如何将一组函数应用于R data.frame中的分组变量的每个组 [英] How to apply a set of functions to each group of a grouping variable in R data.frame
问题描述
我需要在一步中重新整理R 中的data.frame。
简而言之，对象（x1到x6）的值的更改是逐行可见的（从1990到1995）：
> tab1 [1:10，]＃原始数据见tab1的图块
id值年
1 x1 7 1990
2 x1 10 1991
3 x1 11 1992
4 x1 7 1993
5 x1 3 1994
6 x1 1 1995
7 x2 6 1990
8 x2 7 1991
9 x2 9 1992
10 x2 5 1993
我可以一步一步地重新整形，有没有人知道如何一步？
原始数据
表1 - 看到所有时间序列中的最小值为0 / p>
Step1：
表2 - 重新缩放每个每个的最小值将等于0 。
所有时间都在x轴上下降。
Step2：
表3 - 在每个时间轴上应用 diff（）函数。
Step3：
表4 - 对每个时间序列应用 sort（）函数。
我希望图片清楚足够了解每一步。
所以最终的表格如下所示：
> tab4 [1:10，]
id值时间
1 x1 -4 1
2 x1 -4 2
3 x1 -2 3
4 x1 1 4
5 x1 3 5
6 x2 -4 1
7 x2 -3 2
8 x2 1 3
9 x2 1 4
10 x2 2 5
＃源数据：
tab1 < - data.frame（id = rep（c（x1，x2，x3，x4，x5，x6），每个= 6），
value = c（7,10,11,7， 3,1,6,7,9,5,2,3,11,9,7,9,1，
0,1,2,2,4,7,4,2,3,1， 6,4,2,3,5,4,3,5,6），
year = rep（c（1990：1995），times = 6））
tab2< - data.frame（id = rep（c（x1，x2，x3，x4，x5，x6），each = 6），
value = c ，9,10,6,2,0,4,5,7,3,0,1,11,9,7,9,1,0，
0,1,1,3,6,3 ，1,2,0,5,3,1,0,2,1,0,2,3），
year = rep（c（1990：1995），times = 6））
tab3< - data.frame（id = rep（c（x1，x2，x3，x4，x5，x6），each = 5），
value = c（3,1，-4，-4，-2,1,2，-4，-3,1，-2，-2,2，-8，-1，
1 ，0,2,3，-3,1，-2,5，-2，-2,2，-1，1,2,1），
time = rep（c（1：5） ，times = 6））
tab4< - data.frame（id = rep（c（x1，x2，x3，x4，x5 ），每个= 5），
值= c（-4，-4，-2,1,3，-4，-3,1,1,2，-8，-2，-2， -1,2，
-3,0,1,2,3，-2，-2，-2,1,5，-1，-1,1,2,2），
time = rep（c（1：5），times = 6））
这听起来像是要为一组分组变量应用一组函数。在R（从基础R by 和自由插入到附加包如 plyr ， data.table 和 dplyr ）。我一直在学习如何使用包 dplyr ，并提出了以下解决方案。
require（dplyr）
tab4 = tab1％>％
group_by（id）％>％＃group by id
mutate（value = value - min（value），value = value-lag（value））％>％＃group min to 0，差值滞后1
na.omit％>％＃删除由滞后1引起的差异差异$ b $每个id中的排列（id，value）％>％＃按值的顺序
mutate（time = 1：length（value））％>％＃根据当前顺序，将时间变量从1到5
select（-year）＃remove year column to match final OP output
I need to reshape data.frame in R in one step. In short, change of values of objects (x1 to x6) is visible row by row (from 1990 to 1995):
> tab1[1:10, ] # raw data see plot for tab1
id value year
1 x1 7 1990
2 x1 10 1991
3 x1 11 1992
4 x1 7 1993
5 x1 3 1994
6 x1 1 1995
7 x2 6 1990
8 x2 7 1991
9 x2 9 1992
10 x2 5 1993
I am able to do reshaping step by step, does anybody know how do it in one step?
Original data Table 1 - see that minimal value from all timeseries is "0"
Step1: Table 2 - rescale each timeseries that each would have minimal value equal "0". All times fall down on x-axes.
Step2:
Table 3 - apply diff() function on each timeline.
Step3:
Table 4 - apply sort() function on each timeseries.
I hope the pictures are clear enough for understanding each step.
So final table looks like this:
> tab4[1:10, ]
id value time
1 x1 -4 1
2 x1 -4 2
3 x1 -2 3
4 x1 1 4
5 x1 3 5
6 x2 -4 1
7 x2 -3 2
8 x2 1 3
9 x2 1 4
10 x2 2 5
# Source data:
tab1 <- data.frame(id = rep(c("x1","x2","x3","x4","x5","x6"), each = 6),
value = c(7,10,11,7,3,1,6,7,9,5,2,3,11,9,7,9,1,
0,1,2,2,4,7,4,2,3,1,6,4,2,3,5,4,3,5,6),
year = rep(c(1990:1995), times = 6))
tab2 <- data.frame(id = rep(c("x1","x2","x3","x4","x5","x6"), each = 6),
value = c(6,9,10,6,2,0,4,5,7,3,0,1,11,9,7,9,1,0,
0,1,1,3,6,3,1,2,0,5,3,1,0,2,1,0,2,3),
year = rep(c(1990:1995), times = 6))
tab3 <- data.frame(id = rep(c("x1","x2","x3","x4","x5","x6"), each = 5),
value = c(3,1,-4,-4,-2,1,2,-4,-3,1,-2,-2,2,-8,-1,
1,0,2,3,-3,1,-2,5,-2,-2,2,-1,-1,2,1),
time = rep(c(1:5), times = 6))
tab4 <- data.frame(id = rep(c("x1","x2","x3","x4","x5","x6"), each = 5),
value = c(-4,-4,-2,1,3,-4,-3,1,1,2,-8,-2,-2,-1,2,
-3,0,1,2,3,-2,-2,-2,1,5,-1,-1,1,2,2),
time = rep(c(1:5), times = 6))
It sounds like you want to apply a set of functions to each group of a grouping variable. There are many ways to do this in R (from base R by and tapply to add-on packages like plyr, data.table, and dplyr). I've been learning how to use package dplyr, and came up with the following solution.
require(dplyr)
tab4 = tab1 %>%
group_by(id) %>% # group by id
mutate(value = value - min(value), value = value - lag(value)) %>% # group min to 0, difference lag 1
na.omit %>% # remove NA caused by lag 1 differencing
arrange(id, value) %>% # order by value within each id
mutate(time = 1:length(value)) %>% # Make a time variable from 1 to 5 based on current order
select(-year) # remove year column to match final OP output
这篇关于如何将一组函数应用于R data.frame中的分组变量的每个组的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
