks all async tasks from the namespace by the specified label
operation options
Marks a promise by the specified label
Marks a promise or a group of promises by the specified label
additional options
Marks a task (or a group of tasks) from the namespace by the specified label
operation options or a link to the task
Mutes all async tasks
Mutes the specified promise
Mutes the specified promise or a group of promises
options for the operation
Mutes the specified proxy function
Mutes the specified proxy function or a group of functions
options for the operation
Mutes the specified request
Mutes the specified request or a group of requests
options for the operation
Factory for promise clear handlers
Factory for promise merge handlers
Wraps the specified promise.
This method doesn't attach any hook or listeners to the object, but if we cancel the operation by using one of Async methods, like "cancelPromise", the promise will be rejected. The promise can be provided as it is or as a function, that returns a promise.
Terminates the specified promise
name of the destructor method
Wraps the specified function. This method doesn't attach any hook or listeners to the object, but if we cancel the operation by using one of Async methods, like "cancelProxy", the target function won't be invoked.
Registers the specified async task
Wraps the specified external request.
This method doesn't attach any hook or listeners to the object, but if we cancel the operation by using one of Async methods, like "cancelRequest", the promise will be rejected. The request can be provided as a promise or as a function, that returns a promise.
Suspends all async tasks
Suspends the specified promise
Suspends the specified promise or a group of promises
options for the operation
Suspends the specified proxy function
Suspends the specified proxy function or a group of functions
options for the operation
Suspends the specified request
Suspends the specified request or a group of requests
options for the operation
Terminates the specified worker
Terminates the specified worker or a group of workers
options for the operation
Unmutes all async tasks
Unmutes the specified promise
Unmutes the specified promise or a group of promises
options for the operation
Unmutes the specified proxy function
Unmutes the specified proxy function or a group of functions
options for the operation
Unmutes the specified request
Unmutes the specified request or a group of requests
options for the operation
Unsuspends all async tasks
Unsuspends the specified promise
Unsuspends the specified promise or a group of promises
options for the operation
Unsuspends the specified proxy function
Unsuspends the specified proxy function or a group of functions
options for the operation
Unsuspends the specified request
Unsuspends the specified request or a group of requests
options for the operation
Wraps the specified worker object.
This method doesn't attach any hook or listeners to the object, but every time the same object is registered, Async will increment the number of links that relates to this object. And when we try to destroy the worker by using one of Async methods, like "terminateWorker", it will de-increment the links value. When the number of links is equal to zero, Async will try to call a "real" object destructor by using one of possible destructor methods from a whitelist or by the specified destructor name, also if the worker is a function, it is interpreted as the destructor.
Terminates the specified worker
name of the destructor method


News, Hans Haacke 1969/2008/2018
Artistic Collaborations: Lynn Hershman Leeson
Agent Ruby, Lynn Hershman Leeson 1997
Agent Ruby, Lynn Hershman Leeson 1997
Agent Ruby, Lynn Hershman Leeson 1997
Venus of the Anthropocene, Lynn Hershman Leeson 2016
Collaborations with Paul Kos


This class represents a Class Object itself. It enforces that some reflected type is a class and provides facilities for interacting with the class through reflection.
More...
This class represents a Class Object itself. It enforces that some reflected type is a class and provides facilities for interacting with the class through reflection.
Definition at line 15 of file Class.cs.
| HumDrum.Traits.Class.Class |
( |
Type |
type | ) |
|
|
inline |
Attempts to make a Class with a given type.
- Parameters
-
| type | The type to make the class with |
Definition at line 34 of file Class.cs.
| void HumDrum.Traits.Class.AddMethod |
( |
Method |
m | ) |
|
|
inline |
Adds a method to this class
- Parameters
-
Definition at line 84 of file Class.cs.
| Method HumDrum.Traits.Class.GetMethod |
( |
string |
name | ) |
|
|
inline |
Get a method with the specified name
- Returns
- The method to return
- Parameters
-
| name | The name to search for |
Definition at line 62 of file Class.cs.
| bool HumDrum.Traits.Class.HasMethod |
( |
Method |
method | ) |
|
|
inline |
Check to see if this class or its Method Bank have a method
- Returns
true
if this instance has method the specified method; otherwise, false
.
- Parameters
-
Definition at line 75 of file Class.cs.
| IEnumerable<Method> HumDrum.Traits.Class.Methods |
( |
| ) |
|
|
inline |
Returns a list of all the methods that the BasicType of this class has
Definition at line 46 of file Class.cs.
| Type HumDrum.Traits.Class.BasicType |
|
get |
The basic type of this class as a Type object
The type of this object
Definition at line 21 of file Class.cs.
| List<Method> HumDrum.Traits.Class.MethodBank |
|
getset |
Gets or sets a list of methods that this class is picking up for a trait
The method bank
Definition at line 28 of file Class.cs.
The documentation for this class was generated from the following file:


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