R - create a timeseries from filenames - r

Question

I have 900 files named like 20120412_bwDD2yYa.txt. The first part up to the _ is in the year-month-day format. Some days have multiple files associated with them.
I'd like to use the dates extracted from the file names as data to compile a timeseries where the dates are the x axis and the number of files are the y axis.
How can I do this?

Answer 1

Here is a solution with Base R. Since the question does not include a reproducible example, we'll simulate the file names, parse out the dates, and create the counts by date.
# use list.files() to extract files from directory
files <- list.files(path="./data",pattern="*.txt",full.names = FALSE)
# simulate result from list.files()
files <- c("20120101_aaa.txt","20120101_bbb.txt","20120102_ccc.txt")
# extract dates from file names
date <- as.Date(substr(files,1,8),"%Y%m%d")
df <- data.frame(date,count = rep(1,length(date)))
aggregate(count ~ date,data = df, sum)
...and the output:
date count
1 2012-01-01 2
2 2012-01-02 1
dplyr solution
A solution with dplyr::summarise() looks like this:
files <- list.files(path="./data",pattern="*.txt",full.names = FALSE)
# simulate result from list.files()
files <- c("20120101_aaa.txt","20120101_bbb.txt","20120102_ccc.txt")
library(dplyr)
data.frame(date=as.Date(substr(files,1,8),"%Y%m%d")) %>%
group_by(date) %>% summarise(count = n())
# A tibble: 2 x 2
date count
<date> <int>
1 2012-01-01 2
2 2012-01-02 1
Accounting for dates with no files
In response to a comment on my answer, here is a solution that fills in gaps in the file list where there are days with 0 files. We take the minimum and maximum dates from the file list and create a data frame containing the sequence of dates. Then we left_join() this with the previously aggregated data, and recode NA values for count to 0.
# create a gap in dates with files
files <- c("20120101_aaa.txt","20120101_bbb.txt","20120102_ccc.txt",
"20120104_aaa.txt","20120104_aab.txt","20120104_aac.txt")
library(dplyr)
data.frame(date=as.Date(substr(files,1,8),"%Y%m%d")) %>%
group_by(date) %>% summarise(count = n()) -> fileCounts
# create df with all dates, left_join() and recode NA to 0
data.frame(date = as.Date(min(fileCounts$date):max(fileCounts$date),
origin = "1970-01-01")) %>%
left_join(.,fileCounts) %>%
mutate(count = if_else(is.na(count),0,as.numeric(count)))
...and the output:
Joining, by = "date"
date count
1 2012-01-01 2
2 2012-01-02 1
3 2012-01-03 0
4 2012-01-04 3

Answer 2

You can use table to count frequencies and then stack it to get a dataframe.
Using #Len Greski's files.
files <- c("20120101_aaa.txt","20120101_bbb.txt","20120102_ccc.txt")
stack(table(as.Date(sub('_.*', '', files),"%Y%m%d")))[2:1]
# ind values
#1 2012-01-01 2
#2 2012-01-02 1