Data Wrangling with R

Data Wrangling with R#

Data wrangling is the process of cleaning, transforming, and preparing raw data for analysis. In this notebook, we demonstrate essential data wrangling techniques in R using popular packages like dplyr and tidyr. Tasks include subsetting data, renaming columns, handling missing values, performing group-based summaries, and reshaping datasets. These steps are critical for ensuring data quality and extracting meaningful insights from real-world datasets.

Install Packages and Libraries#

This code installs and loads the tidyverse, a collection of R packages designed for data science. The tidyverse includes popular tools like dplyr for data manipulation, ggplot2 for visualization, and readr for reading data, among others.

install.packages("tidyverse") installs the tidyverse package from CRAN. This step only needs to be done once per system.
library(tidyverse) loads the package into the current R session, making its functions available for use.

install.packages("tidyverse")

library(tidyverse)

The downloaded binary packages are in
	/var/folders/2h/84wxzls579b1yv00g4jj02fh0000gn/T//RtmpVhCa6O/downloaded_packages

-- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --
v dplyr     1.1.4     v readr     2.1.5
v forcats   1.0.0     v stringr   1.5.1
v ggplot2   3.5.2     v tibble    3.3.0
v lubridate 1.9.4     v tidyr     1.3.1
v purrr     1.0.4     
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Download Dataset from the URL#

This line reads in a dataset from an online source and stores it as a data frame called NHANES.

read_csv() is a tidyverse function from the readr package that reads comma-separated values (CSV) files into R as a tibble, a modern version of a data frame.
The dataset is being pulled directly from a GitHub repository via its raw URL: 'https://raw.githubusercontent.com/GTPB/PSLS20/master/data/NHANES.csv'
The dataset NHANES likely contains health and demographic data from the National Health and Nutrition Examination Survey.

NHANES <- read_csv('https://raw.githubusercontent.com/GTPB/PSLS20/master/data/NHANES.csv')

Rows: 10000 Columns: 76
-- Column specification --------------------------------------------------------
Delimiter: ","
chr (31): SurveyYr, Gender, AgeDecade, Race1, Race3, Education, MaritalStatu...
dbl (45): ID, Age, AgeMonths, HHIncomeMid, Poverty, HomeRooms, Weight, Lengt...

i Use `spec()` to retrieve the full column specification for this data.
i Specify the column types or set `show_col_types = FALSE` to quiet this message.

Data Exploration#

This code examines the structure and contents of the NHANES dataset:

spec(NHANES) displays the guessed column specifications, showing how R interpreted each column (e.g., character, numeric, double). This helps verify that the data types for each column were correctly detected during import.
head(NHANES) displays the first six rows of the dataset, giving a quick preview of the data to check its structure and contents.

# Using "spec" to find the guessed column specifications and column names
spec(NHANES)
head(NHANES)

cols(
  ID = col_double(),
  SurveyYr = col_character(),
  Gender = col_character(),
  Age = col_double(),
  AgeDecade = col_character(),
  AgeMonths = col_double(),
  Race1 = col_character(),
  Race3 = col_character(),
  Education = col_character(),
  MaritalStatus = col_character(),
  HHIncome = col_character(),
  HHIncomeMid = col_double(),
  Poverty = col_double(),
  HomeRooms = col_double(),
  HomeOwn = col_character(),
  Work = col_character(),
  Weight = col_double(),
  Length = col_double(),
  HeadCirc = col_double(),
  Height = col_double(),
  BMI = col_double(),
  BMICatUnder20yrs = col_character(),
  BMI_WHO = col_character(),
  Pulse = col_double(),
  BPSysAve = col_double(),
  BPDiaAve = col_double(),
  BPSys1 = col_double(),
  BPDia1 = col_double(),
  BPSys2 = col_double(),
  BPDia2 = col_double(),
  BPSys3 = col_double(),
  BPDia3 = col_double(),
  Testosterone = col_double(),
  DirectChol = col_double(),
  TotChol = col_double(),
  UrineVol1 = col_double(),
  UrineFlow1 = col_double(),
  UrineVol2 = col_double(),
  UrineFlow2 = col_double(),
  Diabetes = col_character(),
  DiabetesAge = col_double(),
  HealthGen = col_character(),
  DaysPhysHlthBad = col_double(),
  DaysMentHlthBad = col_double(),
  LittleInterest = col_character(),
  Depressed = col_character(),
  nPregnancies = col_double(),
  nBabies = col_double(),
  Age1stBaby = col_double(),
  SleepHrsNight = col_double(),
  SleepTrouble = col_character(),
  PhysActive = col_character(),
  PhysActiveDays = col_double(),
  TVHrsDay = col_character(),
  CompHrsDay = col_character(),
  TVHrsDayChild = col_double(),
  CompHrsDayChild = col_double(),
  Alcohol12PlusYr = col_character(),
  AlcoholDay = col_double(),
  AlcoholYear = col_double(),
  SmokeNow = col_character(),
  Smoke100 = col_character(),
  Smoke100n = col_character(),
  SmokeAge = col_double(),
  Marijuana = col_character(),
  AgeFirstMarij = col_double(),
  RegularMarij = col_character(),
  AgeRegMarij = col_double(),
  HardDrugs = col_character(),
  SexEver = col_character(),
  SexAge = col_double(),
  SexNumPartnLife = col_double(),
  SexNumPartYear = col_double(),
  SameSex = col_character(),
  SexOrientation = col_character(),
  PregnantNow = col_character()
)

A tibble: 6 x 76
ID	SurveyYr	Gender	Age	AgeDecade	AgeMonths	Race1	Race3	Education	MaritalStatus	...	RegularMarij	AgeRegMarij	HardDrugs	SexEver	SexAge	SexNumPartnLife	SexNumPartYear	SameSex	SexOrientation	PregnantNow
<dbl>	<chr>	<chr>	<dbl>	<chr>	<dbl>	<chr>	<chr>	<chr>	<chr>	...	<chr>	<dbl>	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<chr>	<chr>	<chr>
51624	2009_10	male	34	30-39	409	White	NA	High School	Married	...	No	NA	Yes	Yes	16	8	1	No	Heterosexual	NA
51624	2009_10	male	34	30-39	409	White	NA	High School	Married	...	No	NA	Yes	Yes	16	8	1	No	Heterosexual	NA
51624	2009_10	male	34	30-39	409	White	NA	High School	Married	...	No	NA	Yes	Yes	16	8	1	No	Heterosexual	NA
51625	2009_10	male	4	0-9	49	Other	NA	NA	NA	...	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
51630	2009_10	female	49	40-49	596	White	NA	Some College	LivePartner	...	No	NA	Yes	Yes	12	10	1	Yes	Heterosexual	NA
51638	2009_10	male	9	0-9	115	White	NA	NA	NA	...	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

This code retrieves the column and row names of the NHANES tibble:

colnames(NHANES) returns a vector containing the names of all the columns in the dataset.
rownames(NHANES) returns the row names, but since NHANES is a tibble (a modern data frame), row names are usually not set and this will typically return NULL.

Tibbles generally avoid using row names, favoring row numbers instead.

# find column and row names of the tibble
colnames(NHANES)
rownames(NHANES)

'ID'
'SurveyYr'
'Gender'
'Age'
'AgeDecade'
'AgeMonths'
'Race1'
'Race3'
'Education'
'MaritalStatus'
'HHIncome'
'HHIncomeMid'
'Poverty'
'HomeRooms'
'HomeOwn'
'Work'
'Weight'
'Length'
'HeadCirc'
'Height'
'BMI'
'BMICatUnder20yrs'
'BMI_WHO'
'Pulse'
'BPSysAve'
'BPDiaAve'
'BPSys1'
'BPDia1'
'BPSys2'
'BPDia2'
'BPSys3'
'BPDia3'
'Testosterone'
'DirectChol'
'TotChol'
'UrineVol1'
'UrineFlow1'
'UrineVol2'
'UrineFlow2'
'Diabetes'
'DiabetesAge'
'HealthGen'
'DaysPhysHlthBad'
'DaysMentHlthBad'
'LittleInterest'
'Depressed'
'nPregnancies'
'nBabies'
'Age1stBaby'
'SleepHrsNight'
'SleepTrouble'
'PhysActive'
'PhysActiveDays'
'TVHrsDay'
'CompHrsDay'
'TVHrsDayChild'
'CompHrsDayChild'
'Alcohol12PlusYr'
'AlcoholDay'
'AlcoholYear'
'SmokeNow'
'Smoke100'
'Smoke100n'
'SmokeAge'
'Marijuana'
'AgeFirstMarij'
'RegularMarij'
'AgeRegMarij'
'HardDrugs'
'SexEver'
'SexAge'
'SexNumPartnLife'
'SexNumPartYear'
'SameSex'
'SexOrientation'
'PregnantNow'

'1'
'2'
'3'
'4'
'5'
'6'
'7'
'8'
'9'
'10'
'11'
'12'
'13'
'14'
'15'
'16'
'17'
'18'
'19'
'20'
'21'
'22'
'23'
'24'
'25'
'26'
'27'
'28'
'29'
'30'
'31'
'32'
'33'
'34'
'35'
'36'
'37'
'38'
'39'
'40'
'41'
'42'
'43'
'44'
'45'
'46'
'47'
'48'
'49'
'50'
'51'
'52'
'53'
'54'
'55'
'56'
'57'
'58'
'59'
'60'
'61'
'62'
'63'
'64'
'65'
'66'
'67'
'68'
'69'
'70'
'71'
'72'
'73'
'74'
'75'
'76'
'77'
'78'
'79'
'80'
'81'
'82'
'83'
'84'
'85'
'86'
'87'
'88'
'89'
'90'
'91'
'92'
'93'
'94'
'95'
'96'
'97'
'98'
'99'
'100'
'101'
'102'
'103'
'104'
'105'
'106'
'107'
'108'
'109'
'110'
'111'
'112'
'113'
'114'
'115'
'116'
'117'
'118'
'119'
'120'
'121'
'122'
'123'
'124'
'125'
'126'
'127'
'128'
'129'
'130'
'131'
'132'
'133'
'134'
'135'
'136'
'137'
'138'
'139'
'140'
'141'
'142'
'143'
'144'
'145'
'146'
'147'
'148'
'149'
'150'
'151'
'152'
'153'
'154'
'155'
'156'
'157'
'158'
'159'
'160'
'161'
'162'
'163'
'164'
'165'
'166'
'167'
'168'
'169'
'170'
'171'
'172'
'173'
'174'
'175'
'176'
'177'
'178'
'179'
'180'
'181'
'182'
'183'
'184'
'185'
'186'
'187'
'188'
'189'
'190'
'191'
'192'
'193'
'194'
'195'
'196'
'197'
'198'
'199'
'200'
...
'9801'
'9802'
'9803'
'9804'
'9805'
'9806'
'9807'
'9808'
'9809'
'9810'
'9811'
'9812'
'9813'
'9814'
'9815'
'9816'
'9817'
'9818'
'9819'
'9820'
'9821'
'9822'
'9823'
'9824'
'9825'
'9826'
'9827'
'9828'
'9829'
'9830'
'9831'
'9832'
'9833'
'9834'
'9835'
'9836'
'9837'
'9838'
'9839'
'9840'
'9841'
'9842'
'9843'
'9844'
'9845'
'9846'
'9847'
'9848'
'9849'
'9850'
'9851'
'9852'
'9853'
'9854'
'9855'
'9856'
'9857'
'9858'
'9859'
'9860'
'9861'
'9862'
'9863'
'9864'
'9865'
'9866'
'9867'
'9868'
'9869'
'9870'
'9871'
'9872'
'9873'
'9874'
'9875'
'9876'
'9877'
'9878'
'9879'
'9880'
'9881'
'9882'
'9883'
'9884'
'9885'
'9886'
'9887'
'9888'
'9889'
'9890'
'9891'
'9892'
'9893'
'9894'
'9895'
'9896'
'9897'
'9898'
'9899'
'9900'
'9901'
'9902'
'9903'
'9904'
'9905'
'9906'
'9907'
'9908'
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'9910'
'9911'
'9912'
'9913'
'9914'
'9915'
'9916'
'9917'
'9918'
'9919'
'9920'
'9921'
'9922'
'9923'
'9924'
'9925'
'9926'
'9927'
'9928'
'9929'
'9930'
'9931'
'9932'
'9933'
'9934'
'9935'
'9936'
'9937'
'9938'
'9939'
'9940'
'9941'
'9942'
'9943'
'9944'
'9945'
'9946'
'9947'
'9948'
'9949'
'9950'
'9951'
'9952'
'9953'
'9954'
'9955'
'9956'
'9957'
'9958'
'9959'
'9960'
'9961'
'9962'
'9963'
'9964'
'9965'
'9966'
'9967'
'9968'
'9969'
'9970'
'9971'
'9972'
'9973'
'9974'
'9975'
'9976'
'9977'
'9978'
'9979'
'9980'
'9981'
'9982'
'9983'
'9984'
'9985'
'9986'
'9987'
'9988'
'9989'
'9990'
'9991'
'9992'
'9993'
'9994'
'9995'
'9996'
'9997'
'9998'
'9999'
'10000'

This code extracts a subset of columns from the NHANES dataset and renames one of them:

The %>% pipe operator passes the NHANES dataset into the select() function from dplyr.
select(ID, Age, BMI, BloodPressure = BPSysAve, SurveyYr, Gender):
- Extracts the columns: ID, Age, BMI, BPSysAve, SurveyYr, and Gender.
- Renames the column BPSysAve to BloodPressure while subsetting.

The resulting simplified dataset is stored in a new tibble called nhanes.

head(nhanes) displays the first six rows of the new subset to confirm the selection and renaming.

# Extract only the columns Age, BMI, and BPSysAve, SurveyYr, and Gender
#renamed BPSysAve as BloodPressure while subsetting data
nhanes <- NHANES %>% select(ID, Age, BMI, BloodPressure = BPSysAve, SurveyYr, Gender)
head(nhanes)

A tibble: 6 x 6
ID	Age	BMI	BloodPressure	SurveyYr	Gender
<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
51624	34	32.22	113	2009_10	male
51624	34	32.22	113	2009_10	male
51624	34	32.22	113	2009_10	male
51625	4	15.30	NA	2009_10	male
51630	49	30.57	112	2009_10	female
51638	9	16.82	86	2009_10	male

This code renames multiple columns in the nhanes dataset using the rename() function from dplyr:

The %>% pipe passes the nhanes tibble into the rename() function.
Inside rename():
- BP = BloodPressure renames the BloodPressure column to BP.
- Age_yr = Age renames the Age column to Age_yr.

The updated dataset with the new column names is saved back into nhanes.

head(nhanes) displays the first six rows to confirm the column name changes.

#rename multiple columns of the dataset after subsetting data -- using 'rename' function from dplyr
nhanes <- nhanes %>% 
  rename(
     BP = BloodPressure,
     Age_yr = Age
  )
head(nhanes)

A tibble: 6 x 6
ID	Age_yr	BMI	BP	SurveyYr	Gender
<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
51624	34	32.22	113	2009_10	male
51624	34	32.22	113	2009_10	male
51624	34	32.22	113	2009_10	male
51625	4	15.30	NA	2009_10	male
51630	49	30.57	112	2009_10	female
51638	9	16.82	86	2009_10	male

This code demonstrates different ways to access columns in the nhanes tibble:

nhanes$ID — Accesses the ID column directly as a vector. This is one of the most common and convenient ways to extract a single column.
nhanes['ID'] — Returns the ID column as a tibble with one column. By default, only the first 10 rows are displayed. To see more, you can use print(nhanes['ID'], n = ...).
nhanes[['ID']] — Extracts the ID column as a vector, similar to nhanes$ID, but uses double brackets. This method is especially useful when accessing columns programmatically using variables.

# Ways to access columns
nhanes$ID   # same as nhanes[['ID']]
print(nhanes['ID'])  # prints first 10 values, use print(colname, n=...) to see more rows
nhanes[['ID']]

'51624'
'51624'
'51624'
'51625'
'51630'
'51638'
'51646'
'51647'
'51647'
'51647'
'51654'
'51656'
'51657'
'51659'
'51666'
'51667'
'51671'
'51677'
'51678'
'51679'
'51685'
'51685'
'51691'
'51692'
'51692'
'51694'
'51695'
'51701'
'51702'
'51702'
'51707'
'51710'
'51711'
'51711'
'51715'
'51718'
'51718'
'51719'
'51720'
'51720'
'51722'
'51722'
'51722'
'51722'
'51723'
'51724'
'51731'
'51732'
'51734'
'51741'
'51741'
'51743'
'51743'
'51745'
'51748'
'51752'
'51753'
'51754'
'51754'
'51754'
'51755'
'51758'
'51760'
'51761'
'51761'
'51764'
'51764'
'51764'
'51765'
'51767'
'51767'
'51768'
'51768'
'51771'
'51772'
'51774'
'51774'
'51774'
'51776'
'51780'
'51780'
'51780'
'51781'
'51781'
'51781'
'51781'
'51788'
'51788'
'51789'
'51790'
'51790'
'51794'
'51799'
'51801'
'51803'
'51804'
'51809'
'51810'
'51813'
'51814'
'51819'
'51828'
'51828'
'51829'
'51829'
'51829'
'51832'
'51832'
'51832'
'51840'
'51845'
'51846'
'51846'
'51849'
'51851'
'51856'
'51857'
'51857'
'51857'
'51857'
'51861'
'51862'
'51863'
'51868'
'51875'
'51888'
'51892'
'51893'
'51896'
'51898'
'51899'
'51899'
'51899'
'51905'
'51905'
'51906'
'51907'
'51910'
'51911'
'51911'
'51912'
'51921'
'51928'
'51933'
'51943'
'51943'
'51944'
'51944'
'51945'
'51951'
'51951'
'51952'
'51954'
'51954'
'51958'
'51958'
'51958'
'51958'
'51959'
'51961'
'51962'
'51963'
'51963'
'51964'
'51966'
'51970'
'51971'
'51973'
'51973'
'51974'
'51975'
'51976'
'51976'
'51979'
'51982'
'51982'
'51983'
'51984'
'51985'
'51985'
'51991'
'51996'
'51996'
'51997'
'51997'
'52004'
'52009'
'52009'
'52013'
'52013'
'52016'
'52017'
'52017'
'52019'
'52020'
'52022'
'52023'
'52023'
'52027'
'52030'
...
'71531'
'71531'
'71531'
'71531'
'71532'
'71542'
'71545'
'71547'
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'71552'
'71560'
'71564'
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'71604'
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'71611'
'71611'
'71611'
'71613'
'71619'
'71624'
'71632'
'71637'
'71637'
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'71638'
'71641'
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'71644'
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'71645'
'71645'
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'71654'
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'71654'
'71656'
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'71667'
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'71684'
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'71725'
'71729'
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'71732'
'71733'
'71736'
'71737'
'71740'
'71744'
'71744'
'71745'
'71754'
'71755'
'71756'
'71756'
'71759'
'71762'
'71763'
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'71764'
'71764'
'71766'
'71767'
'71767'
'71768'
'71769'
'71769'
'71773'
'71775'
'71775'
'71782'
'71784'
'71785'
'71786'
'71787'
'71788'
'71792'
'71793'
'71793'
'71794'
'71796'
'71796'
'71797'
'71797'
'71799'
'71799'
'71802'
'71808'
'71809'
'71810'
'71812'
'71816'
'71820'
'71822'
'71823'
'71823'
'71823'
'71823'
'71825'
'71826'
'71826'
'71828'
'71833'
'71835'
'71839'
'71847'
'71847'
'71849'
'71852'
'71855'
'71855'
'71857'
'71857'
'71860'
'71868'
'71868'
'71868'
'71868'
'71869'
'71869'
'71869'
'71873'
'71875'
'71879'
'71880'
'71882'
'71898'
'71907'
'71907'
'71907'
'71908'
'71908'
'71909'
'71909'
'71909'
'71910'
'71911'
'71915'
'71915'

# A tibble: 10,000 x 1
   ID   
   <chr>
51624
51624
51624
51625
51630
51638
51646
51647
51647
51647
# i 9,990 more rows

'51624'
'51624'
'51624'
'51625'
'51630'
'51638'
'51646'
'51647'
'51647'
'51647'
'51654'
'51656'
'51657'
'51659'
'51666'
'51667'
'51671'
'51677'
'51678'
'51679'
'51685'
'51685'
'51691'
'51692'
'51692'
'51694'
'51695'
'51701'
'51702'
'51702'
'51707'
'51710'
'51711'
'51711'
'51715'
'51718'
'51718'
'51719'
'51720'
'51720'
'51722'
'51722'
'51722'
'51722'
'51723'
'51724'
'51731'
'51732'
'51734'
'51741'
'51741'
'51743'
'51743'
'51745'
'51748'
'51752'
'51753'
'51754'
'51754'
'51754'
'51755'
'51758'
'51760'
'51761'
'51761'
'51764'
'51764'
'51764'
'51765'
'51767'
'51767'
'51768'
'51768'
'51771'
'51772'
'51774'
'51774'
'51774'
'51776'
'51780'
'51780'
'51780'
'51781'
'51781'
'51781'
'51781'
'51788'
'51788'
'51789'
'51790'
'51790'
'51794'
'51799'
'51801'
'51803'
'51804'
'51809'
'51810'
'51813'
'51814'
'51819'
'51828'
'51828'
'51829'
'51829'
'51829'
'51832'
'51832'
'51832'
'51840'
'51845'
'51846'
'51846'
'51849'
'51851'
'51856'
'51857'
'51857'
'51857'
'51857'
'51861'
'51862'
'51863'
'51868'
'51875'
'51888'
'51892'
'51893'
'51896'
'51898'
'51899'
'51899'
'51899'
'51905'
'51905'
'51906'
'51907'
'51910'
'51911'
'51911'
'51912'
'51921'
'51928'
'51933'
'51943'
'51943'
'51944'
'51944'
'51945'
'51951'
'51951'
'51952'
'51954'
'51954'
'51958'
'51958'
'51958'
'51958'
'51959'
'51961'
'51962'
'51963'
'51963'
'51964'
'51966'
'51970'
'51971'
'51973'
'51973'
'51974'
'51975'
'51976'
'51976'
'51979'
'51982'
'51982'
'51983'
'51984'
'51985'
'51985'
'51991'
'51996'
'51996'
'51997'
'51997'
'52004'
'52009'
'52009'
'52013'
'52013'
'52016'
'52017'
'52017'
'52019'
'52020'
'52022'
'52023'
'52023'
'52027'
'52030'
...
'71531'
'71531'
'71531'
'71531'
'71532'
'71542'
'71545'
'71547'
'71547'
'71552'
'71560'
'71564'
'71565'
'71567'
'71567'
'71567'
'71568'
'71569'
'71570'
'71570'
'71572'
'71573'
'71575'
'71578'
'71578'
'71578'
'71581'
'71581'
'71587'
'71588'
'71589'
'71589'
'71592'
'71594'
'71594'
'71596'
'71597'
'71597'
'71598'
'71599'
'71604'
'71605'
'71611'
'71611'
'71611'
'71613'
'71619'
'71624'
'71632'
'71637'
'71637'
'71637'
'71638'
'71641'
'71641'
'71644'
'71645'
'71645'
'71645'
'71646'
'71654'
'71654'
'71654'
'71656'
'71656'
'71656'
'71657'
'71657'
'71658'
'71661'
'71662'
'71662'
'71664'
'71667'
'71671'
'71671'
'71675'
'71675'
'71675'
'71676'
'71678'
'71678'
'71679'
'71684'
'71691'
'71691'
'71691'
'71693'
'71697'
'71698'
'71698'
'71699'
'71701'
'71701'
'71702'
'71704'
'71705'
'71707'
'71721'
'71721'
'71725'
'71729'
'71730'
'71732'
'71733'
'71736'
'71737'
'71740'
'71744'
'71744'
'71745'
'71754'
'71755'
'71756'
'71756'
'71759'
'71762'
'71763'
'71763'
'71763'
'71764'
'71764'
'71766'
'71767'
'71767'
'71768'
'71769'
'71769'
'71773'
'71775'
'71775'
'71782'
'71784'
'71785'
'71786'
'71787'
'71788'
'71792'
'71793'
'71793'
'71794'
'71796'
'71796'
'71797'
'71797'
'71799'
'71799'
'71802'
'71808'
'71809'
'71810'
'71812'
'71816'
'71820'
'71822'
'71823'
'71823'
'71823'
'71823'
'71825'
'71826'
'71826'
'71828'
'71833'
'71835'
'71839'
'71847'
'71847'
'71849'
'71852'
'71855'
'71855'
'71857'
'71857'
'71860'
'71868'
'71868'
'71868'
'71868'
'71869'
'71869'
'71869'
'71873'
'71875'
'71879'
'71880'
'71882'
'71898'
'71907'
'71907'
'71907'
'71908'
'71908'
'71909'
'71909'
'71909'
'71910'
'71911'
'71915'
'71915'

This code shows different ways to access specific elements, rows, or columns of the nhanes dataset using standard indexing:

nhanes[2, "Gender"] — Retrieves the value from row 2, column “Gender”, returning a single element.
nhanes[ , c("SurveyYr", "Gender")] — Selects all rows but only the "SurveyYr" and "Gender" columns.
nhanes[1:6, ] — Selects rows 1 through 6 and all columns.
nhanes[ , ] — Returns the entire dataset. Including empty brackets like this means “all rows” and “all columns.”

This indexing style works similarly to data frames in base R but is fully compatible with tibbles.

# Accessing elements of dataset 
nhanes[2, "Gender"]  # a single element
nhanes[ , c("SurveyYr", "Gender")] # all rows, two columns
nhanes[1:6, ]             # rows 1-6, all columns
nhanes[ , ]               # everything

A tibble: 1 x 1
Gender
<chr>
male

A tibble: 10000 x 2
SurveyYr	Gender
<chr>	<chr>
2009_10	male
2009_10	male
2009_10	male
2009_10	male
2009_10	female
2009_10	male
2009_10	male
2009_10	female
2009_10	female
2009_10	female
2009_10	male
2009_10	male
2009_10	male
2009_10	female
2009_10	female
2009_10	male
2009_10	female
2009_10	male
2009_10	male
2009_10	male
2009_10	female
2009_10	female
2009_10	female
2009_10	male
2009_10	male
2009_10	male
2009_10	male
2009_10	male
2009_10	male
2009_10	male
...	...
2011_12	female
2011_12	female
2011_12	male
2011_12	male
2011_12	male
2011_12	male
2011_12	male
2011_12	male
2011_12	male
2011_12	male
2011_12	male
2011_12	male
2011_12	female
2011_12	male
2011_12	male
2011_12	male
2011_12	female
2011_12	female
2011_12	male
2011_12	male
2011_12	male
2011_12	female
2011_12	female
2011_12	male
2011_12	male
2011_12	male
2011_12	female
2011_12	male
2011_12	male
2011_12	male

A tibble: 6 x 6
ID	Age_yr	BMI	BP	SurveyYr	Gender
<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
51624	34	32.22	113	2009_10	male
51624	34	32.22	113	2009_10	male
51624	34	32.22	113	2009_10	male
51625	4	15.30	NA	2009_10	male
51630	49	30.57	112	2009_10	female
51638	9	16.82	86	2009_10	male

A tibble: 10000 x 6
ID	Age_yr	BMI	BP	SurveyYr	Gender
<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
51624	34	32.22	113	2009_10	male
51624	34	32.22	113	2009_10	male
51624	34	32.22	113	2009_10	male
51625	4	15.30	NA	2009_10	male
51630	49	30.57	112	2009_10	female
51638	9	16.82	86	2009_10	male
51646	8	20.64	107	2009_10	male
51647	45	27.24	118	2009_10	female
51647	45	27.24	118	2009_10	female
51647	45	27.24	118	2009_10	female
51654	66	23.67	111	2009_10	male
51656	58	23.69	104	2009_10	male
51657	54	26.03	134	2009_10	male
51659	10	19.20	104	2009_10	female
51666	58	26.22	127	2009_10	female
51667	50	26.60	142	2009_10	male
51671	9	27.40	93	2009_10	female
51677	33	28.54	128	2009_10	male
51678	60	25.84	152	2009_10	male
51679	16	24.74	126	2009_10	male
51685	56	19.73	95	2009_10	female
51685	56	19.73	95	2009_10	female
51691	57	20.66	122	2009_10	female
51692	54	36.32	90	2009_10	male
51692	54	36.32	90	2009_10	male
51694	38	35.84	147	2009_10	male
51695	8	24.32	93	2009_10	male
51701	36	25.95	117	2009_10	male
51702	44	31.43	144	2009_10	male
51702	44	31.43	144	2009_10	male
...	...	...	...	...	...
71855	62	35.9	143	2011_12	female
71855	62	35.9	143	2011_12	female
71857	39	23.8	118	2011_12	male
71857	39	23.8	118	2011_12	male
71860	28	29.8	114	2011_12	male
71868	43	28.4	112	2011_12	male
71868	43	28.4	112	2011_12	male
71868	43	28.4	112	2011_12	male
71868	43	28.4	112	2011_12	male
71869	69	37.8	108	2011_12	male
71869	69	37.8	108	2011_12	male
71869	69	37.8	108	2011_12	male
71873	20	29.3	103	2011_12	female
71875	42	28.5	102	2011_12	male
71879	24	22.4	129	2011_12	male
71880	46	37.5	127	2011_12	male
71882	60	27.3	116	2011_12	female
71898	65	29.4	151	2011_12	female
71907	80	23.2	148	2011_12	male
71907	80	23.2	148	2011_12	male
71907	80	23.2	148	2011_12	male
71908	66	35.1	114	2011_12	female
71908	66	35.1	114	2011_12	female
71909	28	29.4	124	2011_12	male
71909	28	29.4	124	2011_12	male
71909	28	29.4	124	2011_12	male
71910	0	NA	NA	2011_12	female
71911	27	31.3	133	2011_12	male
71915	60	27.5	147	2011_12	male
71915	60	27.5	147	2011_12	male

This code converts the data type of the ID column in the nhanes dataset:

as.character(nhanes$ID) converts the ID column from its current type (double, meaning numeric) to a character (text) type.
The result is assigned back to nhanes$ID, replacing the original column with its character version.

This is often done when ID numbers should be treated as text rather than numerical values for calculations.

# Change the column type of "ID" from "double" to "character"
nhanes$ID <- as.character(nhanes$ID)

This code inspects the nhanes dataset after modifying the ID column:

head(nhanes) displays the first six rows to quickly review the current state of the data.
dim(nhanes) returns the dimensions of the dataset as a numeric vector, showing the total number of rows and columns (rows × columns).
str(nhanes) provides a compact structure summary of the dataset, detailing each column’s name, data type, and a preview of its values.

These commands help confirm that the data looks correct and to understand its overall structure.

# Display the first few rows of the subset after converting column 'ID' to character
head(nhanes)
dim(nhanes) #dimension of the data (Total_rows x Total_columns)
str(nhanes) # Details of the data by columns

A tibble: 6 x 6
ID	Age_yr	BMI	BP	SurveyYr	Gender
<chr>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
51624	34	32.22	113	2009_10	male
51624	34	32.22	113	2009_10	male
51624	34	32.22	113	2009_10	male
51625	4	15.30	NA	2009_10	male
51630	49	30.57	112	2009_10	female
51638	9	16.82	86	2009_10	male

10000
6

tibble [10,000 x 6] (S3: tbl_df/tbl/data.frame)
 $ ID      : chr [1:10000] "51624" "51624" "51624" "51625" ...
 $ Age_yr  : num [1:10000] 34 34 34 4 49 9 8 45 45 45 ...
 $ BMI     : num [1:10000] 32.2 32.2 32.2 15.3 30.6 ...
 $ BP      : num [1:10000] 113 113 113 NA 112 86 107 118 118 118 ...
 $ SurveyYr: chr [1:10000] "2009_10" "2009_10" "2009_10" "2009_10" ...
 $ Gender  : chr [1:10000] "male" "male" "male" "male" ...

This code checks the type of the nhanes object in R:

class(nhanes) returns the class of the object, which indicates what kind of R object it is (likely "tbl_df", "tbl", "data.frame" for a tibble).
typeof(nhanes) returns the internal storage mode of the object, showing the low-level data type used to store it (usually "list" for tibbles and data frames).

Together, these functions help you understand both the high-level object type and the underlying storage structure.

#find the class of the data object "nhanes"
class(nhanes)
typeof(nhanes)

'tbl_df'
'tbl'
'data.frame'

'list'

This code creates a frequency table of the Gender column in the nhanes dataset:

table(nhanes$Gender) counts the number of occurrences of each unique value in the Gender column.
It returns a summary showing how many rows correspond to each gender category, helping to understand the distribution of genders in the data.

# Using 'table' function for frequency table
table(nhanes$Gender)

female   male 
  5020   4980 

This code generates summary statistics for the nhanes dataset and saves the output to a text file, while also printing the summary in the notebook:

capture.output({ ... }) captures all printed output inside the braces into a character vector summary_text.
Inside the capture block:
- cat('Summary statistics of the data before cleaning:....\n') prints a header message.
- summary(as.data.frame(nhanes)) generates summary statistics for the dataset converted to a base data frame (which helps avoid tibble printing quirks).
writeLines(summary_text, 'sumary_stats_before.txt') writes the captured summary text to a file named sumary_stats_before.txt.
Finally, summary(nhanes) prints the summary directly to the console or notebook output.

This approach ensures you have the summary saved for later review while also displaying it immediately.

# Find the summary statistics of the dataset and save the output in another file
summary_text <- capture.output({
  cat('Summary statistics of the data before cleaning:....\n')
  summary(as.data.frame(nhanes))
})

writeLines(summary_text, 'sumary_stats_before.txt')

summary(nhanes)

      ID                Age_yr           BMI              BP       
 Length:10000       Min.   : 0.00   Min.   :12.88   Min.   : 76.0  
 Class :character   1st Qu.:17.00   1st Qu.:21.58   1st Qu.:106.0  
 Mode  :character   Median :36.00   Median :25.98   Median :116.0  
                    Mean   :36.74   Mean   :26.66   Mean   :118.2  
                    3rd Qu.:54.00   3rd Qu.:30.89   3rd Qu.:127.0  
                    Max.   :80.00   Max.   :81.25   Max.   :226.0  
                                    NA's   :366     NA's   :1449   
   SurveyYr            Gender         
 Length:10000       Length:10000      
 Class :character   Class :character  
 Mode  :character   Mode  :character  
                                      
                                      
                                      
                                      

This code demonstrates how to split and then recombine a column in the nhanes dataset using tidyr functions:

separate(nhanes, SurveyYr, into = c("Year1", "Year2"), sep = '_', remove = TRUE):
- Splits the SurveyYr column into two new columns named Year1 and Year2 by splitting at the underscore ('_').
- The original SurveyYr column is removed (remove = TRUE).
- The resulting dataset is stored as nhanes1.
head(nhanes1) shows the first six rows of this modified dataset with the split columns.
unite(nhanes1, "SurveyYr", c("Year1", "Year2"), sep = "_"):
- Combines the two columns Year1 and Year2 back into a single SurveyYr column, joining them with an underscore.
- The result is stored back into nhanes.
head(nhanes) shows the first six rows of the dataset after recombining.

# Using function 'unite' and 'separate' for column 'SurveyYr'
nhanes1 <- separate(nhanes, SurveyYr, into = c("Year1", "Year2"),
                    sep = '_', remove=TRUE) 
head(nhanes1)

nhanes <- unite(nhanes1, "SurveyYr", c("Year1", "Year2"), sep="_")
head(nhanes)

A tibble: 6 x 7
ID	Age_yr	BMI	BP	Year1	Year2	Gender
<chr>	<dbl>	<dbl>	<dbl>	<chr>	<chr>	<chr>
51624	34	32.22	113	2009	10	male
51624	34	32.22	113	2009	10	male
51624	34	32.22	113	2009	10	male
51625	4	15.30	NA	2009	10	male
51630	49	30.57	112	2009	10	female
51638	9	16.82	86	2009	10	male

A tibble: 6 x 6
ID	Age_yr	BMI	BP	SurveyYr	Gender
<chr>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
51624	34	32.22	113	2009_10	male
51624	34	32.22	113	2009_10	male
51624	34	32.22	113	2009_10	male
51625	4	15.30	NA	2009_10	male
51630	49	30.57	112	2009_10	female
51638	9	16.82	86	2009_10	male

Cleaning and Subsetting the Data#

This code checks for duplicate rows in the nhanes dataset:

print('Duplicated rows...') prints a message to indicate the next operation.
duplicated(nhanes) returns a logical vector indicating which rows in the dataset are duplicates of previous rows:
- TRUE means the row is a duplicate.
- FALSE means it’s unique or the first occurrence.

This helps identify if there are any repeated rows in the data.

# Identify duplicate elements
print('Duplicated rows...')
print(duplicated(nhanes))

[1] "Duplicated rows..."
    [1] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
   [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
   [25]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
   [37]  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
   [49] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
   [61] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
   [73]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE
   [85]  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
   [97] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
  [109]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
  [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
  [133]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
  [145] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
  [157]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
  [169]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
  [181] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
  [193]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
  [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
  [217]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
  [229] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
  [241]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
  [253] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
  [265] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
  [277]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE
  [289] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
  [301]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
  [313]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
  [325] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
  [337]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
  [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
  [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
  [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
  [385] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE
  [397] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
  [409]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
  [421] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
  [433]  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
  [445] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
  [457] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
  [469] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
  [481]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
  [493] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
  [505] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
  [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
  [529] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
  [541] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
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 [1765] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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 [1885] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
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 [1909] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
 [1921] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
 [1933] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
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 [2017]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
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 [2053] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
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 [2077]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
 [2089] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
 [2101] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE
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 [2173] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [2185] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
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 [2221] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
 [2233] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE
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 [2257]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
 [2269] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
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 [2293]  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
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 [2413] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
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 [2449] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
 [2461] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
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 [2485] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE
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 [2521] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [2533] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [2545] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
 [2557] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [2569]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
 [2581] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
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 [2605] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
 [2617] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [2629]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
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 [2653] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [2665]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
 [2677] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
 [2689] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [2701] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
 [2713]  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
 [2725]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE
 [2737] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
 [2749]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
 [2761] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [2773] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [2785]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [2797] FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [2809] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
 [2821] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
 [2833]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
 [2845] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
 [2857]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE
 [2869] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
 [2881] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
 [2893] FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE
 [2905] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
 [2917] FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
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 [2941] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
 [2953]  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
 [2965] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [2977] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
 [2989] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
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 [3013] FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
 [3025]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
 [3037] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
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 [3073] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3085]  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [3097] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [3109] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
 [3121] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
 [3133]  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
 [3145]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [3157]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3169] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
 [3181] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
 [3193] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
 [3205] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
 [3217] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [3229]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [3241] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3253] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE
 [3265] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
 [3277] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
 [3289] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [3301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE
 [3313] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
 [3325] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
 [3337] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [3349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3361] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
 [3373] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [3385] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
 [3397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
 [3409] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3433]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
 [3445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [3457]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
 [3469] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [3481] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [3493] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE
 [3505] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [3517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [3529] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
 [3541]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
 [3553] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
 [3565]  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
 [3577]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE
 [3589]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
 [3601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
 [3613]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
 [3625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
 [3637] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
 [3649] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE
 [3661] FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE
 [3673] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [3685] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [3697] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
 [3709]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
 [3721]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [3733] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
 [3745] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
 [3757]  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE
 [3769] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
 [3781] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
 [3793] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
 [3805] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
 [3817] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
 [3829]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
 [3841]  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE
 [3853] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
 [3865] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
 [3877]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [3889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3901] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
 [3913] FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [3925] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
 [3937] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
 [3949]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3961]  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
 [3973] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [3985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE
 [3997] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [4009] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
 [4021] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [4033] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [4045] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
 [4057] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE
 [4069]  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [4081] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [4093]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [4105] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
 [4117] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE
 [4129]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE
 [4141] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
 [4153]  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
 [4165] FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
 [4177] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [4189] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
 [4201] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [4213] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [4225] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [4237] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
 [4249] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [4261] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [4273]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
 [4285]  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
 [4297] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [4309] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [4321] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [4333] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
 [4345] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
 [4357] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
 [4369]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
 [4381] FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
 [4393] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [4405] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
 [4417] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
 [4429] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [4441] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [4453] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
 [4465] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [4477] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [4489] FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
 [4501] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
 [4513] FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [4525] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [4537]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE
 [4549] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [4561]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [4573] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE
 [4585] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
 [4597] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
 [4609] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [4621] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
 [4633] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE
 [4645] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
 [4657] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
 [4669]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
 [4681]  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [4693] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
 [4705] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [4717] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [4729]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [4741]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
 [4753]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [4765] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
 [4777] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
 [4789]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
 [4801] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
 [4813] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
 [4825] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [4837] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [4849] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [4861] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE
 [4873] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [4885] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [4897]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [4909] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
 [4921]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [4933] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
 [4945] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [4957] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
 [4969] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [4981] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [4993]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
 [5005] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
 [5017]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
 [5029] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [5041]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [5053] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE
 [5065] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
 [5077] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
 [5089]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
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 [6445]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
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 [6469] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [6481] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
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 [6625] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
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 [6661] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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 [6841] FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE
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 [6865]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
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 [6961] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
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 [7045] FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [7057] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
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 [7513] FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE
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 [7561] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [7573] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
 [7585] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
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 [7669] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
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 [7693] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
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 [7717]  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
 [7729] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
 [7741]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
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 [7765]  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
 [7777] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [7789] FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
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 [7813] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [7825]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
 [7837] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [7849] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
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 [7885]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE
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 [7921] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [7933] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [7945] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
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 [7969]  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
 [7981] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
 [7993] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
 [8005] FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
 [8017] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [8029] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
 [8041] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [8053] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE
 [8065] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE
 [8077] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
 [8089] FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
 [8101]  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [8113] FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE
 [8125] FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
 [8137] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [8149] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [8161]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [8173] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
 [8185] FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE
 [8197]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [8209] FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE
 [8221]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
 [8233]  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
 [8245] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE
 [8257]  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
 [8269] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
 [8281] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
 [8293] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
 [8305] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [8317] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE
 [8329] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE
 [8341] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE
 [8353]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
 [8365] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE
 [8377] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [8389] FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
 [8401] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
 [8413] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [8425] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE
 [8437] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE
 [8449]  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
 [8461] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [8473]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [8485] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
 [8497] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [8509] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
 [8521] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE
 [8533] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
 [8545] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
 [8557]  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [8569] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
 [8581] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [8593]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [8605] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
 [8617] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE
 [8629]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [8641] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [8653] FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE
 [8665]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [8677]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
 [8689]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
 [8701] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
 [8713] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
 [8725]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [8737]  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
 [8749] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE
 [8761]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
 [8773] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE
 [8785]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [8797] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
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 [8821] FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
 [8833]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [8845] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [8857] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
 [8869] FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
 [8881]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [8893] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [8905] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
 [8917] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [8929]  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [8941] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
 [8953] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
 [8965] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
 [8977] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE
 [8989]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [9001] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
 [9013]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [9025] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
 [9037] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
 [9049] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [9061]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
 [9073] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
 [9085] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
 [9097] FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE
 [9109]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
 [9121] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
 [9133] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [9145]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [9157] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
 [9169] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
 [9181]  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [9193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [9205]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
 [9217]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
 [9229]  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
 [9241]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE
 [9253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
 [9265]  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [9277] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [9289] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
 [9301]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
 [9313] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
 [9325] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
 [9337]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
 [9349] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
 [9361] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [9373]  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE
 [9385] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
 [9397]  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [9409]  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
 [9421]  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
 [9433] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [9445]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE
 [9457] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE
 [9469]  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
 [9481] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE
 [9493] FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE
 [9505] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
 [9517]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [9529]  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
 [9541] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
 [9553] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
 [9565] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE
 [9577] FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
 [9589] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
 [9601] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [9613] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
 [9625] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE
 [9637] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
 [9649] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
 [9661] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
 [9673] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
 [9685]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [9697] FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE
 [9709] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE
 [9721]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [9733] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [9745] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [9757]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
 [9769] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE
 [9781] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [9793] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
 [9805] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [9817] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
 [9829] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
 [9841] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [9853] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE
 [9865]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
 [9877] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
 [9889] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
 [9901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
 [9913] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
 [9925]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [9937] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE
 [9949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
 [9961] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
 [9973] FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
 [9985] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE
 [9997] FALSE FALSE FALSE  TRUE

This code calculates and prints the number of duplicate rows in the nhanes dataset:

print('count of duplicate rows') outputs a message indicating what is being calculated.
sum(duplicated(nhanes)) counts how many rows are duplicates by summing the logical vector returned by duplicated(), where TRUE counts as 1 and FALSE as 0.

This gives the total number of duplicate rows present in the dataset.

# count of duplicated data
print('count of duplicate rows')
sum(duplicated(nhanes))

[1] "count of duplicate rows"

3221

This code creates datasets with unique rows and examines their contents:

data_uniq <- unique(nhanes) creates a new dataset by removing duplicate rows from nhanes using the base R unique() function.
data_dist <- distinct(nhanes) creates a new dataset with distinct rows from nhanes using the distinct() function from dplyr. Both unique() and distinct() generally serve the same purpose here.
head(data_uniq) displays the first six rows of the dataset with duplicates removed.
dim(data_uniq) returns the dimensions (number of rows and columns) of the unique dataset.

This helps confirm how many unique rows remain after removing duplicates.

# remove duplicate rows using both "unique" and "distinct"
data_uniq <- unique(nhanes)
data_dist <- distinct(nhanes)

head(data_uniq)
dim(data_uniq)

A tibble: 6 x 6
ID	Age_yr	BMI	BP	SurveyYr	Gender
<chr>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
51624	34	32.22	113	2009_10	male
51625	4	15.30	NA	2009_10	male
51630	49	30.57	112	2009_10	female
51638	9	16.82	86	2009_10	male
51646	8	20.64	107	2009_10	male
51647	45	27.24	118	2009_10	female

6779
6

This code inspects the data_dist dataset, which contains distinct rows from nhanes:

head(data_dist) displays the first six rows of the data_dist dataset to preview the beginning of the data.
tail(data_dist) displays the last six rows to preview the end of the data.
dim(data_dist) returns the dimensions of data_dist (number of rows and columns).

These commands help verify the contents and size of the dataset after removing duplicates.

# remove duplicate rows using distinct -- needs tidyverse/dplyr
head(data_dist)
tail(data_dist)
dim(data_dist)

A tibble: 6 x 6
ID	Age_yr	BMI	BP	SurveyYr	Gender
<chr>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
51624	34	32.22	113	2009_10	male
51625	4	15.30	NA	2009_10	male
51630	49	30.57	112	2009_10	female
51638	9	16.82	86	2009_10	male
51646	8	20.64	107	2009_10	male
51647	45	27.24	118	2009_10	female

A tibble: 6 x 6
ID	Age_yr	BMI	BP	SurveyYr	Gender
<chr>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
71907	80	23.2	148	2011_12	male
71908	66	35.1	114	2011_12	female
71909	28	29.4	124	2011_12	male
71910	0	NA	NA	2011_12	female
71911	27	31.3	133	2011_12	male
71915	60	27.5	147	2011_12	male

6779
6

This code attempts to perform substitutions on the ID column in data_dist using the sub() function:

sub('5*', 'subj', data_dist$ID) replaces the first occurrence of the pattern '5*' in each element of the ID vector with the string "subj".
- Note: In regex, '5*' means “zero or more 5s,” so this will match in many places, possibly not as intended.
sub('subj', '5', data_dist$ID) replaces the first occurrence of "subj" in each element of ID back to "5".
tail(data_dist) shows the last six rows of the data_dist dataset (note that the substitutions above do not modify data_dist unless you assign the results back).

Important: These sub() calls return modified vectors but do not change the original data_dist unless assigned

# Replacing specific string in the whole column with another string
sub('5*','subj', data_dist$ID)
sub('subj','5', data_dist$ID)
tail(data_dist)

'subj1624'
'subj1625'
'subj1630'
'subj1638'
'subj1646'
'subj1647'
'subj1654'
'subj1656'
'subj1657'
'subj1659'
'subj1666'
'subj1667'
'subj1671'
'subj1677'
'subj1678'
'subj1679'
'subj1685'
'subj1691'
'subj1692'
'subj1694'
'subj1695'
'subj1701'
'subj1702'
'subj1707'
'subj1710'
'subj1711'
'subj1715'
'subj1718'
'subj1719'
'subj1720'
'subj1722'
'subj1723'
'subj1724'
'subj1731'
'subj1732'
'subj1734'
'subj1741'
'subj1743'
'subj1745'
'subj1748'
'subj1752'
'subj1753'
'subj1754'
'subj1755'
'subj1758'
'subj1760'
'subj1761'
'subj1764'
'subj1765'
'subj1767'
'subj1768'
'subj1771'
'subj1772'
'subj1774'
'subj1776'
'subj1780'
'subj1781'
'subj1788'
'subj1789'
'subj1790'
'subj1794'
'subj1799'
'subj1801'
'subj1803'
'subj1804'
'subj1809'
'subj1810'
'subj1813'
'subj1814'
'subj1819'
'subj1828'
'subj1829'
'subj1832'
'subj1840'
'subj1845'
'subj1846'
'subj1849'
'subj1851'
'subj1856'
'subj1857'
'subj1861'
'subj1862'
'subj1863'
'subj1868'
'subj1875'
'subj1888'
'subj1892'
'subj1893'
'subj1896'
'subj1898'
'subj1899'
'subj1905'
'subj1906'
'subj1907'
'subj1910'
'subj1911'
'subj1912'
'subj1921'
'subj1928'
'subj1933'
'subj1943'
'subj1944'
'subj1945'
'subj1951'
'subj1952'
'subj1954'
'subj1958'
'subj1959'
'subj1961'
'subj1962'
'subj1963'
'subj1964'
'subj1966'
'subj1970'
'subj1971'
'subj1973'
'subj1974'
'subj1975'
'subj1976'
'subj1979'
'subj1982'
'subj1983'
'subj1984'
'subj1985'
'subj1991'
'subj1996'
'subj1997'
'subj2004'
'subj2009'
'subj2013'
'subj2016'
'subj2017'
'subj2019'
'subj2020'
'subj2022'
'subj2023'
'subj2027'
'subj2030'
'subj2031'
'subj2037'
'subj2038'
'subj2043'
'subj2044'
'subj2050'
'subj2054'
'subj2060'
'subj2063'
'subj2066'
'subj2067'
'subj2068'
'subj2069'
'subj2075'
'subj2080'
'subj2081'
'subj2083'
'subj2085'
'subj2088'
'subj2089'
'subj2090'
'subj2093'
'subj2094'
'subj2099'
'subj2101'
'subj2102'
'subj2104'
'subj2106'
'subj2107'
'subj2114'
'subj2115'
'subj2119'
'subj2120'
'subj2121'
'subj2122'
'subj2128'
'subj2131'
'subj2134'
'subj2135'
'subj2145'
'subj2148'
'subj2149'
'subj2151'
'subj2152'
'subj2154'
'subj2156'
'subj2157'
'subj2159'
'subj2160'
'subj2163'
'subj2168'
'subj2177'
'subj2179'
'subj2185'
'subj2187'
'subj2189'
'subj2192'
'subj2193'
'subj2194'
'subj2197'
'subj2202'
'subj2204'
...
'subj71306'
'subj71307'
'subj71309'
'subj71310'
'subj71315'
'subj71316'
'subj71326'
'subj71332'
'subj71334'
'subj71337'
'subj71338'
'subj71340'
'subj71346'
'subj71347'
'subj71354'
'subj71356'
'subj71359'
'subj71360'
'subj71361'
'subj71362'
'subj71365'
'subj71366'
'subj71368'
'subj71375'
'subj71377'
'subj71391'
'subj71392'
'subj71393'
'subj71399'
'subj71403'
'subj71409'
'subj71410'
'subj71415'
'subj71419'
'subj71420'
'subj71421'
'subj71422'
'subj71424'
'subj71425'
'subj71428'
'subj71435'
'subj71439'
'subj71440'
'subj71445'
'subj71449'
'subj71450'
'subj71452'
'subj71456'
'subj71459'
'subj71463'
'subj71464'
'subj71467'
'subj71470'
'subj71475'
'subj71478'
'subj71486'
'subj71488'
'subj71493'
'subj71495'
'subj71500'
'subj71510'
'subj71511'
'subj71526'
'subj71529'
'subj71530'
'subj71531'
'subj71532'
'subj71542'
'subj71545'
'subj71547'
'subj71552'
'subj71560'
'subj71564'
'subj71565'
'subj71567'
'subj71568'
'subj71569'
'subj71570'
'subj71572'
'subj71573'
'subj71575'
'subj71578'
'subj71581'
'subj71587'
'subj71588'
'subj71589'
'subj71592'
'subj71594'
'subj71596'
'subj71597'
'subj71598'
'subj71599'
'subj71604'
'subj71605'
'subj71611'
'subj71613'
'subj71619'
'subj71624'
'subj71632'
'subj71637'
'subj71638'
'subj71641'
'subj71644'
'subj71645'
'subj71646'
'subj71654'
'subj71656'
'subj71657'
'subj71658'
'subj71661'
'subj71662'
'subj71664'
'subj71667'
'subj71671'
'subj71675'
'subj71676'
'subj71678'
'subj71679'
'subj71684'
'subj71691'
'subj71693'
'subj71697'
'subj71698'
'subj71699'
'subj71701'
'subj71702'
'subj71704'
'subj71705'
'subj71707'
'subj71721'
'subj71725'
'subj71729'
'subj71730'
'subj71732'
'subj71733'
'subj71736'
'subj71737'
'subj71740'
'subj71744'
'subj71745'
'subj71754'
'subj71755'
'subj71756'
'subj71759'
'subj71762'
'subj71763'
'subj71764'
'subj71766'
'subj71767'
'subj71768'
'subj71769'
'subj71773'
'subj71775'
'subj71782'
'subj71784'
'subj71785'
'subj71786'
'subj71787'
'subj71788'
'subj71792'
'subj71793'
'subj71794'
'subj71796'
'subj71797'
'subj71799'
'subj71802'
'subj71808'
'subj71809'
'subj71810'
'subj71812'
'subj71816'
'subj71820'
'subj71822'
'subj71823'
'subj71825'
'subj71826'
'subj71828'
'subj71833'
'subj71835'
'subj71839'
'subj71847'
'subj71849'
'subj71852'
'subj71855'
'subj71857'
'subj71860'
'subj71868'
'subj71869'
'subj71873'
'subj71875'
'subj71879'
'subj71880'
'subj71882'
'subj71898'
'subj71907'
'subj71908'
'subj71909'
'subj71910'
'subj71911'
'subj71915'

'51624'
'51625'
'51630'
'51638'
'51646'
'51647'
'51654'
'51656'
'51657'
'51659'
'51666'
'51667'
'51671'
'51677'
'51678'
'51679'
'51685'
'51691'
'51692'
'51694'
'51695'
'51701'
'51702'
'51707'
'51710'
'51711'
'51715'
'51718'
'51719'
'51720'
'51722'
'51723'
'51724'
'51731'
'51732'
'51734'
'51741'
'51743'
'51745'
'51748'
'51752'
'51753'
'51754'
'51755'
'51758'
'51760'
'51761'
'51764'
'51765'
'51767'
'51768'
'51771'
'51772'
'51774'
'51776'
'51780'
'51781'
'51788'
'51789'
'51790'
'51794'
'51799'
'51801'
'51803'
'51804'
'51809'
'51810'
'51813'
'51814'
'51819'
'51828'
'51829'
'51832'
'51840'
'51845'
'51846'
'51849'
'51851'
'51856'
'51857'
'51861'
'51862'
'51863'
'51868'
'51875'
'51888'
'51892'
'51893'
'51896'
'51898'
'51899'
'51905'
'51906'
'51907'
'51910'
'51911'
'51912'
'51921'
'51928'
'51933'
'51943'
'51944'
'51945'
'51951'
'51952'
'51954'
'51958'
'51959'
'51961'
'51962'
'51963'
'51964'
'51966'
'51970'
'51971'
'51973'
'51974'
'51975'
'51976'
'51979'
'51982'
'51983'
'51984'
'51985'
'51991'
'51996'
'51997'
'52004'
'52009'
'52013'
'52016'
'52017'
'52019'
'52020'
'52022'
'52023'
'52027'
'52030'
'52031'
'52037'
'52038'
'52043'
'52044'
'52050'
'52054'
'52060'
'52063'
'52066'
'52067'
'52068'
'52069'
'52075'
'52080'
'52081'
'52083'
'52085'
'52088'
'52089'
'52090'
'52093'
'52094'
'52099'
'52101'
'52102'
'52104'
'52106'
'52107'
'52114'
'52115'
'52119'
'52120'
'52121'
'52122'
'52128'
'52131'
'52134'
'52135'
'52145'
'52148'
'52149'
'52151'
'52152'
'52154'
'52156'
'52157'
'52159'
'52160'
'52163'
'52168'
'52177'
'52179'
'52185'
'52187'
'52189'
'52192'
'52193'
'52194'
'52197'
'52202'
'52204'
...
'71306'
'71307'
'71309'
'71310'
'71315'
'71316'
'71326'
'71332'
'71334'
'71337'
'71338'
'71340'
'71346'
'71347'
'71354'
'71356'
'71359'
'71360'
'71361'
'71362'
'71365'
'71366'
'71368'
'71375'
'71377'
'71391'
'71392'
'71393'
'71399'
'71403'
'71409'
'71410'
'71415'
'71419'
'71420'
'71421'
'71422'
'71424'
'71425'
'71428'
'71435'
'71439'
'71440'
'71445'
'71449'
'71450'
'71452'
'71456'
'71459'
'71463'
'71464'
'71467'
'71470'
'71475'
'71478'
'71486'
'71488'
'71493'
'71495'
'71500'
'71510'
'71511'
'71526'
'71529'
'71530'
'71531'
'71532'
'71542'
'71545'
'71547'
'71552'
'71560'
'71564'
'71565'
'71567'
'71568'
'71569'
'71570'
'71572'
'71573'
'71575'
'71578'
'71581'
'71587'
'71588'
'71589'
'71592'
'71594'
'71596'
'71597'
'71598'
'71599'
'71604'
'71605'
'71611'
'71613'
'71619'
'71624'
'71632'
'71637'
'71638'
'71641'
'71644'
'71645'
'71646'
'71654'
'71656'
'71657'
'71658'
'71661'
'71662'
'71664'
'71667'
'71671'
'71675'
'71676'
'71678'
'71679'
'71684'
'71691'
'71693'
'71697'
'71698'
'71699'
'71701'
'71702'
'71704'
'71705'
'71707'
'71721'
'71725'
'71729'
'71730'
'71732'
'71733'
'71736'
'71737'
'71740'
'71744'
'71745'
'71754'
'71755'
'71756'
'71759'
'71762'
'71763'
'71764'
'71766'
'71767'
'71768'
'71769'
'71773'
'71775'
'71782'
'71784'
'71785'
'71786'
'71787'
'71788'
'71792'
'71793'
'71794'
'71796'
'71797'
'71799'
'71802'
'71808'
'71809'
'71810'
'71812'
'71816'
'71820'
'71822'
'71823'
'71825'
'71826'
'71828'
'71833'
'71835'
'71839'
'71847'
'71849'
'71852'
'71855'
'71857'
'71860'
'71868'
'71869'
'71873'
'71875'
'71879'
'71880'
'71882'
'71898'
'71907'
'71908'
'71909'
'71910'
'71911'
'71915'

A tibble: 6 x 6
ID	Age_yr	BMI	BP	SurveyYr	Gender
<chr>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
71907	80	23.2	148	2011_12	male
71908	66	35.1	114	2011_12	female
71909	28	29.4	124	2011_12	male
71910	0	NA	NA	2011_12	female
71911	27	31.3	133	2011_12	male
71915	60	27.5	147	2011_12	male

Subsetting the data using condition on columns using “filter” function#

This code filters the data_dist dataset to create a subset and inspects it:

filter(data_dist, Age_yr == 34, Gender == 'male') selects rows where the Age_yr column equals 34 and the Gender column equals 'male'. This uses the filter() function from dplyr.
The resulting filtered data is saved as age_34_data.
age_34_data prints the filtered dataset to the console or notebook.
dim(age_34_data) returns the dimensions (number of rows and columns) of this subset, showing how many male individuals aged 34 are present.

# Condition -- Find all columns where Age is 34 and gender is 'male'
age_34_data <- filter(data_dist, Age_yr == 34, Gender == 'male')
age_34_data
dim(age_34_data)

A tibble: 36 x 6
ID	Age_yr	BMI	BP	SurveyYr	Gender
<chr>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
51624	34	32.22	113	2009_10	male
52789	34	22.31	107	2009_10	male
54123	34	26.96	104	2009_10	male
54148	34	28.69	124	2009_10	male
54564	34	28.70	116	2009_10	male
54636	34	20.45	106	2009_10	male
56089	34	40.68	124	2009_10	male
56624	34	28.59	120	2009_10	male
57024	34	31.06	123	2009_10	male
57184	34	31.23	124	2009_10	male
57244	34	25.36	115	2009_10	male
57290	34	27.28	125	2009_10	male
57481	34	26.06	129	2009_10	male
58261	34	32.76	118	2009_10	male
58558	34	24.15	124	2009_10	male
58706	34	25.82	129	2009_10	male
60186	34	25.37	118	2009_10	male
61545	34	23.09	115	2009_10	male
63849	34	30.10	121	2011_12	male
65773	34	49.70	138	2011_12	male
65946	34	22.50	102	2011_12	male
66061	34	24.30	124	2011_12	male
66505	34	23.00	115	2011_12	male
66591	34	35.00	120	2011_12	male
66933	34	21.10	114	2011_12	male
67490	34	27.20	107	2011_12	male
67893	34	26.70	122	2011_12	male
68021	34	23.70	107	2011_12	male
68592	34	24.60	127	2011_12	male
70161	34	44.90	98	2011_12	male
70162	34	30.50	147	2011_12	male
70485	34	20.60	102	2011_12	male
70548	34	24.50	114	2011_12	male
71079	34	22.30	116	2011_12	male
71146	34	23.60	121	2011_12	male
71792	34	25.60	103	2011_12	male

36
6

This code creates a subset of data_dist with male individuals whose age falls within a specified range:

The pipe operator %>% passes data_dist into the filter() function.
filter(between(Age_yr, 34, 40), Gender == 'male') selects rows where:
- Age_yr is between 34 and 40 (inclusive), using the between() function.
- Gender is “male”.
The filtered dataset is saved as age_range_data.
age_range_data prints the filtered data to the console or notebook output.

#Condition --  find the data for age between 34 to 40
age_range_data <- data_dist %>% filter(between(Age_yr, 34, 40), Gender == 'male')
age_range_data

A tibble: 304 x 6
ID	Age_yr	BMI	BP	SurveyYr	Gender
<chr>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
51624	34	32.22	113	2009_10	male
51694	38	35.84	147	2009_10	male
51701	36	25.95	117	2009_10	male
51832	38	25.91	118	2009_10	male
51991	35	28.82	121	2009_10	male
52013	36	28.09	102	2009_10	male
52050	39	32.70	127	2009_10	male
52179	40	35.45	117	2009_10	male
52197	39	40.66	140	2009_10	male
52214	38	26.66	125	2009_10	male
52395	35	31.52	NA	2009_10	male
52501	38	24.79	136	2009_10	male
52538	37	30.92	140	2009_10	male
52584	39	26.14	121	2009_10	male
52621	35	32.75	126	2009_10	male
52648	39	29.86	127	2009_10	male
52789	34	22.31	107	2009_10	male
52948	36	32.53	NA	2009_10	male
52975	37	21.96	106	2009_10	male
52980	38	30.90	119	2009_10	male
53127	39	32.65	143	2009_10	male
53221	39	30.40	125	2009_10	male
53325	37	36.26	149	2009_10	male
53418	39	41.49	124	2009_10	male
53476	35	28.98	114	2009_10	male
53569	40	39.16	118	2009_10	male
53621	40	26.39	103	2009_10	male
53672	36	31.70	117	2009_10	male
53702	38	24.27	125	2009_10	male
53742	38	22.85	113	2009_10	male
...	...	...	...	...	...
70161	34	44.9	98	2011_12	male
70162	34	30.5	147	2011_12	male
70196	37	30.9	95	2011_12	male
70213	35	28.2	124	2011_12	male
70485	34	20.6	102	2011_12	male
70535	40	26.3	130	2011_12	male
70548	34	24.5	114	2011_12	male
70551	37	28.3	125	2011_12	male
70757	36	21.7	118	2011_12	male
70851	36	22.9	112	2011_12	male
70869	38	35.4	109	2011_12	male
70973	39	22.4	121	2011_12	male
71079	34	22.3	116	2011_12	male
71119	36	25.2	117	2011_12	male
71146	34	23.6	121	2011_12	male
71158	39	22.3	106	2011_12	male
71183	38	22.6	110	2011_12	male
71279	40	25.1	147	2011_12	male
71368	35	20.6	131	2011_12	male
71415	38	27.3	NA	2011_12	male
71428	35	45.3	111	2011_12	male
71452	36	25.0	NA	2011_12	male
71531	35	22.4	143	2011_12	male
71567	35	26.9	120	2011_12	male
71581	36	38.7	127	2011_12	male
71763	40	20.9	121	2011_12	male
71767	38	26.6	116	2011_12	male
71792	34	25.6	103	2011_12	male
71847	37	26.0	116	2011_12	male
71857	39	23.8	118	2011_12	male

This code filters the data_dist dataset to select male individuals older than 50:

The pipe operator %>% passes data_dist into filter().
filter(Age_yr > 50, Gender == 'male') selects rows where:
- Age_yr is greater than 50.
- Gender is 'male'.
The resulting subset is saved as age_gt_data.
age_gt_data prints this filtered dataset to the console or notebook output.

#Condition --  find the data for age greater than 50
age_gt_data <- data_dist %>% filter(Age_yr > 50, Gender == 'male')
age_gt_data

A tibble: 923 x 6
ID	Age_yr	BMI	BP	SurveyYr	Gender
<chr>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
51654	66	23.67	111	2009_10	male
51656	58	23.69	104	2009_10	male
51657	54	26.03	134	2009_10	male
51678	60	25.84	152	2009_10	male
51692	54	36.32	90	2009_10	male
51748	56	24.98	86	2009_10	male
51803	58	29.27	139	2009_10	male
51804	80	17.87	132	2009_10	male
51819	80	34.04	125	2009_10	male
51861	80	29.34	149	2009_10	male
51888	63	27.46	127	2009_10	male
51921	55	26.97	181	2009_10	male
51933	54	35.82	191	2009_10	male
51975	57	31.88	127	2009_10	male
51976	79	27.78	146	2009_10	male
52023	65	31.28	130	2009_10	male
52027	55	22.81	134	2009_10	male
52031	77	30.34	142	2009_10	male
52043	62	28.97	110	2009_10	male
52068	72	28.15	106	2009_10	male
52081	65	31.77	126	2009_10	male
52099	56	30.27	133	2009_10	male
52122	67	32.35	140	2009_10	male
52134	61	48.22	110	2009_10	male
52135	74	32.93	133	2009_10	male
52152	70	28.64	118	2009_10	male
52168	73	23.52	178	2009_10	male
52192	73	26.39	134	2009_10	male
52193	55	29.86	116	2009_10	male
52221	65	22.01	114	2009_10	male
...	...	...	...	...	...
71377	80	22.1	NA	2011_12	male
71399	55	21.6	110	2011_12	male
71511	64	27.5	116	2011_12	male
71530	68	26.7	138	2011_12	male
71552	65	22.8	159	2011_12	male
71560	80	27.1	145	2011_12	male
71569	56	29.0	147	2011_12	male
71599	59	30.5	145	2011_12	male
71613	75	23.9	174	2011_12	male
71619	55	22.6	122	2011_12	male
71656	58	23.8	114	2011_12	male
71662	74	29.8	139	2011_12	male
71671	65	22.6	128	2011_12	male
71676	80	27.0	120	2011_12	male
71697	74	28.6	143	2011_12	male
71704	77	28.1	135	2011_12	male
71705	67	38.5	122	2011_12	male
71732	63	30.3	123	2011_12	male
71733	80	31.7	139	2011_12	male
71736	74	26.4	105	2011_12	male
71756	63	23.7	131	2011_12	male
71773	53	28.0	86	2011_12	male
71775	62	40.7	109	2011_12	male
71785	64	28.4	138	2011_12	male
71797	80	NA	153	2011_12	male
71799	59	22.5	147	2011_12	male
71802	53	31.3	139	2011_12	male
71869	69	37.8	108	2011_12	male
71907	80	23.2	148	2011_12	male
71915	60	27.5	147	2011_12	male

Handling Missing Data#

This code prints the number of missing (NA) values in specific columns of the data_dist dataset:

For each column (BP, BMI, Age_yr, ID), it calculates the total count of NA values using sum(is.na(...)).
cat() is used to combine a descriptive message and the count into a single output.

# Count the number of rows with column values as NA
cat('Number of missing values in column BloodPressure:', sum(is.na(data_dist$BP)), '\n')
cat('Number of missing values in column BMI:', sum(is.na(data_dist$BMI)), '\n')
cat('Number of missing values in column Age:',sum(is.na(data_dist$Age_yr)), '\n')
cat('Number of missing values in column ID:',sum(is.na(data_dist$ID)), '\n')

Number of missing values in column BloodPressure: 1137 
Number of missing values in column BMI: 304 
Number of missing values in column Age: 0 
Number of missing values in column ID: 0 

Data Imputation - Replacing NA by Some Other Values#

This code performs mean imputation to fill missing values (NAs) in the data_dist dataset and then checks the number of remaining missing values:

impData_mean <- data_dist creates a copy of data_dist called impData_mean to keep the original data intact.
impData_mean$BP[is.na(impData_mean$BP)] <- mean(impData_mean$BP, na.rm = TRUE):
- Calculates the mean of the BP column, ignoring missing values (na.rm = TRUE).
- Replaces all NAs in the BP column with this mean value.
Similarly, missing values in the BMI column are replaced with the column’s mean.
cat(' The number of NA in imputed dataframe', sum(is.na(impData_mean$BP))) prints how many NAs remain in the imputed BP column (should be zero after imputation).

If you want, I can help you write code to check missing values in other columns or implement other imputation methods!

# Replace NA by column 'mean'
impData_mean <- data_dist    #create a copy of original data
impData_mean$BP[is.na(impData_mean$BP)] <- mean(impData_mean$BP, na.rm = TRUE) # replace one column
impData_mean$BMI[is.na(impData_mean$BMI)] <- mean(impData_mean$BMI, na.rm = TRUE)
cat(' The number of NA in imputed dataframe', sum(is.na(impData_mean$BP)))

 The number of NA in imputed dataframe 0

This code performs zero imputation to fill missing values in the BP column:

impData_zero <- data_dist creates a copy of the data_dist dataset named impData_zero.
impData_zero$BP[is.na(impData_zero$BP)] <- 0 replaces all missing (NA) values in the BP column with zero (0).
head(impData_zero) displays the first six rows of the dataset after this replacement.

Zero imputation is a simple method where missing values are replaced with zero, which can be useful in some contexts but may also bias analyses depending on the data.

# Replace NA by some value (say 0)
impData_zero <- data_dist 
impData_zero$BP[is.na(impData_zero$BP)] <- 0
head(impData_zero)

A tibble: 6 x 6
ID	Age_yr	BMI	BP	SurveyYr	Gender
<chr>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
51624	34	32.22	113	2009_10	male
51625	4	15.30	0	2009_10	male
51630	49	30.57	112	2009_10	female
51638	9	16.82	86	2009_10	male
51646	8	20.64	107	2009_10	male
51647	45	27.24	118	2009_10	female

This code removes all rows containing any missing (NA) values from the dataset and reports the number of missing values before and after:

dataNo_na <- data_dist creates a copy of data_dist named dataNo_na.
cat('Before deleting all NA rows', sum(is.na(dataNo_na)), '\n') prints the total count of missing values across the entire dataset before cleaning.
dataNo_na <- na.omit(dataNo_na) removes all rows that have any missing values in any column.
cat('After deleting all NA rows', sum(is.na(dataNo_na)), '\n') prints the count of missing values after deletion (should be zero).

This is a straightforward way to get a dataset with no missing data by dropping incomplete records.

# Remove all the rows with NA entries
dataNo_na <- data_dist
cat('Before deleting all NA rows', sum(is.na(dataNo_na)), '\n')
dataNo_na <- na.omit(dataNo_na)
cat('After deleting all NA rows', sum(is.na(dataNo_na)), '\n')

Before deleting all NA rows 1441 
After deleting all NA rows 0 

This code removes rows with missing values specifically in the BP column and then counts remaining missing values:

rmCol_na <- data_dist %>% drop_na(BP) uses the drop_na() function from tidyr to remove any rows where the BP column has NA.
The pipe %>% passes data_dist into drop_na(BP).
sum(is.na(rmCol_na)) counts all missing values remaining in the resulting dataset across all columns.

This method selectively removes rows with missing BP but keeps rows with missing values in other columns.

# Removing soecific columns with NA
rmCol_na <- data_dist %>% drop_na(BP)
sum(is.na(rmCol_na))

44

Using `apply` Functions For Data With No NA Values#

This code uses the apply() function to perform operations across columns of the dataNo_na dataset:

apply(dataNo_na, 2, min):
- Applies the min function to each column (1 specifies rows, 2 specifies columns).
- Returns the minimum value found in each column.
- Works properly for numeric columns; for character columns, may return unexpected results or errors.
apply(dataNo_na, 2, length):
- Applies the length function to each column.
- Returns the total number of elements (rows) in each column, which should be the same for all columns in a well-formed dataset.

Note: If your dataset has mixed types (numeric, character, factor), it’s often safer to apply such functions to only numeric columns, for example:

apply(dataNo_na[, sapply(dataNo_na, is.numeric)], 2, min)

# Find min, length of ALL the columns
apply(dataNo_na, 2, min)   # MARGIN = 1 (for row), 2 (for column)
apply(dataNo_na, 2, length) # find the length of each column

ID: '51624'
Age_yr: ' 8'
BMI: '12.89'
BP: ' 76'
SurveyYr: '2009_10'
Gender: 'female'

ID: 5598
Age_yr: 5598
BMI: 5598
BP: 5598
SurveyYr: 5598
Gender: 5598

This code calculates the mean for selected numeric columns using both lapply() and sapply():

num_cols <- list(dataNo_na$Age_yr, dataNo_na$BMI, dataNo_na$BP):
- Creates a list containing three numeric vectors from the dataNo_na dataset: Age_yr, BMI, and BP.
lapply(num_cols, mean):
- Applies the mean function to each element (i.e., each column) of the list.
- Returns a list of mean values.
sapply(num_cols, mean):
- Similar to lapply(), but simplifies the result to a vector if possible.
- In this case, it returns a numeric vector containing the mean of each column.

Both approaches compute the mean for each selected column, but sapply() provides a cleaner, simplified output.

#LAPPLY - Apply over a list or vector
num_cols <- list(dataNo_na$Age_yr, dataNo_na$BMI, dataNo_na$BP)
lapply(num_cols, mean)  # output as list
sapply(num_cols, mean) # output as vector

40.2992140050018
27.5776098606645
117.966059306895

40.2992140050018
27.5776098606645
117.966059306895

Using `group_by` For Grouping Data By Columns#

This code calculates summary statistics (means) for different gender groups in the dataset:

dataGrp.gender <- dataNo_na %>% group_by(Gender) %>% summarise(...):
- Groups the dataNo_na dataset by the Gender column using group_by().
- Calculates the mean for Age_yr, BP, and BMI within each gender group using summarise().
- .groups = 'drop' ensures that the result is returned as a regular (ungrouped) data frame.
The results are saved to dataGrp.gender, which contains the average Age_yr, BP, and BMI for each gender.
print('The mean for age, BP, and BMI by gender is:') prints a descriptive message.
print(dataGrp.gender) displays the summarized table with means by gender.

# By one column - 'Gender'
dataGrp.gender <- dataNo_na %>% group_by(Gender)  %>%
  summarise(mean_age = mean(Age_yr), 
            mean_BP = mean(BP), 
            mean_BMI = mean(BMI), 
            .groups = 'drop')
print('The mean for age, BP, and BMI by gender is:')
print(dataGrp.gender)

[1] "The mean for age, BP, and BMI by gender is:"
# A tibble: 2 x 4
  Gender mean_age mean_BP mean_BMI
  <chr>     <dbl>   <dbl>    <dbl>
1 female     41.1    116.     27.9
2 male       39.5    120.     27.3

This code calculates summary statistics (means) grouped by both gender and survey year:

dataGrp.multi <- dataNo_na %>% group_by(Gender, SurveyYr) %>% summarise(...):
- Groups the dataNo_na dataset by both Gender and SurveyYr using group_by().
- Within each group (combination of gender and year), calculates:
  - mean_age: Mean of Age_yr.
  - mean_BP: Mean of BP.
  - mean_BMI: Mean of BMI.
- Note: By default, summarise() keeps the grouping structure unless you specify .groups = 'drop'.
print('The mean for age, BP, and BMI by gender and year is:') prints a descriptive message.
print(dataGrp.multi) displays the summarized table with grouped means by gender and survey year.

# By multiple columns - 'Gender' and 'SurveyYr'
dataGrp.multi <- dataNo_na %>% group_by(Gender, SurveyYr)  %>%
  summarise(mean_age = mean(Age_yr), 
            mean_BP = mean(BP), 
            mean_BMI = mean(BMI))
print('The mean for age, BP, and BMI by gender and year is:')
print(dataGrp.multi)

`summarise()` has grouped output by 'Gender'. You can override using the
`.groups` argument.

[1] "The mean for age, BP, and BMI by gender and year is:"
# A tibble: 4 x 5
# Groups:   Gender [2]
  Gender SurveyYr mean_age mean_BP mean_BMI
  <chr>  <chr>       <dbl>   <dbl>    <dbl>
1 female 2009_10      41.4    116.     28.1
2 female 2011_12      40.6    117.     27.6
3 male   2009_10      39.7    120.     27.5
4 male   2011_12      39.4    120.     27.0

This code calculates the other summary statistics grouped by both gender and survey year:

Groups data by both Gender and SurveyYr.
Calculates:
- min_age: Minimum age in each group.
- max_age: Maximum age in each group.
- sum_BP: Sum of blood pressure values in each group.
- mean_BMI: Mean BMI for each group.
- count: Number of observations (rows) per group.
.groups = 'drop' ensures the result is returned as an ungrouped data frame.

# similarly we can use min, max, sum, count function to aggregate
dataGrp.summary <- dataNo_na %>%
  group_by(Gender, SurveyYr) %>%
  summarise(
    min_age = min(Age_yr),
    max_age = max(Age_yr),
    sum_BP = sum(BP),
    mean_BMI = mean(BMI),
    count = n(),   # Count of rows in each group
    .groups = 'drop'
  )

print('Summary statistics by gender and year (min, max, sum, count):')
print(dataGrp.summary)

[1] "Summary statistics by gender and year (min, max, sum, count):"
# A tibble: 4 x 7
  Gender SurveyYr min_age max_age sum_BP mean_BMI count
  <chr>  <chr>      <dbl>   <dbl>  <dbl>    <dbl> <int>
1 female 2009_10        8      80 172971     28.1  1497
2 female 2011_12        8      80 154983     27.6  1328
3 male   2009_10        8      80 174366     27.5  1458
4 male   2011_12        8      80 158054     27.0  1315

Acknowledgements#

The Python code used in this notebook was originally written by Vandana Srivastava, AI/Data Science Specialist, University Libraries, USC.

Explanatory text, annotations, and additional instructional content were added by Meara Cox, USC 2026 Graduate.