我有2个数据帧。一个有一个ID列，其中有很多ID。 另一个只有第一列的特定行。那些是我的标记。 我需要根据第二列的id值得到特定列中值的总和。 第一列可能是
id goals cards group
1 2 2 1
2 3 2 1
3 4 2 1
4 5 1 1
5 1 2 1
1 2 2 2
2 3 2 2
3 4 2 2
4 5 1 3
5 1 2 3
第二个：
id goals cards group
2 3 2 1
5 1 2 1
2 3 2 2
3 4 2 2
5 1 2 3
我需要得到的东西：
id goals cards group points
1 2 2 1 2-(2+2)
2 3 2 1 0 cause in second list
3 4 2 1 4-(2+1+2)
4 5 1 1 5-(1+2)
5 1 2 1 0 cause in second list
1 2 2 2 2-(2+2)
2 3 2 2 0
3 4 2 2 0
4 5 1 3 5-(1+2)
5 1 2 3 0
类似于：??
df1<- df1%>%
rowwise() %>%
mutate(points=
goals
-(sum( df1$cards[df1$id <= df2$id & df1$id>df1$id])))
答案 0 :(得分：1)
df1 = read.table(text = "
id goals cards
1 2 2
2 3 2
3 4 2
4 5 1
5 1 2
", header=T)
df2 = read.table(text = "
id goals cards
2 3 2
5 1 2
", header=T)
library(dplyr)
# function that gets an id and returns the sum of cards based on df2
GetSumOfCards = function(x) {
ids = min(df2$id[df2$id >= x]) # for a given id of df1 find the minimum id in df2 that is bigger than this id
ifelse(x %in% df2$id, # if the given id exists in df2
0, # sum of cards is zero
sum(df1$cards[df1$id >= x & df1$id <= ids])) # otherwise get sum of cards in df1 from this id until the id obtained before
}
# update function to be vectorised
GetSumOfCards = Vectorize(GetSumOfCards)
df1 %>%
mutate(sum_cards = GetSumOfCards(id), # get sum of cards for each id using the function
points = goals - sum_cards) # get the points
# id goals cards sum_cards points
# 1 1 2 2 4 -2
# 2 2 3 2 0 3
# 3 3 4 2 5 -1
# 4 4 5 1 3 2
# 5 5 1 2 0 1
根据您更新的问题，对每一行应用类似的功能会使进程变得非常慢。因此，此解决方案以一种方式对数据进行分组，您可以在数据/行的块上计算卡片数：
df1 = read.table(text = "
id goals cards group
1 2 2 1
2 3 2 1
3 4 2 1
4 5 1 1
5 1 2 1
1 2 2 2
2 3 2 2
3 4 2 2
4 5 1 3
5 1 2 3
", header=T)
df2 = read.table(text = "
id goals cards group
2 3 2 1
5 1 2 1
2 3 2 2
3 4 2 2
5 1 2 3
", header=T)
library(dplyr)
df1 %>%
arrange(group, desc(id)) %>% # order by group and id descending (this will help with counting the cards)
left_join(df2 %>% # join specific columns of df2 and add a flag to know that this row exists in df2
select(id, group) %>%
mutate(flag = 1), by=c("id","group")) %>%
mutate(flag = ifelse(is.na(flag), 0, flag), # replace NA with 0
flag2 = cumsum(flag)) %>% # this flag will create the groups we need to count cards
group_by(group, flag2) %>% # for each new group (we need both as the card counting will change when we have a row from df2, or if group changes)
mutate(sum_cards = ifelse(flag == 1, 0, cumsum(cards))) %>% # get cummulative sum of cards unless the flag = 1, where we need 0 cards
ungroup() %>% # forget the grouping
arrange(group, id) %>% # back to original order
mutate(points = goals - sum_cards) %>% # calculate points
select(-flag, -flag2) # remove flags
# # A tibble: 10 x 6
# id goals cards group sum_cards points
# <int> <int> <int> <int> <dbl> <dbl>
# 1 1 2 2 1 4 -2
# 2 2 3 2 1 0 3
# 3 3 4 2 1 5 -1
# 4 4 5 1 1 3 2
# 5 5 1 2 1 0 1
# 6 1 2 2 2 4 -2
# 7 2 3 2 2 0 3
# 8 3 4 2 2 0 4
# 9 4 5 1 3 3 2
# 10 5 1 2 3 0 1