Package 'trackclean'

Clean tracking data (complete pipeline)

Description

Master function that runs the complete data cleaning pipeline:

1. Map raw IDs to participant IDs

2. Mark analysis and bell time periods

3. Standardize to fixed time intervals

4. Interpolate gaps (two-phase)

5. Optionally export to CSV

Usage

clean_playground_data(
  data,
  id_mapping,
  exclude_ids = NULL,
  analyze_start,
  analyze_end,
  bell_start = NULL,
  bell_end = NULL,
  unit = "second",
  time_step = 1,
  max_gap_small = 10,
  max_gap_large = NULL,
  max_position_change = 0.3,
  output_file = NULL,
  verbose = TRUE,
  time_col = "At",
  x_col = "X",
  y_col = "Y",
  raw_id_col = "ID",
  id_col = "id_code",
  analyze_col = "Analyze",
  bell_col = "Bell"
)

Arguments

data	Raw tracking data frame
id_mapping	Path to ID mapping CSV file or mapping data frame
exclude_ids	Vector of raw IDs to exclude from analysis
analyze_start	Start time for analysis period (character or POSIXct)
analyze_end	End time for analysis period (character or POSIXct)
bell_start	Start time for bell period (optional)
bell_end	End time for bell period (optional)
unit	Time interval for standardization, passed to standardize_to_seconds() (default: "second"). Use "2 seconds", "5 seconds", etc. for coarser intervals.
time_step	Expected time step in seconds between consecutive observations after standardization (default: 1). Must match the numeric value of unit, e.g. set time_step = 2 when unit = "2 seconds".
max_gap_small	Maximum gap for phase 1 interpolation in seconds (default: 10)
max_gap_large	Maximum gap for phase 2 interpolation in seconds (default: NULL)
max_position_change	Maximum position change for phase 2 in meters (default: 0.3)
output_file	Path to save cleaned data as CSV (optional)
verbose	Print progress messages (default: TRUE)
time_col	Name of the timestamp column (default: "At")
x_col	Name of the x-coordinate column (default: "X")
y_col	Name of the y-coordinate column (default: "Y")
raw_id_col	Name of the raw device ID column in the input data (default: "ID")
id_col	Name of the output column for standardized participant IDs (default: "id_code")
analyze_col	Name of the analysis period flag column (default: "Analyze")
bell_col	Name of the bell period flag column (default: "Bell")

Value

Cleaned data frame

Examples

# Complete pipeline using bundled example data
library(readr)
raw_data <- read_csv(system.file("extdata", "raw_tracking_data.csv",
                                 package = "trackclean"))

cleaned_data <- clean_playground_data(
  data = raw_data,
  id_mapping = system.file("extdata", "id_mapping.csv", package = "trackclean"),
  analyze_start = "2025-03-18 11:47:00",
  analyze_end   = "2025-03-18 11:57:00",
  bell_start    = "2025-03-18 11:53:00",
  bell_end      = "2025-03-18 11:58:00"
)

---

Fix tag replacements in raw data

Description

Handles cases where a participant's tracking tag was replaced during data collection. Renames observations from the new tag to the original ID and removes invalid observations.

Usage

fix_tag_replacement(
  data,
  original_id,
  replacement_id,
  replacement_time,
  time_col = "At",
  id_col = "ID"
)

Arguments

data	A data frame with raw tracking data
original_id	The participant's original tag ID
replacement_id	The new tag ID that replaced the original
replacement_time	Time when tag was replaced (POSIXct or character, e.g. "2025-03-18 11:20:00")
time_col	Name of the timestamp column (default: "At")
id_col	Name of the ID column (default: "ID")

Value

Data frame with corrected IDs:

• Observations from replacement_id >= replacement_time are renamed to original_id

• Observations from original_id >= replacement_time are removed

• Observations from replacement_id < replacement_time are removed

Examples

raw_data <- data.frame(
  ID = c(159L, 159L, 106L, 106L),
  At = as.POSIXct(c("2025-03-18 11:00:00", "2025-03-18 11:30:00",
                     "2025-03-18 11:00:00", "2025-03-18 11:30:00")),
  X  = c(1.0, 2.0, 3.0, 4.0),
  Y  = c(1.0, 2.0, 3.0, 4.0)
)
raw_data <- fix_tag_replacement(
  data = raw_data,
  original_id = 159,
  replacement_id = 106,
  replacement_time = "2025-03-18 11:20:00"
)

---

Interpolate gaps in location tracking data (two-phase approach)

Description

Phase 1: Interpolates small gaps (gap <= max_gap_small seconds) Phase 2: Interpolates larger gaps if position change is <= max_position_change meters

Usage

interpolate_gaps(
  data,
  time_col = "At",
  x_col = "X",
  y_col = "Y",
  id_col = "id_code",
  analyze_col = "Analyze",
  time_step = 1,
  max_gap_small = 10,
  max_gap_large = NULL,
  max_position_change = 0.3,
  verbose = TRUE
)

Arguments

data	A data frame with location tracking data
time_col	Name of the timestamp column (default: "At")
x_col	Name of x-coordinate column (default: "X")
y_col	Name of y-coordinate column (default: "Y")
id_col	Name of ID column (default: "id_code")
analyze_col	Name of column indicating rows to analyze (default: "Analyze")
time_step	Expected time step in seconds between consecutive observations after standardization (default: 1). Set this to match the unit used in standardize_to_seconds(), e.g. time_step = 2 if you standardized to 2-second intervals.
max_gap_small	Maximum gap size for phase 1 in seconds (default: 10)
max_gap_large	Maximum gap size for phase 2 in seconds (default: NULL for no limit)
max_position_change	Maximum position change in meters for phase 2 (default: 0.3)
verbose	Print progress messages (default: TRUE)

Details

Uses linear interpolation: X_t = X_start + (k/gap) * (X_end - X_start)

Value

Data frame with interpolated coordinates and flags:

• imputed: 1 if row was added in phase 1 (small gaps)

• imputed_large: 1 if row was added in phase 2 (large gaps)

• n_entries: 0 for imputed rows

• standardized: 0 for imputed rows

Examples

standardized_data <- data.frame(
  id_code      = c(1L, 1L),
  At           = as.POSIXct(c("2025-03-18 11:00:00", "2025-03-18 11:00:05")),
  X            = c(1.0, 1.0),
  Y            = c(2.0, 2.0),
  Analyze      = c(1L, 1L),
  n_entries    = c(1L, 1L),
  standardized = c(0L, 0L)
)
data_clean <- interpolate_gaps(standardized_data)

---

Map raw tracking IDs to standardized child IDs

Description

Map raw tracking IDs to standardized child IDs

Usage

map_ids(
  data,
  mapping,
  exclude_ids = NULL,
  raw_id_col = "ID",
  id_col = "id_code",
  analyze_col = "Analyze"
)

Arguments

data	A data frame with raw tracking data
mapping	Either: Path to CSV file with columns 'raw_id' and 'child_id' Data frame with columns 'raw_id' and 'child_id' Named vector (raw_id = child_id)
exclude_ids	Vector of raw IDs to exclude from analysis (sets Analyze = 0)
raw_id_col	Name of the raw ID column in data (default: "ID")
id_col	Name of the output column for standardized IDs (default: "id_code")
analyze_col	Name of the Analyze column in data (default: "Analyze")

Value

Data frame with added id_code column and updated Analyze column

Examples

raw_data <- data.frame(
  ID = c(1L, 2L, 3L),
  At = as.POSIXct("2025-03-18 11:45:00"),
  X  = c(1.0, 2.0, 3.0),
  Y  = c(1.0, 2.0, 3.0)
)
id_map <- data.frame(raw_id = c(1L, 2L, 3L), child_id = c(5001L, 5002L, 5003L))
data_mapped <- map_ids(raw_data, id_map)

---

Mark time periods for analysis and bell time in raw data

Description

Creates binary columns indicating whether each timestamp falls within specified time periods (e.g., recess period, bell ringing period)

Usage

mark_time_periods(
  data,
  time_col = "At",
  analyze_start,
  analyze_end,
  bell_start = NULL,
  bell_end = NULL,
  analyze_col = "Analyze",
  bell_col = "Bell"
)

Arguments

data	A data frame with timestamp data
time_col	Name of the timestamp column (default: "At")
analyze_start	Start time for analysis period (POSIXct or character, e.g. "2025-03-18 11:50:00")
analyze_end	End time for analysis period (POSIXct or character)
bell_start	Start time for bell period (POSIXct or character, optional)
bell_end	End time for bell period (POSIXct or character, optional)
analyze_col	Name of column to create for analysis period (default: "Analyze")
bell_col	Name of column to create for bell period (default: "Bell")

Value

Data frame with added binary columns (1 = within period, 0 = outside period)

Examples

raw_data <- data.frame(
  At = as.POSIXct(c("2025-03-18 11:00:00", "2025-03-18 12:00:00",
                     "2025-03-18 13:00:00")),
  ID = 1:3
)
raw_data <- mark_time_periods(
  raw_data,
  analyze_start = "2025-03-18 10:30:00",
  analyze_end   = "2025-03-18 12:30:00",
  bell_start    = "2025-03-18 11:30:00",
  bell_end      = "2025-03-18 12:30:00"
)

---

Standardize location data to fixed time intervals

Description

Rounds timestamps to the nearest interval boundary and averages X/Y coordinates if multiple signals fall within the same interval.

Usage

standardize_to_seconds(
  data,
  time_col = "At",
  x_col = "X",
  y_col = "Y",
  id_col = "id_code",
  unit = "second",
  verbose = TRUE
)

Arguments

data	A data frame with location tracking data
time_col	Name of the timestamp column (default: "At")
x_col	Name of x-coordinate column (default: "X")
y_col	Name of y-coordinate column (default: "Y")
id_col	Name of ID column (default: "id_code")
unit	Time interval to standardize to, passed to lubridate::floor_date() (default: "second"). Use "2 seconds", "5 seconds", etc. for coarser intervals.
verbose	Print summary statistics (default: TRUE)

Value

Data frame with one row per (id, interval) with:

• X, Y: Averaged coordinates

• n_entries: Number of original signals in that interval

• standardized: 1 if multiple signals were aggregated, 0 if single

Examples

raw_data <- data.frame(
  id_code = c(1L, 1L, 1L),
  At = as.POSIXct(c("2025-03-18 11:00:00.0", "2025-03-18 11:00:00.5",
                     "2025-03-18 11:00:01.0")),
  X = c(1.0, 1.2, 2.0),
  Y = c(2.0, 2.1, 3.0)
)
standardized_data <- standardize_to_seconds(raw_data)

---