README.md

wonderapi

Joyce Robbins May 23, 2018

library(tidyverse)
library(wonderapi)

Overview

This package makes it easier to use the CDC Wonder API. It does so by employing hidden default query lists and lookup tables, allowing users to focus only on the variables they’re interested in obtaining, and to write queries using human readable names rather than numeric codes.

getData()

Example

mylist <- list(list("And By", "Gender"))
mydata0 <- getData(TRUE, "Detailed Mortality", mylist)
mydata0 %>% head()
## # A tibble: 6 x 5
##    Year Gender  Deaths Population `Crude Rate`
##   <dbl> <chr>    <dbl>      <dbl>        <dbl>
## 1  1999 Female 1215860  142237295         855.
## 2  1999 Male   1175183  136802873         859 
## 3  2000 Female 1225706  143368343         855.
## 4  2000 Male   1177289  138053563         853.
## 5  2001 Female 1232913  145077463         850.
## 6  2001 Male   1183090  139891492         846.

show_databases() displays available databases by name and code:

wonderapi::show_databases()
## # A tibble: 5 x 2
##   label                    name 
##   <chr>                    <chr>
## 1 Natality for 1995 - 2002 D10  
## 2 Natality for 2003 - 2006 D27  
## 3 Natality for 2007 - 2016 D66  
## 4 Detailed Mortality       D76  
## 5 Heat Wave Days           D104

More databases will be added in the future.

The best way to become familiar with CDC Wonder API options is to use the web interface: https://wonder.cdc.gov, as the options available through the API are nearly identical. The greatest difference is that location variables are not available through the API.

Installation

This package is not on CRAN. It can be installed from Github with the devtools package:

devtools::install_github("socdataR/wonderapi", build_vignettes = TRUE)

(The vignettes are an important component of the package as the codebooks are stored as vignettes, so be sure to include build_vignettes = TRUE.)

Getting started

Queries are composed of parameter name-value pairs. Setting up a query without assistance is complex because the query must be submitted as an .xml file with a long list of required parameters, such as here (Example 1) and here (Example 2). The point of the package is to prevent your having to create requests in this form. It relies on the wondr package which creates converts R lists to xml and makes the query. The value of this package is in the “pre” and “post” stages of the query, that is, the processes of setting up the query and tidying the results.

Codebooks

Codebooks are provided as package vignettes to allow the user to conveniently look up the names and values of available parameters in each dataset. They may be accessed quickly by typing:

> ??codebook

in the console, or searching for “codebook” in the Help window. The codebooks are an important contribution of the package and are not provided by the CDC. They are generated automatically by this script, which scrapes the CDC Wonder web interface form, and displays parameter names and values in human readable form. The benefit of this method is the ability to quickly produce and update codebook vignettes that closely follow the web interface, with parameters appearing in the same order. It also means, however, that the codebooks contain more information than the typical user needs to submit a query. Most users will only need Group By variables (codes beginning with “B_”), Measures (codes beginning with “M_”), and Limiting Variables (codes beginning with “V_”).

Although some of the parameter names are long and/or awkward, for the sake of consistency, we follow the CDC names exactly. The only exception is that any content that appears in parentheses should be dropped. For example, “Fertility Rate” can be substituted for “M_5”, but "Fertility Rate (Census Region, Census Division, HHS Region, State, County, Year, Age of Mother, Race) cannot.

Default query lists and requests

To facilitate the process of designing a query list, this package relies on default query lists. Each default query is set to request a single Group By Results parameter, generally set to "Year". It is set to request the Measures that are listed as default Measures on the web interface (i.e. Births for the Births dataset; Deaths, Population and Crude Rate for the Detailed Mortality dataset.) To see the default settings, perform a query request without specifying a querylist:

natdata <- getData(TRUE, "Natality for 2007 - 2016")
natdata %>% head()
## # A tibble: 6 x 2
##    Year  Births
##   <dbl>   <dbl>
## 1  2007 4316233
## 2  2008 4247694
## 3  2009 4130665
## 4  2010 3999386
## 5  2011 3953590
## 6  2012 3952841
dmdata <- getData(TRUE, "Detailed Mortality")
dmdata %>% head()
## # A tibble: 6 x 4
##    Year  Deaths Population `Crude Rate`
##   <dbl>   <dbl>      <dbl>        <dbl>
## 1  1999 2391043  279040168         857.
## 2  2000 2402995  281421906         854.
## 3  2001 2416003  284968955         848.
## 4  2002 2443030  287625193         849.
## 5  2003 2447946  290107933         844.
## 6  2004 2397269  292805298         819.

The default lists were prepared based on CDC examples, but we make no claim that they are error free. If you have any suggestions for improving them, please make a pull request on Github or send an email to Joyce Robbins. The default lists are available here.

Creating customized queries

There are different types of parameters. Most critical are Group Results By and Measures. The Group Results By parameters serve as keys for grouping the data; the maximum number of Group Results By parameters is five. Limiting Variables may also be used to constrain results behind the scenes.

To make changes to the default list, first create a list of lists, wherein each nested list is a name-value pair. For example, the following changes the first (and currently only) “Group Results By” variable to Weekday:

mylist <- list(list("Group Results By", "Weekday"))
mydata <- getData(TRUE, "Detailed Mortality", mylist)
mydata %>% head()
## # A tibble: 6 x 4
##   Weekday    Deaths Population     `Crude Rate`  
##   <chr>       <dbl> <chr>          <chr>         
## 1 Sunday    6364405 Not Applicable Not Applicable
## 2 Monday    6418650 Not Applicable Not Applicable
## 3 Tuesday   6373491 Not Applicable Not Applicable
## 4 Wednesday 6378520 Not Applicable Not Applicable
## 5 Thursday  6386992 Not Applicable Not Applicable
## 6 Friday    6485908 Not Applicable Not Applicable

As the set up is slightly different depending on the parameter type, more details on setting up the name-value pairs by parameter types are provided below.

Group By variables

Each dataset allows for fixed number (5 or fewer) Group By variables, codes for which are "B_1", "B_2", "B_3", etc. "Group By Results" may be substituted for "B_1" and "And By" for "B_2". "And By" may not, however, be substituted for "B_3" on to avoid ambiguity (this may change in the future.) Values – in this case, the Group By variables – may be specified by code or human readable name. The following, thus, are equivalent:

## not run
mylist <- list(list("B_1", "D66.V2"))
mylist <- list(list("Group Results By", "Race"))
mylist <- list(list("B_1", "Race"))
mylist <- list(list("Group Results By", "D66.V2"))

See the appropriate codebook for all Group By options.

Measures

Measures do not need values; it is sufficient to specify a name only:

mylist <- list(list("Group Results By", "Marital Status"),
               list("And By", "Year"),
               list("Average Age of Mother", ""))
mydata2 <- getData(TRUE, "Natality for 2007 - 2016", mylist)
mydata2 %>% head()
## # A tibble: 6 x 4
##   `Marital Status`  Year  Births `Average Age of Mother`
##   <chr>            <dbl>   <dbl>                   <dbl>
## 1 Married           2007 2601186                    29.5
## 2 Married           2008 2521128                    29.6
## 3 Married           2009 2437007                    29.7
## 4 Married           2010 2365915                    29.8
## 5 Married           2011 2345817                    29.9
## 6 Married           2012 2343222                    30.0

Limiting variables

Queries can be constrained with parameters that limit results in the background. For example, if you’re only interested in February births, you may choose to limit results to February as follows, rather than grouping by Month:

mylist <- list(list("Month", "2"))
getData(TRUE, "D66", mylist)
## # A tibble: 10 x 2
##     Year Births
##    <dbl>  <dbl>
##  1  2007 326891
##  2  2008 338521
##  3  2009 316641
##  4  2010 301994
##  5  2011 297961
##  6  2012 304505
##  7  2013 291748
##  8  2014 298404
##  9  2015 298058
## 10  2016 306015

Note that values for Limiting Variables must be entered as codes; in this case “2” rather than “February.” We hope to add capability for human readable values in the future.

Plotting query results

By returning a tidy data frame, the query results are ready to be plotted without any additional data manipulation:

ggplot(mydata2, aes(x = Year, y = Births, color = `Marital Status`)) +
    geom_line() + ggtitle("Births by Marital Status")

ggplot(mydata2, aes(x = Year, y = `Average Age of Mother`,
                   color = `Marital Status`)) + geom_line() +
    geom_point() + ylab("age (in years)") + 
    ggtitle("Average Age of Mother")

mydata2 <- mydata2 %>% 
    select(-`Average Age of Mother`) %>% 
    spread(key = `Marital Status`, value = `Births`) %>% 
    mutate(Total = Married + Unmarried)
ggplot(mydata2, aes(x = Year, y = Unmarried / Total)) + geom_line() +
    geom_point() + ggtitle("Births to Unmarried Mothers") +
    ylab("Percent of Total Births")

Combining results from multiple datasets

Some of the datasets, such as the Births, are divided into multiple databases by time period. wonderapi makes it easy to combine the data into one data frame. (Care needs to be taken as the variables are not identical in all. For example, the 1995 - 2002 dataset does not have any measure options; it only returns number of births. To find out what’s available, see the codebooks (>??codebook) and crosscheck with the CDC Wonder API web interface.)

births <- rbind(getData(TRUE, "Natality for 1995 - 2002"),
                getData(TRUE, "Natality for 2003 - 2006"),
                getData(TRUE, "Natality for 2007 - 2016"))
ggplot(births, aes(Year, Births)) + geom_line() + 
    ggtitle("U.S. Births by Year, 1995 - 2016")

Errors

The main source of errors is improper query requests. The wonderapi has limited ability to catch problems before the query request is made. It checks the list of parameter names and will reject the name-value pair if the name, either in code or human readable form, is not recognized. (Checking for value problems will be added in the future.) Here is an example of an unrecognized parameter name:

mydata3 <- getData(TRUE, "Detailed Mortality", 
        list(list("Suspect", "Mrs. Peacock")))
## Ignoring: "Suspect",...(not recognized)
mydata3 %>% head()
## # A tibble: 6 x 4
##    Year  Deaths Population `Crude Rate`
##   <dbl>   <dbl>      <dbl>        <dbl>
## 1  1999 2391043  279040168         857.
## 2  2000 2402995  281421906         854.
## 3  2001 2416003  284968955         848.
## 4  2002 2443030  287625193         849.
## 5  2003 2447946  290107933         844.
## 6  2004 2397269  292805298         819.

Errors due to the constraints of the CDC Wonder API are more difficult to catch since there are many quirky rules and it is currently beyond the scope of this package to check for them. The following, for example, appears to be a reasonable request, but results in an error:

mylist <- list(list("And By", "Education"), 
               list("Birth Rate", ""))
mydata4 <- getData(TRUE, "Natality for 2007 - 2016", mylist)
## Error in wondr::make_query(querylist, dbcode): Internal Server Error (HTTP 500).

In this case, the best approach is to visit the CDC Wonder API web interface and try the same query. If all goes well, you will receive more detailed information on what went wrong:

We learn that we can’t include “Education” if we request the “Birth Rate” measure. If we try again with “Bridged Race” instead of “Education”, it works:

mylist <- list(list("And By", "Bridged Race"), 
               list("Birth Rate", ""))
mydata5 <- getData(TRUE, "Natality for 2007 - 2016", mylist)
mydata5 %>% head()
## # A tibble: 6 x 5
##    Year `Bridged Race`               Births `Total Populatio… `Birth Rate`
##   <dbl> <chr>                         <dbl>             <dbl>        <dbl>
## 1  2007 American Indian or Alaska …   49443           3829898         12.9
## 2  2007 Asian or Pacific Islander    254488          15559373         16.4
## 3  2007 Black or African American    675676          40451108         16.7
## 4  2007 White                       3336626         241390828         13.8
## 5  2008 American Indian or Alaska …   49537           3983929         12.4
## 6  2008 Asian or Pacific Islander    253185          16094699         15.7


socdataR/wonderapi documentation built on May 24, 2018, 4:50 a.m.