Week 5 Homework Tutorial: Hate Crimes
Rachel Saidi revised by Steve Dutky
10/4/2019
Hate Crimes Dataset
This dataset, provided by Corey, looks at all types of hate crimes in New York counties by the type of hate crime from 2010 to 2016.
library(tidyverse)## ── Attaching packages ────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.2.1 ✔ purrr 0.3.2
## ✔ tibble 2.1.3 ✔ dplyr 0.8.3
## ✔ tidyr 0.8.3 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0## ── Conflicts ───────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()#install.packages("googledrive")
library(googledrive)Get hateCrimes2010.csv from DATASETS on Google Drive
Use the shareable link to hateCrimes2010.csv to download a copy to the working directory.
# drive_download runs but won't knit
#drive_download(as_id("https://drive.google.com/open?id=1iT6SxAm_gI2srBZuwJK-mEWp7kHCaLdt"),overwrite = TRUE)
hatecrimes<-read_csv("hateCrimes2010.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## County = col_character(),
## `Crime Type` = col_character()
## )## See spec(...) for full column specifications.Clean up the Data: make all headers lowercase and remove spaces
After cleaning up the variable names, look at the structure of the data. Since there are 44 variables considered in this dataset, you can use “summary” to decide which hate crimes to focus on. In the output of “summary”, look at the min/max values. Some have a max-vale of 1.
names(hatecrimes) <- tolower(names(hatecrimes))
names(hatecrimes) <- gsub(" ","",names(hatecrimes))
str(hatecrimes)## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 423 obs. of 44 variables:
## $ county : chr "Albany" "Albany" "Allegany" "Bronx" ...
## $ year : num 2016 2016 2016 2016 2016 ...
## $ crimetype : chr "Crimes Against Persons" "Property Crimes" "Property Crimes" "Crimes Against Persons" ...
## $ anti-male : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-female : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-transgender : num 0 0 0 4 0 0 0 0 0 0 ...
## $ anti-genderidentityexpression : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-age* : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-white : num 0 0 0 1 1 0 0 0 0 0 ...
## $ anti-black : num 1 2 1 0 0 1 0 1 0 2 ...
## $ anti-americanindian/alaskannative : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-asian : num 0 0 0 0 0 1 0 0 0 0 ...
## $ anti-nativehawaiian/pacificislander : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-multi-racialgroups : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-otherrace : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-jewish : num 0 0 0 0 1 0 1 0 0 0 ...
## $ anti-catholic : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-protestant : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-islamic(muslim) : num 1 0 0 6 0 0 0 0 1 0 ...
## $ anti-multi-religiousgroups : num 0 1 0 0 0 0 0 0 0 0 ...
## $ anti-atheism/agnosticism : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-religiouspracticegenerally : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-otherreligion : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-buddhist : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-easternorthodox(greek,russian,etc.): num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-hindu : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-jehovahswitness : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-mormon : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-otherchristian : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-sikh : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-hispanic : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-arab : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-otherethnicity/nationalorigin : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-non-hispanic* : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-gaymale : num 1 0 0 8 0 1 0 0 0 0 ...
## $ anti-gayfemale : num 0 0 0 1 0 0 0 0 0 0 ...
## $ anti-gay(maleandfemale) : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-heterosexual : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-bisexual : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-physicaldisability : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anti-mentaldisability : num 0 0 0 0 0 0 0 0 0 0 ...
## $ totalincidents : num 3 3 1 20 2 3 1 1 1 2 ...
## $ totalvictims : num 4 3 1 20 2 3 1 1 1 2 ...
## $ totaloffenders : num 3 3 1 25 2 3 1 1 1 2 ...
## - attr(*, "spec")=
## .. cols(
## .. County = col_character(),
## .. Year = col_double(),
## .. `Crime Type` = col_character(),
## .. `Anti-Male` = col_double(),
## .. `Anti-Female` = col_double(),
## .. `Anti-Transgender` = col_double(),
## .. `Anti-Gender Identity Expression` = col_double(),
## .. `Anti-Age*` = col_double(),
## .. `Anti-White` = col_double(),
## .. `Anti-Black` = col_double(),
## .. `Anti-American Indian/Alaskan Native` = col_double(),
## .. `Anti-Asian` = col_double(),
## .. `Anti-Native Hawaiian/Pacific Islander` = col_double(),
## .. `Anti-Multi-Racial Groups` = col_double(),
## .. `Anti-Other Race` = col_double(),
## .. `Anti-Jewish` = col_double(),
## .. `Anti-Catholic` = col_double(),
## .. `Anti-Protestant` = col_double(),
## .. `Anti-Islamic (Muslim)` = col_double(),
## .. `Anti-Multi-Religious Groups` = col_double(),
## .. `Anti-Atheism/Agnosticism` = col_double(),
## .. `Anti-Religious Practice Generally` = col_double(),
## .. `Anti-Other Religion` = col_double(),
## .. `Anti-Buddhist` = col_double(),
## .. `Anti-Eastern Orthodox (Greek, Russian, etc.)` = col_double(),
## .. `Anti-Hindu` = col_double(),
## .. `Anti-Jehovahs Witness` = col_double(),
## .. `Anti-Mormon` = col_double(),
## .. `Anti-Other Christian` = col_double(),
## .. `Anti-Sikh` = col_double(),
## .. `Anti-Hispanic` = col_double(),
## .. `Anti-Arab` = col_double(),
## .. `Anti-Other Ethnicity/National Origin` = col_double(),
## .. `Anti-Non-Hispanic*` = col_double(),
## .. `Anti-Gay Male` = col_double(),
## .. `Anti-Gay Female` = col_double(),
## .. `Anti-Gay (Male and Female)` = col_double(),
## .. `Anti-Heterosexual` = col_double(),
## .. `Anti-Bisexual` = col_double(),
## .. `Anti-Physical Disability` = col_double(),
## .. `Anti-Mental Disability` = col_double(),
## .. `Total Incidents` = col_double(),
## .. `Total Victims` = col_double(),
## .. `Total Offenders` = col_double()
## .. )summary(hatecrimes)## county year crimetype anti-male
## Length:423 Min. :2010 Length:423 Min. :0.000000
## Class :character 1st Qu.:2011 Class :character 1st Qu.:0.000000
## Mode :character Median :2013 Mode :character Median :0.000000
## Mean :2013 Mean :0.007092
## 3rd Qu.:2015 3rd Qu.:0.000000
## Max. :2016 Max. :1.000000
## anti-female anti-transgender anti-genderidentityexpression
## Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.01655 Mean :0.04728 Mean :0.05674
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :5.00000 Max. :3.00000
## anti-age* anti-white anti-black
## Min. :0.00000 Min. : 0.0000 Min. : 0.000
## 1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.: 0.000
## Median :0.00000 Median : 0.0000 Median : 1.000
## Mean :0.05201 Mean : 0.3357 Mean : 1.761
## 3rd Qu.:0.00000 3rd Qu.: 0.0000 3rd Qu.: 2.000
## Max. :9.00000 Max. :11.0000 Max. :18.000
## anti-americanindian/alaskannative anti-asian
## Min. :0.000000 Min. :0.0000
## 1st Qu.:0.000000 1st Qu.:0.0000
## Median :0.000000 Median :0.0000
## Mean :0.007092 Mean :0.1773
## 3rd Qu.:0.000000 3rd Qu.:0.0000
## Max. :1.000000 Max. :8.0000
## anti-nativehawaiian/pacificislander anti-multi-racialgroups
## Min. :0 Min. :0.00000
## 1st Qu.:0 1st Qu.:0.00000
## Median :0 Median :0.00000
## Mean :0 Mean :0.08511
## 3rd Qu.:0 3rd Qu.:0.00000
## Max. :0 Max. :3.00000
## anti-otherrace anti-jewish anti-catholic anti-protestant
## Min. :0 Min. : 0.000 Min. : 0.0000 Min. :0.00000
## 1st Qu.:0 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.:0.00000
## Median :0 Median : 0.000 Median : 0.0000 Median :0.00000
## Mean :0 Mean : 3.981 Mean : 0.2695 Mean :0.02364
## 3rd Qu.:0 3rd Qu.: 3.000 3rd Qu.: 0.0000 3rd Qu.:0.00000
## Max. :0 Max. :82.000 Max. :12.0000 Max. :1.00000
## anti-islamic(muslim) anti-multi-religiousgroups anti-atheism/agnosticism
## Min. : 0.0000 Min. : 0.00000 Min. :0
## 1st Qu.: 0.0000 1st Qu.: 0.00000 1st Qu.:0
## Median : 0.0000 Median : 0.00000 Median :0
## Mean : 0.4704 Mean : 0.07565 Mean :0
## 3rd Qu.: 0.0000 3rd Qu.: 0.00000 3rd Qu.:0
## Max. :10.0000 Max. :10.00000 Max. :0
## anti-religiouspracticegenerally anti-otherreligion anti-buddhist
## Min. :0.000000 Min. :0.000 Min. :0
## 1st Qu.:0.000000 1st Qu.:0.000 1st Qu.:0
## Median :0.000000 Median :0.000 Median :0
## Mean :0.007092 Mean :0.104 Mean :0
## 3rd Qu.:0.000000 3rd Qu.:0.000 3rd Qu.:0
## Max. :2.000000 Max. :4.000 Max. :0
## anti-easternorthodox(greek,russian,etc.) anti-hindu
## Min. :0.000000 Min. :0.000000
## 1st Qu.:0.000000 1st Qu.:0.000000
## Median :0.000000 Median :0.000000
## Mean :0.002364 Mean :0.002364
## 3rd Qu.:0.000000 3rd Qu.:0.000000
## Max. :1.000000 Max. :1.000000
## anti-jehovahswitness anti-mormon anti-otherchristian anti-sikh
## Min. :0 Min. :0 Min. :0.00000 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0.00000 1st Qu.:0
## Median :0 Median :0 Median :0.00000 Median :0
## Mean :0 Mean :0 Mean :0.01655 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.00000 3rd Qu.:0
## Max. :0 Max. :0 Max. :3.00000 Max. :0
## anti-hispanic anti-arab anti-otherethnicity/nationalorigin
## Min. : 0.0000 Min. :0.00000 Min. : 0.0000
## 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.0000
## Median : 0.0000 Median :0.00000 Median : 0.0000
## Mean : 0.3735 Mean :0.06619 Mean : 0.2837
## 3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 0.0000
## Max. :17.0000 Max. :2.00000 Max. :19.0000
## anti-non-hispanic* anti-gaymale anti-gayfemale
## Min. :0 Min. : 0.000 Min. :0.0000
## 1st Qu.:0 1st Qu.: 0.000 1st Qu.:0.0000
## Median :0 Median : 0.000 Median :0.0000
## Mean :0 Mean : 1.499 Mean :0.2411
## 3rd Qu.:0 3rd Qu.: 1.000 3rd Qu.:0.0000
## Max. :0 Max. :36.000 Max. :8.0000
## anti-gay(maleandfemale) anti-heterosexual anti-bisexual
## Min. :0.0000 Min. :0.000000 Min. :0.000000
## 1st Qu.:0.0000 1st Qu.:0.000000 1st Qu.:0.000000
## Median :0.0000 Median :0.000000 Median :0.000000
## Mean :0.1017 Mean :0.002364 Mean :0.004728
## 3rd Qu.:0.0000 3rd Qu.:0.000000 3rd Qu.:0.000000
## Max. :4.0000 Max. :1.000000 Max. :1.000000
## anti-physicaldisability anti-mentaldisability totalincidents
## Min. :0.00000 Min. :0.000000 Min. : 1.00
## 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.: 1.00
## Median :0.00000 Median :0.000000 Median : 3.00
## Mean :0.01182 Mean :0.009456 Mean : 10.09
## 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.: 10.00
## Max. :1.00000 Max. :1.000000 Max. :101.00
## totalvictims totaloffenders
## Min. : 1.00 Min. : 1.00
## 1st Qu.: 1.00 1st Qu.: 1.00
## Median : 3.00 Median : 3.00
## Mean : 10.48 Mean : 11.77
## 3rd Qu.: 10.00 3rd Qu.: 11.00
## Max. :106.00 Max. :113.00Select only certain hate-crimes
I decided I would only look at the hate-crime types with a max number or 9 or more. That way I can focus on the most prominent types of hate-crimes.
hatecrimes2 <- hatecrimes %>%
select(county, year, `anti-black`, 'anti-white', `anti-jewish`, 'anti-catholic','anti-age*','anti-islamic(muslim)', 'anti-gaymale', 'anti-hispanic', totalincidents, totalvictims, totaloffenders)
head(hatecrimes2)## # A tibble: 6 x 13
## county year `anti-black` `anti-white` `anti-jewish` `anti-catholic`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Albany 2016 1 0 0 0
## 2 Albany 2016 2 0 0 0
## 3 Alleg… 2016 1 0 0 0
## 4 Bronx 2016 0 1 0 0
## 5 Bronx 2016 0 1 1 0
## 6 Broome 2016 1 0 0 0
## # … with 7 more variables: `anti-age*` <dbl>,
## # `anti-islamic(muslim)` <dbl>, `anti-gaymale` <dbl>,
## # `anti-hispanic` <dbl>, totalincidents <dbl>, totalvictims <dbl>,
## # totaloffenders <dbl>Check Summary to make sure no missing values
Also check the dimensions to count how many variables remain
dim(hatecrimes2)## [1] 423 13summary(hatecrimes2)## county year anti-black anti-white
## Length:423 Min. :2010 Min. : 0.000 Min. : 0.0000
## Class :character 1st Qu.:2011 1st Qu.: 0.000 1st Qu.: 0.0000
## Mode :character Median :2013 Median : 1.000 Median : 0.0000
## Mean :2013 Mean : 1.761 Mean : 0.3357
## 3rd Qu.:2015 3rd Qu.: 2.000 3rd Qu.: 0.0000
## Max. :2016 Max. :18.000 Max. :11.0000
## anti-jewish anti-catholic anti-age* anti-islamic(muslim)
## Min. : 0.000 Min. : 0.0000 Min. :0.00000 Min. : 0.0000
## 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.0000
## Median : 0.000 Median : 0.0000 Median :0.00000 Median : 0.0000
## Mean : 3.981 Mean : 0.2695 Mean :0.05201 Mean : 0.4704
## 3rd Qu.: 3.000 3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 0.0000
## Max. :82.000 Max. :12.0000 Max. :9.00000 Max. :10.0000
## anti-gaymale anti-hispanic totalincidents totalvictims
## Min. : 0.000 Min. : 0.0000 Min. : 1.00 Min. : 1.00
## 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 1.00 1st Qu.: 1.00
## Median : 0.000 Median : 0.0000 Median : 3.00 Median : 3.00
## Mean : 1.499 Mean : 0.3735 Mean : 10.09 Mean : 10.48
## 3rd Qu.: 1.000 3rd Qu.: 0.0000 3rd Qu.: 10.00 3rd Qu.: 10.00
## Max. :36.000 Max. :17.0000 Max. :101.00 Max. :106.00
## totaloffenders
## Min. : 1.00
## 1st Qu.: 1.00
## Median : 3.00
## Mean : 11.77
## 3rd Qu.: 11.00
## Max. :113.00Order the crimes in descending order
Order the data, first by total indcidents, then total offenders, then by total victims. It will be interesting to see if counties and years correlate with certain types of crimes.
ordered <- hatecrimes2 %>%
arrange(desc(totalincidents, totaloffenders, totalvictims))
head(ordered)## # A tibble: 6 x 13
## county year `anti-black` `anti-white` `anti-jewish` `anti-catholic`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Kings 2012 4 1 82 6
## 2 Suffo… 2012 18 0 48 7
## 3 Kings 2010 10 3 34 0
## 4 New Y… 2016 6 5 9 0
## 5 Kings 2015 6 3 35 0
## 6 Kings 2016 4 6 26 1
## # … with 7 more variables: `anti-age*` <dbl>,
## # `anti-islamic(muslim)` <dbl>, `anti-gaymale` <dbl>,
## # `anti-hispanic` <dbl>, totalincidents <dbl>, totalvictims <dbl>,
## # totaloffenders <dbl>Use Facet_Wrap
Look at each set of hate-crimes for each type for each year. Use the package “tidyr” to convert the dataset from wide to long with the command “gather”. It will take each column’s hate-crime type combine them all into one column called “id”. Then each cell count will go into the new column, “crimecount”. Finally, we are only doing this for the quantitiative variables, which are in columns 3 - 13. Note the command facet_wrap requires (~) before “id”.
# install.package("reshape2")
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithshatecrimeslong <- ordered %>% tidyr::gather("id", "crimecount", 3:13)
hatecrimesplot <-hatecrimeslong %>%
ggplot(., aes(year, crimecount))+
geom_point()+
aes(color = id)+
facet_wrap(~id)
hatecrimesplot
## Look deeper into crimes against blacks, gay males, and jews From the facet_wrap plot above, anti-black, anti-gay males, and anti-jewish categories seem to have highest rates of offenses reported. Filter out just for those 3 crimes.
hatenew <- hatecrimeslong %>%
filter( id== "anti-black" | id == "anti-jewish" | id == "anti-gaymale")Plot these three types of hate crimes together
Use the following commands to finalize your barplot: - position = “dodge” makes side-by-side bars, rather than stacked bars - stat = “identity” allows you to plot each set of bars for each year between 2010 and 2016 - ggtitle gives the plot a title - labs gives a title to the legend
plot2 <- hatenew %>%
ggplot() +
geom_bar(aes(x=year, y=crimecount, fill = id),
position = "dodge", stat = "identity") +
ggtitle("Hate Crime Type in NY Counties Between 2010-2016") +
ylab("Number of Hate Crime Incidents") +
labs(fill = "Hate Crime Type")
plot2
## What about the counties? I have not dealt with the counties, but I think that is the next place to explore. I can make bar graphs by county instead of by year.
plot3 <- hatenew %>%
ggplot() +
geom_bar(aes(x=county, y=crimecount, fill = id),
position = "dodge", stat = "identity") +
ggtitle("Hate Crime Type in NY Counties Between 2010-2016") +
ylab("Number of Hate Crime Incidents") +
labs(fill = "Hate Crime Type")
plot3
## So many counties There are too many counties for this plot to make sense, but maybe we can just look at the 10 counties with the highest number of incidents. - use “group_by” to group each row by counties - use summarize to get the total sum of incidents by county - use arrange(desc) to arrange those sums of total incidents by counties in descending order - use top_n to list the 5 counties with highest total incidents
counties <- hatenew %>%
group_by(county)%>%
summarize(sum = sum(crimecount)) %>%
arrange(desc(sum)) %>%
top_n(n=5)## Selecting by sumSelecting by sum
counties## # A tibble: 5 x 2
## county sum
## <chr> <dbl>
## 1 Kings 713
## 2 New York 459
## 3 Suffolk 360
## 4 Nassau 298
## 5 Queens 235Finally, create the barplot above, but only for the 5 counties with the highest incidents of hate-crimes. The command “labs” is nice, because you can get a title, subtitle, y-axis label, and legend title, all in one command.
plot4 <- hatenew %>%
filter(county =="Kings" | county =="New York" | county == "Suffolk" | county == "Nassau" | county == "Queens") %>%
ggplot() +
geom_bar(aes(x=county, y=crimecount, fill = id),
position = "dodge", stat = "identity") +
labs(ylab = "Number of Hate Crime Incidents",
title = "5 Counties in NY with Highest Incidents of Hate Crimes",
subtitle = "Between 2010-2016",
fill = "Hate Crime Type")
plot4
# force new title below plotMy take: Two word clouds:
I will produce one word cloud of the categories and frequencies of hate crime victims (2010-2016) and, for the same time interval, another of the NY counties and frequencies where they occur.
The hatecrimes dataset has too many columns for too many categories containing too many cells filled with zeroes to conveniently represent in two dimensions.
We must first mangle hatecrimes into two distinct tibbles
- hateCrimesSummary
containing county, year, crimetype, and totals
- hateCrimes
containing county, year, crimetype, victimCategory, and incidents
hateCrimesSummary <- hatecrimes %>%
select(county, year, crimetype, totalvictims,totaloffenders,totalincidents)Transform wide hatecrimes to tall hateCrimes:
hateCrimes <- hatecrimes %>%
# eliminate the totals already placed in hateCrimeSummary
select(-totalvictims,-totaloffenders,-totalincidents) %>%
# combine victim categories column counts to two new columns
gather("victimCategory","incidents",-county,-year,-crimetype) %>%
# exclude rows in which there are no victims
filter(incidents>0)
# what do the victimCategories look like?
unique(hateCrimes$victimCategory)## [1] "anti-male"
## [2] "anti-female"
## [3] "anti-transgender"
## [4] "anti-genderidentityexpression"
## [5] "anti-age*"
## [6] "anti-white"
## [7] "anti-black"
## [8] "anti-americanindian/alaskannative"
## [9] "anti-asian"
## [10] "anti-multi-racialgroups"
## [11] "anti-jewish"
## [12] "anti-catholic"
## [13] "anti-protestant"
## [14] "anti-islamic(muslim)"
## [15] "anti-multi-religiousgroups"
## [16] "anti-religiouspracticegenerally"
## [17] "anti-otherreligion"
## [18] "anti-easternorthodox(greek,russian,etc.)"
## [19] "anti-hindu"
## [20] "anti-otherchristian"
## [21] "anti-hispanic"
## [22] "anti-arab"
## [23] "anti-otherethnicity/nationalorigin"
## [24] "anti-gaymale"
## [25] "anti-gayfemale"
## [26] "anti-gay(maleandfemale)"
## [27] "anti-heterosexual"
## [28] "anti-bisexual"
## [29] "anti-physicaldisability"
## [30] "anti-mentaldisability"Describe the victim instead of the offense
Shorter category names can more easily fit on legends and titles. I hope we can do this without offending anyone.
Note the use of camelCase in object names and categories. This eliminates the need for embedded spaces and underscores.
“anti-” -> "" // this is now redundant
“transgender” -> “lgbTq*” // note the uppercase T
“genderidentityexpression” -> “lgbtq*”
“americanindian/alaskannative” -> “nativeAmerican”
“multi-racialgroups” -> “multiRacial”
“islamic.muslim.” -> “islamic”
“multi-religiousgroups” -> “syncretic” //too obscure?
“religiouspracticegenerally” -> “clerical”
“otherreligion” -> “otherReligion”
“easternorthodox.greek.russian.etc..” -> “easternChristian” // per wikipedia
“otherchristian” -> “otherChristian”
“otherethnicity.nationalorigin” -> “ethnicity” // includes nationality
“gaymale” -> “lGbtq*”
“gayfemale” -> “Lgbtq*”
“gay.maleandfemale.” -> “LGbtq*”
“heterosexual” -> “straight” // any objections?
“bisexual” -> “lgBtq*”
“physicaldisability” -> “physicallyDisabled”
“mentaldisability” -> “mentallyDisabled”
# shorten and correct (?) victim categories
hateCrimes <- hateCrimes %>%
select(county,victimCategory,year,crimetype,incidents) %>%
mutate(
victimCategory=str_replace(victimCategory,"anti-","") ,
victimCategory=str_replace(victimCategory,"transgender","lgbTq*") ,
victimCategory=str_replace(victimCategory,"genderidentityexpression","lgbtq*") ,
victimCategory=str_replace(victimCategory,"americanindian/alaskannative","nativeAmerican") ,
victimCategory=str_replace(victimCategory,"multi-racialgroups","multiRacial") ,
victimCategory=str_replace(victimCategory,"islamic.muslim.","islamic") ,
victimCategory=str_replace(victimCategory,"multi-religiousgroups","syncretic") ,
victimCategory=str_replace(victimCategory,"religiouspracticegenerally","clerical") ,
victimCategory=str_replace(victimCategory,"otherreligion","otherReligion") ,
victimCategory=str_replace(victimCategory,"easternorthodox.greek,russian,etc..","easternChristian") ,
victimCategory=str_replace(victimCategory,"otherchristian","otherChristian") ,
victimCategory=str_replace(victimCategory,"otherethnicity.nationalorigin","ethnicity") ,
victimCategory=str_replace(victimCategory,"gaymale","lGbtq*") ,
victimCategory=str_replace(victimCategory,"gayfemale","Lgbtq*") ,
victimCategory=str_replace(victimCategory,"gay.maleandfemale.","LGbtq*") ,
victimCategory=str_replace(victimCategory,"heterosexual","straight") ,
victimCategory=str_replace(victimCategory,"bisexual","lgBtq*") ,
victimCategory=str_replace(victimCategory,"physicaldisability","physicallyDisabled") ,
victimCategory=str_replace(victimCategory,"mentaldisability","mentallyDisabled")
)Visualizing the data
I tried some variations on treemaps and they all looked pretty ugly with illegible labels and legends. I have seen word clouds used in the Washington Post to represent phrases extracted from text. It seemed dubious how phrases of equal weights could be arbitrarily emphasized by how they were positioned on the page. Nevertheless, they can contain many categories legibly named.
wordCloudArgs<-hateCrimes %>%
filter(crimetype!="Property Crimes") %>%
select(victimCategory,incidents,crimetype) %>%
group_by(victimCategory) %>%
summarise(incident=sum(incidents)) %>%
arrange(desc(incident))
# include median to give a better picture of the distribution
wordCloudArgs<- rbind(wordCloudArgs,tibble(
victimCategory="median",
incident=median(wordCloudArgs$incident))
)
# tried to see if log looked better: it didn't
wordCloudArgs<-wordCloudArgs[wordCloudArgs$incident!=0,]
wordCloudArgs$log<-log(wordCloudArgs$incident)
wordcloud::wordcloud(
wordCloudArgs$victimCategory,wordCloudArgs$incident,
min.freq = 1, # default is 3 which hides many rows
# make it look pretty
colors=RColorBrewer::brewer.pal(8, "Dark2"),
random.color = TRUE,
random.order = FALSE,
scale=par("fin")*.35
# figure dimension in inches. .35 seems to best fill the plotting area
)
title(main=paste("Victims of",
sum(hateCrimes$incidents[hateCrimes$crimetype!="Property Crimes"]),
"Hate Crimes in New York State"),sub="Crimes Against Persons reported between 2010 and 2016")
print(wordCloudArgs,n=300)## # A tibble: 29 x 3
## victimCategory incident log
## <chr> <dbl> <dbl>
## 1 lGbtq* 563 6.33
## 2 black 426 6.05
## 3 jewish 424 6.05
## 4 islamic 139 4.93
## 5 hispanic 136 4.91
## 6 white 121 4.80
## 7 Lgbtq* 92 4.52
## 8 ethnicity 70 4.25
## 9 asian 62 4.13
## 10 LGbtq* 26 3.26
## 11 arab 23 3.14
## 12 lgbTq* 19 2.94
## 13 lgbtq* 17 2.83
## 14 catholic 11 2.40
## 15 otherReligion 11 2.40
## 16 multiRacial 9 2.20
## 17 female 6 1.79
## 18 physicallyDisabled 5 1.61
## 19 syncretic 5 1.61
## 20 age* 3 1.10
## 21 mentallyDisabled 3 1.10
## 22 lgBtq* 2 0.693
## 23 male 2 0.693
## 24 nativeAmerican 2 0.693
## 25 clerical 1 0
## 26 easternChristian 1 0
## 27 protestant 1 0
## 28 straight 1 0
## 29 median 11 2.40Package “wordcloud2” produces more dramatic plots but is full of bugs.
note the missing victimCategories:
# make another wordcloud for counties. Pretty much the same as above
wordCloudArgs<-hateCrimesSummary %>%
filter(crimetype!="Property Crimes") %>%
select(county,totalincidents) %>%
group_by(county) %>%
summarise(incident=sum(totalincidents)) %>%
arrange(desc(incident))
# include median to give a better picture of the distribution
wordCloudArgs<- rbind(wordCloudArgs,tibble(
county="median",
incident=median(wordCloudArgs$incident))
)
# tried to see if log looked better: it didn't
wordCloudArgs<-wordCloudArgs[wordCloudArgs$incident!=0,]
wordCloudArgs$log<-log(wordCloudArgs$incident)
wordcloud::wordcloud(
wordCloudArgs$county,wordCloudArgs$log,
min.freq = 1,
colors=RColorBrewer::brewer.pal(8, "Dark2"),
random.color = TRUE,
random.order = FALSE,
scale=par("fin")*.27
)
title(
main=paste(
sum(hateCrimesSummary$totalincidents[hateCrimesSummary$crimetype!="Property Crimes"]),
"Hate Crimes located in",
nrow(wordCloudArgs),
"Counties of New York State"), outer = TRUE,
sub="Crimes Against Persons reported between 2010 and 2016",
line = -1
)
print(wordCloudArgs,n=300)## # A tibble: 60 x 3
## county incident log
## <chr> <dbl> <dbl>
## 1 Kings 483 6.18
## 2 New York 444 6.10
## 3 Queens 169 5.13
## 4 Erie 166 5.11
## 5 Suffolk 158 5.06
## 6 Bronx 117 4.76
## 7 Westchester 87 4.47
## 8 Nassau 82 4.41
## 9 Richmond 72 4.28
## 10 Albany 37 3.61
## 11 Rockland 34 3.53
## 12 Dutchess 33 3.50
## 13 Orange 31 3.43
## 14 Monroe 30 3.40
## 15 Niagara 15 2.71
## 16 Tompkins 14 2.64
## 17 Broome 13 2.56
## 18 Otsego 13 2.56
## 19 Saratoga 12 2.48
## 20 St. Lawrence 11 2.40
## 21 Ulster 11 2.40
## 22 Oswego 10 2.30
## 23 Clinton 9 2.20
## 24 Multiple 9 2.20
## 25 Schenectady 8 2.08
## 26 Madison 7 1.95
## 27 Onondaga 7 1.95
## 28 Cayuga 6 1.79
## 29 Franklin 6 1.79
## 30 Livingston 6 1.79
## 31 Cattaraugus 5 1.61
## 32 Chautauqua 5 1.61
## 33 Chenango 5 1.61
## 34 Cortland 5 1.61
## 35 Oneida 5 1.61
## 36 Essex 4 1.39
## 37 Jefferson 4 1.39
## 38 Putnam 4 1.39
## 39 Rensselaer 4 1.39
## 40 Washington 4 1.39
## 41 Wayne 4 1.39
## 42 Chemung 3 1.10
## 43 Columbia 3 1.10
## 44 Ontario 3 1.10
## 45 Orleans 3 1.10
## 46 Schoharie 3 1.10
## 47 Steuben 3 1.10
## 48 Herkimer 2 0.693
## 49 Tioga 2 0.693
## 50 Allegany 1 0
## 51 Delaware 1 0
## 52 Fulton 1 0
## 53 Genesee 1 0
## 54 Greene 1 0
## 55 Lewis 1 0
## 56 Schuyler 1 0
## 57 Seneca 1 0
## 58 Sullivan 1 0
## 59 Warren 1 0
## 60 median 6 1.79