# Ordenar las palabras de mayor a menor frecuencia top10$palabra <- factor(top10$palabra, levels = rev(top10$palabra[order(top10$Frecuencia)])) # Gráfico estilo imagen ggplot(top10, aes(x = palabra, y = Frecuencia, fill = palabra)) + geom_col(width = 0.9) + geom_label( aes(label = Frecuencia), color = "white", # texto blanco size = 4, label.size = 1, # sin borde fontface = "bold", show.legend = FALSE ) + #35 theme(axis.text.x = element_text(angle = -45, vjust = 1, hjust=-0.3)) + #36 theme_light() + #37 theme(legend.position = "topleft") + #38 facet_grid(~"Palabras más Frecuentes") + #39 coord_flip() + theme_light() + theme( plot.title = element_text(hjust = 0.5, face = "bold"), legend.position = "none" )

Have you ever tried to track something, but some outcomes were still unknown? That's what we call "censoring" in data. This simple guide explains how we still learn from those "unfinished stories" when analyzing how long things take to happen, making sense of real-world data where we don't always have all the answers.

--- title: "Text Prediction App" author: "Anila Mohanan" output: ioslides_presentation --- ## Introduction - Built as part of Coursera Data Science Capstone - Predicts the next word in a user-entered phrase - Uses blogs text dataset is used. --- ## How the Algorithm Works - Text cleaned and tokenized using `tidytext` - Created **unigram**, **bigram**, and **trigram** frequency tables - Uses **n-gram model with backoff**: - If trigram not found → backoff to bigram → backoff to unigram --- ## Prediction Model - Efficient and memory-optimized using RDS storage - Fast lookup using `dplyr` filtering and `slice_max` - Handles unknown inputs with fallback strategy --- ## Shiny App Demo - Built using **Shiny** and hosted on **shinyapps.io** - User enters a phrase → App predicts the next word - Try it live here: **https://x1tkzd-anila-mohanan.shinyapps.io/NextWordPredictor/** --- ## Summary - Accurate, fast predictions with minimal resources - Real-time prediction from cleaned social and web text - Could be extended into mobile keyboards or chat assistants

MDIGI (Multi-Trait Genotype-Ideotype Distance Index) is a multivariate selection index used in plant breeding to evaluate and rank genotypes based on their overall performance across multiple traits. It is a genotype selection tool that integrates mean performance and stability of genotypes while considering the ideal genotype (ideotype) as a reference. The ideotype represents the theoretical best values for all traits. MDIGI calculates the Euclidean distance of each genotype from this ideotype; the lower the MDIGI value, the better the genotype, as it is closer to the ideal. This method helps breeders simultaneously select for high-yielding, stable, and desirable genotypes across multiple traits and environments. It is particularly useful in multi-environment trials (METs) and is preferred over univariate indices for comprehensive genotype ranking.

ELMEB Modul 8 Datenprojekt

First attemp