Thanks to ggforce, you can enhance almost any ggplot by highlighting data groupings, and focusing attention on interesting features of the plot

2.6 The Normal Distribution

Generating random data

Descriptive Statistic

dplyr in R also lets you use a different syntax for querying SQL databases like Postgres, MySQL and SQLite, which is also in a more logical order

We save all of this code, the ui object, the server function, and the call to the shinyApp function, in an R script called app.R

ui

server

I want to get the selected number of bins from the slider and pass that number into a python method and do some calculation/manipulation (return: “You have selected 30bins and I came from a Python Function”) inside of it then return some value back to my R Shiny dashboard and view that result in a text field.

Currently Shiny is far more mature than Dash. Dash doesn’t have a proper layout tool yet, and also not build in theme, so if you are not familiar with Html and CSS, your application will not look good (You must have some level of web development knowledge). Also, developing new components will need ReactJS knowledge, which has a steep learning curve.

You can host standalone apps on a webpage or embed them in R Markdown documents or build dashboards. You can also extend your Shiny apps with CSS themes, Html widgets, and JavaScript actions.

You can either create a one R file named app.R and create two seperate components called (ui and server inside that file) or create two R files named ui.R and server.R

hec <- within(hec,{

“A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).”

enquo() uses some dark magic to look at the argument, see what the user typed, and return that value as a quosure

missing values are managed, without any pre-processing, by the model used to cluster with the assumption that values are missing completely at random.

Some of the best and cheapest tombstones come from India. In 2013 India produced 35,342 million tons of granite, making it the world’s largest producer

Network centralization

Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

Beautiful plotting in R: A ggplot2 cheatsheet

Boxplots and Beyond – Part II: Asymmetry

Using the Median Absolute Deviation to Find Outliers

Using foo:::f instead of foo::f allows access to unexported objects.

In the six states that prohibit ex-felons from voting, one in four African-American men is permanently disenfranchised.

Shiny Server: Running with a Proxy

The host parameter was introduced in Shiny 0.9.0.

toxic waste, poison air, beach goo, eroded 215  roads draw nations together

高齡

HarvardX Biomedical Data Science Open Online Training

argyle: An R Package for Analysis of Illumina Genotyping Arrays

Introduction to R for Data Science

Learn Data Science Online

The Automated R Instructor

National Research Council

The National Research Council (NRC) is an organization within the Government of Canada dedicated to research and development. Today, the NRC works with members of the Canadian industry to provide meaningful research and development for many different types of products. The areas of research and development that the NRC participates in include aerospace, aquatic and crop resource development, automotive and surface transportation, construction, energy, mining, and environment, human health therapeutics, information and communications technologies, measurement science standards, medical devices, astronomy and astrophysics, ocean, coastal, and river engineering, and security and disruptive technologies. The NRC employs scientists, engineers, and business experts. The mission of the NRC is as follows: “Working with clients and partners, we provide innovation support, strategic research, scientific and technical services to develop and deploy solutions to meet Canada's current and future industrial and societal needs.” The main values of the NRC include impact, accountability, leadership, integrity, and collaboration. The most recent success stories of the NRC include research regarding “green buildings,” math games, mechanical insulation, and many more (Government of Canada 2017). Here is a link to their achievement page where these stories and more are posted: http://www.nrc-cnrc.gc.ca/eng/achievements/index.html. Here is a link to the NRC webpage: http://www.nrc-cnrc.gc.ca/eng/index.html.  

Reciprocity

Centralization

Clustering

the last rain

See http://kateto.net/network-visualization↩

THE WESTERN LAND, nervous under the beginning change. The Western States, nervous as horses before a thunder storm

Despite many editors being unpaid or poorly remunerated for their work, plant scientist Jaime A. Teixeira da Silva believes they “should be held accountable” if authors are made to wait for an “excessive or unreasonable amount of time” before a decision is made on their research.

The crawler represented a third option: a way to figure out how humans work.

marine species that calcify have survived through millions of years when CO2 was at much higher levels

Ensures that vital information is provided to educators, families, students, and communities through annual statewide assessments that measure students' progress toward those high standards.

When students are grouped by ability, then collaborative work becomes important because this type of learning environment is heavily dependent on team work.

Students can move at their own pace: When students are grouped together based on skill level, the pressure is lessened of when the topic must be covered.

Between-class grouping - a school's practice of separating students into different classes, courses, or course sequences (curricular tracks) based on their academic achievement

Within-class grouping - a teacher's practice of putting students of similar ability into small groups usually for reading or math instruction

Proponents of ability grouping say that the practice allows teachers to tailor the pace and content of instruction much better to students' needs and, thus, improve student achievement.

Considered by the beef industry to be an impressive innovation, lean finely textured beef is made from the remnant scraps of cattle carcasses that were once deemed too fatty to go into human food.

R Grouping functions: sapply vs. lapply vs. apply. vs. tapply vs. by vs. aggregate var ados = ados || {}; ados.run = ados.run || []; ados.run.push(function () { ados_add_placement(22,8277,"adzerk794974851",4).setZone(43); }); up vote 463 down vote favorite 606 Whenever I want to do something "map"py in R, I usually try to use a function in the apply family. (Side question: I still haven't learned plyr or reshape -- would plyr or reshape replace all of these entirely?) However, I've never quite understood the differences between them [how {sapply, lapply, etc.} apply the function to the input/grouped input, what the output will look like, or even what the input can be], so I often just go through them all until I get what I want. Can someone explain how to use which one when? [My current (probably incorrect/incomplete) understanding is... sapply(vec, f): input is a vector. output is a vector/matrix, where element i is f(vec[i]) [giving you a matrix if f has a multi-element output] lapply(vec, f): same as sapply, but output is a list? apply(matrix, 1/2, f): input is a matrix. output is a vector, where element i is f(row/col i of the matrix) tapply(vector, grouping, f): output is a matrix/array, where an element in the matrix/array is the value of f at a grouping g of the vector, and g gets pushed to the row/col names by(dataframe, grouping, f): let g be a grouping. apply f to each column of the group/dataframe. pretty print the grouping and the value of f at each column. aggregate(matrix, grouping, f): similar to by, but instead of pretty printing the output, aggregate sticks everything into a dataframe.] r sapply tapply r-faq

download and install the ACS package in addition to going to requesting a secret key

R requires forward slashes (/) not back slashes (\) when specifying a file location even if the file is on your hard drive.