Aggiungi funzionalità con i pacchetti

Per aggiungere funzionalità a R base, devi installare e caricare i pacchetti. Per questo esercizio, il pacchetto dplyr è già caricato. Puoi confermare che dplyr è caricato e disponibile eseguendo il comando sessionInfo() per visualizzare lo stato della tua sessione R, inclusi tutti i pacchetti caricati.

Userai poi la funzione library() per caricare il pacchetto Hmisc ed eseguirai di nuovo sessionInfo() per confermare che anche Hmisc è stato caricato.

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Istruzioni dell'esercizio

Esegui sessionInfo() per confermare che il pacchetto dplyr è elencato sotto "other attached packages".
Usa la funzione library() per caricare il pacchetto Hmisc.
Esegui di nuovo sessionInfo() per vedere che il pacchetto Hmisc è stato aggiunto alle funzionalità della tua sessione.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Run sessionInfo() see packages available to this session
___

# Load Hmisc package
library(___)

# Run sessionInfo() again to see updated package list
___

Modifica ed esegui il codice

Questo esercizio fa parte del corso

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This first chapter will get you oriented into the R programming environment. You'll learn how to get help, load a dataset, and increase functionality by adding packages. You'll begin working with the abalone dataset (through the dplyr package workflow) to get descriptive statistics and create helpful visualizations (using the ggplot2 package).

Exercise 1: Ottieni aiuto e carica i dati in R Exercise 2: Ottenere aiuto Exercise 3: Carica il dataset e ottieni dettagli Exercise 4: Aggiungi funzionalità con i pacchetti

Esercizio in corso

Exercise 5: Contenuto dell'insieme di dati e statistiche descrittive Exercise 6: Carica un insieme di dati esterno Exercise 7: Contenuto del dataset Exercise 8: Statistiche descrittive Exercise 9: Visualizzazioni grafiche Exercise 10: Istogrammi Exercise 11: Boxplot e violin plot Exercise 12: Scatterplot

Now that you are oriented in the R environment, this chapter will advance your understanding of R's versatility working with data objects. You'll learn how to create and modify variables in the abalone data set. Using your ggplot2 visualization skills, you will discover the data errors in the abalone data and then create a final cleaned data set ready for analysis and modeling.

Exercise 1: Objects - the building blocks of R Exercise 2: Create data objects in R Exercise 3: Create composite object types Exercise 4: Selecting elements from objects Exercise 5: Determine variable types Exercise 6: Select elements in objects Exercise 7: Manipulating datasets and data objects Exercise 8: Create new variables Exercise 9: Recode variables Exercise 10: Object type conversion Exercise 11: Data quality and cleaning Exercise 12: Variables inspection Exercise 13: Illogical weights Exercise 14: Check dimension measurements Exercise 15: Check final dataset

Once your data set has been cleaned, the next step is exploration. In chapter 3 you will learn how to compute descriptive statistics, explore associations (e.g., correlations) among the variables, and perform bi-variate statistical tests (e.g., t-tests and chi-square tests). You will also create graphical visualizations which illustrate the bi-variate associations and group comparison tests.

Exercise 1: Exploratory data analysis Exercise 2: Descriptive statistics and function masking Exercise 3: Specific statistics for one or more variables Exercise 4: Summary statistics by group Exercise 5: Correlations and t-tests Exercise 6: Bivariate correlations Exercise 7: Scatterplots Exercise 8: Correlations by sex Exercise 9: Tests for two groups Exercise 10: Categorical data: analyze and visualize Exercise 11: Chi-square tests Exercise 12: Mosaic plots Exercise 13: Age categories by shellweight categories

In this final chapter, you will learn how to work with one of the most versatile data object types in R called a list. These skills will enable you to save and manipulate your output from descriptive statistics, associations, and group comparison computations. You will also learn how to perform ANOVA (analysis of variance) and linear regression in R. All your skills are put to use in the final exercises to create the best models for predicting abalone ages from their sex, size, and weight measurements.

Exercise 1: Working with output objects Exercise 2: Descriptive statistics output Exercise 3: Summarise output Exercise 4: Group_by output Exercise 5: Working with lists Exercise 6: Hmisc describe output Exercise 7: Correlations output Exercise 8: t-tests output Exercise 9: Chi-square tests output Exercise 10: ANOVA and linear models Exercise 11: ANOVA Exercise 12: Linear regression Exercise 13: Final models evaluation Exercise 14: Abalone age predictors Exercise 15: Best model by sex Exercise 16: Course summary and recommendations