如何在R中插值数据

Question

I am quite new to R Studio and I have a question:

I have the following data: (Date; Time; Value)

02.01.11;11:00;576
02.01.11;11:05;552
02.01.11;11:10;672
02.01.11;11:15;720
02.01.11;11:20;336
02.01.11;11:25;408
02.01.11;11:30;288
02.01.11;11:35;228
02.01.11;11:40;288
02.01.11;11:45;288
02.01.11;11:50;288
02.01.11;11:55;312
02.01.11;12:00;180
02.01.11;12:05;120
02.01.11;12:10;120
02.01.11;12:15;228
02.01.11;12:20;276
02.01.11;12:25;228
02.01.11;12:30;444
02.01.11;12:35;612
02.01.11;12:40;300
02.01.11;12:45;288
02.01.11;12:50;300
02.01.11;12:55;336
02.01.11;13:00;240
02.01.11;13:05;252
02.01.11;13:10;192
02.01.11;13:15;180
02.01.11;13:20;192
02.01.11;13:25;432
02.01.11;13:30;912
02.01.11;13:35;960
02.01.11;13:40;936
02.01.11;13:45;1260
02.01.11;13:50;1008

For some calculation I need them in 1 minutes time frames. So can somebody help me finding out, how I can interpolate the "missing" values, so that they fit in the present ones?

I used this command to get the Dataframe:

df <- read_delim("~/values.txt", ";", escape_double = FALSE, col_types = cols(Date = col_date(format = "%d.%m.%y"), Value = col_double(), Time = col_time(format = "%H:%M")), trim_ws = TRUE)

Answer 1

To deal with minute data, I would recommend using package xts and the function na.approx from package zoo . In a nutshell, you need to create an empty vector of minute data that you will merge with your original data. Then, you can use na.approx to approximate the missing values.

#Intial data, not by minute    
datetime <- Sys.time()
date_time_init <- Sys.time()+c(0,3,5,8)*60
df1 <- xts(c(1:4),date_time_init)
> df1
                    [,1]
2017-06-02 03:10:20    1
2017-06-02 03:13:20    2
2017-06-02 03:15:20    3
2017-06-02 03:18:20    4

#Create time sequence by minute
date_time_complete <- seq.POSIXt(from=min(date_time_init),
                                 to=max(date_time_init),by="min") 

#Merge initial data with new time sequence
df2 <- merge(df1,xts(,date_time_complete))
                    df1
2017-06-02 03:10:20   1
2017-06-02 03:11:20  NA
2017-06-02 03:12:20  NA
2017-06-02 03:13:20   2
2017-06-02 03:14:20  NA
2017-06-02 03:15:20   3
2017-06-02 03:16:20  NA
2017-06-02 03:17:20  NA
2017-06-02 03:18:20   4

na.approx(df2)
                         df1
2017-06-02 03:07:24 1.000000
2017-06-02 03:08:24 1.333333
2017-06-02 03:09:24 1.666667
2017-06-02 03:10:24 2.000000
2017-06-02 03:11:24 2.500000
2017-06-02 03:12:24 3.000000
2017-06-02 03:13:24 3.333333
2017-06-02 03:14:24 3.666667
2017-06-02 03:15:24 4.000000

Answer 2

Let's assume you a) know how to read in data from a text file with semicolon delimiters. I would advise using stringsAsFators=FALSE . And b) know how to conjoin columns of text with paste . So with a dataframe like

> str(dat)
'data.frame':   35 obs. of  3 variables:
 $ Dates: chr  "02.01.11" "02.01.11" "02.01.11" "02.01.11" ...
 $ Times: chr  "11:00" "11:05" "11:10" "11:15" ...
 $ Vals : int  576 552 672 720 336 408 288 228 288 288 ...

One can use the base function approxfun and give the paste()-ed Dates&Times to as.POSIXct to form the backbone of the solution:

dat$Datetimes <- with(dat, as.POSIXct( paste(Dates,Times), format="%m.%d.%y %H:%M") )

Now create a new dataframe starting with a sequence of "minute-points" made with seq.POSIXct spanning the range of the times:

dat2 <- data.frame(Mins = seq(min(dat$Datetimes), max(dat$Datetimes), by="1 min") )

And then use an the expression of the form: approxfun( <inner args>)(<outer args>) to make the linear interpolations in the missing intervals. approxfun returns a function based on the <inner-args> to which the "minute-points" are given as the <outer-args> :

dat2$interp <- approxfun(dat$Datetimes, dat$Vals)(dat2$Mins)
str(dat2)
#----------
'data.frame':   171 obs. of  2 variables:
 $ Mins  : POSIXct, format: "2011-02-01 11:00:00" "2011-02-01 11:01:00" ...
 $ interp: num  576 571 566 562 557 ...

To "see" the results on the same scale:

with(dat, plot(Datetimes,Vals,col="red") )
with(dat2, points(Mins,interp ,cex=0.2))