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sum up values of compressed time series over time

Rolling sum of time series with factorCan dplyr summarise over several variables without listing each one?R Calculate date based on dates in sequential rows in a data framedplyr summarise over nested group_byGroup rows based on gaps in time seriesAggregating to String and Summing the Values Associated to Aggregate in R 3.3.0 Dplyr v 0.5.0r - Subsetting time-series data.frame based on time and thresholdUsing dplyr in R to create multiple summarise(s)Time series to data frameSelect a time period by day and month

0

I try to describe my problem via the code below. I have a data frame of a 'compressed' time series in the form of data frame: have. It contains the start and end date of a period with a value over time. I want to repeat the data as in data frame: want to ultimately get to the data frame: ultimately_want which sums up the value over time. Maybe I do not need want and get straight to ultimately_want somehow? Thanks.

library(dplyr)

start_date <- as.Date(c("2004-08-02", "2004-08-03"))
end_date <- as.Date(c("2004-08-04", "2004-08-05"))
value <- c(5, 6)
have <- data.frame(start_date, end_date, value)
have

date <- as.Date(c("2004-08-02", "2004-08-03", "2004-08-04", "2004-08-03", "2004-08-04", "2004-08-05"))
value <- c(5, 5, 5, 6, 6, 6)
want <- data.frame(date, value)
want

ultimately_want <- want %>%
 group_by(date) %>%
 summarise(total = sum(value))

ultimately_want

r

share|improve this question

asked yesterday

cs0815cs0815

5,4081978205

add a comment | 

0

I try to describe my problem via the code below. I have a data frame of a 'compressed' time series in the form of data frame: have. It contains the start and end date of a period with a value over time. I want to repeat the data as in data frame: want to ultimately get to the data frame: ultimately_want which sums up the value over time. Maybe I do not need want and get straight to ultimately_want somehow? Thanks.

library(dplyr)

start_date <- as.Date(c("2004-08-02", "2004-08-03"))
end_date <- as.Date(c("2004-08-04", "2004-08-05"))
value <- c(5, 6)
have <- data.frame(start_date, end_date, value)
have

date <- as.Date(c("2004-08-02", "2004-08-03", "2004-08-04", "2004-08-03", "2004-08-04", "2004-08-05"))
value <- c(5, 5, 5, 6, 6, 6)
want <- data.frame(date, value)
want

ultimately_want <- want %>%
 group_by(date) %>%
 summarise(total = sum(value))

ultimately_want

r

share|improve this question

asked yesterday

cs0815cs0815

5,4081978205

add a comment | 

I try to describe my problem via the code below. I have a data frame of a 'compressed' time series in the form of data frame: have. It contains the start and end date of a period with a value over time. I want to repeat the data as in data frame: want to ultimately get to the data frame: ultimately_want which sums up the value over time. Maybe I do not need want and get straight to ultimately_want somehow? Thanks.

library(dplyr)

start_date <- as.Date(c("2004-08-02", "2004-08-03"))
end_date <- as.Date(c("2004-08-04", "2004-08-05"))
value <- c(5, 6)
have <- data.frame(start_date, end_date, value)
have

date <- as.Date(c("2004-08-02", "2004-08-03", "2004-08-04", "2004-08-03", "2004-08-04", "2004-08-05"))
value <- c(5, 5, 5, 6, 6, 6)
want <- data.frame(date, value)
want

ultimately_want <- want %>%
 group_by(date) %>%
 summarise(total = sum(value))

ultimately_want

r

share|improve this question

asked yesterday

cs0815cs0815

5,4081978205

library(dplyr)

start_date <- as.Date(c("2004-08-02", "2004-08-03"))
end_date <- as.Date(c("2004-08-04", "2004-08-05"))
value <- c(5, 6)
have <- data.frame(start_date, end_date, value)
have

date <- as.Date(c("2004-08-02", "2004-08-03", "2004-08-04", "2004-08-03", "2004-08-04", "2004-08-05"))
value <- c(5, 5, 5, 6, 6, 6)
want <- data.frame(date, value)
want

ultimately_want <- want %>%
 group_by(date) %>%
 summarise(total = sum(value))

ultimately_want

share|improve this question

asked yesterday

cs0815cs0815

5,4081978205

share|improve this question

asked yesterday

cs0815cs0815

5,4081978205

asked yesterday

cs0815cs0815

5,4081978205

asked yesterday

cs0815cs0815

5,4081978205

2 Answers
2

active

oldest

votes

1

Here is a data.table approach,

library(data.table)

setDT(have)[, .(value = value, date = seq(start_date, end_date, by = "day")), 
 by = 1:nrow(have)][,.(total = sum(value)), date][]

# date total
#1: 2004-08-02 5
#2: 2004-08-03 11
#3: 2004-08-04 11
#4: 2004-08-05 6

share|improve this answer

answered yesterday

SotosSotos

31k51741

add a comment | 

-1

We can do

library(dplyr)
library(tidyr)
mutate(have,rid=row_number()) %>% gather(key,date, -value,-rid) %>% 
 select(-key)%>%group_by(rid) %>% complete(value, date=full_seq(date,1)) %>%
 group_by(date) %>% summarise(total = sum(value))

# A tibble: 4 x 2
 date total
 <date> <dbl>
1 2004-08-02 5
2 2004-08-03 11
3 2004-08-04 11
4 2004-08-05 6

share|improve this answer

edited yesterday

answered yesterday

A. SulimanA. Suliman

5,63241223

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  thanks but as far as I can tell/see this does not give me the running dates as in the df: ultimately_want
  
  – cs0815
  yesterday
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  The downvote is a bit excessive here. However, grouping on value is an accident waiting to happen. Imagine this same dataframe but both values are 5...
  
  – Sotos
  yesterday
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Sotos thanks I do agree with you on what you've said.
  
  – A. Suliman
  yesterday
  

  
  
  
  

add a comment | 

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1

Here is a data.table approach,

library(data.table)

setDT(have)[, .(value = value, date = seq(start_date, end_date, by = "day")), 
 by = 1:nrow(have)][,.(total = sum(value)), date][]

# date total
#1: 2004-08-02 5
#2: 2004-08-03 11
#3: 2004-08-04 11
#4: 2004-08-05 6

share|improve this answer

answered yesterday

SotosSotos

31k51741

add a comment | 

1

Here is a data.table approach,

library(data.table)

setDT(have)[, .(value = value, date = seq(start_date, end_date, by = "day")), 
 by = 1:nrow(have)][,.(total = sum(value)), date][]

# date total
#1: 2004-08-02 5
#2: 2004-08-03 11
#3: 2004-08-04 11
#4: 2004-08-05 6

share|improve this answer

answered yesterday

SotosSotos

31k51741

add a comment | 

Here is a data.table approach,

library(data.table)

setDT(have)[, .(value = value, date = seq(start_date, end_date, by = "day")), 
 by = 1:nrow(have)][,.(total = sum(value)), date][]

# date total
#1: 2004-08-02 5
#2: 2004-08-03 11
#3: 2004-08-04 11
#4: 2004-08-05 6

share|improve this answer

answered yesterday

SotosSotos

31k51741

library(data.table)

setDT(have)[, .(value = value, date = seq(start_date, end_date, by = "day")), 
 by = 1:nrow(have)][,.(total = sum(value)), date][]

# date total
#1: 2004-08-02 5
#2: 2004-08-03 11
#3: 2004-08-04 11
#4: 2004-08-05 6

share|improve this answer

answered yesterday

SotosSotos

31k51741

answered yesterday

SotosSotos

31k51741

answered yesterday

SotosSotos

31k51741

-1

We can do

library(dplyr)
library(tidyr)
mutate(have,rid=row_number()) %>% gather(key,date, -value,-rid) %>% 
 select(-key)%>%group_by(rid) %>% complete(value, date=full_seq(date,1)) %>%
 group_by(date) %>% summarise(total = sum(value))

# A tibble: 4 x 2
 date total
 <date> <dbl>
1 2004-08-02 5
2 2004-08-03 11
3 2004-08-04 11
4 2004-08-05 6

share|improve this answer

edited yesterday

answered yesterday

A. SulimanA. Suliman

5,63241223

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  thanks but as far as I can tell/see this does not give me the running dates as in the df: ultimately_want
  
  – cs0815
  yesterday
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  The downvote is a bit excessive here. However, grouping on value is an accident waiting to happen. Imagine this same dataframe but both values are 5...
  
  – Sotos
  yesterday
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Sotos thanks I do agree with you on what you've said.
  
  – A. Suliman
  yesterday
  

  
  
  
  

add a comment | 

-1

We can do

library(dplyr)
library(tidyr)
mutate(have,rid=row_number()) %>% gather(key,date, -value,-rid) %>% 
 select(-key)%>%group_by(rid) %>% complete(value, date=full_seq(date,1)) %>%
 group_by(date) %>% summarise(total = sum(value))

# A tibble: 4 x 2
 date total
 <date> <dbl>
1 2004-08-02 5
2 2004-08-03 11
3 2004-08-04 11
4 2004-08-05 6

share|improve this answer

edited yesterday

answered yesterday

A. SulimanA. Suliman

5,63241223

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  thanks but as far as I can tell/see this does not give me the running dates as in the df: ultimately_want
  
  – cs0815
  yesterday
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  The downvote is a bit excessive here. However, grouping on value is an accident waiting to happen. Imagine this same dataframe but both values are 5...
  
  – Sotos
  yesterday
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Sotos thanks I do agree with you on what you've said.
  
  – A. Suliman
  yesterday
  

  
  
  
  

add a comment | 

We can do

library(dplyr)
library(tidyr)
mutate(have,rid=row_number()) %>% gather(key,date, -value,-rid) %>% 
 select(-key)%>%group_by(rid) %>% complete(value, date=full_seq(date,1)) %>%
 group_by(date) %>% summarise(total = sum(value))

# A tibble: 4 x 2
 date total
 <date> <dbl>
1 2004-08-02 5
2 2004-08-03 11
3 2004-08-04 11
4 2004-08-05 6

share|improve this answer

edited yesterday

answered yesterday

A. SulimanA. Suliman

5,63241223

library(dplyr)
library(tidyr)
mutate(have,rid=row_number()) %>% gather(key,date, -value,-rid) %>% 
 select(-key)%>%group_by(rid) %>% complete(value, date=full_seq(date,1)) %>%
 group_by(date) %>% summarise(total = sum(value))

# A tibble: 4 x 2
 date total
 <date> <dbl>
1 2004-08-02 5
2 2004-08-03 11
3 2004-08-04 11
4 2004-08-05 6

share|improve this answer

edited yesterday

answered yesterday

A. SulimanA. Suliman

5,63241223

answered yesterday

A. SulimanA. Suliman

5,63241223

answered yesterday

A. SulimanA. Suliman

5,63241223