CDC BMI reference data

Description

Used for extended BMIz computation

CDCref_d.csv.gz

Used for extended BMI computation

Answers for adjustcarryforward

Description

Determines what should absolutely be reincluded or definitely excluded for a given dataset, already run through cleangrowth.

Usage

acf_answers(
  subjid,
  param,
  agedays,
  sex,
  measurement,
  orig.exclude,
  sd.recenter = NA,
  ewma.exp = -1.5,
  ref.data.path = "",
  quietly = TRUE
)

Arguments

Value

A data frame, containing an index "n" of rows, corresponding to the original order of the input vectors, and "acf_answers", containing the answers on whether a height value should be kept or excluded (returns "Definitely Exclude", "Definitely Include", or "Unknown" for height values, NA for weight values).

adjustcarryforward adjustcarryforward Uses absolute height velocity to identify values excluded as carried forward values for reinclusion.

Description

adjustcarryforward adjustcarryforward Uses absolute height velocity to identify values excluded as carried forward values for reinclusion.

Usage

adjustcarryforward(
  subjid,
  param,
  agedays,
  sex,
  measurement,
  orig.exclude,
  exclude_opt = 0,
  sd.recenter = NA,
  ewma.exp = -1.5,
  ref.data.path = "",
  quietly = TRUE,
  minfactor = 0.5,
  maxfactor = 2,
  banddiff = 3,
  banddiff_plus = 5.5,
  min_ht.exp_under = 2,
  min_ht.exp_over = 0,
  max_ht.exp_under = 0.33,
  max_ht.exp_over = 1.5
)

Arguments

Value

Re-evaluated exclusion assessments based on height velocity.

Examples

# Run on a small subset of given data
df <- as.data.frame(syngrowth)
df <- df[df$subjid %in% unique(df[, "subjid"])[1:2], ]
clean_df <- cbind(df,
                  "gcr_result" = cleangrowth(df$subjid,
                                             df$param,
                                             df$agedays,
                                             df$sex,
                                             df$measurement))

# Adjust carry forward values in cleaned data
adj_clean <- adjustcarryforward(subjid = clean_df$subjid,
                                param = clean_df$param,
                                agedays = clean_df$agedays,
                                sex = clean_df$sex,
                                measurement = clean_df$measurement,
                                orig.exclude = clean_df$gcr_result)

BMI Anthro

Description

Part of default CDC-derived tables

Details

Contains BMI data for calculating BMI

bmianthro.txt.gz

Used in function cleangrowth()

Clean growth measurements

Description

Clean growth measurements

Usage

cleangrowth(
  subjid,
  param,
  agedays,
  sex,
  measurement,
  recover.unit.error = FALSE,
  sd.extreme = 25,
  z.extreme = 25,
  lt3.exclude.mode = "default",
  height.tolerance.cm = 2.5,
  error.load.mincount = 2,
  error.load.threshold = 0.5,
  sd.recenter = NA,
  sdmedian.filename = "",
  sdrecentered.filename = "",
  include.carryforward = FALSE,
  ewma.exp = -1.5,
  ref.data.path = "",
  log.path = NA,
  parallel = FALSE,
  num.batches = NA,
  quietly = TRUE,
  adult_cutpoint = 20,
  weight_cap = Inf,
  adult_columns_filename = "",
  prelim_infants = FALSE
)

Arguments

Value

Vector of exclusion codes for each of the input measurements.

Possible values for each code are:

• 'Include', 'Unit-Error-High', 'Unit-Error-Low', 'Swapped-Measurements', 'Missing',

• 'Exclude-Carried-Forward', 'Exclude-SD-Cutoff', 'Exclude-EWMA-Extreme', 'Exclude-EWMA-Extreme-Pair',

• 'Exclude-Extraneous-Same-Day',

• 'Exclude-EWMA-8', 'Exclude-EWMA-9', 'Exclude-EWMA-10', 'Exclude-EWMA-11', 'Exclude-EWMA-12', 'Exclude-EWMA-13', 'Exclude-EWMA-14',

• 'Exclude-Min-Height-Change', 'Exclude-Max-Height-Change',

• 'Exclude-Pair-Delta-17', 'Exclude-Pair-Delta-18', 'Exclude-Pair-Delta-19',

• 'Exclude-Single-Outlier', 'Exclude-Too-Many-Errors', 'Exclude-Too-Many-Errors-Other-Parameter'

Examples

# Run calculation using a small subset of given data
df_stats <- as.data.frame(syngrowth)
df_stats <- df_stats[df_stats$subjid %in% unique(df_stats[, "subjid"])[1:5], ]

clean_stats <-cleangrowth(subjid = df_stats$subjid,
                         param = df_stats$param,
                         agedays = df_stats$agedays,
                         sex = df_stats$sex,
                         measurement = df_stats$measurement)

# Once processed you can filter data based on result value
df_stats <- cbind(df_stats, "clean_result" = clean_stats)
clean_df_stats <- df_stats[df_stats$clean_result == "Include",]

# Parallel processing: run using 2 cores and batches
clean_stats <- cleangrowth(subjid = df_stats$subjid,
                           param = df_stats$param,
                           agedays = df_stats$agedays,
                           sex = df_stats$sex,
                           measurement = df_stats$measurement,
                           parallel = TRUE,
                           num.batches = 2)

Exponentially Weighted Moving Average (EWMA)

Description

ewma calculates the exponentially weighted moving average (EWMA) for a set of numeric observations over time.

Usage

ewma(agedays, z, ewma.exp, ewma.adjacent = TRUE)

Arguments

Value

Data frame with 3 variables:

• The first variable (ewma.all) contains the EWMA at observation time excluding only the actual observation for that time point.

• The second variable (ewma.before) contains the EWMA for each observation excluding both the actual observation and the immediate prior observation.

• The third variable (ewma.after) contains the EWMA for each observation excluding both the actual observation and the subsequent observation.

Examples

# Run on 1 subject, 1 type of parameter
df_stats <- as.data.frame(syngrowth)
df_stats <- df_stats[df_stats$subjid == df_stats$subjid[1] &
                       df_stats$param == "HEIGHTCM", ]

# Get the uncentered z-scores
measurement_to_z <- read_anthro(cdc.only = TRUE)
sd <- measurement_to_z(df_stats$param,
                       df_stats$agedays,
                       df_stats$sex,
                       df_stats$measurement,
                       TRUE)

# Calculate exponentially weighted moving average
e_df <- ewma(df_stats$agedays, sd, ewma.exp = -1.5)

Calculate extended BMI measures

Description

ext_bmiz Calculates the sigma (scale parameter for the half-normal distribution), extended BMI percentile, extended BMIz, and the CDC LMS Z-scores for weight, height, and BMI for children between 2 and 19.9 years of age. Note that for BMIs <= 95th percentile of the CDC growth charts, the extended values for BMI are equal to the LMS values. The extended values differ only for children who have a BMI > 95th percentile.

Usage

ext_bmiz(
  data,
  age = "agem",
  wt = "wt",
  ht = "ht",
  bmi = "bmi",
  adjust.integer.age = TRUE,
  ref.data.path = ""
)

Arguments

Details

This function should produce output equivalent to the SAS macro provided at https://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm. The macro was updated in December, 2022, according to the findings of the NCHS report available at https://dx.doi.org/10.15620/cdc:121711. This function has been updated to match it as of growthcleanr v2.1.0.

The extended BMIz is the inverse cumulative distribution function (CDF) of the extended BMI percentile. If the extended percentile is very close to 100, the qnorm function in R produces an infinite value. This occurs only if the the extended BMI percentile is > 99.99999999999999. This occurs infrequently, such as a 48-month-old with a BMI > 39, and it is likely that these BMIs represent data entry errors. For these cases, extended BMIz is set to 8.21, a value that is slightly greater than the largest value that can be calculated.

See the README.md file for descriptions of the output columns generated by this function.

data must have columns for at least age, sex, weight, height, and bmi.

age should be coded in months, using the most precise values available. To convert to months from age in years, multiply by 12. To convert to months from age in days, divide by 30.4375 (365.25 / 12).

sex is coded as 1, boys, Boys, b, B, males, Males, m, or M for male subjects or 2, girls, Girls, g, G, females, Females, f, or F for female subjects. Note that this is different from cleangrowth, which uses 0 (Male) and 1 (Female).

wt should be in kilograms.

ht should be in centimeters.

Specify the input data parameter names for age, wt, ht, bmi using quotation marks. See example below.

If the parameter adjust.integer.age is TRUE (the default), 0.5 will be added to all age if all input values are integers. Set to FALSE to disable.

By default, the reference data file CDCref_d.csv, made available at https://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm, is included in this package for convenience. If you are developing this package, use ref.data.path to adjust the path to this file from your working directory if necessary.

Value

Expanded data frame containing computed BMI values

Examples

# Run on a small subset of given data
df <- as.data.frame(syngrowth)
df <- df[df$subjid %in% unique(df[, "subjid"])[1:5], ]
df <- cbind(df,
            "gcr_result" = cleangrowth(df$subjid,
                                       df$param,
                                       df$agedays,
                                       df$sex,
                                       df$measurement))
df_wide <- longwide(df) # convert to wide format for ext_bmiz
df_wide_bmi <- simple_bmi(df_wide) # compute simple BMI

# Calling the function with default column names
df_bmiz <- ext_bmiz(df_wide_bmi)

# Specifying different column names; note that quotes are used
dfc <- simple_bmi(df_wide)
colnames(dfc)[colnames(dfc) %in% c("agem", "wt", "ht")] <-
  c("agemos", "weightkg", "heightcm")
df_bmiz <- ext_bmiz(dfc, age="agemos", wt="weightkg", ht="heightcm")

# Disabling conversion of all-integer age in months to (age + 0.5)
dfc <- simple_bmi(df_wide)
df_bmiz <- ext_bmiz(dfc, adjust.integer.age=FALSE)

Fenton Growth Curves

Description

Fenton growth curves with premature infant data with sex, age, and integer weight

fentlms_foraga.csv.gz

Used in function cleangrowth()

Fenton Growth Curve Z-Scores

Description

Fenton growth curves with premature infant z-scores for height and head circumference

fentlms_forz.csv.gz

Used in function cleangrowth()

CDC Growth Percentile Table

Description

Part of default CDC-derived tables

Details

Contains percentiles for various ages, gender, and weights, pre-calculated by CDC

growthfile_cdc_ext.csv.gz

Used in function cleangrowth()

CDC Growth Percentile Table for Infants

Description

Part of default CDC-derived tables

Details

Contains percentiles for various ages, gender, and weights, pre-calculated by CDC for infants algorithm

growthfile_cdc_ext_infants.csv.gz

Used in function cleangrowth()

WHO Growth Percentile Table

Description

Part of default WHO-derived tables

Details

Contains percentiles for various ages, gender, and weights, pre-calculated by WHO

growthfile_who.csv.gz

Used in function cleangrowth()

Length to Age Table

Description

Part of default CDC-derived tables

Details

Contains percentiles for various ages, gender, and weights, pre-calculated by CDC

lenanthro.txt.gz

Used in function cleangrowth()

Transform data in growthcleanr format into wide structure for BMI calculation

Description

longwide transforms data from long to wide format. Ideal for transforming output from growthcleanr::cleangrowth() into a format suitable for growthcleanr::ext_bmiz().

Usage

longwide(
  long_df,
  id = "id",
  subjid = "subjid",
  sex = "sex",
  agedays = "agedays",
  param = "param",
  measurement = "measurement",
  gcr_result = "gcr_result",
  include_all = FALSE,
  inclusion_types = c("Include"),
  extra_cols = NULL,
  keep_unmatched_data = FALSE
)

Arguments

Value

Returns a data frame transformed from long to wide. Includes only values flagged with indicated inclusion types. Potentially includes additional columns if arguments are passed to extra_cols. For each subject, heights without corresponding weights for a given age (and vice versa) will be dropped unless keep_unmatched_data is set to TRUE.

Examples

# Run on a small subset of given data
df <- as.data.frame(syngrowth)
df <- df[df$subjid %in% unique(df[, "subjid"])[1:2], ]
df <- cbind(df,
            "gcr_result" = cleangrowth(df$subjid,
                                       df$param,
                                       df$agedays,
                                       df$sex,
                                       df$measurement))
# Convert to wide format
wide_df <- longwide(df)

# Include all inclusion types
wide_df <- longwide(df, include_all = TRUE)

# Specify all inclusion codes
wide_df <- longwide(df, inclusion_types = c("Include", "Exclude-Carried-Forward"))

NHANES reference medians

Description

Contains reference median values for default recentering, derived from NHANES years 2009-2018

nhanes-reference-medians.csv.gz

Used in function cleangrowth()

Infants reference medians

Description

Contains reference median values for default recentering in the infants algorithm

rcfile-2023-08-15_format.csv.gz

Used in function cleangrowth()

Function to calculate z-scores and csd-scores based on anthro tables.

Description

Function to calculate z-scores and csd-scores based on anthro tables.

Usage

read_anthro(path = "", cdc.only = FALSE, prelim_infants = FALSE)

Arguments

Value

Function for calculating BMI based on measurement, age in days, sex, and measurement value.

Examples

# Return calculating function with all defaults
afunc <- read_anthro()

# Return calculating function while specifying a path and using only CDC data
afunc <- read_anthro(path = system.file("extdata", package = "growthcleanr"),
                     cdc.only = TRUE)

Recode binary sex variable for compatibility

Description

recode_sex recodes a binary sex variable for a given source column in a data frame or data table. Useful in transforming output from growthcleanr::cleangrowth() into a format suitable for growthcleanr::ext_bmiz().

Usage

recode_sex(
  input_data,
  sourcecol = "sex",
  sourcem = "0",
  sourcef = "1",
  targetcol = "sex_recoded",
  targetm = 1L,
  targetf = 2L
)

Arguments

Value

Returns a data table with recoded sex variables.

Examples

# Run on given data
df <- as.data.frame(syngrowth)

# Run with all defaults
df_r <- recode_sex(df)

# Specify different targets
df_rt <- recode_sex(df, targetcol = "sexr", targetm = "Male", targetf = "Female")

# Specify different inputs
df_ri <- recode_sex(df_rt, sourcecol = "sexr", sourcem = "Male", sourcef = "Female")

Calculate median SD score by age for each parameter.

Description

Calculate median SD score by age for each parameter.

Usage

sd_median(param, sex, agedays, sd.orig)

Arguments

Value

Table of data with median SD-scores per day of life by gender and parameter.

Examples

# Run on 1 subject
df_stats <- as.data.frame(syngrowth)
df_stats <- df_stats[df_stats$subjid == df_stats$subjid[1], ]

# Get the original standard deviations
measurement_to_z <- read_anthro(cdc.only = TRUE)
sd.orig <- measurement_to_z(df_stats$param,
                       df_stats$agedays,
                       df_stats$sex,
                       df_stats$measurement,
                       TRUE)

# Calculate median standard deviations
sd.m <- sd_median(df_stats$param,
                  df_stats$sex,
                  df_stats$agedays,
                  sd.orig)

Compute BMI using standard formula

Description

simple_bmi Computes BMI using standard formula. Assumes input compatible with output from longwide().

Usage

simple_bmi(wide_df, wtcol = "wt", htcol = "ht")

Arguments

Value

Returns a data table with the added column "bmi"

Examples

# Simple usage
# Run on a small subset of given data
df <- as.data.frame(syngrowth)
df <- df[df$subjid %in% unique(df[, "subjid"])[1:2], ]
df <- cbind(df,
            "gcr_result" = cleangrowth(df$subjid,
                                       df$param,
                                       df$agedays,
                                       df$sex,
                                       df$measurement))
# Convert to wide format
wide_df <- longwide(df)
wide_df_with_bmi <- simple_bmi(wide_df)

# Specifying different column names; note that quotes are used
colnames(wide_df)[colnames(wide_df) %in% c("wt", "ht")] <-
  c("weight", "height")
wide_df_with_bmi <- simple_bmi(wide_df, wtcol = "weight", htcol = "height")

Split input data into multiple files

Description

splitinput Splits input based on keepcol specified, yielding csv files each with at least the mininum number of rows that are written and saved separately (except for the last split file written, which may be smaller). Allows splitting input data while ensuring all records for each individual subject will stay together in one file. Pads split filenames with zeros out to five digits for consistency, assuming < 100,000 file count result.

Usage

splitinput(
  df,
  fname = deparse(substitute(df)),
  fdir = NA,
  min_nrow = 10000,
  keepcol = "subjid"
)

Arguments

Value

the count number referring to the last split file written

Examples

# Run on given data
df <- as.data.frame(syngrowth)

# Run with all defaults (specifying directory)
splitinput(df, fdir = tempdir())

# Specifying the name, directory and minimum row size
splitinput(df, fname = "syngrowth", fdir = tempdir(), min_nrow = 5000)

# Specifying a different subject ID column
colnames(df)[colnames(df) == "subjid"] <- "sub_id"
splitinput(df, fdir = tempdir(), keepcol = "sub_id")

syngrowth

Description

A synthetic set of measurements from ~3,500 subjects generated using Synthea, with measurement errors for testing with growthcleanr. Contains both pediatric and adult data.

Usage

syngrowth

Format

A data frame with six variables: id, subjid, sex, agedays, param, and measurement

Details

Example electronic health record (heightcm, weightkg) data.

Tanner Growth Velocity Table

Description

Part of default CDC-derived tables

Details

Contains velocities for growth pre-calculated by CDC

tanner_ht_vel.csv.gz

Used in function cleangrowth()

Tanner Growth Velocity Table for Infants

Description

Part of default CDC-derived tables

Details

Contains velocities for growth pre-calculated by CDC, used for the infants algorithm

tanner_ht_vel_rev.csv.gz

Used in function cleangrowth()

Tanner Growth Velocity Table with (2\sigma)

Description

Part of default CDC-derived tables

Details

Contains velocities for growth pre-calculated by CDC, including those 2 standard deviations away.

tanner_ht_vel_with_2sd.csv.gz

Used in function acf_answers()

CDC SAS BMI Output

Description

Contains results of CDC SAS macro for calculating BMI values.

test_syngrowth_sas_output_compare.csv.gz

Used to test function ext_bmiz()

CDC SAS BMI Input

Description

Contains input data for CDC SAS macro for calculating BMI values.

test_syngrowth_wide.csv.gz

Used to test function ext_bmiz()

Function to test adjust carried forward

Description

The goal of this script is to consider the height values that growthcleanr excludes as “carried forward” for potential re-inclusion by using a reverse absolute height velocity check based on step 15 of the Daymont et al. algorithm

Usage

testacf(
  infile,
  seed = 7,
  searchtype = "random",
  grid.length = 9,
  writeout = FALSE,
  outfile = paste0("test_adjustcarryforward_", format(Sys.time(),
    "%m-%d-%Y_%H-%M-%S")),
  quietly = FALSE,
  param = "none",
  debug = FALSE,
  maxrecs = 0,
  exclude_opt = 0,
  add_answers = TRUE
)

Arguments

Value

A list containing: testacf_res: data frame with adjustcarryforward results for each run, params: a data frame containing parameter values for each run. debug_filtered_data: data frame with original data, returned if debug TRUE

Weight Anthro Table

Description

Part of default CDC-derived tables

Details

Contains median and standard deviation for weight by age and gender

weianthro.csv.gz

Used in function cleangrowth()

WHO Maximum Head Circumference Velocity for (3\sigma)

Description

Part of default WHO-derived tables

Details

Contains three standard deviations for the World Health Organization values of maximum head circumference velocities.

who_hc_maxvel_3sd_infants.csv.gz

Used in function cleangrowth()

WHO Head Circumference Velocity for (3\sigma)

Description

Part of default WHO-derived tables

Details

Contains three standard deviations for the World Health Organization values of head circumference velocities.

who_hc_vel_3sd_infants.csv.gz

Used in function cleangrowth()

WHO Maximum Height Velocity for (3\sigma)

Description

Part of default WHO-derived tables

Details

Contains three standard deviations for the World Health Organization values of maximum height velocities.

who_ht_maxvel_3sd.csv.gz

Used in function cleangrowth()

WHO Maximum Height Velocity for (2\sigma)

Description

Part of default WHO-derived tables

Details

Contains two standard deviations for the World Health Organization values of maximum height velocities.

who_ht_maxvel_2sd.csv.gz

Used in function acf_answers()

WHO Height Velocity for (2\sigma)

Description

Part of default WHO-derived tables

Details

Contains two standard deviations for the World Health Organization values of height velocities.

who_ht_vel_2sd.csv.gz

Used in function acf_answers()

WHO Height Velocity for (3\sigma)

Description

Part of default WHO-derived tables

Details

Contains three standard deviations for the World Health Organization values of height velocities.

who_ht_vel_3sd.csv.gz

Used in function cleangrowth()