Domestic terrorism analysis

Please switch between tabs in each section to see the text compared to the charts and the summary tables.

packages <- c("tidyverse", "readxl", "janitor",
              "geofacet", "forcats",  "scales",
              "lubridate", "DT", "knitr")

if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
  install.packages(setdiff(packages, rownames(installed.packages())), repos = "http://cran.us.r-project.org")  
}

library(tidyverse)
library(readxl)
library(janitor)
library(geofacet)
library(forcats)
library(scales)
library(lubridate)
library(DT)
library(knitr)

#df2 <- read_excel("../raw_data/CSIS_TNT_Terrorism_US_Jan1994-Aug2020.xlsx")
#df2 <- read_excel("../raw_data/CSIS_TNT_Terrorism_US_Jan94-Jan21_Mar2021.xlsx")

df2 <- read_csv("../../data/clean_data/csis_wapo_domestic_terrorism.csv")

df2 <- clean_names(df2)

df2 <- df2 %>% 
  rename(tnt_orientation=orientation_csis,
         year=year_csis,
         state=state_csis,
         month=month_csis,
         vict_killed=vict_killed_csis,
         weapon=weapon_csis,
         target=target_csis,
         lat=lat_csis,
         long=long_csis,
         target_category=victim_category,
         target_specific1=victim_specific1,
         target_specific2=victim_specific2,
         mil_status=mil_status_csis,
         le_status=le_status_csis,
         perp_social_media=susp_social_media_use)

a. Terror incidents, plots since ’94

Text

text_of <- df2 %>% 
  filter(tnt_orientation=="Violent Far-right") %>% 
  count() %>% 
  pull(n) 

left_total <-  df2 %>% 
  filter(tnt_orientation=="Violent Far-left") %>% 
  count() %>% 
  pull(n)

There have been 561 violent far-right incidents since 1994. (In contrast, there have been 251 violent far-left incidents since then.)

In 2020, there were 73 violent far-right attacks in the United States– the most since recorded by CSIS since 1994. The previous high was 49 in 2017.

In January 2021, CSIS documented 11 violent far-right attacks, including the Jan. 6 insurrection at the Capitol. That is the highest number for any January in the CSIS database and among the top four for any month.

Chart

df2 %>% 
  filter(tnt_orientation=="Violent Far-right") %>% 
  count(year) %>% 
  ggplot(aes(x=year, y=n)) +
  geom_col() +
  theme_minimal() +
  labs(x="", y="incidents",
       title="Violent Far-right incidents since 1994",
       subtitle="Data: CSIS")

Data

df2 %>% 
  filter(tnt_orientation=="Violent Far-right") %>% 
  count(year) %>% 
  datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "total_since_1994_data",              exportOptions = list(                modifier = list(page = "all")              )))))

b. Proportion of incidents

Text

df_summary <- df2 %>% 
  count(year, tnt_orientation) %>% 
  group_by(year) %>% 
  mutate(percent=round(n/sum(n, na.rm=T)*100))

more_than50 <- df_summary %>% 
  filter(tnt_orientation=="Violent Far-right") %>% 
  filter(percent>50) %>% 
  ungroup () %>% 
  count() %>% 
  pull(n)

max_year <- df_summary %>% 
  ungroup() %>% 
  filter(year==max(year)) %>% 
  pull(year) %>% unique()

min_year <- df_summary %>% 
  ungroup() %>% 
  filter(year==min(year)) %>% 
  pull(year) %>% unique()

total_years <- max_year-min_year

ratio50 <- round(more_than50/total_years*100,1)

Incidents involving right wing extremists made up more than half the attacks of all categories in 17 out of 27 years, almost two out of three years.

In recent years, right-wing incidents made up the majority of all domestic terror incidents. There have been 267 right wing attacks and plots since 2015.

Chart

df_summary <- df2 %>% 
  count(year, tnt_orientation) %>% 
  group_by(year) %>% 
  mutate(percent=round(n/sum(n, na.rm=T)*100))

# How many per year?
df_summary %>% 
  rename(Group=tnt_orientation) %>% 
  ggplot(aes(x=year, y=percent, fill=Group)) +
  geom_bar(position="stack", stat="identity") +
  theme_minimal() +
  labs(x="", title="Percent of incidents by year", 
       caption="Data: CSIS")

Data

df_summary %>% 
  rename(Group=tnt_orientation, incidents=n) %>% 
  datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "proportion_data",              exportOptions = list(                modifier = list(page = "all")              )))))

c. Fatalities

Text

#fatalities
#injuries
#vict_killed
df_summary <- df2 %>% 
  group_by(year, tnt_orientation) %>% 
  #group_by(tnt_orientation) %>% 
  summarize(vict_killed=sum(vict_killed, na.rm=T)) %>%
  group_by(year) %>% 
  mutate(percent=round(vict_killed/sum(vict_killed, na.rm=T)*100))
  
  #mutate(percent=round(n/sum(n, na.rm=T)*100))

more_than50 <- df_summary %>% 
  filter(tnt_orientation=="Violent Far-right") %>% 
  filter(percent>50) %>% 
  ungroup () %>% 
  count() %>% 
  pull(n)

max_year <- df_summary %>% 
  ungroup() %>% 
  filter(year==max(year)) %>% 
  pull(year) %>% unique()

min_year <- df_summary %>% 
  ungroup() %>% 
  filter(year==min(year)) %>% 
  pull(year) %>% unique()

total_years <- max_year-min_year

ratio50 <- round(more_than50/total_years*100,1)

There have been 329 fatalities since 1994 from right-wing attacks compared to the 31 from left-wing attacks.

Fatalities occurred in a total 21 years in the database. In 14 of the years when a fatality was recorded, the fatality was the result of right wing attacks.

Islamic religious terrorists on 911 caused the largest number of terror-related fatalities on American soil between 1994 and January 2021.

But in any year, right-wing attackers were more likely to cause more deaths than any other category of domestic terrorism.

Chart

df_summary %>% 
  rename(Group=tnt_orientation) %>% 
  ggplot(aes(x=year, y=percent, fill=Group)) +
  geom_bar(position="stack", stat="identity") +
  theme_minimal() +
  labs(x="", title="Percent of fatalities killed by year", 
       caption="Data: CSIS")

Data

df_summary %>% 
  rename(Group=tnt_orientation) %>% 
  datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "vict_killed_data",              exportOptions = list(                modifier = list(page = "all")              )))))

d. Targets

Text

targets <- df2 %>% count(tnt_orientation, target) %>% 
  filter(tnt_orientation=="Violent Far-right" | tnt_orientation=="Violent Far-left") %>% 
  pivot_wider(names_from="target", values_from="n") %>% 
  pivot_longer(cols=2:13, names_to="target", values_to="attacks") %>% 
  rename(Group=tnt_orientation) %>% 
  group_by(Group) %>% 
  mutate(percent=round(attacks/sum(attacks, na.rm=T)*100))
#type_target

This is based on CSIS designations

Most of these left-wing attacks targeted property associated with animal research, farming, or construction and were claimed by the Animal Liberation Front or the Earth Liberation Front.

There are three broad types of right-wing terrorist individuals and networks in the United States: white supremacists, anti-government extremists, and incels. Right wing attack increasingly focus on individuals and religious institutions, including black churches, synagogues etc.

Chart

ggplot(targets) +
  geom_col(aes(x=attacks, y=fct_reorder(target, attacks, na.rm=T), 
               fill=Group, group=Group),position="dodge") +
  scale_fill_manual(values = c("lightslategray", "burlywood")) +
  #scale_color_manual(values = rev(cols))+
  labs(y="Target", x="Incidents", title="Attacks and plots since 1994",
       caption="Data: CSIS") +
  theme_minimal()  +
    theme(legend.position="top")

Data

targets %>% datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "targets_data",              exportOptions = list(                modifier = list(page = "all")              )))))

e. Targets over time

Text

targets_year <- df2 %>% count(tnt_orientation, year, target) %>% 
  filter(tnt_orientation=="Violent Far-right" | tnt_orientation=="Violent Far-left") %>% 
  pivot_wider(names_from="target", values_from="n") %>% 
  pivot_longer(cols=3:14, names_to="target", values_to="attacks") %>% 
  rename(Group=tnt_orientation)

#type_target

Violent acts from the far-left in the early 2000s focused on businesses.

Then in 2015, right wing attacks began focusing on individuals and churches, synagogues, mosques, and other religious institutions, as well as private individuals.

In 2019, left-wing and right-wing violence saw the most incidents against government, military, and police targets (between 15 and 20). The following year, left-wing violence against those targets dropped to just …. while right-wing violence just dropped to 10.

In 2020, demonstrators became the largest targeted group among right-wing incidents. Nearly 25 incidents– the most of any targeted groups since 1994.

This is based on CSIS designations

Chart

ggplot(targets_year) +
  geom_col(aes(x=year, y=attacks, 
               fill=Group, group=Group),position="dodge") +
  facet_wrap(~target, ncol=3) +
  scale_fill_manual(values = c("lightslategray", "burlywood")) +
  #scale_color_manual(values = rev(cols))+
  labs(y="Incidents", x="", title="Targets of attacks since 1994",
       caption="Data: CSIS") +
  theme_minimal() +
    theme(legend.position="top")

Data

targets_year %>% datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "targets_time_data",              exportOptions = list(                modifier = list(page = "all")              )))))

f. Types of Far Right Extremism

Text

#ideology_subtype
#type_extremism
#
ideologies1 <- df2 %>% 
  #filter(!is.na(ideology_subtype)) %>% 
  filter(year>2014) %>% 
  filter(tnt_orientation=="Violent Far-right") %>% 
  select(year, month, extremism_type=type_extremism_1, vict_killed) 

ideologies2 <- df2 %>% 
  #filter(!is.na(ideology_subtype)) %>% 
  filter(year>2014) %>% 
  filter(tnt_orientation=="Violent Far-right") %>% 
  select(year, month, extremism_type=type_extremism_2, vict_killed) 

ideologies <- rbind(ideologies1, ideologies2) %>% 
  filter(!is.na(extremism_type)) %>% 
  group_by(extremism_type) %>% 
  summarize(incidents=n(), killed=sum(vict_killed, na.rm=T)) 


ideologies_month <- rbind(ideologies1, ideologies2) %>% 
  filter(!is.na(extremism_type)) %>% 
  group_by(year, month, extremism_type) %>% 
  summarize(incidents=n(), killed=sum(vict_killed, na.rm=T))

ideologies_year <- rbind(ideologies1, ideologies2) %>% 
  filter(!is.na(extremism_type)) %>% 
  group_by(year, month, extremism_type) %>% 
  summarize(incidents=n(), killed=sum(vict_killed, na.rm=T))

Attacks and plots involving white supremacist accounted for the most extreme right-wing terror incidents since 2015.

In all, there were 72 incidents and 37 deaths from white supremacist attacks. Another way to put it is more than 1 out of 4 incidents were white supremacist attacks and about 40% all deaths tied to right-wing violence came from white supremacists.

The most-popular type of right-wing incidents:

white supremacist: 72 incidents and 37 deaths (27% and 41%)
anti-muslim: 43 incidents - 16%
anti government: 43 incidents - 13%
racist: 10.9 - 11%

Right-wing incidents with the most fatalities:

anti-immigrant: 27 - 30%
incel: 14 - 15%
racist: 10.9 - 11%

Other things to note:

Incel-related incidents made up just over 3 percent of all violent right-wing incidents but made up 15 percent of all deaths.
Anti immigrant-related incidents consisted of nearly 6 percent of all violent right-wing incidents but made up about 30 percent of all deaths.

Analysis based on Post designations

Chart

ideologies %>%
  pivot_longer(cols=2:3, names_to="type", values_to="values") %>% 
  ggplot(aes(y=extremism_type, x=values)) +
  geom_col() +
  facet_wrap(~type, ncol=2, scales="free") +
  theme_minimal() +
  labs(title="Right-wing extremism incidents and deaths since 2015",
       y="", x="")

Data

inc_kill <- df2 %>% 
  #filter(!is.na(ideology_subtype)) %>% 
  filter(year>2014) %>% 
  filter(tnt_orientation=="Violent Far-right") %>% 
  summarize(incidents=n(),
            killed=sum(vict_killed, na.rm=T))

incidents_total <- inc_kill %>% 
  pull(incidents)

killed_total <- inc_kill %>% 
  pull(killed)


ideologies %>% 
  ungroup() %>% 
  mutate(`incidents %`=round(incidents/incidents_total*100,1),
         `killed %`=round(killed/killed_total*100,1)) %>% 
  arrange(desc(`incidents %`)) %>% 
           datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "types_of_far_right_data",              exportOptions = list(                modifier = list(page = "all")              )))))

g. Annual types of FRE incidents

Text

#ideology_subtype
#type_extremism
#
ideologies_year <- rbind(ideologies1, ideologies2) %>% 
  filter(!is.na(extremism_type)) %>% 
  group_by(year, extremism_type) %>% 
  summarize(incidents=n(), killed=sum(vict_killed, na.rm=T))

In 2020, right-wing attacks against Black Lives Matters protesters outnumbered any other target in a given year (26). The next highest year was in 2017 with white supremacist-related attacks (21) and in 2015 against muslims (16).

Anti muslim attacks started high at 16 in 2015 but has declined every year since and since disappeared in 2020 and 2021.

Since 2019, attacks and plots rising year over year have been driven by:

conspiracy theorists,
anti covid lockdowns
anti police
incels
white supremacists

Chart

ideologies_year %>%
  #pivot_longer(cols=2:3, names_to="type", values_to="values") %>% 
  ggplot(aes(x=year, y=incidents)) +
  geom_col() +
  facet_wrap(~extremism_type, ncol=3) +
  theme_minimal() +
  labs(title="Right-wing extremism incidents by type since 2015",
       y="", x="")

Data

ideologies_year %>% 
           datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "types_of_far_right_year_data",              exportOptions = list(                modifier = list(page = "all")              )))))

h. Monthly FRE incidents in 2020

Text

#ideology_subtype
#type_extremism
#
ideologies_month <- rbind(ideologies1, ideologies2) %>% 
  filter(!is.na(extremism_type)) %>% 
  filter(year>2019) %>% 
  group_by(year, month, extremism_type) %>% 
  summarize(incidents=n(), killed=sum(vict_killed, na.rm=T)) %>% 
  mutate(date=ymd(paste0(year, "/", month, "/1")))

Looking specifically at 2020 and 2021, the majority of attacks were against Black Lives Matters demonstrators with the largest spike in June at the same time when protestors across the country were most active.

Anti-government and anti-police attacks began in May, following widespread covid-19 lockdowns by the government. Anti-government and Stop the Steal violence began peaking in October of 2020 clmbing to their highest points in January 2021.

Chart

ideologies_month %>%
  #pivot_longer(cols=2:3, names_to="type", values_to="values") %>% 
  ggplot(aes(x=date, y=incidents)) +
  geom_col() +
  facet_wrap(~extremism_type, ncol=2) +
  theme_minimal() +
  labs(title="Monthly right-wing extremism incidents by type in 2020 and 2021",
       y="", x="")

Data

ideologies_month %>% 
           datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "types_of_far_right_month_data",              exportOptions = list(                modifier = list(page = "all")              )))))

i. Fatalities and targets

Text

victims1 <- df2 %>% 
  #filter(!is.na(ideology_subtype)) %>% 
  filter(year>2014) %>% 
  filter(tnt_orientation=="Violent Far-right") %>% 
  #mutate(target1 = paste0(target_category, "-", target_specific1)) %>% 
  #mutate(target2 = paste0(target_category, "-", target_specific2)) %>% 
  #select(year, month, target_type=target1, vict_killed) 
  select(year, month, target_category, target_specified=target_specific1, vict_killed)

victims2 <- df2 %>% 
  #filter(!is.na(ideology_subtype)) %>% 
  filter(year>2014) %>% 
  filter(tnt_orientation=="Violent Far-right") %>% 
  filter(!is.na(target_specific1)) %>% 
  #mutate(target1 = paste0(target_category, "-", target_specific1)) %>% 
  #mutate(target2 = paste0(target_category, "-", target_specific2)) %>% 
  #select(year, month, target_type=target2, vict_killed) 
  select(year, month, target_category, target_specified=target_specific2, vict_killed) %>% 
  filter(!is.na(target_specified))

victims <- rbind(victims1, victims2) %>% 
  #filter(!is.na(target_type)) %>% 
  #group_by(target_type) %>% 
  
  group_by(target_category, target_specified) %>% 
  summarize(incidents=n(), killed=sum(vict_killed, na.rm=T)) #%>% 
  #mutate(target_type=gsub("-NA", "", target_type))


#victims_month <- rbind(ideologies1, ideologies2) %>% 
#  filter(!is.na(extremism_type)) %>% 
#  group_by(year, month, extremism_type) %>% 
#  summarize(incidents=n(), killed=sum(vict_killed, na.rm=T))

#ideologies_year <- rbind(ideologies1, ideologies2) %>% 
#  filter(!is.na(extremism_type)) %>% 
#  group_by(year, month, extremism_type) %>% 
#  summarize(incidents=n(), killed=sum(vict_killed, na.rm=T))
# df2 %>% 
#   filter(year>2014) %>% 
#   filter(tnt_orientation=="Violent Far-right") %>% 
#   mutate(race_victim=gsub("black men protesters", "Black", race_victim)) %>% 
#   mutate(race_victim=gsub("black protesters", "Black", race_victim)) %>% 
#   mutate(race_victim=gsub("Latina", "latino", race_victim)) %>% 
#   mutate(race_victim=gsub("white black asian", "multiple races", race_victim)) %>% 
#   mutate(race_victim=case_when(
#     grepl(",", race_victim) ~ "multiple races",
#     TRUE ~ race_victim)) %>% 
#   mutate(race_victim=case_when(
#     grepl(" and ", race_victim) ~ "multiple races",
#     TRUE ~ race_victim)) %>% 
#   mutate(race_victim=str_to_title(race_victim)) %>% 
#   count(race_victim) %>% 
#   arrange(desc(n)) %>% 
#   kable()
#race_victims
#type_target

Since 2015, the most frequent targets of right-wing violence, the targets accounting for the most fatalities are social justice and Black Lives Matters demonstrators. Black and muslim individuals account for the next most common targets.

Among buildings that were attacked by right-wing extremists, abortion clinics and government institutions were hit the most often.

Analysis based on our designations

Chart

ggplot(victims, aes(x=incidents, y=target_specified)) +
  geom_col() +
  facet_wrap(~target_category, ncol=2) +
  theme_minimal() +
  labs(title="Fatalities and targets of right-wing violence since 2015",
       y="", x="", caption="Data: CSIS")

Data

victims %>% datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "victims_data",              exportOptions = list(                modifier = list(page = "all")              )))))

j. Weapons

Text

weapons <- df2 %>% 
  filter(tnt_orientation=="Violent Far-right") %>% 
  count(year, weapon)

Firearms, explosives and incendiaries were used in right-wing attacks often since 1994 (a high of 32 in 1995) and fell and leveled off at about 6 times per year in the 2000s. But that started climbing again starting in 2015.

Firearms use also climbed until reaching a high point in 2020.

Vehicles were used in 11 attacks in 2020, a recent development.

Chart

ggplot(weapons) +
  geom_col(aes(x=year, y=n)) +
  facet_wrap(~weapon) +
  labs(y="Target", x="Incidents", title="Attacks since 1994") +
  theme_minimal()

Data

weapons %>% datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "weapons_data",              exportOptions = list(                modifier = list(page = "all")              )))))

k. Former police or military

Text

po_mil <- df2 %>% 
  filter(year>2014) %>% 
  filter(tnt_orientation=="Violent Far-right") %>% 
  mutate(mil_vet=case_when(
    mil_status=="Active" ~ "military-active/reserve",
    mil_status=="Reserve"~ "military-active/reserve",
    grepl("Veteran", mil_status) ~ "military-veteran",
    grepl("Reserve, Veteran", mil_status) ~ "military-veteran",
    le_status=="Active" ~ "law enforcement-active",
    le_status=="Former" ~ "law enforcement-former",
    TRUE ~ NA_character_
  )) %>% 
  count(mil_vet)

lea <- po_mil %>% 
  filter(mil_vet=="law enforcement-active") %>% 
  pull(n)

lef <- po_mil %>% 
  filter(mil_vet=="law enforement-former") %>% 
  pull(n)

if (length(lef)==0) { lef <- 0}

mar <- po_mil %>% 
  filter(mil_vet=="military-active/reserve") %>% 
  pull(n)

mv <- po_mil %>% 
  filter(mil_vet=="military-veteran") %>% 
  pull(n)

#le
#mil
#military_vet


po_mil_annual <- df2 %>% 
  filter(year>2014) %>% 
    filter(tnt_orientation=="Violent Far-right") %>% 
  mutate(mil_vet=case_when(
    mil_status=="Active" ~ "military-active/reserve",
    mil_status=="Reserve"~ "military-active/reserve",
    grepl("Veteran", mil_status) ~ "military-veteran",
    grepl("Reserve, Veteran", mil_status) ~ "military-veteran",
    le_status=="Active" ~ "law enforcement-active",
    le_status=="Former" ~ "law enforcement-former",
    TRUE ~ NA_character_
  )) %>% 
  count(year, mil_vet)

Since 2015, military veterans or law enforcement have been involved in 29 right-wing violent attacks.

Active during and reserve military duty have been part of 7 attacks.

To a lesser degree, a total of 1 former and active law enforcement officers have participated in attacks.

2020 saw the most attacks from suspects who were in active or reserve military.

Chart

po_mil_annual %>% 
  filter(!is.na(mil_vet)) %>% 
  ggplot(aes(x=year, y=n)) +
  geom_col() +
  theme_minimal() +
  facet_wrap(~mil_vet)

Data

po_mil_annual %>% 
  filter(!is.na(mil_vet)) %>% 
  datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "frmr_mil_data",              exportOptions = list(                modifier = list(page = "all")              )))))

l. Police targets

Police

cops <- df2 %>% 
    filter(tnt_orientation=="Violent Far-right") %>% 

  filter(target_specific1 == "police" | target_specific2 == "police")

Police have been a target of right-wing violence 7 times since 2015.

Attacks didn’t begin until 2017 and in 2020, the highest amount was reached (4).

Chart

 df2 %>% 
  filter(target_specific1 == "police" | target_specific2 == "police") %>% 
  count(year) %>% 
  ggplot(aes(x=year, y=n)) +
  geom_col() +
  theme_minimal() +
  labs(title="Attacks against police since 2015", x="", y="incidents",
       caption="Data: CSIS")

Data

df2 %>% 
    filter(tnt_orientation=="Violent Far-right") %>% 

  filter(target_specific1 == "police" | target_specific2 == "police") %>% 
  count(year) %>% datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "police_data",              exportOptions = list(                modifier = list(page = "all")              )))))

m. Religious places

Text

relig <- df2 %>% 
      filter(tnt_orientation=="Violent Far-right") %>% 

  filter(year>2014) %>% 
  count(target) %>% 
  mutate(percent=round(n/sum(n, na.rm=T)*100)) %>% 
  filter(target=="Religious Institutions")

Since 2015, there have been 61 attacks on religious institutions (about 23 percent)`.

The most frequent targets were 24 mosques as well as 15 synagogues and 15 Black churches.

Chart

df2 %>% 
  filter(year>2014) %>% 
    filter(tnt_orientation=="Violent Far-right") %>% 

  filter(target=="Religious Institutions") %>% 
  count(target_specific1) %>% 
  filter(!is.na(target_specific1)) %>% 
  ggplot(aes(x=n, y=target_specific1)) +
  geom_col()+
  theme_minimal()+
  labs(title="Targets of religious institution attacks by right-wing extremism",
       y="",x="", caption="Data: CSIS")

Data

df2 %>% 
  filter(year>2014) %>% 
    filter(tnt_orientation=="Violent Far-right") %>% 

  filter(target=="Religious Institutions") %>% 
  count(target_specific1) %>% 
  filter(!is.na(target_specific1)) %>% datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "religious_data",              exportOptions = list(                modifier = list(page = "all")              )))))

n. Planned Parenthood and clinics

Text

abor <- df2 %>% 
    filter(tnt_orientation=="Violent Far-right") %>% 

  #filter(year>2014) %>% 
  count(target) %>% 
  mutate(percent=round(n/sum(n, na.rm=T)*100))
#target=="Abortion Related"

Since 1994, there have been 114 attacks on Planned Parenthood and abortion (about 20 percent).

Chart

  df2 %>% 
    filter(tnt_orientation=="Violent Far-right") %>% 

  filter(target=="Abortion Related") %>% 
  #filter(year>2014) %>% 
  group_by(year) %>%
  summarize(incidents=n(), fatalities=sum(vict_killed, na.rm=T)) %>% 
  pivot_longer(cols=2:3, names_to="type", values_to="values") %>% 
  ggplot(aes(x=year, y=values)) +
  geom_col() +
  facet_wrap(~type) +
  theme_minimal() +
  labs(title="Fatalities and incidents against Planned Parenthood and womens' clinics",
       x="", y="", caption="Data: CSIS")

Data

df2 %>% 
    filter(tnt_orientation=="Violent Far-right") %>% 

  filter(target=="Abortion Related") %>% 
  #filter(year>2014) %>% 
  group_by(year) %>%
  summarize(incidents=n(), fatalities=sum(vict_killed, na.rm=T)) %>%
  datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "pp_data",              exportOptions = list(                modifier = list(page = "all")              )))))

o. Attacks on govt or process

Text

govt <- df2 %>% 
    filter(tnt_orientation=="Violent Far-right") %>% 

  filter(target_specific1=="government" | target_specific2=="government") %>% 
  summarize(incidents=n(), fatalities=sum(vict_killed, na.rm=T))

#govt

Since 2015, there have been 30 attacks on government institutions or representatives and 1 death.

Annually, these attaks have grown steadily culminating with the highest amount ever in a year in 2020 (15) followed by 10 in the first month of 2021.

The insurrection at the nation’s capitol was just one of those logged incidents.

Chart

df2 %>% 
    filter(tnt_orientation=="Violent Far-right") %>% 

  filter(target_specific1=="government" | target_specific2=="government") %>% 
  group_by(year) %>% 
  summarize(incidents=n(), fatalities=sum(vict_killed, na.rm=T)) %>% 
  ggplot(aes(x=year, y=incidents)) +
  geom_col() +
  theme_minimal() +
  labs(title="Attacks against government institutions or representatives since 2015",
       x="", y="", caption="Data: CSIS")

Data

df2 %>% 
    filter(tnt_orientation=="Violent Far-right") %>% 

  filter(target_specific1=="government" | target_specific2=="government") %>% 
  group_by(year) %>% 
  summarize(incidents=n(), fatalities=sum(vict_killed, na.rm=T)) %>% 
  datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "govt_data",              exportOptions = list(                modifier = list(page = "all")              )))))

p. Hometown of perps

In 2020, at least 11 attacks were outside of the perpatrator’s hometown. That year was the second-highest since 2015. The most was in 2017 with 14 attacks. The annual average distance traveled by these perpetrators range from 20 to miles to 68 miles between 2015 and 2020. However in the first month of 2021, the average distance traveled by perpetrators was 143 miles.

Text

distances <- read_csv("../../data/clean_data/distances.csv")
distances%>% 
     filter(!is.na(distance)) %>% 
     mutate(further=case_when(
         distance>0 ~ "further",
         TRUE ~ "same place")) %>% 
     count(year, further) %>% 
  filter(further!="same place") %>% 
  kable()

year	further	n
2015	further	8
2016	further	9
2017	further	14
2018	further	5
2019	further	5
2020	further	11
2021	further	2

Chart

distances %>% 
  filter(!is.na(distance)) %>% 
  filter(distance!=0) %>%
  group_by(year) %>%  
  summarize(miles=median(distance)) %>% 
  kable()

year	miles
2015	67.5
2016	67.0
2017	71.5
2018	54.0
2019	20.0
2020	50.0
2021	142.5

Data

distances %>% 
  filter(!is.na(distance)) %>% 
  filter(distance!=0) %>%
  group_by(year) %>%  
  summarize(miles=median(distance)) %>% 
  datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "hometown_data",              exportOptions = list(                modifier = list(page = "all")              )))))

q. Social media

Text

df2$sm <- ifelse(!is.na(df2$perp_social_media), 1, 0)

#!is.na(perp_social_media)
#perp_social_media!="unknown"

According to a review from the Post, mentions of perpetrators using social media have grown steadily since 2015 (with the exception of 2018). But the most by far was in 2020 when 24 incidents involved messaging on social media such as Facebook or an encrypted chat service. That’s more than one out of five incidents of right wing violence in 2020.

Chart

df2 %>% 
  filter(year>2014) %>% 
  mutate(sm=case_when(
    perp_social_media=="unclear" ~ 0,
    TRUE ~ sm)) %>% 
  group_by(year) %>% 
  summarize(total=n(),
            social_media=sum(sm, na.rm=T)) %>% 
  mutate(percent=round(social_media/total*100,2)) %>% 
  ggplot(aes(x=year, y=total)) +
  geom_col() +
  theme_minimal() +
  labs(title="Social media was used by perpetrator", 
       x="", y="", caption="Data: CSIS")

Data

df2 %>% 
  filter(year>2014) %>% 
  mutate(sm=case_when(
    perp_social_media=="unclear" ~ 0,
    TRUE ~ sm)) %>% 
  group_by(year) %>% 
  summarize(total=n(),
            social_media=sum(sm, na.rm=T)) %>% 
  mutate(percent=round(social_media/total*100,2)) %>% 
  datatable(     extensions = c("Buttons"),     options = list(       dom = 'Bfrtip',       buttons = list(         list(extend = "csv", text = "Download Table", filename = "sm_data",              exportOptions = list(                modifier = list(page = "all")              )))))

r. Advanced analysis

Text

# guns
# social media
# attacks by month
monthly_guns <- read_csv("../../data/raw_data/monthly_national.csv") %>% 
  filter(year >=2020) %>% 
  mutate(type="gun sales estimate rate") %>% 
  select(year, month, date, month, type, values=rate) %>% 
  mutate(date=ymd(paste0(year, "/", month, "/1")))
  
social_media <- read_excel("../../data/raw_data/ncri_temporal_data.xlsx", sheet=1) %>% 
  mutate(year=year(Date),
         month=month(Date)) %>% 
  group_by(year, month) %>% 
  summarize(antifa_gab=sum(antifa_gab, na.rm=T),
            all_lives_parler=sum(all_lives_parler, na.rm=T),
            flatten=sum(Flatten, na.rm=T),
            reopen=sum(Reopen, na.rm=T),
            liberate=sum(Liberate, na.rm=T),
            covid=sum(COVID, na.rm=T),
            anti_blm=sum(anti_BLM, na.rm=T),
            elex=sum(Elex, na.rm=T),
            stopthesteal=sum(StoptheSteal, na.rm=T)) %>%
  mutate(date=ymd(paste0(year, "/", month, "/1"))) %>% 
  pivot_longer(cols=3:11, names_to="type", values_to="values") %>% 
  select(-year)

combined <- monthly_guns %>% rbind(social_media)

  
ideologies_condensed <- ideologies_month %>%
  #pivot_longer(cols=2:3, names_to="type", values_to="values") %>% 
  filter(year>2019) %>% 
  mutate(date=ymd(paste0(year, "/", month, "/1"))) %>% 
  mutate(category_combined=case_when(
    extremism_type=="anti abortion" ~ "anti-abortion, anti-asian, anti-semitic, other",
    extremism_type=="anti asian" ~ "anti-abortion, anti-asian, anti-semitic, other",
    extremism_type=="anti semitic" ~ "anti-abortion, anti-asian, anti-semitic, other",
    extremism_type=="other" ~ "anti-abortion, anti-asian, anti-semitic, other",
    extremism_type=="anti government" ~ "anti-covid lockdown, anti-government, anti-police, conspiracy theorist",
    extremism_type=="anti police" ~ "anti-covid lockdown, anti-government, anti-police, conspiracy theorist",
    extremism_type=="anti covid lockdown" ~ "anti-covid lockdown, anti-government, anti-police, conspiracy theorist",
    extremism_type=="incel" ~ "incel, racist, white supremacist",
    extremism_type=="conspiracy theorist" ~ "anti-covid lockdown, anti-government, anti-police, conspiracy theorist",
    extremism_type=="white supremacist" ~ "incel, racist, white supremacist",
    extremism_type=="racist" ~ "incel, racist, white supremacist",
    extremism_type=="anti blm" ~ "anti blm, anti left",
    extremism_type=="anti left" ~ "anti blm, anti left",
    TRUE ~ extremism_type)) %>% 
  group_by(year, month, date, category_combined) %>% 
  summarize(incidents=sum(incidents),
            killed=sum(killed)) 

#ideologies_condensed$category_combined <- reorder(ideologies_condensed$category_condensed, ideologies_condensed$incidents)
#ideologies_condensed$category_combined <- factor(ideologies_condensed$category_condesed, levels=rev(levels(ideologies_condensed$category_combined)))

Monthly attacks in 2020 compared to gun sales and social media and social distancing metrics in that time frame

Chart 1

ideologies_condensed %>% 
  #ggplot(aes(x=date, y=incidents)) +
  ggplot(aes(x=date, y=incidents, fill=category_combined)) +
  geom_bar(stat="identity") +
  #facet_wrap(~extremism_type, ncol=2) +
  #facet_wrap(~category_combined, ncol=5) +
  theme_minimal() +
  labs(title="Monthly right-wing extremism incidents by type in 2020 and 2021",
       y="", x="")

Chart 2

combined %>% 
  filter(date!=ymd("2021-02-01")) %>% 
  ggplot(aes(x=date, y=values)) +
  geom_col() +
  facet_wrap(~type, ncol=3, scales="free_y") +
  theme_minimal()

Domestic terrorism analysis

Andrew Ba Tran

a. Terror incidents, plots since ’94

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Data

b. Proportion of incidents

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c. Fatalities

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d. Targets

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e. Targets over time

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f. Types of Far Right Extremism

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g. Annual types of FRE incidents

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h. Monthly FRE incidents in 2020

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i. Fatalities and targets

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j. Weapons

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k. Former police or military

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l. Police targets

Police

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m. Religious places

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n. Planned Parenthood and clinics

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o. Attacks on govt or process

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p. Hometown of perps

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q. Social media

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r. Advanced analysis

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Chart 2