Excel or R: Preparing time series from multiple sources?

Question

Lately I often had to handle time series data from multiple .csv sources in the same analysis. Let\'s assume for simplicity that all series are regular quarterly series (no

Answer 1

My strategy for problems of this type is:

1. Read each data source into a standard data.frame
2. Clean each data.frame, i.e. get data into desired format, process missing values, etc.
3. Merge or join into standard data.frame
4. Perform any aggregate data cleanup, e.g. adding blank lines, removing duplicates, etc.
5. Only then pass data to the next step, such as conversion to ts object, plot it, etc.

Using your example data:

v1 <- "27.05.11;5965.95
26.05.11;5947.06
25.05.11;5942.82
24.05.11;5939.98"

v2 <- "Germany;Switzerland;USA;OECDEurope
69,90974;61,8241;55,60966;64,96157
67,0394;62,18966;56,47361;64,15152
70,56651;63,6347;56,87237;65,43568"


v3 <- "1984-04-01,33.3238396624473
1984-07-01,63.579833082501
1984-10-01,35.8375401560349"

# Read and clean data
dat1 <- read.table(textConnection(v1), header=FALSE, sep=";", dec=".")
names(dat1) <- c("date", "V1")
dat1$date <- as.Date(dat1$date, format="%d.%m.%y")
dat1

dat2 <- read.table(textConnection(v2), header=TRUE, sep=";", dec=",")
dat2$date <- seq(as.Date("2011/1/1"), by="3 months", length.out=3)
dat2

dat3 <- read.table(textConnection(v3), header=FALSE, sep=",", dec=".")
names(dat3) <- c("date", "V2")
dat3$date <- as.Date(dat3$date)
dat3

# Merge separate data.frames.
# I use join() in package plyr, you may wish to use merge(), rbind.fill, etc
library(plyr)
join(join(dat1, dat2, type="full"), dat3, type="full")

The results:

         date      V1  Germany Switzerland      USA OECDEurope       V2
1  2011-05-27 5965.95       NA          NA       NA         NA       NA
2  2011-05-26 5947.06       NA          NA       NA         NA       NA
3  2011-05-25 5942.82       NA          NA       NA         NA       NA
4  2011-05-24 5939.98       NA          NA       NA         NA       NA
5  2011-01-01      NA 69.90974    61.82410 55.60966   64.96157       NA
6  2011-04-01      NA 67.03940    62.18966 56.47361   64.15152       NA
7  2011-07-01      NA 70.56651    63.63470 56.87237   65.43568       NA
8  1984-04-01      NA       NA          NA       NA         NA 33.32384
9  1984-07-01      NA       NA          NA       NA         NA 63.57983
10 1984-10-01      NA       NA          NA       NA         NA 35.83754