dipr

The dipr package is an R package that loads and provides means of caching of .dat.gz files. Functions are also provided to convert from .dat.gz files to Apache Arrow formats.

Installation

While this package is intended for us within the SRE you can install the package from GitHub:

if(!requireNamespace("remotes")) install.packages("remotes")
remotes::install_github("bcgov/dipr")

Installation inside the SRE

Installation of dipr is slightly different than typical R package installation. To begin with you will need to clone the dipr repo. To do this you need access to the SRE instance of GitLab.

Go to this website: https://projectsc.popdata.bc.ca/

Scroll down and click on “Sign in with Pop Data Sso”

You will need to do this step everytime you log into the SRE.

Next go to this site: https://projectsc.popdata.bc.ca/profile/personal_access_tokens

Create a new Personal Access Token enabling each scope and typing in your username. This should create a new personal access token. Those are your “git credentials”. Save those on your “U” drive along with your user name.

Install from R

## First you need to store your credentials created in the step above. This is a one time only thing
token <- credentials::git_credential_ask("https://projectsc.popdata.bc.ca")$password

## These next two lines download the package to a temporary folder and install it. 
resp <- httr::GET("https://projectsc.popdata.bc.ca/api/v4/projects/90/repository/archive.tar.gz",
                  query = list(sha = "1.3.1"),
                  httr::write_disk(tempfile(fileext = ".tar.gz")),
                  config = httr::add_headers(`Private-token` = token))
remotes::install_local(resp[["content"]])

Usage

The dipr package comes with some sample fake data to illustrate the usage of the package and facilitate unit tests. Specifically, the package provides a fake data dictionary and a compressed data file. Example fake data can be seen like this:

library(dipr)
dipr_examples()
#>  [1] "sample_hlth_dict.csv"             "starwars-csv.dat"                
#>  [3] "starwars-csv.dat.gz"              "starwars-csv2.dat"               
#>  [5] "starwars-csv2.dat.gz"             "starwars-dict-with-comments.nflt"
#>  [7] "starwars-dict.nflt"               "starwars-dict.txt"               
#>  [9] "starwars-fwf.dat"                 "starwars-fwf.dat.gz"             
#> [11] "starwars-fwf2.dat"                "starwars-fwf2.dat.gz"

Individual paths can be extracted like this:

data_dict_path <- dipr_example("starwars-dict.txt")
dict <- read.table(data_dict_path)
dict
#>       name start stop col_type
#> 1     name     1   21        c
#> 2   height    22   24        i
#> 3     mass    25   30        d
#> 4 has_hair    31   35        l
#> 5  species    36   49        c
#> 6     date    50   57        c
dat_path <- dipr_example("starwars-fwf.dat.gz")

The main function in the package is read_dat() which allows you to directly read `.dat.gz files:

raw <- read_dat(data_path = dat_path,
                data_dict = dict)
#> ✔ Reading starwars-fwf
#> Rows: 15 Columns: 6
#> ── Column specification ────────────────────────────────────────────────────────
#> 
#> chr (2): name, species
#> dbl (3): height, mass, date
#> lgl (1): has_hair
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(raw)
#> # A tibble: 6 × 6
#>   name           height  mass has_hair species     date
#>   <chr>           <dbl> <dbl> <lgl>    <chr>      <dbl>
#> 1 Luke Skywalker    172    77 FALSE    Human   19871219
#> 2 C-3PO             167    75 TRUE     Droid   19020105
#> 3 R2-D2              96    32 TRUE     Droid   19631118
#> 4 Darth Vader       202   136 FALSE    Human   20000414
#> 5 Leia Organa       150    49 FALSE    Human   19730828
#> 6 Owen Lars         178   120 FALSE    Human   19760713

If two files share a common data dictionary you can supply a vector of filenames to the data_path argument and read_dat will combine them for you into one dataframe:

dat_path2 <- dipr_example("starwars-fwf2.dat.gz")
raw_two_files <- read_dat(
    data_path = c(dat_path, dat_path2),
    data_dict = dict,
    id = "file"
  )
#> ✔ Reading starwars-fwf and starwars-fwf2
#> Rows: 30 Columns: 7
#> ── Column specification ────────────────────────────────────────────────────────
#> 
#> chr (2): name, species
#> dbl (3): height, mass, date
#> lgl (1): has_hair
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Working with the data.table package for data manipulation is also possible in dipr using the read_dat_dt function:

raw_dt <- read_dat_dt(
    data_path = dat_path,
    data_dict = dict
  )
#> ✔ Reading starwars-fwf
#> Rows: 15 Columns: 6
#> ── Column specification ────────────────────────────────────────────────────────
#> 
#> chr (2): name, species
#> dbl (3): height, mass, date
#> lgl (1): has_hair
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Data Dictionary

Data Dictionaries need to be in a data frame format that includes ‘name`, ’start’, ‘stop’, and ‘length’ columns (in any order):

> dict
# A tibble: 64 x 4
   start  stop length name                      
   <dbl> <dbl>  <dbl> <chr>   

Project Status

Under development

License

Copyright 2019 Province of British Columbia

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at 

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.