Answer to: Interpolate data using indicator in time series

You can use a linear regression to predict the missing values: library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union df <- tibble( year = 2015:2025, Variable = c(1000, NA, NA, NA, 1200, NA, NA, NA, 1500, NA, NA), Indicator = c(700, 730, 775, 790, 815, 870, 910, 930, 970, 980, 1030) ) df2 <- df %>% mutate(Prediction = if_else( is.na(Variable), predict(lm(Variable ~ year + Indicator, data = .), newdata = .), Variable )) library(ggplot2) library(tidyr) df2 |> pivot_longer(-year) %>% ggplot(aes(x = year, y = value, color = name)) + geom_line() + geom_point() + # highlight original values geom_point(data = ~filter(.x, name == "Variable"), size = 3) + labs(x = "Year", y = "Value", color = "Variable") + guides(color = guide_legend(reverse = T)) #> Warning: Removed 2 rows containing missing values or values outside the scale range #> (`geom_line()`). #> Warning: Removed 8 rows containing missing values or values outside the scale range #> (`geom_point()`). #> Removed 8 rows containing missing values or values outside the scale range #> (`geom_point()`). Created on 2025-11-03 with reprex v2.1.1