> head(df2)
# A tibble: 20 x 2

   `Date (US)`              Value
   <dttm>                   <dbl>
 1 2016-01-02 02:24:00 243814121.
 2 2016-01-04 16:48:00 243821146.
 3 2016-01-29 21:36:00 243837845.
 4 2016-01-30 21:36:00 243838871.
 5 2016-02-04 21:36:00 243841925.
 6 2016-02-06 04:48:00 243843313.

rm(list=ls())

#http://sma.epfl.ch/~lbelzile/math342/pract_sup.html
library(readxl)
library(forecast)
library(imputeTS)
library(xts)
library(zoo)
library(data.table)
library(xlsx)

#import, combine, and sort data
df1 <- read_excel("C:/Users/PC/Desktop/Time_Series/upwork-data.xlsx")
df2 <- read_excel("C:/Users/PC/Desktop/Time_Series/upwork-data.xlsx", sheet = 2)
df <- rbind(df1,df2)
df <- df[order(df$`Date (US)`),]

#plot irregular time series
itimeseries <- as.xts(x = df$Value, order.by = df$`Date (US)`)
plot(itimeseries, grid.ticks.on = "year", yaxis.right = FALSE)

#plot interpolation
plot(na.interpolation(itimeseries))

#get interpolated values
df$interp <- na.interpolation(itimeseries)

#use only those interpolated values where data are avalailable within a day on either side
df$dateplus <- df$`Date (US)` + 24*60*60 # time moved up 24 hours
df$dateminus <- df$`Date (US)` - 24*60*60 #time moved back 24 hours
setDT(df)[, laguptime:= shift(df$"Date (US)")][] #time moved by 1 row down
setDT(df)[, lagdowntime:= shift(df$"Date (US)",type="lead")][] #time moved by 1 row up
df$new <- ifelse(df$lagdowntime < df$dateplus | df$laguptime > df$dateminus , df$interp, NA)

#keep only regular midnight values
df$time <- strftime(df$`Date (US)`, format="%H:%M:%S")
df$new <- ifelse(df$time == "00:00:00" | df$time == "01:00:00", df$new, NA)
df3 <- df[which(df$time == "00:00:00" | df$time == "01:00:00"),]

#make regular time series of all midnights
timeseries <- as.xts(x = df3$new, order.by = df3$`Date (US)`)
plot(timeseries, grid.ticks.on = "year", yaxis.right = FALSE)

#visualize missing data
plotNA.distributionBar(timeseries)