Looking at Data Gaps

Couting the gaps in a csv data with all the nodes is easy. Let’s look at the top 5 nodes with data gaps.

network load_file("./data/ohio.network")
network clip()
network csv_count_na(
	"./data/ts/observed.csv",
	sort=true,
	head = 5
)

*Error*:

----8<----

network function: "csv_count_na" not found

Running it for two timeseries, and comparing them base don network information. We can see the downstream part have more missing data on natural timeseries.

network load_file("./data/ohio.network")
network csv_count_na("./data/ts/observed.csv", outattr = "observed_missing")
network csv_count_na("./data/ts/natural.csv", outattr = "natural_missing")
network table_to_svg(
	template="
<Node=> {_NAME}
>Observed => {observed_missing}
>Natural => {natural_missing}
",
	outfile="./output/natural-gaps.svg"
)
network clip()
network echo("
<center>
Number of Missing Days in Timeseries Data

 ![](../output/natural-gaps.svg)
<center>
")

*Error*:

network function: "csv_count_na" not found