Add fish and wells datasets and update show_loading

Added new Quarto markdown files for the fish and wells datasets in the data directory, providing descriptions and example analyses. Updated the show_loading function in quarto_related.R to accept the dataset directly instead of its attributes, improving usability and consistency with show_code.

Author: oduerr

R/edudat/R/quarto_related.R

@@ -21,7 +21,7 @@ show_code <- function(df) {
 
 #' Generate R and Python code showing how to load a dataset, which is needed in quarto documents
 #'
-#' @param attributes A list containing the attributes of the dataset, including 'download_url' and 'source'
+#' @param df The edudat dataset, including 'download_url' and 'source' in the attributes
 #' @return No return value, called for side effects. Outputs formatted R and Python code for loading the data.
 #' @export
 #' @examples 
@@ -32,7 +32,8 @@ show_code <- function(df) {
 #' attributes = attributes(df)
 #' show_loading(attributes(df))
 #' ```
-show_loading <- function(attributes, show_python = FALSE) {
+show_loading <- function(df, show_python = FALSE) {
+  attributes <- attributes(df)
   r_code <-  sprintf("df <- read.csv(\"%s\")", attributes$download_url)
   r_code2 <- sprintf("df <- edudat::load_data(\"%s\")", attributes$source)
 

data/fish.qmd

@@ -0,0 +1,42 @@
+---
+title: "Fish Data"
+---
+
+## Description
+The data-set contains the number of Fish a group of camper caught in a state park (taken from https://stats.idre.ucla.edu/r/dae/zip/). The task here is to predict the number of fish caught by a fishing party. We have a small data set, of 250 groups, which visited a state park and provided the following information:
+
+- how many people are in the group
+- the number children in the group
+- the use of live bait
+- whether the group came with a camper to the park.
+
+```{r, echo=FALSE, eval=FALSE}
+  # Importing of the data
+  df <- read.csv("https://stats.idre.ucla.edu/stat/data/fish.csv")
+  write.csv(df, "fish.csv", row.names = FALSE)
+```
+
+
+
+## Investiating the Zero Inflated Data
+
+```{r plot_data, echo=TRUE, eval=TRUE}
+data <- edudat::load_data("fish.csv")
+plot(table(data$count), xlim=c(0, 10), col="lightblue", main="Histogram of Fish Caught", xlab="Number of Fish Caught")
+```
+
+
+## Investiating the Zero Inflated Data
+```{r zib, echo=TRUE, eval=FALSE}
+data <- edudat::load_data("fish.csv")
+# Creating factor variables
+data <- within(data, {
+    nofish <- factor(nofish)
+    livebait <- factor(livebait)
+    camper <- factor(camper)
+})
+str(data)
+library(pscl)
+summary(m1 <- zeroinfl(count ~ child + camper | persons, data = data))
+```
+

data/wells.qmd

@@ -0,0 +1,68 @@
+---
+title: "Wells Data"
+---
+
+## Description (from rstanarm)
+A survey of 3200 residents in a small area of Bangladesh suffering from arsenic contamination of groundwater. 
+Respondents with elevated arsenic levels in their wells had been encouraged to switch their water source to a safe public or private well in the nearby area (dist meters away) and the survey was conducted several years later to learn which of the affected residents had switched wells.
+
+Souce: Gelman and Hill (2007)
+
+3020 obs. of 5 variables
+
+- switch Indicator for well-switching
+
+- arsenic Arsenic level in respondent's well
+
+- dist Distance (meters) from the respondent's house to the nearest well with safe drinking water.
+
+- assoc Indicator for member(s) of household participate in community organizations
+
+- educ Years of education (head of household)
+
+Gelman, A. and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, Cambridge, UK. https://stat.columbia.edu/~gelman/arm/
+
+
+
+
+```{r, echo=TRUE, eval=FALSE}
+  # Importing of the data
+  library(rstanarm)
+  data(wells)
+  ?wells
+  write.csv(wells, "wells.csv", row.names = FALSE)
+  
+  if (FALSE){ 
+    # More info
+    install.packages("haven")
+    library(haven)
+    d = read_dta('/Users/oli/Downloads/ARM_Data/arsenic/all.dta')
+  }
+  
+```
+
+
+
+## Data
+
+```{r plot_data, echo=TRUE, eval=TRUE}
+data <- edudat::load_data("wells.csv")
+summary(data)
+```
+
+```{r plot1, echo=FALSE, eval=FALSE}
+library(ggplot2)
+
+```
+
+```{r plot, echo=FALSE, eval=FALSE}
+library(ggplot2)
+ggplot(wells, aes(x = dist, y = ..density.., fill = switch == 1)) +
+  geom_density(alpha=0.5) +
+  scale_fill_manual(values = c("gray30", "skyblue"))
+```
+So especially people which are living close to a contaminated well are more likely to switch to a safe well. 
+
+
+
+