数据转换 data transfer,是将数据从一种表示形式变为另一种表现形式的过程。我们喂给程序的数据在任何一个阶段都要符合这个程序的数据要求，可视化也好，聚类也好，每一种函数，每一个公式都有其特定的输入。所以在我们进行数据分析时，很大一部分工作是数据形式的不断转化。有时候数据的存储会以一种比较特殊的形式，比如需要节约空间等原因，在数据分析的第一步就要进行数据格式的转化，不转化无法读入。
在R语言中是为了获得数据摘要而进行的转化，一般包括长宽转化、取特定子集、缺失值处理、不同数据框的整合。
> library(nycflights13)
Warning message:
程辑包‘nycflights13’是用R版本3.5.3 来建造的
> library(tidyverse)
-- Attaching packages --------------------------------------- tidyverse 1.2.1 --
√ ggplot2 3.1.1 √ purrr 0.3.2
√ tibble 2.1.1 √ dplyr 0.8.0.1
√ tidyr 0.8.3 √ stringr 1.4.0
√ readr 1.3.1 √ forcats 0.4.0
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::collapse() masks IRanges::collapse()
x dplyr::combine() masks Biobase::combine(), BiocGenerics::combine()
x dplyr::count() masks matrixStats::count()
x dplyr::desc() masks IRanges::desc()
x tidyr::expand() masks S4Vectors::expand()
x dplyr::filter() masks stats::filter()
x dplyr::first() masks S4Vectors::first()
x dplyr::lag() masks stats::lag()
x ggplot2::Position() masks BiocGenerics::Position(), base::Position()
x purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce()
x dplyr::rename() masks S4Vectors::rename()
x purrr::simplify() masks DelayedArray::simplify()
x dplyr::slice() masks IRanges::slice()
Warning messages:
1: 程辑包‘tidyverse’是用R版本3.5.3 来建造的
2: 程辑包‘ggplot2’是用R版本3.5.3 来建造的
3: 程辑包‘tibble’是用R版本3.5.3 来建造的
4: 程辑包‘tidyr’是用R版本3.5.3 来建造的
5: 程辑包‘readr’是用R版本3.5.2 来建造的
6: 程辑包‘purrr’是用R版本3.5.3 来建造的
7: 程辑包‘dplyr’是用R版本3.5.2 来建造的
8: 程辑包‘stringr’是用R版本3.5.2 来建造的
masks 的意思是说，tidyverse的函数会对已有函数的覆盖。如果要使用被覆盖的函数，需要输入他们的完整名称，以::连接包名和函数名。
示例数据
> flights
# A tibble: 336,776 x 19
year month day dep_time sched_dep_time dep_delay arr_time
1 2013 1 1 517 515 2 830
2 2013 1 1 533 529 4 850
3 2013 1 1 542 540 2 923
4 2013 1 1 544 545 -1 1004
5 2013 1 1 554 600 -6 812
6 2013 1 1 554 558 -4 740
7 2013 1 1 555 600 -5 913
8 2013 1 1 557 600 -3 709
9 2013 1 1 557 600 -3 838
10 2013 1 1 558 600 -2 753
# ... with 336,766 more rows, and 12 more variables:
# sched_arr_time , arr_delay , carrier , flight ,
# tailnum , origin , dest , air_time ,
# distance , hour , minute , time_hour
tibble是R语言中一个用来替换data.frame类型的扩展的数据框，tibble继承了data.frame，是弱类型的，同时与data.frame有相同的语法，使用起来更方便。tibble包，也是由Hadley开发的R包。
tibble对data.frame做了重新的设定：
tibble，不关心输入类型，可存储任意类型，包括list类型
tibble，没有行名设置 row.names
tibble，支持任意的列名
tibble，会自动添加列名
tibble，类型只能回收长度为1的输入
tibble，会懒加载参数，并按顺序运行
tibble，是tbl_df类型
tibble的数据类型：
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基础
- 按值筛选观测，filter()
- 对行进行重新排序，arrange()
- 按名称选取变量，select()
- 使用现有变量的函数创建新变量，mutate()
- 将多个值总结为一个统计摘要，summarise()
这些函数均可以和group_by()函数联合起来使用，group_by()函数可以改变以上每个函数的作用范围，让其从整个数据集上的操作，变为在每个分组上的分别操作。
使用filter() 筛选行
> (dec25 <- filter(flights, month == 12, day == 25))
# A tibble: 719 x 19
year month day dep_time sched_dep_time dep_delay arr_time
1 2013 12 25 456 500 -4 649
2 2013 12 25 524 515 9 805
3 2013 12 25 542 540 2 832
4 2013 12 25 546 550 -4 1022
5 2013 12 25 556 600 -4 730
6 2013 12 25 557 600 -3 743
7 2013 12 25 557 600 -3 818
8 2013 12 25 559 600 -1 855
9 2013 12 25 559 600 -1 849
10 2013 12 25 600 600 0 850
# ... with 709 more rows, and 12 more variables: sched_arr_time ,
# arr_delay , carrier , flight , tailnum ,
# origin , dest , air_time , distance ,
# hour , minute , time_hour
比较运算符
>、<、>=、<=、==、!=
有限精度运算
filter(flights, month = 1)
Error: `month` (`month = 1`) must not be named, do you need `==`?
Call `rlang::last_error()` to see a backtrace
> sqrt(2) ^ 2 == 2
[1] FALSE
> 1 / 49 * 49 == 1
[1] FALSE
> near(sqrt(2) ^ 2, 2)
[1] TRUE
> near(1 / 49 * 49, 1)
[1] TRUE
逻辑运算符
&、|、!、&&、||、xor
运算符“逻辑与”和“逻辑或”存在两种形式，“&”和“|”作用在对象中的每一个元素上并且返回和比较次数相等长度的逻辑值；“&&”和“||”只作用在对象的第一个元素上。
xor为异或，两值不等为真，两值相等为假。例：xor(0, 1)
> filter(flights, month == 11 | month == 12)
#filter(flights, month %in% c(11, 12))
# A tibble: 55,403 x 19
year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier flight tailnum origin
1 2013 11 1 5 2359 6 352 345 7 B6 745 N568JB JFK
2 2013 11 1 35 2250 105 123 2356 87 B6 1816 N353JB JFK
3 2013 11 1 455 500 -5 641 651 -10 US 1895 N192UW EWR
4 2013 11 1 539 545 -6 856 827 29 UA 1714 N38727 LGA
5 2013 11 1 542 545 -3 831 855 -24 AA 2243 N5CLAA JFK
6 2013 11 1 549 600 -11 912 923 -11 UA 303 N595UA JFK
7 2013 11 1 550 600 -10 705 659 6 US 2167 N748UW LGA
8 2013 11 1 554 600 -6 659 701 -2 US 2134 N742PS LGA
9 2013 11 1 554 600 -6 826 827 -1 DL 563 N912DE LGA
10 2013 11 1 554 600 -6 749 751 -2 DL 731 N315NB LGA
# ... with 55,393 more rows, and 6 more variables: dest , air_time , distance , hour ,
# minute , time_hour
filter(flights, arr_delay <= 120, dep_delay <= 120)
#filter(flights, !(arr_delay > 120 | dep_delay > 120))
# A tibble: 316,050 x 19
year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier flight tailnum origin
1 2013 1 1 517 515 2 830 819 11 UA 1545 N14228 EWR
2 2013 1 1 533 529 4 850 830 20 UA 1714 N24211 LGA
3 2013 1 1 542 540 2 923 850 33 AA 1141 N619AA JFK
4 2013 1 1 544 545 -1 1004 1022 -18 B6 725 N804JB JFK
5 2013 1 1 554 600 -6 812 837 -25 DL 461 N668DN LGA
6 2013 1 1 554 558 -4 740 728 12 UA 1696 N39463 EWR
7 2013 1 1 555 600 -5 913 854 19 B6 507 N516JB EWR
8 2013 1 1 557 600 -3 709 723 -14 EV 5708 N829AS LGA
9 2013 1 1 557 600 -3 838 846 -8 B6 79 N593JB JFK
10 2013 1 1 558 600 -2 753 745 8 AA 301 N3ALAA LGA
# ... with 316,040 more rows, and 6 more variables: dest , air_time , distance , hour ,
# minute , time_hour
使用arrange()排列行
> arrange(flights, year, month, day)
# A tibble: 336,776 x 19
year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier flight tailnum origin
1 2013 1 1 517 515 2 830 819 11 UA 1545 N14228 EWR
2 2013 1 1 533 529 4 850 830 20 UA 1714 N24211 LGA
3 2013 1 1 542 540 2 923 850 33 AA 1141 N619AA JFK
4 2013 1 1 544 545 -1 1004 1022 -18 B6 725 N804JB JFK
5 2013 1 1 554 600 -6 812 837 -25 DL 461 N668DN LGA
6 2013 1 1 554 558 -4 740 728 12 UA 1696 N39463 EWR
7 2013 1 1 555 600 -5 913 854 19 B6 507 N516JB EWR
8 2013 1 1 557 600 -3 709 723 -14 EV 5708 N829AS LGA
9 2013 1 1 557 600 -3 838 846 -8 B6 79 N593JB JFK
10 2013 1 1 558 600 -2 753 745 8 AA 301 N3ALAA LGA
# ... with 336,766 more rows, and 6 more variables: dest , air_time , distance , hour ,
# minute , time_hour
>
arrange(flights, desc(dep_delay))
# A tibble: 336,776 x 19
year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier flight tailnum origin
1 2013 1 9 641 900 1301 1242 1530 1272 HA 51 N384HA JFK
2 2013 6 15 1432 1935 1137 1607 2120 1127 MQ 3535 N504MQ JFK
3 2013 1 10 1121 1635 1126 1239 1810 1109 MQ 3695 N517MQ EWR
4 2013 9 20 1139 1845 1014 1457 2210 1007 AA 177 N338AA JFK
5 2013 7 22 845 1600 1005 1044 1815 989 MQ 3075 N665MQ JFK
6 2013 4 10 1100 1900 960 1342 2211 931 DL 2391 N959DL JFK
7 2013 3 17 2321 810 911 135 1020 915 DL 2119 N927DA LGA
8 2013 6 27 959 1900 899 1236 2226 850 DL 2007 N3762Y JFK
9 2013 7 22 2257 759 898 121 1026 895 DL 2047 N6716C LGA
10 2013 12 5 756 1700 896 1058 2020 878 AA 172 N5DMAA EWR
# ... with 336,766 more rows, and 6 more variables: dest , air_time , distance , hour ,
# minute , time_hour
>
df <- tibble(x = c(5, 2, NA))
arrange(df, x)
#> # A tibble: 3 x 1
#> x
#>
#> 1 2
#> 2 5
#> 3 NA
arrange(df, desc(x))
#> # A tibble: 3 x 1
#> x
#>
#> 1 5
#> 2 2
#> 3 NA
使用select()选择列
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. You’ll learn more about regular expressions in strings.num_range("x", 1:3): matchesx1,x2andx3.
# Select columns by name
select(flights, year, month, day)
# Select all columns between year and day (inclusive)
select(flights, year:day)
# Select all columns except those from year to day (inclusive)
select(flights, -(year:day))
rename(flights, tail_num = tailnum)
select(flights, time_hour, air_time, everything())
使用mutate()添加新变量
flights_sml <- select(flights,
year:day,
ends_with("delay"),
distance,
air_time
)
mutate(flights_sml,
gain = dep_delay - arr_delay,
speed = distance / air_time * 60
)
# A tibble: 336,776 x 9
year month day dep_delay arr_delay distance air_time gain speed
1 2013 1 1 2 11 1400 227 -9 370.
2 2013 1 1 4 20 1416 227 -16 374.
3 2013 1 1 2 33 1089 160 -31 408.
4 2013 1 1 -1 -18 1576 183 17 517.
5 2013 1 1 -6 -25 762 116 19 394.
6 2013 1 1 -4 12 719 150 -16 288.
7 2013 1 1 -5 19 1065 158 -24 404.
8 2013 1 1 -3 -14 229 53 11 259.
9 2013 1 1 -3 -8 944 140 5 405.
10 2013 1 1 -2 8 733 138 -10 319.
# ... with 336,766 more rows
Note that you can refer to columns that you’ve just created:
mutate(flights_sml,
gain = dep_delay - arr_delay,
hours = air_time / 60,
gain_per_hour = gain / hours
)
transmute(flights,
gain = dep_delay - arr_delay,
hours = air_time / 60,
gain_per_hour = gain / hours
)
A tibble: 336,776 x 3
gain hours gain_per_hour
1 -9 3.78 -2.38
2 -16 3.78 -4.23
3 -31 2.67 -11.6
4 17 3.05 5.57
5 19 1.93 9.83
6 -16 2.5 -6.4
7 -24 2.63 -9.11
8 11 0.883 12.5
9 5 2.33 2.14
10 -10 2.3 -4.35
# ... with 336,766 more rows
使用summarize()进行分组摘要
by_day <- group_by(flights, year, month, day)
summarise(by_day, delay = mean(dep_delay, na.rm = TRUE))
# A tibble: 365 x 4
# Groups: year, month [12]
year month day delay
1 2013 1 1 11.5
2 2013 1 2 13.9
3 2013 1 3 11.0
4 2013 1 4 8.95
5 2013 1 5 5.73
6 2013 1 6 7.15
7 2013 1 7 5.42
8 2013 1 8 2.55
9 2013 1 9 2.28
10 2013 1 10 2.84
# ... with 355 more rows
使用管道组合多种操作
解决中间变量不断命名的问题。
by_dest <- group_by(flights, dest)
delay <- summarise(by_dest,
count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE)
)
delay <- filter(delay, count > 20, dest != "HNL")
与下面的相同
delays <- flights %>%
group_by(dest) %>%
summarise(
count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE)
) %>%
filter(count > 20, dest != "HNL")
计数
not_cancelled <- flights %>%
filter(!is.na(dep_delay), !is.na(arr_delay))
not_cancelled %>%
group_by(year, month, day) %>%
summarise(mean = mean(dep_delay))
# A tibble: 365 x 4
# Groups: year, month [12]
year month day delay
1 2013 1 1 11.5
2 2013 1 2 13.9
3 2013 1 3 11.0
4 2013 1 4 8.95
5 2013 1 5 5.73
6 2013 1 6 7.15
7 2013 1 7 5.42
8 2013 1 8 2.55
9 2013 1 9 2.28
10 2013 1 10 2.84
# ... with 355 more rows
delays <- not_cancelled %>%
group_by(tailnum) %>%
summarise(
delay = mean(arr_delay)
)
ggplot(data = delays, mapping = aes(x = delay)) +
geom_freqpoly(binwidth = 10)
install.packages("Lahman")
library(Lahman)
# Convert to a tibble so it prints nicely
batting <- as_tibble(Lahman::Batting)
batters <- batting %>%
group_by(playerID) %>%
summarise(
ba = sum(H, na.rm = TRUE) / sum(AB, na.rm = TRUE),
ab = sum(AB, na.rm = TRUE)
)
batters %>%
filter(ab > 100) %>%
ggplot(mapping = aes(x = ab, y = ba)) +
geom_point() +
geom_smooth(se = FALSE)
常用摘要函数
- 位置度量
mean(x), median(x)
- 分散度
sd(x), IQR(x), mad(x)
- 秩
min(x), quantile(x, 0.25), max(x)
- 定位
first(x), nth(x, 2), last(x)
- 计数
n(),sum(!is.na(x)),n_distinct(x).
- 逻辑值
sum(x > 10), mean(y == 0)
# How many flights left before 5am? (these usually indicate delayed
# flights from the previous day)
not_cancelled %>%
group_by(year, month, day) %>%
summarise(n_early = sum(dep_time < 500))
#> # A tibble: 365 x 4
#> # Groups: year, month [?]
#> year month day n_early
#>
#> 1 2013 1 1 0
#> 2 2013 1 2 3
#> 3 2013 1 3 4
#> 4 2013 1 4 3
#> 5 2013 1 5 3
#> 6 2013 1 6 2
#> # … with 359 more rows
r4ds
详解《R数据科学》--第三章dplyr
https://swcarpentry.github.io/r-novice-gapminder/13-dplyr/
R语言数据科学新类型tibble
https://github.com/gadenbuie/tidyexplain#left-join