Bir ilçedeki kadın yüzdesini hesaplama

Veri kümesi, her ilçe için erkek ve kadınların toplam sayısını (yüzde değil) içeren sütunlar barındırıyor. Bunu, population değişkeniyle birlikte kullanarak her ilçedeki erkek (veya kadın) oranını hesaplayabilirsin.

Bu egzersizde, ilgili sütunları kendin seçeceksin.

Bu egzersiz

dplyr ile Veri Manipülasyonu

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

state, county, population, men ve women sütunlarını seç.
İlçe nüfusunun kadınlardan oluşan payını içeren proportion_women adlı yeni bir değişken ekle.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

counties_selected <- counties %>%
  # Select the columns state, county, population, men, and women
  ___

counties_selected %>%
  # Calculate proportion_women as the fraction of the population made up of women
  ___

Kodu Düzenle ve Çalıştır

Bu egzersiz

dplyr ile Veri Manipülasyonu

kursunun bir parçasıdır

InicianteNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

Learn verbs you can use to transform your data, including select, filter, arrange, and mutate. You'll use these functions to modify the counties dataset to view particular observations and answer questions about the data.

Exercise 1: dplyr ile verileri keşfetme Exercise 2: Verini anlamak Exercise 3: Sütunları seçme Exercise 4: filter ve arrange fiilleri Exercise 5: Gözlemleri sıralama Exercise 6: Koşullara göre filtreleme Exercise 7: Filtreleme ve sıralama Exercise 8: mutate() fiili Exercise 9: Kamu çalışanlarının sayısını hesaplama Exercise 10: Bir ilçedeki kadın yüzdesini hesaplama

Geçerli Egzersiz

Exercise 11: Mutate, filter ve arrange

Now that you know how to transform your data, you'll want to know more about how to aggregate your data to make it more interpretable. You'll learn a number of functions you can use to take many observations in your data and summarize them, including count, group_by, summarize, ungroup, and slice_min/slice_max.

Exercise 1: The count verb Exercise 2: Counting by region Exercise 3: Counting citizens by state Exercise 4: Mutating and counting Exercise 5: The group_by, summarize, and ungroup verbs Exercise 6: Summarizing Exercise 7: Summarizing by state Exercise 8: Summarizing by state and region Exercise 9: The slice_min and slice_max verbs Exercise 10: Selecting a county from each region Exercise 11: Finding the lowest-income state in each region Exercise 12: Using summarize, slice_max, and count together

Learn advanced methods to select and transform columns. Also, learn about select helpers, which are functions that specify criteria for columns you want to choose, as well as the rename verb.

Exercise 1: Selecting Exercise 2: Selecting columns Exercise 3: Select helpers Exercise 4: The rename verb Exercise 5: Renaming a column after count Exercise 6: Renaming a column as part of a select Exercise 7: The relocate verb Exercise 8: Using relocate Exercise 9: Choosing among the four verbs

Work with a new dataset that represents the names of babies born in the United States each year. Learn how to use grouped mutates and window functions to ask and answer more complex questions about your data. And use a combination of dplyr and ggplot2 to make interesting graphs to further explore your data.

Exercise 1: The babynames data Exercise 2: Filtering and arranging for one year Exercise 3: Finding the most popular names each year Exercise 4: Visualizing names with ggplot2 Exercise 5: Grouped mutates Exercise 6: Finding the year each name is most common Exercise 7: Adding the total and maximum for each name Exercise 8: Visualizing the normalized change in popularity Exercise 9: Window functions Exercise 10: Using ratios to describe the frequency of a name Exercise 11: Biggest jumps in a name Exercise 12: Congratulations!