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orm which is model likelihood?Accomplished
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Minor Comments Along the Way
Typo in "Vectors, Lists, Data Frames" I think you mean multiply not multiple, but technically you haven't introduced the multiply " * " operator yet (or subtraction, division, etc.)
A common issue is that a student wants to check if x is less than or equal to -3, and writes "x<-3" and accidentally reassigns x. Worth mentioning?
The verbal explanations of && and || were not clear to me, but the student didn't need to use either.
Your tasks ask to create objects, but the student can do it correctly and not have it display if they assign the object...
data.frame(x,y) vs. >z <- data.frame(x,y) is what I have in mind. This could cause confusion. At first I thought my code wasn't running but then I realized I was only assigning and not displaying.
For you exercise on adding a column to an object df, I think the computer scientists would bristle at using the same name "df" for the new object. But I break this rule all the time. What it means is that if a student runs their code more than once they could accidentally keep adding columns. Not an issue for chunks in RMarkdown but an issue for R scripts or working in the console.
Any reason you spell is "summarise"? Seems British to me.
I am in the Assessment, did you introduce a command to check the dimension of a data frame? I don't recall. A student can click the buttons at the bottom of the window displaying the data to see the answers, but they couldn't do that in an actual R analysis.
The word "filter" is unfamiliar to me. I have only ever heard subset. I keep thinking you must mean something more specific. You've used this term a few times by now.
Larger Overall Comments
The number of activities throughout seems low relative to the number of concepts/commands you are teaching. I am inclined to think students need to use most if not all of the commands you introduce in exercises. By the time I got to "Indexing Vectors" I thought this may be moving too fast for some. To a student who still doesn't immediately know what c() and brackets mean, the expression "y[c(TRUE, FALSE)]" is not likely to be clear.
For what it's worth, I have never used nor heard of the R commands >aggregate() or >subset(). They are probably more efficient than what I do. For subset I just treat a data frame as a matrix and write stuff like mtcars[(mpg>20),]. Maybe I am just outdated in this regard. I mention this only because you wanted to know what this looks like to a base R user.
Finally I'll level with you, I just completed it and the syntax for >subset() has already left my short-term memory. I am not sure how long you intend students to work on this, but if these are the topics you want to cover I might suggest a few more exercises along the way, add a few that take 2-4 lines of code to complete "(first make ..., and then merge it with ..."), and I would revisit all of this a few days later with a guided lab or more open-ended task to make sure things stick.
UseMethod? purrr::walk(.x = c(rep("A", 4), rep("B", 5)),
~ get_username(.x,
'teaching-r-study/')) What I accomplished this week
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Adding a 30 minute meeting on Wed/Thurs? @LucyMcGowan send out when2meet for this!
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Went through chapter 5 and summarized two course objectives
Sent course objectives for chapter 1 to 4
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Revise objectives if feedback received
Continue working on chapter 6 and beyond
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surveyUI() and surveyServer().What I accomplished this week
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if (is.null(sex) & is.null(race) {
names(output) <- c("id",
paste0(variable, "_1"),
paste0(variable, "_2"),
paste0(variable, "_diff"),
paste0(variable, "_mean"))
} else if (is.null(sex) & !is.null(race)) {
names(output) <- c("id",
paste0(variable, "_1"),
paste0(variable, "_2"),
paste0(variable, "_diff"),
paste0(variable, "_mean").
paste0(race, "_1"),
paste0(race, "_2"))
} else if (!is.null(sex) & is.null(race)) {
names(output) <- c("id",
paste0(variable, "_1"),
paste0(variable, "_2"),
paste0(variable, "_diff"),
paste0(variable, "_mean").
paste0(sex, "_1"),
paste0(sex, "_2"))
} else if (!is.null(sex) & !is.null(race)) {
names(output) <- c("id",
paste0(variable, "_1"),
paste0(variable, "_2"),
paste0(variable, "_diff"),
paste0(variable, "_mean").
paste0(sex, "_1"),
paste0(race, "_2"),
paste0(race, "_1"),
paste0(race, "_2"))
}Accomplished this week
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Look into prolific: https://www.prolific.co
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Added bio
LearnR modules
errors
Suggestions:
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getUICode from the PR).General Questions
session$clientData$url_port will provide a unique ID? That should be able to interface with Shiny/learnr documents.taskdesignr and next steps.transmute() with user-supplied column names:
# the following is inside a function
# id, S1, and S2, are strings
# diff and mean are numeric vectors
output <- data %>%
dplyr::transmute(id = .data[[id]],
"{{variable}}_1" := data[[S1]],
"{{variable}}_2" := data[[S2]],
"{{variable}}_diff" := diff,
"{{variable}}_mean" := mean)Projects/ research tasks:
tidyverse packageGoals:
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