Normally, I'd have suggested converting the Incident.date object to a dttm object, from which you can easily extract the year for summarization. However, the second entry is malformed: 1/12016
So, I'd use stringr to strip everything except the last four digits
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(stringr))
sample_data <- data.frame(Incident.Date = c('1/1/2015','1/12016','2/2/2015','2/2/2018','5/5/2016','4/4/2015','4/4/2017','4/4/2018','6/6/2018','5/5/2018','1/4/2015'))
sample_data %>% mutate(Incident.Date = str_extract(Incident.Date, "\\d{4}$")) %>% group_by(Incident.Date) %>% count()
#> # A tibble: 4 x 2
#> # Groups: Incident.Date [4]
#> Incident.Date n
#> <chr> <int>
#> 1 2015 4
#> 2 2016 2
#> 3 2017 1
#> 4 2018 4
Created on 2020-01-26 by the reprex package (v0.3.0)