Sum row of list of dataframe in r - r

Question

I have a list of dataframes. Each dataframe is a Stock quote whose row names are dates and column names are buy price, sell price, shares and PL.
I want to obtain a column that contains the percentage of every positive PL contribution to the total daily PL.
Making it simplier. I have the following Data:
mylist= structure(list(`1` = structure(list(ID = c(35L, '2009-01-03', '2009-01-04', '2009-01-05'), Income = c(100, 200, 300, 400)), .Names = c("Date", "Income"), row.names = c(1L, 2L, 3L, 4L), class = "data.frame"), `2` = structure(list(ID = c('2009-01-02', '2009-01-03', '2009-01-04', '2009-01-05'), Income = c(500, -600, 700, 800)), .Names = c("Date", "Income"), row.names = c(1L, 2L, 3L, 4L), class = "data.frame"), `3` = structure(list(ID = c('2009-01-02', '2009-01-03', '2009-01-04'), Income = c(100, 200, 300)), .Names = c("Date", "Income"), row.names = c(1L, 2L, 3L), class = "data.frame")))
Which looks like this:
$`1`
Date Income
1 2009-01-01 100
2 2009-01-03 200
3 2009-01-04 300
4 2009-01-05 400
$`2`
Date Income
1 2009-01-02 500
2 2009-01-03 -600
3 2009-01-04 700
4 2009-01-05 800
$`3`
Date Income
1 2009-01-02 100
2 2009-01-03 200
3 2009-01-04 300
I want to obtain something that looks like this:
$`1`
Date Income Perc
1 2009-01-03 100 1.00
2 2009-01-03 200 0.20
3 2009-01-04 300 0.23
4 2009-01-05 400 0.33
$`2`
Date Income Perc
1 2009-01-02 500 0.83
2 2009-01-03 600 -1.50
3 2009-01-04 700 0.54
4 2009-01-05 800 0.67
$`3`
Date Income Perc
1 2009-01-02 100 0.17
2 2009-01-03 200 0.20
3 2009-01-04 300 0.23

Answer 1

I have two solutions for your problem. I highly recommend combining your data frame in one master data frame in order to reduce the complexity of the code if at all possible. I am sure there are better solutions to the "Separate Data Frame" problem, but most of them will involve multiple loops and thus negatively impact performance.
Data
mylist= structure(list(`1` = structure(list(ID = c('2009-01-02', '2009-01-03', '2009-01-04', '2009-01-05'), Income = c(100, 200, 300, 400)), .Names = c("Date", "Income"), row.names = c(1L, 2L, 3L, 4L), class = "data.frame"), `2` = structure(list(ID = c('2009-01-02', '2009-01-03', '2009-01-04', '2009-01-05'), Income = c(500, -600, 700, 800)), .Names = c("Date", "Income"), row.names = c(1L, 2L, 3L, 4L), class = "data.frame"), `3` = structure(list(ID = c('2009-01-02', '2009-01-03', '2009-01-04'), Income = c(100, 200, 300)), .Names = c("Date", "Income"), row.names = c(1L, 2L, 3L), class = "data.frame")))
Combined Data Frame
library(dplyr)
# add an ID to each data frame
for(i in 1:length(mylist)){
mylist[[i]] <- cbind(mylist[[i]], stock_id = names(mylist)[i])
}
# create data frame with all observations
my_data_frame <- do.call(rbind, mylist)
rownames(my_data_frame) <- NULL
my_data_frame %>%
group_by(Date) %>%
mutate(Perc = Income/sum(Income[Income > 0]))
# A tibble: 11 x 4
# Groups: Date [4]
Date Income stock_id Perc
<chr> <dbl> <chr> <dbl>
1 2009-01-02 100 1 0.143
2 2009-01-03 200 1 0.5
3 2009-01-04 300 1 0.231
4 2009-01-05 400 1 0.333
5 2009-01-02 500 2 0.714
6 2009-01-03 -600 2 -1.5
7 2009-01-04 700 2 0.538
8 2009-01-05 800 2 0.667
9 2009-01-02 100 3 0.143
10 2009-01-03 200 3 0.5
11 2009-01-04 300 3 0.231
Separate Data Frames
library(dplyr)
all_dates <- unique(unlist(lapply(mylist, function(x) unique(x$Date))))
for(i in 1:length(mylist)){
mylist[[i]] <- cbind(mylist[[i]], stock_id = names(mylist)[i])
}
perc_all <- list()
for(i in 1:length(all_dates)){
temporary <- lapply(mylist, function(x) x[x$Date == all_dates[i], ])
all_obs_date <- do.call(rbind, temporary)
all_obs_date$Perc <- all_obs_date$Income/sum(all_obs_date$Income[all_obs_date$Income > 0])
perc_all[[i]] <- all_obs_date
}
perc_final <- do.call(rbind, perc_all)
lapply(mylist, function(x) {
left_join(x, perc_final) %>% select(-stock_id)
})
$`1`
Date Income Perc
1 2009-01-02 100 0.1428571
2 2009-01-03 200 0.5000000
3 2009-01-04 300 0.2307692
4 2009-01-05 400 0.3333333
$`2`
Date Income Perc
1 2009-01-02 500 0.7142857
2 2009-01-03 -600 -1.5000000
3 2009-01-04 700 0.5384615
4 2009-01-05 800 0.6666667
$`3`
Date Income Perc
1 2009-01-02 100 0.1428571
2 2009-01-03 200 0.5000000
3 2009-01-04 300 0.2307692