应用group_by和summarise(sum)，但保留具有不相关冲突数据的列? [英] Applying group_by and summarise(sum) but keep columns with non-relevant conflicting data?
问题描述
My question is very similar to Applying group_by and summarise on data while keeping all the columns' info but I would like to keep columns which get excluded because they conflict after grouping.
Label <- c("203c","203c","204a","204a","204a","204a","204a","204a","204a","204a")
Type <- c("wholefish","flesh","flesh","fleshdelip","formula","formuladelip",
"formula","formuladelip","wholefish", "wholefishdelip")
Proportion <- c(1,1,0.67714,0.67714,0.32285,0.32285,0.32285,
0.32285, 0.67714,0.67714)
N <- (1:10)
C <- (1:10)
Code <- c("c","a","a","b","a","b","c","d","c","d")
df <- data.frame(Label,Type, Proportion, N, C, Code)
df
Label Type Proportion N C Code
1 203c wholefish 1.0000 1 1 c
2 203c flesh 1.0000 2 2 a
3 204a flesh 0.6771 3 3 a
4 204a fleshdelip 0.6771 4 4 b
5 204a formula 0.3228 5 5 a
6 204a formuladelip 0.3228 6 6 b
7 204a formula 0.3228 7 7 c
8 204a formuladelip 0.3228 8 8 d
9 204a wholefish 0.6771 9 9 c
10 204a wholefishdelip 0.6771 10 10 d
total <- df %>%
#where the Label and Code are the same the Proportion, N and C
#should be added together respectively
group_by(Label, Code) %>%
#total proportion should add up to 1
#my way of checking that the correct task has been completed
summarise_if(is.numeric, sum)
# A tibble: 6 x 5
# Groups: Label [?]
Label Code Proportion N C
<fctr> <fctr> <dbl> <int> <int>
1 203c a 1.00000 2 2
2 203c c 1.00000 1 1
3 204a a 0.99999 8 8
4 204a b 0.99999 10 10
5 204a c 0.99999 16 16
6 204a d 0.99999 18 18
直到这里我得到我想要的.现在，我想包括类型"列，但由于值冲突而被排除在外.这是我想要获得的结果
Up until here I get what I want. Now I would like to include the column Type though it is excluded because values are conflicting. this is the result I would like to obtain
# A tibble: 6 x 5
# Groups: Label [?]
Label Code Proportion N C Type
<fctr> <fctr> <dbl> <int> <int> <fctr>
1 203c a 1.00000 2 2 wholefish
2 203c c 1.00000 1 1 flesh
3 204a a 0.99999 8 8 flesh_formula
4 204a b 0.99999 10 10 fleshdelip_formuladelip
5 204a c 0.99999 16 16 wholefish_formula
6 204a d 0.99999 18 18 wholefishdelip_formuladelip
我尝试了ungroup()以及mutate和unite的一些变体，但无济于事，任何建议将不胜感激
I have tried ungroup() and some variations of mutate and unite but to no avail, any suggestions would be greatly appreciated
推荐答案
这里是data.table解决方案，我假设您要的是mean()比例，因为这些分组的比例可能不具有累加性.
Here's the data.table solution, I'm assuming you want the mean() of Proportion, since these grouped proportions are likely not additive.
setDT(df)
df[, .(Type =paste(Type,collapse="_"),
Proportion=mean(Proportion),N= sum(N),C=sum(C)), by=.(Label,Code)]
[order(Label)]
Label Code Type Proportion N C
1: 203c c wholefish 1.000000 1 1
2: 203c a flesh 1.000000 2 2
3: 204a a flesh_formula 0.499995 8 8
4: 204a b fleshdelip_formuladelip 0.499995 10 10
5: 204a c formula_wholefish 0.499995 16 16
6: 204a d formuladelip_wholefishdelip 0.499995 18 18
我不确定这是否是最干净的dplyr解决方案，但是它可以正常工作:
I'm not sure this is the cleanest dplyr solution, but it works:
df %>% group_by(Label, Code) %>%
mutate(Type = paste(Type,collapse="_")) %>%
group_by(Label,Type,Code) %>%
summarise(N=sum(N),C=sum(C),Proportion=mean(Proportion))
请注意，这里的关键是在创建合并的Type列后重新分组.
Note the key here is to re-group once you create the combined Type column.
Label Type Code N C Proportion
<fctr> <chr> <fctr> <int> <int> <dbl>
1 203c flesh a 2 2 1.000000
2 203c wholefish c 1 1 1.000000
3 204a flesh_formula a 8 8 0.499995
4 204a fleshdelip_formuladelip b 10 10 0.499995
5 204a formula_wholefish c 16 16 0.499995
6 204a formuladelip_wholefishdelip d 18 18 0.499995
这篇关于应用group_by和summarise(sum)，但保留具有不相关冲突数据的列?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！
