README.md
In John-Brandon/CornHuskerCrime:
Cornhusker Crime Data and the Legalization of Marijuana in The Centenial State
John R. Brandon, PhD
2017-03-21
It seems that the storm of U.S. political news has been unavoidable recently. The stakes are high under the new Republican Administration. It only took one executive order before the Federal District Courts began to restrain the administration from constitutional overreach.
This document was motivated by comments Attorney General Jeff Sessions (AL) made recently regarding marijuana. Context is provided on Nebraska's role as an opponent of legalization in the courts, and background is provided regarding Session's recent comments proposing a link between marijuana and violent crime.
This letter will examine crime data from Nebraska with an eye on the marijuana legalization timeline of bordering state Colorado. Of particular interest is the question, "Are there trends in Nebraska crime through time, and if so, do they correspond with the timeline of legalization in Colorado?"
Nebraska and Oklahoma v Colorado: 2014 -- 2016
The legalization of marijuana at the state level could be another consistutional issue put before the courts in the near future. Nebraska and Oklahoma tried to bring a suit before the U.S. Supreme Court against Colorado during 2014 -- 2016. This attempt started the same year Colorado put regulations for recreational pot into effect, ammending the state constitution in the process.
Attorney Generals Doug Peterson (NE) and E. Scott Pruit (OK; now the head of the EPA) argued that Colorado's pot was being smuggled across state lines and claimed this led to an increase in other crimes in the states. The Supreme Court ultimately declined to consider the matter (6 -- 2), with Justices Thomas and Alito dissenting that such matters were under the court's constitutional jurisdiction.
Reefer Madness and the Agents of :tangerine:
After his nomination to U.S. Attorney General, Jeff Sessions has made it clear he wants the Department of Justice to be 'Tough on Crime' under his watch. This is a central theme of the administration. He has also repeatedly mentioned marijuana.
Session's rhetoric indicates his DOJ will be looking to take a harder line against state legalized marijuana than was taken under the previous administration's more hands off approach. Sessions has compared marijuana as "only slightly less awful" than herion.
It was Sessions' recent comments linking marijuana and violent crime, however, that led me to a well researched article on Snopes.com by Alex Kasprak. Kasprak presents interesting background into Session's claim, and cites published scientific research based on the legalization of medical marijuana that is contrary to Session's claims; ultimately concluding that Jeff Session's comments linking marijuana and violent crime are false..
[As far as I can tell, the published science Kasprak cites are all studies focused on medical marijuana. That makes sense because medical marijuana has been legalized in more states than recreational marijuana, and for a longer period of time. The sample size for medical marijuana is larger than it is for recreational marijuana in the U.S.]
BadCornHusker.csv
Introduce publically available crime data, e.g. .dat\BadCornHusker.csv here:
# Packages
library(tidyverse) # For wrangling data
library(lubridate) # Dates and times
library(purrr) # Mapping functions to each element of a vector
# Read Nebraska crime data and glimpse -----------------------------------------
badcorn_wide = read_csv(file = "./dat/BadCornHusker.csv")
# Glimpse data
badcorn_wide
## # A tibble: 31 × 18
## Offense Violent `2000` `2001` `2002` `2003` `2004` `2005`
## <chr> <lgl> <int> <int> <int> <int> <int> <int>
## 1 Unknown NA 4 4 5 3 9 6
## 2 Criminal_Homicide TRUE 49 51 42 56 35 41
## 3 Death_by_Negligence NA 16 22 24 20 15 21
## 4 Forcible_Rape TRUE 158 156 155 157 200 169
## 5 Robbery TRUE 316 282 331 282 317 324
## 6 Aggravated_Assault TRUE 959 895 849 876 1044 1274
## 7 Burglary FALSE 1196 1033 1015 1031 1012 965
## 8 Larceny FALSE 9904 9797 9315 8256 8795 8468
## 9 Motor_Vehicle_Theft FALSE 471 558 530 457 399 370
## 10 Simple_Assault TRUE 10229 9809 9838 9589 9607 9975
## # ... with 21 more rows, and 10 more variables: `2006` <int>,
## # `2007` <int>, `2008` <int>, `2009` <int>, `2010` <int>, `2011` <int>,
## # `2012` <int>, `2013` <int>, `2014` <int>, `2015` <int>
# Define a function to map legal status to year --------------------------------
legality = function(year) {
if (year < 2009) {
"Illegal"
} else if (year <= 2012) {
"Medical"
} else {
"Legal"
}
}
# Wrangle data before plotting -------------------------------------------------
badcorn_long = badcorn_wide %>%
# Gather table from wide to long
gather(key = yr, value = Count, -Offense, -Violent) %>%
# Format yr as POSIXct date
mutate(Year = paste(yr, "12", "31", sep = "-"),
Year = ymd(Year)) %>%
# Map legal status to year
mutate(Status = map_chr(.x = yr, .f = legality),
Legal = ifelse(Status != "Illegal", TRUE, FALSE)) %>%
mutate(Offense = as.factor(Offense))
# Glimpse data
badcorn_long
## # A tibble: 496 × 7
## Offense Violent yr Count Year Status Legal
## <fctr> <lgl> <chr> <int> <date> <chr> <lgl>
## 1 Unknown NA 2000 4 2000-12-31 Illegal FALSE
## 2 Criminal_Homicide TRUE 2000 49 2000-12-31 Illegal FALSE
## 3 Death_by_Negligence NA 2000 16 2000-12-31 Illegal FALSE
## 4 Forcible_Rape TRUE 2000 158 2000-12-31 Illegal FALSE
## 5 Robbery TRUE 2000 316 2000-12-31 Illegal FALSE
## 6 Aggravated_Assault TRUE 2000 959 2000-12-31 Illegal FALSE
## 7 Burglary FALSE 2000 1196 2000-12-31 Illegal FALSE
## 8 Larceny FALSE 2000 9904 2000-12-31 Illegal FALSE
## 9 Motor_Vehicle_Theft FALSE 2000 471 2000-12-31 Illegal FALSE
## 10 Simple_Assault TRUE 2000 10229 2000-12-31 Illegal FALSE
## # ... with 486 more rows
# First look at criminal homicides ---------------------------------------------
badcorn_long %>% filter(Offense == "Criminal_Homicide") %>%
ggplot(aes(x = Year, y = Count, color = Status)) +
theme_classic() +
geom_point() +
stat_smooth(method = lm) +
labs(title = "Criminal Homicides",
subtitle = "Nebraska: 2000 -- 2015")
John-Brandon/CornHuskerCrime documentation built on May 7, 2019, 11:16 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Cornhusker Crime Data and the Legalization of Marijuana in The Centenial State
John R. Brandon, PhD 2017-03-21
It seems that the storm of U.S. political news has been unavoidable recently. The stakes are high under the new Republican Administration. It only took one executive order before the Federal District Courts began to restrain the administration from constitutional overreach.
This document was motivated by comments Attorney General Jeff Sessions (AL) made recently regarding marijuana. Context is provided on Nebraska's role as an opponent of legalization in the courts, and background is provided regarding Session's recent comments proposing a link between marijuana and violent crime.
This letter will examine crime data from Nebraska with an eye on the marijuana legalization timeline of bordering state Colorado. Of particular interest is the question, "Are there trends in Nebraska crime through time, and if so, do they correspond with the timeline of legalization in Colorado?"
Nebraska and Oklahoma v Colorado: 2014 -- 2016
The legalization of marijuana at the state level could be another consistutional issue put before the courts in the near future. Nebraska and Oklahoma tried to bring a suit before the U.S. Supreme Court against Colorado during 2014 -- 2016. This attempt started the same year Colorado put regulations for recreational pot into effect, ammending the state constitution in the process.
Attorney Generals Doug Peterson (NE) and E. Scott Pruit (OK; now the head of the EPA) argued that Colorado's pot was being smuggled across state lines and claimed this led to an increase in other crimes in the states. The Supreme Court ultimately declined to consider the matter (6 -- 2), with Justices Thomas and Alito dissenting that such matters were under the court's constitutional jurisdiction.
Reefer Madness and the Agents of :tangerine:
After his nomination to U.S. Attorney General, Jeff Sessions has made it clear he wants the Department of Justice to be 'Tough on Crime' under his watch. This is a central theme of the administration. He has also repeatedly mentioned marijuana.
Session's rhetoric indicates his DOJ will be looking to take a harder line against state legalized marijuana than was taken under the previous administration's more hands off approach. Sessions has compared marijuana as "only slightly less awful" than herion.
It was Sessions' recent comments linking marijuana and violent crime, however, that led me to a well researched article on Snopes.com by Alex Kasprak. Kasprak presents interesting background into Session's claim, and cites published scientific research based on the legalization of medical marijuana that is contrary to Session's claims; ultimately concluding that Jeff Session's comments linking marijuana and violent crime are false..
[As far as I can tell, the published science Kasprak cites are all studies focused on medical marijuana. That makes sense because medical marijuana has been legalized in more states than recreational marijuana, and for a longer period of time. The sample size for medical marijuana is larger than it is for recreational marijuana in the U.S.]
BadCornHusker.csv
Introduce publically available crime data, e.g. .dat\BadCornHusker.csv here:
# Packages
library(tidyverse) # For wrangling data
library(lubridate) # Dates and times
library(purrr) # Mapping functions to each element of a vector
# Read Nebraska crime data and glimpse -----------------------------------------
badcorn_wide = read_csv(file = "./dat/BadCornHusker.csv")
# Glimpse data
badcorn_wide
## # A tibble: 31 × 18
## Offense Violent `2000` `2001` `2002` `2003` `2004` `2005`
## <chr> <lgl> <int> <int> <int> <int> <int> <int>
## 1 Unknown NA 4 4 5 3 9 6
## 2 Criminal_Homicide TRUE 49 51 42 56 35 41
## 3 Death_by_Negligence NA 16 22 24 20 15 21
## 4 Forcible_Rape TRUE 158 156 155 157 200 169
## 5 Robbery TRUE 316 282 331 282 317 324
## 6 Aggravated_Assault TRUE 959 895 849 876 1044 1274
## 7 Burglary FALSE 1196 1033 1015 1031 1012 965
## 8 Larceny FALSE 9904 9797 9315 8256 8795 8468
## 9 Motor_Vehicle_Theft FALSE 471 558 530 457 399 370
## 10 Simple_Assault TRUE 10229 9809 9838 9589 9607 9975
## # ... with 21 more rows, and 10 more variables: `2006` <int>,
## # `2007` <int>, `2008` <int>, `2009` <int>, `2010` <int>, `2011` <int>,
## # `2012` <int>, `2013` <int>, `2014` <int>, `2015` <int>
# Define a function to map legal status to year --------------------------------
legality = function(year) {
if (year < 2009) {
"Illegal"
} else if (year <= 2012) {
"Medical"
} else {
"Legal"
}
}
# Wrangle data before plotting -------------------------------------------------
badcorn_long = badcorn_wide %>%
# Gather table from wide to long
gather(key = yr, value = Count, -Offense, -Violent) %>%
# Format yr as POSIXct date
mutate(Year = paste(yr, "12", "31", sep = "-"),
Year = ymd(Year)) %>%
# Map legal status to year
mutate(Status = map_chr(.x = yr, .f = legality),
Legal = ifelse(Status != "Illegal", TRUE, FALSE)) %>%
mutate(Offense = as.factor(Offense))
# Glimpse data
badcorn_long
## # A tibble: 496 × 7
## Offense Violent yr Count Year Status Legal
## <fctr> <lgl> <chr> <int> <date> <chr> <lgl>
## 1 Unknown NA 2000 4 2000-12-31 Illegal FALSE
## 2 Criminal_Homicide TRUE 2000 49 2000-12-31 Illegal FALSE
## 3 Death_by_Negligence NA 2000 16 2000-12-31 Illegal FALSE
## 4 Forcible_Rape TRUE 2000 158 2000-12-31 Illegal FALSE
## 5 Robbery TRUE 2000 316 2000-12-31 Illegal FALSE
## 6 Aggravated_Assault TRUE 2000 959 2000-12-31 Illegal FALSE
## 7 Burglary FALSE 2000 1196 2000-12-31 Illegal FALSE
## 8 Larceny FALSE 2000 9904 2000-12-31 Illegal FALSE
## 9 Motor_Vehicle_Theft FALSE 2000 471 2000-12-31 Illegal FALSE
## 10 Simple_Assault TRUE 2000 10229 2000-12-31 Illegal FALSE
## # ... with 486 more rows
# First look at criminal homicides ---------------------------------------------
badcorn_long %>% filter(Offense == "Criminal_Homicide") %>%
ggplot(aes(x = Year, y = Count, color = Status)) +
theme_classic() +
geom_point() +
stat_smooth(method = lm) +
labs(title = "Criminal Homicides",
subtitle = "Nebraska: 2000 -- 2015")
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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