Yeni değişkenler oluştur

Sıradaki egzersizlerde abalone veri kümesini değiştireceksin. İlk adımın, orijinal veri kümesi abalone üzerinde değişiklik yapmamak için iyi bir yöntem olarak, bir kopya oluşturup buna abaloneMod adını vermek olacak.

Özellikle bu egzersizde, abaloneMod'a dört yeni değişken eklemek için dplyr::mutate() fonksiyonunu kullanacaksın. Egzersizin sonunda yeni değişkenleri kontrol etmek için summary() çalıştıracaksın. Hesaplanan yeni yüzdelerin maksimum değerlerine dikkat et.

abalone veri kümesi ve dplyr paketi senin için yüklendi.

Bu egzersiz

SAS Kullananlar İçin R

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Create abaloneMod from abalone, add new variable age
abaloneMod <- ___ %>%
  mutate(___)

Kodu Düzenle ve Çalıştır

Bu egzersiz

SAS Kullananlar İçin R

kursunun bir parçasıdır

InicianteNível de habilidade

4.8+

27 reviews

Kursa Ücretsiz Başlayın

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: Get help and load data in R Exercise 2: Getting help Exercise 3: Load dataset and get details Exercise 4: Add functionality with packages Exercise 5: Dataset contents and descriptive statistics Exercise 6: Load external dataset Exercise 7: Dataset contents Exercise 8: Descriptive statistics Exercise 9: Graphical visualizations Exercise 10: Histograms Exercise 11: Boxplots and violin plots Exercise 12: Scatterplots

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: Nesneler - R'nin yapı taşları Exercise 2: R'de veri nesneleri oluştur Exercise 3: Bileşik nesne türleri oluştur Exercise 4: Nesnelerden ögeler seçme Exercise 5: Değişken türlerini belirle Exercise 6: Nesnelerden öğe seçme Exercise 7: Veri kümelerini ve veri nesnelerini değiştirme Exercise 8: Yeni değişkenler oluştur

Geçerli Egzersiz

Exercise 9: Değişkenleri yeniden kodla Exercise 10: Nesne türü dönüştürme Exercise 11: Veri kalitesi ve temizleme Exercise 12: Değişkenleri inceleme Exercise 13: Mantıksız ağırlıklar Exercise 14: Boyut ölçümlerini kontrol et Exercise 15: Nihai veri kümesini kontrol et

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