Un dot plot di base

Come mostrato nel video, usa solo geom_path() per creare la struttura di base del dot plot.

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Istruzioni dell'esercizio

Crea un nuovo oggetto ggplot che utilizzi ilo_data come insieme di dati.
Aggiungi una geometria geom_path() mappando le ore lavorate settimanali sull’asse x e i Paesi sull’asse y.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create the dot plot
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Questo esercizio fa parte del corso

Comunicare con i dati nel Tidyverse

InicianteNível de habilidade

4.8+

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In this chapter, you will have a first look at the data you're going to work with throughout this course: the relationship between weekly working hours and monetary compensation in European countries, according to the International Labour Organization (ILO). After that, you'll dive right in and discover a stunning correlation by employing an exploratory visualization. You will then apply a custom look to that graphic – you'll turn an ordinary plot into an aesthetically pleasing and unique data visualization.

Exercise 1: Introduction to the data Exercise 2: Join the two data sets together Exercise 3: Change variable types Exercise 4: Filtering and plotting the data Exercise 5: Filter the data for plotting Exercise 6: Some summary statistics Exercise 7: A basic scatter plot Exercise 8: Add labels to the plot Exercise 9: Custom ggplot2 themes Exercise 10: Apply a default theme Exercise 11: Change the appearance of titles Exercise 12: Alter background color and add margins

Barcharts, scatter plots, and histograms are probably the most common and effective data visualizations. Yet, sometimes, there are even better ways to visually highlight the finding you want to communicate to your audience. So-called "dot plots" make us better grasp and understand changes in data: development over time, for example. In this chapter, you'll build a custom and unique visualization that emphasizes and explains exactly one aspect of the story you want to tell.

Exercise 1: Visualizzare aspetti dei dati con i facet Exercise 2: Prepara l'insieme di dati per il grafico con facet Exercise 3: Aggiungi i facet al grafico Exercise 4: Definisci la tua funzione di tema Exercise 5: Applica la nuova funzione di tema al grafico Exercise 6: Un grafico personalizzato per mettere in evidenza il cambiamento Exercise 7: Un dot plot di base

Esercizio in corso

Exercise 8: Aggiungi frecce alle linee nel grafico Exercise 9: Aggiungi delle etichette a ciascun paese Exercise 10: Rifinire il dot plot Exercise 11: La funzione fct_reorder Exercise 12: Riordinare gli elementi nel grafico Exercise 13: Correggi le etichette mal posizionate Exercise 14: Finalizzare il grafico per pubblici e dispositivi diversi Exercise 15: Modifica il viewport per evitare che le etichette si sovrappongano al bordo del grafico Exercise 16: Ottimizzare il grafico per i dispositivi mobili

Back in the old days, researchers and data analysts used to generate plots in R and then tediously copy them into their LaTeX or Word documents. Nowadays, whole reports can be produced and reproduced from within R and RStudio, using the RMarkdown language – combining R chunks, formatted prose, tables and plots. In this chapter, you'll take your previous findings, results, and graphics and integrate them into such a report to tell the story that needs to be told.

Exercise 1: What is RMarkdown?Exercise 2: When is a document reproducible?Exercise 3: Introduction to the RMarkdown exercise interface Exercise 4: Formatting with Markdown Exercise 5: Give your document a structure Exercise 6: Change formatting of text snippets Exercise 7: R code in RMarkdown documents Exercise 8: Specify packages in the first code chunk Exercise 9: R code chunk options Exercise 10: Inline code statements Exercise 11: Images in RMarkdown files Exercise 12: Adjusting figure options, optimizing them for mobile devices.Exercise 13: Add auxiliary images

Your boss, your client, or your professor usually expects your results to be accurate and presented in a clear and concise structure. However, coming up with a nicely formatted and unique report on top of that is certainly a plus and RMarkdown can be customized to accomplish this. In this last chapter, you'll take your report from the last chapter and brand it with your own custom and unique style.

Exercise 1: Advanced YAML settings Exercise 2: Change the overall appearance of your report Exercise 3: Add a table of contents Exercise 4: More YAML hacks Exercise 5: Custom stylesheets Exercise 6: CSS selectors Exercise 7: Change style attributes of text elements Exercise 8: Reference the style sheet Exercise 9: Beautiful tables Exercise 10: Beautify a table with kable Exercise 11: Repetition: CSS Exercise 12: Summary