I have a dataset that looks like this:
ID created_at
MUM-0001 2014-04-16
MUM-0002 2014-01-14
MUM-0003 2014-04-17
MUM-0004 2014-04-12
MUM
You could use data.table
to get the sequence
of Dates from 'created_at' to '2015-07-12', grouped by the 'ID' column.
library(data.table)
setDT(df1)[, list(date=seq(created_at, as.Date('2015-07-12'), by='1 day')) , ID]
If you need an option with dplyr
, use do
library(dplyr)
df1 %>%
group_by(ID) %>%
do( data.frame(., Date= seq(.$created_at,
as.Date('2015-07-12'), by = '1 day')))
If you have duplicate IDs, then we may need to group by row_number()
df1 %>%
group_by(rn=row_number()) %>%
do(data.frame(ID= .$ID, Date= seq(.$created_at,
as.Date('2015-07-12'), by = '1 day'), stringsAsFactors=FALSE))
Based on @Frank's commment, the new idiom for tidyverse
is
library(tidyverse)
df1 %>%
group_by(ID) %>%
mutate(d = list(seq(created_at, as.Date('2015-07-12'), by='1 day')), created_at = NULL) %>%
unnest()
In the case of data.table
setDT(df1)[, list(date=seq(created_at,
as.Date('2015-07-12'), by = '1 day')), by = 1:nrow(df1)]
df1 <- structure(list(ID = c("MUM-0001", "MUM-0002", "MUM-0003",
"MUM-0004",
"MUM-0005", "MUM-0006"), created_at = structure(c(16176, 16084,
16177, 16172, 16178, 16177), class = "Date")), .Names = c("ID",
"created_at"), row.names = c(NA, -6L), class = "data.frame")