Project A Data

Most Recent Update: 2022-10-18 07:39:43.

What Data Will I Use?

Your Project A will use the 2022 version of the analytic data from County Health Rankings (CHR). You have already seen some versions of these data in Labs 02 and 03, but you’ll create and clean a new data set of your own for Project A.

The data are gathered at the County Health Rankings Data & Documentation site.

  • The key elements we’ll use are in the Rankings Data & Documentation section, specifically the National Data & Documentation section site for the 2022 County Health Rankings.
    • Do not use data from previous editions of the CHR, and do not use the trends data available on their website for this project.

Specifically, you’ll need three files:

These files are also available in the data folder for Project A on Github.

You’ll wind up selecting five measures, which you’ll also want to look up at this CHR 2022 Measures link for further details, including who created the measure, how it is measured, and when it was measured.

Developing the Data: Overview

Obtaining and cleaning your data takes a little while, but you can start right now. There are seven main tasks to complete. You will create a clean tibble containing 200-400 rows (counties) and eleven variables, and then present the data, a codebook and some brief summaries, by following the steps below. Detailed instructions follow this overview.

  1. Ingest the data into a raw tibble in R called chr_2022_raw, carefully.
  2. Select the states (3-5 plus Ohio) that you want to study, creating a new chr_2022 tibble that contains only the counties from those states, carefully.
  3. Select nine variables (4 we’ll specify and 5 more you’ll select), rename them in useful ways, clean up problems, and then save this smaller tibble as chr_2022.
  4. Create new factors (categorical variables) from two of your 5 selected variables from the previous task, and add them to chr_2022 so you now have 11 variables.
  5. Save the resulting chr_2022 tibble, and share it with us in your Proposal.
  6. Create a codebook for your chr_2022 tibble which briefly but sufficiently describes your selected variables and subjects (counties.)
  7. Print your chr_2022 tibble (to prove it is a tibble) and then provide a modest set of appropriate numerical summaries of each variable.

Here are some key additional details for each step of this process. - Please refer to Dr. Love’s materials in the rest of these Project A Instructions, particularly in the Examples & Tips section for more help.

Task 1. Ingest the raw data into chr_2022_raw

Begin an R Project just for Project A, and create an R Markdown file within that project where you will do your data development work. Working from a template, or from your own best understanding of what works well for you, start by loading the packages you’ll need, including the tidyverse and any other packages you plan to use.

You’ll use read_csv() to ingest your raw .csv file into R and call the resulting tibble chr_2022_raw. But as you do this, you’ll need to remove the top row from the .csv within your R code. You should probably look at the raw .csv in Excel or another spreadsheet system so that you know why we need to do this.

To accomplish this, use the skip = 1 command within your read_csv(). Sample code follows:

data_url <- 
  "https://www.countyhealthrankings.org/sites/default/files/media/document/analytic_data2022.csv"

chr_2022_raw <- read_csv(data_url, skip = 1, guess_max = 4000,
                         show_col_types = FALSE)

You should at this point have a chr_2022_raw tibble with 3194 rows and 725 columns.

Finally, to complete Task 1, you should now write code to restrict the chr_2022_raw data to only include the 3082 rows which have “county_ranked” values of 1, since the other rows will not be used by us in this project.

Task 2. Filter your data to the counties in the states you’ve chosen

Now we’ll filter the chr_2022_raw data containing 3082 rows and 725 columns to a new tibble called chr_2022 which contains the 200-400 rows (counties) you will actually study in your project. Each row you’ll keep should have a county_ranked value of 1, as we’ve ensured at the end of Task 1. To go from 3082 counties to our final sample, you will need to select 4-6 states, including Ohio.

Your selection must include:

  • the 88 counties of Ohio, and
  • all of the counties in a subset of 3-5 additional states in the US

The number of counties (which have county_ranked values of 1) associated with each state (specified using its two-letter postal abbreviation code) is listed below, for your convenience.

  • Do not include any of the six “states” (District of Columbia, Connecticut, Delaware, Hawaii, New Hampshire and Rhode Island) with fewer than 12 counties.

States You Are Permitted to Select

chr_2022_raw |> count(statecode, state) |> filter(n > 12) |> print(n = 50)
# A tibble: 45 × 3
   statecode state     n
   <chr>     <chr> <int>
 1 01        AL       67
 2 02        AK       24
 3 04        AZ       15
 4 05        AR       75
 5 06        CA       58
 6 08        CO       59
 7 12        FL       67
 8 13        GA      159
 9 16        ID       43
10 17        IL      102
11 18        IN       92
12 19        IA       99
13 20        KS      104
14 21        KY      120
15 22        LA       64
16 23        ME       16
17 24        MD       24
18 25        MA       14
19 26        MI       83
20 27        MN       87
21 28        MS       82
22 29        MO      115
23 30        MT       47
24 31        NE       79
25 32        NV       16
26 34        NJ       21
27 35        NM       32
28 36        NY       62
29 37        NC      100
30 38        ND       48
31 39        OH       88    ## note: must be selected
32 40        OK       77
33 41        OR       35
34 42        PA       67
35 45        SC       46
36 46        SD       61
37 47        TN       95
38 48        TX      244
39 49        UT       28
40 50        VT       14
41 51        VA      133
42 53        WA       39
43 54        WV       55
44 55        WI       72
45 56        WY       23

So, including OH, you will need a total of 200-400 counties, from 4-6 states. For example,

  • one possible combination would be the states of TX (244 counties), AZ (15 counties) and NM (32 counties) in addition to OH (88 counties), which would yield 379 counties in 4 states
  • another combination would be the states of WA (39 counties), WI (72 counties), WV (55 counties) and WY (23 counties) to join OH (88 counties) yielding 277 counties in 5 states
  • and yet another would be PA (67), NY (62), NJ (21), MD (24) and VA (133) in addition to OH (88) yielding 395 counties in 6 states

After making your selection, and applying it, you should have a tibble called chr_2022 which contains 200-400 rows, and 725 columns. In Task 3, we’ll cut down those columns.

Additional Task 2 Notes

  1. Choose your subset of states with the knowledge in mind that some variables in the CHR data are not available for some counties, and that each state you select must have more than 12 counties.
  2. You should have some reason for selecting the states that you do, and you will need to describe that reason in a complete sentence or two in your proposal and your final report.
  3. Since we’ve studied five Midwest states (and 437 counties) in Lab 2, we want you to look at a minimum of one state not included in that list, so your final selection of states (besides Ohio) must include at least one state other than Indiana, Illinois, Michigan and Wisconsin.
  4. Don’t forget to filter the rows at the end of Task 1 so that only those rows with county_ranked values of 1 are included. Otherwise, your counts won’t match those shown above, and you’ll have lots of problems.

Task 3. Select five variables, add four required ones, and clean up chr_2022

Next, you will identify variables from the your existing chr_2022 tibble describing your selected sample of counties to include only nine variables (columns) instead of the 725 you should have at the start of Task 3. These nine variables must include:

  • the five-digit fips code for the county, which will be a convenient ID variable, which is called fipscode in the raw data.
  • the name of the county, which will be useful for labeling and identifying the counties, and which is called county in the raw data.
  • the state, which will be a multi-categorical (with 4-6 categories) variable
  • the county_ranked variable, which tells us whether the row should be included in our data (all of the rows you include in your data should have county_ranked == 1)
  • followed by five variables you select from the 84 variables in the raw CHR data set that we have listed below.
    • Each of these must be of the form vXXX_rawvalue (note: to select the entire group of 86 variables, you might try select(ends_with("rawvalue")) as part of a pipe.)
    • Note that we have not included two of these 86 in our list below of the 84 variables you can choose from.

The 84 variables you must select five from

You must select your five variables from the 84 variables in the raw (.csv) data file listed below. We show them here in the order in which they appear in the raw file. The listing “v001” in this table actually refers to the variable named “v001_rawvalue”.

 [1] "v001" "v002" "v036" "v042" "v037" "v009" "v011" "v133" "v070" "v132"
[11] "v049" "v134" "v045" "v014" "v085" "v004" "v088" "v062" "v005" "v050"
[21] "v155" "v168" "v069" "v023" "v024" "v044" "v082" "v140" "v043" "v135"
[31] "v125" "v124" "v136" "v067" "v137" "v173" "v147" "v127" "v128" "v129"
[41] "v144" "v145" "v060" "v061" "v139" "v083" "v138" "v039" "v143" "v003"
[51] "v122" "v021" "v149" "v159" "v160" "v167" "v169" "v151" "v063" "v065"
[61] "v141" "v142" "v171" "v172" "v015" "v161" "v148" "v158" "v156" "v153"
[71] "v154" "v166" "v051" "v052" "v053" "v054" "v055" "v081" "v080" "v056"
[81] "v126" "v059" "v057" "v058"

For example, v001_rawvalue shows the raw values for the premature death measure. If you select this variable, it is up to you to use the documentation in the two PDF files I have linked to, as well as the information on the County Health Rankings website, to get a reasonable understanding of what the variable measures, and how it was collected.

The 2022 CHR Analytic Data Documentation file (PDF), and the 2022 Data Dictionary file (also PDF) are crucial here, as those are the ones that explain what the available variables mean, and how they should be labeled.

As noted below, you will also need look up each measure you wind up selecting to learn more about it at this CHR 2022 Measures link.

Some important notes for Task 3

  1. Be sure to read this entire Data page (especially the material on How to Clean Your Variables) before selecting your variables, so you are aware of some features of the data we disclose below.

  2. The five variables you select must be five different variables selected from the 84 variables listed above. Some variables in that set of 84 are better choices than others.

  3. Each variable you select should be of some interest to you on its own, in terms of either providing a health outcome of interest, or potentially providing useful information about a feature of the county that might relate to that health outcome. Your five selected quantitative variables, selected by you from the 84 available “raw value” CHR variables, will need be treated as follows:

  • variable 1 will be treated as quantitative, and as an outcome of interest
  • two others (variables 2 and 3) will be treated as quantitative predictors of interest for variable 1
    • Each of your quantitative variables (1, 2 and 3) must have at least 15 distinct values within your final tibble.
  • variable 4 will be categorized into 2 mutually exclusive and collectively exhaustive levels to create a binary categorical variable of interest in predicting variable 1 (this is a terrible idea in practical work.)
    • Exactly one of the variables in the data (v124 about drinking water violations) is already a binary (1 = Yes, 0 = No) variable (all other variables are quantitative.) You are permitted to use v124 as your variable 4, if you like. v124 may not be used as anything other than variable 4 in your work.
  • variable 5 will be categorized into 3-5 mutually exclusive and collectively exhaustive levels to create a multi-categorical variable of interest in predicting variable 1 (this is almost as bad as what we’ll do to variable 4 in practical work)
  • the state will serve as another multi-categorical (with 4-6 categories) predictor of variable 1, so this will also be part of your tibble
  1. Each of the five variables you select must have data for at least 75% of the counties in each state you plan to study. This is something you will have to check on, in R, and you’ll have to present the code, and demonstrate with complete English sentences that you’ve verified this. The use of a tool (or more than one) from the naniar package to do this checking is encouraged.

Caution: Some Variables have Lots of Missing Data

The variables listed below have more than 10% missing values across all 3082 ranked counties in the US. While you are welcome to use any of these variables, you may want to look elsewhere to avoid problems with the “minimum 75% complete data” requirement. (Note that variable v170_rawvalue is missing for all counties in the data so we don’t allow its use.)

Variable Description % missing
v129_rawvalue infant mortality 60.5
v149_rawvalue disconnected youth 60.4
v015_rawvalue homicides 57.0
v138_rawvalue drug overdose deaths 41.7
v128_rawvalue child mortality 38.9
v158_rawvalue juvenile arrests 37.3
v141_rawvalue residential segregation 32.6
v148_rawvalue firearm fatalities 26.3
v161_rawvalue suicides 21.1
v021_rawvalue High School graduation 19.5
v173_rawvalue COVID-19 age-adjusted mortality 16.8
v061_rawvalue HIV prevalence 14.2
v160_rawvalue Math scores 12.8
v039_rawvalue Motor vehicle crash deaths 12.7

  1. The descriptions provided in these instructions associated with each variable are available as the first (deleted in R) row in the .csv file, and are also specified in the 2022 CHR Analytic Data Documentation PDF file.

  2. Across your complete set of 4-6 selected states, the raw versions of each of your five selected variables must have at least ten distinct non-missing values. You’ll need to show R code to do this checking (the best choice is often Hmisc::describe since you’ll run that anyway), and you’ll need to demonstrate with a complete English sentence or two that you have checked this to be true.

How To Clean Your Selected Variables

Find each of your five selected variables on one of the lists below.

  • If you plan to use the variable as quantitative, do what is suggested below as part of your data development work, rename the variable appropriately, and use that version in your codebook.
  • If you plan to use the variable as a categorical predictor, you should still make the appropriate change to the original quantitative version as indicated below.

The Binary Category

v124 is about Drinking Water Violations, is the only binary (1 = Yes, 0 = No) variable in the data. (all other variables are quantitative.) You are permitted to use v124 as your variable 4, if you like. v124 may not be used as anything other than variable 4 in your work.

Ratios That Need Converting

These variables are specified as providers per population in the raw data file. You will want to take the reciprocal (1/raw value) to rescale the results in terms of population per provider, which should be much more interpretable.

  • You’ll note that this rescaled ratio is also provided in the raw file if you want it, for example, for primary care physicians, v004_rawvalue is providers per population, but v004_other_data_1 is the ratio of population (residents) per provider.
  • Note that after rescaling by taking the reciprocal, you may see some counties with infinite ratios, which should then be changed to missing values.
Code Variable Description
v004 Primary care physicians
v062 Mental health providers
v088 Dentists
  • Note that we don’t allow the use of variable v131 in Project A because of the large number of counties with very small numbers of these providers listed.

Variables that should be rescaled

Code Variable Description What to do?
v001 Premature death Divide by 100 to represent losses per 1000 population
v051 Population Either use log10(population), or divide population by 1000 to represent population in thousands.
v061 HIV prevalence Divide by 100 to represent cases per 1000 population (caution: substantial missing data)
v063 Median household income Divide by 1000 to represent in thousands of dollars

Variables that are Proportions should be converted to Percentages

Each of the variables listed below are proportions (between 0 and 1). Before you use them in any analyses, please multiply them by 100 in your data development (using mutate) to turn their values into percentages (between 0 and 100) and this will seriously ease the interpretation of slopes and transformations for these variables.

Code Variable Description
v002 Poor or fair health
v003 Uninsured adults (pick v003 or v085, but not both)
v009 Adult smoking
v011 Adult obesity
v021 High school graduation (pick v021 or v168, but not both, see note)
v023 Unemployment
v024 Children in poverty
v037 Low birthweight
v049 Excessive drinking
v050 Mammography screening
v052 Proportion below 18 years of age
v053 Proportion 65 and older
v054 Proportion Non-Hispanic Black (see note below)
v055 Proportion American Indian and Alaska Native (see note below)
v056 Proportion Hispanic (see note below)
v057 Proportion Females
v058 Proportion Rural
v059 Proportion not proficient in English
v060 Diabetes prevalence
v065 Children eligible for free or reduced price lunch
v067 Driving alone to work
v069 Some college
v070 Physical inactivity
v080 Proportion Native Hawaiian/Other Pacific Islander (see note below)
v081 Proportion Asian (see note below)
v082 Children in single-parent households
v083 Limited access to healthy foods
v085 Uninsured (pick v003 or v085, but not both)
v122 Uninsured children (pick v085 or v122, but not both)
v126 Proportion Non-Hispanic White (see note below)
v132 Access to exercise opportunities
v134 Proportion of driving deaths with alcohol involvement
v136 Severe housing problems
v137 Long commute - driving alone
v139 Food insecurity
v143 Insufficient sleep
v144 Frequent physical distress (pick v036 or v144, but not both)
v145 Frequent mental distress (pick v042 or v145, but not both)
v149 Disconnected youth (caution: lots of missing data)
v153 Homeownership
v154 Severe housing cost burden
v155 Flu vaccinations
v166 Broadband access
v168 High school completion (pick v021 or v168, but not both, see note)
v171 Childcare cost burden
  • If you are interested in studying race and ethnicity and their impact on a health outcome, these data aren’t particularly well suited to a detailed look. Instead we suggest using v126 to incorporate this dimension as a predictor (or its inverse, 1 - v126), rather than including any of the other race/ethnicity variables, specifically v053, v054, v055, v056, v080, or v081. This is because there’s more variation in the v126 data across the reported counties. Again, a serious look at the impact of race/ethnicity is beyond the scope of Project A.
  • Note that variable v021 generally has more missing data than v168, should you be trying to choose between them.

Variables that should be OK as is

The variables listed below should be useful as they are. Most of them are ratios, although a few are averages or indexes. The main issue for these variables is correctly specifying the units of measurement (note that the indexes don’t have units.)

Code Variable Description
v005 Preventable hospital stays
v014 Teen births
v015 Homicide rate (caution: lots of missing data)
v036 Poor physical health days (pick v036 or v144, but not both)
v039 Motor vehicle crash deaths (caution: substantial missing data)
v042 Poor mental health days (pick v042 or v145, but not both)
v043 Violent crime
v044 Income inequality
v045 Sexually transmitted infections
v125 Air pollution - particulate matter
v127 Premature age-adjusted mortality
v128 Child mortality (caution: lots of missing data)
v129 Infant mortality (caution: lots of missing data)
v133 Food environment index
v135 Injury deaths
v138 Drug overdose deaths (caution: lots of missing data)
v140 Social associations
v141 Residential segregation - Black/White (pick v141 or v142, see note)
v142 Residential segregation - non-White/White (pick v141 or v142, see note)
v147 Life expectancy
v148 Firearm fatalities (caution: substantial missing data)
v151 Gender pay gap
v156 Traffic volume
v158 Juvenile arrests (caution: lots of missing data)
v159 Reading scores
v160 Math scores (caution: substantial missing data)
v161 Suicides (caution: substantial missing data)
v167 School segregation (index from 0-1 with no units of measurement)
v169 School funding adequacy (gap measured in dollars per pupil)
v172 Childcare centers per 1000 population under 5 years old
v173 COVID-19 age-adjusted mortality (caution: substantial missing data)
  • Note that variable v141 has more missing data than v142, should you be trying to choose between them.
  • For any variable you select, be sure that it has at least 75% complete data across the counties in your selected states, and within each state you have selected.

Task 4. Create factors for two of the five variables you chose in Task 2

Create new categorical variables (factors) based on two of your previously selected variables, and add them to your chr_2022 tibble. You’ll also retain the original (quantitative) versions of those two variables, so your tibble will now have 11 variables.

As mentioned, you will need to provide code to categorize two of your variables. Specifically…

  • variable 4 will be categorized into 2 mutually exclusive and collectively exhaustive levels to create a binary categorical variable of interest in predicting variable 1 (this is a terrible idea in practical work.)
  • variable 5 will be categorized into 3-5 mutually exclusive and collectively exhaustive levels to create a multi-categorical variable of interest in predicting variable 1 (this is almost as bad as what we’ll do to variable 4 in practical work.)

Additional Task 4 Notes

  1. You will need to verify that all levels (categories) in each of your two categorical variables contain at least ten counties. Using tabyl is a good approach to check that this is true.
  2. Verify that your state variable and your two categorical variables are now recognized by R as factors with appropriate levels.

Task 5. Save and share the final chr_2022 tibble

Your final chr_2022 tibble will thus contain 200-400 rows (counties) and 11 variables. You’ll save this tibble as an R data set (.Rds file) and share that file with us as part of your proposal.

In your R Markdown file, you will need to present all code necessary to take the original .csv data file and wind up with your chr_2022 tibble so that we can run that code and get the same results you do.

Your main data set for analysis then should be gathered into a tibble called chr_2022 containing the following information in this order:

  • the identifying variables for each county, specifically:
    • fipscode = the five-digit fips code for the state and county,
    • county = the name of the county
    • state = the postal abbreviation code for the state
  • the variable county_ranked, which must be 1 for all rows in your data
  • your three selected quantitative variables, one of which is your planned outcome
  • your binary categorical variable derived from your fourth selected variable
  • your multi-categorical variable derived from your fifth selected variable
  • the “original” quantitative versions of the two variables (4 and 5) that you later categorized (if you chose the drinking water violations variable for variable 4, then you don’t need to repeat it here.)

All of these variables should be renamed (and also have clean_names() from the janitor package applied) so as to have descriptive and maximally helpful variable names.

  • For example, if you have decided to use as a quantitative variable something like v009_rawvalue, which is about adult smoking, you should rename the variable v009_rawvalue to adult_smoking in your tibble.
  • If v009_rawvalue (about adult smoking) is to be one of your categorical variables, then you should include both the original quantitative value (renamed adult_smoking_raw) and your categorical variable that you’ll actually use in analyses, which should be named adult_smoking_cat.

Save the Tibble: You should provide code that saves your tibble as an R data set into your R Project, with the file name chr_2022_YOURNAME.Rds. If you like, you can store this .Rds file in a data subdirectory within your R Project.

Additional Task 5 Notes

  1. After your cleaning is done, each row in your chr_2022 tibble should contain all of the counties within the 4-6 states you are studying, and no other counties should be included in your tibble. All rows of your chr_2022 tibble must display a county_ranked value of 1.
  2. If data for some counties are missing in the raw data for one or more of your variables, then these data should be indicated as missing (using NA) in the tibble.
  3. There is no need to set a seed in this process, as you are not doing anything that involves selecting a random sample.

Task 6. Provide a Codebook for Your Tibble

Next, you will provide a codebook as part of your R Markdown file (and HTML/PDF output) that specifies the name of each variable in your tibble and its definition. After you select your variables, use the County Health Rankings website’s 2022 Measures list, and in particular the linked information on that page for full descriptions, definitions and limitations of the variables you have selected.

Demo Codebook

The start of your Codebook might look something like this (note that I’ve used states you’re not allowed to use here.) Here, I’ve split the codebook into two parts - first describing the states, and then the variables.

My states are:

State Name Abbreviation # of counties
Ohio OH 88
Connecticut CT 8
Delaware DE 3
Hawaii HI 4
New Hampshire NH 10
Rhode Island RI 5
Total (6 states) – 118

Remember that you need to have 4-6 states, including Ohio, with 200-400 counties, so my choice of states wouldn’t be acceptable.

My variables are:

Variable Description Original Name # Missing
fipscode five-digit identifying code for state and county fipscode 0
county name of the county county 0
state postal abbreviation for the state state 0
poorfair (variable 1: outcome) % of adults reporting fair or poor health (age-adjusted). v002_rawvalue 0
fluvax (variable 2) % of fee-for-service (FFS) Medicare enrollees that had an annual flu vaccination. v155_rawvalue 0
infmort (variable 3) Infant deaths (within 1 year) per 1,000 live births. v129_rawvalue 31
discyouth_raw % of teens and young adults ages 16-19 who are neither working nor in school. v149_rawvalue 44
homicide_raw Deaths due to homicide per 100,000 population. v015_rawvalue 48
discyouth_cat (variable 4) Low (below A%) vs. High (A% or higher) based on discyouth_raw v149_rawvalue 44
homicide_cat (variable 5) Low (below B), Medium (between B and C), High (above C) based on homicide_raw v015_rawvalue 48
  • A% is the median value for discyouth_raw
  • B and C represent the tertiles of homicide_raw.

Remember that you need to have at least 75% complete cases for each variable, so several of my variables here wouldn’t be acceptable given this selection of states.

Additional Task 6 Notes

  1. Be sure to include a description for each of the 11 variables (4 required including county_ranked + 5 you select + 2 categorical versions you create) in your codebook for your final tibble. The descriptions I’ve used here come from the County Health Rankings website’s 2022 Measures list site, which is what you should use, as well.

  2. Include a description of what the cut points are for the two categorical variables you create, and specify how those cut points were chosen, as I have done above - you should include the actual numbers, not just A, B and C.

  3. Tools from the naniar package, especially the miss_var_summary() tool, can be of help in preparing this codebook.

Task 7. Print and Summarize Your Tibble

  1. Print the Tibble: You will then prove that your tibble is in fact a tibble and not just a data frame by listing it, so that the first 10 rows are printed, and the columns are appropriately labeled. The command you want is just chr_2022.

  2. Numerical Summaries: You will then demonstrate main numerical summaries from the tibble by running some of the following summaries.

  • At a minimum in the proposal, you will need to show the results of Hmisc::describe run on the whole tibble. This can also be used to demonstrate that you have at least 15 distinct values for each quantitative variable (variables 1-3) in your data set.
  • You may need an additional summary to prove that you have at least 75% complete data within each state for each variable.
  • You may need an additional summary to demonstrate that there are at least ten observations within each level of each of your categorical variables.
  • Note that in the project report, there are two additional requirements, so you might consider them now.
    • mosaic::favstats on each of your quantitative variables (so variables 1, 2, and 3 and the original versions of variables 4 and 5)
    • janitor::tabyl on your categorical variables 4 and 5 as well as on state.