《R for Data Science》第九、十章 Tibbles 啃书知识点积累
参考链接：R for Data Science
参考书籍
- 《R for data science》
- 《R数据科学》
Data wrangling
Creating tibbles
- 强转data.frame
as_tibble(iris) # as.tibble()已弃用
#> # A tibble: 150 x 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> # … with 144 more rows
- 常规创建tibble
tibble(
x = 1:5,
y = 1,
z = x ^ 2 + y
)
#> # A tibble: 5 x 3
#> x y z
#> <int> <dbl> <dbl>
#> 1 1 1 2
#> 2 2 1 5
#> 3 3 1 10
#> 4 4 1 17
#> 5 5 1 26
It’s possible for a tibble to have column names that are not valid R variable names, aka non-syntactic names.
tb <- tibble(
`:)` = "smile",
` ` = "space",
`2000` = "number"
); tb
#> # A tibble: 1 x 3
#> `:)` ` ` `2000`
#> <chr> <chr> <chr>
#> 1 smile space number
- tribble()
允许少量数据横向构建tibble，易于观察
（常规用#注释标注表头）
tb1 <- tribble(
~x, ~y, ~z,
#--|--|----
"a", 2, 3.6,
"b", 1, 8.5
)
#> # A tibble: 2 x 3
#> x y z
#> <chr> <dbl> <dbl>
#> 1 a 2 3.6
#> 2 b 1 8.5
class(tb1)
# [1] "tbl_df" "tbl" "data.frame"
Tibbles vs. data.frame
- print()
参数n控制显示的行数
tibble(
a = lubridate::now() + runif(1e3) * 86400,
b = lubridate::today() + runif(1e3) * 30,
c = 1:1e3,
d = runif(1e3),
e = sample(letters, 1e3, replace = TRUE)
)
#> # A tibble: 1,000 x 5
#> a b c d e
#> <dttm> <date> <int> <dbl> <chr>
#> 1 2020-01-15 20:43:23 2020-01-22 1 0.368 n
#> 2 2020-01-16 14:48:32 2020-01-27 2 0.612 l
#> 3 2020-01-16 09:12:12 2020-02-06 3 0.415 p
#> 4 2020-01-15 22:33:29 2020-02-05 4 0.212 m
#> 5 2020-01-15 18:57:45 2020-02-02 5 0.733 i
#> 6 2020-01-16 05:58:42 2020-01-29 6 0.460 n
#> # … with 994 more rows
tb1 %>%
print()
tb1 %>%
print(n = 3)
参数width设置Inf则显示所有列，默认则为适应宽度
参数n_extra设置不直接显示而在页脚出现的列名个数，默认全显示
width: Width of text output to generate. This defaults to NULL, which means use getOption("tibble.width") or (if also NULL) getOption("width"); the latter displays only the columns that fit on one screen. You can also set options(tibble.width = Inf) to override this default and always print all columns.
n_extra: Number of extra columns to print abbreviated information for, if the width is too small for the entire tibble. If NULL, the default, will print information about at most tibble.max_extra_cols extra columns.
nycflights13::flights %>%
print(n = 3)
nycflights13::flights %>%
print(n = 3, width = Inf)
nycflights13::flights %>%
print(n = 3, n_extra = 5)
You can also control the default print behaviour by setting options:
options(tibble.print_max = n, tibble.print_min = m): if more than n rows, print only m rows. Use options(tibble.print_min = Inf) to always show all rows.- Use
options(tibble.width = Inf)to always print all columns, regardless of the width of the screen.
- lubridate包
补充
lubridate包用法，部分参考：Learn R | 日期时间处理之lubridate包
(1) 解析时点
library(lubridate)
ymd(20200406)
# [1] "2020-04-06"
# tz可以指定时区
ymd_hms("20200406 21:30:30", tz = "Pacific/Auckland")
# [1] "2020-04-06 21:30:30 NZST"
# 智能识别杂乱格式
test_date<- c("2020-04-06", "2020 04 06", "2020-4-6",
"2020-4, 6", "Created on 2020 4 06", "202004 !!! 06")
ymd(test_date)
# [1] "2020-04-06" "2020-04-06" "2020-04-06" "2020-04-06" "2020-04-06" "2020-04-06"
# parse_date_time可分布解析时间
test_date <- c('20200406','120315','12/17/1996','09-01-01',
'2015 12 23','2009-1, 5','Created on 2013 4 6')
parse_date_time(test_date,order = c('ymd','mdy','dmy','ymd'))
# [1] "2020-04-06 UTC" "2015-12-03 UTC" "1996-12-17 UTC" "2009-01-01 UTC"
# [5] "2015-12-23 UTC" "2009-01-05 UTC" "2013-04-06 UTC"
(2) 查改时点
second()，minute()，hour()，day()，wday()，yday()，week()，month()，year()，tz()分别可以提取秒，分，小时，天，周的第几天，年的第几天，星期，月，年和时区的信息
test_date <- ymd_hms('2020/04/06/21/10/42')
second(test_date)
# [1] 42
second(test_date) <- 32
second(test_date)
# [1] 32
wday(test_date)
# [1] 2
wday(test_date, label = TRUE)
# [1] 周一
# Levels: 周日 < 周一 < 周二 < 周三 < 周四 < 周五 < 周六
(3) 日期计算
# periods
> minutes(1)
[1] "1M 0S"
# durations[加前缀'd']
> dminutes(1)
[1] "60s"
leap_year(2020)
# [1] TRUE
ymd(20200101)+years(1)
# [1] "2021-01-01"
ymd(20200101)+dyears(1)
# [1] "2020-12-31"
test_date + weeks(0:5)
# [1] "2020-04-06 21:10:32 UTC" "2020-04-20 21:10:32 UTC" "2020-05-04 21:10:32 UTC"
# [4] "2020-05-18 21:10:32 UTC" "2020-06-01 21:10:32 UTC" "2020-06-15 21:10:32 UTC"
# 日期加减需要用 %m+%
test_date <- as.Date('2020-04-06')
test_date %m+% months(20:30)
# [1] "2021-12-06" "2022-01-06" "2022-02-06" "2022-03-06" "2022-04-06" "2022-05-06"
# [7] "2022-06-06" "2022-07-06" "2022-08-06" "2022-09-06" "2022-10-06"
- Subsetting
df <- tibble(
x = runif(5),
y = rnorm(5)
)
# Extract by name
df$x
#> [1] 0.7330 0.2344 0.6604 0.0329 0.4605
df[["x"]]
#> [1] 0.7330 0.2344 0.6604 0.0329 0.4605
# Extract by position
df[[1]]
#> [1] 0.7330 0.2344 0.6604 0.0329 0.4605
结合管道标识符
df %>%
.$x
#> [1] 0.7330 0.2344 0.6604 0.0329 0.4605
df %>%
.[["x"]]
#> [1] 0.7330 0.2344 0.6604 0.0329 0.4605
- Q: Compare and contrast the following operations on a data.frame and equivalent tibble. What is different?
df <- data.frame(abc = 1, xyz = "a")
df$x
# [1] a
# Levels: a
# df中没有`x`列，只有`xyz`列，但开头模糊匹配也能取出数据
df <- tibble(abc = 1, xyz = "a")
df$x
# NULL
# Warning message:
# Unknown or uninitialised column: 'x'.
# 如果用tibble则无法模糊匹配
- Q: If you have the name of a variable stored in an object, e.g. var <- "mpg", how can you extract the reference variable from a tibble?
df <- tribble(~x, ~y, ~mpg,
#--/--/----
1, 2, 'a',
3, 4, 'b'
)
var <- 'mpg'
df$var
# NULL
# Warning message:
# Unknown or uninitialised column: 'var'.
# $方法无法取出
df[[var]]
# [1] "a" "b"
- Q: Practice referring to non-syntactic names
annoying <- tibble(
`1` = 1:10,
`2` = `1` * 2 + rnorm(length(`1`))
)
#> Extracting the variable called 1.
annoying$`1`
# 等价于
annoying[[1]]
# [1] 1 2 3 4 5 6 7 8 9 10
#> Plotting a scatterplot of 1 vs 2.
annoying %>%
ggplot(aes(x = `1`, y = `2`)) +
geom_point()
#> Creating a new column called 3 which is 2 divided by 1.
annoying %>%
mutate(`3` = `2` / `1`)
#> Renaming the columns to one, two and three.
annoying %>%
mutate(`3` = `2` / `1`) %>%
rename(one = `1`,
two = `2`,
three = `3`)
- Q: What does tibble::enframe() and deframe() do
'''
enframe() converts named atomic vectors or lists to one- or two-column data frames. For a list, the result will be a nested tibble with a column of type list. For unnamed vectors, the natural sequence is used as name column.
deframe() converts two-column data frames to a named vector or list, using the first column as name and the second column as value. If the input has only one column, an unnamed vector is returned.
'''
enframe(1:3)
enframe(c(a = 5, b = 7))
enframe(list(one = 1, two = 2:3, three = 4:6))
deframe(enframe(1:3))
deframe(tibble(a = 1:3))
deframe(tibble(a = as.list(1:3)))
deframe(enframe(list(one = 1, two = 2:3, three = 4:6))
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