Sum group values by lubridate %within% interval - r

Question

There are 10 projects split between group A & B, each with different start and end dates. For each day within a given period the sum of outputX and outputY needs to be calculated. I manage to do this for all projects together, but how to split the results per group?
I've made several attempts with lapply() and purrr:map(), also looking at filters and splits, but to no avail. An example that doesn't distinguish between groups is found below.
library(tidyverse)
library(lubridate)
df <- data.frame(
project = 1:10,
group = c("A","B"),
outputX = rnorm(2),
outputY = rnorm(5),
start_date = sample(seq(as.Date('2018-01-3'), as.Date('2018-1-13'), by="day"), 10),
end_date = sample(seq(as.Date('2018-01-13'), as.Date('2018-01-31'), by="day"), 10))
df$interval <- interval(df$start_date, df$end_date)
period <- data.frame(date = seq(as.Date("2018-01-08"), as.Date("2018-01-17"), by = 1))
df_sum <- do.call(rbind, lapply(period$date, function(x){
index <- x %within% df$interval;
list("X" = sum(df$outputX[index]),
"Y" = sum(df$outputY[index]))}))
outcome <- cbind(period, df_sum) %>% gather("id", "value", 2:3)
outcome
Ultimately, it should be a 40x4 table. Some suggestions are much appreciated!

Answer 1

If I understand you correctly, you need to use inner join. SO can suggest us to use sqldf. See https://stackoverflow.com/a/11895368/9300556
With your data we can do smth like this. There is no need to calculate df$interval but we need to add ID to period, otherwise sqldf wont work.
df <- data.frame(
project = 1:10,
group = c("A","B"),
outputX = rnorm(2),
outputY = rnorm(5),
start = sample(seq(as.Date('2018-01-3'), as.Date('2018-1-13'), by="day"), 10),
end = sample(seq(as.Date('2018-01-13'), as.Date('2018-01-31'), by="day"), 10))
# df$interval <- interval(df$start_date, df$end_date)
period <- data.frame(date = seq(as.Date("2018-01-08"), as.Date("2018-01-17"), by = 1)) %>%
mutate(id = 1:nrow(.))
Then we can use sqldf
sqldf::sqldf("select * from period inner join df
on (period.date > df.start and period.date <= df.end) ") %>%
as_tibble() %>%
group_by(date, group) %>%
summarise(X = sum(outputX),
Y = sum(outputY)) %>%
gather(id, value, -group, -date)
# A tibble: 40 x 4
# Groups: date [10]
date group id value
<date> <fct> <chr> <dbl>
1 2018-01-08 A X 3.04
2 2018-01-08 B X 2.34
3 2018-01-09 A X 3.04
4 2018-01-09 B X 3.51
5 2018-01-10 A X 3.04
6 2018-01-10 B X 4.68
7 2018-01-11 A X 4.05
8 2018-01-11 B X 4.68
9 2018-01-12 A X 4.05
10 2018-01-12 B X 5.84
# ... with 30 more rows