The New York Times web site provides a rich set of APIs, as described here: https://developer.nytimes.com/apis You’ll need to start by signing up for an API key. Your task is to choose one of the New York Times APIs, construct an interface in R to read in the JSON data, and transform it into an R DataFrame. The api data I used from NY times was on books by Stephen King.

ANOVA

# Load ggplot2 library(ggplot2) # Create a contingency table and convert it to a data frame for ggplot contingency_table <- table(clinical_data$Smoking_Status, clinical_data$Diabetes) plot_data <- as.data.frame(contingency_table) colnames(plot_data) <- c("Smoking_Status", "Diabetes", "Count") # Plot ggplot(plot_data, aes(x = Smoking_Status, y = Count, fill = Diabetes)) + geom_bar(stat = "identity", position = "fill") + labs(title = "Proportion of Diabetes Status by Smoking Status", x = "Smoking Status", y = "Proportion") + scale_y_continuous(labels = scales::percent) + theme_minimal() + theme(legend.position = "top")

# Create a mosaic plot from the contingency table mosaicplot(contingency_table, main = "Mosaic Plot of Smoking Status and Diabetes", xlab = "Smoking Status", ylab = "Diabetes", color = TRUE)

In this RPubs post, I dive into my personal exploration of vectors in R. I find myself drawn to the powerful built-in constants like LETTERS and month.abb, which I use to effortlessly create sequences of letters and months. As I experiment, I discover how I can create named vectors and realize how intuitive it feels to assign labels to my data. I enjoy generating number sequences using the : operator and the seq() function, giving me control over the steps and ranges. Working with different data types in vectors excites me, as I learn how to manipulate and index elements easily. I also explore the rep() function, which opens up new ways for me to repeat and structure my data efficiently. Through these exercises, I feel more confident in my ability to handle data in R, and I’m thrilled to share my journey with colorful, easy-to-follow examples.

Mtcars data adjusted multiple lineal regression

# Scatter plot with trend line ggplot(clinical_data, aes(x = Age, y = Cholesterol, color = Smoking_Status)) + geom_point() + geom_smooth(method = "lm", se = FALSE) + labs(title = "Age vs Cholesterol with Trend Line", x = "Age (years)", y = "Cholesterol (mg/dL)")