.jpg "A picture of Thomas Hollis passed through multiple iterations of Google Deep Dream [1] algorithms")
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Formerly a Bachelor of Electrical and Electronic Engineering at the University of Manchester (2015-2018), I am now a candidate in the Master of Science in Applied Computing at University of Toronto (2018-2020). Biology novice.
Email address: mailto:thollis@cs.toronto.edu
Below is a collection of useful code snippets for subsequent reuse and referral. I hope that future-me finds them useful, as well as hopefully other classmates.
This is a cheat sheet for Git syntax that I wrote to help myself and classmates remember any git command for the future.
| Command example | Operation explanation |
|---|---|
git clone https://github.com/YOUR-USERNAME/YOUR-REPOSITORY | Clones the linked repository to local storage |
git push origin master | Pushes the updated file to the master branch with the version as a comment |
This is a cheat sheet for R syntax that I wrote for myself and my classmates to avoid silly mistakes and learn the R language more thoroughly.
| Command example | Operation explanation |
|---|---|
library(RWeka) | Installs the RWeka package and all its dependencies |
save(x, y, z, file = “mydata.RData”) | Saves objects x, y, z regardless of whether they are vectors, factors, lists or data frames into a file of given name. |
load(“mydata.RData”) | Recreates the x, y, z data structures |
save.image() | Saves current session to a file called .RData (R will look for this file automatically next time you start R) |
CTRL+L | Clears terminal |
mydata <- read.csv("data.csv", stringsAsFactors = FALSE, header = FALSE)
| By default R assumes that CSV files include headers as the first row of the file thus header = FALSE must be used for headless CSV files. |
write.csv(pt_data, file = "pt_data.csv") | Used to create a CSV file from an R object |
object_name | Prints the information stored in an R object |
remove(object_name) | Removes an R object |
CVector_name <- c(“John”) | Writes a character vector |
NVector_name <- c(9.81) | Writes a numeric vector |
IVector_name <- c(12, 13) | Writes an integer vector (two entries) |
LVector_name <- c(TRUE, FALSE) | Writes a logical vector (two entries) |
NULL | Special vector type used in machine learning used to indicate absence of a value |
NA | Special vector type used in machine learning used to indicate missing value (used for uninitialized values in vectors) |
& | ! | AND, OR, NOT logical operators |
%>% | Pipe operator |
Vector_name[1:4] | Prints elements of vector from 1st to 4th in the form: [1] 12, 13, NA, NA |
blood <- factor(c("O", "AB", "A"), levels = c("A", "B", "AB", "O"))
| Creating a factor with 3 blood types and adding a level that did not appear in the data before writing to the var ‘blood’ in the form: [1] O AB A Levels: A B AB O |
list() | List function which creates a list, a fast way of assigning/displaying data of an object |
m <- matrix(c('a', 'b', 'c', 'd'), nrow = 2)
| Matrix creation results in the following: "a" "c" "b" "d" |
summary(patients$year) | Useful for investigating numeric variables (displays several common summary statistics). |
mean() | Function used to find the mean of data |
median() | Function used to find the median of data |
range() | Returns minimum and maximum values of data |
IQR() | Used to find the inter-quartile range of data |
var() | Outputs the variance of dataset |
sd() | Outputs the standard deviation of dataset |
plot(x = patiens$age, y = patients$diseases, main = "Scatterplot of Age vs. Concurrent Diseases", xlab = "Patient age (years)", ylab = "Number of concurrent diseases") | Typical scatterplot command |
Github account: https://github.com/PsiPhiTheta (lots of additional material here that this course inspired me to do)
Google scholar account: https://scholar.google.co.uk/citations?user=Gmr1zVUAAAAJ&hl=en (my current publications)
UofT landing page: http://www.cs.toronto.edu/~thollis/
Personal landing page: http://www.thomashollis.com
- ^ Google. (2015, August 12). Google/deepdream. Retrieved September 15, 2018, from https://github.com/google/deepdream