Calculate Sum of Random observations as sum per week in R

Question

I have a dataset of random, sometimes infrequent, events that I want to count as a sum per week. Due to the randomness they are not linear so other examples I have tried so far are not applicable.

The data is similar to this:

df_date <- data.frame( Name = c("Jim","Jim","Jim","Jim","Jim","Jim","Jim","Jim","Jim","Jim",
                                "Sue","Sue","Sue","Sue","Sue","Sue","Sue","Sue","Sue","Sue"),
                       Dates = c("2010-1-1", "2010-1-2", "2010-01-5","2010-01-17","2010-01-20",
                                 "2010-01-29","2010-02-6","2010-02-9","2010-02-16","2010-02-28",
                                 "2010-1-1", "2010-1-2", "2010-01-5","2010-01-17","2010-01-20",
                                 "2010-01-29","2010-02-6","2010-02-9","2010-02-16","2010-02-28"),
                       Event = c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1) )

What I'm trying to do is create a new table that contains the sum of events per week in the calendar year.

In this case producing something like this:

Name   Week   Events
Jim    1      3
Sue    1      3
Jim    2      0
Sue    x ...  x 

and so on...

Answer 1

Update OP request for multiple years:

We could use isoweek also from lubridate instead of week

OR:

We could add the year as follows:

df_date %>% 
  as_tibble() %>% 
  mutate(Week = week(ymd(Dates))) %>% 
  mutate(Year = year(ymd(Dates))) %>% 
  count(Name, Year, Week)

We could use lubridates Week function after transforming character Dates to date format with lubridates ymd function. Then we can use count which is the short for group_by(Name, Week) %>% summarise(Count = n()) :

library(dplyr)
library(lubridate)
df_date %>% 
  as_tibble() %>% 
  mutate(Week = week(ymd(Dates))) %>% 
  count(Name, Week)

  Name   Week     n
   <chr> <dbl> <int>
 1 Jim       1     3
 2 Jim       3     2
 3 Jim       5     1
 4 Jim       6     2
 5 Jim       7     1
 6 Jim       9     1
 7 Sue       1     3
 8 Sue       3     2
 9 Sue       5     1
10 Sue       6     2
11 Sue       7     1
12 Sue       9     1