Help for package cchsflow

Type:

Package

Title:

Transforming and Harmonizing CCHS Variables

Version:

2.1.0

Date:

2022-05-05

Depends:

R (≥ 3.5.0), haven (≥ 1.1.2), dplyr (≥ 0.8.2), sjlabelled (≥ 1.0.17), stringr (≥ 1.2.0), magrittr

Description:

Supporting the use of the Canadian Community Health Survey (CCHS) by transforming variables from each cycle into harmonized, consistent versions that span survey cycles (currently, 2001 to 2018). CCHS data used in this library is accessed and adapted in accordance to the Statistics Canada Open Licence Agreement. This package uses rec_with_table(), which was developed from 'sjmisc' rec(). Lüdecke D (2018). "sjmisc: Data and Variable Transformation Functions". Journal of Open Source Software, 3(26), 754. <doi:10.21105/joss.00754>.

License:

MIT + file LICENSE

Encoding:

UTF-8

LazyData:

true

URL:

https://github.com/Big-Life-Lab/cchsflow

BugReports:

https://github.com/Big-Life-Lab/cchsflow/issues

RoxygenNote:

7.1.2

Suggests:

testthat (≥ 3.0.0)

Config/testthat/edition:

NeedsCompilation:

Packaged:

2022-05-26 13:12:18 UTC; Kitty

Author:

Doug Manuel

[aut, cph], Warsame Yusuf [aut], Rostyslav Vyuha [aut], Kitty Chen [aut, cre], Carol Bennett [aut], Yulric Sequeira [ctb], The Ottawa Hospital [cph]

Maintainer:

Kitty Chen <kitchen@ohri.ca>

Repository:

CRAN

Date/Publication:

2022-05-26 14:10:02 UTC

Type of drinker (12 months)

Description

NOTE: this is not a function.

This is a categorical variable derived by Statistics Canada that uses various intermediate alcohol variables to categorize individuals into 3 distinct groups:

Regular Drinker
Occasional Drinker
No drink in the last 12 months.

Usage

ALCDTTM(ALCDTTM)

Arguments

ALCDTTM

cchsflow variable name for type of drinker (12 months)

Details

This variable was introduced in the 2007-2008 cycle of the CCHS, and became the sole derived variable that categorized people into various drinker types from 2009 onwards. Unlike ALCDTYP, this variable does not distinguish between former and never drinkers.

Examples

library(cchsflow)
 ?ALCDTTM

Type of drinker

Description

NOTE: this is not a function.

This is a categorical variable derived by Statistics Canada that uses various intermediate alcohol variables to categorize individuals into 4 distinct groups:

Regular Drinker
Occasional Drinker
Former Drinker
Never Drinker

Usage

ALCDTYP(ALCDTYP)

Arguments

ALCDTYP

cchsflow variable name for type of drinker

Details

This variable is used in CCHS cycles from 2001 to 2007. How it was derived remained consistent during these years.

Starting in 2007, Statistics Canada created a derived variable that looked at drinking type in the last 12 months. This new derived variable did not distinguish between former and never drinkers. If your research requires you to differentiate between former and never drinkers, we recommend using earlier cycles of the CCHS.

Examples

library(cchsflow)
 ?ALCDTYP

Average daily alcohol consumption

Description

NOTE: this is not a function.

This is a continuous variable derived by Statistics Canada that quantifies the mean daily consumption of alcohol. This takes the value of ALWDWKY and divides it by 7.

Usage

ALWDDLY(ALWDDLY)

Arguments

ALWDDLY

cchsflow variable name for average daily alcohol consumption

Details

This variable is present in every CCHS cycle used in cchsflow, and how it was derived remains consistent.

Examples

library(cchsflow)
 ?ALWDDLY

Number of drinks consumed in the past week

Description

NOTE: this is not a function.

This is a continuous variable derived by Statistics Canada that quantifies the amount of alcohol that is consumed in a week. This is calculated by adding the number of drinks consumed each day in the past week. Respondents of each CCHS cycle are asked how much alcohol they have consumed each day in the past week (ie. how much alcohol did you consume on Sunday, how much did you consume on Monday etc.). Each day is considered an individual variable and ALWDWKY takes the sum of all daily variables.

Usage

ALWDWKY(ALWDWKY)

Arguments

ALWDWKY

cchsflow variable name for number of drinks consumed in the past week

Details

This variable is present in every CCHS cycle used in cchsflow, and how it was derived remains consistent.

Examples

library(cchsflow)
 ?ALWDWKY

Any alcohol past week

Description

NOTE: this is not a function.

This is a categorical variable derived by Statistics Canada that determines if alcohol was consumed in the past week. The variable is optional in selected provinces and territories.

Usage

ALW_1(ALW_1)

Arguments

ALW_1

cchsflow variable name for any alcohol past week

Details

This variable is present in every CCHS cycle used in cchsflow. In 2007 and 2008, the variable is optional for Newfoundland and Labrador, Nova Scotia, Ontario, British Columbia and Nunavut.In 2009 and 2010, the variable is optional for Newfoundland and Labrador, Ontario, and Saskatchewan. In 2011, the variable is optional for Newfoundland and Labrador, Quebec, Ontario, Manitoba, and Saskatchewan. In 2012, the variable is optional for Newfoundland and Labrador, Quebec, Ontario, Manitoba, Nunavut, and Saskatchewan.In 2013, the variable is optional for Quebec, Ontario, Prince Edward Island, Manitoba, Yukon, and Saskatchewan. In 2014, the variable is optional for Nunavut, Quebec, Ontario, Prince Edward Island, Manitoba, Newfoundland and Labrador, Saskatchewan, and British Columbia.

Examples

library(cchsflow)
 ?ALW_1

Number of drinks - Sunday

Description

NOTE: this is not a function.

This is a continuous variable derived by Statistics Canada that quantifies the number of alcoholic drinks consumed on Sunday. The variable is optional in selected provinces and territories.

Usage

ALW_2A1(ALW_2A1)

Arguments

ALW_2A1

cchsflow variable name for number of drinks on Sunday

Details

Examples

library(cchsflow)
 ?ALW_2A1

Number of drinks - Monday

Description

NOTE: this is not a function.

This is a continuous variable derived by Statistics Canada that quantifies the number of alcoholic drinks consumed on Monday. The variable is optional in selected provinces and territories.

Usage

ALW_2A2(ALW_2A2)

Arguments

ALW_2A2

cchsflow variable name for number of drinks on Monday

Details

Examples

library(cchsflow)
 ?ALW_2A2

Number of drinks - Tuesday

Description

NOTE: this is not a function.

This is a continuous variable derived by Statistics Canada that quantifies the number of alcoholic drinks consumed on Tuesday. The variable is optional in selected provinces and territories.

Usage

ALW_2A3(ALW_2A3)

Arguments

ALW_2A3

cchsflow variable name for number of drinks on Tuesday

Details

Examples

library(cchsflow)
 ?ALW_2A3

Number of drinks - Wednesday

Description

NOTE: this is not a function.

This is a continuous variable derived by Statistics Canada that quantifies the number of alcoholic drinks consumed on Wednesday. The variable is optional in selected provinces and territories.

Usage

ALW_2A4(ALW_2A4)

Arguments

ALW_2A4

cchsflow variable name for number of drinks on Wednesday

Details

Examples

library(cchsflow)
 ?ALW_2A4

Number of drinks - Thursday

Description

NOTE: this is not a function.

This is a continuous variable derived by Statistics Canada that quantifies the number of alcoholic drinks consumed on Thursday. The variable is optional in selected provinces and territories.

Usage

ALW_2A5(ALW_2A5)

Arguments

ALW_2A5

cchsflow variable name for number of drinks on Thursday

Details

Examples

library(cchsflow)
 ?ALW_2A5

Number of drinks - Friday

Description

NOTE: this is not a function.

This is a continuous variable derived by Statistics Canada that quantifies the number of alcoholic drinks consumed on Friday. The variable is optional in selected provinces and territories.

Usage

ALW_2A6(ALW_2A6)

Arguments

ALW_2A6

cchsflow variable name for number of drinks on Friday

Details

Examples

library(cchsflow)
 ?ALW_2A6

Number of drinks - Saturday

Description

NOTE: this is not a function.

This is a continuous variable derived by Statistics Canada that quantifies the number of alcoholic drinks consumed on Saturday. The variable is optional in selected provinces and territories.

Usage

ALW_2A7(ALW_2A7)

Arguments

ALW_2A7

cchsflow variable name for number of drinks on Saturday

Details

Examples

library(cchsflow)
 ?ALW_2A7

COPD_Emph_der_fun1

Description

This is one of 2 functions used to create a derived variable (COPD_Emph_der) that determines if a respondents has either COPD or Emphysema. 2 different functions have been created to account for the fact that different respiratory variables are used across CCHS cycles. This function is for CCHS cycles (2005-2008) that use COPD and Emphysema as a combined variable.

Usage

COPD_Emph_der_fun1(DHHGAGE_cont, CCC_91E, CCC_91F)

Arguments

DHHGAGE_cont

continuous age variable.

CCC_91E

variable indicating if respondent has Emphysema

CCC_91F

variable indicating if respondent has COPD

Value

a categorical variable (COPD_Emph_der) with 3 levels:

respondent is over the age of 35 and has a respiratory condition
respondent is under the age of 35 and has a respiratory condition
respondent does not have a respiratory condition

Examples

# COPD_Emph_der_fun1() to create values across CCHS cycles
# (2005-2008) COPD_Emph_der_fun1() is specified in
# variable_details.csv along with the CCHS variables and cycles included.

# To transform COPD_Emph_der, use rec_with_table() for each CCHS cycle
# and specify COPD_Emph_der, along with the various respiratory
# variables. Then by using merge_rec_data() you can combine COPD_Emph_der
# across cycles.

library(cchsflow)

COPD2005 <- suppressWarnings(rec_with_table(
  cchs2005_p,  c(
    "DHHGAGE_cont", "CCC_91E", "CCC_91F",
    "COPD_Emph_der"
  )
))

head(COPD2005)

COPD2007_2008 <- suppressWarnings(rec_with_table(
  cchs2007_2008_p, c(
    "DHHGAGE_cont", "CCC_91E", "CCC_91F", 
    "COPD_Emph_der"
  )
))

tail(COPD2007_2008)

combined_COPD <- suppressWarnings(merge_rec_data(COPD2005, COPD2007_2008))

head(combined_COPD)
tail(combined_COPD)

COPD_Emph_der_fun2

Description

This is one of 2 functions used to create a derived variable (COPD_Emph_der) that determines if a respondents has either COPD or Emphysema. 2 different functions have been created to account for the fact that different respiratory variables are used across CCHS cycles. This function is for CCHS cycles (2001-2003, 2009-2014) that use COPD and Emphysema as a combined variable.

Usage

COPD_Emph_der_fun2(DHHGAGE_cont, CCC_091)

Arguments

DHHGAGE_cont

continuous age variable.

CCC_091

variable indicating if respondent has either COPD or Emphysema

Value

a categorical variable (COPD_Emph_der) with 3 levels:

respondent is over the age of 35 and has a respiratory condition
respondent is under the age of 35 and has a respiratory condition
respondent does not have a respiratory condition

Examples

# COPD_Emph_der_fun2() to create values across CCHS cycles
# (2001-2003, 2009-2014) COPD_Emph_der_fun2() is specified in
# variable_details.csv along with the CCHS variables and cycles included.

# To transform COPD_Emph_der, use rec_with_table() for each CCHS cycle
# and specify COPD_Emph_der, along with the various respiratory
# variables. Then by using merge_rec_data() you can combine COPD_Emph_der
# across cycles.

library(cchsflow)

COPD2001 <- suppressWarnings(rec_with_table(
  cchs2001_p,  c(
    "DHHGAGE_cont", "CCC_091",
    "COPD_Emph_der"
  )
))

head(COPD2001)

COPD2014 <- suppressWarnings(rec_with_table(
  cchs2007_2008_p, c(
    "DHHGAGE_cont", "CCC_091", 
    "COPD_Emph_der"
  )
))

tail(COPD2014)

combined_COPD <- suppressWarnings(merge_rec_data(COPD2001, COPD2014))

head(combined_COPD)
tail(combined_COPD)

Depression Scale - Predicted Probability

Description

NOTE: this is not a function.

This is categorical variable derived by Statistics Canada that predicts the probability that a respondent would be diagnosed as having a major depressive episode if a diagnostic interview was completed. This variable is derived from DPSDSF in which probabilities are assigned to respondents based on their depression scale score. For more details on how the variable was derived click here.

Usage

DPSDPP(DPSDPP)

Arguments

DPSDPP

cchsflow variable name for derived depression scale predicted probability.

Details

While this variable was considered to be categorical in CCHS documentation, the values range from 0 to 0.90 with no distinct names or metadata for each category. As such, this variable was specified as a continuous variable in cchsflow. This has no bearing on the final output of the variable as there are no recode changes. This means that a respondent who was coded with a probability of 0.50 will still have a probability value of 0.50 when the variable goes through harmonization.

This variable is present in every CCHS cycle used in cchsflow, and how it was derived remains consistent.

Examples

library(cchsflow)
 ?DPSDPP

Derived Depression Scale - Short Form Score

Description

NOTE: this is not a function.

This is a continuous variable derived by Statistics Canada that assesses the level of depression of respondents who have identified that they have felt depressed or loss of interest within the last two weeks. This variable is scaled from 0 to 8, with 0 indicating a respondent has not felt depressed or loss of interest, and 8 representing the highest level of depression.

Usage

DPSDSF(DPSDSF)

Arguments

DPSDSF

cchsflow variable name for derived depression scale.

Details

The derivation of this variable is based on the work of Kessler & Mroczek from the University of Michigan. For more details on the items used and how the variable was derived click here.

This variable is present in every CCHS cycle used in cchsflow, and how it was derived remains consistent.

Examples

library(cchsflow)
 ?DPSDSF

Satisfaction with life (GEN_02A/GEN_02A2)

Description

NOTE: this is not a function.

These are two variables asked in the CCHS that asks respondents to rate their satisfaction with their lives. The variable GEN_02A is a categorical variable with 5 categories:

Very satisfied
Satisfied
Neither satisfied nor unsatisfied
Dissatisfied
Very dissatisfied

The GEN_02A2 is a continuous variable from 0 to 10, where 0 represents very dissatisfied and 10 represents very satisfied.

Usage

GEN_02A2(GEN_02A, GEN_02A2)

Arguments

GEN_02A

- categorical life satisfaction variable asked from 2003-2007

GEN_02A2

- continuous life satisfaction variable asked from 2009-2014, and derived for 2003-2007

Details

GEN_02A was asked to respondents in the 2003, 2005, and 2007-2008 CCHS survey cycles; while GEN_02A2 was asked to respondents in CCHS survey cycles from 2009 to 2014. To harmonize GEN_02A2 across more cycles, GEN_02A2 was derived for earlier cycles by converting GEN_02A values to match the scale used in GEN_02A2. The very satisfied category was converted to a score of 10; the satisfied category was converted to a score of 7; the neither satisfied nor unsatisfied category was converted to a score of 5; the dissatisfied category was converted to a score of 2; and the very dissatisfied category was converted to a score of 0.

When using earlier CCHS cycles (2003-2007), it is appropriate to use GEN_02A. When using multiple CCHS cycles that include cycles from 2009-2014, GEN_02A2 is recommended.

Examples


library(cchsflow)
?GEN_02A2

Occupation Group (9 categories)

Description

NOTE: this is not a function.

This is a 9 category variable (LBFA_31A) that is in the CCHS that asks which occupation group best describes a respondent. Occupation group is asked in the 2001 CCHS cycle and in CCHS cycles from 2007-2014.

Usage

LBFA_31A(LBFA_31A)

Arguments

LBFA_31A

cchsflow variable name for Occupation Group (9 categories)

Details

While occupation group is asked in many survey cycles, the 2001 CCHS survey cycle is the only survey that has 9 categories. The categories are as follows:

Management
Professional (including accountants)
Technologist, Technician or Tech Occupation
Administrative, Financial or Clerical
Sales or Service
Trades, Transport or Equipment Operator
Farming, Forestry, Fishing, Mining
Processing, Manufacturing, Utilities
Other

To harmonize the 2001 CCHS cycle with other survey cycles, LBFA_31A_a and LBFA_31A_b were created in which categories in the 2001 survey cycle were collapsed.

Examples

library(cchsflow)
 ?LBFA_31A

Occupation Group (5 categories)

Description

NOTE: this is not a function.

This is a 5 category variable (LBFA_31A_a) that is in the CCHS that asks which occupation group best describes a respondent. Occupation group is asked in the 2001 CCHS cycle and in CCHS cycles from 2007-2014.

Usage

LBFA_31A_a(LBFA_31A_a)

Arguments

LBFA_31A_a

cchsflow variable name for Occupation Group (5 categories)

Details

In the 2007-2014 CCHS survey cycles, occupation group has 5 categories. The categories are as follows:

Management, Health, Education, Art, Culture
Business, Finance, Admin
Sales or Service
Trades, Transport or Equipment Operator
Unique to Primary Industry/Processing/Manufacturing

In this variable, categories from the 2001 CCHS survey cycle were collapsed to harmonize with the other survey cycles. "Management, Professional (including accountants), Technologist, Technician or Tech Occupation" were combined into one category "Management, Health, Education, Art, Culture". "Farming, Forestry, Fishing, Mining" and "Processing, Manufacturing, Utilities", were combined into one category "Farming, Forestry, Fishing, Mining, Processing, Manufacturing, Utilities".

The "other" category in the 2001 CCHS survey cycle was assigned to missing (NA(b)). This is consistent with other studies (doi: 10.4103/IJCIIS.IJCIIS_43_18) that group the "other" category as "missing". LBFA_31A_b is a 6 category variable that keeps the "other" category in the 2001 survey cycle as "other".

Examples

library(cchsflow)
?LBFA_31A_a

Occupation Group (6 categories)

Description

NOTE: this is not a function.

This is a 6 category variable (LBFA_31A_b) that is in the CCHS that asks which occupation group best describes a respondent. Occupation group is asked in the 2001 CCHS cycle and in CCHS cycles from 2007-2014.

Usage

LBFA_31A_b(LBFA_31A_b)

Arguments

LBFA_31A_b

cchsflow variable name for Occupation Group (6 categories)

Details

In the 2007-2014 CCHS survey cycles, occupation group has 5 categories. This variable, however, includes a sixth category to account for the "other" category asked in the 2001 CCHS survey cycle. The categories are as follows:

Management, Health, Education, Art, Culture
Business, Finance, Admin
Sales or Service
Trades, Transport or Equipment Operator
Unique to Primary Industry/Processing/Manufacturing
Other

Examples

library(cchsflow)
?LBFA_31A_b

Participation and Activity Limitation

Description

This is a derived variable used in the CCHS (RACDPAL) to classify respondents according to the frequency with which they experience activity limitations due to disability.

Usage

RACDPAL_fun(RAC_1, RAC_2A, RAC_2B, RAC_2C)

Arguments

RAC_1

Has difficulty with activities due to disability

RAC_2A

Reduction in activities at home due to disability

RAC_2B

Reduction in activities at school or work due to disability

RAC_2C

Reduction in other activities

Details

This derived variable is generated in CCHS cycles 2003-2014. The 2001 CCHS cycle, however, contains the same base variables used to derive this variable. To include respondents in the 2001 CCHS cycle, this custom function was created using the same derivation conditions used in later cycles.

Value

the CCHS derived variable RACDPAL with 3 categories:

Sometimes
Often
Never

Examples

# Using RACDPAL_fun() to transform RACDPAL in 2001. 
# RACDPAL_fun() is specified in variable_details.csv along with the
# CCHS variables and cycles included.
 
# To transform RACDPAL, use rec_with_table() for each the 2001 cycle
# and specify RACDPAL, along with the various ADL variables.

library(cchsflow)

RACDPAL_2001 <- rec_with_table(
  cchs2001_p, c(
    "RAC_1", "RAC_2A", "RAC_2B", "RAC_2C", "RACDPAL"
  )
)

head(RACDPAL_2001)

# Note: In other CCHS cycles you only need to specify RACDPAL as the variable
# was included in those survey cycles.

# Using RACDPAL_fun() with user inputted data.

# Let's say you're an individual that sometimes has difficulties with
# activities due to disability, sometimes has a reduction in activities at
# home, often has a reduction at school or work, and never has a reduction
# in other activities. Your participation and activity limitation can be
# determined as follows:

library(cchsflow)
RACDPAL <- RACDPAL_fun(1, 1, 2, 3)
print(RACDPAL)

Type of smokers

Description

This function creates a derived variable (SMKDSTY_A) for smoker type with 5 categories:

daily smoker
current occasional smoker (former daily)
current occasional smoker (never daily)
current nonsmoker (former daily)
current nonsmoker (never daily)
nonsmoker

Usage

SMKDSTY_fun(SMK_005, SMK_030, SMK_01A)

Arguments

SMK_005

type of smoker presently

SMK_030

smoked daily - lifetime (occasional/former smoker)

SMK_01A

smoked 100 or more cigarettes in lifetime

Details

For CCHS 2001-2014, smoker type is derived from smoking more than 100 cigarettes in lifetime, type of smoker at present time, and ever smoked daily. For CCHS 2015-2018, smoker type was derived differently with different variables and categories. A function was created for a consistent smoker status across all cycles.

Value

value for smoker type in the SMKDSTY_A variable

Examples

 
# Using SMKDSTY_fun() to derive smoke type values across CCHS cycles
# SMKDSTY_fun() is specified in variable_details.csv along with the 
# CCHS variables and cycles included.

# To transform SMKDSTY_A across cycles, use rec_with_table() for each
# CCHS cycle and specify SMKDSTY_A.
# For CCHS 2001-2014, only specify SMKDSTY_A for smoker type.
# For CCHS 2015-2018, specify the parameters and SMKDSTY_A for smoker type.

library(cchsflow)

smoker_type_2009_2010 <- rec_with_table(
  cchs2009_2010_p, "SMKDSTY_A")

head(smoker_type_2009_2010)

smoker_type_2017_2018 <- rec_with_table(
  cchs2017_2018_p,c(
    "SMK_01A", "SMK_005","SMK_030","SMKDSTY_A"
  )
)

tail(smoker_type_2017_2018)

combined_smoker_type <- suppressWarnings(merge_rec_data
(smoker_type_2009_2010,smoker_type_2017_2018))

head(combined_smoker_type)
tail(combined_smoker_type)

Age started smoking daily - daily/former daily smokers

Description

This function creates a continuous derived variable (SMKG040_fun) that calculates the approximate age that a daily or former daily smoker began smoking daily.

Usage

SMKG040_fun(SMKG203_cont, SMKG207_cont)

Arguments

SMKG203_cont

age started smoking daily. Variable asked to daily smokers.

SMKG207_cont

age started smoking daily. Variable asked to former daily smokers.

Details

SMKG203 (daily smoker) and SMKG207 (former daily) are present in CCHS 2001-2014, and are separate variables. For CCHS 2015 and onward, SMKG040 (daily/former daily) combines the two previous variables. SMKG040_fun takes the continuous functions (SMKG203_cont and SMKG207_cont) to create SMKG040 for 2001-2014.

Value

value for age started smoking daily for daily/former daily smokers in the SMKG040_cont variable

Note

In previous cycles, both SMKG203 and SMKG207 included respondents who did not state their smoking status. From CCHS 2015 and onward, SMKG040 only included respondents who specified daily smoker or former daily smoker. As a result, SMKG040 has a large number of missing respondents for CCHS 2015 survey cycles and onward.

Examples

 
# Using SMKG040_fun() to create age values across CCHS cycles
# SMKG040_fun() is specified in variable_details.csv under SMKG040_cont.

# To create a continuous harmonized variable for SMKG040, use rec_with_table() 
# for each CCHS cycle and specify SMKG040_cont.

library(cchsflow)

age_smoke_dfd_2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "SMKG203_cont", "SMKG207_cont","SMKG040_cont"
  )
)

head(age_smoke_dfd_2009_2010)

age_smoke_dfd_2011_2012 <- rec_with_table(
  cchs2011_2012_p,c(
    "SMKG203_cont", "SMKG207_cont","SMKG040_cont"
  )
)

tail(age_smoke_dfd_2011_2012)

combined_age_smoke_dfd <- suppressWarnings(merge_rec_data
(age_smoke_dfd_2009_2010,age_smoke_dfd_2011_2012))

head(combined_age_smoke_dfd)
tail(combined_age_smoke_dfd)

Age started to smoke daily - daily smoker

Description

This function creates a continuous derived variable (SMKG203_cont) for age started to smoke daily for daily smokers.

Usage

SMKG203_fun(SMK_005, SMKG040)

Arguments

SMK_005

type of smoker presently

SMKG040

age started to smoke daily - daily/former daily smoker

Details

For CCHS 2015-2018, age started to smoke daily was combined for daily and former daily smokers.Previous cycles had separate variables for age started to smoke daily. Type of smoker presently is used to define daily smoker.

Value

value for continuous age started to smoke daily for daily smokers in the SMKG203_cont variable

Examples

 
# Using SMKG203_fun() to derive age started to smoke daily values across 
# CCHS cycles.
# SMKG203_fun() is specified in variable_details.csv along with the 
# CCHS variables and cycles included.

# To transform SMKG203_A across cycles, use rec_with_table() for each
# CCHS cycle and specify SMKG203_A.
# For CCHS 2001-2014, only specify SMKG203_A.
# For CCHS 2015-2018, specify the parameters and SMKG203_A for daily smoker 
# age.

library(cchsflow)

agecigd_2009_2010 <- rec_with_table(
  cchs2009_2010_p, "SMKG203_A")

head(agecigd_2009_2010)

agecigd_2017_2018 <- rec_with_table(
  cchs2017_2018_p,c(
    "SMK_005","SMKG040","SMKG203_A"
  )
)

tail(agecigd_2017_2018)

combined_agecigd <- suppressWarnings(merge_rec_data
(agecigd_2009_2010,agecigd_2017_2018))

head(combined_agecigd)
tail(combined_agecigd)

Age started to smoke daily - former daily smoker

Description

This function creates a continuous derived variable (SMKG207_cont) for age started to smoke daily for former daily smokers.

Usage

SMKG207_fun(SMK_030, SMKG040)

Arguments

SMK_030

smoked daily - lifetime (occasional/former smoker)

SMKG040

age started to smoke daily - daily/former daily smoker

Details

For CCHS 2015-2018, age started to smoke daily was combined for daily and former daily smokers.Previous cycles had separate variables for age started to smoke daily. Smoked daily in lifetime is used to define former daily smoker.

Value

value for continuous age started to smoke daily for former daily smokers in the SMKG207_cont variable

Examples

 
# Using SMKG207_fun() to derive age started to smoke daily values across 
# CCHS cycles.
# SMKG207_fun() is specified in variable_details.csv along with the 
# CCHS variables and cycles included.

# To transform SMKG207_A across cycles, use rec_with_table() for each
# CCHS cycle and specify SMKG207_A.
# For CCHS 2001-2014, only specify SMKG207_A.
# For CCHS 2015-2018, specify the parameters and SMKG207_A for former daily 
# smoker age.

library(cchsflow)

agecigfd_2009_2010 <- rec_with_table(
  cchs2009_2010_p, "SMKG207_A")

head(agecigfd_2009_2010)

agecigfd_2017_2018 <- rec_with_table(
  cchs2017_2018_p,c(
    "SMK_030","SMKG040","SMKG207_A"
  )
)

tail(agecigfd_2017_2018)

combined_agecigfd <- suppressWarnings(merge_rec_data
(agecigfd_2009_2010,agecigfd_2017_2018))

head(combined_agecigfd)
tail(combined_agecigfd)

Five-item social provision scale (SPS-5)

Description

This function creates a derived variable for the five-item social provision scale (SPS_5_fun). The range is 0-20, where a higher score reflects a higher level of perceived social support.

Usage

SPS_5_fun(SPS_03, SPS_04, SPS_05, SPS_07, SPS_10)

Arguments

SPS_03

close relationships that provide sense of emotional security and well-being

SPS_04

talk to about important decisions with someone

SPS_05

relationships where competence and skill are recognized

SPS_07

part of a group who share attitudes and beliefs

SPS_10

strong emotional bond with a least one person

Details

The Social Provisions Scale (SPS) is commonly used to measure social support. The ten-item social provisions scale (SPS-10) has been reduced to a five-item scale (SPS-5).Reducing the SPS-10 items by half decreases the respondent burden on surveys. SPS-5 is a valid measure of social support while maintaining adequate measurement properties (Orpana et al., 2019). Validation of SDS-5 using Canadian national survey data can be found here.

SPS-10 and their items were available in CCHS from 2011-2018.

Examples

# Using the SPS_5_fun function to create the derived SPS5_der variable  
# across CCHS cycles.
# SPS_5_fun() is specified in the variable_details.csv.

# To create a harmonized SPS5_der variable across CCHS cycles, use 
# rec_with_table() for each CCHS cycle and specify SPS_5_fun and the
# required base variables.
# Using merge_rec_data(), you can combine SPS5_der across cycles.

library(cchsflow)

SPS5_2011_2012 <- rec_with_table(
  cchs2011_2012_p, c(
    "SPS_03", "SPS_04", "SPS_05", "SPS_07", "SPS_10", "SPS5_der"
  )
)

head(SPS5_2011_2012)

SPS5_2017_2018 <- rec_with_table(
  cchs2017_2018_p,c(
    "SPS_03", "SPS_04", "SPS_05", "SPS_07", "SPS_10", "SPS5_der"
  )
)

tail(SPS5_2017_2018)

combined_SPS5 <- suppressWarnings(merge_rec_data(SPS5_2011_2012,
 SPS5_2017_2018))

head(combined_SPS5)
tail(combined_SPS5)

Daily active transportation (2001-2005)

Description

This function creates a derived variable for daily time spent traveling in active ways. This includes walking and biking. This function is used for CCHS 2001-2005.

Usage

active_transport1_fun(PAC_4A_cont, PAC_4B_cont)

Arguments

PAC_4A_cont

number of hours walk work/school in week in the past 3 months.

PAC_4B_cont

number of hours bike work/school in week in the past 3 months.

Value

Continuous variable for active transportation (active_transport)

Examples

# Using active_transport1_fun() to determine daily time spent 
# traveling in active ways values across CCHS 2001-2005.

# active_transport1_fun() is specified in variable_details.csv along with the CCHS
# variables and cycles included.

# To transform active_transport across cycles, use rec_with_table() for each
# CCHS cycle and specify active_transport, along with each activity variable.
# Then by using merge_rec_data(), you can combine active_transport across
# cycles

library(cchsflow)
active_transport2001 <- rec_with_table(
  cchs2001_p, c(
    "PAC_4A_cont", "PAC_4B_cont", "active_transport"
  )
)

head(active_transport2001)

active_transport2005 <- rec_with_table(
  cchs2005_p, c(
    "PAC_4A_cont", "PAC_4B_cont", "active_transport"
  )
)

tail(active_transport2005)

combined_active_transport <- suppressWarnings(merge_rec_data(active_transport2001,
 active_transport2005))

head(combined_active_transport)
tail(combined_active_transport)

Daily active transportation (2007-2014)

Description

This function creates a derived variable for daily time spent traveling in active ways. This includes walking and biking. This function is used for CCHS 2007-2014.

Usage

active_transport2_fun(PAC_7, PAC_7A, PAC_7B_cont, PAC_8, PAC_8A, PAC_8B_cont)

Arguments

PAC_7

have walked to work or school in the past 3 months?

PAC_7A

number of times walked to work/school in the past 3 months.

PAC_7B_cont

number of minutes walk to work/school.

PAC_8

have biked to work or school in the past 3 months?

PAC_8A

number of times biked to work/school in the past 3 months.

PAC_8B_cont

number of minutes bike to work/school.

Value

Continuous variable for active transportation (active_transport)

Examples

# Using active_transport2_fun() to determine daily time spent 
# traveling in active ways values across CCHS 2007-2014.

# active_transport2_fun() is specified in variable_details.csv along with the CCHS
# variables and cycles included.

# To transform active_transport across cycles, use rec_with_table() for each
# CCHS cycle and specify active_transport, along with each activity variable.
# Then by using merge_rec_data(), you can combine active_transport across
# cycles

library(cchsflow)
active_transport2007_2008 <- rec_with_table(
  cchs2007_2008_p, c(
    "PAC_7", "PAC_7A", "PAC_7B_cont", "PAC_8", "PAC_8A", "PAC_8B_cont", 
    "active_transport"
  )
)

head(active_transport2007_2008)

active_transport2013_2014 <- rec_with_table(
  cchs2013_2014_p, c(
    "PAC_7", "PAC_7A", "PAC_7B_cont", "PAC_8", "PAC_8A", "PAC_8B_cont", 
    "active_transport"
  )
)

tail(active_transport2013_2014)

combined_active_transport <- suppressWarnings(merge_rec_data(
 active_transport2007_2008, active_transport2013_2014))

head(combined_active_transport)
tail(combined_active_transport)

Daily active transportation (2015-2018)

Description

This function creates a derived variable for daily time spent traveling in active ways. This includes walking and biking. This function is used for CCHS 2015-2018.

Usage

active_transport3_fun(PAYDVTTR, PAADVTRV)

Arguments

PAYDVTTR

number of minutes of active transportation in a week for 12-17 years old.

PAADVTRV

number of minutes of active transportation in a week for 18+ years old.

Value

Continuous variable for active transportation (active_transport)

Examples

# Using active_transport3_fun() to determine daily time spent 
# traveling in active ways values across CCHS 2015-2018.

# active_transport3_fun() is specified in variable_details.csv along with the CCHS
# variables and cycles included.

# To transform active_transport across cycles, use rec_with_table() for each
# CCHS cycle and specify active_transport, along with each activity variable.
# Then by using merge_rec_data(), you can combine active_transport across
# cycles

library(cchsflow)
active_transport2015_2016 <- rec_with_table(
  cchs2015_2016_p, c(
    "PAYDVTTR", "PAADVTRV","active_transport"
  )
)

head(active_transport2015_2016)

active_transport2017_2018 <- rec_with_table(
  cchs2017_2018_p, c(
    "PAYDVTTR", "PAADVTRV","active_transport"
  )
)

tail(active_transport2017_2018)

combined_active_transport <- suppressWarnings(merge_rec_data(
 active_transport2015_2016, active_transport2017_2018))

head(combined_active_transport)
tail(combined_active_transport)

Adjusted Body Mass Index (BMI) derived variable

Description

This function creates a harmonized adjusted BMI variable. A systematic review of the literature concluded that the use of self-reported data among adults underestimates weight and overestimates height, resulting in lower estimates of obesity than those obtained from measured data. Using data from the 2005 Canadian Community Health Survey (CCHS) subsample, where both measured and self-reported values were collected, correction equations have been developed (Connor Gorber et al. 2008). Differences between corrected estimates of obesity from the CCHS and measured estimates from the Canadian Health Measures Survey is monitored over time to determine if the bias in self-reported values is changing and if new correction equations need to be developed. Adjusted BMI variable is first introduced in the CCHS 2015 cycle.

adjusted_bmi_fun() creates a derived variable (HWTGCOR_der) that is harmonized across all CCHS cycles. This function takes the BMI by dividing weight by the square of height, and adds a correction value based on sex.

Usage

adjusted_bmi_fun(DHH_SEX, HWTGHTM, HWTGWTK)

Arguments

DHH_SEX

CCHS variable for sex; 1 = male, 2 = female

HWTGHTM

CCHS variable for height (in meters)

HWTGWTK

CCHS variable for weight (in kilograms)

Details

For HWTGCOR_der, there are no restrictions to age, height, weight, or pregnancy status. While pregnancy was consistent across all CCHS cycles, its variable (MAM_037) was not available in the PUMF CCHS datasets so it could not be harmonized and included into the function.

HWTGCOR_der uses the CCHS variables for sex, height and weight that have been transformed by cchsflow. In order to generate a value for adjusted BMI across CCHS cycles, sex, height and weight must be transformed and harmonized.

Value

numeric value for adjusted BMI in the HWTGCOR_der variable

Note

In earlier CCHS cycles (2001 and 2003), height was collected in inches; while in later CCHS cycles (2005+) it was collected in meters. To harmonize values across cycles, height was converted to meters (to 3 decimal points). Weight was collected in kilograms across all CCHS cycles, so no transformations were required in the harmonization process.

Examples

# Using adjusted_bmi_fun() to create adjusted BMI values between cycles
# adjusted_bmi_fun() is specified in variable_details.csv along with the
# CCHS variables and cycles included.

# To transform the derived BMI variable, use rec_with_table() for each cycle
# and specify HWTGCOR_der, along with sex (DHH_SEX), height (HWTGHTM) and 
# weight (HWTGWTK).Then by using merge_rec_data(), you can combined 
# HWTGBMI_der across cycles.

library(cchsflow)
adjustedbmi2001 <- rec_with_table(
  cchs2001_p, c(
    "HWTGHTM",
    "HWTGWTK", 
    "DHH_SEX",
    "HWTGCOR_der"
  )
)

head(adjustedbmi2001)

adjustedbmi2011_2012 <- rec_with_table(
  cchs2011_2012_p, c(
    "HWTGHTM",
    "HWTGWTK", 
    "DHH_SEX",
    "HWTGCOR_der"
  )
)

tail(adjustedbmi2011_2012)

combined_bmi <- merge_rec_data(adjustedbmi2001, adjustedbmi2011_2012)
head(combined_bmi)
tail(combined_bmi)

# adjusted_bmi_fun() can also generate a value for BMI if you input your sex, 
# and a value for height and weight. Let's say your sex is male, height is 
# 170cm (1.7m) and your weight is 50kg, your BMI can be calculated as follows:

library(cchsflow)
adjusted_BMI <- adjusted_bmi_fun(DHH_SEX = 1, HWTGHTM = 1.7, HWTGWTK = 50)
print(adjusted_BMI)

Derived needs help with tasks

Description

This derived variable (ADL_der) is based on the CCHS derived variable ADLF6R which flags respondents who need help with tasks based on their response to the various activities of daily living (ADL) variables.

Usage

adl_fun(ADL_01, ADL_02, ADL_03, ADL_04, ADL_05)

Arguments

ADL_01

Needs help preparing meals

ADL_02

Needs help getting to appointments/errands

ADL_03

Needs help doing housework

ADL_04

Needs help doing personal care

ADL_05

Needs help moving inside house

Details

The CCHS derived variable ADLF6R uses different ADL variables across the various CCHS survey cycles. This newly derived variable (ADL_der) uses ADL variables that are consistent across CCHS cycles.

In the 2001 CCHS survey cycle, the ADLF6R variable examines the following ADL variables:

ADL_01 - Needs help preparing meals
ADL_02 - Needs help getting to appointments/errands
ADL_03 - Needs help doing housework
ADL_04 - Needs help doing personal care
ADL_05 - Needs help moving inside house
ADL_07 - Needs help doing heavy household chores

In the 2003-2005 CCHS survey cycles, the ADLF6R variable examines the following ADL variables:

ADL_01 - Needs help preparing meals
ADL_02 - Needs help getting to appointments/errands
ADL_03 - Needs help doing housework
ADL_04 - Needs help doing personal care
ADL_05 - Needs help moving inside house
ADL_06 - Needs help doing finances
ADL_07 - Needs help doing heavy household chores

In the 2007-2014 CCHS survey cycles, the ADLF6R variable examines the following ADL variables:

ADL_01 - Needs help preparing meals
ADL_02 - Needs help getting to appointments/errands
ADL_03 - Needs help doing housework
ADL_04 - Needs help doing personal care
ADL_05 - Needs help moving inside house
ADL_06 - Needs help doing finances

This newly derived variable (ADL_der) uses ADL_01 to ADL_05 which are consistent across all survey cycles. For any single CCHS survey year, it is appropriate to use ADLF6R. ADL_der is recommended when using multiple survey cycles.

Value

A derived variable (ADL_der) with 2 categories:

- Needs help with tasks
- Does not need help with tasks

Examples

# Using adl_fun() to create ADL_der values across CCHS cycles
# adl_fun() is specified in variable_details.csv along with the
# CCHS variables and cycles included.

# To transform ADL_der, use rec_with_table() for each CCHS cycle
# and specify ADL_der, along with the various ADL variables.
# Then by using merge_rec_data() you can combine ADL_der across cycles.

library(cchsflow)
adl2001 <- rec_with_table(
  cchs2001_p, c(
    "ADL_01", "ADL_02", "ADL_03", "ADL_04", "ADL_05", "ADL_der"
  )
)

head(adl2001)

adl2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "ADL_01", "ADL_02", "ADL_03", "ADL_04", "ADL_05", "ADL_der"
  )
)

tail(adl2009_2010)

combined_adl <- merge_rec_data(adl2001, adl2009_2010)

head(combined_adl)

tail(combined_adl)

# Using adl_fun() to generate to ADL_der based on user inputted values.
# 
# Let's say you do not need help preparing meals, you need help getting to
# appointments or errands, you need help doing housework, do not need help
# doing personal care, and do not need help moving inside the house. Using
# adl_fun() we can check if you need help doing tasks

ADL_der <- adl_fun(2, 1, 1, 2, 2)

print(ADL_der)

The number of activities of daily living tasks that require help.

Description

A 6 category variable (ADL_score_5) representing the number of activities of daily living tasks that require help. This variable tallies the number of daily living tasks that a respondent requires help with based on various ADL variables that a respondent answered yes or no to. The ADL variables used are common across all CCHS cycles from 2001 to 2014.

Usage

adl_score_5_fun(ADL_01, ADL_02, ADL_03, ADL_04, ADL_05)

Arguments

ADL_01

Needs help preparing meals.

ADL_02

Needs help getting to appointments/errands.

ADL_03

Needs help doing housework.

ADL_04

Needs help doing personal care.

ADL_05

Needs help moving inside house.

Value

A derived variable (ADL_score_5) with 6 categories:

0 - Needs help with 0 tasks
1 - Needs help with at least 1 task
2 - Needs help with at least 2 tasks
3 - Needs help with at least 3 tasks
4 - Needs help with at least 4 tasks
5 - Needs help with at least 5 tasks

Examples

# Use adl_score_5_fun() to create the variable ADL_score_5 across CCHS 
# cycles adl_score_5_fun() is specified in variable_details.csv along with 
# the CCHS variables and cycles included.

# To transform ADL_score_5, use rec_with_table() for each CCHS cycle
# and specify ADL_score_5, along with the various ADL variables.
# Then by using merge_rec_data() you can combine ADL_der across cycles.

library(cchsflow)
adl2001 <- rec_with_table(
  cchs2001_p, c(
    "ADL_01", "ADL_02", "ADL_03", "ADL_04", "ADL_05", "ADL_score_5"
  )
)

head(adl2001)

adl2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "ADL_01", "ADL_02", "ADL_03", "ADL_04", "ADL_05", "ADL_score_5"
  )
)

tail(adl2009_2010)

combined_adl <- merge_rec_data(adl2001, adl2009_2010)

head(combined_adl)

tail(combined_adl)

# Using adl_score_5_fun() to generate to ADL_score_5 based on user inputted
# values.
# Let's say you do not need help preparing meals, you need help getting to
# appointments or errands, you need help doing housework, do not need help
# doing personal care, and do not need help moving inside the house. Using
# adl_score_5_fun() we can check the number of tasks you need help with

ADL_score_5 <- adl_score_5_fun(2, 1, 1, 2, 2)

print(ADL_score_5)

Derived categorical age

Description

This is a derived categorical age variable (DHHGAGE_C) that groups various age categories across all CCHS cycles. This is based on the continuous age variable (DHHGAGE_cont) that is harmonious across all CCHS cycles.

The categories of this new age variable are as follows:

12 to 14 years
15 to 17 years
18 to 19 years
20 to 24 years
25 to 29 years
30 to 34 years
35 to 39 years
40 to 44 years
45 to 49 years
50 to 54 years
55 to 59 years
60 to 64 years
65 to 69 years
70 to 74 years
75 to 79 years
80 years or more

Usage

age_cat_fun(DHHGAGE_cont)

Arguments

DHHGAGE_cont

continuous age variable

Details

The categories in the grouped age variable (DHHGAGE) vary between CCHS cycles. As such, a continuous age variable (DHHGAGE_cont) was created that harmonized age across all CCHS cycles by taking the midpoint of each age category. This new age variable (DHHGAGE_C) categorizes age based on the categories used in CCHS cycles from 2007 to 2014.

Value

a categorical age variable (DHHGAGE_C)

Examples

# Using age_cat_fun() to create categorical age values from DHHGAGE_cont
# age_cat_fun() is specified in variable_details.csv along with the CCHS
# variables and cycles included.

# To generate DHHGAGE_C in a cycle, use rec_with_table() and specify
# DHHGAGE_C along with DHHGAGE_cont.

library(cchsflow)

cat_age2009_2010 <- rec_with_table(
   cchs2009_2010_p,  c(
     "DHHGAGE_cont", "DHHGAGE_C"
     )
   )

Binge drinking

Description

This function creates a derived categorical variable that flags for binge drinking based on the number drinks consumed on a single day.

Usage

binge_drinker_fun(
  DHH_SEX,
  ALW_1,
  ALW_2A1,
  ALW_2A2,
  ALW_2A3,
  ALW_2A4,
  ALW_2A5,
  ALW_2A6,
  ALW_2A7
)

Arguments

DHH_SEX

sex of respondent (1 - male, 2 - female)

ALW_1

Drinks in the last week (1 - yes, 2 - no)

ALW_2A1

Number of drinks on Sunday

ALW_2A2

Number of drinks on Monday

ALW_2A3

Number of drinks on Tuesday

ALW_2A4

Number of drinks on Wednesday

ALW_2A5

Number of drinks on Thursday

ALW_2A6

Number of drinks on Friday

ALW_2A7

Number of drinks on Saturday

Details

In health research, binge drinking is defined as having an excess amount of alcohol in a single day. For males, this is defined as having five or more drinks; and for females it is four or more drinks. In the CCHS, respondents are asked to count the number of drinks they had during each day of the last week.

Value

Categorical variable (binge_drinker) with two categories:

1 - binge drinker
2 - non-binge drinker

Examples

 
# Using binge_drinker_fun() to create binge_drinker values across CCHS cycles
# binge_drinker_fun() is specified in variable_details.csv along with the
# CCHS variables and cycles included.

# To transform binge_drinker, use rec_with_table() for each CCHS cycle
# and specify binge_drinker, along with the various alcohol and sex
# variables. Then by using bind_rows() you can combine binge_drinker
# across cycles.

library(cchsflow)
binge2001 <- rec_with_table(
  cchs2001_p, c(
    "ALW_1", "DHH_SEX", "ALW_2A1", "ALW_2A2", "ALW_2A3", "ALW_2A4",
    "ALW_2A5", "ALW_2A6", "ALW_2A7", "binge_drinker"
  )
)

head(binge2001)

binge2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "ALW_1", "DHH_SEX", "ALW_2A1", "ALW_2A2", "ALW_2A3", "ALW_2A4",
    "ALW_2A5", "ALW_2A6", "ALW_2A7", "binge_drinker"
  )
)

tail(binge2009_2010)

combined_binge <- bind_rows(binge2001, binge2009_2010)

head(combined_binge)

tail(combined_binge)

# Using binge_drinker_fun() to generate binge_drinker with user-inputted
# values.
#
# Let's say you are a male, and you had drinks in the last week. Let's say
# you had 3 drinks on Sunday, 1 drink on
# Monday, 6 drinks on Tuesday, 0 drinks on Wednesday, 3 drinks on Thurday,
# 8 drinks on Friday, and 2 drinks on Saturday. Using binge_drinker_fun(),
# we can check if you would be classified as a drinker.

binge <- binge_drinker_fun(DHH_SEX = 1, ALW_1 = 1, ALW_2A1 = 3, ALW_2A2 = 1,
                          ALW_2A3 = 6, ALW_2A4 = 0, ALW_2A5 = 3,
                          ALW_2A6 = 8, ALW_2A7 = 2)

print(binge)

Body Mass Index (BMI) derived variable

Description

This function creates a harmonized BMI variable. The BMI variable provided by the CCHS calculates BMI using methods that vary across cycles, leading to measurement error when using multiple CCHS cycles. In certain CCHS cycles (2001-2003, 2007+), there are age restrictions in which respondents under the age of 20 and over the age of 64 were not included. Across all CCHS cycles, female respondents who identified as being pregnant were excluded; and in certain CCHS cycles (2003-2007, 2013-2014), females who did not answer the pregnancy question were coded as NS (not stated) for HWTGBMI. As well, in certain CCHS cycles (2001-2003, 2009-2014), respondents outside certain height and weight ranges (0.914-2.108m for height, 0-260kg for weight) were excluded from HWTGBMI.

bmi_fun() creates a derived variable (HWTGBMI_der) that is harmonized across all CCHS cycles. This function divides weight by the square of height.

Usage

bmi_fun(HWTGHTM, HWTGWTK)

Arguments

HWTGHTM

CCHS variable for height (in meters)

HWTGWTK

CCHS variable for weight (in kilograms)

Details

For HWTGBMI_der, there are no restrictions to age, height, weight, or pregnancy status. While pregnancy was consistent across all CCHS cycles, its variable (MAM_037) was not available in the PUMF CCHS datasets so it could not be harmonized and included into the function.

For any single CCHS survey year, it is appropriate to use the CCHS BMI variable (HWTGBMI) that is also available on cchsflow. HWTGBMI_der is recommended when using multiple survey cycles.

HWTGBMI_der uses the CCHS variables for height and weight that have been transformed by cchsflow. In order to generate a value for BMI across CCHS cycles, height and weight must be transformed and harmonized.

Value

numeric value for BMI in the HWTGBMI_der variable

Note

Examples

# Using bmi_fun() to create BMI values between cycles
# bmi_fun() is specified in variable_details.csv along with the
# CCHS variables and cycles included.

# To transform the derived BMI variable, use rec_with_table() for each cycle
# and specify HWTGBMI_der, along with height (HWTGHTM) and weight (HWTGWTK).
# Then by using merge_rec_data(), you can combined HWTGBMI_der across
# cycles.

library(cchsflow)
bmi2001 <- rec_with_table(
  cchs2001_p, c(
    "HWTGHTM",
    "HWTGWTK", "HWTGBMI_der"
  )
)

head(bmi2001)

bmi2011_2012 <- rec_with_table(
  cchs2011_2012_p, c(
    "HWTGHTM",
    "HWTGWTK", "HWTGBMI_der"
  )
)

tail(bmi2011_2012)

combined_bmi <- merge_rec_data(bmi2001, bmi2011_2012)
head(combined_bmi)
tail(combined_bmi)

# Using bmi_fun() to generate a BMI value with user inputted height and
# weight values. bmi_fun() can also generate a value for BMI if you input a
# value for height and weight. Let's say your height is 170cm (1.7m) and
# your weight is 50kg, your BMI can be calculated as follows:

library(cchsflow)
BMI <- bmi_fun(HWTGHTM = 1.7, HWTGWTK = 50)
print(BMI)

Categorical BMI (international standard)

Description

This function creates a categorical derived variable (HWTGBMI_der_cat4) that categorizes derived BMI (HWTGBMI_der).

Usage

bmi_fun_cat(HWTGBMI_der)

Arguments

HWTGBMI_der

derived variable that calculates numeric value for BMI. See bmi_fun for documentation on how variable was derived.

Details

The categories were based on international standards and are divided into four categories: underweight for BMI < 18.5 (1), normal weight for BMI between 18.5 to 25 (2), overweight for BMI between 25 to 30 (3), and obese for BMI over 30 (4).

HWTGBMI_der_cat4 uses the derived variable HWTGBMI_der. HWTGBMI_der uses height and weight that have been transformed by cchsflow. In order to categorize BMI across CCHS cycles, height and weight variables must be transformed and harmonized.

Value

value for BMI categories in the HWTGBMI_der_cat4 variable.

Examples

 
# Using bmi_fun_cat() to categorize BMI across CCHS cycles
# bmi_fun_cat() is specified in variable_details.csv along with the 
# CCHS variables and cycles included.

# To transform HWTGBMI_der_cat4 across all cycles, use rec_with_table() for 
# each CCHS cycle.
# Since HWTGBMI_der is also a derived variable, you will have to specify 
# the variables that are derived from it.

library(cchsflow)

bmi_cat_2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "HWTGHTM",
    "HWTGWTK",
    "HWTGBMI_der",
    "HWTGBMI_der_cat4"
  )
)

head(bmi_cat_2009_2010)

bmi_cat_2011_2012 <- rec_with_table(
  cchs2011_2012_p,c(
    "HWTGHTM",
    "HWTGWTK",
    "HWTGBMI_der",
    "HWTGBMI_der_cat4"
  )
)

tail(bmi_cat_2011_2012)

combined_bmi_cat <- suppressWarnings(merge_rec_data
(bmi_cat_2009_2010,bmi_cat_2011_2012))

head(combined_bmi_cat)
tail(combined_bmi_cat)

2001 CCHS PUMF subset data (200 respondents)

Description

This is a subset of 200 observations from the 2001 cycle of the Canadian Community Health Survey (CCHS) Public Use Microdata file (PUMF) dataset. The CCHS survey is conducted by Statistics Canada.

Details

See here for the open license. Source from Statistics Canada, Canadian Community Health Survey PUMF, accessed Jan 2020. Reproduced and distributed on an "as is" basis with the permission of Statistics Canada.

Long name: cchs-82M0013-E-2001-c1-1-general-file

DDI: https://osf.io/jtd9h/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2001_p

a data frame

Source

https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=3359

Examples

data(cchs2001_p)
str(cchs2001_p)

2003 CCHS PUMF subset data (200 respondents)

Description

This is a subset of 200 observations from the 2003 cycle of the Canadian Community Health Survey (CCHS) Public Use Microdata file (PUMF) dataset. The CCHS survey is conducted by Statistics Canada.

Details

Long name: cchs-82M0013-E-2003-c2-1-General File

DDI: https://osf.io/nzq37/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2003_p

a data frame

Source

https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=4995

Examples

data(cchs2003_p)
str(cchs2003_p)

2005 CCHS PUMF subset data (200 respondents)

Description

This is a subset of 200 observations from the 2005 cycle of the Canadian Community Health Survey (CCHS) Public Use Microdata file (PUMF) dataset. The CCHS survey is conducted by Statistics Canada.

Details

Long name: cchs-82M0013-E-2005-c3-1-main-file

DDI: https://osf.io/35mhq/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2005_p

a data frame

Source

https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=22642

Examples

data(cchs2005_p)
str(cchs2005_p)

2007-2008 CCHS PUMF subset data (200 respondents)

Description

This is a subset of 200 observations from the 2007-2008 cycle of the Canadian Community Health Survey (CCHS) Public Use Microdata file (PUMF) dataset. The CCHS survey is conducted by Statistics Canada.

Details

Long name: cchs-E-2007-2008-AnnualComponent

DDI: https://osf.io/emzsp/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2007_2008_p

a data frame

Source

https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=29539

Examples

data(cchs2007_2008_p)
str(cchs2007_2008_p)

2009-2010 CCHS PUMF subset data (200 respondents)

Description

This is a subset of 200 observations from the 2009-2010 cycle of the Canadian Community Health Survey (CCHS) Public Use Microdata file (PUMF) dataset. The CCHS survey is conducted by Statistics Canada.

Details

Long name: CCHS-82M0013-E-2009-2010-Annualcomponent

DDI: https://osf.io/ynzpe/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2009_2010_p

a data frame

Source

https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=67251

Examples

data(cchs2009_2010_p)
str(cchs2009_2010_p)

2009 CCHS synthetic subset data (200 respondents)

Description

This is a subset of 200 observations from the 2009 cycle of the Canadian Community Health Survey (CCHS) synthetic dataset. The CCHS survey is conducted by Statistics Canada.

Details

NOTE: this subset of respondents may also be in the 2009 synthetic subset. Please see the "CCHS datasets that overlap each other" article to see how the two datasets contain overlap.

See here for the open license. Source from Statistics Canada, Canadian Community Health Survey PUMF, accessed May 2022. Reproduced and distributed on an "as is" basis with the permission of Statistics Canada.

Long name: synthetic-CCHS-E-2009-FullSampleFile_F1

DDI: https://osf.io/q8g7y/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2009_s

a data frame

Source

https://www.statcan.gc.ca/en/statistical-programs/document/3226_D56_T9_V1

Examples

data(cchs2009_s)
str(cchs2009_s)

2010 CCHS PUMF subset data (200 respondents)

Description

This is a subset of 200 observations from the 2010 cycle of the Canadian Community Health Survey (CCHS) Public Use Microdata file (PUMF) dataset. The CCHS survey is conducted by Statistics Canada.

Details

NOTE: this subset of respondents may also be in the 2009-2010 PUMF subset. Please see the "CCHS datasets that overlap each other" article to see how the two datasets contain overlap.

Long name: CCHS-82M0013-E-2010-AnnualComponent

DDI: https://osf.io/7stpz/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2010_p

a data frame

Source

https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=81424

Examples

data(cchs2010_p)
str(cchs2010_p)

2010 CCHS synthetic subset data (200 respondents)

Description

This is a subset of 200 observations from the 2010 cycle of the Canadian Community Health Survey (CCHS) synthetic dataset. The CCHS survey is conducted by Statistics Canada.

Details

NOTE: this subset of respondents may also be in the 2010 synthetic subset. Please see the "CCHS datasets that overlap each other" article to see how the two datasets contain overlap.

Long name: synthetic-CCHS-E-2010-AnnualComponent_F1

DDI: https://osf.io/q8g7y/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2010_s

a data frame

Source

https://www.statcan.gc.ca/en/statistical-programs/document/3226_D56_T9_V1

Examples

data(cchs2010_s)
str(cchs2010_s)

2011-2012 CCHS PUMF subset data (200 respondents)

Description

This is a subset of 200 observations from the 2011-2012 cycle of the Canadian Community Health Survey (CCHS) Public Use Microdata file (PUMF) dataset. The CCHS survey is conducted by Statistics Canada.

Details

Long name: cchs-82M0013-E-2011-2012-Annual-component

DDI: https://osf.io/zk2vw/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2011_2012_p

a data frame

Source

https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=114112

Examples

data(cchs2011_2012_p)
str(cchs2011_2012_p)

2012 CCHS PUMF subset data (200 respondents)

Description

This is a subset of 200 observations from the 2012 cycle of the Canadian Community Health Survey (CCHS) Public Use Microdata file (PUMF) dataset. The CCHS survey is conducted by Statistics Canada.

Details

NOTE: this subset of respondents may also be in the 2011-2012 PUMF subset. Please see the "CCHS datasets that overlap each other" article to see how the two datasets contain overlap.

Long name: cchs-82M0013-E-2012-Annual-component

DDI: https://osf.io/sbem8/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2012_p

a data frame

Source

https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=135927

Examples

data(cchs2012_p)
str(cchs2012_p)

2012 CCHS synthetic subset data (200 respondents)

Description

This is a subset of 200 observations from the 2012 cycle of the Canadian Community Health Survey (CCHS) synthetic dataset. The CCHS survey is conducted by Statistics Canada.

Details

NOTE: this subset of respondents may also be in the 2012 synthetic subset. Please see the "CCHS datasets that overlap each other" article to see how the two datasets contain overlap.

Long name: synthetic-CCHS-E-2012-AnnualComponent_F1

DDI: https://osf.io/q8g7y/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2012_s

a data frame

Source

https://www.statcan.gc.ca/en/statistical-programs/document/3226_D56_T9_V1

Examples

data(cchs2012_s)
str(cchs2012_s)

2013-2014 CCHS PUMF subset data (200 respondents)

Description

This is a subset of 200 observations from the 2013-2014 cycle of the Canadian Community Health Survey (CCHS) Public Use Microdata file (PUMF) dataset. The CCHS survey is conducted by Statistics Canada.

Details

Long name: cchs-82M0013-E-2013-2014-Annual-component

DDI: https://osf.io/gy25d/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2013_2014_p

a data frame

Source

https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=144170

Examples

data(cchs2013_2014_p)
str(cchs2013_2014_p)

2014 CCHS PUMF subset data (200 respondents)

Description

This is a subset of 200 observations from the 2014 cycle of the Canadian Community Health Survey (CCHS) Public Use Microdata file (PUMF) dataset. The CCHS survey is conducted by Statistics Canada.

Details

NOTE: this subset of respondents may also be in the 2013-2014 PUMF subset. Please see the "CCHS datasets that overlap each other" article to see how the two datasets contain overlap.

Long name: cchs-82M0013-E-2014-Annual-component

DDI: https://osf.io/tbmdn/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2014_p

a data frame

Source

https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=164081

Examples

data(cchs2014_p)
str(cchs2014_p)

2015-2016 CCHS PUMF subset data (200 respondents)

Description

This is a subset of 200 observations from the 2015-2016 cycle of the Canadian Community Health Survey (CCHS) Public Use Microdata file (PUMF) dataset. The CCHS survey is conducted by Statistics Canada.

Details

NOTE: this subset of respondents may also be in the 2015-2016 PUMF subset. Please see the "CCHS datasets that overlap each other" article to see how the two datasets contain overlap.

See here for the open license. Source from Statistics Canada, Canadian Community Health Survey PUMF, accessed Oct 2021. Reproduced and distributed on an "as is" basis with the permission of Statistics Canada.

Long name: cchs-82M0013-E-2015-2016-Annual-component

DDI: https://osf.io/m948q/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2015_2016_p

a data frame

Source

https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=238854

Examples

data(cchs2015_2016_p)
str(cchs2015_2016_p)

2017-2018 CCHS PUMF subset data (200 respondents)

Description

This is a subset of 200 observations from the 2017-2018 cycle of the Canadian Community Health Survey (CCHS) Public Use Microdata file (PUMF) dataset. The CCHS survey is conducted by Statistics Canada.

Details

NOTE: this subset of respondents may also be in the 2017-2018 PUMF subset. Please see the "CCHS datasets that overlap each other" article to see how the two datasets contain overlap.

Long name: cchs-82M0013-E-2017-2018-Annual-component

DDI: https://osf.io/q8g7y/

Additional documentation (PDFs): https://osf.io/hkuy3/

Value

cchs2017_2018_p

a data frame

Source

https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=329241

Examples

data(cchs2017_2018_p)
str(cchs2017_2018_p)

Compare Value Based On Interval

Description

Compare values on the scientific notation interval

Usage

compare_value_based_on_interval(
  left_boundary,
  right_boundary,
  data,
  compare_columns,
  interval
)

Arguments

left_boundary

the min value

right_boundary

the max value

data

the data that contains values being compared

compare_columns

The columns inside data being checked

interval

The scientific notation interval

Value

a boolean vector containing true for rows where the comparison is true

Diet score

Description

This function creates a derived diet variable (diet_score) based on consumption of fruit, salad, potatoes, carrots, other vegetables and juice. 2 baseline points plus summation of total points for diet attributes. Negative overall scores are recoded to 0, resulting in a range from 0 to 10.

1 point per daily fruit and vegetable consumption, excluding fruit juice (maximum 8 points).
-2 points for high potato intake (>=7 (males), >=5 (females) times/week)
-2 points for no carrot intake
-2 points per daily frequency of fruit juice consumption greater than once/day (maximum -10 points)

Usage

diet_score_fun(FVCDFRU, FVCDSAL, FVCDPOT, FVCDCAR, FVCDVEG, FVCDJUI, DHH_SEX)

Arguments

FVCDFRU

daily consumption of fruit

FVCDSAL

daily consumption of green salad

FVCDPOT

daily consumption of potatoes

FVCDCAR

daily consumption of carrots

FVCDVEG

daily consumption of other vegetables

FVCDJUI

daily consumption of fruit juice

DHH_SEX

sex; 1 = male, 2 = female

Details

While diet score can be calculated for all survey respondents, in the 2005 CCHS survey cycle, fruit and vegetable consumption was an optional section in which certain provinces had opted in to be asked to respondents. In this survey cycle, fruit and vegetable consumption was asked to respondents in British Columbia, Ontario, Alberta, and Prince Edward Island. As such, diet score has a large number of missing respondents for this cycle.

Examples

# Using the diet_score_fun function to create the derived diet variable  
# across CCHS cycles.
# diet_score_fun() is specified in the variable_details.csv.

# To create a harmonized diet_score variable across CCHS cycles, use 
# rec_with_table() for each CCHS cycle and specify diet_score_fun and the
# required base variables.
# Using merge_rec_data(), you can combine diet_score across cycles.

library(cchsflow)

diet_score2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "FVCDFRU", "FVCDSAL", "FVCDPOT", "FVCDCAR", "FVCDVEG", "FVCDJUI", 
    "DHH_SEX", "diet_score"
  )
)

head(diet_score2009_2010)

diet_score2011_2012 <- rec_with_table(
  cchs2011_2012_p,c(
    "FVCDFRU", "FVCDSAL", "FVCDPOT", "FVCDCAR", "FVCDVEG", "FVCDJUI", 
    "DHH_SEX", "diet_score"
  )
)

tail(diet_score2011_2012)

combined_diet_score <- suppressWarnings(merge_rec_data(diet_score2009_2010,
 diet_score2011_2012))

head(combined_diet_score)
tail(combined_diet_score)

Categorized diet score

Description

This function creates a categorical derived diet variable (diet_score_cat3) that categorizes derived diet score (diet_score).

Usage

diet_score_fun_cat(diet_score)

Arguments

diet_score

derived variable that calculates diet score. See diet_score_fun for documentation on how variable was derived.

Details

The diet score is based on consumption of fruit, salad, potatoes, carrots, other vegetables and juice. 2 baseline points plus summation of total points for diet attributes. Negative overall scores are recoded to 0, resulting in a range from 0 to 10.The categories were based on the Mortality Population Risk Tool (Douglas Manuel et al. 2016).

diet_score_cat3 uses the derived variable diet_score. diet_score uses sex, and fruit and vegetable variables that have been transformed by cchsflow (see documentation on diet_score). In order to categorize diet across CCHS cycles, sex, and fruit and vegetable variables must be transformed and harmonized.

Value

value for diet score categories using diet_score_cat3 variable.

Examples

# Using the diet_score_fun_cat function to categorize the derived diet 
# variable across CCHS cycles.
# diet_score_fun_cat() is specified in the variable_details.csv.

# To create a harmonized diet_score_cat3 variable across CCHS cycles, use 
# rec_with_table() for each CCHS cycle.
# Since diet_score is also a derived variable, you will have to specify 
# the variables that are derived from it.
# Using merge_rec_data(), you can combine diet_score_cat3 across cycles.

library(cchsflow)

diet_score_cat2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "FVCDFRU", "FVCDSAL", "FVCDPOT", "FVCDCAR", "FVCDVEG", "FVCDJUI", 
    "DHH_SEX", "diet_score", "diet_score_cat3"
  )
)

head(diet_score_cat2009_2010)

diet_score_cat2011_2012 <- rec_with_table(
  cchs2011_2012_p,c(
    "FVCDFRU", "FVCDSAL", "FVCDPOT", "FVCDCAR", "FVCDVEG", "FVCDJUI", 
    "DHH_SEX", "diet_score", "diet_score_cat3"
  )
)

tail(diet_score_cat2011_2012)

combined_diet_score_cat <- suppressWarnings(merge_rec_data(
diet_score_cat2009_2010, diet_score_cat2011_2012))

head(combined_diet_score_cat)
tail(combined_diet_score_cat)

Daily energy expenditure in leisure activity

Description

This function creates a derived variable for daily leisure energy expenditure.A MET is a conceptual value that represents energy expended during physical activity. The volume of activity is calculated by multiplying the amount of minutes of activity (by level of intensity) by the MET value associated with that intensity. A MET (metabolic equivalent) is the energy cost of activity expressed as kilocalories expended per kilogram of body weight per hour of activity.

In CCHS 2001-2014, PACDEE is the variable used to determine the daily expenditure of leisure activity for all ages. In CCHS 2015-2018, ages 12-17 and 18+ years old have separate activity variables, where 12-17 year olds use PAY_XXX and 18+ year olds use PAA_XXX. Leisure activity is not directly measured. We used the derived variable, PAADVVOL, and removed active transportation in the new function. With this function, we combined leisure activity for ages 12+. We calculate the daily energy expenditure which uses the frequency and duration per session of the physical activity as well as the MET value (3 METS for leisure and 6 METS for vigorous activity).

EE (Daily Energy Expenditure) = ((N X D X METvalue) / 60)/7 Where: N = the number of times a respondent engaged in an activity over a 7 day period D = the average duration in minutes of the activity MET value = the energy cost of the activity expressed as kilocalories expended per kilogram of body weight per hour of activity (kcal/kg per hour)

Usage

energy_exp_fun(
  DHHGAGE_cont,
  PAA_045,
  PAA_050,
  PAA_075,
  PAA_080,
  PAADVDYS,
  PAADVVIG,
  PAYDVTOA,
  PAYDVADL,
  PAYDVVIG,
  PAYDVDYS
)

Arguments

DHHGAGE_cont

continuous age variable.

PAA_045

number of hours of sports, fitness, or recreational activity that make you sweat or breathe harder for CCHS 2015-2018 for 18+ years old.

PAA_050

number of minutes of sports, fitness, or recreational activity that make you sweat or breathe harder for CCHS 2015-2018 for 18+ years old.

PAA_075

number of hours of other physical activity while at work, home or volunteering for CCHS 2015-2018 for 18+ years old.

PAA_080

number of minutes of other physical activity while at work, home or volunteering for CCHS 2015-2018 for 18+ years old.

PAADVDYS

number of active days - 7 day for CCHS 2015-2018 for 18+ years old.

PAADVVIG

number of minutes of vigorous activity over 7 days or CCHS 2015-2018 for 18+ years old.

PAYDVTOA

total minutes of other activities - 7 day for CCHS 2015-2018 for 12-17 years old.

PAYDVADL

total minutes of physical activity - leisure - 7 day for CCHS 2015-2018 for 12-17 years old.

PAYDVVIG

total minutes - vigorous physical activity - 7 d for CCHS 2015-2018 for 12-17 years old.

PAYDVDYS

total days physically active - 7 day for CCHS 2015-2018 for 12-17 years old.

Value

Continuous variable for energy expenditure (energy_exp)

Examples

# Using energy_exp_fun() to create energy expenditure values across CCHS 
# cycles
# energy_exp_fun() is specified in variable_details.csv along with the CCHS
# variables and cycles included.

# To transform energy_exp across cycles, use rec_with_table() for each
# CCHS cycle and specify energy_exp, along with each activity variable.
# Then by using merge_rec_data(), you can combine energy_exp across
# cycles

library(cchsflow)
energy_exp2015_2016 <- rec_with_table(
  cchs2015_2016_p, c(
    "DHHGAGE_cont", "PAA_045", "PAA_050", "PAA_075", "PAA_080", "PAADVDYS", 
    "PAADVVIG", "PAYDVTOA", "PAYDVADL", "PAYDVVIG", "PAYDVDYS", "energy_exp"
  )
)

head(energy_exp2015_2016)

energy_exp2017_2018 <- rec_with_table(
  cchs2017_2018_p, c(
    "DHHGAGE_cont", "PAA_045", "PAA_050", "PAA_075", "PAA_080", "PAADVDYS", 
    "PAADVVIG", "PAYDVTOA", "PAYDVADL", "PAYDVVIG", "PAYDVDYS", "energy_exp"
  )
)

tail(energy_exp2015_2016)

combined_energy_exp <- suppressWarnings(merge_rec_data(energy_exp2015_2016,
 energy_exp2017_2018))

head(combined_energy_exp)
tail(combined_energy_exp)

Food insecurity

Description

NOTE: this is not a function.

This is a derived variable that uses the different food insecurity variables from all CCHS cycles to generate food_insecurity_der that is harmonized across all cycles. food_insecurity_der is a categorical variable with two categories:

no food insecurity in the last 12 months
food insecurity in the last 12 months

Usage

food_insecurity_der(FINF1, FSCDHFS, FSCDHFS2)

Arguments

FINF1

variable used in 2001 and 2003 survey cycles indicating food insecurity in the past 12 months

FSCDHFS

variable used in the 2005 survey cycle measuring food insecurity & hunger in the last 12 months

FSCDHFS2

variable used in 2007-2014 survey cycles measuring household food insecurity in the last 12 months

Details

Food insecurity is measured differently across CCHS cycles. In 2001 and 2003, FINF1 is used; in 2005, FSCDHFS is used; and in 2007 to 2014, FSCDHFS2 is used. Each variable examines food insecurity in the household over the past 12 months, but use different base variables to derive food insecurity.

If you are using cchsflow for CCHS survey years that use consistent food insecurity variables, it is appropriate to use FINF1, FSCDHFS, or FSCDHFS2 that are available on cchsflow. If you are using cchsflow for only the 2001 and 2003 cycles, it is appropriate to use FINF1. If you are using cchsflow for only the 2005 cycle, FSCDHFS is appropriate. If you are using cchsflow for cycles between 2007 and 2014, FSCDHFS2 is appropriate. For multiple CCHS survey years that do not use the same food insecurity variables (i.e. using cchsflow for years 2001 to 2007), food_insecurity_der is recommended.

Examples


library(cchsflow)
?food_insecurity_der

Get Data Variable Name

Description

Retrieves the name of the column inside data to use for calculations

Usage

get_data_variable_name(
  data_name,
  data,
  row_being_checked,
  variable_being_checked
)

Arguments

data_name

name of the database being checked

data

database being checked

row_being_checked

the row from variable details that contains information on this variable

variable_being_checked

the name of the recoded variable

Value

the data equivalent of variable_being_checked

if_else2

Description

Custom ifelse function that evaluates missing (NA) values. If the logical argument (x) compares to a value that is 'NA', it is set to 'FALSE'

Usage

if_else2(x, a, b)

Arguments

x

A logical argument

a

value if 'x' is 'TRUE'

b

value if 'x' is 'FALSE'

Details

unlike the base ifelse() function, if_else2() is able to evaluate NA as either a or b. In base ifelse(), anything compared to NA will produce NA, which can break a function. When dealing with large datasets like the CCHS, there are many missing (NA) values. That means a special ifelse function like if_else2() is needed in order for other functions to not break

Value

a or b based on the evaluation of x

Examples

age <- 12
status <- if_else2((age < 18), "child", "invalid age")
print(status)

age <- NA
status <- if_else2((age < 18), "child", "invalid age")
print(status)

Immigration by ethnicity and settlement

Description

This function creates a categorical variable based on immigrant status (SDCFIMM), country of birth (SDCGCBG), ethnicity (SDCGCGT), and time in Canada (SDCGRES).

Usage

immigration_fun(SDCFIMM, SDCGCBG, SDCGCGT, SDCGRES)

Arguments

SDCFIMM

Immigrant status (1-immigrant, 2-non-immigrant)

SDCGCBG

Country of birth (1-Canada, 2-Outside of Canada)

SDCGCGT

Cultural or racial origin (1-white, 2-visible minority)

SDCGRES

Length/time in Canada since immigration (1- 0-9 years, 2- 10+ years)

Details

immigration_der uses the CCHS variables that have been transformed by cchsflow. In order to generate a value for BMI across CCHS cycles, the following SDC variables must be transformed and harmonized.

Value

Categorical variable (immigration_der) with six categories:

1 - White Canada-born
2 - Non-white Canadian born
3 - White immigrant born outside of Canada (0-9 years in Canada)
4 - Non-white immigrant born outside of Canada (0-9 years in Canada)
5 - White immigrant born outside of Canada (10+ years in Canada)
6 - Non-white immigrant born outside of Canada (10+ years in Canada)

Examples

# Using immigration_fun() to create immigration_der values across CCHS cycles
# immigration_fun() is specified in variable_details.csv along with the
# CCHS variables and cycles included.

# To transform immigration_der, use rec_with_table() for each CCHS cycle
# and specify immigration_der, along with the various SDC variables.
# Then by using merge_rec_data() you can combine immigration_der across cycles.

library(cchsflow)
immigration2001 <- rec_with_table(
  cchs2001_p, c(
    "SDCFIMM", "SDCGCBG", "SDCGCGT", "SDCGRES", "immigration_der"
  )
)

head(immigration2001)

immigration2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "SDCFIMM", "SDCGCBG", "SDCGCGT", "SDCGRES", "immigration_der"
  )
)

tail(immigration2009_2010)

combined_immigration <- merge_rec_data(immigration2001, immigration2009_2010)

head(combined_immigration)

tail(combined_immigration)

is equal

Description

Function to compare even with NA present This function returns TRUE wherever elements are the same, including NA's, and false everywhere else.

Usage

is_equal(v1, v2)

Arguments

v1

variable 1

v2

variable 2

Value

boolean value of whether or not v1 and v2 are equal

Examples

library(cchsflow)
is_equal(1,2)
# FALSE

is_equal(1,1)
# TRUE

1==NA
# NA

is_equal(1,NA)
# FALSE

NA==NA
# NA

is_equal(NA,NA)
# TRUE

label_data

Description

Attaches labels to the DataToLabel to preserve metadata

Usage

label_data(label_list, data_to_label)

Arguments

label_list

the label list object that contains extracted labels from variable details

data_to_label

The data that is to be labeled

Value

Returns labeled data

Long term risks due to drinking

Description

This function creates a categorical variable that flags for increased long term health risks due to their drinking habits, according to Canada's Low-Risk Alcohol Drinking Guideline.

Usage

low_drink_long_fun(
  DHH_SEX,
  ALWDWKY,
  ALC_1,
  ALW_1,
  ALW_2A1,
  ALW_2A2,
  ALW_2A3,
  ALW_2A4,
  ALW_2A5,
  ALW_2A6,
  ALW_2A7
)

Arguments

DHH_SEX

Sex of respondent (1 - male, 2 - female)

ALWDWKY

Number of drinks consumed in the past week

ALC_1

Drinks in the past year (1 - yes, 2 - no)

ALW_1

Drinks in the last week (1 - yes, 2 - no)

ALW_2A1

Number of drinks on Sunday

ALW_2A2

Number of drinks on Monday

ALW_2A3

Number of drinks on Tuesday

ALW_2A4

Number of drinks on Wednesday

ALW_2A5

Number of drinks on Thursday

ALW_2A6

Number of drinks on Friday

ALW_2A7

Number of drinks on Saturday

Details

The classification of drinkers according to their long term health risks comes from guidelines in Alcohol and Health in Canada: A Summary of Evidence and Guidelines for Low-risk Drinking, and is based on the alcohol consumption reported over the past week. Short-term or acute risks include injury and overdose.

Categories are based on CCHS 2015-2016's variable (ALWDVLTR) where long term health risk are increased when drinking more than 10 drinks a week for women, with no more than 2 drinks a day most days, and more than 15 drinks a week for men, with no more than 3 drinks a day most days.

See https://osf.io/ykau5/ for more details on the guideline. See https://osf.io/ycxaq/ for more details on the derivation of the function on page 8.

Value

Categorical variable (ALWDVLTR_der) with two categories:

1 - Increased long term health risk
2 - No increased long term health risk

Examples

 
# Using low_drink_long_fun() to create ALWDVLTR_der values across CCHS cycles
# low_drink_long_fun() is specified in variable_details.csv along with the
# CCHS variables and cycles included.

# To transform ALWDVLTR_der, use rec_with_table() for each CCHS cycle
# and specify ALWDVLTR_der, along with the various alcohol and sex
# variables. 
# Using merge_rec_data(), you can combine ALWDVLTR_der across cycles.

library(cchsflow)
long_low_drink2001 <- rec_with_table(
  cchs2001_p, c(
    "ALW_1", "DHH_SEX", "ALW_2A1", "ALW_2A2", "ALW_2A3", "ALW_2A4",
    "ALW_2A5", "ALW_2A6", "ALW_2A7", "ALWDWKY", "ALC_1","ALWDVLTR_der"
  )
)

head(long_low_drink2001)

long_low_drink2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "ALW_1", "DHH_SEX", "ALW_2A1", "ALW_2A2", "ALW_2A3", "ALW_2A4",
    "ALW_2A5", "ALW_2A6", "ALW_2A7", "ALWDWKY", "ALC_1","ALWDVLTR_der"
  )
)

tail(long_low_drink2009_2010)

combined_long_low_drink <- bind_rows(long_low_drink2001, 
long_low_drink2009_2010)

head(combined_long_low_drink)

tail(combined_long_low_drink)

# Using low_drink_long_fun() to generate ALWDVLTR_der with user-inputted
# values.
#
# Let's say you are a male, you had drinks in the last week and in the last 
# year. Let's say you had 5 drinks on Sunday, 1 drink on Monday, 6 drinks on 
# Tuesday, 4 drinks on Wednesday, 4 drinks on Thursday, 8 drinks on Friday, 
# and 2 drinks on Saturday with a total of 30 drinks in a week. 
# Using low_drink_long_fun(), we can check if you would be classified as
# having an increased long term health risk due to drinking.

long_term_drink <- low_drink_long_fun(DHH_SEX = 1, ALWDWKY = 30, ALC_1 = 1,
 ALW_1 = 1, ALW_2A1 = 5, ALW_2A2 = 1, ALW_2A3 = 6, ALW_2A4 = 4, ALW_2A5 = 4, 
 ALW_2A6 = 8, ALW_2A7 = 2)

print(long_term_drink)

Low drinking score (all cycles)

Description

This function creates a derived variable based on their drinking habits and flags for health and social problems from their pattern of alcohol use according to Canada's Low-Risk Alcohol Drinking Guideline.

Usage

low_drink_score_fun(DHH_SEX, ALWDWKY)

Arguments

DHH_SEX

Sex of respondent (1 - male, 2 - female)

ALWDWKY

Number of drinks consumed in the past week

Details

The low risk drinking score is based on the scoring system in Canada's Low-Risk Alcohol Drinking Guideline. The score is divided into two steps. Step 1 allocates points based on sex and the number of drinks that you usually have each week. In step 2, one point will be awarded for each item that is true related to drinking habits. The total score is obtained from adding the points in step 1 and step 2.

Value

Low risk drinking score (low_drink_score) with four categories:

1 - Low risk (0 points)
2 - Marginal risk (1-2 points)
3 - Medium risk (3-4 points)
4 - High risk (5-9 points)

Note

Step 2 is not included in this function because the questions in step 2 are not asked in any of the CCHS cycles. The score is only based on step 1.

See https://osf.io/eprg7/ for more details on the guideline and score.

Examples

 
# Using low_drink_score_fun() to create low_drink_score values across 
# CCHS cycles low_drink_score_fun() is specified in variable_details.csv 
# along with the CCHS variables and cycles included.

# To transform low_drink_score, use rec_with_table() for each CCHS cycle
# and specify low_drink_score, along with the various alcohol and sex
# variables. 
# Using merge_rec_data(), you can combine low_drink_score across cycles.

library(cchsflow)
low_drink2001 <- rec_with_table(
  cchs2001_p, c(
    "DHH_SEX", "ALWDWKY", "low_risk_score"
  )
)

head(low_drink2001)

low_drink2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "DHH_SEX", "ALWDWKY", "low_risk_score"
  )
)

tail(low_drink2009_2010)

combined_low_drink <- bind_rows(low_drink2001, 
low_drink2009_2010)

head(combined_low_drink)

tail(combined_low_drink)

Low drinking score (select cycles)

Description

Usage

low_drink_score_fun1(DHH_SEX, ALWDWKY, ALC_005, ALC_1)

Arguments

DHH_SEX

Sex of respondent (1 - male, 2 - female)

ALWDWKY

Number of drinks consumed in the past week

ALC_005

In lifetime, ever had a drink? (1 - yes, 2 - no)

ALC_1

Past year, have you drank alcohol? (1 - yes, 2 - no)

Details

This score has two 0 point categories: low risk (never drank) and low risk (former drinker). The two drinking groups are derived from 'ever had a drink in lifetime'. 'Ever had a drink in lifetime' is only available in CCHS 2001-2008 and 2015-2018.

Value

Low risk drinking score (low_drink_score1) with four categories:

1 - Low risk - never drank (0 points)
2 - Low risk - former drinker (0 points)
3 - Marginal risk (1-2 points)
4 - Medium risk (3-4 points)
5 - High risk (5-9 points)

Note

Step 2 is not included in this function because the questions in step 2 are not asked in any of the CCHS cycles. The score is only based on step 1.

See https://osf.io/eprg7/ for more details on the guideline and score.

Examples

 
# Using low_drink_score_fun1() to create low_drink_score values across 
# CCHS cycles low_drink_score_fun1() is specified in variable_details.csv 
# along with the CCHS variables and cycles included.

# To transform low_drink_score1, use rec_with_table() for each CCHS cycle
# and specify low_drink_score1, along with the various alcohol and sex
# variables. 
# Using merge_rec_data(), you can combine low_drink_score1 across cycles.

library(cchsflow)
low_drink2001 <- rec_with_table(
  cchs2001_p, c(
    "DHH_SEX", "ALWDWKY", "ALC_005", "ALC_1", "low_risk_score"
  )
)

head(low_drink2001)

low_drink2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "DHH_SEX", "ALWDWKY", "ALC_005", "ALC_1", "low_risk_score"
  )
)

tail(low_drink2009_2010)

combined_low_drink1 <- bind_rows(low_drink2001, 
low_drink2009_2010)

head(combined_low_drink1)

tail(combined_low_drink1)

Short term risks due to drinking

Description

This function creates a categorical variable that flags for increased short term health risks due to their drinking habits, according to Canada's Low-Risk Alcohol Drinking Guideline.

Usage

low_drink_short_fun(
  DHH_SEX,
  ALWDWKY,
  ALC_1,
  ALW_1,
  ALW_2A1,
  ALW_2A2,
  ALW_2A3,
  ALW_2A4,
  ALW_2A5,
  ALW_2A6,
  ALW_2A7
)

Arguments

DHH_SEX

Sex of respondent (1 - male, 2 - female)

ALWDWKY

Number of drinks consumed in the past week

ALC_1

Drinks in the past year (1 - yes, 2 - no)

ALW_1

Drinks in the last week (1 - yes, 2 - no)

ALW_2A1

Number of drinks on Sunday

ALW_2A2

Number of drinks on Monday

ALW_2A3

Number of drinks on Tuesday

ALW_2A4

Number of drinks on Wednesday

ALW_2A5

Number of drinks on Thursday

ALW_2A6

Number of drinks on Friday

ALW_2A7

Number of drinks on Saturday

Details

The classification of drinkers according to their short term health risks comes from guidelines in Alcohol and Health in Canada: A Summary of Evidence and Guidelines for Low-risk Drinking, and is based on the alcohol consumption reported over the past week. Short-term or acute risks include injury and overdose.

Categories are based on CCHS 2015-2016's variable (ALWDVSTR) where short term health risk are increased when drinking more than 3 drinks (for women) or 4 drinks (for men) on any single occasion.

See https://osf.io/ykau5/ for more details on the guideline. See https://osf.io/ycxaq/ for more details on derivation of the function on page 9.

Value

Categorical variable (ALWDVSTR_der) with two categories:

1 - Increased short term health risk
2 - No increased short term health risk

Examples

 
# Using low_drink_short_fun() to create ALWDVSTR_der values across CCHS cycles
# low_drink_short_fun() is specified in variable_details.csv along with the
# CCHS variables and cycles included.

# To transform ALWDVSTR_der, use rec_with_table() for each CCHS cycle
# and specify ALWDVSTR_der, along with the various alcohol and sex
# variables. 
# Using merge_rec_data(), you can combine ALWDVSTR_der across cycles.

library(cchsflow)
short_low_drink2001 <- rec_with_table(
  cchs2001_p, c(
    "ALW_1", "DHH_SEX", "ALW_2A1", "ALW_2A2", "ALW_2A3", "ALW_2A4",
    "ALW_2A5", "ALW_2A6", "ALW_2A7", "ALWDWKY", "ALC_1","ALWDVSTR_der"
  )
)

head(short_low_drink2001)

short_low_drink2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "ALW_1", "DHH_SEX", "ALW_2A1", "ALW_2A2", "ALW_2A3", "ALW_2A4",
    "ALW_2A5", "ALW_2A6", "ALW_2A7", "ALWDWKY", "ALC_1","ALWDVSTR_der"
  )
)

tail(short_low_drink2009_2010)

combined_short_low_drink <- bind_rows(short_low_drink2001, 
short_low_drink2009_2010)

head(combined_short_low_drink)

tail(combined_short_low_drink)

# Using low_drink_short_fun() to generate ALWDVSTR_der with user-inputted
# values.
#
# Let's say you are a male, you had drinks in the last week and in the last 
# year. Let's say you had 5 drinks on Sunday, 1 drink on Monday, 6 drinks on 
# Tuesday, 4 drinks on Wednesday, 4 drinks on Thursday, 8 drinks on Friday, 
# and 2 drinks on Saturday with a total of 30 drinks in a week. 
# Using low_drink_short_fun(), we can check if you would be classified as
# having an increased short term health risk due to drinking.

short_term_drink <- low_drink_short_fun(DHH_SEX = 1, ALWDWKY = 30, ALC_1 = 1,
 ALW_1 = 1, ALW_2A1 = 5, ALW_2A2 = 1, ALW_2A3 = 6, ALW_2A4 = 4, ALW_2A5 = 4, 
 ALW_2A6 = 8, ALW_2A7 = 2)

print(short_term_drink)

Merge recoded data

Description

This function allows users to merge CCHS data transformed by the rec_with_table function. This function generates a labelled merged data frame with multiple transformed CCHS cycles.

Usage

merge_rec_data(...)

Arguments

...

recoded data frames to be merged.

Details

When merging recoded CCHS data, there are variables that are missing in certain CCHS cycles. This function tags missing variable observations as NA(c), indicating that the variable was not asked or included in the CCHS cycle of the respondent.

Click here for more details on how NA's are treated in cchsflow.

Value

a merged data frame consisting of multiple recoded CCHS cycles with labels for variable names and tags for variables not included in particular CCHS cycles.

Examples

# Merging two CCHS cycles with variables missing in each cycle.

# INCGHH_A is a cchsflow variable available for the 2001 CCHS cycle, while
# INCGHH_B is a cchsflow variable available for the 2003 CCHS cycle. 
# Using merge_rec_data(), datasets containing INCGHH_A & INCGHH_B can be
# merged and tagged.

library(cchsflow)
income2001 <- rec_with_table(cchs2001_p, "INCGHH_A")
income2003 <- rec_with_table(cchs2001_p, "INCGHH_B")

income_merged <- merge_rec_data(income2001, income2003)
head(income_merged)
tail(income_merged)

Number of chronic conditions (5 chronic conditions)

Description

This function generates a derived variable (number_conditions) that counts the number of chronic conditions a respondent has. This function takes 5 CCHS-defined conditions (heart disease, cancer, stroke, bowel disorder, and arthritis), and well one derived variable (respiratory condition) to count the number of conditions a respondent has.

Usage

multiple_conditions_fun1(
  CCC_121,
  CCC_131,
  CCC_151,
  CCC_171,
  resp_condition_der,
  CCC_051
)

Arguments

CCC_121

variable indicating if respondent has heart disease (1 = respondent has heart disease, 2 = respondent does not have heart disease)

CCC_131

variable indicating if respondent has active cancer (1 = respondent has active cancer, 2 = respondent does not have active cancer)

CCC_151

variable indicating if respondent suffers from the effects of a stroke (1 = respondent suffers from stroke effects, 2 = respondent does not suffer from stroke effects)

CCC_171

variable indicating if respondent has a bowel disorder (1 = respondent has bowel disorder, 2 = respondent does not have a bowel disorder)

resp_condition_der

derived variable indicating if respondent has a respiratory condition (1 = respondent is over the age of 35 and has a respiratory condition, 2 = respondent is under the age of 35 and has a respiratory conditions, 3 = respondent does not have a respiratory condition). See resp_condition_fun1 for documentation on how variable was derived.

CCC_051

variable indicating if respondent has arthritis or rheumatism (1 = respondent has arthritis or rheumatism, 2 = respondent does not have arthritis or rheumatism)

Details

mood disorder (CCC_280) was not asked to respondents in the 2001 CCHS survey cycle. This mean respondents in this cycle will only be able to have a maximum of 6 chronic conditions as opposed to 7 for respondents in other cycles. multiple_conditions_fun2 is used for CCHS cycles from 2003 to 2014.

Value

A categorical variable indicating the number of chronic conditions a respondent has. Respondents with 5 or more conditions are grouped in the "5+" category.

Examples

 # Using rec_with_table() to generate multiple_conditions in a CCHS
 # cycle.
 
 # multiple_conditions_fun1() is specified in variable_details.csv along with
 # the CCHS variables and cycles included.
 
 # To generate multiple_conditions, use rec_with_table() and specify the
 # multiple_conditions, along with the variables that are derived from it.
 # Since resp_condition_der is also a derived variable, you will have to
 # specify the variables that are derived from it. In this example, data
 # from the 2001 CCHS will be used, so DHHGAGE_cont, CCC_091, and CCC_91A,
 # and CCC_031 will be specified along with resp_condition_der.
 
library(cchsflow)
 conditions_2001 <- suppressWarnings(rec_with_table(cchs2001_p,
 c("DHHGAGE_cont", "CCC_091",
 "CCC_91A", "CCC_031", "CCC_121","CCC_131","CCC_151", "CCC_171","CCC_280",
 "resp_condition_der","CCC_051", "number_conditions")))
 
 head(conditions_2001)
 
 # Generating multiple_conditions with user inputted values
 # Let's say you are an individual that has heart disease, bowel disorder,
 # and arthritis. multiple_conditions_fun1() can be used to count the number
 # of chronic conditions you have
 
library(cchsflow)
 num_conditions <- multiple_conditions_fun1(CCC_121 = 1, CCC_131 = 2, 
 CCC_151 = 2, CCC_171 = 1, resp_condition_der = 3, CCC_051 = 1) 
 
print(num_conditions)

Number of chronic conditions (6 chronic conditions)

Description

This function generates a derived variable (number_conditions) that counts the number of chronic conditions a respondent has. This function takes 6 CCHS-defined conditions (heart disease, cancer, stroke, bowel disorder, mood disorder and arthritis), and well one derived variable (respiratory condition) to count the number of conditions a respondent has.

Usage

multiple_conditions_fun2(
  CCC_121,
  CCC_131,
  CCC_151,
  CCC_171,
  CCC_280,
  resp_condition_der,
  CCC_051
)

Arguments

CCC_121

variable indicating if respondent has heart disease (1 = respondent has heart disease, 2 = respondent does not have heart disease)

CCC_131

variable indicating if respondent has active cancer (1 = respondent has active cancer, 2 = respondent does not have active cancer)

CCC_151

variable indicating if respondent suffers from the effects of a stroke (1 = respondent suffers from stroke effects, 2 = respondent does not suffer from stroke effects)

CCC_171

variable indicating if respondent has a bowel disorder (1 = respondent has bowel disorder, 2 = respondent does not have a bowel disorder)

CCC_280

variable indicating if respondent has a mood disorder (1 = respondent has a mood disorder, 2 = respondent does not have a mood disorder. Note, variable was not asked to respondents in the 2001 CCHS survey cycle.

resp_condition_der

derived variable indicating if respondent has a respiratory condition. (1 = respondent is over the age of 35 and has a respiratory condition, 2 = respondent is under the age of 35 and has a respiratory conditions, 3 = respondent does not have a respiratory condition). See resp_condition_fun1 for documentation on how variable was derived.

CCC_051

variable indicating if respondent has arthritis or rheumatism (1 = respondent has arthritis or rheumatism, 2 = respondent does not have arthritis or rheumatism)

Details

Value

A categorical variable indicating the number of chronic conditions a respondent has. Respondents with 5 or more conditions are grouped in the "5+" category.

Examples

 # Using rec_with_table() to generate multiple_conditions in a CCHS
 # cycle.
 
 # multiple_conditions_fun2() is specified in variable_details.csv along with
 # the CCHS variables and cycles included.
 
 # To generate multiple_conditions, use rec_with_table() and specify the
 # multiple_conditions, along with the variables that are derived from it.
 # Since resp_condition_der is also a derived variable, you will have to
 # specify the variables that are derived from it. In this example, data
 # from the 2010 CCHS will be used, so DHHGAGE_cont, CCC_091, and CCC_031
 # will be specified along with resp_condition_der.
 
library(cchsflow)
 conditions_2009_2010 <- suppressWarnings(rec_with_table(cchs2009_2010_p,
 c("DHHGAGE_cont", "CCC_091",
 "CCC_031", "CCC_121","CCC_131","CCC_151", "CCC_171","CCC_280",
 "resp_condition_der","CCC_051", "number_conditions")))
 
 head(conditions_2009_2010)
 
 # Generating multiple_conditions with user inputted values
 # Let's say you are an individual that has heart disease, bowel disorder,
 # and arthritis. multiple_conditions_fun2() can be used to count the number
 # of chronic conditions you have
 
library(cchsflow)
 num_conditions <- multiple_conditions_fun2(CCC_121 = 1, CCC_131 = 2, 
 CCC_151 = 2, CCC_171 = 1, CCC_280 = 2, resp_condition_der = 3, CCC_051 = 1) 

print(num_conditions)

Smoking pack-years

Description

This function creates a derived variable (pack_years_der) that measures an individual's smoking pack-years based on various CCHS smoking variables. This is a popular variable used by researchers to quantify lifetime exposure to cigarette use.

Usage

pack_years_fun(
  SMKDSTY_A,
  DHHGAGE_cont,
  time_quit_smoking,
  SMKG203_cont,
  SMKG207_cont,
  SMK_204,
  SMK_05B,
  SMK_208,
  SMK_05C,
  SMKG01C_cont,
  SMK_01A
)

Arguments

SMKDSTY_A

variable used in CCHS cycles 2001-2014 that classifies an individual's smoking status.

DHHGAGE_cont

continuous age variable.

time_quit_smoking

derived variable that calculates the approximate time a former smoker has quit smoking. See time_quit_smoking_fun for documentation on how variable was derived

SMKG203_cont

age started smoking daily. Variable asked to daily smokers.

SMKG207_cont

age started smoking daily. Variable asked to former daily smokers.

SMK_204

number of cigarettes smoked per day. Variable asked to daily smokers.

SMK_05B

number of cigarettes smoked per day. Variable asked to occasional smokers

SMK_208

number of cigarettes smoked per day. Variable asked to former daily smokers

SMK_05C

number of days smoked at least one cigarette

SMKG01C_cont

age smoked first cigarette

SMK_01A

smoked 100 cigarettes in lifetime (y/n)

Details

pack-years is calculated by multiplying the number of cigarette packs per day (20 cigarettes per pack) by the number of years. Example 1: a respondent who is a current smoker who smokes 1 package of cigarettes for the last 10 years has smoked 10 pack-years. Pack-years is also calculated for former smokers. Example 2: a respondent who started smoking at age 20 years and smoked half a pack of cigarettes until age 40 years smoked for 10 pack-years.

Value

value for smoking pack-years in the pack_years_der variable

Examples

# Using pack_years_fun() to create pack-years values across CCHS cycles
# pack_years_fun() is specified in variable_details.csv along with the CCHS
# variables and cycles included.

# To transform pack_years_der across cycles, use rec_with_table() for each
# CCHS cycle and specify pack_years_der, along with each smoking variable.
# Since time_quit_smoking_der is also a derived 
# variable, you will have to specify the variables that are derived from it.
# Then by using merge_rec_data(), you can combine pack_years_der across
# cycles

library(cchsflow)

pack_years2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "SMKDSTY_A", "DHHGAGE_cont", "SMK_09A_B", "SMKG09C", "time_quit_smoking",
    "SMKG203_cont", "SMKG207_cont", "SMK_204", "SMK_05B", "SMK_208",
    "SMK_05C", "SMK_01A", "SMKG01C_cont", "pack_years_der"
  )
)

head(pack_years2009_2010)

pack_years2011_2012 <- rec_with_table(
  cchs2011_2012_p,c(
    "SMKDSTY_A", "DHHGAGE_cont", "SMK_09A_B", "SMKG09C", "time_quit_smoking",
    "SMKG203_cont", "SMKG207_cont", "SMK_204", "SMK_05B", "SMK_208",
    "SMK_05C", "SMK_01A", "SMKG01C_cont", "pack_years_der"
  )
)

tail(pack_years2011_2012)

combined_pack_years <- suppressWarnings(merge_rec_data(pack_years2009_2010,
 pack_years2011_2012))

head(combined_pack_years)
tail(combined_pack_years)

Categorical smoking pack-years

Description

This function creates a categorical derived variable (pack_years_cat) that categorizes smoking pack-years (pack_years_der).

Usage

pack_years_fun_cat(pack_years_der)

Arguments

pack_years_der

derived variable that calculates smoking pack-years See pack_years_fun for documentation on how variable was derived.

Details

pack-years is calculated by multiplying the number of cigarette packs per day (20 cigarettes per pack) by the number of years.The categories were based on the Cardiovascular Disease Population Risk Tool (Douglas Manuel et al. 2018).

pack_years_cat uses the derived variable pack_years_der. Pack_years_der uses age and various smoking variables that have been transformed by cchsflow (see documentation on pack_year_der). In order to categorize pack years across CCHS cycles, age and smoking variables must be transformed and harmonized.

Value

value for pack year categories in the pack_years_cat variable.

Examples

 
# Using pack_years_fun_cat() to categorize pack year values across CCHS cycles
# pack_years_fun_cat() is specified in variable_details.csv along with the 
# CCHS variables and cycles included.

# To transform pack_years_cat across cycles, use rec_with_table() for each
# CCHS cycle and specify pack_years_cat.
# Since pack_year_der is also also derived variable, you will have to specify 
# the variables that are derived from it.
# Since time_quit_smoking_der is also a derived variable in pack_year_der, 
# you will have to specify the variables that are derived from it.
# Then by using merge_rec_data(), you can combine pack_years_cat across
# cycles.

library(cchsflow)

pack_years_cat_2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "SMKDSTY_A", "DHHGAGE_cont", "SMK_09A_B", "SMKG09C", "time_quit_smoking",
    "SMKG203_cont", "SMKG207_cont", "SMK_204", "SMK_05B", "SMK_208",
    "SMK_05C", "SMK_01A", "SMKG01C_cont", "pack_years_der", "pack_years_cat"
  )
)

head(pack_years_cat_2009_2010)

pack_years_cat_2011_2012 <- rec_with_table(
  cchs2011_2012_p,c(
    "SMKDSTY_A", "DHHGAGE_cont", "SMK_09A_B", "SMKG09C", "time_quit_smoking",
    "SMKG203_cont", "SMKG207_cont", "SMK_204", "SMK_05B", "SMK_208",
    "SMK_05C", "SMK_01A", "SMKG01C_cont", "pack_years_der", "pack_years_cat"
  )
)

tail(pack_years_cat_2011_2012)

combined_pack_years_cat <- suppressWarnings(merge_rec_data
(pack_years_cat_2009_2010,pack_years_cat_2011_2012))

head(combined_pack_years_cat)
tail(combined_pack_years_cat)

Percent time in Canada

Description

This function creates a derived variable (pct_time_der) that provides an estimated percentage of the time a person's life was spent in Canada.

Usage

pct_time_fun(DHHGAGE_cont, SDCGCBG, SDCGRES)

Arguments

DHHGAGE_cont

continuous age variable.

SDCGCBG

whether or not someone was born in Canada (1 - born in Canada, 2 - born outside Canada)

SDCGRES

how long someone has lived in Canada. Note: in the PUMF CCHS datasets, this is a categorical variable with two categories (1 - 0-9 years; 2 - 10+ years).

Value

Numeric value between 0 and 100 that represents percentage of a respondent's time in Canada

Note

Since SDCGRES is a categorical variable measuring length of time, we've set midpoints in the function. A respondent identified as being in Canada for 0-9 years is assigned a value of 4.5 years, and someone who has been in Canada for over 10 years is assigned a value of 15 years.

Examples

# Using pct_time_fun() to create percent time values between CCHS cycles
# pct_time_fun() is specified in variable_details.csv along with the CCHS
# variables and cycles included.

# To transform pct_time_der across cycles, use rec_with_table() for each CCHS
# cycle and specify pct_time_der, along with age (DHHGAGE_cont), whether or
# not someone was born in Canada (SDCGCBG), how long someone has lived in
# Canada (SDCGRES). Then by using merge_rec_data(), you can combine
# pct_time_der across cycles

library(cchsflow)
pct_time2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "DHHGAGE_cont", "SDCGCBG",
    "SDCGRES", "pct_time_der"
  )
)
head(pct_time2009_2010)

pct_time2011_2012 <- rec_with_table(
  cchs2011_2012_p,  c(
    "DHHGAGE_cont", "SDCGCBG",
    "SDCGRES", "pct_time_der"
  )
)
tail(pct_time2011_2012)

combined_pct_time <- merge_rec_data(pct_time2009_2010, pct_time2011_2012)
head(combined_pct_time)
tail(combined_pct_time)

# Using pct_time_fun() to generate a value for percent time spent in Canada
# with user inputted values Let's say you are 27 years old who was born
# outside of Canada and have been living in Canada for less than 10 years.
# Your estimated percent time spent in Canada can be calculated as follows:

pct_time <- pct_time_fun(DHHGAGE_cont = 27, SDCGCBG = 2, SDCGRES = 1)

print(pct_time)

Categorical percent time in Canada

Description

This function creates a categorical derived variable (pct_time_der_cat10) that categorizes the derived percent time in Canada variable (pct_time_der).

Usage

pct_time_fun_cat(pct_time_der)

Arguments

pct_time_der

derived continuous percent time in Canada. See pct_time_fun for documentation on how variable was derived.

Details

The percent time in Canada provides an estimated percentage of the time a person's life was spent in Canada.The categorical percent time in Canada divides the continuous value into 10 percent intervals.

pct_time_der_cat10 uses the derived variable pct_time_der. pct_time_der uses various variables that have been transformed by cchsflow (see documentation on pct_time_der). In order to categorize percent time in Canada across CCHS cycles, the variables must be transformed and harmonized.

Value

value for categorical percent time in Canada using pct_time_der variable.

Examples

# Using pct_time_fun_cat() to create categorical percent time values 
# between CCHS cycles.
# pct_time_fun_cat() is specified in variable_details.csv along with the CCHS
# variables and cycles included.

# To transform pct_time_der_cat10 across cycles, use rec_with_table() for 
# each CCHS cycle.
# Since pct_time_der is a derived variable, you will have to specify the 
# variables that are derived from it.
# Then by using merge_rec_data(), you can combine pct_time_der_cat10 across 
# cycles.

library(cchsflow)
pct_time_cat2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "DHHGAGE_cont", "SDCGCBG",
    "SDCGRES", "pct_time_der", "pct_time_der_cat10"
  )
)
head(pct_time_cat2009_2010)

pct_time_cat2011_2012 <- rec_with_table(
  cchs2011_2012_p,  c(
    "DHHGAGE_cont", "SDCGCBG",
    "SDCGRES", "pct_time_der", "pct_time_der_cat10"
  )
)
tail(pct_time_cat2011_2012)

combined_pct_time_cat <- merge_rec_data(pct_time_cat2009_2010, 
pct_time_cat2011_2012)
head(combined_pct_time_cat)
tail(combined_pct_time_cat)

Recode with Table

Description

Recode with Table is responsible for recoding values of a dataset based on the specifications in variable_details.

Usage

rec_with_table(
  data,
  variables = NULL,
  database_name = NULL,
  variable_details = NULL,
  else_value = NA,
  append_to_data = FALSE,
  log = FALSE,
  notes = TRUE,
  var_labels = NULL,
  custom_function_path = NULL,
  attach_data_name = FALSE
)

Arguments

data

A dataframe containing the variables to be recoded. Can also be a list of dataframes

variables

character vector containing variable names to recode or a variables csv containing additional variable info

database_name

String, the name of the dataset containing the variables to be recoded. Can also be a vector of strings if data is a list

variable_details

A dataframe containing the specifications (rules) for recoding.

else_value

Value (string, number, integer, logical or NA) that is used to replace any values that are outside the specified ranges (no rules for recoding).

append_to_data

Logical, if TRUE (default), recoded variables will be appended to the data.

log

Logical, if FALSE (default), a log of recoding will not be printed.

notes

Logical, if FALSE (default), will not print the content inside the 'Note“ column of the variable being recoded.

var_labels

labels vector to attach to variables in variables

custom_function_path

path to location of the function to load

attach_data_name

to attach name of database to end table

Details

The variable_details dataframe needs the following variables to function:

variable: name of new (mutated) variable that is recoded
toType: type the variable is being recoded to cat = categorical, cont = continuous
databaseStart: name of dataframe with original variables to be recoded
variableStart: name of variable to be recoded
fromType: variable type of start variable. cat = categorical or factor variable cont = continuous variable (real number or integer)
recTo: Value to recode to
recFrom: Value/range being recoded from

Each row in variable_details comprises one category in a newly transformed variable. The rules for each category the new variable are a string in recFrom and value in recTo. These recode pairs are the same syntax as sjmisc::rec(), except in sjmisc::rec() the pairs are a string for the function attribute rec =, separated by '='. For example in rec_w_table variable_details$recFrom = 2; variable_details$recTo = 4 is the same as sjmisc::rec(rec = "2=4"). the pairs are obtained from the RecFrom and RecTo columns

recode pairs: each recode pair is row. see above example or PBC-variableDetails.csv
multiple values: multiple old values that should be recoded into a new single value may be separated with comma, e.g. recFrom = "1,2"; recTo = 1
value range: a value range is indicated by a colon, e.g. recFrom= "1:4"; recTo = 1 (recodes all values from 1 to 4 into 1)
value range for doubles: for double vectors (with fractional part), all values within the specified range are recoded; e.g. recFrom = "1:2.5'; recTo = 1 recodes 1 to 2.5 into 1, but 2.55 would not be recoded (since it's not included in the specified range)
"min" and "max": minimum and maximum values are indicates by min (or lo) and max (or hi), e.g. recFrom = "min:4"; recTo = 1 (recodes all values from minimum values of x to 4 into 1)
"else": all other values, which have not been specified yet, are indicated by else, e.g. recFrom = "else"; recTo = NA (recode all other values (not specified in other rows) to "NA")
"copy": the "else"-token can be combined with copy, indicating that all remaining, not yet recoded values should stay the same (are copied from the original value), e.g. recFrom = "else"; recTo = "copy"
NA's: NA values are allowed both as old and new value, e.g. recFrom "NA"; recTo = 1. or "recFrom = "3:5"; recTo = "NA" (recodes all NA into 1, and all values from 3 to 5 into NA in the new variable)

Value

a dataframe that is recoded according to rules in variable_details.

Examples

library(cchsflow)
bmi2001 <- rec_with_table(
  data = cchs2001_p, c(
    "HWTGHTM",
    "HWTGWTK", "HWTGBMI_der"
  )
)

head(bmi2001)

bmi2011_2012 <- rec_with_table(
  data = cchs2011_2012_p,  c(
    "HWTGHTM",
    "HWTGWTK", "HWTGBMI_der"
  )
)

tail(bmi2011_2012)

combined_bmi <- bind_rows(bmi2001, bmi2011_2012)
head(combined_bmi)
tail(combined_bmi)

recode_columns

Description

Recodes columns from passed row and returns just table with those columns and same rows as the data

Usage

recode_columns(
  data,
  variables_to_process,
  data_name,
  log,
  print_note,
  else_default
)

Arguments

data

The source database

variables_to_process

rows from variable details that are applicable to this DB

data_name

Name of the database being passed

log

The option of printing log

print_note

the option of printing the note columns

else_default

default else value to use if no else is present

Value

Returns recoded and labeled data

Recode NA formatting

Description

Recodes the NA depending on the var type

Usage

recode_variable_NA_formating(cell_value, var_type)

Arguments

cell_value

The value inside the recTo column

var_type

the toType of a variable

Value

an appropriately coded tagged NA

resp_condition_fun1

Description

This is one of 3 functions used to create a derived variable (resp_condition_der) that determines if a respondents has a respiratory condition. 3 different functions have been created to account for the fact that different respiratory variables are used across CCHS cycles. This function is for CCHS cycles (2009-2014) that only use COPD and Emphysema as a combined variable. Asthma is used across CCHS cycles as a separate variable.

Usage

resp_condition_fun1(DHHGAGE_cont, CCC_091, CCC_031)

Arguments

DHHGAGE_cont

continuous age variable.

CCC_091

variable indicating if respondent has either COPD or Emphysema

CCC_031

variable indicating if respondent has asthma

Value

a categorical variable (resp_condition_der) with 3 levels:

respondent is over the age of 35 and has a respiratory condition
respondent is under the age of 35 and has a respiratory condition
respondent does not have a respiratory condition

Examples

# Using resp_condition_fun1() to create values across CCHS cycles
# (2009-2014) resp_condition_fun1() is specified in
# variable_details.csv along with the CCHS variables and cycles included.

# To transform resp_condition_der, use rec_with_table() for each CCHS cycle
# and specify resp_condition_der, along with the various respiratory
# variables. Then by using merge_rec_data() you can combine
# resp_condition_der across cycles.

library(cchsflow)

resp2009_2010 <- suppressWarnings(rec_with_table(
  cchs2009_2010_p,  c(
    "DHHGAGE_cont", "CCC_091", "CCC_031",
    "resp_condition_der"
  )
))

head(resp2009_2010)

resp2011_2012 <- suppressWarnings(rec_with_table(
  cchs2011_2012_p, c(
    "DHHGAGE_cont", "CCC_091", "CCC_031",
    "resp_condition_der"
  )
))

tail(resp2011_2012)

combined_resp <-
 suppressWarnings(merge_rec_data(resp2009_2010, resp2011_2012))

head(combined_resp)
tail(combined_resp)

resp_condition_fun2

Description

This is one of 3 functions used to create a derived variable (resp_condition_der) that determines if a respondents has a respiratory condition. This function is for CCHS cycles (2005-2007) that use COPD & Emphysema as separate variables, as well as Bronchitis. Asthma is used across CCHS cycles as a separate variable.

Usage

resp_condition_fun2(DHHGAGE_cont, CCC_91E, CCC_91F, CCC_91A, CCC_031)

Arguments

DHHGAGE_cont

continuous age variable.

CCC_91E

variable indicating if respondent has emphysema

CCC_91F

variable indicating if respondent has COPD

CCC_91A

variable indicating if respondent has chronic bronchitis

CCC_031

variable indicating if respondent has asthma

Value

a categorical variable (resp_condition_der) with 3 levels:

respondent is over the age of 35 and has a respiratory condition
respondent is under the age of 35 and has a respiratory condition
respondent does not have a respiratory condition

Examples


# Using resp_condition_fun2() to create values across CCHS cycles
# (2005-2007) resp_condition_fun2() is specified in
# variable_details.csv along with the CCHS variables and cycles included.

# To transform resp_condition_der, use rec_with_table() for each CCHS cycle
# and specify resp_condition_der, along with the various respiratory
# variables. Then by using merge_rec_data() you can combine
# resp_condition_der across cycles.

library(cchsflow)

resp2005 <- suppressWarnings(rec_with_table(
  cchs2005_p, c(
    "DHHGAGE_cont", "CCC_91E", "CCC_91F", "CCC_91A", "CCC_031",
    "resp_condition_der"
  )
))

head(resp2005)

resp2007_2008 <- suppressWarnings(rec_with_table(
  cchs2007_2008_p,  c(
    "DHHGAGE_cont", "CCC_91E", "CCC_91F", "CCC_91A", "CCC_031",
    "resp_condition_der"
  )
))

tail(resp2007_2008)

combined_resp <- suppressWarnings(merge_rec_data(resp2005, resp2007_2008))

head(combined_resp)
tail(combined_resp)

resp_condition_fun3

Description

This is one of 3 functions used to create a derived variable (resp_condition_der) that determines if a respondents has a respiratory condition. This function for CCHS cycles (2001-2003) that use COPD and Emphysema as a combined variable, as well as Bronchitis. Asthma is used across CCHS cycles as a separate variable.

Usage

resp_condition_fun3(DHHGAGE_cont, CCC_091, CCC_91A, CCC_031)

Arguments

DHHGAGE_cont

continuous age variable.

CCC_091

variable indicating if respondent has either COPD or Emphysema

CCC_91A

variable indicating if respondent has chronic bronchitis

CCC_031

variable indicating if respondent has asthma

Value

a categorical variable (resp_condition_der) with 3 levels:

respondent is over the age of 35 and has a respiratory condition
respondent is under the age of 35 and has a respiratory condition
respondent does not have a respiratory condition

Examples

# Using resp_condition_fun3() to create values across CCHS cycles
# (2001-2003) resp_condition_fun3() is specified in
# variable_details.csv along with the CCHS variables and cycles included.

# To transform resp_condition_der, use rec_with_table() for each CCHS cycle
# and specify resp_condition_der, along with the various respiratory
# variables. Then by using merge_rec_data() you can combine
# resp_condition_der across cycles.

library(cchsflow)

resp2001 <- suppressWarnings(rec_with_table(
  cchs2001_p, c(
    "DHHGAGE_cont", "CCC_091", "CCC_91A", "CCC_031",
    "resp_condition_der"
  )
))

head(resp2001)

resp2003 <- suppressWarnings(rec_with_table(
  cchs2003_p,c(
    "DHHGAGE_cont", "CCC_091", "CCC_91A", "CCC_031",
    "resp_condition_der"
  )
))

tail(resp2003)

combined_resp <- suppressWarnings(merge_rec_data(resp2001, resp2003))

head(combined_resp)
tail(combined_resp)

Set Data Labels

Description

sets labels for passed database, Uses the names of final variables in variable_details/variables_sheet as well as the labels contained in the passed dataframes

Usage

set_data_labels(data_to_label, variable_details, variables_sheet = NULL)

Arguments

data_to_label

newly transformed dataset

variable_details

variable_details.csv

variables_sheet

variables.csv

Value

labeled data_to_label

Examples

library(cchsflow)
library(sjlabelled)
bmi2001 <- rec_with_table(
 cchs2001_p, c(
    "HWTGHTM",
    "HWTGWTK", "HWTGBMI_der"
  )
)

bmi2003 <- rec_with_table(
  cchs2003_p, c(
    "HWTGHTM",
    "HWTGWTK", "HWTGBMI_der"
  )
)

combined_bmi <- bind_rows(bmi2001, bmi2003)

get_label(combined_bmi)

labeled_combined_data <- set_data_labels(combined_bmi,
 variable_details,
 variables)

get_label(labeled_combined_data)

Simple smoking status

Description

This function creates a derived smoking variable (smoke_simple) with four categories:

non-smoker (never smoked)
current smoker (daily and occasional?)
former daily smoker quit =<5 years or former occasional smoker
former daily smoker quit >5 years

Usage

smoke_simple_fun(SMKDSTY_cat5, time_quit_smoking)

Arguments

SMKDSTY_cat5

derived variable that classifies an individual's smoking status. This variable captures cycles 2001-2018.

time_quit_smoking

derived variable that calculates the approximate time a former smoker has quit smoking. See time_quit_smoking_fun for documentation on how variable was derived.

Examples

# Using the 'smoke_simple_fun' function to create the derived smoking   
# variable across CCHS cycles.
# smoke_simple_fun() is specified in the variable_details.csv

# To create a harmonized smoke_simple variable across CCHS cycles, use 
# rec_with_table() for each CCHS cycle and specify smoke_simple_fun and 
# the required base variables. Since time_quit_smoking_der is also a derived 
# variable, you will have to specify the variables that are derived from it.
# Using merge_rec_data(), you can combine smoke_simple across cycles.

library(cchsflow)

smoke_simple2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "SMKDSTY", "SMK_09A_B", "SMKG09C", "time_quit_smoking",
    "smoke_simple"
  )
)

head(smoke_simple2009_2010)

smoke_simple2011_2012 <- rec_with_table(
  cchs2011_2012_p,c(
   "SMKDSTY", "SMK_09A_B", "SMKG09C", "time_quit_smoking",
   "smoke_simple"
  )
)

tail(smoke_simple2011_2012)

combined_smoke_simple <- 
suppressWarnings(merge_rec_data(smoke_simple2009_2010,smoke_simple2011_2012))

head(combined_smoke_simple)
tail(combined_smoke_simple)

Time since quit smoking

Description

This function creates a derived variable (time_quit_smoking_der) that calculates the approximate time a former smoker has quit smoking based on various CCHS smoking variables. This variable is for CCHS respondents in CCHS surveys 2003-2014.

Usage

time_quit_smoking_fun(SMK_09A_B, SMKG09C)

Arguments

SMK_09A_B

number of years since quitting smoking. Variable asked to former daily smokers who quit <3 years ago.

SMKG09C

number of years since quitting smoking. Variable asked to former daily smokers who quit >=3 years ago.

Value

value for time since quit smoking in time_quit_smoking_der.

Examples

# Using time_quit_smoking_fun() to create pack-years values across CCHS 
# cycles.
# time_quit_smoking_fun() is specified in variable_details.csv along with the
# CCHS variables and cycles included.

# To transform time_quit_smoking across cycles, use rec_with_table() for each
# CCHS cycle and specify time_quit_smoking, along with each smoking variable.
# Then by using merge_rec_data(), you can combine time_quit_smoking across
# cycles.

library(cchsflow)

time_quit2009_2010 <- rec_with_table(
  cchs2009_2010_p, c(
    "SMK_09A_B", "SMKG09C", "time_quit_smoking"
  )
)

head(time_quit2009_2010)

time_quit2011_2012 <- rec_with_table(
  cchs2011_2012_p, c(
    "SMK_09A_B", "SMKG09C", "time_quit_smoking"
  )
)

tail(time_quit2011_2012)

combined_time_quit <- suppressWarnings(merge_rec_data(time_quit2009_2010,
 time_quit2011_2012))

head(combined_time_quit)
tail(combined_time_quit)

variable_details.csv

Description

This dataset provides details on how variables are recoded in cchsflow.

Details

See the below link for more details about how the worksheet is structured https://big-life-lab.github.io/cchsflow/articles/variable_details.html

Value

variable_details

a data frame

Examples

data(variable_details)
str(variable_details)

variables.csv

Description

This dataset lists all the variables that are present in cchsflow.

Details

See the below link for more details about how the worksheet is structured https://big-life-lab.github.io/cchsflow/articles/variables_sheet.html

Value

variables

a data frame

Examples

data(variables)
str(variables)

Type of drinker (12 months)

Description

Usage

Arguments

Details

Examples

Type of drinker

Description

Usage

Arguments

Details

Examples

Average daily alcohol consumption

Description

Usage

Arguments

Details

Examples

Number of drinks consumed in the past week

Description

Usage

Arguments

Details

Examples

Any alcohol past week

Description

Usage

Arguments

Details

Examples

Number of drinks - Sunday

Description

Usage

Arguments

Details

Examples

Number of drinks - Monday

Description

Usage

Arguments

Details

Examples

Number of drinks - Tuesday

Description

Usage

Arguments

Details

Examples

Number of drinks - Wednesday

Description

Usage

Arguments

Details

Examples

Number of drinks - Thursday

Description

Usage

Arguments

Details

Examples

Number of drinks - Friday

Description

Usage

Arguments

Details

Examples

Number of drinks - Saturday

Description

Usage

Arguments

Details

Examples

COPD_Emph_der_fun1

Description

Usage

Arguments

Value

See Also

Examples

COPD_Emph_der_fun2