Keseragaman mata uang

Setelah tanggal Anda sudah beres, Anda perlu memperbaiki perbedaan satuan. Saat pertama kali memvisualisasikan data, Anda akan melihat sekelompok nilai yang sangat tinggi dan sekelompok nilai yang relatif lebih rendah. Bank memiliki dua kantor berbeda — satu di New York dan satu di Tokyo — sehingga Anda menduga akun yang dikelola kantor Tokyo menggunakan yen Jepang, bukan dolar AS. Untungnya, Anda memiliki data frame bernama account_offices yang menunjukkan kantor mana yang mengelola akun setiap nasabah, sehingga Anda dapat menggunakan informasi ini untuk menentukan total mana yang perlu dikonversi dari yen ke dolar.

Rumus untuk mengonversi yen ke dolar adalah USD = JPY / 104.

dplyr dan ggplot2 sudah dimuat dan data frame accounts serta account_offices tersedia.

Latihan ini adalah bagian dari kursus

Membersihkan Data di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Scatter plot of opening date and total amount
accounts %>%
  ggplot(aes(x = ___, y = ___)) +
  ___

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Membersihkan Data di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

In this chapter, you'll learn how to overcome some of the most common dirty data problems. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double-counting.

Exercise 1: Data type constraints Exercise 2: Common data types Exercise 3: Converting data types Exercise 4: Trimming strings Exercise 5: Range constraints Exercise 6: Ride duration constraints Exercise 7: Back to the future Exercise 8: Uniqueness constraints Exercise 9: Full duplicates Exercise 10: Removing partial duplicates Exercise 11: Aggregating partial duplicates

Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.

Exercise 1: Checking membership Exercise 2: Members only Exercise 3: Not a member Exercise 4: Categorical data problems Exercise 5: Identifying inconsistency Exercise 6: Correcting inconsistency Exercise 7: Collapsing categories Exercise 8: Cleaning text data Exercise 9: Detecting inconsistent text data Exercise 10: Replacing and removing Exercise 11: Invalid phone numbers

In this chapter, you’ll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. You’ll also gain invaluable skills that will help you verify that values have been added correctly and that missing values don’t negatively impact your analyses.

Exercise 1: Keseragaman Exercise 2: Keseragaman tanggal Exercise 3: Keseragaman mata uang

Latihan Saat Ini

Exercise 4: Validasi lintas kolom Exercise 5: Memvalidasi total Exercise 6: Memvalidasi usia Exercise 7: Kelengkapan Exercise 8: Jenis-jenis missingness Exercise 9: Memvisualisasikan data hilang Exercise 10: Menangani data hilang

Record linkage is a powerful technique used to merge multiple datasets together, used when values have typos or different spellings. In this chapter, you'll learn how to link records by calculating the similarity between strings—you’ll then use your new skills to join two restaurant review datasets into one clean master dataset.

Exercise 1: Comparing strings Exercise 2: Calculating distance Exercise 3: Small distance, small difference Exercise 4: Fixing typos with string distance Exercise 5: Generating and comparing pairs Exercise 6: Link or join?Exercise 7: Pair blocking Exercise 8: Comparing pairs Exercise 9: Scoring and linking Exercise 10: Score then select or select then score?Exercise 11: Putting it together Exercise 12: Congratulations!