This package exists to allow easier access to the data provided by Austin L. Wright Et Al. in the working paper Tracking Mask Mandates during the COVID-19 Pandemic.
I had the wonderful opportunity to spend the summer in the University of Chicago DPSS program and worked on the capstone/lab that helped collect and validate the data. I wanted to make the data which was available easier to access for R users. Using this package users can easily make visualizations showing where and when mandates were adopted.
This package is currently only available via github.
You can install it from Github using the following commands
#install using devtools
devtools::install_github("delabj/USMaskMandates)
#install using remotes
remotes::install_github("delabj/USMaskMandates)Data were collected and refined by students and staff at the University of Chicago, led by Austin L. Wright. In particular the data was collected and validated by two labs of students, the IPAL Lab, and the DPSS Lab. As the data is ever changing, corrections and revisions can be recommended to the original data authors via a form.
Two data sets are provided. One is called mask_mandates and the other
raw_mandates.
This data set comes directly from the raw data provided by the paper
authors. See ?raw_mandates for specific details.
The following is a sampling of the data from this source.
raw_mandates[sample(nrow(raw_mandates), 10),] ## # A tibble: 10 x 19
## state_fips state_name county_fips county_name county_mask_pol~
## <int> <chr> <int> <chr> <chr>
## 1 37 NORTH CAR~ 37131 Northampto~ 26Jun2020
## 2 13 GEORGIA 13019 Berrien Co~ <NA>
## 3 17 ILLINOIS 17087 Johnson Co~ <NA>
## 4 55 WISCONSIN 55069 Lincoln Co~ <NA>
## 5 6 CALIFORNIA 6005 Amador Cou~ <NA>
## 6 12 FLORIDA 12041 Gilchrist ~ <NA>
## 7 19 IOWA 19191 Winneshiek~ <NA>
## 8 36 NEW YORK 36001 Albany Cou~ 17Apr2020
## 9 21 KENTUCKY 21229 Washington~ 10Jul2020
## 10 54 WEST VIRG~ 54101 Webster Co~ 07Jul2020
## # ... with 14 more variables: county_mask_policy_end <chr>,
## # county_conditions <chr>, county_source <chr>, county_escalation <chr>,
## # county_defiance <chr>, county_edate <int>, state_mask_policy_start <chr>,
## # state_mask_policy_end <chr>, state_conditions <chr>, state_source <chr>,
## # state_edate <int>, earliest_policy_edate <int>, county_fips_str <int>,
## # date_format <chr>
I’ve taken the liberty of cleaning the data, by adding appropriate
padding to FIPS codes, standardizing data formats and dropping duplicate
columns. More details can be found by using ?mask_mandates in the R
console.
The following is a sample of random rows from the cleaned data
mask_mandates[sample(nrow(mask_mandates), 10),] ## # A tibble: 10 x 15
## state_fips state_name county_fips country_name county_policy_s~
## <chr> <chr> <chr> <chr> <date>
## 1 54 West Virg~ 54031 Hardy County 2020-07-20
## 2 13 Georgia 13011 Banks County NA
## 3 34 New Jersey 34021 Mercer Coun~ 2020-08-20
## 4 45 South Car~ 45005 Allendale C~ NA
## 5 36 New York 36009 Cattaraugus~ NA
## 6 <NA> <NA> <NA> 22098 NA
## 7 48 Texas 48137 Edwards Cou~ NA
## 8 51 Virginia 51103 Lancaster C~ NA
## 9 51 Virginia 51145 Powhatan Co~ NA
## 10 48 Texas 48295 Lipscomb Co~ NA
## # ... with 10 more variables: county_policy_end <date>,
## # county_policy_conditions <chr>, county_policy_source <chr>,
## # county_policy_defiance <chr>, county_policy_escalation <chr>,
## # state_policy_start <date>, state_policy_end <date>,
## # state_policy_conditions <chr>, state_policy_source <chr>,
## # earliest_policy_date <date>
mask_mandates %>%
mutate(defy_status = if_else(is.na(county_policy_defiance), "Comply", "Defy" )) %>%
group_by(state_name, defy_status) %>%
count() %>%
pivot_wider(names_from = defy_status, values_from = n ) %>%
transmute(Comply = replace_na(Comply, 0),
Defy = replace_na(Defy, 0),
percent_compliant = Comply/(Comply+Defy),
state = state_name) %>%
na.omit() %>%
arrange(percent_compliant) %>%
head( 10) %>%
ggplot(aes(y= forcats::fct_reorder(state, percent_compliant),
x=percent_compliant,
fill = forcats::fct_reorder(state, percent_compliant)))+
geom_col()+
labs(title = "10 Least Compliant States",
y=NULL)+
theme_minimal()+
delabj::scale_fill_delabj()+
delabj::legend_none()+
theme(plot.title.position = "plot")
county_shp is a local shapefile I have, that I’m unsure of distribution rights.
valid_states <- unique(mask_mandates$state_fips)
# join to shape file
plotting_data <- county_shp %>%
left_join(mask_mandates %>%
mutate(STATEFP = state_fips,
COUNTYFP = stringr::str_sub(county_fips, -3, -1))) %>%
# Filter out non lower 48 states
filter(!c(state_fips %in% c("02", "15" ,"11", "60", "66", "69", "72", "78")),
STATEFP %in% valid_states,
!is.na(state_fips)) %>% arrange(state_name)## Joining, by = c("STATEFP", "COUNTYFP")
ggplot(plotting_data)+
geom_sf(aes(fill = earliest_policy_date),
size = 0.1)+
labs(
title = "Mask Mandate Starting Dates",
subtitle = "as of August 4th 2020",
fill = "Mandate Start Date"
)+
theme_minimal()+
theme(plot.title = element_markdown(),
axis.text = element_blank(),
plot.title.position = "plot")+
delabj::gridlines_off()+
scale_fill_date(low = "#8856a7",
high = "#e0ecf4",
na.value = "#fc8d59")+
theme(legend.position = c(.15,.15),
legend.direction = "horizontal")+
guides(fill = guide_colorbar(title.position = "top",
title.hjust = 0,
barwidth = 10,
frame.colour = "black")
)
In order to make this data more accessible, Austin L. Wright. Published this data With their COVID-19 research. They ask that anyone using this data cite the working paper and acknowledge the source of the data. I have provided a link and citation below for the working paper.
Data Release and Working Paper:
- Wright, Austin L. and Chawla, Geet and Chen, Luke and Farmer, Anthony, Tracking Mask Mandates During the Covid-19 Pandemic (August 4, 2020). University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2020-104, Available at SSRN: https://ssrn.com/abstract=3667149
Related Paper on Mask Use and Partisanship:
- Milosh, Maria and Painter, Marcus and Van Dijcke, David and Wright, Austin L., Unmasking Partisanship: How Polarization Influences Public Responses to Collective Risk (July 31, 2020). University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2020-102, Available at SSRN: https://ssrn.com/abstract=3664779 or http://dx.doi.org/10.2139/ssrn.3664779