Buat variabel baru

Pada latihan berikut, Anda akan memodifikasi himpunan data abalone. Langkah pertama adalah membuat abaloneMod sebagai salinan dari himpunan data asli abalone. Ini adalah praktik yang baik untuk memastikan himpunan data asli tidak diubah.

Secara khusus pada latihan ini, Anda akan menggunakan fungsi dplyr::mutate() untuk menambahkan empat variabel baru ke abaloneMod. Di akhir latihan, Anda akan menjalankan summary() untuk memeriksa variabel baru tersebut. Perhatikan nilai maksimum untuk persentase baru yang dihitung.

Himpunan data abalone dan paket dplyr telah dimuat untuk Anda.

Latihan ini adalah bagian dari kursus

R untuk Pengguna SAS

Lihat Kursus

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

R untuk Pengguna SAS

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Mulai Kursus Gratis

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: Objek - fondasi pembentuk R Exercise 2: Membuat objek data di R Exercise 3: Membuat tipe objek komposit Exercise 4: Memilih elemen dari objek Exercise 5: Menentukan tipe variabel Exercise 6: Memilih elemen dalam objek Exercise 7: Memanipulasi himpunan data dan objek data Exercise 8: Buat variabel baru

Latihan Saat Ini

Exercise 9: Mengkoding ulang variabel Exercise 10: Konversi tipe objek Exercise 11: Kualitas data dan pembersihan Exercise 12: Inspeksi variabel Exercise 13: Bobot tidak logis Exercise 14: Periksa pengukuran dimensi Exercise 15: Periksa himpunan data final

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