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ipumsr_webinar.Rmd
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---
title: "Enhancing reproducibility with the IPUMS API and the ipumsr package"
author: "Derek Burk, Dan Ehrlich, & Kara Fisher"
date: "4/11/2022"
output:
xaringan::moon_reader:
lib_dir: libs
nature:
highlightStyle: github
highlightLines: true
highlightSpans: true
countIncrementalSlides: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = TRUE,
collapse = TRUE,
comment = "#>"
)
hook_source <- knitr::knit_hooks$get('source')
knitr::knit_hooks$set(source = function(x, options) {
x <- gsub(
"^[[:blank:]]?([^*].+?)[[:blank:]]*#<<[[:blank:]]*$",
"*\\1",
x,
perl = TRUE
)
hook_source(x, options)
})
options(width = 50)
```
<style>
.derek-pet {
background-color: #000000;
background-image: url(images/derek2.jpg);
background-size: cover;
width: 120px;
height: 160px;
position: absolute;
left: 125px;
top: 150px;
}
</style>
<style>
.dan-pet1 {
background-color: #d3d3d3;
background-image: url(images/felix.jpg);
background-size: cover;
width: 120px;
height: 160px;
position: absolute;
left: 305px;
top: 225px;
}
</style>
<style>
.dan-pet2 {
background-color: #000000;
background-image: url(images/luna.jpg);
background-size: cover;
width: 120px;
height: 120px;
position: absolute;
left: 450px;
top: 250px;
}
</style>
<style>
.kara-pet1 {
background-color: #000000;
background-image: url(images/Ru.jpg);
background-size: cover;
width: 120px;
height: 160px;
position: absolute;
left: 600px;
bottom: 200px;
}
</style>
<style>
.kara-pet2 {
background-color: #000000;
background-image: url(images/harvey.jpg);
background-size: cover;
width: 120px;
height: 160px;
position: absolute;
left: 730px;
bottom: 200px;
}
</style>
<style>
.grid-logo {
background-image: url(images/grid.png);
background-size: cover;
width: 400px;
height: 400px;
position: absolute;
right: 75px;
}
</style>
<style>
.isrdi-logo {
background-image: url(images/isrdi2.jpg);
background-size: cover;
height: 186px;
width: 604px;
position: absolute;
bottom: 15px;
left: 15;
}
</style>
<style>
.names {
font-size: 1.5em;
}
</style>
<style>
.greyed-out {
color: #D3D3D3;
text-indent: 20px;
}
</style>
<style>
.strong {
color: #000000;
text-indent: 20px;
}
</style>
# Who we are
.isrdi-logo[
]
--
.left[
Derek Burk, PhD
Sociology
]
--
.center[
Dan Ehrlich, MA
Anthropology
]
--
.right[
Kara Fisher,
MPH
Public Health
]
???
(Name, pronouns, academic field, time with IPUMS)
So, my name is Derek Burk, my pronouns are he/him, I've been working on the
IPUMS International team for the past four years, and my academic training is in
sociology.
---
# Who we are
.isrdi-logo[]
.derek-pet[
]
.dan-pet1[]
.dan-pet2[]
.kara-pet1[]
.kara-pet2[]
???
Rather than our faces, we thought everyone would appreciate pets.
**A FEW NOTES**
This presentation will be recorded and will be available along with slides so don't worry about trying to take notes.
You can always reach out to through our **github**, or via **user support**.
---
# Overview
1. What is IPUMS?
--
2. What is ipumsr, and why use it?
--
3. How to create a data extract
--
4. Reading data into R
--
5. Exploring and manipulating metadata
--
6. Intro to the IPUMS USA API
--
7. IPUMS API use cases
--
8. Q & A
---
# Overview
.greyed-out[
.strong[`1.` What is IPUMS?]
`2.` What is ipumsr, and why use it?
`3.` How to create a data extract
`4.` Reading data into R
`5.` Exploring and manipulating metadata
`6.` Intro to the IPUMS USA API
`7.` IPUMS API use cases
`8.` Q & A
]
---
class: center
# What is IPUMS?
--
## IPUMS is **data**
## from censuses and surveys around the world,
## **harmonized** across space and time,
## thoroughly documented,
## and available for free at ipums.org
???
ipums has grown substantially since its first beta release in 1993,
started with **US census data** has grown to include 9 collections
---
# Harmonization
```{r echo=FALSE, fig.alt="Table illustrating IPUMS harmonization process.", fig.height=3, fig.width=5}
knitr::include_graphics("images/harmonization_4.png")
```
---
#
- U.S. Census and American Community Survey **microdata** from 1850 to the present.
- 180,755,919 unique person records from decennial census and American Community Survey.
- 191,983,898 historical person records from full count decennial census from 1850-1940 (1890 census lost due to fire).
- https://usa.ipums.org/usa/
???
---
#
- Current Population Survey **microdata** from 1962 to the present.
- Monthly labor force surveys and supplements.
- https://cps.ipums.org/cps/
---
#
- Health **survey** data from the National Health Interview Survey (NHIS) from the 1960s to the present and the Medical Expenditure Panel Survey (MEPS) from 1996.
- Supplements on cost of healthcare.
- https://healthsurveys.ipums.org/
???
---
#
- Scientists and Engineers Statistical Data System (SESTAT), the leading surveys for studying the science and engineering (STEM) workforce in the United States
- Data from the National Surveys of College Graduates (NSCG), Recent College Graduates (NSRCG) and Doctorate Recipients (SDR) are integrated from 1993 to the present.
- https://highered.ipums.org/highered/
---
#
- Census **microdata** covering 103 countries from 1960 to the present
- International historic **microdata** from the 19th and early 20th centuries available for some samples.
- Labor Force surveys provide high resolution **microdata** about work conditions
- Administered quarterly (usually) with records going back at least 10 years (usually)
- Currently available for Italy (2011-2020), Spain (2005-2020), and Mexico (2005-2020)
- https://international.ipums.org/international/
---
#
.pull-left[
- “Climate-Induced migration and unemployment in middle-income Africa”
- Valerie Mueller, Clark Gray, and Douglas Hopping
]
.pull-right[
]
---
#
- Demographic and Health Surveys (DHS) provide integrated **microdata** for analysis across time and space.
- From the 1980s to the present.
- Covering Africa and South Asia
- Performance Monitoring for Action (PMA) surveys
- Focus on fertility, contraception, hygiene, and health
- Administered frequently to monitor trends in select high-fertility countries.
- https://globalhealth.ipums.org/
---
#
.pull-left[
]
.pull-right[
- https://tech.popdata.org/pma-data-hub/
]
???
Check out blog using IPUMS Global Health Data
---
.pull-left[
]
.pull-right[
]
.pull-left[
- **NHGIS** Shapefiles for all levels of US geography including tracts, from 1790 to the present
- https://www.nhgis.org/
]
.pull-right[
- **IHGIS** Shapefiles for admin level 2
- https://ihgis.ipums.org/
]
- Summary tables and time series of population, housing, agriculture, and economic data
---
- “Migrant Selection and Sorting during the Great American Drought.”
- Christopher Sichko
.pull-left[
]
.pull-right[
]
---
#
- Historical and contemporary time use data from 1965 to the present.
- Extensive time diary data from respondents in the US and *7* other countries.
- https://timeuse.ipums.org/
---
#
.pull-left[
- Nathan Yau of Flowing Data
- http://flowingdata.com/projects/2015/timeuse-simulation/
]
.pull-right[
]
---
# So what is IPUMS?
.grid-logo[
]
.pull-left[
- IPUMS is **a lot** of data
- Individual-level microdata
- Summarized tabular data
- GIS shapefiles
- Consistent and extensively documented **metadata**
]
???
So ipums really is **data** and a whole lot of it. These 9 different projects interact with different types of data and at different scales but they are united in the use of metadata that helps contextualize the data
*So I know you're asking yourselves..*
---
# So what is IPUMS?
.grid-logo[
]
.pull-left[
- IPUMS is **a lot** of data
- Individual-level microdata
- Summarized tabular data
- GIS shapefiles
- Consistent and extensively documented **metadata**
- *How can I work with all this IPUMS data?*
]
???
---
# Overview
.greyed-out[
`1.` What is IPUMS?
.strong[`2.` What is ipumsr, and why use it?]
`3.` How to create a data extract
`4.` Reading data into R
`5.` Exploring and manipulating metadata
`6.` Intro to the IPUMS USA API
`7.` IPUMS API use cases
`8.` Q & A
]
---
# What is ipumsr?
<style>
.ipumsr-logo {
background-image: url(images/ipumsr.png);
background-size: cover;
height: 298px;
width: 257px;
position: absolute;
right: 50px;
bottom: 50px;
}
</style>
.ipumsr-logo[
]
- R package developed by Greg Freedman Ellis
- Released in 2017
- Over 100,000 CRAN downloads
- Includes functions for
- Reading IPUMS data
- Exploring and manipulating IPUMS metadata
- **SOON**: Interacting with the IPUMS API
???
(Metadata such as value labels, variable labels, and detailed variable
descriptions.)
Initial API support will be for IPUMS USA, with more projects to follow soon.
---
# Why use ipumsr?
- One package for IPUMS microdata, aggregate data, and geography
- Specialized functions for viewing and manipulating IPUMS metadata
- Bundled how-to guides (vignettes)
- Potential to add more features (e.g. API support); let us know what you want!
- File an issue at https://github.com/ipums/ipumsr/issues
- Email [email protected]
???
Regarding "One package": Without ipumsr, you'd need to use a variety of
different approaches from different packages to read in and explore IPUMS
**microdata** from IPUMS: USA, CPS, and International, IPUMS
**aggregate data** (from NHGIS or IHGIS),
and **IPUMS shapefiles**. ipumsr provides one
package with a consistent interface for working with all these different types
of IPUMS data.
*a one stop shop that makes it easy to work with ipums data*
Regarding "More features": The aforementioned IPUMS API support will be the next
big feature. Another potential new feature is adding tools for properly handling
survey weights. Let us know what would be helpful to you via GitHub or email.
---
class: center, middle
# To run the code in this presentation
???
Repeat: slides/recording will be available. To run this code yourself you can clone/download the repo from our github. This provides the data extracts you'll need.
However, you may need to install some additional packages used in the example code below, as shown on the next slide.
---
.pull-left[
# Install R packages (as needed)
```{r, eval = FALSE}
install.packages("ipumsr")
## Tidyverse
install.packages("dplyr")
install.packages("ggplot2")
install.packages("stringr")
install.packages("purrr")
## HTML tables
install.packages("DT")
## GitHub helper functions
install.packages("usethis")
```
]
.pull-right[
# Load R packages (each time)
```{r, eval = FALSE}
library(ipumsr)
## Tidyverse
library(dplyr)
library(ggplot2)
library(stringr)
library(purrr)
## HTML tables
library(DT)
## GitHub helper functions
library(usethis)
```
]
```{r, echo = FALSE, include=FALSE}
library(ipumsr)
## Tidyverse
library(dplyr)
library(ggplot2)
library(stringr)
library(purrr)
## HTML tables
library(DT)
## GitHub helper functions
library(usethis)
```
???
reminder, installing packages only needs to happen once, but some packages do update frequently so it can be a good idea to re-install once in a while
---
# Overview
.greyed-out[
`1.` What is IPUMS?
`2.` What is ipumsr, and why use it?
.strong[`3.` How to create a data extract]
`4.` Reading data into R
`5.` Exploring and manipulating metadata
`6.` Intro to the IPUMS USA API
`7.` IPUMS API use cases
`8.` Q & A
]
???
Dan passes to Derek. Derek switches to a web demo of extract system.
---
# Overview
.greyed-out[
`1.` What is IPUMS?
`2.` What is ipumsr, and why use it?
`3.` How to create a data extract
.strong[`4.` Reading data into R]
`5.` Exploring and manipulating metadata
`6.` Intro to the IPUMS USA API
`7.` IPUMS API use cases
`8.` Q & A
]
???
Website demo ends, back to slides. Derek passes to Kara.
---
# Downloading your data extract
```{r echo=FALSE, fig.alt="Screenshot of IPUMS data download page with overlaid instructions: 1. Click the 'Download .DAT' link to download the data. 2. Right click the 'DDI' link. 3. In the right-click menu, choose 'Save link as' in Firefox or Chrome, or 'Download Linked File' in Safari."}
knitr::include_graphics("images/microdata_annotated_screenshot.png")
```
--
- You must download both the data and DDI codebook
- Save both files in the same folder
???
Kind of confusing how to save the DDI/.xml file. THIS IS HOW.
DDI is EXTREMELY important, as it contains all the instructions regarding the METADATA
Once your extract is complete, download the data file and the DDI. Downloading
the DDI is a little bit different depending on your browser. On most browsers
you should right-click the file and select “Save As…”. If this saves a file with
a .xml file extension, then you should be ready. However, Safari users must
select “Download Linked File” instead of “Download Linked File As”. On Safari,
selecting the wrong version of these two will download a file with a .html file
extension instead of a .xml extension.
In case anyone was curious, DDI stands for "Data Documentation Initiative" --
the DDI project sets standards for documenting datasets, and the codebooks for
most IPUMS projects follow this standard.
Make sure to save the data and DDI files in the same location.
---
# Downloading your data extract
- Optional: "R" link contains code to read in your data with ipumsr
```{r echo=FALSE, fig.alt="Screenshot of IPUMS data download page with the 'R' link highlighted"}
knitr::include_graphics("images/download_screenshot_2.png")
```
???
The links under "Command Files" contain program-specific code for reading in the
data. The R one contains the code we'll show on the next slide.
This helper code checks that you have ipumsr installed, and if you do, it reads
in the DDI codebook and data into separate objects.
---
# Read in the data
```{r include=FALSE}
if (!file.exists("prcs_migration_extract.xml")) {
# Load extract definition from JSON
prcs_migration_extract <- define_extract_from_json(
"prcs_migration_extract.json",
"usa"
)
# Submit, wait for, and download extract
ddi_filename <- submit_extract(prcs_migration_extract) %>%
wait_for_extract() %>%
download_extract() %>%
basename()
# Infer data file name from DDI file name
data_filename <- str_replace(ddi_filename, "\\.xml$", ".dat.gz")
# Standardize DDI and data file names
file.rename(ddi_filename, "prcs_migration_extract.xml")
file.rename(data_filename, "prcs_migration_extract.dat.gz")
}
ddi <- read_ipums_ddi("prcs_migration_extract.xml")
data <- read_ipums_micro(ddi, data_file = "prcs_migration_extract.dat.gz")
```
- Using functions `read_ipums_ddi()` and `read_ipums_micro()`
```{r eval=FALSE}
ddi <- read_ipums_ddi("usa_00013.xml")
data <- read_ipums_micro(ddi)
```
- Note: supply the codebook, *not* the data file, to `read_ipums_micro()`
???
So you've downloaded both the data and the DDI codebook, and saved them in the
same folder. Here's how you actually read the data into R.
The first option, and the one I'd recommend, is to read the DDI codebook into an
object named "ddi" using the `read_ipums_ddi()` function, and then supply that
ddi object to `read_ipums_micro()`.