This shows that the 1928 Anderson Iris dataset that was collected over a much larger number of locations and a wider geographical distribution has a larger degree of scatter than the Fisher Iris data and it suggest that models trained on the Fisher data will underperform with real world data

This code maps counties top 5/1 percentile and bottom 5/1 percentile by bbd usage

This portfolio presents my work for Statistical Modeling 1 using the Airbnb listings dataset. It includes exploratory data analysis, multiple regression, multinomial logistic regression, Poisson regression, and a LASSO model. Each section connects the modeling choices to the course objectives and explains the results in clear, accessible language. The portfolio highlights how I applied probability, inference, GLMs, cross-validation, and tidymodels workflows to answer practical questions from the data.

Se presenta un flujo analítico completo utilizando el paquete {biostats}, aplicado sobre un conjunto de datos clínicos simulados. Se incluyen tablas de línea base, comparaciones entre grupos, análisis longitudinal, pruebas de supuestos, medidas de efecto epidemiológico, visualizaciones estandarizadas y cálculos de tamaño de muestra. El objetivo es mostrar cómo {biostats} optimiza, estandariza y acelera procesos estadísticos comúnmente empleados en ensayos clínicos, manteniendo reproducibilidad y rigor metodológico.

Auto datset

Comparison of Red Maple and Sugar Maple to investigate if the Red Maple has muted dynamics.

BASE DE DATOS Y DESCRIPTIVAS

--- title: "U.S. COVID-19 Sentiment Timeline" output: html_document --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE) library(dplyr) library(ggplot2) library(lubridate) library(crosstalk) library(plotly) library(DT) set.seed(123) ``` ```{r load_and_prepare} # Load data ------------------------------------------------------------- # If the CSV is on your Desktop: sent <- read.csv("~/Desktop/usa_covid_2020_2022.csv", stringsAsFactors = FALSE) # If it's in the project folder instead, use: # sent <- read.csv("usa_covid_2020_2022.csv", stringsAsFactors = FALSE) sent$Date <- as.Date(sent$Date) sent <- sent[order(sent$Date), ] sent <- sent %>% mutate( Year = year(Date), Month = month(Date), YearMonth = format(Date, "%Y-%m") ) # Theme tagging --------------------------------------------------------- assign_theme <- function(text) { txt <- tolower(text) if (grepl("vaccine|vaccination|shot|booster|dose|warp speed", txt)) { "Vaccines & Boosters" } else if (grepl("lockdown|stay-at-home|shutdown|restrictions|social distancing|face covering|mask", txt)) { "Restrictions & Reopening" } else if (grepl("delta|omicron|variant|ba\\.5|ba5", txt)) { "Variants" } else if (grepl("economy|economic|jobs|unemployment|market|business|stimulus|checks|rescue plan|relief", txt)) { "Economy & Relief" } else if (grepl("school|schools|students|classroom|kids", txt)) { "Schools & Education" } else if (grepl("misinformation|disinformation|false information|myths|rumors", txt)) { "Misinformation & Communication" } else { "General Pandemic Response" } } sent$Theme <- vapply(sent$Summary, assign_theme, character(1)) # Stopword lists for word scrambles ------------------------------------- stopwords_simple <- c( "the","and","to","of","in","a","for","on","as","is","are","this","that", "with","be","from","at","it","by","an","into","about","has","have","will", "was","were","but","more","many","most","less","very","their","his","her", "its","our","us","we","they","over","again","now","than","still","even" ) stopwords_domain <- c( "covid","coronavirus","pandemic","virus","viruses", "case","cases","death","deaths", "vaccine","vaccines","shot","shots", "lockdown","lockdowns","restrictions","guidelines", "americans","american","people","country", "united","states","state","u","s","us","u.s", "economy","economic","hospital","hospitals" ) make_scramble <- function(text) { clean <- gsub("[^A-Za-z ]", " ", tolower(text)) words <- unlist(strsplit(clean, "\\s+")) words <- words[ words != "" & !(words %in% c(stopwords_simple, stopwords_domain)) & nchar(words) >= 5 ] if (length(words) == 0) return("") unique_words <- unique(words) paste(sample(unique_words, min(5, length(unique_words))), collapse = " ") } # Monthly aggregates ----------------------------------------------------- monthly_info <- sent %>% group_by(YearMonth) %>% summarize( MonthStart = min(Date), AvgSentiment = mean(SentimentScore), HoverText = paste(Summary, collapse = "<br>• "), MonthScramble = make_scramble(paste(Summary, collapse = " ")), .groups = "drop" ) %>% mutate( SentSign = ifelse(AvgSentiment > 0, "Positive", "Negative/Neutral") ) # Join month scramble back to row level --------------------------------- sent <- sent %>% left_join(monthly_info %>% select(YearMonth, MonthScramble), by = "YearMonth") # Theme-level summary --------------------------------------------------- theme_summary <- sent %>% group_by(Theme) %>% summarize( AvgSentiment = mean(SentimentScore), Count = n(), .groups = "drop" ) %>% mutate( SentSign = ifelse(AvgSentiment > 0, "Positive", "Negative/Neutral") ) ``` ```{r monthly_interactive} plot_ly( monthly_info, x = ~MonthStart, y = ~AvgSentiment, type = "scatter", mode = "lines+markers", color = ~SentSign, colors = c("Negative/Neutral" = "#d95f02", "Positive" = "#1b9e77"), text = ~paste0( "<b>", YearMonth, "</b><br>", "Average Sentiment: ", round(AvgSentiment, 3), "<br>", "Category: ", SentSign, "<br><br>", "<b>Summaries:</b><br>• ", HoverText ), hoverinfo = "text" ) %>% layout( title = "Monthly Average Sentiment (Color-Coded by Positive vs Negative)", xaxis = list(title = "Month"), yaxis = list(title = "Average Sentiment Score (-1 to +1)") ) ``` ```{r theme_chart} ggplot( theme_summary, aes(x = reorder(Theme, AvgSentiment), y = AvgSentiment, fill = SentSign) ) + geom_col(width = 0.6) + geom_hline(yintercept = 0, linetype = "dashed") + coord_flip() + scale_fill_manual( values = c("Negative/Neutral" = "#d95f02", "Positive" = "#1b9e77"), name = "Average Sentiment" ) + theme_minimal(base_size = 13) + labs( title = "Average Sentiment by Theme", x = "Theme", y = "Average Sentiment Score (-1 to +1)" ) ``` ```{r interactive_timeline} shared_sent <- SharedData$new(sent, key = ~Date, group = "covid") filter_date <- filter_slider( id = "date_filter", label = "Date Range", sharedData = shared_sent, column = ~Date ) filter_score <- filter_slider( id = "score_filter", label = "Sentiment Score", sharedData = shared_sent, column = ~SentimentScore, step = 0.05 ) filter_month <- filter_select( id = "month_filter", label = "Month", sharedData = shared_sent, group = ~YearMonth ) timeline_plot <- plot_ly( shared_sent, x = ~Date, y = ~SentimentScore, type = "scatter", mode = "lines+markers", text = ~paste0( "Date: ", Date, "<br>", "Sentiment: ", SentimentScore, "<br>", "Theme: ", Theme, "<br><br>", Summary ), hoverinfo = "text" ) %>% layout( title = "Daily Sentiment Timeline", xaxis = list(title = "Date"), yaxis = list(title = "Sentiment Score (-1 to +1)") ) bscols( widths = c(3, 9), list(filter_date, filter_score, filter_month), timeline_plot ) ``` ```{r word_scramble_table} table_data <- sent %>% select(Date, SentimentScore, YearMonth, MonthScramble, Theme, Summary) shared_table <- SharedData$new(table_data, key = ~Date, group = "covid") datatable( shared_table, options = list(pageLength = 5, lengthChange = FALSE), colnames = c( "Date", "Sentiment", "Month", "Word Scramble", "Theme", "Summary" ), rownames = FALSE ) ```

Auto dataset

A quick guide for finding similar episodes of interest.