1 Download and Prepare Data

First we prepare the data by downloading it and writing the dataset to .parquet files. This needs to be done only once and is shown for reproducibility reasons only, the actual dataverifyr code is shown below the next block

library(arrow)
url <- "https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2018-01.parquet"
file <- "yellow_tripdata_2018-01.parquet"
if (!file.exists(file)) download.file(url, file, method = "curl")
file.size(file) / 1e6 # in MB
#> [1] 123.6685

# quick check of the filesize
d <- read_parquet(file)
dim(d)
#> [1] 8760687      19
names(d)
#>  [1] "VendorID"              "tpep_pickup_datetime"  "tpep_dropoff_datetime" "passenger_count"      
#>  [5] "trip_distance"         "RatecodeID"            "store_and_fwd_flag"    "PULocationID"         
#>  [9] "DOLocationID"          "payment_type"          "fare_amount"           "extra"                
#> [13] "mta_tax"               "tip_amount"            "tolls_amount"          "improvement_surcharge"
#> [17] "total_amount"          "congestion_surcharge"  "airport_fee"

# write the dataset to disk
write_dataset(d, "nyc-taxi-data")

2 Create Rules in yaml

Next, we can create some rules that we will use to check our data. As we saw earlier, we can create the rules in R using the rule() and ruleset() functions, there is however, the (in my opinion) preferred option to separate the code from the rules by writing the rules in a separate yaml file and reading them into R.

First we display the hand-written contents of the nyc_data_rules.yaml file.

- name: 'Rule for: passenger_count'
  expr: passenger_count >= 0 & passenger_count <= 10
  allow_na: no
  negate: no
  index: 1
- name: 'Rule for: trip_distance'
  expr: trip_distance >= 0 & trip_distance <= 1000
  allow_na: no
  negate: no
  index: 2
- name: 'Rule for: payment_type'
  expr: payment_type %in% c(0, 1, 2, 3, 4)
  allow_na: no
  negate: no
  index: 3

Then, we can load, display, and finally check the rules against the data

rules <- read_rules("nyc_data_rules.yaml")
rules
#> <Verification Ruleset with 3 elements>
#>   [1] 'Rule for: passenger_count' matching `passenger_count >= 0 & passenger_count <= 10` (allow_na: FALSE)
#>   [2] 'Rule for: trip_distance' matching `trip_distance >= 0 & trip_distance <= 1000` (allow_na: FALSE)
#>   [3] 'Rule for: payment_type' matching `payment_type %in% c(0, 1, 2, 3, 4)` (allow_na: FALSE)

3 Verify that the Data matches the given Rules

Now we can check if the data follows our rules or if we have unexpected data points:

# open the dataset 
ds <- open_dataset("nyc-taxi-data/")

# perform the data validation check
res <- check_data(ds, rules)
res
#> # A tibble: 3 × 10
#>   name                      expr              allow…¹ negate   tests    pass  fail warn  error time 
#>   <chr>                     <chr>             <lgl>   <lgl>    <int>   <int> <int> <chr> <chr> <drt>
#> 1 Rule for: passenger_count passenger_count … FALSE   FALSE  8760687 8760687     0 ""    ""    0.56…
#> 2 Rule for: trip_distance   trip_distance >=… FALSE   FALSE  8760687 8760686     1 ""    ""    0.43…
#> 3 Rule for: payment_type    payment_type %in… FALSE   FALSE  8760687 8760687     0 ""    ""    0.42…
#> # … with abbreviated variable name ¹​allow_na

plot_res(res)

Using the power of arrow, we were able to scan 8+mln observations for three rules in about 1.5 seconds (YMMV). As we can see from the results, there is one unexpected value, lets quickly investigate using the filter_fails() function, which filters a dataset for the failed rule matches

res |>
  filter_fails(ds) |> 
  # only select a couple of variables for brevity
  dplyr::select(tpep_pickup_datetime, tpep_dropoff_datetime, trip_distance)
#> # A tibble: 1 × 3
#>   tpep_pickup_datetime tpep_dropoff_datetime trip_distance
#>   <dttm>               <dttm>                        <dbl>
#> 1 2018-01-30 12:41:02  2018-01-30 12:42:09         189484.

As we can see, this is probably a data error (a trip distance of 190k miles in 1 minute seems - ehm stellar…).