-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy path02_agg_analysis.R
409 lines (337 loc) · 16.3 KB
/
02_agg_analysis.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
"
Law & Order, SVU, and CI
1. Gender and Race of Criminals and Victims (Aggregated)
2. Comparison to NCVS, UCR, and Census
"
# Set directory
setwd(basedir)
setwd("face_of_crime/")
# Set library
library(ggplot2)
library(dplyr)
library(reshape2)
# Execute data cleaner
source("scripts/01_get_data_ready.R")
# Merge
#
lo_ci$series <- "Criminal Intent"
lo_svu$series <- "SVU"
lo_or$series <- "Original"
lo_all <- rbind(lo_ci, lo_svu, lo_or)
# Total shows, n_victims, n_criminals
# -----------------------------------------
nrow(lo_all)
sum(as.numeric(lo_all$n_victims), na.rm = T)
sum(as.numeric(lo_all$n_criminals), na.rm = T)
# What kind of crime
# ---------------------------
lo_all$rape <- grepl("rape", tolower(lo_all$crime))
lo_all$murder <- grepl("murder", tolower(lo_all$crime))
lo_all$assault <- grepl("assault", tolower(lo_all$crime))
sum(lo_all$rape | lo_all$murder)
sum(lo_all$rape | lo_all$murder | lo_all$assault)
# Split by series
table(lo_all$murder, lo_all$series)
table(lo_all$rape, lo_all$series)
# Split by crime
lo_rape <- lo_all[lo_all$rape == 1, ]
lo_murder <- lo_all[lo_all$murder == 1, ]
lo_mrdraslt <- lo_all[lo_all$murder == 1 | lo_all$assault == 1, ]
# Add Census, UCR, Crime Victimization Data
#----------------------------------------------
# Read and modify headers of census
census <- read.csv("data/census/census.csv")
names(census) <- c("year", "white_us", "black_us", "men_us", "white_ny", "black_ny", "men_ny")
census_means <- colMeans(census)
# Read in NCVS
ncvs <- read.csv("data/ncvs/ncvs.csv")
ncvs_means <- colMeans(ncvs)
# NCVS Hispanic
ncvs_hisp <- read.csv("data/ncvs/ncvs_hispanic.csv")
ncvs_hisp_means <- colMeans(ncvs_hisp)
# Read in murder victim data
murder_victims <- read.csv("data/ucr/murder_victim_race_gender.csv")
murder_victims[, 2:4] <- sapply(murder_victims[, 2:4], function(x) as.numeric(gsub("%", "", x)))
names(murder_victims) <- tolower(names(murder_victims))
murder_v_means <- colMeans(murder_victims)
# UCR
ucr <- read.csv("data/ucr/race_gender_criminals.csv")
ucr_means <- colMeans(ucr)
# UCR Hispanic
ucr_hisp <- read.csv("data/ucr/race_gender_criminals_hispanic.csv")
ucr_hisp_means <- colMeans(ucr_hisp, na.rm = T)
# NY Data
ny <- read.csv("data/ny_enforcement/nyc_enforcement.csv")
names(ny) <- tolower(names(ny))
ny <- as.data.frame(lapply(ny, function(x) as.numeric(gsub("%", "", x))))
ny_means <- colMeans(ny, na.rm = T)
# Custom theme
#-----------------
cust_theme <- theme_minimal() +
theme(panel.grid.major = element_line(color="#e1e1e1", linetype = "dotted"),
panel.grid.minor = element_blank(),
legend.position ="bottom",
legend.key = element_blank(),
legend.key.width = unit(1, "cm"),
axis.title = element_text(size = 10, color = "#555555"),
axis.text = element_text(size = 10, color = "#555555"),
axis.ticks.y = element_blank(),
axis.title.x = element_text(vjust = -1, margin = margin(10, 0, 0, 0)),
axis.title.y = element_text(vjust = 1),
axis.ticks = element_line(color = "#e3e3e3", size = .2),
plot.margin = unit(c(0, 1, 0, 0), "cm"))
# Plot 1a
# --------------------------
# Victims by Gender in shows where one of the crimes was rape
out_rape <- lo_rape %>%
group_by(series) %>%
summarise(male = sum(n_v_male, na.rm = T),
female = sum(n_v_female, na.rm = T),
pfemale = female*100/sum(male, female))
rout_rape <- out_rape[3, c("series", "pfemale")] # as number of rapes portrayed in other shows not large enough
# Victims by Gender in shows where one of the crimes was murder
out_murder <- lo_murder %>%
group_by(series) %>%
summarise(male = sum(n_v_male, na.rm = T),
female = sum(n_v_female, na.rm = T),
pfemale = female*100/sum(male, female))
rout_murder <- out_murder[, c("series", "pfemale")]
# Victims by gender
out <- lo_all %>%
group_by(series) %>%
summarise(male = sum(n_v_male, na.rm = T),
female = sum(n_v_female, na.rm = T),
pfemale = female*100/sum(male, female))
rout <- out[, c("series", "pfemale")]
# UCR and NCVS
ucr_v_sex <- data.frame(series = "UCR", pfemale = 100 - murder_v_means["murder_victim_men"])
ncvs_v_sex <- data.frame(series = "NCVS", pfemale = 100 - ncvs_means[c("rape_sexual_assault_male", "serious_violent_victimization_male")])
# All/Murder
rout2 <- cbind(rbind(rout, rout_murder, rout_rape, ucr_v_sex, ncvs_v_sex), crime = c(rep("All", 3), rep("Murder", 3), "Rape", "Murder","Rape", "Serious Violent Victimization"))
rout2$series <- factor(rout2$series, levels = c("UCR", "NCVS", "SVU", "Criminal Intent", "Original"))
# Plot
ggplot(rout2, aes(series, pfemale, color = crime)) +
geom_point(pch = 16, size = 3, alpha = .55) + #position=position_dodge(.1),
scale_colour_manual(values = c("#aaaaaa", "#5628a6", "#56a628", "#a65628"), guide = guide_legend(title = "")) +
coord_flip() +
scale_x_discrete(expand = c(.3, 1)) +
scale_y_continuous(name = "Percentage of Female Victims", limits = c(0, 100), breaks = seq(0, 100, 10), labels = paste0(seq(0, 100, 10), "%")) +
xlab("") +
cust_theme
ggsave("figs/all_victims_by_gender.pdf", scale = .8)
# Plot 1b
# ---------------------------------
# Criminals by Gender in shows where one of the crimes was rape
out_rape <- lo_rape %>%
group_by(series) %>%
summarise(male = sum(n_c_male, na.rm = T),
female = sum(n_c_female, na.rm = T),
pfemale = female*100/sum(male, female))
rout_rape <- out_rape[3, c("series", "pfemale")] # as number of rapes portrayed in other shows not large enough
# Criminals by Gender in shows where one of the crimes was murder
out_murder <- lo_murder %>%
group_by(series) %>%
summarise(male = sum(n_c_male, na.rm = T),
female = sum(n_c_female, na.rm = T),
pfemale = female*100/sum(male, female))
rout_murder <- out_murder[, c("series", "pfemale")]
# Criminals by Gender
out <- lo_all %>%
group_by(series) %>%
summarise(male = sum(n_c_male, na.rm = T),
female = sum(n_c_female, na.rm = T),
pfemale = female*100/sum(male, female))
rout <- out[, c("series", "pfemale")]
# UCR
ucr_c_sex <- data.frame(series = "UCR", pfemale = 100 - ucr_means[c("all_crime_perc_men", "homicides_perc_men", "rape_perc_men")])
# All/Rape/Murder
rout2 <- cbind(rbind(rout, rout_murder, rout_rape, ucr_c_sex), crime = c(rep("All", 3), rep("Murder", 3), "Rape", c("All", "Murder", "Rape")))
rout2$series <- factor(rout2$series, levels = c("UCR", "NCVS", "SVU", "Criminal Intent", "Original"))
# Plot
ggplot(rout2, aes(series, pfemale, color = crime)) +
geom_point(pch = 16, size = 3, alpha = .55) +
scale_colour_manual(values = c("#aaaaaa","#5628a6", "#56a628"), guide = guide_legend(title = "")) +
coord_flip() +
scale_x_discrete(expand = c(.3, 1)) +
scale_y_continuous(name = "Percentage of Female Criminals", limits = c(0, 100), breaks = seq(0, 100, 10), labels = paste0(seq(0, 100, 10), "%")) +
xlab("") +
cust_theme
ggsave("figs/all_criminals_by_gender.pdf", scale = .8)
# Plot 2a
# -------------------
# Victims by Race in shows where one of the crimes was rape
out_rape <- lo_rape %>%
group_by(series) %>%
summarise(white = sum(n_v_white, na.rm = T),
black = sum(n_v_black, na.rm = T),
hispanic = sum(n_v_hispanic, na.rm = T),
asian = sum(n_v_asian, na.rm = T),
pblack = black*100/sum(white, black, hispanic, asian))
rout_rape <- out_rape[3, c("series", "pblack")] # as number of rapes portrayed in other shows not large enough
# Victims by Race in shows where one of the crimes was murder
out_murder <- lo_murder %>%
group_by(series) %>%
summarise(white = sum(n_v_white, na.rm = T),
black = sum(n_v_black, na.rm = T),
hispanic = sum(n_v_hispanic, na.rm = T),
asian = sum(n_v_asian, na.rm = T),
pblack = black*100/sum(white, black, hispanic, asian))
rout_murder <- out_murder[, c("series", "pblack")]
# Victims by Race
out <- lo_all %>%
group_by(series) %>%
summarise(white = sum(n_v_white, na.rm = T),
black = sum(n_v_black, na.rm = T),
hispanic = sum(n_v_hispanic, na.rm = T),
asian = sum(n_v_asian, na.rm = T),
pblack = black*100/sum(white, black, hispanic, asian))
rout <- out[, c("series", "pblack")]
# UCR and NCVS and NY
ucr_v_race <- data.frame(series = "UCR", pblack = murder_v_means["murder_victim_black"])
ncvs_v_race <- data.frame(series = "NCVS", pblack = ncvs_means[c("rape_sexual_assault_black", "serious_violent_victimization_black")])
ny_v_race <- data.frame(series = "NYPD", pblack = ny_means[c("homicides_black_victim", "rapes_black_victim")])
# All/Rape/Murder
rout2 <- cbind(rbind(rout, rout_murder, rout_rape, ucr_v_race, ncvs_v_race, ny_v_race), crime = c(rep("All", 3), rep("Murder", 3), "Rape", "Murder", "Rape", "Serious Violent Victimization", "Murder", "Rape"))
rout2$series <- factor(rout2$series, levels = c("NYPD", "UCR", "NCVS", "SVU", "Criminal Intent", "Original"))
ggplot(rout2, aes(series, pblack, color = crime)) +
geom_point(pch = 16, size = 3, alpha = .55) +
scale_colour_manual(values = c("#aaaaaa","#5628a6", "#56a628", "#a65628"), guide = guide_legend(title = "")) +
coord_flip() +
scale_x_discrete(expand = c(.3, 1)) +
scale_y_continuous(name = "Percentage of Black Victims", limits = c(0, 100), breaks = seq(0, 100, 10), labels = paste0(seq(0, 100, 10), "%")) +
xlab("") +
cust_theme
ggsave("figs/all_victims_by_race.pdf", scale = .8)
# Plot 2a/SI: Hispanics
# ---------------------------
# Hispanic victims of rape
out_rape <- lo_rape %>%
group_by(series) %>%
summarise(white = sum(n_v_white, na.rm = T),
black = sum(n_v_black, na.rm = T),
hispanic = sum(n_v_hispanic, na.rm = T),
asian = sum(n_v_asian, na.rm = T),
phispanic = hispanic*100/sum(white, black, hispanic, asian))
rout_rape <- out_rape[3, c("series", "phispanic")] # as number of rapes portrayed in other shows not large enough
# Hispanic Victims in shows where one of the crimes was murder
out_murder <- lo_murder %>%
group_by(series) %>%
summarise(white = sum(n_v_white, na.rm = T),
black = sum(n_v_black, na.rm = T),
hispanic = sum(n_v_hispanic, na.rm = T),
asian = sum(n_v_asian, na.rm = T),
phispanic = hispanic*100/sum(white, black, hispanic, asian))
rout_murder <- out_murder[, c("series", "phispanic")]
out <- lo_all %>%
group_by(series) %>%
summarise(white = sum(n_v_white, na.rm = T),
black = sum(n_v_black, na.rm = T),
hispanic = sum(n_v_hispanic, na.rm=T),
asian = sum(n_v_asian, na.rm = T),
phispanic = hispanic*100/sum(white, black, hispanic, asian))
rout <- out[, c("series", "phispanic")]
ny_v_race <- data.frame(series = "NYPD", phispanic = ny_means[c("homicides_hispanic_victim", "rapes_hispanic_victim")])
ncvs_v_race <- data.frame(series = "NCVS", phispanic = ncvs_hisp_means[c("rape_hispanic", "serious_violent_victimization_hispanic")])
# All/Rape/Murder Hispanic
rout2 <- cbind(rbind(rout, rout_murder, rout_rape, ny_v_race, ncvs_v_race), crime = c(rep("All", 3), rep("Murder", 3), "Rape", "Murder", "Rape", "Rape", "Serious Violent Victimization"))
rout2$series <- factor(rout2$series, levels = c("NYPD", "NCVS", "SVU", "Criminal Intent", "Original"))
ggplot(rout2, aes(series, phispanic, color = crime)) +
geom_point(pch = 16, size = 3, alpha = .55) +
scale_colour_manual(values = c("#aaaaaa","#5628a6", "#56a628", "#a65628"), guide = guide_legend(title = "")) +
coord_flip() +
scale_x_discrete(expand = c(.3, 1)) +
scale_y_continuous(name = "Percentage of Hispanic Victims", limits = c(0, 100), breaks = seq(0, 100, 10), labels = paste0(seq(0, 100, 10), "%")) +
xlab("") +
cust_theme
ggsave("figs/all_victims_by_race_hispanic.pdf", scale = .8)
# Plot 2b
# --------------------------
# Criminals by Race in shows where one of the crimes was rape
out_rape <- lo_rape %>%
group_by(series) %>%
summarise(white = sum(n_c_white, na.rm = T),
black = sum(n_c_black, na.rm = T),
hispanic = sum(n_c_hispanic, na.rm = T),
asian = sum(n_v_asian, na.rm = T),
pblack = black*100/sum(white, black, hispanic, asian))
rout_rape <- out_rape[3, c("series", "pblack")] # as number of rapes portrayed in other shows not large enough
# Criminals by Race in shows where one of the crimes was murder
out_murder <- lo_murder %>%
group_by(series) %>%
summarise(white = sum(n_c_white, na.rm = T),
black = sum(n_c_black, na.rm = T),
hispanic = sum(n_c_hispanic, na.rm = T),
asian = sum(n_v_asian, na.rm = T),
pblack = black*100/sum(white, black, hispanic, asian))
rout_murder <- out_murder[, c("series", "pblack")]
# Criminals by Race
out <- lo_all %>%
group_by(series) %>%
summarise(white = sum(n_c_white, na.rm = T),
black = sum(n_c_black, na.rm = T),
hispanic = sum(n_c_hispanic, na.rm = T),
asian = sum(n_v_asian, na.rm = T),
pblack = black*100/sum(white, black, hispanic, asian))
rout <- out[, c("series", "pblack")]
# UCR
ucr_c_race <- data.frame(series = "UCR", pblack = ucr_means[c("all_crime_perc_black", "homicides_perc_black", "rape_perc_black")])
ny_c_race <- data.frame(series = "NYPD", pblack=ny_means[c("homicides_black_suspect", "rapes_black_suspect")])
# All/Rape/Murder
rout2 <- cbind(rbind(rout, rout_murder, rout_rape, ucr_c_race, ny_c_race), crime = c(rep("All",3), rep("Murder", 3), "Rape", c("All", "Murder","Rape"), "Murder", "Rape"))
rout2$series <- factor(rout2$series, levels = c("NYPD", "UCR", "NCVS", "SVU", "Criminal Intent", "Original"))
ggplot(rout2, aes(series, pblack, color = crime)) +
geom_point(pch = 16, size = 3, alpha = .55) +
scale_colour_manual(values = c("#aaaaaa","#5628a6", "#56a628"), guide = guide_legend(title = "")) +
coord_flip() +
scale_x_discrete(expand= c(.3, 1)) +
scale_y_continuous(name = "Percentage of Black Criminals", limits = c(0, 100), breaks = seq(0, 100, 10), labels = paste0(seq(0, 100, 10), "%")) +
xlab("") +
cust_theme
ggsave("figs/all_criminals_by_race.pdf", scale = .8)
# Plot 2b/SI/Hispanics
# --------------
# Criminals by Race in shows where one of the crimes was rape
out_rape <- lo_rape %>%
group_by(series) %>%
summarise(white = sum(n_c_white, na.rm = T),
black = sum(n_c_black, na.rm = T),
hispanic = sum(n_c_hispanic, na.rm = T),
asian = sum(n_v_asian, na.rm = T),
phispanic = hispanic*100/sum(white, black, hispanic, asian))
rout_rape <- out_rape[3, c("series", "phispanic")] # as number of rapes portrayed in other shows not large enough
# Criminals by Race in shows where one of the crimes was murder
out_murder <- lo_murder %>%
group_by(series) %>%
summarise(white = sum(n_c_white, na.rm = T),
black = sum(n_c_black, na.rm = T),
hispanic = sum(n_c_hispanic, na.rm = T),
asian = sum(n_v_asian, na.rm = T),
phispanic = hispanic*100/sum(white, black, hispanic, asian))
rout_murder <- out_murder[, c("series", "phispanic")]
# Criminals by Race
out <- lo_all %>%
group_by(series) %>%
summarise(white = sum(n_c_white, na.rm = T),
black = sum(n_c_black, na.rm = T),
hispanic = sum(n_c_hispanic, na.rm = T),
asian = sum(n_v_asian, na.rm = T),
phispanic = hispanic*100/sum(white, black, hispanic, asian))
rout <- out[, c("series", "phispanic")]
# UCR
ucr_c_race <- data.frame(series = "UCR", phispanic = ucr_hisp_means[c("all_crime_perc_hispanic", "homicides_perc_hispanic", "rape_perc_hispanic")])
ny_c_race <- data.frame(series = "NYPD", phispanic = ny_means[c("homicides_hispanic_suspect", "rapes_hispanic_suspect")])
# All/Rape/Murder
rout2 <- cbind(rbind(rout, rout_murder, rout_rape, ucr_c_race, ny_c_race), crime = c(rep("All", 3), rep("Murder", 3), "Rape", c("All", "Murder", "Rape"), "Murder", "Rape"))
rout2$series <- factor(rout2$series, levels = c("NYPD", "UCR", "NCVS", "SVU", "Criminal Intent", "Original"))
ggplot(rout2, aes(series, phispanic, color = crime)) +
geom_point(pch = 16, size = 3, alpha = .55) +
scale_colour_manual(values = c("#aaaaaa","#5628a6", "#56a628"), guide = guide_legend(title = "")) +
coord_flip() +
scale_x_discrete(expand = c(.3, 1)) +
scale_y_continuous(name = "Percentage of Hispanic Criminals",
limits = c(0, 100),
breaks = seq(0, 100, 10),
labels = paste0(seq(0, 100, 10), "%")) +
xlab("") +
cust_theme
ggsave("figs/all_criminals_by_race_hispanic.pdf", scale = .8)