View source: R/patterns_lookup.R
patterns_lookup | R Documentation |
This function, given a date or range of dates, will return a character vector of folder paths in the weekly (new or backfill) data you will need to run through list.files(pattern = '.csv.gz', full.names = TRUE)
after downloading files (or just set list_files = TRUE
. This is done because the subfolder after this is based on the hour the data is released, which can't be predicted ahead of time for future weeks.
patterns_lookup( dates, dir = NULL, old_dir = NULL, new_dir = NULL, subfolder = "patterns", silent = FALSE, add_ma = 0, patterns_backfill_date = "2021/08/02/22/", old_date_split = lubridate::ymd("2021-07-11"), old_bucket = "weekly-backfill", new_bucket = "weekly", key = NULL, secret = NULL, list_files = FALSE, ... )
dates |
A vector of |
dir |
If specified, will append |
old_dir |
If specified, will append |
new_dir |
If specified, will append |
subfolder |
Which folder in the AWS bucket to look at. Will append "_backfill" for backfill data. Usually this is "patterns", "normalization_data", or "home_panel_summary". |
silent |
If specified, will omit the warning for using any dates after the package author last checked the consistency of the SafeGraph file structure. |
add_ma |
Also looks at the |
patterns_backfill_date |
Character variable with the folder structure for the most recent |
old_date_split |
Date indicating the last day on which "old" data is present, before switching to the "new" data structure. |
old_bucket, new_bucket |
The |
key |
A character string containing an AWS Access Key ID. If |
secret |
A character string containing an AWS Secret Access Key. |
list_files |
After creating folderpaths (and, possibly, downloading files), run each of them through |
... |
Arguments to be passed to |
# We have already downloaded all of AWS data into the working directory and just need to locate and load it # (if we also wanted to download, we could leave off list_files and pass this to safegraph_aws, # or add our key and secret here and it would download) filelist <- patterns_lookup(lubridate::ymd('2020-9-01') + lubridate::days(0:100), list_files = TRUE) dt <- read_many_patterns(filelist = filelist, by = 'brands', expand_int = 'visits_by_day') # Now let's get the normalization files normlist <- patterns_lookup(lubridate::ymd('2020-9-01') + lubridate::days(0:100), subfolder = 'normalization_stats', list_files = TRUE) norm <- read_many_csvs(filelist = normlist, makedate = TRUE)
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