Fill missing dates in several time series stored in same database - r

Question

I'm a complete beginner to R and I just need to do some quick cleaning of my data. But I ran into a problem I can't wrap my head around.
So I have a Postgres db with timeseries, Columns are ID, DATE and VALUE (temperature). Each ID is a new measuring station, so I have a time serie for each id (around 2000 unique ids, 4m rows). The dates span from 1915-2016, some series are overlapping some are not. If there is missing measurement from a week I want to fill those weeks with an NA value (which i interpolate after).
The problem i run into is that complete(Date.seq) creates NA values for all weeks between 1915 and 2016, I clearly understand why it happens. How can I make so it only fills values between the actual start and end date of the specific timeserie? I want a moving min and max which is dependent on the start date and end date of each specific ID and than fill missing dates between the start and end date of each ID.
library("RpostgreSQL")
library("tidyverse")
library("lubridate")
con <- dbConnect(PostgreSQL(), user = "postgres",
dbname="", password = "", host = "localhost", port= "5432")
out <- dbGetQuery(con, "SELECT * FROM *******.Weekly_series")
out %>%
group_by(ID)%>%
mutate(DATE = as.Date(DATE)) %>%
complete(DATE = seq(ymd("1915-04-14"), ymd("2016-03-30"), by= "week"))
Ignore errors in the connect line.
Thanks in advance.
Edit1
Sample data
ID DATE VALUE
1 2015-10-01 1
1 2015-10-08 1
1 2015-10-15 1
1 2015-10-29 1
2 1956-01-01 1
2 1956-01-15 1
2 1956-01-22 1
3 1982-01-01 1
3 1982-01-15 1
3 1982-01-22 1
3 1982-01-29 1
Excpected output
ID DATE VALUE
1 2015-10-01 1
1 2015-10-08 1
1 2015-10-15 1
1 2015-10-22 NA
1 2015-10-29 1
2 1956-01-01 1
2 1956-01-08 NA
2 1956-01-15 1
2 1956-01-22 1
3 1982-01-01 1
3 1982-01-08 NA
3 1982-01-15 1
3 1982-01-22 1
3 1982-01-29 1

Answer 1

Using the data you provided, this works. I don't know why this works and your whole code does not, but possibly in your code, the data structure is not what is needed. If so, something like out <- tibble::as_tibble(out) might work. My other guess is that complete isn't drawing from the package you need. Using tidyr::complete works on the sample.
library(lubridate)
library(dplyr)
library(tidyr)
a <- "ID DATE VALUE
1 2015-10-01 1
1 2015-10-08 1
1 2015-10-15 1
1 2015-10-29 1
2 1956-01-01 1
2 1956-01-15 1
2 1956-01-22 1
3 1982-01-01 1
3 1982-01-15 1
3 1982-01-22 1
3 1982-01-29 1"
df <- read.table(text = a, header = TRUE)
big_df1 <- df %>%
filter(ID == 1)%>%
mutate(DATE = as.Date(DATE)) %>%
tidyr::complete(DATE = seq(ymd(min(DATE)), ymd(max(DATE)), by= "week"))
big_df2 <- df %>%
filter(ID == 2)%>%
mutate(DATE = as.Date(DATE)) %>%
tidyr::complete(DATE = seq(ymd(min(DATE)), ymd(max(DATE)), by= "week"))
big_df3 <- df %>%
filter(ID == 3)%>%
mutate(DATE = as.Date(DATE)) %>%
tidyr::complete(DATE = seq(ymd(min(DATE)), ymd(max(DATE)), by= "week"))
big_df <- rbind(big_df1, big_df2, big_df3)
big_df
DATE ID VALUE
<date> <int> <int>
1 2015-10-01 1 1
2 2015-10-08 1 1
3 2015-10-15 1 1
4 2015-10-22 NA NA
5 2015-10-29 1 1
6 1956-01-01 2 1
7 1956-01-08 NA NA
8 1956-01-15 2 1
9 1956-01-22 2 1
10 1982-01-01 3 1
11 1982-01-08 NA NA
12 1982-01-15 3 1
13 1982-01-22 3 1
14 1982-01-29 3 1