Am trying to compute the correlations of the below countries, with USA. I have a relatively big dataset with 80 variables & 3000 observations in my first df as below, so am trying to use R to automate this instead of using excel.

I am trying to compute correlations for the countries in the first df (i.e. Germany, Italy, Japan and more) with USA in the 2nd df. So it should go Germany - USA, Italy - USA, Japan - USA and so on.

Not too sure how should I begin - should I loop every column in the first table to correlate with USA in the 2nd? Help is much appreciated.

Thanks!

df1

and on and on

df2

and on and on

CodePudding user response：

Something like that should do the trick:

df1 <- 
  tibble(
    date = 2001:2010,
    Germany = runif(10),
    Italy = runif(10),
    Japan = runif(10)
  )

df2 <-
  tibble(
    date = 2001:2010,
    USA = runif(10)
  )

df.cor <-
  df1 %>%
  summarise(across(-one_of('date'), ~ cor(.x, df2$USA)))

df.cor

Note: You have to be sure that dates are consistent between df1 and df2. You can use join function (e.g. left_join) to ensure this

CodePudding user response：

You could use this approach:

countries = c("Germany", "Italy", "Japan")
left_join(df1, df2) %>% summarise(across(countries, ~cor(., USA)))

1. left_join merges df1 and df2 together so that the dates always match up with one another
2. summarise allows you to perform column-wise operations
3. across tells you which columns you want to make a correlation with USA
4. ~cor(., USA) says take each country and perform the correlation with USA

Germany  Italy  Japan
    <dbl>  <dbl>  <dbl>
1  -0.393 -0.147 -0.214

Thank you Damien Georges for the data.