In Westat-Transportation/summarizeNHTS: National Household Travel Survey R Analysis Toolkit

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Overview

Workshop Goals

• Demonstrate how to work with NHTS data in R
• Generate national travel estimates with statistical confidence
• Create a publish-ready report with estimates and visuals from today's workshop
• Preview the report we are going to create

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Topics

1. Setting Up an R Analysis Environment
2. Accessing the Data
3. Generating Estimates
4. Creating New Variables
5. Visualizing Estimates
6. Producing a Travel Analysis Report

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"House Cleaning"

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This presentation is intended for re-use

• Focus on the presentation itself today
  ↪ Later, follow along in RStudio at your own pace

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Next Topic {#transition_slide}

1. Setting Up an R Analysis Environment
2. Accessing the Data
3. Generating Estimates
4. Creating New Variables
5. Visualizing Estimates
6. Producing a Travel Analysis Report

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Setting Up an R Analysis Environment

Click here for latest installation instructions

Instructions provide explicit download links for

• R
• R Tools
• RStudio

Once installed, open RStudio

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Setting Up an R Analysis Environment (cont.)

Make sure you have the "summarizeNHTS" R software package installed

install.packages("devtools")
devtools::install_github("Westat-Transportation/summarizeNHTS")

And now that we have installed the necessary software, load the software

library(summarizeNHTS)

OK, we're ready!

• Your R environment for NHTS analysis is set up
• You only need to set up an environment once
• From now on, just open RStudio and load summarizeNHTS

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Next Topic {#transition_slide}

1. Setting Up an R Analysis Environment
2. Accessing the Data
3. Generating Estimates
4. Creating New Variables
5. Visualizing Estimates
6. Producing a Travel Analysis Report

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Accessing the Data

• Instructions for downloading the data
• Read the 2017 data into our R enviornment
• Explore the data structure and object content
• Select a subset of an NHTS data table
• Explore the codebook in R

Before we begin, let's make sure the summarizeNHTS package is loaded.

library(summarizeNHTS)

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Accessing the Data: Downloading NHTS Data

# Not Run
download_nhts_data("2001")
download_nhts_data("2009")
download_nhts_data("2017")

• You can download the 2001, 2009, and 2017 using the download_nhts_data function.
  • Downloads directly from the Oak Ridge NHTS website.
• download_nhts_data parameters
  • Dataset year
  • Dataset directory (defaults to current working directory)
• Recommend creating an RStudio Project and using the project directory.

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Accessing the Data: Reading the 2017 NHTS data

nhts_data <- read_data("2017", "C:/NHTS")

• read_data is a function that reads and compiles data from CSVs.
  • Data files are packaged up (in memory) as an R object.
  • Any alterations to this object will NOT affect the CSVs.
• nhts_data is the new object we created.
  • Note: It can be named anything (within R's syntax rules).
• Which NHTS dataset?
  • Pass '2017' to specify that we are working with the 2017 dataset.
  • Can specify directory where the data is stored (recall the download_nhts_data function).

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Accessing the Data: Summarizing the data object

• The data / weights are contained within the nhts_data object we created.
  • Access these contents using $
• Use the summary function to get an overview of the data structure.

summary(nhts_data$data)

• The contents of the data element include four data.table objects:
  • trip
  • person
  • household
  • vehicle
• data.tables are 2-dimensional data structures (rows X columns).
  • An extension of R's base data.frame structure (with enhanced functionality).
• The length refers to the number of columns in each table.

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Accessing the Data: Snapshot of the vehicle data

• How do I access the raw data?
• Let's hop over to Rstudio to explore in more detail

nhts_data$data$vehicle

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Accessing the Data: Subsetting

• Select specific columns

# By position
nhts_data$data$vehicle[, c(1, 3)]

# By name (single variable)
nhts_data$data$vehicle$ANNMILES

# By name
nhts_data$data$vehicle[, list(HOUSEID, ANNMILES)]

• Select specific rows

# By row numbers (first 5 rows)
nhts_data$data$vehicle[1:5, ]

# By condition
nhts_data$data$vehicle[VEHTYPE == "01", ]

# By condition (multiple values)
nhts_data$data$vehicle[VEHTYPE %in% c("01","02"), ]

• Note: Conditional subsetting will be revisited in the "Generating Estimates" secition.
• Putting it together
  • Live example: print the MAKE and MODEL of vehicles from year 2012

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Accessing the Data: Codebook objects

• Access the variables and values tables for different years
  • codebook_2001, codebook_2009, codebook_2017

# 2017 variables table
head(codebook_2017$variables)

# 2017 values table
head(codebook_2017$values)

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Next Topic {#transition_slide}

1. Setting Up an R Analysis Environment
2. Accessing the Data
3. Generating Estimates
4. Creating New Variables
5. Visualizing Estimates
6. Producing a Travel Analysis Report

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Generating Estimates

• Introduction to the summarize_data function
• Understanding summarize_data parameters
• Statistics grouped by variables
• Exploring aggregation options
• Estimates using a subset condition
• Referencing the documentation

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Generating Estimates: Introduction to summarize_data

• The summarizeNHTS package has built in functions for running complex queries on the NHTS dataset
• summarize_data is the workhorse function behind these queries.

summarize_data(
  data = nhts_data,
  agg = "household_count"
)

• What do these values mean?

  • W - Weighted statistic.
    • Count of households weighted to the population
  • E - Standard error of the weighted statistic.
    • Standard error of the weighted count of households
  • S - Surveyed/sampled statistic (unweighted statistic).
    • The count of sampled households
  • N - Number of observations/sample size.
    • The number of observations is the same as the count of sampled households in this example

• Every summarize_data query will return these fields.

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Generating Estimates: Exploring summarize_data Parameters

summarize_data(
  data = nhts_data,
  agg = "household_count"
)

• Required parameters
  • data - NHTS dataset object
    • Will always be the output of read_data.
    • In our example, we stored the output in the nhts_data object.
  • agg - Aggregate function label
    • Our example used 'household_count' but agg could be a number of other labels.
• Let's explore some of the additional parameters!
  • Categorical grouping
  • Aggregation options
  • Subset conditions
  • More...

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Generating Estimates: Grouping by Variables

• What if I wanted to group these statistics by another variable?
  • Let's use the by parameter to group by metropolitan status.

summarize_data(
  data = nhts_data,
  agg = "household_count",
  by = "IS_METRO"
)

• You can specify any number of variables using the by parameter.

summarize_data(
  data = nhts_data,
  agg = "household_count",
  by = c("IS_METRO","HOMEOWN")
)

---

Generating Estimates: Frequencies/Proportions

• You can use any of the following count aggregates
  • 'household_count', 'person_count', 'trip_count', 'vehicle_count'

# Person count
summarize_data(
  data = nhts_data,
  agg = "person_count"
)

• You can also choose to view counts as a proportion of the total using the prop parameter
  • Only useful when a by variable is specified

# Proportion of persons by WORKER, worker status
summarize_data(
  data = nhts_data,
  agg = "person_count",
  by = "WORKER",
  prop = TRUE
)

---

Generating Estimates: Numeric Aggregates

• You can use any of the following numeric aggregates
  • 'sum', 'avg', 'median'
• Must also specify a variable name using the agg_var parameter
  • Required that the variable be numeric

# Average TRPMILES, trip distance in miles
summarize_data(
  data = nhts_data,
  agg = "avg",
  agg_var = "TRPMILES"
)

Notes

• summarize_data handles missing value (-1,-7,-8,-9) exclusion for numeric aggregates
• The N in the example above refers to the number of trips used in the calculation

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Generating Estimates: Trip Rates

• You can use either of the following trip rate aggregates
  • 'household_trip_rate' - Daily Person Trips per Household
  • 'person_trip_rate' - Daily Person Trips per Person

# Daily person Trips by worker status
summarize_data(
  data = nhts_data,
  agg = "person_trip_rate",
  by = "WORKER"
)

---

Generating Estimates: Subsetting in summarize_data

• Pre-aggregation subset conditions can be specified using the subset parameter.

  • Argument should be passed as a string.

• Subsetting character variables

# Distribution of social/recreational trips by travel day
summarize_data(
  data = nhts_data,
  agg = "trip_count",
  by = "TRAVDAY",
  prop = TRUE,
  subset = "WHYTRP90 %in% c('07','08','10')"
)

• Subsetting numeric variables

# Person trip rate by Sex (for millennials)
summarize_data(
  data = nhts_data,
  agg = "person_trip_rate",
  by = "R_SEX",
  subset = "R_AGE >= 18 & R_AGE <= 34"
)

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Generating Estimates: Documentation

• Comprehensive function documentation is accessible within R!

?summarize_data

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Next Topic {#transition_slide}

1. Setting Up an R Analysis Environment
2. Accessing the Data
3. Generating Estimates
4. Creating New Variables
5. Visualizing Estimates
6. Producing a Travel Analysis Report

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Creating New Variables

• Derive information from other variables in the dataset
• Find new ways to to expand your analysis
• Types of derived variables include:
  • Flags (yes/no, is/is-not, has/has-not, etc)
  • Collapsing/Binning (e.g. grouping Bus and Rail into one Public Transit value)
  • (re)Categorization (manipulating categorical values to fit your needs)
  • Mathematical Calculations (variable * factor, variable / variable, etc)

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Creating New Variables: Example Scenario

Example Derived Variable Coding Scenario

1) Someone's interested in querying the NHTS for a particular travel behavior

Anthony: "I am interested in exploring how financial burden may affect travel."
Alex: "Remember that question about walking to save money? I would include that in your analysis."

2) Consider suggested variable's usefulness for Anthony's analysis:

WALK2SAVE: "I walk to places to save money."

Values:

| | | |:------|---------------------------| | 01 | Strongly agree | | 02 | Agree | | 03 | Neither Agree or Disagree | | 04 | Disagree | | 05 | Strongly disagree |

3) Look for potential other ways of maniupulating this variable for analysis

4) Create variable called WALK_FINANCE, a yes/no variable for the binary analysis question, "who does or does not walk to save money?"

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Creating New Variables: Configuration

• A derived variable template file is included in summarizeNHTS

• Create basic and complex variables with your own logic

• Variables loaded automatically for you by read_data()

• The derived variable template file preserves details of coding for your documentation

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Creating New Variables: Configuration (cont.)

Derived variable file requirements

| Item | Description | |:-------|:----------------------------------------------------------| | NAME | The name of the variable as it will appear in the dataset | | TABLE | The table level this variable is being computed for | | TYPE | Data type (numeric or character) | | DOMAIN | Logical expression that decides value assignment | | VALUE | A variable code value | | LABEL | Description of code value |

Review File

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Creating New Variables: Example 1 (Has/Has-not)

Using the Derived Variables file, create a variable with the following requirements:

• Level: Household
• Idea: Household has at least one vehicle
• Values: 1=Yes, 2=No
• Name: HAS_VEHICLE

\

| NAME | TABLE | TYPE | DOMAIN | VALUE | LABEL | |:------------|:----------|:----------|:--------------|:------|:------| | HAS_VEHICLE | household | character | HHVEHCNT > 0 | 1 | Yes | | HAS_VEHICLE | household | character | HHVEHCNT == 0 | 2 | No |

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Creating New Variables: Example 2 (Grouping)

Using the Derived Variables file, create a variable with the following requirements:

• Level: Person
• Idea: Person age categorized into four large groups
• Values: 0 to 17 = Child, 18 to 44 = Young Adult, 45 to 65 = Middle Adult, 66 and up = Older Adult
• Name: AGE_GROUP

\

| NAME | TABLE | TYPE | DOMAIN | VALUE | LABEL | |:----------|:-------|:----------|:--------------------------|:------|:-------------| | AGE_GROUP | person | character | R_AGE >= 0 & R_AGE <= 17 | 1 | Child | | AGE_GROUP | person | character | R_AGE >= 18 & R_AGE <= 44 | 2 | Young Adult | | AGE_GROUP | person | character | R_AGE >= 45 & R_AGE <= 65 | 3 | Middle Adult | | AGE_GROUP | person | character | R_AGE >= 66 | 4 | Older Adult |

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Creating New Variables: Example 3 (Uses/Does-not-use)

Using the Derived Variables file, create a variable with the following requirements:

• Level: Person
• Idea: Person reports using a transportation network company (Uber/Lyft) in the last 30 days
• Values: 1=Yes, 2=No
• Name: USES_TNC

\

| NAME | TABLE | TYPE | DOMAIN | VALUE | LABEL | |:----------|:-------|:----------|:---------------|:------|:------| | USES_TNC | person | character | RIDESHARE > 0 | 1 | Yes | | USES_TNC | person | character | RIDESHARE == 0 | 2 | No |

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Creating New Variables: Example 4 (Is/Is-not)

Using the Derived Variables file, create a variable with the following requirements:

• Level: Household
• Idea: Is this household in a Metropolitan Statistical Area?
• Values: 1=Yes, 2=No
• Name: IS_METRO

\

| NAME | TABLE | TYPE | DOMAIN | VALUE | LABEL | |:---------|:----------|:----------|:------------------------------|:------|:------| | IS_METRO | household | character | MSACAT %in% c('01','02','03') | 1 | Yes | | IS_METRO | household | character | MSACAT %in% c('04') | 2 | No |

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Creating New Variables: Summary

• Include new ways of grouping your statistic
• Variables become available on next data read
• This is a preferred organization method, but not necessary
  • Alternative: attach them to the appropriate csv file yourself
  • Alternative: compute and attach to the appropriate table level in R after read_data()

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Next Topic {#transition_slide}

1. Setting Up an R Analysis Environment
2. Accessing the Data
3. Generating Estimates
4. Creating New Variables
5. Visualizing Estimates
6. Producing a Travel Analysis Report

---

Visualizing Estimates

• Demonstrate the 3 core visualization functions
  • make_table - Create report-ready, formatted tables.
  • make_chart - Create interactive bar charts.
  • make_map - Create interactive choropleth maps.
• Quick and easy steps for visuals:
  1. Assign the output of summarize_data to a new object.
  2. Feed that object to one of the visualization functions to output the results.
• Numeric formatting supported for all 3 viz functions.

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Visualizing Estimates: Tables (Introductory)

statistic <- summarize_data(
  data = nhts_data,
  agg = "person_trip_rate",
  by = "WORKER"
)

make_table(statistic)

---

Visualizing Estimates: Tables (Advanced)

• Example of a 3-way table with custom configuration

statistic <- summarize_data(
  data = nhts_data,
  agg = "person_count",
  by = c("TRAVDAY","OCCAT","EDUC"),
  exclude_missing = TRUE
)

make_table(
  tbl = statistic,
  title = "Table 1: Distribution of Persons (%) by Travel Day, Job Category, and Educational Attainment",
  output = c(W = "Weighted Percentage", N = "Sample Size"),
  row_vars = c("EDUC","OCCAT")
)

---

Visualizing Estimates: Charts (Introductory)

statistic <- summarize_data(
  data = nhts_data,
  agg = "person_trip_rate",
  by = "WORKER",
  exclude_missing = TRUE
)

make_chart(statistic)

---

Visualizing Estimates: Charts (Advanced)

Person Trip Rate by Sex, Worker Status, and Travel Day of Week

statistic <- summarize_data(
  data = nhts_data,
  agg = "person_trip_rate",
  by = c("R_SEX","WORKER","TRAVDAY"),
  exclude_missing = TRUE
)

# Specify fill and facet
make_chart(
  tbl = statistic, 
  fill = "WORKER",
  facet = "TRAVDAY",
  palette = "Accent"
)

---

Visualizing Estimates: Maps (Introductory)

statistic <- summarize_data(
  data = nhts_data,
  agg = "person_count",
  by = "CENSUS_D"
)

make_map(statistic)

---

Visualizing Estimates: Maps - Built in Geography Layers

• Census Regions

  • Variable: CENSUS_R
  • Layer: census_region_layer

• Census Divisions

  • Variable: CENSUS_D
  • Layer: census_division_layer

• States

  • Variable: HHSTFIPS
  • Layer: state_layer / state_tile_layer

• CBSA

  • Variable: HH_CBSA
  • Layer: cbsa_layer

---

Visualizing Estimates: Maps (Advanced)

• We can even embed charts in map geographies!

Include a second table grouping by the original geography plus one variable.

statistic1 <- summarize_data(
  data = nhts_data,
  agg = "person_trip_rate",
  by = "HHSTFIPS",
  exclude_missing = TRUE
)

statistic2 <- summarize_data(
  data = nhts_data,
  agg = "person_trip_rate",
  by = c("HHSTFIPS","WORKER"),
  exclude_missing = TRUE
)

map <- make_map(
  tbl = statistic1, 
  tbl2 = statistic2
)

map

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Formatting

• All 3 visualization function support the same value formatting options:

  • digits - Number of decimal places to use
  • percentage - Treat proportions as percentages
    • TRUE or FALSE
  • scientific - Use scientific notation
    • TRUE or FALSE
  • multiplier - A value multiplier
    • Ex: To display a value "In Thousands", use multiplier = 1000

• Formatting example with make_table

statistic <- summarize_data(
  data = nhts_data,
  agg = "trip_count",
  by = "PRMACT",
  exclude_missing = TRUE
)

make_table(
  tbl = statistic,
  title = "Trip Count by Primary Activity (in Millions)",
  output = c(W = "Trip Count (Millions)", E = "SE"),
  digits = 0,
  multiplier = 1000000
)

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Next Topic {#transition_slide}

1. Setting Up an R Analysis Environment
2. Accessing the Data
3. Generating Estimates
4. Creating New Variables
5. Visualizing Estimates
6. Producing a Travel Analysis Report

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Producing a Travel Analysis Report

• Put your analysis and results directly into a shareable document
• Use simple Markdown syntax in RStudio to create word, pdf, and html reports
• Use RStudio and today"s examples to produce an NHTS data based report right now
• Start with a template file to jump off from
  • Template R Markdown file

Westat-Transportation/summarizeNHTS documentation built on May 17, 2020, 8:57 p.m.