Create a categorical variable from date data in R - r

Question

I have data that includes dates (dd/mm/yyyy) and am wanting to summarise the data by year. I'm sure that there is an easier way to do it but the route that I've taken is to try to create a new categorical variable using the "cut" function.
For example:
# create sample dataframe
dates<-c("01/01/2013", "01/02/2013", "01/01/2014", "01/02/2014", "01/01/2015", "01/02/2015")
cases<-c(3,5,2,6,8,4)
df<-as.data.frame(cbind(dates, cases))
df$dates <- as.Date(df$dates,"%d/%m/%Y")
# categorise by year
df$year <- cut(df$dates, c(2013-01-01, 2013-12-31, 2014-12-31, 2015-12-31))
This gives an error:
invalid specification of 'breaks'
How do I tell R to cut at various "date" intervals? Is my approach to this all wrong? Still new to R (sorry about the basic question).
Greg

Answer 1

How should your output look like?
Your code works when you define your breaks with as.Date:
breaks <- as.Date(c("2013-01-01", "2013-12-31", "2014-12-31", "2015-12-31"))
# categorise by year
df$year <- cut(df$dates, breaks)
dates cases year
1 2013-01-01 3 2013-01-01
2 2013-02-01 5 2013-01-01
3 2014-01-01 2 2013-12-31
4 2014-02-01 6 2013-12-31
5 2015-01-01 8 2014-12-31
6 2015-02-01 4 2014-12-31
I'm guessing you want your variable year to look different, though? You can define labels when using cut:
# categorise by year
df$year <- cut(df$dates, breaks, labels = c(2013, 2014, 2015))
dates cases year
1 2013-01-01 3 2013
2 2013-02-01 5 2013
3 2014-01-01 2 2014
4 2014-02-01 6 2014
5 2015-01-01 8 2015
6 2015-02-01 4 2015

Answer 2

if you are just looking for the year, maybe this helps:
df$year <- format(df$dates, format="%Y")
dates cases year
1 2013-01-01 3 2013
2 2013-02-01 5 2013
3 2014-01-01 2 2014
4 2014-02-01 6 2014
5 2015-01-01 8 2015
6 2015-02-01 4 2015

Answer 3

I think the solutions based on cut are a bit overkill. You can use the year function from the lubridate package to extract the year from the date:
library(dplyr)
library(lubridate)
df %>% mutate(year = year(dates))
# dates cases year
# 1 2013-01-01 3 2013
# 2 2013-02-01 5 2013
# 3 2014-01-01 2 2014
# 4 2014-02-01 6 2014
# 5 2015-01-01 8 2015
# 6 2015-02-01 4 2015
lubridate is such an awesome package when it comes to dealing with time data.
After the year column is constructed you can apply all kinds of summaries. I use the dplyr style here:
# Note that as.numeric(as.character()) is needed as `cbind` forces `cases` to be a factor
df %>% mutate(year = year(dates), cases = as.numeric(as.character(cases))) %>%
group_by(year) %>% summarise(tot_cases = sum(cases))
# # A tibble: 3 × 2
# year tot_cases
# <dbl> <dbl>
# 1 2013 8
# 2 2014 8
# 3 2015 12
Note that group_by ensures that all operations after that are done per unique category mentioned there, in this case per year.

Answer 4

A simple solution would be using the dplyr package. Here is a simple example:
library(dplyr)
df_grouped <- df %>%
mutate(
dates = as_date(dates),
cases = as.numeric(cases)) %>%
group_by(year = year(dates)) %>%
summarise(tot_cases = sum(cases))
In the mutate statement we convert the variables to a more suitable format, in group_by we select which variable is going to do the grouping and in summarise we create any new variables that we want.
df_grouped looks like this:
# A tibble: 3 × 2
year tot_cases
<dbl> <dbl>
1 2013 6
2 2014 6
3 2015 9