47 Matching Annotations
  1. Feb 2019
    1. Network centralization

      degree.cent <- centr_degree(g, mode = "all") degree.cent$res degree.cent$centralization degree.cent$theoretical_max

  2. Dec 2018
    1. I came across this via the cran.r-project, referred to be a computer scientist at an NIH lecture. It might be an interesting source to see code-sharing norms and practices.

  3. Sep 2018
  4. May 2018
    1. hi there please check on the Recent Updated SAS Training and Tutorial Course which can explain about the SAS and its integration with the R as well so please go through the Link:-


  5. Apr 2018
  6. Mar 2018
  7. Feb 2018
    1. In the six states that prohibit ex-felons from voting, one in four African-American men is permanently disenfranchised.
  8. Jan 2018
  9. Dec 2017
  10. Nov 2017
  11. Oct 2017
  12. Aug 2017
  13. Jul 2017
  14. Jun 2017
  15. May 2017
    1. 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.  


      Government of Canada. 2017. National Research Council Canada. May 5. Accessed May 8, 2017. http://www.nrc-cnrc.gc.ca/eng/index.html.

  16. Feb 2017
    1. Reciprocity

      This one was easy! Getting good a good directed network to play around with it with into R and able to be modified was... way harder than getting this info.

      reciprocity(g, ignore.loops = TRUE)

      There is an additional mode operator where if you put the mode = ("ratio") it calculates (unordered) vertex pairs are classified into three groups: (1) not-connected, (2) non-reciprocaly connected, (3) reciprocally connected. The result is the size of group (3), divided by the sum of group sizes (2)+(3).

    2. Centralization

      Centralization interests me for analyzing discussion forums--are there key players, and do these key players show higher degrees of cognitive presence?

      Calculating for centralization by number of connections seems quite straightforward in R: centralization.degree

    3. Clustering

      I am very interested in clustering measures, because I plan to analyze data from a Slack group that I am a part of, where I suspect there are many subgroups who only interact with each other.

      After looking around for some different clustering algorithms, I found the "cluster_label_prop" function in the igraph package, which seems to do what I would like to do. To summarize, this function automatically detects groups within a network by initially labeling every node with a unique label and at every step each node adopts the label that most of its neighbors currently have. In this iterative process densely connected groups of nodes form a consensus on a unique label to form communities.

      There seem to be many different ways to define clustering though, so I am sure that I will need to do more research on the topic of clustering as I move forward with my research project.

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

      This is incredible! Thank you for sharing this link.

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

      Steinbeck groups the Western states together in one entity that feels and experiences the same nervous energy, like that of horses. The repetition of this idea throughout Ch. 14 serves to underscore the unity of these states as a single group separate and distinct from the rest of the country.

    1. 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.

      How would this be enforced exactly?

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

      Good way to look at it.

  17. Jan 2017
    1. marine species that calcify have survived through millions of years when CO2 was at much higher levels

      Some calcifying species were indeed abundant in the Cretaceous, a time at which the atmospheric CO2 concentration was high. However, seawater alkalinity was also high due to intense weathering on land. Hence, the concentration of carbonate ions (CO3, which controls calcification) was elevated. That compensation does not happen today and will not happen in the near future because total alkalinity does not change significantly on time scales of centuries. There is ample evidence in the literature for that.

  18. Jun 2016
  19. Mar 2016
    1. Ensures that vital information is provided to educators, families, students, and communities through annual statewide assessments that measure students' progress toward those high standards.

      Naturally, not every student is capable of reaching those high standards but, ability based grouping will help those students reach those standards.

  20. Feb 2016
    1. When students are grouped by ability, then collaborative work becomes important because this type of learning environment is heavily dependent on team work.

      This prevents the one or two "smart" students in the group from doing all the work because all the students are on the same academic level.

    2. 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.

      This is probably the most apparent benefit to ability based grouping.

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

      This is how i envision the education system should look.

    2. Within-class grouping - a teacher's practice of putting students of similar ability into small groups usually for reading or math instruction
    3. 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.
  21. Dec 2015
    1. 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.

      The textured beef is made of just scraps and waste that was not going to be put into food.

  22. Aug 2015
    1. 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

      very useful article on apply functions in r

  23. Jun 2015
    1. download and install the ACS package in addition to going to requesting a secret key

      Troubleshooting csv file - step 1.

  24. Jan 2015
    1. R requires forward slashes (/) not back slashes (\) when specifying a file location even if the file is on your hard drive.