Fill dates and create new rows in dataframe - r

Question

rptdate st
1 2/18/2017 2/12/2017
2 2/25/2017 2/19/2017
3 3/4/2017 2/26/2017
4 3/11/2017 3/5/2017
5 3/18/2017 3/12/2017
6 3/25/2017 3/19/2017
7 4/1/2017 3/26/2017
8 4/8/2017 4/2/2017
9 4/15/2017 4/9/2017
10 4/22/2017 4/16/2017
11 4/29/2017 4/23/2017
12 5/6/2017 4/30/2017
13 5/13/2017 5/7/2017
14 5/20/2017 5/14/2017
15 5/27/2017 5/21/2017
16 6/3/2017 5/28/2017
17 6/10/2017 6/4/2017
So basically rptdate is a bunch of Saturdays and st is each previous Sunday.
I would like to reshape this dataframe (the data is in date format) in this manner:
what I would like to do is this:
i=1
j=1
While (rptdate[i][j]>=st[i][j])
{add a new row where rptdate[i][j+1]= rptdate[i][j] and st[i][j+1]=rptdate[i][j]+1}
So basically, my desired new dataframe should be like this:
rptdate st
1 2/18/2017 2/12/2017
2/18/2017 2/13/2017
2/18/2017 2/14/2017
2/18/2017 2/15/2017
2/18/2017 2/16/2017
2/18/2017 2/17/2017
2/18/2017 2/18/2017
2 2/25/2017 2/19/2017
2/25/2017 2/20/2017
2/25/2017 2/21/2017
2/25/2017 2/22/2017
2/25/2017 2/23/2017
2/25/2017 2/24/2017
2/25/2017 2/25/2017
Thank you very much for your time.

Answer 1

Here is an idea via base R. You need to convert you variables to dates first. Then expand the data frame with extra 7 rows (1 week) for each date. Generate all the missing dates using seq and add them in your st variable.
d2[] <- lapply(d2, function(i) as.Date(i, format = '%m/%d/%Y'))
d3 <- d2[rep(row.names(d2), each = 7),]
d3$st<- do.call(c, Map(function(x, y)seq(x, y, by = 1), d2$st, d2$rptdate))
head(d3, 10)
# rptdate st
#1 2017-02-18 2017-02-12
#1.1 2017-02-18 2017-02-13
#1.2 2017-02-18 2017-02-14
#1.3 2017-02-18 2017-02-15
#1.4 2017-02-18 2017-02-16
#1.5 2017-02-18 2017-02-17
#1.6 2017-02-18 2017-02-18
#2 2017-02-25 2017-02-19
#2.1 2017-02-25 2017-02-20
#2.2 2017-02-25 2017-02-21
...

Answer 2

library(data.table)
dt <- data.table(V1=as.Date(c("2/18/2017","2/25/2017","3/4/2017","3/11/2017"),format = "%m/%d/%Y"),
V2=as.Date(c("2/12/2017","2/19/2017","2/26/2017","3/5/2017"),format = "%m/%d/%Y"))
for(i in 0:6){
dt[,paste0("colomn_i",i):=V1-i]
}
dt[,V2:=NULL]
temp <- melt(dt,id.vars = "V1")
setorder(temp,V1,value)
temp[,variable:=NULL]
Even though, eventually V2, is not needed

Answer 3

Here is an example using functions from dplyr and lubridate. dt2 would be the final output.
# Create example data frame
dt <- read.table(text = "rptdate st
2/18/2017 2/12/2017
2/25/2017 2/19/2017
3/4/2017 2/26/2017
3/11/2017 3/5/2017
3/18/2017 3/12/2017
3/25/2017 3/19/2017
4/1/2017 3/26/2017
4/8/2017 4/2/2017
4/15/2017 4/9/2017
4/22/2017 4/16/2017
4/29/2017 4/23/2017
5/6/2017 4/30/2017
5/13/2017 5/7/2017
5/20/2017 5/14/2017
5/27/2017 5/21/2017
6/3/2017 5/28/2017
6/10/2017 6/4/2017",
header = TRUE, stringsAsFactors = FALSE)
# Load packages
library(dplyr)
library(lubridate)
# Process the data
dt2 <- dt %>%
mutate(rptdate = mdy(rptdate), st = mdy(st)) %>%
rowwise() %>%
do(data_frame(rptdate = rep(.$rptdate[1], 7),
st = seq(.$st[1], .$rptdate[1], by = 1))) %>%
mutate(rptdate = format(rptdate, "%m/%d/%Y"),
st = format(st, "%m/%d/%Y"))
Or you can use the map2 and unnest functions from tidyverse.
# Load packages
library(tidyverse)
library(lubridate)
# Process the data
dt2 <- dt %>%
mutate(rptdate = mdy(rptdate), st = mdy(st)) %>%
mutate(st = map2(st, rptdate, seq, by = 1)) %>%
unnest() %>%
mutate(rptdate = format(rptdate, "%m/%d/%Y"),
st = format(st, "%m/%d/%Y"))