Home/ Questions/Q 3660472
In Process

The Archive Base Latest Questions

Editorial Team
  • 0
Asked: 2026-05-19T01:12:39+00:00

I have a quite long and (for me) complex question. I have voting data

  • 0

I have a quite long and (for me) complex question. I have voting data from the Council of the European Union, where the voting behaviour for each country has been coded according to a nominal scale:

0: yes
1: no
2: Abstention
3: no with a negative statement
4: Abstention with a negative statement
5: yes with a negative statement

The data is in the following format (see the end of the post for a dump of 20 observations from the data set):

Country1 Country2 Country3 ... Date
1        0        0        ... 2004-12-12
1        2        0        ... 2003-02-14
2        0        1        ... 2004-05-22
...      ...      ...      ... ...

First of all I would like to aggregate the data into monthly intervals, where for each month we have a sum of how many 0,1,2 etc there were for each country. Ideally the data should look like this:

Month    Country   sum of 0s    sum of 1s   Sum of 2s
January  Country1  2            0           1  
January  Country2  4            0           0
...      ...       ...          ...         ...

Once this has been done I would like to put the data into panel format like this:

Country   Month    sum of 0s   sum of 1s   sum of 2s
Country1  January  2           0           1 
Country1  February 0           1           3
...       ...      ...         ...         ...
Country2  January  4           0           0
Country2  February 2           2           0
...       ...      ...         ...         ...

I am sorry if this is a very time consuming question, but I have been playing around with aggregate, by and different apply functions forever, without being able to get the desired result. Any help will be greatly appreciated!

20 observations from the data set (output from the dput() function):

    structure(list(Recitals = c(29L, 13L, 2L, 20L, 10L, 18L, 29L, 
6L, 4L, 16L, 7L, 6L, 12L, 23L, 6L, 10L, 2L, 6L, 9L, 8L, 7L), 
    Voting_Rule = structure(c(4L, 4L, 5L, 5L, 5L, 5L, 5L, 4L, 
    4L, 4L, 5L, 4L, 5L, 4L, 5L, 4L, 4L, 5L, 4L, 4L, 4L), .Label = c("0", 
    "Qualified Majority", "Simple Majority", "Unanimity", "Qualified majority", 
    "Simple majority"), class = "factor"), Belgium = c(0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L), Denmark = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), 
    Czech.Republic = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Germany = c(0L, 
    0L, 0L, 0L, 0L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L), Estonia = c(0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
    ), Greece = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Spain = c(0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L), France = c(0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Ireland = c(0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L), Italy = c(0L, 0L, 0L, 0L, 0L, 0L, 5L, 
    0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), 
    Cyprus = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Latvia = c(0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L), Lithuania = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Luxembourg = c(0L, 
    0L, 0L, 0L, 0L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L), Hungary = c(0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
    ), Malta = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Netherlands = c(0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L), Austria = c(0L, 0L, 0L, 1L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
    ), Poland = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Portugal = c(0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L), Slovenia = c(0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
    ), Slovakia = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Finland = c(0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L), Sweden = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), 
    UK = c(0L, 0L, 0L, 0L, 0L, 0L, 5L, 0L, 0L, 0L, 5L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Dates = structure(c(12716, 
    12716, 12716, 12674, 12674, 12698, 12705, 12724, 12738, 12738, 
    12716, 12741, 12744, 12754, 12754, 12758, 12758, 12758, 12759, 
    12759, 12759), class = "Date")), .Names = c("Recitals", "Voting_Rule", 
"Belgium", "Denmark", "Czech.Republic", "Germany", "Estonia", 
"Greece", "Spain", "France", "Ireland", "Italy", "Cyprus", "Latvia", 
"Lithuania", "Luxembourg", "Hungary", "Malta", "Netherlands", 
"Austria", "Poland", "Portugal", "Slovenia", "Slovakia", "Finland", 
"Sweden", "UK", "Dates"), row.names = c(752L, 753L, 762L, 774L, 
775L, 776L, 777L, 780L, 789L, 790L, 793L, 794L, 797L, 816L, 817L, 
818L, 819L, 820L, 824L, 825L, 826L), class = "data.frame")
Share

Leave an answer

You must login to add an answer.

Need An Account,

1 Answer

  1. Editorial Team

    Hadley’s reshape2 and plyr package would greatly help your work:

    library(reshape2)

# load your data into variable d1
# d1 <- struct( ... your data ...)

# first, melt the data.
# molten data is very very useful for further transformation.
d2 <- reshape2:::melt.data.frame(d1[,-(1:2)], # drop unused variables
                                 id.vars="Dates",
                                 variable.name="Country")

# create a variable "Month" from Date
d2$Month <- months(d2$Date)

# cast the data to the desired format using appropriate function (here, length())
d3 <- dcast(d2, Country+Month~value, length)

# finally change the names of newly created variables
names(d3)[-(1:2)] <- sprintf("sum of %ss", names(d3)[-(1:2)])
    • 0