基于多列和多行条件扩展 R 数据框 [英] Expand R dataframe based on multiple column and row criteria
问题描述
我在 R studio 中有以下数据框
I have the following dataframe in R studio
DF1<-data.frame('X_F'=c(1,2,3,4,5, NA, NA, NA, 1,2,3,4,5), "X_A"=c(.1,.2,.3,.4,.5, NA, NA, NA, .2,.3,.4, .5,.6),"Y_F"=c(2,3,5,NA, 7, 1,3, 4, 1,NA,3,4,5), "Y_A"=c(.2,.3,.4,NA, .7, .1,.2,.7,.1,NA, .3,.4,.5),'ID'=c("A", "A", "A", "A", "A", "B", "B", "B", "C", "C", "C","C",'C'))
数据框由 5 列 - AN ID 列组成，用于标识每组和两组参数 - X_F、Y_F 和相应的一组 A 值 - X_A、Y_A.
The dataframe consists of 5 columns- AN ID column to identify each set and two sets of parameters- X_F, Y_F and a corresponding set of A values- X_A, Y_A.
数据框如下所示.
X_F X_A Y_F Y_A ID
1 0.1 2 0.2 A
2 0.2 3 0.3 A
3 0.3 5 0.4 A
4 0.4 NA NA A
5 0.5 7 0.7 A
NA NA 1 0.1 B
NA NA 3 0.2 B
NA NA 4 0.7 B
1 0.2 1 0.1 C
2 0.3 NA NA C
3 0.4 3 0.3 C
4 0.5 4 0.4 C
5 0.6 5 0.5 C
我想通过扩展上面的数据框来获得下面的数据框.扩展的数据框将有一个名为 SF 的额外列.SF的价值派生为一系列 X_F、Y_F 列，按 ID 分组.此范围由每个步骤的值 1 分隔
I want to obtain the following dataframe by expanding the above dataframe. The expanded dataframe will have an extra column called SF. The values of SF are derived as a range of X_F, Y_F columns, grouped by ID. this range is separated by a value of 1 for each step
ID SF X_F X_A Y_F Y_A
1 A 1 1 0.1 1 NA
2 A 2 2 0.2 2 0.2
3 A 3 3 0.3 3 0.3
4 A 4 4 0.4 4 NA
5 A 5 5 0.5 5 0.4
6 A 6 6 NA 6 NA
7 A 7 7 NA 7 0.7
8 B 1 1 NA 1 0.1
9 B 2 2 NA 2 NA
10 B 3 3 NA 3 0.2
11 B 4 4 NA 4 0.7
12 C 1 1 0.2 1 0.1
13 C 2 2 0.3 2 NA
14 C 3 3 0.4 3 0.3
15 C 4 4 0.5 4 0.4
16 C 5 5 0.6 5 0.5
我已经尝试过这种方法来获得所需的结果.
I have tried this approach to obtain the required result.
library(dplyr)
library(tidyr)
DF1
DF2<-DF1%>%group_by(ID)%>% mutate(SF=pmax(X_F, Y_F, na.rm = T))%>%
complete(SF=(full_seq(SF ,1)))
与上面的预期输出相比，我得到了以下输出
I have got the following output as against the expected output above
ID SF X_F X_A Y_F Y_A
<fct> <dbl> <dbl> <dbl> <dbl> <dbl>
A 2 1 0.1 2 0.2
A 3 2 0.2 3 0.3
A 4 4 0.4 NA NA
A 5 3 0.3 5 0.4
A 6 NA NA NA NA
A 7 5 0.5 7 0.7
B 1 NA NA 1 0.1
B 2 NA NA NA NA
B 3 NA NA 3 0.2
B 4 NA NA 4 0.7
C 1 1 0.2 1 0.1
C 2 2 0.3 NA NA
C 3 3 0.4 3 0.3
C 4 4 0.5 4 0.4
C 5 5 0.6 5 0.5
我请人帮忙.我无法解决这个问题
I request someone to help. Am unable to solve this
推荐答案
在complete中获取SF的max值并使用seq 而不是 full_seq 因为
Get max value of SF in complete and use seq instead of full_seq because
full_seq(2:4, 1) #gives
#[1] 2 3 4
#whereas
seq(max(2:4)) #gives
#[1] 1 2 3 4
那就试试吧
library(dplyr)
library(tidyr)
DF1 %>%
group_by(ID) %>%
mutate(SF= pmax(X_F, Y_F, na.rm = T)) %>%
complete(SF = seq(max(SF)))
# ID SF X_F X_A Y_F Y_A
# <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 A 1 NA NA NA NA
# 2 A 2 1 0.1 2 0.2
# 3 A 3 2 0.2 3 0.3
# 4 A 4 4 0.4 NA NA
# 5 A 5 3 0.3 5 0.4
# 6 A 6 NA NA NA NA
# 7 A 7 5 0.5 7 0.7
# 8 B 1 NA NA 1 0.1
# 9 B 2 NA NA NA NA
#10 B 3 NA NA 3 0.2
#11 B 4 NA NA 4 0.7
#12 C 1 1 0.2 1 0.1
#13 C 2 2 0.3 NA NA
#14 C 3 3 0.4 3 0.3
#15 C 4 4 0.5 4 0.4
#16 C 5 5 0.6 5 0.5
<小时>
要使用 full_seq 获得预期输出，您可以在向量中添加 1
To get your expected output with full_seq you could add 1 in the vector
DF1 %>%
group_by(ID) %>%
mutate(SF= pmax(X_F, Y_F, na.rm = T)) %>%
complete(SF = full_seq(c(1, SF), 1))
这篇关于基于多列和多行条件扩展 R 数据框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！
