Summing the values of several R Lists based on a date key [duplicate]

Answer 1

Assuming your list is called list_df, you can bind them all together using bind_rows, group_by date and then sum all the other columns.

library(dplyr)

list_df %>%
   bind_rows() %>%
   group_by(date) %>%
   summarise_all(sum)

# A tibble: 3 x 3
#  date       clicks impressions
#  <fct>       <int>       <int>
#1 2019-06-01      3          16
#2 2019-06-02      3          14
#3 2019-06-03    111         149

which in base R could be achieved using Reduce

aggregate(.~date, Reduce(rbind, list_df), sum)

Answer 2

We can do:

cbind(date=df1[,1],do.call(`+`, list(df1[,-1],df2[,-1])),
            row.names = NULL)
        date clicks impressions
1 2019-06-01      3          16
2 2019-06-02      3          14
3 2019-06-03    111         149

If you are not sure about the presence of dates(can then cbind as above):

do.call(`+`,lapply(list(df1,df2), function(x) x[,-1]))
  clicks impressions
1      3          16
2      3          14
3    111         149

This assumes that the data sets will have the same structure always.

Answer 3

We can use data.table

library(data.table)
rbindlist(list_df)[, lapply(.SD, sum), date]
#         date clicks impressions
#1: 2019-06-01      3          16
#2: 2019-06-02      3          14
#3: 2019-06-03    111         149

data

list_df <- mget(paste0("df", 1:2))