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dataprep (version 0.1.8)

data1: Example data (aggregated particle number concentrations, SMEAR I Varrio forest)

Description

An already-aggregated seven-column version of data. The 61 size bins of the raw size distribution have been collapsed into three log-normal modes (nucleation, Aitken, accumulation) plus two total concentrations (tconc, TPNC). The dataset covers the same two-month window as data (January and July 2020) and is intended for read-only demos.

Usage

data1

Arguments

Format

A data frame with 7,640 observations on the following 7 variables.

date

a POSIXct vector, sampling time (10-minute resolution).

monthyear

a character vector, e.g. "January 2020".

Nucleation

a numeric vector, number concentration in the nucleation mode (nominally 3--25 nm).

Aitken

a numeric vector, number concentration in the Aitken mode (nominally 25--100 nm).

Accumulation

a numeric vector, number concentration in the accumulation mode (nominally 100--1000 nm).

tconc

a numeric vector, total number concentration.

TPNC

a numeric vector, total particle number concentration.

Details

Unlike data, which is the raw size-resolved table, data1 contains only mode-integrated values. The three integrated values can remain non-zero even when the smallest raw size bins (1 through 2) are entirely NA in the two-month subset. It has:

  • very few missing values,

  • no long consecutive missing runs,

  • no obvious outliers.

Because of this, data1 is not a useful input for the cleaning pipeline (varidele, obsedele, condextr, shorvalu, dataprep): those steps have nothing to remove on an already-clean table. It is intended for demos that only read the data:

  • descdata / descplot --- descriptive statistics and plots,

  • percdata / percplot --- percentile summaries,

  • na_diagnose --- missing-value diagnostics,

  • data_report --- compact quality report.

Use data for anything that modifies values.

References

Liang, C.-S., Wu, H., Li, H.-Y., Zhang, Q., Li, Z. & He, K.-B. (2020). Efficient data preprocessing, episode classification, and source apportionment of particle number concentrations. Science of the Total Environment, 741, 140923. tools:::Rd_expr_doi("10.1016/j.scitotenv.2020.140923")

Examples

Run this code
data1

dim(data1)
names(data1)

# The three log-normal modes and the two total concentrations
summary(data1[, 3:7])

# A quick look at how the modes relate to the totals
cor(data1[, 3:7], use = "complete.obs")

# data1 has very few missing values compared with the raw size bins
colMeans(is.na(data1))

# For a read-only descriptive plot
if (interactive()) {
  descplot(data1, cols = 3:7)
}

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