这是英文版的4、5章节
4 Workflow: basics
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括号好东西，能帮你在赋值的时候同时打印变量，这点在Rmarkdown的时候也很有帮助
y <- seq(1, 10, length.out = 5) y # > [1] 1.00 3.25 5.50 7.75 10.00 (y <- seq(1, 10, length.out = 5)) #> [1] 1.00 3.25 5.50 7.75 10.00 -
快捷键 Alt + Shift + K. 会帮你把所有R studio的快捷键呈现出来
有一点要注意的是，R studio的一些快捷键在输入法状态下是不能用的
5.1 Introduction
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这章讲了5个函数
- Pick observations by their values (
filter()).
- Pick observations by their values (
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Reorder the rows (
arrange()).- Pick variables by their names (
select()).
- Pick variables by their names (
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Create new variables with functions of existing variables (
mutate()).- Collapse many values down to a single summary (
summarise()).
- Collapse many values down to a single summary (
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All verbs work similarly:
The first argument is a data frame.
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The subsequent arguments describe what to do with the data frame, using the variable names (without quotes).
# tab可以自动打出变量名（比如这里的year）的，这点很方便，但似乎只有在这种情况下才可以 flights %>% select(., year) # 这种似乎就不行 select(flights, year) # 还有就是记住变量名是没有引号的 filter(flights, month == 1, day == 1) The result is a new data frame.
5个函数可以和group_by 函数联用，which changes the scope of each function from operating on the entire dataset to operating on it group-by-group
但有时候得记得ungroup
5.2 Filter rows with filter()
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浮点数的问题
sqrt(2) ^ 2 == 2 #> [1] FALSE 1 / 49 * 49 == 1 #> [1] FALSE
这是因为计算机是用有限精度来处理这些问题的。碰到这些问题的时候，考虑使用near函数
near(sqrt(2) ^ 2, 2)
#> [1] TRUE
near(1 / 49 * 49, 1)
#> [1] TRUE
comparison operators（比较运算符） 以及 Logical operators（逻辑运算符） 再次记住这两个英文，方便你有问题查询google
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这个图片挺好的
image -
在执行 | 的时候有一个有意思的事情
# 我们会看到下面的输出结果是month==1的时候，而不是我们预期的是11或者12 > head(filter(flights, month == (11 | 12)), n = 3) # A tibble: 3 x 19 year month day dep_time sched_dep_time dep_delay arr_time <int> <int> <int> <int> <int> <dbl> <int> 1 2013 1 1 517 515 2 830 2 2013 1 1 533 529 4 850 3 2013 1 1 542 540 2 923 # ... with 12 more variables: sched_arr_time <int>, # arr_delay <dbl>, carrier <chr>, flight <int>, # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, # distance <dbl>, hour <dbl>, minute <dbl>, # time_hour <dttm> # 是因为首先 > 11 | 12 [1] TRUE # 然后TRUE在数字环境的时候，就会变成1 # 那么结果就变成了 month == 1 > TRUE == 1 [1] TRUE > head(filter(flights, month == 1), n = 3) # A tibble: 3 x 19 year month day dep_time sched_dep_time dep_delay arr_time <int> <int> <int> <int> <int> <dbl> <int> 1 2013 1 1 517 515 2 830 2 2013 1 1 533 529 4 850 3 2013 1 1 542 540 2 923 # ... with 12 more variables: sched_arr_time <int>, # arr_delay <dbl>, carrier <chr>, flight <int>, # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, # distance <dbl>, hour <dbl>, minute <dbl>, # time_hour <dttm> -
x %in% y 是挺好的函数。This will select every row where x is one of the values in y .
nov_dec <- filter(flights, month %in% c(11, 12))这会让你的函数会更加的压缩，比如这里有4,11,12的话，相比于 == 而言会更好写一点
filter(flights, month %in% c(4, 11, 12)) filter(flights, month == 4 | month == 11 | month == 12)
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remembering De Morgan’s law:
!(x & y)is the same as!x | !y, and!(x | y)is the same as!x & !y.# 这里原书有错误：weren’t delayed (on arrival or departure) by more than two hours # 原书是或，但这里表述的是且 filter(flights, !(arr_delay > 120 | dep_delay > 120)) filter(flights, arr_delay <= 120, dep_delay <= 120) && 和 || 我们在会后面遇到，记住这里不要在这里用！我们应该用的是 | 和 &
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Missing Value，即 NA (“not availables”) 。我能想到的NA场景就是在RNA-Seq这类的p-value里面的结果遇到。
# NA是具有传染性的 # 因为NA代表着有这个数据，但你不知道是什么，所以对于NA的一切操作结果，你都只能是不知道，即NA # 当然，也有例子，见下面 NA > 5 #> [1] NA 10 == NA #> [1] NA NA + 10 #> [1] NA NA / 2 #> [1] NA ------------------------------------------------ NA == NA #> [1] NA # It’s easiest to understand why this is true with a bit more context: # Let x be Mary's age. We don't know how old she is. x <- NA # Let y be John's age. We don't know how old he is. y <- NA # Are John and Mary the same age? x == y #> [1] NA # We don't know! -------------------------------------------------- is.na(x) #> [1] TRUE # filter只会包含condition是TRUE的，并不会保留 NA 的 df <- tibble(x = c(1, NA, 3)) filter(df, x > 1) #> # A tibble: 1 x 1 #> x #> <dbl> #> 1 3 # 想要NA结果的话得可以自己搞一个 filter(df, is.na(x) | x > 1) #> # A tibble: 2 x 1 #> x #> <dbl> #> 1 NA #> 2 3
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Exercise 5.2.2
Another useful dplyr filtering helper is
between(). What does it do? Can you use it to simplify the code needed to answer the previous challenges?关于between的
Description
This is a shortcut for x >= left & x <= right, implemented efficiently in C++ for local values, and translated to the appropriate SQL for remote tables.Usage
between(x, left, right)# 之前我们写 filter(flights, month %in% 7:9) # 现在我们还可以用between filter(flights, between(month, 7,9)) # 其实我感觉filter就是把下面步骤整合了 flights[between(flights$month, 7, 9),] # 对于between，我们还可以验证标准正态分布的-1到1占据了68.3%的面积 > x <- rnorm(1e7) > length(x[between(x, -1, 1)]) / 1e7 [1] 0.6827031
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Exercise 5.2.3
How many flights have a missing
dep_time? What other variables are missing? What might these rows represent?filter(flights, is.na(dep_time)) #> # A tibble: 8,255 x 19 #> year month day dep_time sched_dep_time dep_delay arr_time #> <int> <int> <int> <int> <int> <dbl> <int> #> 1 2013 1 1 NA 1630 NA NA #> 2 2013 1 1 NA 1935 NA NA #> 3 2013 1 1 NA 1500 NA NA #> 4 2013 1 1 NA 600 NA NA #> 5 2013 1 2 NA 1540 NA NA #> 6 2013 1 2 NA 1620 NA NA #> # … with 8,249 more rows, and 12 more variables: sched_arr_time <int>, #> # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>, #> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, #> # minute <dbl>, time_hour <dttm>
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Exercise 5.2.4
Why is
NA ^ 0not missing? Why isNA | TRUEnot missing? Why isFALSE & NAnot missing? Can you figure out the general rule? (NA * 0is a tricky counter example!)
# 任何数的0次方都是1
# 这个特性对于scale的时候很有用，因为有时候你对于所有数都是均等进行scale的话，会由于方差是0（因为方差是分母）而无法返回值，这时候你就可以 x - mean(x) / (sd(x) ^ 0) 来解决这个问题了
> NA ^ 0
[1] 1
# 因为
# anything and FALSE is always FALSE.
# anything or TRUE is always TRUE
> NA | TRUE
[1] TRUE
> FALSE & NA
[1] FALSE
# 而这里就不是了，我们不确定NA是什么，而结果又是会根据NA值的不同而变化的，所以返回值就是NA了
> NA | FALSE
[1] NA
> NA & TRUE
[1] NA
# 关于这个问题，我更喜欢Quaro的回答
# https://www.quora.com/In-R-why-is-NA*0-not-equal-to-0
# 即NA可以代表任何值，可以代表0也可以代表NaN。
# 而 x * 0 == 0 这一特性只在值是有限的时候，
# 而在无限的时候结果则是NaN，即无意义数
# 所以这里等于用NA代表了两种结果
> NA * 0
[1] NA
> Inf * 0
[1] NaN
NaN 和 Null 要区分， NaN代表无意义，即 not a number。而 Null 代表的是一种特殊的对象，表示函数没有被赋予任何内容。
5.3 Arrange rows with arrange()
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arrange排序的时候，缺失值在最后
df <- tibble(x = c(5, 2, NA)) arrange(df, x) #> # A tibble: 3 x 1 #> x #> <dbl> #> 1 2 #> 2 5 #> 3 NA arrange(df, desc(x)) #> # A tibble: 3 x 1 #> x #> <dbl> #> 1 5 #> 2 2 #> 3 NA
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Exercise 5.3.1
How could you use
arrange()to sort all missing values to the start? (Hint: useis.na()).> (df <- tibble(x = c(5, 2, 8, NA, NA, NA), + y = c(4, 3, 1, 4, 3, 8))) # A tibble: 6 x 2 x y <dbl> <dbl> 1 5 4 2 2 3 3 8 1 4 NA 4 5 NA 3 6 NA 8 # 默认排序总是会把NA排在最后 # 然后我们还应该注意的一点是NA对应的4、3、8其实是不会排序的 #（虽然理论上来说如果x列相同，y列会再一次排序） # 我们看起来NA和NA是“一样的”，但前面的知识告诉我们，NA == NA的结果是NA，即不知道，所以这里并没有二次排序 > arrange(df, x) # A tibble: 6 x 2 x y <dbl> <dbl> 1 2 3 2 5 4 3 8 1 4 NA 4 5 NA 3 6 NA 8 # 我看了solution之后，明白了 # is.na返回的是TRUE或者FALSE，而TRUE在与FALSE比较的时候，TRUE > FALSE # 然后再按desc（降序排列，从大到小排序）的话，就是 NA 的在前面了 # 在is.na排完之后，我们就不再排序了，还是按照原来的顺序 5 -> 2 -> 8 > arrange(df, desc(is.na(x))) # A tibble: 6 x 2 x y <dbl> <dbl> 1 NA 4 2 NA 3 3 NA 8 4 5 4 5 2 3 6 8 1 # 如果你想在TRUE和FALSE排完之后，再做一波排序的话，可以在加上x # 那就是先排is.na(x)的结果，is.na(x)排完，再排一次x > arrange(df, desc(is.na(x)),x) # A tibble: 6 x 2 x y <dbl> <dbl> 1 NA 4 2 NA 3 3 NA 8 4 2 3 5 5 4 6 8 1
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Exercise 5.3.3
Sort flights to find the fastest flights.
# 还可以加上数学表达式的结果排序 flights %>% arrange(., desc(distance / air_time))
5.4 Select columns with select()
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select挑选
# 你既可以挑选某几列 # 也可以去掉某几列 select(flights, -(year:day)) #> # A tibble: 336,776 x 16 #> dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier #> <int> <int> <dbl> <int> <int> <dbl> <chr> #> 1 517 515 2 830 819 11 UA #> 2 533 529 4 850 830 20 UA #> 3 542 540 2 923 850 33 AA #> 4 544 545 -1 1004 1022 -18 B6 #> 5 554 600 -6 812 837 -25 DL #> 6 554 558 -4 740 728 12 UA #> # … with 3.368e+05 more rows, and 9 more variables: flight <int>, #> # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, #> # hour <dbl>, minute <dbl>, time_hour <dttm> -
There are a number of helper functions you can use within
select():-
starts_with("abc"): matches names that begin with “abc”.> flights %>% + select(., - starts_with("ye")) # A tibble: 336,776 x 18 month day dep_time sched_dep_time dep_delay arr_time <int> <int> <int> <int> <dbl> <int> 1 1 1 517 515 2 830 2 1 1 533 529 4 850 3 1 1 542 540 2 923 4 1 1 544 545 -1 1004 5 1 1 554 600 -6 812 6 1 1 554 558 -4 740 7 1 1 555 600 -5 913 8 1 1 557 600 -3 709 9 1 1 557 600 -3 838 10 1 1 558 600 -2 753 # ... with 336,766 more rows, and 12 more variables: # sched_arr_time <int>, arr_delay <dbl>, carrier <chr>, # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, # time_hour <dttm> 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.
See
?selectfor more details. -
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rename 和 select的区别
# 可以看到rename好用多了 > rename(as_tibble(iris), petal_length = Petal.Length) # 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 7 4.6 3.4 1.4 0.3 setosa 8 5 3.4 1.5 0.2 setosa 9 4.4 2.9 1.4 0.2 setosa 10 4.9 3.1 1.5 0.1 setosa # ... with 140 more rows # select只会保留你选的 > select(as_tibble(iris), petal_length = Petal.Length) # A tibble: 150 x 1 petal_length <dbl> 1 1.4 2 1.4 3 1.3 4 1.5 5 1.4 6 1.7 7 1.4 8 1.5 9 1.4 10 1.5 # ... with 140 more rows -
everything也是很好用的
# 可以把某几列调到前面 select(flights, time_hour, air_time, everything()) #> # A tibble: 336,776 x 19 #> time_hour air_time year month day dep_time sched_dep_time #> <dttm> <dbl> <int> <int> <int> <int> <int> #> 1 2013-01-01 05:00:00 227 2013 1 1 517 515 #> 2 2013-01-01 05:00:00 227 2013 1 1 533 529 #> 3 2013-01-01 05:00:00 160 2013 1 1 542 540 #> 4 2013-01-01 05:00:00 183 2013 1 1 544 545 #> 5 2013-01-01 06:00:00 116 2013 1 1 554 600 #> 6 2013-01-01 05:00:00 150 2013 1 1 554 558 #> # … with 3.368e+05 more rows, and 12 more variables: dep_delay <dbl>, #> # arr_time <int>, sched_arr_time <int>, arr_delay <dbl>, carrier <chr>, #> # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, distance <dbl>, #> # hour <dbl>, minute <dbl>
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Exercise 5.4.1
Brainstorm as many ways as possible to select
dep_time,dep_delay,arr_time, andarr_delayfrom flights.来自solution
# 关于变量名加不加引号这点，我一直没怎么搞清楚 # 不过后面我搞清楚了一点点 select(flights, dep_time, dep_delay, arr_time, arr_delay) select(flights, "dep_time", "dep_delay", "arr_time", "arr_delay") select(flights, 4, 6, 7, 9) variables <- c("dep_time", "dep_delay", "arr_time", "arr_delay") select(flights, one_of(c("dep_time", "dep_delay", "arr_time", "arr_delay"))) select(flights, one_of(variables)) select(flights, starts_with("dep_"), starts_with("arr_"))
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Exercise 5.4.2
What happens if you include the name of a variable multiple times in a
select()call?# 这点对于我们的everything特别有用 # 即select变量名冗余会只保留一个 select(flights, year, month, day, year, year) #> # A tibble: 336,776 x 3 #> year month day #> <int> <int> <int> #> 1 2013 1 1 #> 2 2013 1 1 #> 3 2013 1 1 #> 4 2013 1 1 #> 5 2013 1 1 #> 6 2013 1 1 #> # … with 3.368e+05 more rows # arr_delay这个变量是冗余的 select(flights, arr_delay, everything()) #> # A tibble: 336,776 x 19 #> arr_delay year month day dep_time sched_dep_time dep_delay arr_time #> <dbl> <int> <int> <int> <int> <int> <dbl> <int> #> 1 11 2013 1 1 517 515 2 830 #> 2 20 2013 1 1 533 529 4 850 #> 3 33 2013 1 1 542 540 2 923 #> 4 -18 2013 1 1 544 545 -1 1004 #> 5 -25 2013 1 1 554 600 -6 812 #> 6 12 2013 1 1 554 558 -4 740 #> # … with 3.368e+05 more rows, and 11 more variables: sched_arr_time <int>, #> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>, #> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, #> # time_hour <dttm>
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Exercise 5.4.3
What does the
one_of()function do? Why might it be helpful in conjunction with this vector?这个solution部分的解答非常值得好好看
vars <- c("year", "month", "day", "dep_delay", "arr_delay") select(flights, one_of(vars)) #> # A tibble: 336,776 x 5 #> year month day dep_delay arr_delay #> <int> <int> <int> <dbl> <dbl> #> 1 2013 1 1 2 11 #> 2 2013 1 1 4 20 #> 3 2013 1 1 2 33 #> 4 2013 1 1 -1 -18 #> 5 2013 1 1 -6 -25 #> 6 2013 1 1 -4 12 #> # … with 3.368e+05 more rows select(flights, vars) #> # A tibble: 336,776 x 5 #> year month day dep_delay arr_delay #> <int> <int> <int> <dbl> <dbl> #> 1 2013 1 1 2 11 #> 2 2013 1 1 4 20 #> 3 2013 1 1 2 33 #> 4 2013 1 1 -1 -18 #> 5 2013 1 1 -6 -25 #> 6 2013 1 1 -4 12 #> # … with 3.368e+05 more rows如果 vars 是flight里面的变量，那么就会返回名字是vars的变量列，如果不是flight里面的变量，就会查找 vars的值，对应的变量列
# 举个例子 > year <- "month" > flights %>% + select(., year) %>% + head(., n = 2) # A tibble: 2 x 1 year <int> 1 2013 2 2013 > year_another <- "month" > flights %>% + select(., year_another) %>% + head(., n = 2) # A tibble: 2 x 1 month <int> 1 1 2 1 > year_second <- "month_another" > flights %>% + select(., year_second) %>% + head(., n = 2) Error: Unknown column `month_another` Run `rlang::last_error()` to see where the error occurred.如果要消除这种“混淆”，利用 !!!
> flights %>% + select(., !!!year) %>% + head(., n = 2) # A tibble: 2 x 1 month <int> 1 1 2 1This behavior, which is used by many tidyverse functions, is an example of what is called non-standard evaluation (NSE) in R. See the dplyr vignette, Programming with dplyr, for more information on this topic.
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Exercise 5.4.4
Does the result of running the following code surprise you? How do the select helpers deal with case by default? How can you change that default?
select(flights, contains("TIME")) #> # A tibble: 336,776 x 6 #> dep_time sched_dep_time arr_time sched_arr_time air_time #> <int> <int> <int> <int> <dbl> #> 1 517 515 830 819 227 #> 2 533 529 850 830 227 #> 3 542 540 923 850 160 #> 4 544 545 1004 1022 183 #> 5 554 600 812 837 116 #> 6 554 558 740 728 150 #> # … with 3.368e+05 more rows, and 1 more variable: time_hour <dttm>这是因为大小写不敏感的问题
select(flights, contains("TIME", ignore.case = FALSE)) #> # A tibble: 336,776 x 0
5.5 Add new variables with mutate()
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我喜欢Mutate这个特性
Note that you can refer to columns that you’ve just created:
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 #> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 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. #> # … with 3.368e+05 more rows mutate(flights_sml, gain = dep_delay - arr_delay, hours = air_time / 60, gain_per_hour = gain / hours ) #> # A tibble: 336,776 x 10 #> year month day dep_delay arr_delay distance air_time gain hours #> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 2013 1 1 2 11 1400 227 -9 3.78 #> 2 2013 1 1 4 20 1416 227 -16 3.78 #> 3 2013 1 1 2 33 1089 160 -31 2.67 #> 4 2013 1 1 -1 -18 1576 183 17 3.05 #> 5 2013 1 1 -6 -25 762 116 19 1.93 #> 6 2013 1 1 -4 12 719 150 -16 2.5 #> # … with 3.368e+05 more rows, and 1 more variable: gain_per_hour <dbl>
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如果你只想保留新产生的变量
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 #> <dbl> <dbl> <dbl> #> 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 #> # … with 3.368e+05 more rows
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有许多函数你都可以和mutate联用，从而创造新的变量。这些函数的关键特性就是向量化，即你输入一个向量，输出也是一个向量，输入输出向量里面包含的值是对应的。
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Arithmetic operators:
+,-,*,/,^. These are all vectorised, using the so called “recycling rules”. If one parameter is shorter than the other, it will be automatically extended to be the same length. This is most useful when one of the arguments is a single number:air_time / 60,hours * 60 + minute, etc.Arithmetic operators are also useful in conjunction with the aggregate functions you’ll learn about later. For example,
x / sum(x)calculates the proportion of a total, andy - mean(y)computes the difference from the mean. -
Modular arithmetic（所谓的模运算）:
%/%(integer division) and%%(remainder), wherex == y * (x %/% y) + (x %% y). Modular arithmetic is a handy tool because it allows you to break integers up into pieces. For example, in the flights dataset, you can computehourandminutefromdep_timewith:transmute(flights, dep_time, hour = dep_time %/% 100, minute = dep_time %% 100 ) #> # A tibble: 336,776 x 3 #> dep_time hour minute #> <int> <dbl> <dbl> #> 1 517 5 17 #> 2 533 5 33 #> 3 542 5 42 #> 4 544 5 44 #> 5 554 5 54 #> 6 554 5 54 #> # … with 3.368e+05 more rows -
Logs:
log(),log2(),log10(). Logarithms are an incredibly useful transformation for dealing with data that ranges across multiple orders of magnitude. They also convert multiplicative relationships to additive（累乘关系变成累加关系，最大似然估计那边会用到吧）, a feature we’ll come back to in modelling.All else being equal, I recommend using
log2()because it’s easy to interpret: a difference of 1 on the log scale corresponds to doubling on the original scale and a difference of -1 corresponds to halving.（对数标度下的数值增加 1 个单位，意味着初始数值加倍；减少 1 个单位，则意味着初始数值减半。常见的就是log2FoldChange了） -
Offsets:
lead()andlag()allow you to refer to leading or lagging values.（感觉是整体数据往前或者往后挪一格） This allows you to compute running differences (e.g.x - lag(x)) or find when values change (x != lag(x)). They are most useful in conjunction withgroup_by(), which you’ll learn about shortly.(x <- 1:10) #> [1] 1 2 3 4 5 6 7 8 9 10 lag(x) #> [1] NA 1 2 3 4 5 6 7 8 9 lead(x) #> [1] 2 3 4 5 6 7 8 9 10 NA -
Cumulative and rolling aggregates: R provides functions for running sums, products, mins and maxes:
cumsum(),cumprod(),cummin(),cummax(); and dplyr providescummean()for cumulative means. If you need rolling aggregates (i.e. a sum computed over a rolling window), try the RcppRoll package.x #> [1] 1 2 3 4 5 6 7 8 9 10 # 累积求和 cumsum(x) #> [1] 1 3 6 10 15 21 28 36 45 55 # 累积求平均 cummean(x) #> [1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5RcppRoll包的使用（我觉得RcppRoll包对于一些基因组的数据应该很有用）
install.packages("RcppRoll") library(RcppRoll) # 看参数说明 # n 滚动求和窗口的大小 # by 表示每次窗口移动的距离 # 也就是说如果 n = by的话，就是我们常见的bw压缩或者分bin求值的基因组了吧 (x <- 1:10) [1] 1 2 3 4 5 6 7 8 9 10 > roll_sum(x, n = 3, by = 3) [1] 6 15 24 > roll_sum(x, n = 3, by = 2) [1] 6 12 18 24 > roll_sum(x, n = 3, by = 1) [1] 6 9 12 15 18 21 24 27又让我想起了Sliding window 和 fixed windows。
我估计是sliding windows会有bin（这里的bin经常和window是混用的）（bin就是RcppRoll的n）和step（Step就是RcppRoll的by）两个参数。但fixed windows只有一个参数，即bin，应该就没有step了，或者说step等于window。或者说 sliding windows 又会分overlap or be disjoint。overlap就是我们step < bin，而 disjoint 就是 step >= bin 了。
参考
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Logical comparisons,
<,<=,>,>=,!=, and==, which you learned about earlier. If you’re doing a complex sequence of logical operations it’s often a good idea to store the interim values in new variables so you can check that each step is working as expected.-
Ranking: there are a number of ranking functions, but you should start with
min_rank(). It does the most usual type of ranking (e.g. 1st, 2nd, 2nd, 4th). The default gives smallest values the small ranks; usedesc(x)to give the largest values the smallest ranks.y <- c(1, 2, 2, NA, 3, 4) min_rank(y) #> [1] 1 2 2 NA 4 5 min_rank(desc(y)) #> [1] 5 3 3 NA 2 1If
min_rank()doesn’t do what you need, look at the variantsrow_number(),dense_rank(),percent_rank(),cume_dist(),ntile(). See their help pages for more details.row_number(y) # row_number(): equivalent to rank(ties.method = "first") #> [1] 1 2 3 NA 4 5 dense_rank(y) # dense_rank(): like min_rank(), but with no gaps between ranks #> [1] 1 2 2 NA 3 4 percent_rank(y) # 这是分位数（向量化版本） #> [1] 0.00 0.25 0.25 NA 0.75 1.00 cume_dist(y) # 这是累积分布函数（向量化版本） #> [1] 0.2 0.6 0.6 NA 0.8 1.0Exercise 5.5.4的solution有一个解释我觉得蛮好的
rankme <- tibble( x = c(10, 5, 1, 5, 5) ) rankme <- mutate(rankme, x_row_number = row_number(x), x_min_rank = min_rank(x), x_dense_rank = dense_rank(x) ) arrange(rankme, x) #> # A tibble: 5 x 4 #> x x_row_number x_min_rank x_dense_rank #> <dbl> <int> <int> <int> #> 1 1 1 1 1 #> 2 5 2 2 2 #> 3 5 3 2 2 #> 4 5 4 2 2 #> 5 10 5 5 3
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Exercise 5.5.1
Currently
dep_timeandsched_dep_timeare convenient to look at, but hard to compute with because they’re not really continuous numbers. Convert them to a more convenient representation of number of minutes since midnight.看了solution才理解，这题意思应该是，现在 dep_time 里面的时间，表示很方便（517代表了5:17）， 但计算很麻烦（517-417是100，但代表的不是100min，而是60分钟）。所以我们应该进行转换。转换成距离午夜12点的时间
# 以1504（15:04）举例 # 这里产生的904，就是距离午夜12点（24：00）904min 1504 %/% 100 * 60 + 1504 %% 100 #> [1] 904 # 然后有一个小问题就是，距离午夜1440分钟，恰好是午夜12点，刚好一个轮回 # 那么应该是距离午夜12点 0分钟，而不是1440 # 所以最后是变成了 (1504 %/% 100 * 60 + 1504 %% 100) %% 1440 # 转换的话 flights %>% select(., dep_time, sched_dep_time) %>% mutate(., dep_time_minus = (dep_time %/% 100 * 60 + dep_time %% 100) %% 1440, sched_dep_time_minus = (sched_dep_time %/% 100 * 60 + sched_dep_time %% 100) %% 1440) # A tibble: 336,776 x 4 dep_time sched_dep_time dep_time_minus sched_dep_time_minus <int> <int> <dbl> <dbl> 1 517 515 317 315 2 533 529 333 329 3 542 540 342 340 4 544 545 344 345 5 554 600 354 360 6 554 558 354 358 7 555 600 355 360 8 557 600 357 360 9 557 600 357 360 10 558 600 358 360 # ... with 336,766 more rows # 还可以写成函数 time2mins <- function(x) { (x %/% 100 * 60 + x %% 100) %% 1440 }
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Exercise 5.5.6
What trigonometric functions does R provide?
这部分solution写的特别详细，大家有需要的自己去看就行
5.6 Grouped summaries with summarise()
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It collapses a data frame to a single row
summarise()is not terribly useful unless we pair it withgroup_by(). This changes the unit of analysis from the complete dataset to individual groups. Then, when you use the dplyr verbs on a grouped data frame they’ll be automatically applied “by group”. For example, if we applied exactly the same code to a data frame grouped by date, we get the average delay per date:summarise(flights, delay = mean(dep_delay, na.rm = TRUE)) #> # A tibble: 1 x 1 #> delay #> <dbl> #> 1 12.6 # 记得NA.rm，有传染性 flights %>% group_by(year, month, day) %>% summarise(mean = mean(dep_delay)) #> # A tibble: 365 x 4 #> # Groups: year, month [12] #> year month day mean #> <int> <int> <int> <dbl> #> 1 2013 1 1 NA #> 2 2013 1 2 NA #> 3 2013 1 3 NA #> 4 2013 1 4 NA #> 5 2013 1 5 NA #> 6 2013 1 6 NA #> # … with 359 more rows我有时候感觉group就相当于把观测变得有层次了，比如上面就是先根据year划一层，然后根据month划一层，最后根据day划一层。
Whenever you do any aggregation, it’s always a good idea to include either a count (
n()), or a count of non-missing values (sum(!is.na(x))). That way you can check that you’re not drawing conclusions based on very small amounts of data.
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Just using means, counts, and sum can get you a long way, but R provides many other useful summary functions:
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Measures of location: we’ve used
mean(x), butmedian(x)is also useful. The mean is the sum divided by the length; the median is a value where 50% ofxis above it, and 50% is below it.It’s sometimes useful to combine aggregation with logical subsetting. We haven’t talked about this sort of subsetting yet, but you’ll learn more about it in subsetting.
not_cancelled %>% group_by(year, month, day) %>% summarise( avg_delay1 = mean(arr_delay), avg_delay2 = mean(arr_delay[arr_delay > 0]) # the average positive delay ) #> # A tibble: 365 x 5 #> # Groups: year, month [12] #> year month day avg_delay1 avg_delay2 #> <int> <int> <int> <dbl> <dbl> #> 1 2013 1 1 12.7 32.5 #> 2 2013 1 2 12.7 32.0 #> 3 2013 1 3 5.73 27.7 #> 4 2013 1 4 -1.93 28.3 #> 5 2013 1 5 -1.53 22.6 #> 6 2013 1 6 4.24 24.4 #> # … with 359 more rows -
Measures of spread:
sd(x),IQR(x)（IQR就是boxplot里面的那个上四分位点 - 下四分位点）,mad(x). The root mean squared deviation, or standard deviationsd(x), is the standard measure of spread. The interquartile rangeIQR(x)and median absolute deviationmad(x)are robust equivalents that may be more useful if you have outliers.# Why is distance to some destinations more variable than to others? not_cancelled %>% group_by(dest) %>% summarise(distance_sd = sd(distance)) %>% arrange(desc(distance_sd)) #> # A tibble: 104 x 2 #> dest distance_sd #> <chr> <dbl> #> 1 EGE 10.5 #> 2 SAN 10.4 #> 3 SFO 10.2 #> 4 HNL 10.0 #> 5 SEA 9.98 #> 6 LAS 9.91 #> # … with 98 more rows -
Measures of rank:
min(x),quantile(x, 0.25),max(x). Quantiles are a generalisation of the median. For example,quantile(x, 0.25)will find a value ofxthat is greater than 25% of the values, and less than the remaining 75%.# When do the first and last flights leave each day? not_cancelled %>% group_by(year, month, day) %>% summarise( first = min(dep_time), last = max(dep_time) ) #> # A tibble: 365 x 5 #> # Groups: year, month [12] #> year month day first last #> <int> <int> <int> <int> <int> #> 1 2013 1 1 517 2356 #> 2 2013 1 2 42 2354 #> 3 2013 1 3 32 2349 #> 4 2013 1 4 25 2358 #> 5 2013 1 5 14 2357 #> 6 2013 1 6 16 2355 #> # … with 359 more rows -
Measures of position:
first(x),nth(x, 2),last(x). These work similarly tox[1],x[2], andx[length(x)]but let you set a default value if that position does not exist (i.e. you’re trying to get the 3rd element from a group that only has two elements). For example, we can find the first and last departure for each day:not_cancelled %>% group_by(year, month, day) %>% summarise( first_dep = first(dep_time), last_dep = last(dep_time) ) #> # A tibble: 365 x 5 #> # Groups: year, month [12] #> year month day first_dep last_dep #> <int> <int> <int> <int> <int> #> 1 2013 1 1 517 2356 #> 2 2013 1 2 42 2354 #> 3 2013 1 3 32 2349 #> 4 2013 1 4 25 2358 #> 5 2013 1 5 14 2357 #> 6 2013 1 6 16 2355 #> # … with 359 more rowsThese functions are complementary to filtering on ranks. Filtering gives you all variables, with each observation in a separate row:
not_cancelled %>% group_by(year, month, day) %>% mutate(r = min_rank(desc(dep_time))) %>% filter(r %in% range(r)) #> # A tibble: 770 x 20 #> # Groups: year, month, day [365] #> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time #> <int> <int> <int> <int> <int> <dbl> <int> <int> #> 1 2013 1 1 517 515 2 830 819 #> 2 2013 1 1 2356 2359 -3 425 437 #> 3 2013 1 2 42 2359 43 518 442 #> 4 2013 1 2 2354 2359 -5 413 437 #> 5 2013 1 3 32 2359 33 504 442 #> 6 2013 1 3 2349 2359 -10 434 445 #> # … with 764 more rows, and 12 more variables: arr_delay <dbl>, carrier <chr>, #> # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, #> # distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>, r <int> # 上面的不太清楚 # 其实这就等于是把最大的dep_time和最小的dep_time同时输出了 > not_cancelled %>% + group_by(year, month, day) %>% + select(., year:day, dep_time) %>% + mutate(r = min_rank(desc(dep_time))) %>% + filter(r %in% range(r)) # A tibble: 770 x 5 # Groups: year, month, day [365] year month day dep_time r <int> <int> <int> <int> <int> 1 2013 1 1 517 831 2 2013 1 1 2356 1 3 2013 1 2 42 928 4 2013 1 2 2354 1 5 2013 1 3 32 900 6 2013 1 3 2349 1 7 2013 1 4 25 908 8 2013 1 4 2358 1 9 2013 1 4 2358 1 10 2013 1 5 14 717 # ... with 760 more rows -
Counts: You’ve seen
n(), which takes no arguments, and returns the size of the current group. To count the number of non-missing values, usesum(!is.na(x)). To count the number of distinct (unique) values, usen_distinct(x).（这函数可以唉，类似于Linux里面的uniq -c？）# Which destinations have the most carriers? not_cancelled %>% group_by(dest) %>% summarise(carriers = n_distinct(carrier)) %>% arrange(desc(carriers)) #> # A tibble: 104 x 2 #> dest carriers #> <chr> <int> #> 1 ATL 7 #> 2 BOS 7 #> 3 CLT 7 #> 4 ORD 7 #> 5 TPA 7 #> 6 AUS 6 #> # … with 98 more rowsCounts are so useful that dplyr provides a simple helper if all you want is a count:
# 你可以看到not_cancelled是没有group的 > not_cancelled # A tibble: 327,346 x 19 # count自动完成了group_by+n() not_cancelled %>% count(dest) #> # A tibble: 104 x 2 #> dest n #> <chr> <int> #> 1 ABQ 254 #> 2 ACK 264 #> 3 ALB 418 #> 4 ANC 8 #> 5 ATL 16837 #> 6 AUS 2411 #> # … with 98 more rows # 我其实感觉 n() 就类似于 length() 吧 # 来自solution的exercise 5.6.2 > not_cancelled %>% + group_by(dest) %>% + summarise(n = length(dest)) %>% + head(n = 3) # A tibble: 3 x 2 dest n <chr> <int> 1 ABQ 254 2 ACK 264 3 ALB 418 > not_cancelled %>% + group_by(dest) %>% + summarise(n = n()) %>% + head(n = 3) # A tibble: 3 x 2 dest n <chr> <int> 1 ABQ 254 2 ACK 264 3 ALB 418You can optionally provide a weight variable. For example, you could use this to “count” (sum) the total number of miles a plane flew:
之前是数每个group下的个数，这里是对每个group下，你指定的varible进行求和
# 数个数 > not_cancelled %>% + count(tailnum) %>% + head(., n = 3) # A tibble: 3 x 2 tailnum n <chr> <int> 1 D942DN 4 2 N0EGMQ 352 3 N10156 145 > not_cancelled %>% + group_by(tailnum) %>% + summarise(n = n()) %>% + head(n = 3) # A tibble: 3 x 2 tailnum n <chr> <int> 1 D942DN 4 2 N0EGMQ 352 3 N10156 145 # 求和 > not_cancelled %>% + count(tailnum, wt = distance) %>% + head(., n = 3) # A tibble: 3 x 2 tailnum n <chr> <dbl> 1 D942DN 3418 2 N0EGMQ 239143 3 N10156 109664 > not_cancelled %>% + select(tailnum, distance) %>% + group_by(tailnum) %>% + summarise(n = sum(distance)) %>% + head(n = 3) # A tibble: 3 x 2 tailnum n <chr> <dbl> 1 D942 3418 2 N0EGMQ 239143 3 N10156 109664 Counts and proportions of logical values:
sum(x > 10),mean(y == 0). When used with numeric functions,TRUEis converted to 1 andFALSEto 0. This makessum()andmean()very useful:sum(x)gives the number ofTRUEs inx, andmean(x)gives the proportion.
这个操作是很好的，举个最简单的例子
# 下面这个例子可以用来说明 # 正态分布的中位数是0 > set.seed(19960203) > mean(rnorm(1E6) > 0) [1] 0.500266 > set.seed(19960203) > sum(rnorm(1E6) > 0) / 1E6 [1] 0.500266# 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 [12] #> year month day n_early #> <int> <int> <int> <int> #> 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 # What proportion of flights are delayed by more than an hour? not_cancelled %>% group_by(year, month, day) %>% summarise(hour_prop = mean(arr_delay > 60)) #> # A tibble: 365 x 4 #> # Groups: year, month [12] #> year month day hour_prop #> <int> <int> <int> <dbl> #> 1 2013 1 1 0.0722 #> 2 2013 1 2 0.0851 #> 3 2013 1 3 0.0567 #> 4 2013 1 4 0.0396 #> 5 2013 1 5 0.0349 #> 6 2013 1 6 0.0470 #> # … with 359 more rows -
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有意思的功能：当使用多个变量进行分组时，每次的摘要统计会用掉一个分组变量。这样就可以轻松地对数据集进行循序渐进的分析：
daily <- group_by(flights, year, month, day) (per_day <- summarise(daily, flights = n())) #> # A tibble: 365 x 4 #> # Groups: year, month [12] #> year month day flights #> <int> <int> <int> <int> #> 1 2013 1 1 842 #> 2 2013 1 2 943 #> 3 2013 1 3 914 #> 4 2013 1 4 915 #> 5 2013 1 5 720 #> 6 2013 1 6 832 #> # … with 359 more rows (per_month <- summarise(per_day, flights = sum(flights))) #> # A tibble: 12 x 3 #> # Groups: year [1] #> year month flights #> <int> <int> <int> #> 1 2013 1 27004 #> 2 2013 2 24951 #> 3 2013 3 28834 #> 4 2013 4 28330 #> 5 2013 5 28796 #> 6 2013 6 28243 #> # … with 6 more rows (per_year <- summarise(per_month, flights = sum(flights))) #> # A tibble: 1 x 2 #> year flights #> <int> <int> #> 1 2013 336776
在循序渐进地进行摘要分析时，需要小心：使用求和与计数操作是没问题的，但如果想要使用加权平均和方差的话，就要仔细考虑一下，在基于秩的统计数据（如中位数）上是无法进行这些操作的。换句话说，对分组求和的结果再求和就是对整体求和，但分组中位数的中位数可不是整体的中位数。(这话来自中文版)
我觉得意思就是
# 整体求和 和 把整体分割成组，然后分组求和的结果是一样的
# 这个你可以通过自己列公式看出来
> sum(1:8)
[1] 36
> sum(sum(1:4),sum(5:6),sum(7:8))
[1] 36
# 但整体求中位数 和 把整体分割成组，然后分组求中位数，再求中位数的结果是不一样的
> median(1:8)
[1] 4.5
> median(median(1:4),median(5:6),median(7:8))
[1] 2.5
# 均值同理
> mean(1:8)
[1] 4.5
> mean(mean(1:4),mean(5:6),mean(7:8))
[1] 2.5
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Exercise 5.6.2
Come up with another approach that will give you the same output as
not_cancelled %>% count(dest)andnot_cancelled %>% count(tailnum, wt = distance)(without usingcount()).not_cancelled <- flights %>% filter(!is.na(dep_delay), !is.na(arr_delay)) > not_cancelled %>% + count(dest) %>% + head(n = 3) # A tibble: 3 x 2 dest n <chr> <int> 1 ABQ 254 2 ACK 264 3 ALB 418 > not_cancelled %>% + group_by(dest) %>% + summarise(n = length(dest)) %>% + head(n = 3) # A tibble: 3 x 2 dest n <chr> <int> 1 ABQ 254 2 ACK 264 3 ALB 418 -------------------------------------------------------------------------------------------------- # 其实group_by加summarise我觉得可以认为是下面这个操作的多次循环 # 以ABQ为例 > not_cancelled %>% + filter(., dest == "ABQ") %>% + nrow() [1] 254 ------------------------------------------------------------------------------------------------------ > not_cancelled %>% + group_by(dest) %>% + summarise(n = n()) %>% + head(n = 3) # A tibble: 3 x 2 dest n <chr> <int> 1 ABQ 254 2 ACK 264 3 ALB 418 # Another alternative to count() is to use the combination of the group_by() and tally() verbs. # In fact, count() is effectively a short-cut for group_by() followed by tally(). > not_cancelled %>% + group_by(dest) %>% + tally() %>% + head(n = 3) # A tibble: 3 x 2 dest n <chr> <int> 1 ABQ 254 2 ACK 264 3 ALB 418
> not_cancelled %>%
+ count(tailnum, wt = distance) %>%
+ head(n = 3)
# A tibble: 3 x 2
tailnum n
<chr> <dbl>
1 D942DN 3418
2 N0EGMQ 239143
3 N10156 109664
> not_cancelled %>%
+ group_by(tailnum) %>%
+ summarise(n = sum(distance)) %>%
+ head(n = 3)
# A tibble: 3 x 2
tailnum n
<chr> <dbl>
1 D942DN 3418
2 N0EGMQ 239143
3 N10156 109664
# 同样举个例子
> not_cancelled %>%
+ filter(., tailnum == "D942DN") %>%
+ pull(distance) %>%
+ sum()
[1] 3418
# Like the previous example, we can also use the combination group_by() and tally().
# Any arguments to tally() are summed.
> not_cancelled %>%
+ group_by(tailnum) %>%
+ tally(distance) %>%
+ head(n = 3)
# A tibble: 3 x 2
tailnum n
<chr> <dbl>
1 D942DN 3418
2 N0EGMQ 239143
3 N10156 109664
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Exercise 5.6.6
What does the sort argument to
count()do? When might you use it?> not_cancelled %>% + count(dest, sort = T) %>% + head(n = 3) # A tibble: 3 x 2 dest n <chr> <int> 1 ATL 16837 2 ORD 16566 3 LAX 16026 # 等价于 > not_cancelled %>% + count(dest) %>% + arrange(., desc(n)) %>% + head(n = 3) # A tibble: 3 x 2 dest n <chr> <int> 1 ATL 16837 2 ORD 16566 3 LAX 16026
5.7 Grouped mutates (and filters)
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虽然与 summarize() 函数结合起来使用是最有效的，但分组也可以与 mutate() 和 filter()函数结合，以完成非常便捷的操作。（中文版这段话）
flights_sml <- select(flights, year:day, ends_with("delay"), distance, air_time ) # Find the worst members of each group: # 这里对arr_delay进行排序，按从大到小排。然后再得到rank，rank越靠前就是arr_delay越大的 # 这里就是得到每组前10多延迟的 flights_sml %>% group_by(year, month, day) %>% filter(rank(desc(arr_delay)) < 10) %>% head(n = 3) # A tibble: 3 x 7 # Groups: year, month, day [1] year month day dep_delay arr_delay distance air_time <int> <int> <int> <dbl> <dbl> <dbl> <dbl> 1 2013 1 1 853 851 184 41 2 2013 1 1 290 338 1134 213 3 2013 1 1 260 263 266 46 # Find all groups bigger than a threshold: popular_dests <- flights %>% group_by(dest) %>% filter(n() > 365) # Standardise to compute per group metrics: # 这里等于是计算了百分比 # 始终记住：向量化，向量化 popular_dests %>% filter(arr_delay > 0) %>% mutate(prop_delay = arr_delay / sum(arr_delay)) %>% select(year:day, dest, arr_delay, prop_delay) #> # A tibble: 131,106 x 6 #> # Groups: dest [77] #> year month day dest arr_delay prop_delay #> <int> <int> <int> <chr> <dbl> <dbl> #> 1 2013 1 1 IAH 11 0.000111 #> 2 2013 1 1 IAH 20 0.000201 #> 3 2013 1 1 MIA 33 0.000235 #> 4 2013 1 1 ORD 12 0.0000424 #> 5 2013 1 1 FLL 19 0.0000938 #> 6 2013 1 1 ORD 8 0.0000283 #> # … with 1.311e+05 more rows # 这样可能直观一点 > popular_dests %>% + filter(arr_delay > 0) %>% + mutate(prop_delay = arr_delay / sum(arr_delay)) %>% + select(year:day, dest, arr_delay, prop_delay) %>% + arrange(dest) # A tibble: 131,106 x 6 # Groups: dest [77] year month day dest arr_delay prop_delay <int> <int> <int> <chr> <dbl> <dbl> 1 2013 1 1 ALB 40 0.00418 2 2013 1 1 ALB 44 0.00459 3 2013 1 2 ALB 71 0.00741 4 2013 1 2 ALB 82 0.00856 5 2013 1 3 ALB 40 0.00418 6 2013 1 4 ALB 30 0.00313 7 2013 1 6 ALB 95 0.00992 8 2013 1 6 ALB 4 0.000418 9 2013 1 7 ALB 41 0.00428 10 2013 1 10 ALB 120 0.0125 # ... with 131,096 more rows
