Mengevaluasi imputasi: Skala

Meskipun imputasi rata-rata mungkin tidak terlihat terlalu buruk saat kita membandingkannya menggunakan box plot, penting untuk memahami variasi dalam data. Inilah sebabnya mengapa penting untuk menelusuri bagaimana skala dan sebaran nilai imputasi berubah dibandingkan dengan data aslinya.

Salah satu cara untuk mengevaluasi kesesuaian skala imputasi adalah menggunakan scatter plot untuk melihat apakah nilai-nilainya tampak sesuai atau tidak.

Latihan ini adalah bagian dari kursus

Menangani Data Hilang di R

Petunjuk latihan

Dengan menggunakan data yang sudah diimputasi, ocean_imp_mean:

Telusuri imputasi pada suhu udara (sumbu x) dan kelembapan (sumbu y) menggunakan scatter plot, jangan lupa gunakan color = any_missing.
Kembangkan visualisasi sebelumnya dengan membuat facet berdasarkan tahun.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Explore imputations in air temperature and humidity,  
# coloring by the variable, any_missing
ggplot(___, 
       aes(x = ___, y = ___, color = ___)) + 
  geom_point()

# Explore imputations in air temperature and humidity,  
# coloring by the variable, any_missing, and faceting by year
ggplot(___, 
       aes(x = ___, y = ___, color = ___)) + 
  ___() +  
  facet_wrap(~___)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Menangani Data Hilang di R

SkillTag.level.beginnerSkillTag.label

4.8+

Mulai Kursus Gratis

Chapter 1 introduces you to missing data, explaining what missing values are, their behavior in R, how to detect them, and how to count them. We then introduce missing data summaries and how to summarise missingness across cases, variables, and how to explore across groups within the data. Finally, we discuss missing data visualizations, how to produce overview visualizations for the entire dataset and over variables, cases, and other summaries, and how to explore these across groups.

Exercise 1: Introduction to missing data Exercise 2: Using and finding missing values Exercise 3: How many missing values are there?Exercise 4: Working with missing values Exercise 5: Why care about missing values?Exercise 6: Summarizing missingness Exercise 7: Tabulating Missingness Exercise 8: Other summaries of missingness Exercise 9: How do we visualize missing values?Exercise 10: Your first missing data visualizations Exercise 11: Visualizing missing cases and variables Exercise 12: Visualizing missingness patterns

In chapter two, you will learn how to uncover hidden missing values like "missing" or "N/A" and replace them with `NA`. You will learn how to efficiently handle implicit missing values - those values implied to be missing, but not explicitly listed. We also cover how to explore missing data dependence, discussing Missing Completely at Random (MCAR), Missing At Random (MAR), Missing Not At Random (MNAR), and what they mean for your data analysis.

Exercise 1: Searching for and replacing missing values Exercise 2: Using miss_scan_count Exercise 3: Using replace_with_na Exercise 4: Using replace_with_na scoped variants Exercise 5: Filling down missing values Exercise 6: Fix implicit missings using complete()Exercise 7: Fix explicit missings using fill()Exercise 8: Using complete() and fill() together Exercise 9: Missing Data dependence Exercise 10: Differences between MCAR and MAR Exercise 11: Exploring missingness dependence Exercise 12: Further exploring missingness dependence

In this chapter, you will learn about workflows for working with missing data. We introduce special data structures, the shadow matrix, and nabular data, and demonstrate how to use them in workflows for exploring missing data so that you can link summaries of missingness back to values in the data. You will learn how to use ggplot to explore and visualize how values changes as other variables go missing. Finally, you learn how to visualize missingness across two variables, and how and why to visualize missings in a scatterplot.

Exercise 1: Tools to explore missing data dependence Exercise 2: Creating shadow matrix data Exercise 3: Performing grouped summaries of missingness Exercise 4: Further exploring more combinations of missingness Exercise 5: Visualizing missingness across one variable Exercise 6: Nabular data and filling by missingness Exercise 7: Nabular data and summarising by missingness Exercise 8: Explore variation by missingness: box plots Exercise 9: Visualizing missingness across two variables Exercise 10: Exploring missing data with scatter plots Exercise 11: Using facets to explore missingness Exercise 12: Faceting to explore missingness (multiple plots)

In this chapter, you will learn about filling in the missing values in your data, which is called imputation. You will learn how to impute and track missing values, and what the good and bad features of imputations are so that you can explore, visualise, and evaluate the imputed data against the original values. You will learn how to use, evaluate, and compare different imputation models, and explore how different imputation models affect the inferences you can draw from the models.

Exercise 1: Mengisi yang kosong Exercise 2: Imputasi data di bawah rentang dengan data nabular Exercise 3: Visualisasikan nilai imputasi dalam plot sebar Exercise 4: Buat histogram data hasil imputasi Exercise 5: Apa yang membuat imputasi menjadi baik Exercise 6: Mengevaluasi imputasi yang buruk Exercise 7: Mengevaluasi imputasi: Skala

Latihan Saat Ini

Exercise 8: Mengevaluasi imputasi: Pada banyak variabel Exercise 9: Melakukan imputasi Exercise 10: Menggunakan simputation untuk mengimputasi data Exercise 11: Mengevaluasi dan membandingkan imputasi Exercise 12: Mengevaluasi imputasi (banyak model & variabel)Exercise 13: Mengevaluasi imputasi dan model Exercise 14: Menggabungkan dan membandingkan banyak model imputasi Exercise 15: Mengevaluasi berbagai parameter dalam model Exercise 16: Pelajaran Terakhir