Setting Paths

pckgs <- c("tidyverse", "sf", "dplyr", "tigris","lubridate","purrr","ggplot2","RColorBrewer","hrbrthemes")
lapply(pckgs, library, character.only = T)

# set paths
OS <- .Platform$OS.type
if (OS == "unix"){
  input_dir <- "~/Dropbox/chyn_campos_bruhn/Gangs/data"
} else {
  input_dir <- "C:/Users/at3981/Dropbox/chyn_campos_bruhn/Gangs/data"
}

Gang Distance to Gang Presence data

Here, I have a dataset of each censusblock-year pair with its distance from a gang. Negative distance means that the gang is inside that census block. Missing values mean that the gang does not exist that year, so I imputed the missing distance value with a proxy positive value.

To locate blocks with gang presence, I did a rowwise operation where if ONLY one distance across the row is found to be negative, the block contains a gang inside. In contrast, census blocks without gang presence are rows where all the distances are postive. I can also found out the number of gangs inside each block by counting the number of distinct negative values within the row.

#load gang distance data
load(str_interp("${input_dir}/gang_distance.rda"))
gang_data <- gang.distance

#fill in missing data with positive values since missing values mean no gangs
gang_data <- gang_data %>% mutate(across(.cols = 3:380, ~case_when(is.na(.) ~ 1, TRUE ~ .)))
#count the number of gangs by counting negative distances across rows
gang_data$no_gang <- rowSums(gang_data[,3:380] <= 0)


#extract blocks with gang presence by finding blocks with any negative distance across rows
block_with_gangs <- gang_data %>% rowwise %>% 
  filter(any(c(-1,0) %in% sign(c_across(`?????`:Young_Crowd)))) %>% mutate(gang_presence = 1)
#extract blocks without gang presence by finding blocks with all positive distances across rows
block_without_gangs <- gang_data %>% rowwise %>% 
  filter(all(rep(1,dim(gang_data[,3:380])[2]) == sign(c_across(`?????`:Young_Crowd)))) %>% mutate(gang_presence = 0)

#combine and sort the two data extracts with and without gangs
blocks_gang_presence <- rbind(block_with_gangs,block_without_gangs)
blocks_gang_presence <- blocks_gang_presence %>% select(c("block.id","year","no_gang","gang_presence")) %>% 
  arrange(block.id, year)
write.csv(blocks_gang_presence, file = str_interp("${input_dir}/block_gang_presence.csv"))

The data now looks like this:

#load yearly data on gang presence - load here instead of using the above output if want to avoid having to run the above trunks again, which takes quite a few minutes
blocks_gang_presence <- read_csv(str_interp("${input_dir}/block_gang_presence.csv")) %>% 
  rename(census_block = block.id) %>% select(census_block, year, no_gang, gang_presence)
knitr::kable(head(blocks_gang_presence, 10), col.names = c("Census Block ID", "Year","Number of Gangs Inside", "Gang presence"))
Census Block ID Year Number of Gangs Inside Gang presence
060371011101000 2006 0 0
060371011101000 2007 0 0
060371011101000 2008 0 0
060371011101000 2009 0 0
060371011101000 2011 0 0
060371011101000 2016 0 0
060371011101000 2019 0 0
060371011101001 2006 0 0
060371011101001 2007 0 0
060371011101001 2008 0 0

LAPD Crime Data 2010-2019

First, I collapsed the monthly crime data into a yearly dataset so that I can merge it with the yearly gang presence data.

Note that the yearly data on gang presence covers the year 2006, 2007, 2008, 2009, 2011, 2016, 2019. However, our crime data covers the year 2010-2019 (LAPD) and 2005-2021 (LA Sheriff). Therefore, simply merging 1:1 on years and census block id will get a lot of crime data being thrown away. To avoid this, I imputed the missing gang presence value in years we do not have data by the closest earlier gang presence value of that particular census block. For example, gang presence data from the year 2013 will come from the gang presence data of the year 2011. Note that we do not have gang presence data on year 2005 so that year data from the Sheriff crime data will be unmerged.

After filling in, there will still be missing values from observations who only have one year of data and that year is not covered by the gang presence data. Thus, there is no other observations for us to impute and I removed those observations.

#load LAPD crime data
lapd_crime <- read_csv(str_interp("${input_dir}/crime/LAPD/output/panel_data_crimela_1019.csv"))

#collapse monthly data to yearly data.
lapd_crime_yearly <- lapd_crime %>% group_by(census_block, year) %>% 
  summarize(robbery = sum(robbery), homicide = sum(homicide), burglary = sum(burglary),
            shots_fired = sum(shots_fired), assault = sum(assault), rape = sum(rape), 
            theft = sum(theft), reckless_driving = sum(reckless_driving),
            vandalism = sum(vandalism), kidnapping = sum(kidnapping), order_violation = sum(order_violation),
            lynching = sum(lynching), counterfeit = sum(counterfeit), vandalism = sum(vandalism)) %>% 
  ungroup() %>% arrange(census_block, year)


#fill in the missing values with the closest earlier year
#change 2009 rto 2010 so that the 2009 gang presence data can be merged in with the 2010 LAPD crime data
blocks_gang_presence2 <- blocks_gang_presence %>% mutate(year = case_when(year == 2009 ~ 2010, TRUE ~ year))
lapd_crime_gang <- lapd_crime_yearly %>% 
  left_join(blocks_gang_presence2, by = c("census_block","year"))  %>% group_by(census_block) %>% 
  fill(c(gang_presence, no_gang), .direction = "down") %>% ungroup() %>% drop_na(gang_presence)

Here, I documented the number of census block with and without gangs over the year.

lapd_crime_total <- lapd_crime_gang %>% group_by(year, gang_presence) %>% tally()
knitr::kable(lapd_crime_total, col.names = c("Year", "Presence of Gangs","Number of Census Blocks"))
Year Presence of Gangs Number of Census Blocks
2010 0 7080
2010 1 7527
2011 0 6701
2011 1 7834
2012 0 6430
2012 1 7621
2013 0 6319
2013 1 7573
2014 0 6292
2014 1 7533
2015 0 6231
2015 1 7532
2016 0 7323
2016 1 7112
2017 0 7113
2017 1 7103
2018 0 7069
2018 1 7095
2019 0 6484
2019 1 8175

After merging the data, I collapsed it on the year and gang presence variable to calculate some summary statistics of different crime types. Specifically, I calculated the sum, mean, and standard deviation. Note that the mean for a given crime type here is calculated as:

\[ \overline{Crime_{kjt}} = \frac{\sum_i Case_{ikjt}}{\sum_{j \in \{J_0,J_1\}} Block_{jt}} \] where the subscript \(i\) denotes a particular case of that crime type \(k\) in blocks of gang presence status \(j\), and \(t\) denotes year. \(J_0\) is the set of census blocks without gang presence, and \(J_1\) is the set of census blocks with gang presence. In words, the average for each crime type here is the count of all cases of that crime type within all the census blocks with/without gang presence, divided by the number of census blocks with/without gang presence. For illustration purposes, I multiplied all of these mean by 100.

lapd_crime_grouped <- lapd_crime_gang  %>% 
  group_by(year, gang_presence) %>% summarise(count_robbery = sum(robbery), mean_robbery = 100*mean(robbery), sd_robbery = sd(robbery),
                                             count_homicide = sum(homicide), mean_homicide = 100*mean(homicide), sd_homicide = sd(homicide),
                                             count_burglary = sum(burglary), mean_burglary = 100*mean(burglary), sd_burglary = sd(burglary),
                                             count_shots_fired = sum(shots_fired), mean_shots_fired = 100*mean(shots_fired), sd_shots_fired = sd(shots_fired),
                                             count_assault = sum(assault), mean_assault = 100*mean(assault), sd_assault = sd(assault),
                                             count_rape = sum(rape), mean_rape = 100*mean(rape), sd_rape = sd(rape),
                                             count_theft = sum(theft), mean_theft = 100*mean(theft), sd_theft = sd(theft),
                                             count_counterfeit = sum(counterfeit), mean_counterfeit = 100*mean(counterfeit), sd_counterfeit = sd(counterfeit),
                                             count_reckless_driving = sum(reckless_driving), mean_reckless_driving = 100*mean(reckless_driving), sd_reckless_driving = sd(reckless_driving),
                                             count_lynching = sum(lynching), mean_lynching = 100*mean(lynching), sd_lynching = sd(lynching),
                                             count_vandalism = sum(vandalism), mean_vandalism = 100*mean(vandalism), sd_vandalism = sd(vandalism),
                                             count_order_violation = sum(order_violation), mean_order_violation = 100*mean(order_violation), sd_order_violation = sd(order_violation),
                                             count_kidnapping = sum(kidnapping), mean_kidnapping = 100*mean(kidnapping), sd_kidnapping = sd(kidnapping)) %>% ungroup() %>% 
  mutate(gang_present = as.factor(gang_presence), `Presence of Gangs` = ifelse(gang_presence == 1, "Yes","No"))

knitr::kable(lapd_crime_grouped[,1:41] , col.names = c('Year', 'Presence of Gangs', 'Count of Robbery', 'Average:Robbery', 'SD:Robbery','Count of Homicide', 'Average:Homicide', 'SD:Homicide', 'Count of Burglary', 'Average:Burglary', 'SD:Burglary','Count of Shots fired', 'Average:Shots fired', 'SD:Shots fired','Count of Assault', 'Average:Assualt', 'SD:Assault','Count of Sexual cases', 'Average:Sexual cases', 'SD:sexual cases','Count of Theft', 'Average:Theft', 'SD:Theft','Count of Counterfeit', 'Average:Counterfeit', 'SD:Counterfeit','Count of Reckless Driving', 'Average:Reckless Driving', 'SD:Reckless Driving','Count of Lynching', 'Average:Lynching', 'SD:Lynching','Count of Vandalism', 'Average:Vandalism', 'SD:Vandalism','Count of Order violation', 'Average:Order violation', 'SD:Order violation','Count of Kidnapping', 'Average:Kidnapping', 'SD:Kidnapping'))
Year Presence of Gangs Count of Robbery Average:Robbery SD:Robbery Count of Homicide Average:Homicide SD:Homicide Count of Burglary Average:Burglary SD:Burglary Count of Shots fired Average:Shots fired SD:Shots fired Count of Assault Average:Assualt SD:Assault Count of Sexual cases Average:Sexual cases SD:sexual cases Count of Theft Average:Theft SD:Theft Count of Counterfeit Average:Counterfeit SD:Counterfeit Count of Reckless Driving Average:Reckless Driving SD:Reckless Driving Count of Lynching Average:Lynching SD:Lynching Count of Vandalism Average:Vandalism SD:Vandalism Count of Order violation Average:Order violation SD:Order violation Count of Kidnapping Average:Kidnapping SD:Kidnapping
2010 0 2573 36.34181 1.1024588 50 0.7062147 0.0902406 12843 181.3983 2.764986 104 1.468927 0.1282701 10287 145.2966 2.993621 1417 20.01412 0.5977544 278 3.926554 0.7800200 51 0.7203390 0.0894432 9 0.1271186 0.0356335 0 0.0000000 0.0000000 6008 84.85876 1.525953 1167 16.48305 0.6993875 128 1.8079096 0.1394629
2010 1 5776 76.73708 1.7205696 187 2.4843895 0.1606996 13823 183.6455 2.513151 385 5.114920 0.2570576 20695 274.9435 4.369638 2698 35.84429 0.8367173 252 3.347947 0.7619683 48 0.6377043 0.0796066 12 0.1594261 0.0651873 3 0.0398565 0.0199614 9062 120.39325 1.840792 2007 26.66401 0.9159360 258 3.4276604 0.2059301
2011 0 2045 30.51783 0.9478105 42 0.6267721 0.0826219 11516 171.8549 2.689926 85 1.268467 0.1269162 8949 133.5472 2.932470 1229 18.34055 0.6049047 283 4.223250 0.7810296 29 0.4327712 0.0761719 6 0.0895389 0.0345431 1 0.0149231 0.0122160 5348 79.80898 1.553514 1014 15.13207 0.6872572 89 1.3281600 0.1183329
2011 1 5548 70.81950 1.7290237 184 2.3487363 0.1620430 13317 169.9898 2.432688 385 4.914475 0.2412993 20751 264.8838 4.265822 2616 33.39290 0.8050366 259 3.306102 0.7880472 45 0.5744192 0.0963667 3 0.0382946 0.0195665 4 0.0510595 0.0225920 9008 114.98596 1.837732 2037 26.00204 0.8047905 261 3.3316314 0.2041021
2012 0 1868 29.05132 0.9536890 52 0.8087092 0.0929791 11109 172.7683 2.679899 93 1.446345 0.1352799 8621 134.0747 2.881828 1239 19.26905 0.5794018 276 4.292380 0.7999604 43 0.6687403 0.1134263 4 0.0622084 0.0249358 2 0.0311042 0.0249416 5251 81.66407 1.524458 974 15.14774 0.7741006 85 1.3219285 0.1169134
2012 1 4867 63.86301 1.5695924 182 2.3881380 0.1650792 13047 171.1980 2.408286 422 5.537331 0.2663628 20653 271.0012 4.392925 2716 35.63837 0.8407332 248 3.254166 0.7266875 38 0.4986222 0.0740739 6 0.0787298 0.0280496 4 0.0524866 0.0229054 8787 115.29983 1.787225 1949 25.57407 0.8160594 252 3.3066527 0.1969809
2013 0 1690 26.74474 0.9178003 39 0.6171863 0.0803200 10770 170.4384 2.650765 78 1.234373 0.1187124 8325 131.7455 2.837846 1209 19.13277 0.6461326 314 4.969141 0.8326585 49 0.7754392 0.2763840 1 0.0158253 0.0125799 2 0.0316506 0.0177892 4863 76.95838 1.505846 985 15.58791 0.7495339 89 1.4084507 0.1293729
2013 1 4191 55.34134 1.4564303 156 2.0599498 0.1501832 12519 165.3110 2.395787 365 4.819754 0.2737426 19551 258.1672 4.269617 2503 33.05163 0.7797386 326 4.304767 1.0887679 35 0.4621682 0.0734392 10 0.1320481 0.0363168 6 0.0792288 0.0363322 8618 113.79902 1.822585 1952 25.77578 0.8249331 210 2.7730094 0.1788384
2014 0 1607 25.54037 0.8490007 44 0.6993007 0.0974091 9625 152.9720 2.445510 95 1.509854 0.1390099 9173 145.7883 3.210365 1249 19.85060 0.6395369 321 5.101717 0.7535877 25 0.3973299 0.0629138 3 0.0476796 0.0281879 1 0.0158932 0.0126068 5031 79.95868 1.571497 1087 17.27591 0.8944637 80 1.2714558 0.1215742
2014 1 4266 56.63082 1.4708230 163 2.1638126 0.1569225 11587 153.8165 2.239221 376 4.991371 0.2595066 21152 280.7912 4.592835 2543 33.75813 0.7781307 410 5.442719 1.7123805 26 0.3451480 0.0630166 8 0.1061994 0.0325731 3 0.0398248 0.0257619 8928 118.51852 1.846854 2227 29.56326 0.9457276 212 2.8142838 0.1865192
2015 0 1871 30.02728 1.1108100 38 0.6098540 0.0818802 10317 165.5753 2.660048 82 1.316001 0.1427324 9730 156.1547 3.467856 1284 20.60664 0.6669702 334 5.360295 0.8134204 25 0.4012197 0.0657097 8 0.1283903 0.0358115 0 0.0000000 0.0000000 5107 81.96116 1.680272 795 12.75879 0.6744081 69 1.1073664 0.1120622
2015 1 4633 61.51089 1.5783525 182 2.4163569 0.1715382 12121 160.9267 2.503353 392 5.204461 0.2554930 22800 302.7084 5.016749 2559 33.97504 0.8410825 386 5.124801 1.1811278 36 0.4779607 0.0708729 19 0.2522570 0.0527457 5 0.0663834 0.0257581 9638 127.96070 2.064655 1916 25.43813 0.8055218 223 2.9607010 0.1866610
2016 0 2267 30.95726 1.0677154 55 0.7510583 0.1087455 12172 166.2160 2.893922 118 1.611361 0.1332973 10311 140.8029 3.316312 1405 19.18613 0.6514576 303 4.137649 0.6638172 36 0.4916018 0.0737483 16 0.2184897 0.0466950 1 0.0136556 0.0116857 5990 81.79708 1.695647 1060 14.47494 0.6278789 87 1.1880377 0.1258494
2016 1 5327 74.90157 1.8438171 171 2.4043870 0.1697436 11963 168.2087 2.610414 469 6.594488 0.3065111 23244 326.8279 5.196817 2687 37.78121 0.8755992 446 6.271091 1.5811624 27 0.3796400 0.0615022 15 0.2109111 0.0458798 5 0.0703037 0.0313671 10117 142.25253 2.260285 2124 29.86502 1.0024346 205 2.8824522 0.1952505
2017 0 2470 34.72515 1.1552862 55 0.7732321 0.0967517 13088 184.0011 3.097541 118 1.658934 0.1537141 11002 154.6745 3.492936 1471 20.68044 0.6685518 275 3.866161 0.4411943 20 0.2811753 0.0555469 12 0.1687052 0.0410420 0 0.0000000 0.0000000 6116 85.98341 1.787866 1089 15.31000 0.6510218 88 1.2371714 0.1214550
2017 1 5536 77.93890 1.9476861 154 2.1680980 0.1594931 12675 178.4457 2.867166 395 5.561031 0.2957109 23939 337.0266 5.485371 2557 35.99887 0.8721826 398 5.603266 1.0650286 22 0.3097283 0.0555709 17 0.2393355 0.0488668 4 0.0563142 0.0237256 10002 140.81374 2.218297 2250 31.67676 0.9693466 170 2.3933549 0.1618028
2018 0 2372 33.55496 1.1169627 50 0.7073136 0.0978304 12664 179.1484 3.168237 133 1.881454 0.1589161 11421 161.5646 3.984703 1451 20.52624 0.6719335 320 4.526807 0.5550502 16 0.2263404 0.0557449 11 0.1556090 0.0394194 0 0.0000000 0.0000000 5972 84.48154 1.732490 1152 16.29651 0.7079331 69 0.9760928 0.0997496
2018 1 5244 73.91121 1.7962188 153 2.1564482 0.1564787 12140 171.1064 2.688553 343 4.834390 0.2422809 23882 336.6032 5.399235 2410 33.96758 0.8118259 391 5.510923 0.9873354 25 0.3523608 0.0592595 20 0.2818887 0.0530220 3 0.0422833 0.0205600 9790 137.98450 2.157373 2185 30.79634 0.9763268 146 2.0577872 0.1543449
2019 0 1910 29.45713 1.0548266 41 0.6323257 0.0867078 10248 158.0506 2.956651 94 1.449722 0.1397632 9602 148.0876 4.043048 1115 17.19618 0.6149034 281 4.333745 0.6762563 21 0.3238742 0.0568221 6 0.0925355 0.0304079 2 0.0308452 0.0175614 4890 75.41641 1.662395 933 14.38927 0.6403989 64 0.9870450 0.1004143
2019 1 5167 63.20489 1.5902506 152 1.8593272 0.1455539 11770 143.9755 2.345601 450 5.504587 0.2774516 25210 308.3792 5.099686 2315 28.31804 0.7179612 451 5.516820 1.1709789 21 0.2568807 0.0529830 19 0.2324159 0.0481564 2 0.0244648 0.0156403 10351 126.61774 2.024284 2442 29.87156 0.9475179 173 2.1162080 0.1638101

Figures: Average cases of crimes over the years by gang presence - reported by LAPD

#define custom color scale
myColors <- brewer.pal(6, "PuOr")
names(myColors) <- levels(lapd_crime_grouped$`Presence of Gangs`)
custom_colors <- scale_colour_manual(name = "Presence of Gangs", values = myColors)

#plots of average cases of crimes reported by LAPD
lapd_crime_grouped %>% ggplot(aes(x = year, y = mean_robbery, color = `Presence of Gangs`)) +
  geom_line()  + theme(legend.title = element_text(size=8)) + 
  ylab("")   + scale_x_continuous(breaks=seq(2010,2019,1)) +
  custom_colors + theme_ipsum() + ggtitle("LAPD - Average Cases of Robbery (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

lapd_crime_grouped %>% ggplot(aes(x = year, y = mean_homicide, color = `Presence of Gangs`)) +
  geom_line()  + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2010,2019,1)) + 
  custom_colors + theme_ipsum() + ggtitle("LAPD - Average Cases of Homicide (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

lapd_crime_grouped %>% ggplot(aes(x = year, y = mean_burglary, color = `Presence of Gangs`)) +
  geom_line()  + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2010,2019,1)) + 
  custom_colors + theme_ipsum() + ggtitle("LAPD - Average Cases of Burglary (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

lapd_crime_grouped %>% ggplot(aes(x = year, y = mean_shots_fired, color = `Presence of Gangs`)) +
  geom_line()  + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2010,2019,1)) +
  custom_colors + theme_ipsum() + ggtitle("LAPD - Average Cases of Shots Fired (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

lapd_crime_grouped %>% ggplot(aes(x = year, y = mean_assault, color = `Presence of Gangs`)) +
  geom_line()  + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2010,2019,1)) +
  custom_colors + theme_ipsum() + ggtitle("LAPD - Average Cases of Assault (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

lapd_crime_grouped %>% ggplot(aes(x = year, y = mean_rape, color = `Presence of Gangs`)) +
  geom_line()  + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2010,2019,1)) +
  custom_colors + theme_ipsum() + ggtitle("LAPD - Average Cases of Rape (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

lapd_crime_grouped %>% ggplot(aes(x = year, y = mean_theft, color = `Presence of Gangs`)) +
  geom_line()  + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2010,2019,1)) + 
  custom_colors + theme_ipsum() + ggtitle("LAPD - Average Cases of Theft (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

lapd_crime_grouped %>% ggplot(aes(x = year, y = mean_counterfeit, color = `Presence of Gangs`)) +
  geom_line()  + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2010,2019,1)) + 
  custom_colors + theme_ipsum() + ggtitle("LAPD - Average Cases of Counterfeit (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

lapd_crime_grouped %>% ggplot(aes(x = year, y = mean_reckless_driving, color = `Presence of Gangs`)) +
  geom_line() + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2010,2019,1)) + 
  custom_colors + theme_ipsum() + ggtitle("LAPD - Average Cases of Reckless Driving (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

lapd_crime_grouped %>% ggplot(aes(x = year, y = mean_lynching, color = `Presence of Gangs`)) +
  geom_line()  + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2010,2019,1)) + 
  custom_colors + theme_ipsum() + ggtitle("LAPD - Average Cases of Lynching (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

lapd_crime_grouped %>% ggplot(aes(x = year, y = mean_vandalism, color = `Presence of Gangs`)) +
  geom_line() + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2010,2019,1)) + 
  custom_colors + theme_ipsum() + ggtitle("LAPD - Average Cases of Vandalism (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

lapd_crime_grouped %>% ggplot(aes(x = year, y = mean_order_violation, color = `Presence of Gangs`)) +
  geom_line() + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2010,2019,1)) + 
  custom_colors + theme_ipsum() + ggtitle("LAPD - Average Cases of Order Violation (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

lapd_crime_grouped %>% ggplot(aes(x = year, y = mean_kidnapping, color = `Presence of Gangs`)) +
  geom_line() + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2010,2019,1))  + 
  custom_colors + theme_ipsum() + ggtitle("LAPD - Average Cases of Kidnapping (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10))

Sheriff Crime Data 2005-2021

The below section repeats all the same steps with the Sheriff crime data. However, it seems that there are very few census blocks with gang presence in this data. This means that the count of crimes in census blocks without gang presence is much larger than the corresponding figure from blocks with gangs. Even when taking the mean, the pattern still holds for all crime types.

#Sheriff Crime Data
sheriff_crime <- read_csv(str_interp("${input_dir}/crime/Sheriff/output/panel_data_crimela_0521.csv"))
#collapse to yearly data
sheriff_crime_yearly <- sheriff_crime %>% group_by(census_block, year) %>% 
  summarize(robbery = sum(robbery), homicide = sum(homicide), burglary = sum(burglary),
            weapons = sum(weapons), assault = sum(assault), rape_sexual = sum(rape_sexual), 
            theft = sum(theft), reckless_driving = sum(reckless_driving), fraud = sum(fraud), 
            vandalism = sum(vandalism), alcohol = sum(alcohol)) %>% 
  ungroup() %>% arrange(census_block, year)

#join with gang presnce data
sheriff_crime_gang <- sheriff_crime_yearly %>% 
  left_join(blocks_gang_presence, by = c("census_block","year")) %>% group_by(census_block) %>% 
  fill(c(gang_presence), .direction = "down") %>% ungroup() %>% drop_na(gang_presence)
sheriff_crime_total <- sheriff_crime_gang %>% group_by(year, gang_presence) %>% tally()
knitr::kable(sheriff_crime_total, col.names = c("Year", "Presence of Gangs","Number of Census Blocks"))
Year Presence of Gangs Number of Census Blocks
2006 0 2849
2006 1 731
2007 0 3006
2007 1 869
2008 0 2839
2008 1 1023
2009 0 2769
2009 1 922
2010 0 2635
2010 1 639
2011 0 2799
2011 1 1308
2012 0 2565
2012 1 938
2013 0 2516
2013 1 920
2014 0 2495
2014 1 909
2015 0 2527
2015 1 885
2016 0 2713
2016 1 1158
2017 0 2501
2017 1 794
2018 0 2414
2018 1 658
2019 0 2745
2019 1 611
2020 0 2614
2020 1 475
2021 0 2605
2021 1 419 
sheriff_crime_grouped <- sheriff_crime_gang %>% group_by(year, gang_presence) %>% summarise(count_robbery = sum(robbery), mean_robbery = 100*mean(robbery), sd_robbery = sd(robbery),
                                                       count_homicide = sum(homicide), mean_homicide = 100*mean(homicide), sd_homicide = sd(homicide),
                                                       count_burglary = sum(burglary), mean_burglary = 100*mean(burglary), sd_burglary = sd(burglary),
                                                       count_weapons = sum(weapons), mean_weapons = 100*mean(weapons), sd_weapons = sd(weapons),
                                                       count_assault = sum(assault), mean_assault = 100*mean(assault), sd_assault = sd(assault),
                                                       count_rape_sexual = sum(rape_sexual), mean_rape_sexual = 100*mean(rape_sexual), sd_rape_sexual = sd(rape_sexual),
                                                       count_theft = sum(theft), mean_theft = 100*mean(theft), sd_theft = sd(theft),
                                                       count_reckless_driving = sum(reckless_driving), mean_reckless_driving = 100*mean(reckless_driving), sd_reckless_driving = sd(reckless_driving),
                                                       count_fraud = sum(fraud), mean_fraud = 100*mean(fraud), sd_fraud = sd(fraud),
                                                       count_vandalism = sum(vandalism), mean_vandalism = 100*mean(vandalism), sd_vandalism = sd(vandalism),
                                                       count_alcohol = sum(alcohol), mean_alcohol = 100*mean(alcohol), sd_alcohol = sd(alcohol)) %>% ungroup() %>% 
  mutate(gang_presence = as.factor(gang_presence), `Presence of Gangs` = ifelse(gang_presence == 1, "Yes","No")) 

knitr::kable(sheriff_crime_grouped[-36], col.names = c('Year', 'Presence of Gangs', 'Count of Robbery', 'Average:Robbery', 'SD:Robbery','Count of Homicide', 'Average:Homicide', 'SD:Homicide', 'Count of Burglary', 'Average:Burglary', 'SD:Burglary','Count of Weapon cases', 'Average:Weapon cases', 'SD:Weapon cases','Count of Assault', 'Average:Assualt', 'SD:Assault','Count of Sexual cases', 'Average:Sexual cases', 'SD:sexual cases','Count of Theft', 'Average:Theft', 'SD:Theft','Count of Reckless Driving', 'Average:Reckless Driving', 'SD:Reckless Driving','Count of Fraud', 'Average:Fraud', 'SD:Fraud','Count of Vandalism', 'Average:Vandalism', 'SD:Vandalism','Count of Alcohol', 'Average:Alcohol', 'SD:Alcohol'))
Year Presence of Gangs Count of Robbery Average:Robbery SD:Robbery Count of Homicide Average:Homicide SD:Homicide Count of Burglary Average:Burglary SD:Burglary Count of Weapon cases Average:Weapon cases SD:Weapon cases Count of Assault Average:Assualt SD:Assault Count of Sexual cases Average:Sexual cases SD:sexual cases Count of Theft Average:Theft SD:Theft Count of Reckless Driving Average:Reckless Driving SD:Reckless Driving Count of Fraud Average:Fraud SD:Fraud Count of Vandalism Average:Vandalism SD:Vandalism Count of Alcohol Average:Alcohol SD:Alcohol
2006 0 1183 41.523342 1.1304920 69 2.4219024 0.1668959 1666 58.476658 1.1815201 943 33.099333 3.4583636 2845 99.85960 20.7569874 434 15.233415 0.9145145 3630 127.41313 4.561260 282 9.8982099 0.5287012 922 32.362232 0.7720586 2180 76.51808 3.3971602 647 22.709723 1.1477977
2006 1 93 12.722298 0.5429098 0 0.0000000 0.0000000 65 8.891929 0.4843019 59 8.071135 0.3356409 173 23.66621 0.8226731 31 4.240766 0.2711855 208 28.45417 2.298545 17 2.3255814 0.1596428 51 6.976744 0.2654587 253 34.61012 2.1938383 10 1.367989 0.1162378
2007 0 1266 42.115769 1.2504661 60 1.9960080 0.1445659 1797 59.780439 1.1325975 936 31.137725 3.4894922 2791 92.84764 18.0143373 465 15.469062 0.6047700 4171 138.75582 5.901902 292 9.7139055 0.4229818 1031 34.298071 0.8205730 2365 78.67598 3.2522519 767 25.515635 1.1247178
2007 1 122 14.039125 0.6447879 0 0.0000000 0.0000000 87 10.011508 0.4429296 83 9.551208 0.3856177 160 18.41197 0.7639191 22 2.531646 0.1712082 260 29.91945 1.548081 23 2.6467204 0.1743694 42 4.833141 0.2301324 574 66.05293 6.9829331 37 4.257767 0.2434029
2008 0 1169 41.176471 1.1686001 53 1.8668545 0.1404846 1869 65.833040 1.3059980 988 34.800986 4.2856993 2622 92.35646 18.4437550 508 17.893625 0.7135802 4119 145.08630 4.898508 302 10.6375484 0.4543559 1122 39.520958 0.9645906 2258 79.53505 3.4678681 965 33.990842 1.2886708
2008 1 155 15.151515 0.7305971 7 0.6842620 0.0824770 106 10.361681 0.6931427 128 12.512219 0.7520255 304 29.71652 3.3088953 31 3.030303 0.1825579 352 34.40860 2.630337 30 2.9325513 0.1905811 87 8.504399 0.2927720 335 32.74682 2.2346012 55 5.376344 0.2542073
2009 0 1075 38.822680 1.0911587 40 1.4445648 0.1252486 1827 65.980498 1.3729341 840 30.335861 4.7254530 2449 88.44348 18.2975272 410 14.806790 0.6068290 4108 148.35681 6.653574 255 9.2091008 0.4092133 965 34.850126 0.9470627 2172 78.43987 3.7288075 744 26.868906 1.4332568
2009 1 146 15.835141 0.7859244 2 0.2169197 0.0465494 71 7.700651 0.4882246 155 16.811280 2.5200483 277 30.04338 3.1263268 30 3.253796 0.2004985 255 27.65727 1.923192 13 1.4099783 0.1268370 51 5.531453 0.2425412 262 28.41649 2.0074640 36 3.904555 0.2648302
2010 0 1139 43.225806 1.1999522 54 2.0493359 0.1545235 1926 73.092979 1.2848379 777 29.487666 3.3034278 2454 93.13093 15.0900432 441 16.736243 0.9199537 4052 153.77609 7.739421 304 11.5370019 0.5332268 945 35.863378 1.0177042 1876 71.19545 1.9823034 799 30.322581 1.1808267
2010 1 111 17.370892 0.7012286 3 0.4694836 0.0684114 95 14.866980 0.6394506 99 15.492958 1.1927563 234 36.61972 2.5804637 31 4.851330 0.2221867 293 45.85290 2.411652 16 2.5039124 0.2079885 45 7.042254 0.3010717 203 31.76839 1.4233334 53 8.294210 0.6089768
2011 0 901 32.190068 0.9626639 28 1.0003573 0.1161078 1703 60.843158 1.1707113 624 22.293676 1.7865841 2117 75.63416 12.2659041 390 13.933548 0.6841562 4360 155.76992 7.124203 341 12.1829225 0.5503447 876 31.296892 0.9207013 1604 57.30618 1.7508100 674 24.080029 1.1693153
2011 1 250 19.113150 0.6765371 13 0.9938838 0.0992350 331 25.305810 0.7601583 202 15.443425 0.4876472 360 27.52294 0.8357530 66 5.045872 0.2514985 709 54.20489 2.803251 43 3.2874618 0.2170724 91 6.957186 0.2662744 331 25.30581 0.6737156 126 9.633028 0.5983031
2012 0 952 37.115010 1.0293405 22 0.8576998 0.0922320 1729 67.407407 1.3379038 609 23.742690 1.9006534 2538 98.94737 18.8906938 393 15.321637 0.7807118 4635 180.70175 7.431263 307 11.9688109 0.5409357 996 38.830409 0.9793598 1581 61.63743 2.3188262 759 29.590643 1.1458409
2012 1 260 27.718550 1.0340684 9 0.9594883 0.0975344 319 34.008529 0.8434017 144 15.351812 0.4641852 449 47.86780 1.3548890 80 8.528785 0.3566370 753 80.27719 3.759089 15 1.5991471 0.1337423 125 13.326226 0.4871258 308 32.83582 0.8087473 170 18.123667 1.1564813
2013 0 801 31.836248 0.9604488 30 1.1923688 0.1085643 1662 66.057234 1.2611536 622 24.721781 2.2658525 2815 111.88394 26.1396575 354 14.069952 0.9302071 4125 163.95072 4.933193 258 10.2543720 0.5553083 877 34.856916 0.8363760 1588 63.11606 3.9178298 728 28.934817 1.2068978
2013 1 282 30.652174 1.1557496 11 1.1956522 0.1087493 313 34.021739 0.9437452 143 15.543478 0.4508158 464 50.43478 1.5577329 77 8.369565 0.3882881 912 99.13043 5.495268 19 2.0652174 0.1497463 113 12.282609 0.4390125 344 37.39130 1.0675620 162 17.608696 1.0987548
2014 0 758 30.380761 0.9318635 18 0.7214429 0.0846478 1424 57.074148 1.0430324 586 23.486974 2.6105368 3131 125.49098 29.6136840 328 13.146293 0.7494666 4025 161.32265 5.654010 241 9.6593186 0.4599959 809 32.424850 0.7715581 1611 64.56914 4.3191073 650 26.052104 1.1594356
2014 1 259 28.492849 1.0299499 14 1.5401540 0.1232113 332 36.523652 1.0004082 136 14.961496 0.5264506 516 56.76568 1.4021259 63 6.930693 0.2866979 797 87.67877 3.669166 24 2.6402640 0.2028604 107 11.771177 0.3760519 409 44.99450 1.3581912 190 20.902090 1.0957899
2015 0 711 28.136130 0.8886885 26 1.0288880 0.1120819 1402 55.480807 1.1181507 669 26.474080 3.2833880 3441 136.16937 33.3922643 296 11.713494 0.7341161 4266 168.81678 6.035402 184 7.2813613 0.4002188 875 34.626039 0.8497641 1824 72.18045 4.9852287 446 17.649387 0.8264443
2015 1 273 30.847458 1.0072062 13 1.4689266 0.1203738 233 26.327684 0.8426261 159 17.966102 0.5790790 507 57.28814 1.4662682 81 9.152542 0.4195268 780 88.13559 4.653192 15 1.6949153 0.1376342 111 12.542373 0.4134038 383 43.27684 1.5636024 135 15.254237 0.8875756
2016 0 895 32.989311 0.9824691 39 1.4375230 0.1190538 1381 50.903059 1.1239595 749 27.607814 2.8273982 3703 136.49097 34.0119748 325 11.979359 1.0736292 4788 176.48360 5.160905 153 5.6395135 0.2952247 890 32.805013 0.7903444 2148 79.17435 4.9036188 1019 37.559897 7.2047659
2016 1 331 28.583765 1.2411790 9 0.7772021 0.0878538 240 20.725389 0.6795310 151 13.039724 0.4191917 550 47.49568 1.4789721 71 6.131261 0.3092451 866 74.78411 3.604517 14 1.2089810 0.1309190 107 9.240069 0.3541495 451 38.94646 1.3269859 121 10.449050 0.9400145
2017 0 954 38.144742 1.1214453 29 1.1595362 0.1070770 1501 60.015994 1.2958642 762 30.467813 2.8617362 3337 133.42663 31.3245881 348 13.914434 1.2012623 4516 180.56777 5.815137 141 5.6377449 0.2764422 777 31.067573 0.7614736 1998 79.88804 4.4270910 764 30.547781 3.7851613
2017 1 204 25.692695 0.7394375 8 1.0075567 0.0999331 218 27.455919 0.7500703 172 21.662468 0.5712098 404 50.88161 2.0095730 52 6.549118 0.2897894 667 84.00504 3.649990 9 1.1335013 0.1059276 95 11.964735 0.3851491 416 52.39295 1.7939387 74 9.319899 0.6323591
2018 0 808 33.471417 1.0136963 25 1.0356255 0.1012583 1237 51.242751 1.0465991 759 31.441591 3.2275466 3598 149.04722 37.7353495 308 12.758906 1.0922442 4624 191.54930 7.340986 205 8.4921292 0.4568361 743 30.778790 0.7816571 1762 72.99089 4.5082188 508 21.043910 1.3901401
2018 1 151 22.948328 0.6601831 5 0.7598784 0.0869052 179 27.203647 0.7805722 159 24.164134 0.6853312 338 51.36778 2.3680962 23 3.495441 0.2213699 623 94.68085 3.512348 7 1.0638298 0.1165559 82 12.462006 0.3857846 347 52.73556 1.5712445 55 8.358663 0.4455133
2019 0 901 32.823315 1.0328535 32 1.1657559 0.1073586 1128 41.092896 0.8942789 863 31.438980 2.9831696 3081 112.24044 23.4843543 329 11.985428 0.7948376 4693 170.96539 6.474147 188 6.8488160 0.3687522 800 29.143898 0.7356124 1864 67.90528 2.5860022 650 23.679417 1.6359754
2019 1 62 10.147300 0.4466793 5 0.8183306 0.0901645 70 11.456629 0.7377195 102 16.693944 0.4453258 164 26.84124 2.0252425 20 3.273322 0.2603445 286 46.80851 3.092065 6 0.9819967 0.0986888 33 5.400982 0.2535572 140 22.91326 1.2930593 34 5.564648 0.4325070
2020 0 774 29.609793 0.8402477 50 1.9127774 0.1370003 1154 44.146901 0.9813697 909 34.774292 3.4473183 2518 96.32747 17.2944212 209 7.995409 0.6187186 3848 147.20735 3.759894 123 4.7054323 0.2529673 601 22.991584 0.6057577 1869 71.49962 1.6170580 401 15.340474 0.6853909
2020 1 37 7.789474 0.3186135 2 0.4210526 0.0648201 73 15.368421 0.8391540 115 24.210526 0.5794619 118 24.84211 1.5238514 8 1.684211 0.1288153 159 33.47368 1.032976 12 2.5263158 0.1570888 24 5.052632 0.2377255 131 27.57895 1.2055354 49 10.315789 1.0633373
2021 0 675 25.911708 0.7559533 76 2.9174664 0.1750386 1097 42.111324 0.9940373 770 29.558541 1.6351552 2138 82.07294 11.3631455 144 5.527831 0.2629444 3917 150.36468 4.288430 143 5.4894434 0.2927373 734 28.176583 0.6970378 1724 66.18042 1.5890188 282 10.825336 0.5085568
2021 1 34 8.114558 0.3289866 2 0.4773270 0.0690062 70 16.706444 0.8969719 108 25.775656 0.6637617 129 30.78759 1.7093075 9 2.147971 0.1882092 195 46.53938 1.447856 4 0.9546539 0.0973551 24 5.727924 0.2326532 131 31.26492 1.1820457 15 3.579952 0.2516121 
#define custom color scale
myColors <- brewer.pal(4, "Spectral")
names(myColors) <- levels(sheriff_crime_grouped$`Presence of Gangs`)
custom_colors <- scale_colour_manual(name = "Presence of Gangs", values = myColors)

#visualization
sheriff_crime_grouped %>% ggplot(aes(x = year, y = mean_robbery, color = `Presence of Gangs`)) +
  geom_line() + theme(legend.title = element_text(size=8)) + 
  ylab("") +
  custom_colors + theme_ipsum() + ggtitle("LA Sheriff - Average Cases of Robbery (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

sheriff_crime_grouped %>% ggplot(aes(x = year, y = mean_homicide, color = `Presence of Gangs`)) +
  geom_line() + theme_minimal() + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2005,2021,2)) +
  custom_colors + theme_ipsum() + ggtitle("LA Sheriff - Average Cases of Homicide (x100)") + theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

sheriff_crime_grouped %>% ggplot(aes(x = year, y = mean_burglary, color = `Presence of Gangs`)) +
  geom_line() + theme_minimal() + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2005,2021,2)) +
  custom_colors + theme_ipsum() + ggtitle("LA Sheriff - Average Cases of Burglary (x100)") + theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

sheriff_crime_grouped %>% ggplot(aes(x = year, y = mean_weapons, color = `Presence of Gangs`)) +
  geom_line() + theme_minimal() + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2005,2021,2)) +
  custom_colors + theme_ipsum() + ggtitle("LA Sheriff - Average Cases of Weapon Crimes (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

sheriff_crime_grouped %>% ggplot(aes(x = year, y = mean_assault, color = `Presence of Gangs`)) +
  geom_line() + theme_minimal() + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2005,2021,2)) +
  custom_colors + theme_ipsum() + ggtitle("LA Sheriff - Average Cases of Assault (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

sheriff_crime_grouped %>% ggplot(aes(x = year, y = mean_rape_sexual, color = `Presence of Gangs`)) +
  geom_line() + theme_minimal() + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2005,2021,2)) + 
  custom_colors + theme_ipsum() + ggtitle("LA Sheriff - Average Cases of Sexual Crimes (x100)") +
 theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

sheriff_crime_grouped %>% ggplot(aes(x = year, y = mean_theft, color = `Presence of Gangs`)) +
  geom_line() + theme_minimal() + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2005,2021,2)) + 
  custom_colors + theme_ipsum() + ggtitle("LA Sheriff - Average Cases of Theft (x100)") +
 theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

sheriff_crime_grouped %>% ggplot(aes(x = year, y = mean_reckless_driving, color = `Presence of Gangs`)) +
  geom_line() + theme_minimal() + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2005,2021,2)) + 
  custom_colors + theme_ipsum() + ggtitle("LA Sheriff - Average Cases of Reckless Driving (x100)") +
 theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

sheriff_crime_grouped %>% ggplot(aes(x = year, y = mean_fraud, color = `Presence of Gangs`)) +
  geom_line() + theme_minimal() + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2005,2021,2)) +
  custom_colors + theme_ipsum() + ggtitle("LA Sheriff - Average Cases of Fraud (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

sheriff_crime_grouped %>% ggplot(aes(x = year, y = mean_vandalism, color = `Presence of Gangs`)) +
  geom_line() + theme_minimal() + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2005,2021,2)) +
  custom_colors + theme_ipsum() + ggtitle("LA Sheriff - Average Cases of Vandalism (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10)) 

sheriff_crime_grouped %>% ggplot(aes(x = year, y = mean_alcohol, color = `Presence of Gangs`)) +
  geom_line() + theme_minimal() + theme(legend.title = element_text(size=8)) + 
  ylab("") + scale_x_continuous(breaks=seq(2005,2021,2)) + 
  custom_colors + theme_ipsum() + ggtitle("LA Sheriff - Average Cases of Alcohol Offenses (x100)") +
  theme(plot.title = element_text(size = 10, face = "bold"), axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 10))