[英]Group/bin/bucket data in R and get count per bucket and sum of values per bucket
I wish to bucket/group/bin data : 我想分组/分组/ bin数据:
C1 C2 C3
49488.01172 0.0512 54000
268221.1563 0.0128 34399
34775.96094 0.0128 54444
13046.98047 0.07241 61000
2121699.75 0.00453 78921
71155.09375 0.0181 13794
1369809.875 0.00453 12312
750 0.2048 43451
44943.82813 0.0362 49871
85585.04688 0.0362 18947
31090.10938 0.0362 13401
68550.40625 0.0181 14345
I want to bucket it by C2 values but I wish to define the buckets eg <=0.005, <=.010, <=.014 etc. As you can see, the bucketing will be uneven intervals. 我想用C2值进行存储,但我希望定义存储桶,例如<= 0.005,<=。010,<=。014等。正如您所看到的,存储区间将是不均匀的。 I want the count of C1 per bucket as well as the total sum of C1 for every bucket.
我想要每桶的C1计数以及每个桶的C1总和。
I don't know where to begin as I am fairly new a user of R. Is there anyone willing to help me figure out the code or direct to me to an example that will work for my needs? 我不知道从哪里开始,因为我是一个相当新的R用户。有没有人愿意帮我弄清楚代码或指导我找一个能满足我需求的例子?
EDIT: added another column C3. 编辑:添加了另一列C3。 I need sum of C3 per bucket as well at the same time as sum and count of C1 per bucket
我需要每桶的C3总和以及每桶的C1和数量
From the comments, "C2" seems to be "character" column with %
as suffix. 从评论中,“C2”似乎是“字符”列,后缀为
%
。 Before, creating a group, remove the %
using sub
, convert to "numeric" ( as.numeric
). 在创建组之前,删除
%
using sub
,转换为“numeric”( as.numeric
)。 The variable "group" is created ( transform(df,...)
) by using the function cut
with breaks
(group buckets/intervals) and labels
(for the desired group labels) arguments. 通过使用带有
breaks
(组桶/间隔)和labels
(用于所需的组标签)参数的函数cut
来创建变量“group”( transform(df,...)
)。 Once the group variable is created, the sum
of the "C1" by "group" and the "count" of elements within "group" can be done using aggregate
from "base R" 一旦组变量被创建,所述
sum
的“C1”,由“基团”和“基团”中的元素“计数”的可利用来完成aggregate
从“基R”
df1 <- transform(df, group=cut(as.numeric(sub('[%]', '', C2)),
breaks=c(-Inf,0.005, 0.010, 0.014, Inf),
labels=c('<0.005', 0.005, 0.01, 0.014)))
res <- do.call(data.frame,aggregate(C1~group, df1,
FUN=function(x) c(Count=length(x), Sum=sum(x))))
dNew <- data.frame(group=levels(df1$group))
merge(res, dNew, all=TRUE)
# group C1.Count C1.Sum
#1 <0.005 2 3491509.6
#2 0.005 NA NA
#3 0.01 2 302997.1
#4 0.014 8 364609.5
or you can use data.table
. 或者您可以使用
data.table
。 setDT
converts the data.frame
to data.table
. setDT
的转换data.frame
到data.table
。 Specify the "grouping" variable with by=
and summarize/create the two variables "Count" and "Sum" within the list(
. .N
gives the count of elements within each "group". 使用
by=
指定“grouping”变量,并在list(
汇总/创建两个变量“Count”和“Sum” list(
. .N
给出每个“group”中元素的计数。
library(data.table)
setDT(df1)[, list(Count=.N, Sum=sum(C1)), by=group][]
Or using dplyr
. 或者使用
dplyr
。 The %>%
connect the LHS with RHS arguments and chains them together. %>%
将LHS与RHS参数连接起来并将它们链接在一起。 Use group_by
to specify the "group" variable, and then use summarise_each
or summarise
to get summary count and sum
of the concerned column. 使用
group_by
指定“组”变量,然后用summarise_each
或summarise
得到汇总数量和sum
有关列。 summarise_each
would be useful if there are more than one column. 如果有多个列,
summarise_each
将非常有用。
library(dplyr)
df1 %>%
group_by(group) %>%
summarise_each(funs(n(), Sum=sum(.)), C1)
Using the new dataset df
使用新数据集
df
df1 <- transform(df, group=cut(C2, breaks=c(-Inf,0.005, 0.010, 0.014, Inf),
labels=c('<0.005', 0.005, 0.01, 0.014)))
res <- do.call(data.frame,aggregate(cbind(C1,C3)~group, df1,
FUN=function(x) c(Count=length(x), Sum=sum(x))))
res
# group C1.Count C1.Sum C3.Count C3.Sum
#1 <0.005 2 3491509.6 2 91233
#2 0.01 2 302997.1 2 88843
#3 0.014 8 364609.5 8 268809
and you can do the merge
as detailed above. 你可以按照上面的详细说明进行
merge
。
The dplyr
approach would be the same except specifying the additional variable 除了指定附加变量之外,
dplyr
方法是相同的
df1%>%
group_by(group) %>%
summarise_each(funs(n(), Sum=sum(.)), C1, C3)
#Source: local data frame [3 x 5]
# group C1_n C3_n C1_Sum C3_Sum
#1 <0.005 2 2 3491509.6 91233
#2 0.01 2 2 302997.1 88843
#3 0.014 8 8 364609.5 268809
df <-structure(list(C1 = c(49488.01172, 268221.1563, 34775.96094,
13046.98047, 2121699.75, 71155.09375, 1369809.875, 750, 44943.82813,
85585.04688, 31090.10938, 68550.40625), C2 = c("0.0512%", "0.0128%",
"0.0128%", "0.07241%", "0.00453%", "0.0181%", "0.00453%", "0.2048%",
"0.0362%", "0.0362%", "0.0362%", "0.0181%")), .Names = c("C1",
"C2"), row.names = c(NA, -12L), class = "data.frame")
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