As a Core Curriculum Teaching Fellow, my project will develop a new course called “Statistics for Research” focusing on innovation, undergraduate research, and writing across the curriculum as an alternative to the traditional Introduction to Statistics, Stat 145. Intro Stats is one of the largest courses on campus with 1000-1200 students per semester in over 20 sections. This Statistics for Research course will cover the traditional material, but in a modern evidence-based way by integrating real data in the context of case studies, fostering active learning, and using technology to explore concepts and analyze large real datasets and communicate the results. This is a computer- and a project-based course in the style of Dierker’s Passion-Driven Statistics and modeled directly after the already-successful UNM Stat 427/527 Advanced Data Analysis 1 and 2 where I’ve piloted these learning strategies for advanced undergraduates and graduate students for four semesters. Unlike my previous six-section intervention study showing active learning increases success by a third of a letter grade for everyone, this will be a pilot course developed for one or two sections. This implementation is just one of the many evidence-based recommendations I made as a 2016-17 Teaching Fellow for improving statistics education at UNM, including offering multiple versions (Stat Literacy with a first-year learning community, Stats for Research, Advanced Math-Stats); train/mentor our TAs prior to their teaching a course; offer a new Undergraduate Stat Ed Practicum course for all undergrad stat majors to serve as peer learning facilitators in at least one intro stat course; use multidisciplinary project-based learning to improve the outcomes of statistical interest, attitude, and con dence for under-represented minority students; and make other small changes such as moving the final from the first day of finals at 7:30 AM to a later time when students are shown to have greater success. A long-term goal is to hire a professor of practice in statistics for continued leadership and research in statistical education at UNM.
... more
Workshop at The UNM Dept of Math & Stats on Friday 8/17/2018 10AM-5PM.
Erik Erhardt
rmarkdown::render(fn). This has the advantage of using the console environment instead of the RMarkdown environment, so all the objects are available for manipulation in the console. However, it is hard to debug when there are scores of unnamed chunks. Now, I can easily name all the code chunk and can quickly identify where issues are.
Install from github: https://github.com/erikerhardt/RmdNameChunk
devtools::install_github("erikerhardt/RmdNameChunk")
Read an Rmd file, update existing prefixed code chunks, and renumber.
library(RmdNameChunk) rmd_name_chunks( fn_in = "test_in.Rmd" , fn_out = "test_out.Rmd" , prefix_chunk_name = "chunk-" )Review the input and output files to see how the chunk header names have been updated. Below is an example of the two Rmd files in the vignette. test_in.Rmd was read in and test_out.Rmd was created. The chunk headers are shown below from each file.
test_in.Rmd
8 : ```{r setup, include=FALSE}
22 : ```{r cars}
28 : ```{r}
32 : ```{ r }
36 : ```{r, echo=FALSE}
40 : ```{ r, eval=FALSE}
44 : ```{r , eval=FALSE}
48 : ```{r chunk-2, eval=FALSE}
52 : ```{r chunk-XXX , eval=FALSE}
56 : ```{r chunk-XXX2 , eval=FALSE}
60 : ```{r chunk-XXX3 , eval=FALSE}
64 : ```{r chunk-XXX4 , eval=FALSE}
68 : ```{r chunk-XXX5 , eval=FALSE}
72 : ```{r chunk-XXX6 , eval=FALSE}
81 : ```{r pressure, echo=FALSE}
These code chunk headers were changed to those below:
test_out.Rmd
8 : ```{r setup, include=FALSE}
22 : ```{r cars}
28 : ```{r chunk-01}
32 : ```{r chunk-02}
36 : ```{r chunk-03, echo=FALSE}
40 : ```{r chunk-04, eval=FALSE}
44 : ```{r chunk-05, eval=FALSE}
48 : ```{r chunk-06, eval=FALSE}
52 : ```{r chunk-07, eval=FALSE}
56 : ```{r chunk-08, eval=FALSE}
60 : ```{r chunk-09, eval=FALSE}
64 : ```{r chunk-10, eval=FALSE}
68 : ```{r chunk-11, eval=FALSE}
72 : ```{r chunk-12, eval=FALSE}
81 : ```{r pressure, echo=FALSE}
Overview of the formulation of a scientific manuscript. (O’Connor)[/caption]
1. Figures/Tables
“Construction of first-draft figures and tables based on data is the critical first step toward preparation of an outline for the manuscript.” (O’Connor)
“Paste the figures and tables on a wall in their approximate order of appearance. This will provide the skeleton from which to begin the construction of a more detailed outline and draft.” (O’Connor)
“Write a first draft of figure legends. Make each legend’s opening sentence into a figure title to describe the variables compared.” (O’Connor)
“The entire story of your paper should be comprehensible from this abstract and the figures and tables.” (2b. Baldwin)
“Each figure should illustrate at least one important take-home message of your paper and ideally be understandable in 30 seconds to an uninformed scientific reader.” (Baldwin)
Caption: “the first sentence should summarize the main point of the figure.” “The entire figures should be fully comprehensible from the figure caption.” “Table legends should include only a stand-alone first sentence.” (Baldwin)
For more on figures, see our chapter in the 4th ed of the “Handbook of Psychophysiology”.
2. Summary Statements
“These important statements are conclusions summarizing the major contributions of the manuscript to the scientific community. Use short rigid statements usually containing cause/effect words.” (O’Connor)
3. Scientific Audience
Identify the scientific audience and journals. (O’Connor)
4. Materials and Methods
“Write the materials and methods section to supplement and explain the figure legends.” (O’Connor)
“Write while you are still conducting experiments.” (1.+4. Baldwin)
5. Re-evaluate Data
“For each figure and table, make a note of which summary statement it addresses.” (O’Connor)
6. Results
“Once each figure and table has a legend describing the mathematical variables compared, then the first sentence of each results paragraph is simply the results of this comparison.” (O’Connor)
“The Results section should highlight all of the conclusions that you can draw from your data.” (3. Baldwin)
7. Discussion/Conclusions
“Convert by logical arguments the relations of mathematical variables stated in the results section into mechanistic interpretations of cause and effect. Simply restate the data relation from each results paragraph and convert each to mechanistic conclusions.” (O’Connor)
Table 1 (O’Connor) gives examples of results words for data relations vs. discussion words for logic and mechanism.
“The discussion should start with a paragraph which succinctly states your motivation, and the conclusions that you draw from your data that do not require discussion and ends with an introduction to the weaker conclusions that you would like to draw, but do require discussion.” “The following paragraphs should discuss the conclusions that you would like to draw from your data, but require discussion because the inferences are indirect, or your data or the data in the literature are contradictory.” “The last paragraph should summarize all major conclusions from the discussion of your results and indicate future research directions.” (6. Baldwin)
8. References
This is taken care of by a reference manager with well-curated reference data and style file.
9. Introduction
“First, summarize the subject and review the literature to allow the reader to (i) understand the statements in the Results and Discussion, (ii) understand how the statements fit into the extant scientific body of knowledge, and (iii) Understand that the conclusions are indeed novel, the next step in the knowledge of the subject.” (O’Connor)
“Write the last paragraph of the Introduction first; this should be an abbreviated road map of the question that you are addressing and the means by which you answered the question” (5. Baldwin)
“Circle all of the words and concepts that you used in this last paragraph that need to be introduced and elaborated on in preceding paragraphs of the Introduction. In this way, you will be reverse engineering the entire Introduction from your last paragraph.” (Baldwin)
Remove the fluff.
10. Title
“The title should be a positive statement from the summary statements.” (O’Connor)
11. Conclusion Paragraph
“In this paragraph, restate the logical conclusions and explain why these conclusions are important, how they will influence future thinking in this and other fields. In the introduction, these conclusions were the next step. Now discuss the future based on the conclusions in this manuscript or an alternative path to further substantiate the validity of your conclusions. Also, state the relevance of results in the present manuscript to other fields.” (O’Connor)
12. Abstract
“Use the abstract in general terms to describe the most important points in the work.” (O’Connor)
“Write a rough draft of the abstract … to create the roadmap of the logic of the results and conclusions that you will be developing.” (2b. Baldwin)
13. Revise, revise, revise.
Erase your memory of your writing and see it fresh.
Short course for the Albuquerque Chapter of the American Statistical Association (ACASA) on Friday 2/16/2018 12:30-5pm, UNM SUB.
Erik Erhardt
SOLD OUT!
Please navigate to https://tinyurl.com/EE-Tidyverse18-ACASA for the Course resources.
Previous course landing page: https://tinyurl.com/EE-Tidyverse18
Course page: https://tinyurl.com/EE-Tidyverse18-ACASA
Albuquerque The Magazine, November 2015, p. 35[/caption]