DataCamp课程 <用dplyr操作数据> Chapter4

用dplyr操作数据课程目录

Chapter1. 数据变形
Chapter2. 数据统计
Chapter3. 数据选择和变形
Chapter4. 实战演练

是时候通过实战演练来复习一下目前为止所学习到的内容了。
实战演练中会用到的数据是babynames。数据里一共有三个变量year,name,number。

对数据进行筛选和排序

选择1990年的数据，并根据number进行排序。

babynames %>%
  # Filter for the year 1990
  filter(year==1990) %>% 
  # Sort the number column in descending order 
  arrange(desc(number))
# A tibble: 21,223 x 3
    year name        number
   <dbl> <chr>        <int>
 1  1990 Michael      65560
 2  1990 Christopher  52520
 3  1990 Jessica      46615
 4  1990 Ashley       45797
 5  1990 Matthew      44925
 6  1990 Joshua       43382
 7  1990 Brittany     36650
 8  1990 Amanda       34504
 9  1990 Daniel       33963
10  1990 David        33862
# ... with 21,213 more rows

显示每年最常用的名字。这里会用到group_by()进行分组，用top_n()显示排名。

# Find the most common name in each year
babynames %>%
  group_by(year) %>% 
  top_n(1,number)
# A tibble: 28 x 3
# Groups:   year [28]
    year name  number
   <dbl> <chr>  <int>
 1  1880 John    9701
 2  1885 Mary    9166
 3  1890 Mary   12113
 4  1895 Mary   13493
 5  1900 Mary   16781
 6  1905 Mary   16135
 7  1910 Mary   22947
 8  1915 Mary   58346
 9  1920 Mary   71175
10  1925 Mary   70857
# ... with 18 more rows

选择名字是Steven, Thomas, Matthew的数据，并以year为x轴，number为y轴，看每个name的year和number的差别。

# Filter for the names Steven, Thomas, and Matthew 
selected_names <- babynames %>%
  filter(name %in% c("Steven", "Thomas", "Matthew"))

# Plot the names using a different color for each name
ggplot(selected_names, aes(x = year , y = number, color = name)) +
  geom_line()

首先根据year分组，计算每个year里number的总和命名为year_total。然后根据这个year_total计算每年每个名字占到的比例命名为fraction。最后计算每个name里fraction排名第一的数据。

# Calculate the fraction of people born each year with the same name
babynames %>% 
  group_by(year) %>% 
  mutate(year_total=sum(number)) %>% 
  ungroup() %>%
  mutate(fraction = number/year_total)
# A tibble: 332,595 x 5
    year name    number year_total  fraction
   <dbl> <chr>    <int>      <int>     <dbl>
 1  1880 Aaron      102     201478 0.000506 
 2  1880 Ab           5     201478 0.0000248
 3  1880 Abbie       71     201478 0.000352 
 4  1880 Abbott       5     201478 0.0000248
 5  1880 Abby         6     201478 0.0000298
 6  1880 Abe         50     201478 0.000248 
 7  1880 Abel         9     201478 0.0000447
 8  1880 Abigail     12     201478 0.0000596
 9  1880 Abner       27     201478 0.000134 
10  1880 Abraham     81     201478 0.000402 
# ... with 332,585 more rows