MulaiMulai sekarang secara gratis

Beranda
Kursus
Studi Kasus: Exploratory Data Analysis di R

Himpunan Data Pemungutan Suara Perserikatan Bangsa-Bangsa

Create Your Free Account

or

Email Address

Password

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Latihan ini adalah bagian dari kursus

Studi Kasus: Exploratory Data Analysis di R

SkillTag.level.beginnerSkillTag.label

5.0+

Mulai Kursus Gratis

The best way to learn data wrangling skills is to apply them to a specific case study. Here you'll learn how to clean and filter the United Nations voting dataset using the dplyr package, and how to summarize it into smaller, interpretable units.

Exercise 1: Himpunan Data Pemungutan Suara Perserikatan Bangsa-Bangsa

Latihan Saat Ini

Exercise 2: Menyaring baris Exercise 3: Menambahkan kolom year Exercise 4: Menambahkan kolom negara Exercise 5: Pengelompokan dan perangkuman Exercise 6: Meringkas seluruh himpunan data Exercise 7: Meringkas per tahun Exercise 8: Meringkas berdasarkan negara Exercise 9: Mengurutkan dan memfilter data yang telah diringkas Exercise 10: Mengurutkan berdasarkan persentase suara "yes"Exercise 11: Menyaring hasil yang telah diringkas

Once you've cleaned and summarized data, you'll want to visualize them to understand trends and extract insights. Here you'll use the ggplot2 package to explore trends in United Nations voting within each country over time.

Exercise 1: Visualization with ggplot2 Exercise 2: Choosing an aesthetic Exercise 3: Plotting a line over time Exercise 4: Other ggplot2 layers Exercise 5: Visualizing by country Exercise 6: Summarizing by year and country Exercise 7: Plotting just the UK over time Exercise 8: Plotting multiple countries Exercise 9: Faceting by country Exercise 10: Faceting the time series Exercise 11: Faceting with free y-axis Exercise 12: Choose your own countries

While visualization helps you understand one country at a time, statistical modeling lets you quantify trends across many countries and interpret them together. Here you'll learn to use the tidyr, purrr, and broom packages to fit linear models to each country, and understand and compare their outputs.

Exercise 1: Linear regression Exercise 2: Linear regression on the United States Exercise 3: Finding the slope of a linear regression Exercise 4: Finding the p-value of a linear regression Exercise 5: Tidying models with broom Exercise 6: Tidying a linear regression model Exercise 7: Combining models for multiple countries Exercise 8: Nesting for multiple models Exercise 9: Nesting a data frame Exercise 10: List columns Exercise 11: Unnesting Exercise 12: Fitting multiple models Exercise 13: Performing linear regression on each nested dataset Exercise 14: Tidy each linear regression model Exercise 15: Unnesting a data frame Exercise 16: Working with many tidy models Exercise 17: Filtering model terms Exercise 18: Filtering for significant countries Exercise 19: Sorting by slope

In this chapter, you'll learn to combine multiple related datasets, such as incorporating information about each resolution's topic into your vote analysis. You'll also learn how to turn untidy data into tidy data, and see how tidy data can guide your exploration of topics and countries over time.

Exercise 1: Joining datasets Exercise 2: Joining datasets with inner_join Exercise 3: Filtering the joined dataset Exercise 4: Visualizing colonialism votes Exercise 5: Tidy data Exercise 6: Tidy data observations Exercise 7: Using gather to tidy a dataset Exercise 8: Recoding the topics Exercise 9: Summarize by country, year, and topic Exercise 10: Visualizing trends in topics for one country Exercise 11: Tidy modeling by topic and country Exercise 12: Nesting by topic and country Exercise 13: Interpreting tidy models Exercise 14: Steepest trends by topic Exercise 15: Checking models visually Exercise 16: Conclusion