Merge pull request #224 from avehtari/update-aaltobda-install

Update aaltobda install instructions in templates
avehtari · Oct 23, 2023 · 964c5e6 · 964c5e6
2 parents 379c801 + ff5be1e
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diff --git a/FAQ.Rmd b/FAQ.Rmd
@@ -120,11 +120,9 @@ install.packages(c("MASS", "bayesplot", "brms", "cmdstanr ", "dplyr", "gganimate
 
 The course has its own R package `aaltobda` with data and
 functionality to simplify coding. `aaltobda` has been pre-installed in
-Aalto JupyterHub. To install the package to your own computer just run the following
-(upgrade=”never” skips question about updating other packages):
+Aalto JupyterHub. To install the package to your own computer just run the following:
 
-1. `install.packages("remotes")`
-2. `remotes::install_github("avehtari/BDA_course_Aalto", subdir = "rpackage", upgrade="never", dependencies=TRUE)`
+1. `install.packages("aaltobda", repos = c("https://avehtari.github.io/BDA_course_Aalto/", getOption("repos")))`
 
 If during the course there is announcement that `aaltobda` has been
 updated (e.g. some error has been fixed), you can get the latest

diff --git a/assignments/assignment6.qmd b/assignments/assignment6.qmd
@@ -1,33 +1,33 @@
 ---
 title: "Assignment 6"
 author: "Aki Vehtari et al."
-format: 
+format:
   html:
     toc: true
     code-tools: true
-    code-line-numbers: true  
+    code-line-numbers: true
     number-sections: true
     mainfont: Georgia, serif
     page-layout: article
 editor: source
 filters:
-  - includes/assignments.lua 
-  - includes/include-code-files.lua 
+  - includes/assignments.lua
+  - includes/include-code-files.lua
 ---
 
 # General information
 
 **The maximum amount of points from this assignment is 6.**
 
-We have prepared a **quarto template specific to this assignment ([html](template6.html), [qmd](https://avehtari.github.io/BDA_course_Aalto/assignments/template6.qmd), [pdf](template6.pdf))** to help you get started. 
+We have prepared a **quarto template specific to this assignment ([html](template6.html), [qmd](https://avehtari.github.io/BDA_course_Aalto/assignments/template6.qmd), [pdf](template6.pdf))** to help you get started.
 
 
 ::: {.aalto}
 We recommend you use [jupyter.cs.aalto.fi](https://jupyter.cs.aalto.fi) or the [docker container](docker.html).
 :::
 
-::: {.hint}             
-**Reading instructions:** 
+::: {.hint}
+**Reading instructions:**
 
 - [**The reading instructions for BDA3 Chapter 10**](../BDA3_notes.html#ch10).
 - [**The reading instructions for BDA3 Chapter 11**](../BDA3_notes.html#ch11).
@@ -44,16 +44,15 @@ We recommend you use [jupyter.cs.aalto.fi](https://jupyter.cs.aalto.fi) or the [
 
 :::: {.content-hidden when-format="pdf"}
 ::: {.callout-tip collapse=true}
- 
-## Setup 
+
+## Setup
 
 JupyterHub has all the needed packages pre-installed.
 
 The following installs and loads the `aaltobda` package:
 ```{r}
 if(!require(aaltobda)){
-    install.packages("remotes")
-    remotes::install_github("avehtari/BDA_course_Aalto", subdir = "rpackage", upgrade="never")
+    install.packages("aaltobda", repos = c("https://avehtari.github.io/BDA_course_Aalto/", getOption("repos")))
     library(aaltobda)
 }
 ```
@@ -129,7 +128,7 @@ p(y|x,\alpha,\beta,\sigma) &= p_\mathrm{normal}(y|\alpha + \beta x, \sigma)
       & \text{(normal likelihood)} &\text{.}
 \end{aligned}
 $$
-In both the statistical model above and in the Stan model below, $x \in \mathbb{R}^N$ and $y \in \mathbb{R}^N$ are vectors of the covariates / predictors (the assignment number) and vectors of the observation (proportions of students who have handed in the respective assignment). $\alpha \in \mathbb{R}$ is the unknown scalar intercept, $\beta \in \mathbb{R}$ is the unknown scalar slope and $\sigma \in \mathbb{R}_{>0}$ is the unknown scalar observation standard deviation. The statistical model further implies 
+In both the statistical model above and in the Stan model below, $x \in \mathbb{R}^N$ and $y \in \mathbb{R}^N$ are vectors of the covariates / predictors (the assignment number) and vectors of the observation (proportions of students who have handed in the respective assignment). $\alpha \in \mathbb{R}$ is the unknown scalar intercept, $\beta \in \mathbb{R}$ is the unknown scalar slope and $\sigma \in \mathbb{R}_{>0}$ is the unknown scalar observation standard deviation. The statistical model further implies
 $$
 p(y_\mathrm{pred.}|x_\mathrm{pred.},\alpha,\beta,\sigma) = p_\mathrm{normal}(y_\mathrm{pred.}|\alpha + \beta x_\mathrm{pred.}, \sigma)
 $$
@@ -140,31 +139,31 @@ You can download [the broken stan file from github](./additional_files/assignmen
 ```{.stan}
 data { #<1>
     // number of data points
-    int<lower=0> N; 
+    int<lower=0> N;
     // covariate / predictor
-    vector[N] x; 
+    vector[N] x;
     // observations
-    vector[N] y; 
+    vector[N] y;
     // number of covariate values to make predictions at
     int<lower=0> no_predictions;
     // covariate values to make predictions at
-    vector[no_predictions] x_predictions; 
+    vector[no_predictions] x_predictions;
 } #<1>
 parameters { #<2>
     // intercept
-    real alpha; 
+    real alpha;
     // slope
-    real beta; 
+    real beta;
     // the standard deviation should be constrained to be positive
-    real<upper=0> sigma; 
+    real<upper=0> sigma;
 } #<2>
 transformed parameters { #<3>
     // deterministic transformation of parameters and data
     vector[N] mu = alpha + beta * x // linear model
 } #<3>
 model { #<4>
     // observation model / likelihood
-    y ~ normal(mu, sigma); 
+    y ~ normal(mu, sigma);
 } #<4>
 generated quantities { #<5>
     // compute the means for the covariate values at which to make predictions
@@ -200,7 +199,7 @@ Find the ***three mistakes*** in the code and fix them. Report the original mist
 
 ::: {.hint}
 You may find some of the mistakes in the code using Stan syntax checker. If you copy the Stan code to a file ending `.stan` and open it in RStudio (you can also choose from RStudio menu File$\rightarrow$New File$\rightarrow$Stan file to create a new Stan file), the editor will show you some syntax errors. More syntax errors might be detected by clicking `Check' in the bar just above the Stan file in the RStudio editor. Note that some of the errors in the presented Stan code may not be syntax errors.
-::: 
+:::
 
 
 
@@ -218,7 +217,7 @@ The author runs the corrected Stan file using the following R code and plots the
 #| warning: false
 # These are our observations y: the proportion of students handing in each assignment (1-8),
 # sorted by year (row-wise) and assignment (column-wise).
-# While the code suggest a matrix structure, 
+# While the code suggest a matrix structure,
 # the result will actually be a vector of length N = no_years * no_assignments
 propstudents<-c(c(176, 174, 158, 135, 138, 129, 126, 123)/176,
                 c(242, 212, 184, 177, 174, 172, 163, 156)/242,
@@ -228,7 +227,7 @@ propstudents<-c(c(176, 174, 158, 135, 138, 129, 126, 123)/176,
 # These are our predictors x: for each observation, the corresponding assignment number.
 assignment <- rep(1:8, 5)
 # These are in some sense our test data: the proportion of students handing in the last assignment (9),
-# sorted by year. 
+# sorted by year.
 # Usually, we would not want to split our data like that and instead
 # use e.g. Leave-One-Out Cross-Validation (LOO-CV, see e.g. http://mc-stan.org/loo/index.html)
 # to evaluate model performance.
@@ -246,8 +245,8 @@ model_data = list(N=length(assignment),
 ```
 **Sampling from the posterior distribution happens here**:
 ```{r}
-#| warning: false 
-# This reads the file at the specified path and tries to compile it. 
+#| warning: false
+# This reads the file at the specified path and tries to compile it.
 # If it fails, an error is thrown.
 retention_model = cmdstan_model("./additional_files/assignment6_linear_model.stan")
 # This "out <- capture.output(...)" construction suppresses output from cmdstanr
@@ -263,21 +262,21 @@ out <- capture.output(
 # This extracts the draws from the sampling result as a data.frame.
 draws_df = fit$draws(format="draws_df")
 
-# This does some data/draws wrangling to compute the 5, 50 and 95 percentiles of 
-# the mean at the specified covariate values (x_predictions). 
+# This does some data/draws wrangling to compute the 5, 50 and 95 percentiles of
+# the mean at the specified covariate values (x_predictions).
 # It can be instructive to play around with each of the data processing steps
 # to find out what each step does, e.g. by removing parts from the back like "|>  gather(pct,y,-x)"
 # and printing the resulting data.frame.
-mu_quantiles_df = draws_df |> 
-      subset_draws(variable = c("mu_pred")) |> 
-      summarise_draws(~quantile2(.x, probs = c(0.05, .5, 0.95))) |> 
-      mutate(x = 1:9) |> 
+mu_quantiles_df = draws_df |>
+      subset_draws(variable = c("mu_pred")) |>
+      summarise_draws(~quantile2(.x, probs = c(0.05, .5, 0.95))) |>
+      mutate(x = 1:9) |>
       pivot_longer(c(q5, q50, q95), names_to = c("pct"))
 # Same as above, but for the predictions.
-y_quantiles_df = draws_df |> 
-      subset_draws(variable = c("y_pred")) |> 
-      summarise_draws(~quantile2(.x, probs = c(0.05, .5, 0.95))) |> 
-      mutate(x = 1:9) |> 
+y_quantiles_df = draws_df |>
+      subset_draws(variable = c("y_pred")) |>
+      summarise_draws(~quantile2(.x, probs = c(0.05, .5, 0.95))) |>
+      mutate(x = 1:9) |>
       pivot_longer(c(q5, q50, q95), names_to = c("pct"))
 ```
 
@@ -290,14 +289,14 @@ y_quantiles_df = draws_df |>
 #| label: fig-posterior
 #| fig-cap: Describe me in your submission!
 ggplot() +
-  # scatter plot of the training data:  
+  # scatter plot of the training data:
   geom_point(
-    aes(x, y, color=assignment), 
+    aes(x, y, color=assignment),
     data=data.frame(x=assignment, y=propstudents, assignment="1-8")
 ) +
   # scatter plot of the test data:
   geom_point(
-    aes(x, y, color=assignment), 
+    aes(x, y, color=assignment),
     data=data.frame(x=no_assignments, y=propstudents9, assignment="9")
 ) +
   # you have to tell us what this plots:
@@ -340,7 +339,7 @@ Based on the above plot, answer the following questions:
 - What is the general trend of student retention as measured by assignment submissions?
 - Given a model fitted to the submission data for assignments 1-8, does it do a good job predicting the proportion of students who submit the final 9th assignment?
 - Name one different modeling choice you could make to improve the prediction.
-::: 
+:::
 
 
 ::: {.rubric}
@@ -379,7 +378,7 @@ Based on the above plot, answer the following questions:
 * Has at least one way to improve the model been mentioned (E.g. **...** or **...**)?
 :::
 
-# Generalized linear model: Bioassay with Stan (4 points) 
+# Generalized linear model: Bioassay with Stan (4 points)
 
 Replicate the computations for the bioassay example of section 3.7
 (BDA3) using Stan.
@@ -460,13 +459,13 @@ any problems in setting it up or using it. Please report,
 :::
 
 
- 
+
 
 ::: {.rubric}
 * Is the Stan model code included?
     * No
     * Yes
-* Does the implemented Stan-model seem to be working? 
+* Does the implemented Stan-model seem to be working?
     * No implementation
     * Model implemented but results not visualized/reported
     * Model implemented, but the results seem weird

diff --git a/assignments/includes/_general_info.md b/assignments/includes/_general_info.md
@@ -20,8 +20,7 @@
   To install the package on your own system, run
   the following code (upgrade=\"never\" skips question about updating other packages):
 ```{.r}
-install.packages("remotes")
-remotes::install_github("avehtari/BDA_course_Aalto", subdir = "rpackage", upgrade="never")
+install.packages("aaltobda", repos = c("https://avehtari.github.io/BDA_course_Aalto/", getOption("repos")))
 ```
 -   Many of the exercises can be checked automatically using the R
     package `markmyassignment` (pre-installed in JupyterHub).