Menghitung persentase perempuan di suatu county

Himpunan data ini mencakup kolom jumlah total (bukan persentase) laki-laki dan perempuan di setiap county. Anda dapat menggunakan ini, bersama dengan variabel population, untuk menghitung porsi laki-laki (atau perempuan) dalam setiap county.

Pada latihan ini, Anda akan memilih sendiri kolom-kolom yang relevan.

Latihan ini adalah bagian dari kursus

Manipulasi Data dengan dplyr

Petunjuk latihan

Pilih kolom state, county, population, men, dan women.
Tambahkan variabel baru bernama proportion_women yang berisi porsi populasi county yang merupakan perempuan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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
  ___

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Manipulasi Data dengan dplyr

SkillTag.level.beginnerSkillTag.label

4.8+

Mulai Kursus Gratis

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: Mengeksplorasi data dengan dplyr Exercise 2: Memahami data Anda Exercise 3: Memilih kolom Exercise 4: Verba filter dan arrange Exercise 5: Mengurutkan observasi Exercise 6: Menyaring berdasarkan kondisi Exercise 7: Menyaring dan mengurutkan Exercise 8: Kata kerja mutate()Exercise 9: Menghitung jumlah pegawai pemerintah Exercise 10: Menghitung persentase perempuan di suatu county

Latihan Saat Ini

Exercise 11: Mutate, filter, dan 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!