通常我们的数据不能直接用于可视化处理，因此我们要对它们进行转化整理（transform），比如创建新的变量或重命名变量或者重新整理观测值的顺序等等。

1. 安装

这里使用 nycflights13 和 tidyverse 两个包，其中主要用到 dplyr 包中函数：

library(nycflights13)
library(tidyverse)

nycflights13 中的 flights 数据对象含有 336776 个 2013 年纽约的航班信息：

flights.png

注意 ：

• int stands for integers.
• dbl stands for doubles, or real numbers.
• chr stands for character vectors, or strings.
• dttm stands for date-times (a date + a time).
• lgl stands for logical, vectors that contain only TRUE or FALSE.
• fctr stands for factors, which R uses to represent categorical variables with fixed possible values.
• date stands for dates.
  dplyr basics
• filter() : Pick observations by their values.
• arrange() : Reorder the rows.
• select() : Pick variables by their names.
• mutate() : Create new variables with functions of existing variables.
• summarise() : Collapse many values down to a single summary.

2. filter() 筛选观测值（行）

选取特定值：

filter1.png

near().png

& is "and", | is "or", and ! is "not". “与或非”
x %in% y: This will select every row where x is one of the values in y.
!(x & y) is the same as !x | !y, and !(x | y) is the same as !x & !y.

logical operators.png

filter(flights, month == 11 | month == 12)
filter(flights, month %in% c(11, 12))
filter(flights, !(arr_delay > 120 | dep_delay > 120))
filter(flights, arr_delay <= 120, dep_delay <= 120)

filter2.png

2.3 缺失值：

NA represents an unknown value so missing values are “contagious”: almost any operation involving an unknown value will also be unknown.

判断一个值是否是 NA， 使用 is.na()。
尝试：

filter(df, is.na(x) | x > 1)

3. arrange() 对行进行重排序

默认情况下按升序排列。使用desc() 可以降序排列, NA 值进行排序时候再末尾：

desc.png

4. select() 筛选特征值（列）

flights 对象有 19 个特征值，可以直接选择所需要的特征值进行后续分析：

select.png

There are a number of helper functions you can use within select():

• starts_with("abc"): matches names that begin with “abc”.
• ends_with("xyz"): matches names that end with “xyz”.
• contains("ijk"): matches names that contain “ijk”.
• matches("(.)\\1"): selects variables that match a regular expression. This one matches any variables that contain repeated characters.
• num_range("x", 1:3): matches x1, x2 and x3.

rename() 函数可以用来重命名变量：

rename.png

5. mutate() 添加新变量

一般把新变量添加在数据末尾：

mutate.png

6. summarise() 分组统计

能将一整个数据框统计成一行。同时会用到 group_by() 来进行分组：

image.png