数据转换：从R中的二元数据到观测数据

Question

I have a (directed) dyadic dataset that looks something like this (see below). What I want to do now is to keep just one observation per year. So in this case only one observation for 1992 (AFG 1992) and one in 1993 (AFG 1993), while deleting other observations. It doesn't matter which observation from the same year I keep in the data (not interested in country2).

 country1   country2    year    X   X1
Afghanistan Colombia    1992    1   0.44
Afghanistan Venezuela   1992    1   0.45
Afghanistan Peru        1992    1   0.46
Afghanistan Brazil      1992    1   0.47
Afghanistan Bolivia     1992    1   0.48
Afghanistan Chile       1992    1   0.49
Afghanistan Argentina   1992    1   0.50
Afghanistan Uruguay     1993    0   0.51
Afghanistan USA         1993    0   0.52
Afghanistan Canada      1993    0   0.53
Afghanistan UK          1993    0   0.54
Afghanistan Netherlands 1993    0   0.55
Afghanistan Belgium     1993    0   0.56
Afghanistan Luxembourg  1993    0   0.57
Afghanistan France      1993    0   0.58

My try:

newdata<- data %>% 
  group_by(country1,year) %>%
  summarise() %>%
  select(unique.x=country1, unique.y=year)

This works BUT how do I keep all other variables from "data" in the "newdata"? I can't think of any way of doing this (which I find more practical). Any help?

Desired outcome

    country1     year   X
    Afghanistan 1991   1
    Afghanistan 1992   0

dput(data) structure(list(country1 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Afghanistan", class = "factor"), country2 = structure(c(8L, 33L, 24L, 5L, 4L, 7L, 1L, 32L, 31L, 6L, 30L, 21L, 3L, 19L, 14L, 29L, 27L, 26L, 15L, 25L, 2L, 17L, 10L, 18L, 13L, 28L, 23L, 11L, 9L, 16L, 12L, 20L, 22L), .Label = c("Argentina", "Austria", "Belgium", "Bolivia, Plurinational State of", "Brazil", "Canada", "Chile", "Colombia", "Cuba", "Czech Republic", "Denmark", "Dominican Republic", "Finland", "France", "Germany", "Guinea-Bissau", "Hungary", "Italy", "Luxembourg", "Mauritania", "Netherlands", "Niger", "Norway", "Peru", "Poland", "Portugal", "Spain", "Sweden", "Switzerland", "United Kingdom", "United States", "Uruguay", "Venezuela, Bolivarian Republic of"), class = "factor"), year = c(1992L, 1992L, 1992L, 1992L, 1992L, 1992L, 1992L, 1993L, 1993L, 1993L, 1993L, 1993L, 1993L, 1993L, 1993L, 19 94L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L), X = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), X1 = c(0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76)), .Names = c("country1", "country2", "year", "X", "X1"), class = "data.frame", row.names = c(NA, -33L))

Answer 1

newdata <- olddata[!duplicated(olddata$year),]

Answers the question

newdata <- olddata[!duplicated(paste(olddata$country1, olddata$year)),]

Gives you what you want

Answer 2

I don't truly understand your question, but to get your desired output you can use:

data %>% 
  group_by(country1, year) %>%
  summarise(X = mean(X))

When you apply this to your entire data.frame, bear in mind this code will return the mean of all values in X for unique combinations of country1 and year .

Answer 3

you can try:

data %>%
    group_by(year) %>%
    top_n(1) %>%
    select(country1, X)