Question

我有以下数据结构。

pos <- c(4532568,4541529,4586529,4591235,4712360,4732504,4740231,10532655,10542365,10564587,45312567,45326354,45369874,124832658,124845829,124869874)
cm <- c(2.21,2.25,2.26,2.29,3.31,3.35,3.36,4.32,4.35,4.39,5.23,5.27,5.29,7.36,7.45,7.49)
data <- cbind(pos,cm)

            pos   cm
 [1,]   4532568 2.21
 [2,]   4541529 2.25
 [3,]   4586529 2.26
 [4,]   4591235 2.29
 [5,]   4712360 3.31
 [6,]   4732504 3.35
 [7,]   4740231 3.36
 [8,]  10532655 4.32
 [9,]  10542365 4.35
 [10,]  10564587 4.39
 [11,]  45312567 5.23
 [12,]  45326354 5.27
 [13,]  45369874 5.29
 [14,] 124832658 7.36
 [15,] 124845829 7.45
 [16,] 124869874 7.49

我的目的是总结在“pos”列中按100000个单位分组的行，并获得每个类的“CM”列的平均值。此示例中的结果如下所示：

pos <- c(4500000,4700000,10500000,45300000,124800000)
cm <- c(2.2525,3.34,4.35333,5.26333,7.43333)
newdata <- cbind(pos,cm)

           pos      cm
[1,]   4500000 2.25250
[2,]   4700000 3.34000
[3,]  10500000 4.35333
[4,]  45300000 5.26333
[5,] 124800000 7.43333

我不知道如何自动化处理庞大数据帧的过程。

回答Akrun：所以。如果我在真实数据集中使用以下脚本：

 Ch1<- ch1 %>%
 as.data.frame %>% 
 group_by(Pos = plyr::round_any(Pos, 1e5, f = floor))

然后我得到以下结果（仅前10行）

 structure(list(Chr = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
 1L, 1L, 1L), .Label = "1", class = "factor"), Pos = c(0, 0, 0, 
 2e+05, 5e+05, 5e+05, 5e+05, 5e+05, 5e+05, 7e+05), CM = c(0, 0.080572, 
 0.092229, 0.439456, 1.478148, 1.478214, 1.480558, 1.488889, 1.489481, 
 1.931794)), .Names = c("Chr", "Pos", "CM"), row.names = c(NA, 
 -10L), class = c("grouped_df", "tbl_df", "tbl", "data.frame"), vars = "Pos", drop = TRUE, indices = list(
 0:2, 3L, 4:8, 9L), group_sizes = c(3L, 1L, 5L, 1L), biggest_group_size = 5L, labels = structure(list(
 Pos = c(0, 2e+05, 5e+05, 7e+05)), row.names = c(NA, -4L), class = "data.frame", vars = "Pos", drop = TRUE, .Names = "Pos"))

但是，如果我使用整个脚本来获取Ch1 $ CM的平均值：

 Ch1<- ch1 %>%
 as.data.frame %>% 
 group_by(Pos = plyr::round_any(Pos, 1e5, f = floor)) %>% 
 summarise(cm = mean(cm))

然后我得到以下data.frame：

 structure(list(Pos = c(0, 2e+05, 5e+05, 7e+05, 8e+05, 9e+05, 
 1e+06, 1100000, 1200000, 1300000), cm = c(4.528498, 4.528498, 
 4.528498, 4.528498, 4.528498, 4.528498, 4.528498, 4.528498, 4.528498, 
 4.528498)), .Names = c("Pos", "cm"), row.names = c(NA, -10L), class = c("tbl_df", 
 "tbl", "data.frame"))

正如你所看到的那样，平均值是错误的，因为所有这些都是错误的。我不知道为什么会这样。

Answer 1

我们可以使用round_any

library(dplyr)
data %>%
    as.data.frame %>% 
    group_by(grp = plyr::round_any(pos, 1e5, f = floor)) %>% 
    summarise(cm = mean(cm))
# A tibble: 5 x 2
#        grp       cm
#      <dbl>    <dbl>
#1   4500000 2.252500
#2   4700000 3.340000
#3  10500000 4.353333
#4  45300000 5.263333
#5 124800000 7.433333

总结一个矩阵。获得每个100000单位的平均值

1 个答案: