Amusement Park Injuries

Overview

These data are from the #TidyTuesday (10 Sept 2019) project. Tidy Tuesday is a weekly data project aimed at the R ecosystem. As this project was borne out of the R4DS Online Learning Community and the R for Data Science textbook, an emphasis was placed on understanding how to summarize and arrange data to make meaningful charts with {ggplot2}, {tidyr}, {dplyr}, and other tools in the {tidyverse} ecosystem.

The intent of Tidy Tuesday is to provide a safe and supportive forum for individuals to practice their wrangling and data visualization skills independent of drawing conclusions. While we understand that the two are related, the focus of this practice is purely on building skills with real-world data.

Amusement Park Injuries

This particular dataset is from the SaferParks Database.

These data contain a lot free text, some inconsistent NAs (n/a, N/A) and dates (ymd, dmy). A good chance to do some data cleaning and then take a look at frequency, incident types, injury types, and accident types using free text analysis.

The safer_parks dataset contains 8351 incidents that were recorded from 17 different types of parks. Below is a table containing the number on incidents according to the type of parks.

safer_parks %>% 
  count(`Park Type` = bus_type, sort = TRUE, name = "Number of Incidents") %>% 
  kable() %>% kable_styling(bootstrap_options = "striped", full_width = FALSE) %>% 
  scroll_box(height = "400px") 
Park Type Number of Incidents
Amusement park 3667
Water park 1767
Carnival or rental 701
Trampoline park 698
Family entertainment center 484
Go kart track 228
Sports or recreation facility 178
Mountain resort 174
School or church 95
City or county park 90
Adventure course 86
Zoo or museum 61
Mall, store or restaurant 53
Pool waterslide 27
Unknown 19
Camp 14
Other 9


For this analysis, I will focus on the amusement park industry. The first thing I did was filter these data to only include incidents from either an Amusement park or Carnival or rental when the industry sector was listed as amusement ride.

dat <- safer_parks %>% 
  filter(bus_type %in% c("Amusement park", "Carnival or rental") &
                              industry_sector == "amusement ride") %>% 
  mutate(acc_date = mdy(acc_date), 
         month = factor(month(acc_date, label = TRUE, abbr = TRUE)),
         manufacturer = ifelse(is.na(manufacturer), "Unknown", manufacturer),
         category2 = ifelse(str_detect(category, ":"),
                             str_extract(category, "^([^:])+"), category),
         category2 = ifelse(str_detect(category, "Illness"),
                             "Illness", category2))

The resulting dataset contains 3438 incidents that we will analyze further.

Type of Incident

First, we will look at the ten most common types of incidents that occur with respect to amusement rides:

dat %>% 
  count(category2) %>% 
  mutate(category2 = fct_reorder(category2, n)) %>% 
  top_n(n=10) %>%
  ggplot(aes(category2, n, fill = category2)) + 
  geom_col(show.legend = FALSE) + 
  coord_flip() + 
  scale_fill_grey(start = 0.8, end = 0.3) +
  labs(x = NULL, 
       y = "Number of Incidents",
       title = "Ten Most Common Types of Incidents",
       subtitle = "Reported for amusement rides")

Incidents According to State

Second, we will look at the most common states where incidents occur. Below is a table that contains the number of incidents according to state.

dat %>% 
  count(State = acc_state, name = "Number of Incidents", sort = TRUE) %>% 
  kable() %>% kable_styling(bootstrap_options = "striped", full_width = FALSE) %>% 
  scroll_box(height = "300px")
State Number of Incidents
CA 2067
PA 494
NJ 143
TX 141
OK 138
FL 130
IL 43
NH 42
MI 39
KY 38
WI 28
NC 25
OH 19
IA 14
ME 13
MD 11
CT 10
MA 8
WA 7
TN 5
WV 4
MO 3
SC 3
AR 2
CO 2
MN 2
GA 1
MT 1
NE 1
NM 1
OR 1
UT 1
VA 1


Although interesting, this may be misleading given the population differences between these states. For example, Pennsylvania is substantially smaller in population than California, yet it has almost 1/2 of the number of incidents. To better understand these data, we will pull in state population data from the 2010 decennial US Census using the {tidycensus} package and match state names with the built in state abbreviations.

st_crosswalk <- tibble(state = state.name) %>%
  bind_cols(tibble(abb = state.abb))

state_pop <- as_tibble(tidycensus::get_decennial(geography = "state", 
                           variables = "P001001") %>% select(state = NAME, pop = value)) %>% 
  left_join(st_crosswalk, by = "state") %>% select(state = abb, pop)

state_dat <- left_join(dat%>% count(acc_state), state_pop, by = c("acc_state" = "state"))

state_dat %>% 
  mutate(incident_per_100k = ((n / pop)*100000),
         acc_state = fct_reorder(acc_state, incident_per_100k)) %>% 
  top_n(n=10) %>%
  ggplot(aes(acc_state, incident_per_100k, fill = acc_state)) + 
  geom_col(show.legend = FALSE) + 
  coord_flip() + 
  scale_fill_grey(start = 0.8, end = 0.3) +
  labs(x = "State", 
       y = "Number of Incidents per 100k",
       title = "Ten states with highest per-capita incidents",
       subtitle = "Reported for amusement rides")

Incidents According to Date

Next, we can look at when the incidents tend to cluster. We would expect incidents to occur around the summer time as it is the most likely time that people tend to go to amusement parks.

p1 <- dat %>%  
  count(acc_date) %>% 
  ggplot(aes(acc_date, n)) +
  geom_line(color = "red") +
  scale_x_date(date_breaks = "years" , date_labels = "%Y") +
  labs(x = "Date of incident",
       y = "Number of incidents",
       title = "Seasonality of incidents")

p2 <- dat %>%  
  count(month) %>% 
  ggplot(aes(month, n)) +
  geom_col() +
  labs(x = NULL,
       y = "Number of incidents",
       title = "Month when incidents occur")

(p1 / p2)


We do in fact see that incidents tend to occur more often during the summer months (June-August).

Incidents by type of ride

We can also look at the number of incidents according to the type of amusement ride to see if one type of ride has a higher number of incidents. Keep in mind, we are not controlling for the amount of a particular type that is at an amusement park. That is, we see that coasters have a high number of incidents, however, how common are coasters at an amusement park compared to vertical drops? We need to be careful when interpreting these data beyond using it for data exploration.

dat %>% 
  count(`Ride type` = device_category, name = "Number of Incidents", sort = TRUE) %>% 
  kable() %>% 
  kable_styling(bootstrap_options = "striped", full_width = FALSE) 
Ride type Number of Incidents
coaster 1162
spinning 834
cars & track rides 554
water ride 507
other attraction 167
pendulum 122
vertical drop 92

Incidents by manufacturer

Next we will look at the number of incidents by manufacturer.

dat %>% 
  count(manufacturer, sort = TRUE) %>% 
  mutate(manufacturer = fct_reorder(manufacturer, n)) %>% 
  top_n(n = 10) %>% 
  ggplot(aes(manufacturer, n, fill = manufacturer)) + 
  geom_col(show.legend = FALSE)+ 
  coord_flip() +
  scale_fill_grey(start = 0.8, end = 0.3) + 
  labs(x = "Manufacturer",
       y = "Number of incidents",
       title = "Ten manufacturers with the highest reported incidents")

It appears that in-house made amusement rides have the highest reported number of incidents followed by Intamin AG.

Within these data, a column also lists if an incident was related to a mechanical issue, let’s look at the manufacturers with the highest reported mechanical issues.

dat %>%  
  filter(mechanical == 1) %>% 
  count(manufacturer) %>% 
  mutate(manufacturer = fct_reorder(manufacturer, n)) %>% 
  top_n(n = 10) %>% 
  ggplot(aes(manufacturer, n, fill = manufacturer)) + 
  geom_col(show.legend = FALSE) + 
  coord_flip() + 
  scale_fill_grey(start = 0.8, end = 0.3) + 
  labs(x = "Manufacturer",
       y = "Number of mechanical incidents",
       title = "Ten manufacturers with the highest reported mechanical incidents")

Injury Text Analysis

Now for the fun part, several columns of these data have free text. We will look at two in particular. One describes the injury itself, and one describes the accident and how it occurred.

Text Analysis Overview

For text analysis, we will use the {tidytext} package. First, we need to isolate the injury text and tokenize it. That is, we will split each row so that one token (word) is in each row. Additionally, punctuation is removed and words are converted to lowercase. I have also removed numbers that exist in the text (e.g. number of stitches).

injury_tokens <- dat %>% 
  select(acc_id, injury_desc) %>% 
  unnest_tokens(word, injury_desc) %>% 
  filter(is.na(as.numeric(word)))

Next, we will remove stop words, or words that are extremely common such as “the”, “of”, “to”, etc. I have also removed the words injury and pain as they are not descriptive for our purposes and appear to be commonly used in the injury description.

data(stop_words) 
my_stop_words <- c("injury", "pain")

injury_tokens <- injury_tokens %>% anti_join(stop_words) %>% filter(!word %in% my_stop_words)
Bar-plot

Q: What types of injuries occur most often?

We can use three visualizations to understand this a little easier. The first is a bar-plot showing the 20 most commonly used words when describing the injury.

injury_tokens %>% count(word, sort = TRUE) %>% 
  mutate(word = fct_reorder(word, n)) %>% 
  slice(1:20) %>% 
  ggplot(aes(x = word, y = n, fill = word)) + 
  geom_col(show.legend = FALSE) +
  coord_flip() +
  scale_fill_grey(start = 0.8, end = 0.3) + 
  labs(x = "", y = "Number of Incidents",
       title = "Twenty most common reported words in injury description")

Word-cloud

The second is a word-cloud showing the frequency that words appear in the injury description column.

injury_tokens %>% count(word) %>% with(wordcloud(word, n, max.words = 100, colors =  c( "#56B4E9", "#E69F00")))

Bi-gram graph

The third type of visualization is the relationships between words (n-grams). We are looking at how often words co-occur in these data.

injury_bigrams <- dat %>%  
  unnest_tokens(bigram, injury_desc, token = "ngrams", n = 2)
  
injury_bigrams_sep <- injury_bigrams %>% 
  separate(bigram, c("word1", "word2"), sep = " ")

injury_bigrams_filtered <- injury_bigrams_sep %>% 
  filter(!word1 %in% c(stop_words$word, my_stop_words),
         !word2 %in% c(stop_words$word, my_stop_words),
         !is.na(word1),
         is.na(as.numeric(word1)),
         is.na(as.numeric(word2)))

injury_bigrams_filtered %>% 
  unite(bigram, word1, word2, sep = " ") %>% 
  count(bigram, name = "Number of Incidents", sort = TRUE) %>% 
  kable() %>% 
  kable_styling(bootstrap_options = "striped", full_width = FALSE) %>% 
  scroll_box(height = "400px")
bigram Number of Incidents
chipped tooth 35
left knee 34
blood pressure 26
fractured arm 21
left shoulder 20
difficulty breathing 19
dislocated knee 17
dislocated shoulder 17
left arm 16
requiring stitches 16
dizziness nausea 15
facial injuries 15
nausea vomiting 15
lower leg 14
left leg 13
fractured wrist 12
hospital treated 11
low blood 11
quot laceration 11
twisted ankle 11
fractured ankle 10
laceration requiring 10
left eye 10
left hand 10
left wrist 10
lost consciousness 10
panic attack 10
paramedics transported 10
foreign body 9
fractured rib 9
light headed 9
middle finger 9
nausea dizziness 9
pinched finger 9
severe headache 9
sprained ankle 9
chipped teeth 8
critical condition 8
left eyebrow 8
left hip 8
local hospital 8
lower lip 8
minor injuries 8
requiring surgery 8
bloody nose 7
chest pains 7
chest pressure 7
dizzy nauseous 7
fractured foot 7
fractured ribs 7
head bump 7
head laceration 7
headache dizziness 7
heart rate 7
index finger 7
left ankle 7
left elbow 7
left foot 7
motion sickness 7
nose bleed 7
facial laceration 6
fractured elbow 6
head neck 6
headache nausea 6
inch laceration 6
personal physician 6
pre existing 6
pregnancy complication 6
upper arm 6
anxiety attack 5
asthma attack 5
dizziness transported 5
fractured tailbone 5
front teeth 5
knee abrasion 5
left eyelid 5
patron transported 5
pinky finger 5
racing heart 5
rib cage 5
slurred speech 5
swollen knee 5
upper leg 5
weakness dizziness 5
arm fracture 4
bear weight 4
bruised ribs 4
child complained 4
children’s hospital 4
crushed finger 4
description redacted 4
dizziness chest 4
dizziness vomiting 4
existing condition 4
eyebrow requiring 4
finger requiring 4
forehead requiring 4
front tooth 4
head contusion 4
hospital injuries 4
injuries reported 4
knee transported 4
left forearm 4
nbsp guest 4
neck injuries 4
neck strain 4
patron complained 4
pregnancy complications 4
puncture wound 4
requiring sutures 4
severe neck 4
slight swelling 4
sprained knee 4
traumatic brain 4
twisted knee 4
unspecified injuries 4
vomiting dizziness 4
allergic reaction 3
bee sting 3
bottom lip 3
broken arm 3
broken foot 3
broken ribs 3
broken teeth 3
broken tooth 3
bruised hand 3
bruised left 3
chest discomfort 3
chipped front 3
cm laceration 3
cut finger 3
deep laceration 3
dislocated thumb 3
dizziness headache 3
dizziness loss 3
electric shock 3
electrical shock 3
febrile seizure 3
finger transported 3
foot ankle 3
forehead laceration 3
fractured nose 3
fractured skull 3
heart palpitations 3
hospitalized overnight 3
injuries unknown 3
irregular heartbeat 3
knee requiring 3
left rib 3
leg numbness 3
leg requiring 3
lower abdominal 3
lower left 3
medical center 3
moderate bleeding 3
nausea headache 3
neck sprain 3
park nurse 3
patron lost 3
rapid heart 3
requiring staples 3
ring finger 3
shoulder arm 3
shoulder dislocation 3
shoulder injuries 3
shoulder neck 3
sprained wrist 3
syncopal episode 3
teeth knocked 3
upper lip 3
vaginal bleeding 3
vomiting transported 3
wrist fracture 3
16yo girl 2
17yo female 2
1st degree 2
2cm laceration 2
6yo boy 2
administered albuterol 2
ambulance treated 2
ankle fracture 2
ankle sprain 2
arm emts 2
arm neck 2
arm requiring 2
arm shoulder 2
arms nausea 2
asthma chest 2
badly bruised 2
bilateral upper 2
bleeding gums 2
blood vessels 2
blurry vision 2
brain skull 2
breath chest 2
breath headache 2
breath vomiting 2
breathing treatment 2
broken finger 2
broken fingers 2
broken leg 2
broken nose 2
bruised leg 2
bumped head 2
chest wall 2
compression fracture 2
consciousness amnesia 2
consciousness seizure 2
contusion inside 2
crushed fingers 2
cut requiring 2
deformity possibly 2
degree burns 2
difficulty moving 2
difficulty walking 2
dislocated hip 2
dizziness head 2
dizziness low 2
dizziness neck 2
dizziness shortness 2
dizziness weakness 2
dizzy chest 2
dizzy lightheaded 2
dizzy lost 2
dizzy sweaty 2
elevated blood 2
elevated heart 2
ems declined 2
exact injuries 2
eye irritation 2
eye lid 2
facial drooping 2
feeling light 2
feet legs 2
foot laceration 2
foot transported 2
forehead contusion 2
forehead dizziness 2
fractured bone 2
fractured finger 2
fractured thumb 2
fractured toe 2
friction burn 2
gaping laceration 2
goose egg 2
hairline fracture 2
hand middle 2
hand wrist 2
head emt 2
head requiring 2
head transported 2
heart attack 2
heart beat 2
herniated disk 2
hurt head 2
ice pack 2
injuries including 2
injuries treated 2
insect sting 2
jammed finger 2
laceration transported 2
left cheek 2
left chest 2
left corner 2
left hands 2
left index 2
left middle 2
left pinky 2
left ribcage 2
left shin 2
left temple 2
left thigh 2
left thumb 2
left upper 2
leg foot 2
leg weakness 2
lip laceration 2
loose tooth 2
lower chin 2
lower ribs 2
mechanical failure 2
medical attention 2
medical condition 2
medical facility 2
memory loss 2
mid center 2
mid lower 2
migraine headache 2
minor bruising 2
multiple injuries 2
multiple lacerations 2
nail bed 2
nausea loss 2
nbsp transported 2
nose abrasion 2
nose bleeding 2
numbness chest 2
paramedics administered 2
partial amputation 2
patient admission 2
patron’s mother 2
patrons ages 2
patrons complained 2
patrons injured 2
physical therapy 2
possibly fractured 2
pressure transported 2
probable fractured 2
pulled muscle 2
rapid heartbeat 2
red mark 2
refused treatment 2
requested ambulance 2
requiring hospitalization 2
requiring tissue 2
scalp laceration 2
seizure loss 2
severe nausea 2
severe stomach 2
shock burns 2
shoulder collarbone 2
skin grafts 2
skull neck 2
soft tissue 2
sore neck 2
sought medical 2
sprained arm 2
sprained neck 2
sting inside 2
suffered minor 2
sustained minor 2
swollen ankle 2
swollen lump 2
thumb nail 2
tissue adhesion 2
torn meniscus 2
upper abdominal 2
upper cheek 2
upper left 2
upper thigh 2
vomiting chest 2
vomiting loss 2
weak dizzy 2
10yo 15yo 1
10yo broken 1
10yo bruises 1
119lbs died 1
11yo males 1
13yo female 1
13yo male 1
13yo patrons 1
14yo 31yo 1
14yo girl 1
15yo female 1
15yo girl 1
15yo reported 1
16yo female 1
16yo hospitalized 1
16yo sprained 1
18yo male 1
1cm cut 1
20yo male 1
2nd degree 1
2nd toe 1
2yo laceration 1
30yo male 1
31yo 35yo 1
39yo injured 1
3yo struck 1
4yo boy 1
5mo pregnant 1
5yo boy 1
68yo heart 1
6yo broken 1
6yo traumatic 1
7yo 14yo 1
8yo boy 1
8yo girl 1
8yo received 1
abdomen reported 1
abdominal bleeding 1
abrasion redness 1
abrasions 16yo 1
abrasions hematoma 1
activity paramedics 1
acute blood 1
acute cervical 1
acute intense 1
acute respiratory 1
administerd guest 1
adult tooth 1
adults suffered 1
advised stitches 1
aid office 1
aid station 1
aid transported 1
alcohol consumption 1
alergic reaction 1
alleged arthroscopy 1
alleged bruised 1
alleged chemical 1
alleged laceration 1
alleged staph 1
allegedly due 1
altered consciousness 1
ambulance responded 1
amp front 1
amp left 1
amputated thumb 1
aneurysm pre 1
ankle examined 1
ankle foot 1
ankle laceration 1
ankle neck 1
ankle patron 1
ankle requiring 1
ankle scraped 1
ankle swelling 1
ankle tops 1
ankle wrist 1
ankles feet 1
anxiety difficulty 1
anxiety inability 1
anxiety racing 1
anxiety transported 1
approx thickness 1
arm 10yo 1
arm amp 1
arm difficulty 1
arm hospitalized 1
arm knee 1
arm laceration 1
arm left 1
arm leg 1
arm numbness 1
arm pre 1
arm splinted 1
arm transported 1
arm unable 1
arm wrist 1
arms panic 1
arms transported 1
artery resulting 1
assessment report 1
assessment ruled 1
asthma paramedics 1
asthma transported 1
asthmas attack 1
atrial fibrillation 1
attack asthmas 1
attack hyperventilating 1
baby’s head 1
bacterial infection 1
badly sprained 1
balance equilibrium 1
bar caused 1
bed avulsion 1
bend knee 1
bilateral feet 1
bilateral knee 1
bilateral leg 1
bilateral lower 1
bilateral shoulder 1
bit lip 1
bladder surgery 1
bladder tear 1
bleed nausea 1
bleeding complication 1
bleeding controlled 1
bleeding contusion 1
bleeding distal 1
bleeding heavily 1
bleeding transported 1
blindness hospitalized 1
blood blister 1
blood loss 1
bloody swollen 1
blunt force 1
blunt trauma 1
blurred vision 1
boats collided 1
body injuries 1
bone bruise 1
bone bruising 1
bone fracture 1
bone laceration 1
bone tip 1
boy fractured 1
boy suffered 1
boy transported 1
bra strap 1
brain aneurysm 1
brain requiring 1
breast implant 1
breath ambulance 1
breath dizziness 1
breath elevated 1
breath irregular 1
breath numbness 1
breath potential 1
breath stomach 1
breath tightness 1
breathing difficulty 1
breathing excruciating 1
breathing hyperventilation 1
breathing minor 1
breathing treatments 1
breathing vomiting 1
broken ankle 1
broken bones 1
broken cheek 1
broken hand 1
broken knee 1
broken neck 1
broken tibia 1
broken transported 1
brow requiring 1
bruise left 1
bruise nbsp 1
bruised bone 1
bruised knee 1
bruised legs 1
bruised lump 1
bruised rib 1
bruised rt 1
bruised shin 1
bruised thumb 1
bruises emts 1
brush burn 1
brusing emotional 1
bump decreasing 1
bump found 1
bump transported 1
bumped nose 1
burst blood 1
buttocks 1st 1
calf abrasions 1
calf muscle 1
cannabis intoxication 1
car accident 1
cardiac history 1
cardiac monitor 1
care due 1
carotid arteries 1
carotid artery 1
causing difficulting 1
center abdominal 1
center nbsp 1
cervical lumbar 1
cervical strain 1
cheek bone 1
cheeks red 1
chemical burn 1
chest bone 1
chest head 1
chest injuries 1
chest tightening 1
chest transported 1
child fire 1
child hit 1
child nbsp 1
child stopped 1
child sustained 1
child unable 1
child’s arm 1
children injured 1
children suffered 1
childrens hospital 1
chin abrasion 1
chin laceration 1
chin patron 1
chin requiring 1
chin transported 1
clavical fracture 1
clavicle approx 1
clavicle collarbone 1
collapsed lung 1
collarbone anxiety 1
collarbone fracture 1
collerbone hurts 1
coma fractures 1
community medical 1
competitive swimming 1
compound fracture 1
concussion missed 1
concussion neck 1
concussion physical 1
condition 10yo 1
condition multiple 1
condition surgery 1
conditions nbsp 1
confusion slurred 1
consciousness blood 1
consciousness chest 1
consciousness difficulty 1
consciousness emt 1
consciousness helicoptered 1
consciousness loss 1
consciousness nausea 1
consciousness neck 1
consciousness paramedics 1
consciousness persistent 1
consciousness revived 1
consciousness severe 1
consciousness suspected 1
consciousness vomiting 1
consciousness weak 1
continual left 1
contusion bone 1
contusion strain 1
contusion transported 1
contusions blook 1
contusions child 1
contusions shoulder 1
cord injuries 1
cortisone shots 1
coughing dizziness 1
county paramedics 1
cracked crown 1
cramps pregnancy 1
cramps shortness 1
critical injuries 1
crush fracture 1
crush injuries 1
crushed pinky 1
crushing hematoma 1
ct mri 1
ct scan 1
cut head 1
cuts scrapes 1
day prior 1
day woke 1
days diagnosed 1
declined transport 1
decreased ability 1
deep breath 1
deep cut 1
deformity abraisons 1
deformity nbsp 1
deformity wide 1
degree burn 1
degree sunburn 1
dental care 1
department ambulance 1
details redacted 1
detected irregular 1
detected low 1
diabetic episode 1
diarrhea dizziness 1
difficultly breathing 1
difficulty received 1
difficulty standing 1
dime sized 1
discomfort nurse 1
dislocated elbow 1
dislocated left 1
dislocated nose 1
dislocated toe 1
dislocation fracture 1
dislocation shoulder 1
disoriented panicked 1
distal edge 1
dizziness ambulance 1
dizziness difficulty 1
dizziness heart 1
dizziness increased 1
dizziness numbness 1
dizziness pale 1
dizziness paramedics 1
dizziness pressure 1
dizziness racing 1
dizziness seizure 1
dizziness slow 1
dizziness slurred 1
dizziness tightness 1
dizziness treated 1
dizziness trouble 1
dizziness weaknesss 1
dizzy anxious 1
dizzy disoriented 1
dizzy head 1
dizzy headache 1
dizzy hot 1
dizzy lightl 1
dizzy low 1
dizzy nauseated 1
dizzy shaky 1
dizzy short 1
dizzy transported 1
dizzy vomiting 1
dizzyness rapid 1
doctor told 1
doctor’s appointment 1
double fracture 1
droop slurred 1
ear 2yo 1
ear drum 1
ear infection 1
ear requiring 1
earlobe laceration 1
earring pulled 1
earring puntured 1
egg bump 1
ejected sustained 1
elbow deformity 1
elbow laceration 1
elbow lower 1
elbow transported 1
elbow xray 1
electrical burns 1
emotional distress 1
emotional trauma 1
employee received 1
ems called 1
emt advised 1
emt noted 1
emt recommended 1
emt’s transported 1
emts evaluated 1
emts examined 1
emts splinted 1
emts transported 1
epileptic seizure 1
er bill 1
esophagus pneumonediastinum 1
exacerbated pre 1
exacerbated symptoms 1
excruciating headache 1
existing concussion 1
experienced lower 1
extensive abrasions 1
extensive cardiac 1
extreme fright 1
eye complained 1
eye conjunctivitus 1
eye infection 1
eye lacerations 1
eye left 1
eye minor 1
eye retina 1
eye sprained 1
eye strain 1
eyebrow head 1
eyebrow laceration 1
eyebrow nose 1
eyelid contusion 1
eyelid hospital 1
eyelid laceration 1
eyes looked 1
eyes nose 1
facial bones 1
facial contusions 1
facial droop 1
facial lacerations 1
facial nose 1
facial numbness 1
facial rash 1
factor individual 1
failure nbsp 1
faint low 1
fairly deep 1
fall lacerations 1
fall trauma 1
fall treated 1
fast heart 1
fatal injuries 1
fatigue stomach 1
feel heavy 1
feel nauseated 1
feeling ill 1
feeling sick 1
feeling unwell 1
feet evaluated 1
feet head 1
fell unconcious 1
female neck 1
female sustained 1
fib fracture 1
finger bone 1
finger child 1
finger contusions 1
finger emt 1
finger finger 1
finger knuckle 1
finger nail 1
finger painful 1
finger stitches 1
fingernail bed 1
fingers child 1
fingers emt 1
fingers nbsp 1
fire department 1
flex foot 1
floating ribs 1
focus concerns 1
foot bruise 1
foot facial 1
foot injuries 1
foot lasceration 1
foot leg 1
foot torn 1
foot twisted 1
fore arm 1
forearm abrasion 1
forearm bruise 1
forearm contusions 1
forearm fracture 1
forearm hospitalized 1
forearms 15yo 1
forehead bruising 1
forehead ems 1
forehead nose 1
foreign material 1
fracture arm 1
fracture collarbone 1
fracture transported 1
fractured facial 1
fractured hip 1
fractured jaw 1
fractured knee 1
fractured left 1
fractured leg 1
fractured pelvis 1
fractured ring 1
fractured shoulder 1
fractured t1 1
fractured teeth 1
fractures employee 1
fractures neck 1
fractures requiring 1
futher evaluation 1
genitals admitted 1
girl bruising 1
girl injured 1
glued cut 1
gluten allergy 1
graft surgery 1
grafts required 1
grandfather reported 1
grandfather sustained 1
groin treated 1
ground transported 1
guest requested 1
guest’s left 1
gum bleeding 1
gums contusions 1
hand abrasion 1
hand finger 1
hand fracture 1
hand hospitalized 1
hand laceration 1
hand pinky 1
hand radiating 1
hand received 1
hand requiring 1
hands legs 1
hands medics 1
hands nausea 1
hands numb 1
hands treated 1
hands wrists 1
head 17yo 1
head abrasions 1
head admitted 1
head bruise 1
head child 1
head ear 1
head foot 1
head headache 1
head legs 1
head mid 1
head mild 1
head mother 1
head nausea 1
head possibel 1
head seizure 1
head shoulder 1
headache admitted 1
headache ambulance 1
headache difficulty 1
headache illness 1
headache neck 1
headache negative 1
headache transported 1
headache vomiting 1
headaches diagnosed 1
headaches vomiting 1
headaches whiplash 1
health conditions 1
health issue 1
hearing loss 1
heart ambulance 1
heart discomfort 1
heart flutter 1
heart issues 1
heart numb 1
heart palpatations 1
heart shortness 1
heart symptoms 1
heart transported 1
heartbeat diagnosis 1
heavy bleeding 1
hernia surgery 1
herniated discs 1
hip ankle 1
hip elbows 1
hip groin 1
hip ribs 1
hit child 1
hit head 1
hospital diagnosed 1
hospital died 1
hospital due 1
hospital glued 1
hospital nbsp 1
hospital park 1
hospitalization 15yo 1
hospitalization shoulder 1
hospitalization skin 1
hospitalized 16yo 1
humerus requiring 1
hurt knee 1
hurt left 1
hurt leg 1
hurt neck 1
hurt shoulder 1
hydraulic oil 1
hyperextended fingers 1
hyperextended muscle 1
hyperextended neck 1
hyperventilating lost 1
hyperventilation fast 1
hypothermia dehydration 1
icu injuries 1
illness low 1
illness related 1
inch wide 1
incision site 1
incision sore 1
included scrapes 1
including herniated 1
including leg 1
increased blood 1
increased suddenly 1
indentation redness 1
index fingers 1
individual refused 1
induced coma 1
infected wound 1
infection cold 1
inhalation redness 1
inhaled insect 1
inher arms 1
initial exam 1
injured 30yo 1
injured 7yo 1
injured ages 1
injured ankle 1
injured arm 1
injured finger 1
injured knee 1
injured left 1
injured muscle 1
injured patron’s 1
injured patrons 1
injured persons 1
injuries 17yo 1
injuries cuts 1
injuries due 1
injuries included 1
injuries mechanical 1
injuries mid 1
injuries neck 1
injuries noted 1
injuries person 1
injuries requiring 1
injuries scrapes 1
injuries transported 1
injuries yest 1
insect patron 1
inside lower 1
inside mouth 1
inside throat 1
intense abdominal 1
intoxication ambulance 1
irregular heart 1
irregulatr heartbeat 1
jaw ache 1
jaw broken 1
jaw fracture 1
killed 15yo 1
knee abrasions 1
knee ankle 1
knee bruised 1
knee cap 1
knee chipped 1
knee contusion 1
knee hip 1
knee laceration 1
knee left 1
knee loss 1
knee nbsp 1
knee painful 1
knee parents 1
knee shoulder 1
knee split 1
knee tib 1
knee unable 1
knee wrist 1
knees rib 1
knocked loose 1
laceration abrasion 1
laceration approx 1
laceration approximately 1
laceration bleeding 1
laceration close 1
laceration contusion 1
laceration crush 1
laceration injuries 1
laceration mother 1
lacerations requiring 1
lasceration strained 1
lateral aspect 1
layers deep 1
leave information 1
left 2nd 1
left breast 1
left clavicle 1
left collerbone 1
left ear 1
left finger 1
left flank 1
left front 1
left glute 1
left palm 1
left scapula 1
leg 16yo 1
leg abrasion 1
leg abrasions 1
leg amp 1
leg ankle 1
leg cramps 1
leg fracture 1
leg hip 1
leg lower 1
leg paramedics 1
leg shifted 1
leg shin 1
leg spasms 1
leg sprain 1
leg treated 1
legs abrasion 1
legs allegedly 1
legs feel 1
legs fractured 1
legs neck 1
legs numbness 1
legs paramedics 1
lethargic loss 1
lethargy disorientation 1
lethargy jaw 1
level transported 1
life support 1
lifted numbness 1
lightheaded shortness 1
lightl headed 1
line bruise 1
lip contusions 1
lip loose 1
lip md 1
lip requiring 1
lip swelling 1
lip tooth 1
liquid stitches 1
listless baby 1
loc injuries 1
location ice 1
log hit 1
loose front 1
loose teeth 1
loss disorientation 1
low calcium 1
low glucose 1
low heart 1
low mid 1
lower abdomen 1
lower body 1
lower extremities 1
lower groin 1
lower legs 1
lower lumbar 1
lower neck 1
lower rib 1
lower stomach 1
lower thighs 1
lumbar region 1
lumbar spine 1
lumbosacral strain 1
lump bilateral 1
male sustained 1
md recommended 1
mechanical operational 1
medical care 1
medical history 1
medical issue 1
medical records 1
medical treatment 1
medically induced 1
medics detected 1
medics reported 1
medics stabilized 1
memory responds 1
meniscus requiring 1
mentioned unverified 1
mid forehead 1
mid thigh 1
middle fingers 1
middle left 1
migraine elevated 1
migraine transported 1
mild concussion 1
mild electric 1
mild traumatic 1
mild whiplash 1
minor abrasion 1
minor bleeding 1
minor deformity 1
minor edema 1
minor head 1
minor laceration 1
moderate neck 1
monitor advised 1
months post 1
months prior 1
mother checked 1
mother stated 1
mouth laceration 1
mouth transported 1
move hand 1
move leg 1
move legs 1
movement nbsp 1
moving neck 1
moving wrist 1
multiple fractures 1
multiple seizures 1
multiple surgeries 1
muscle cartilage 1
muscle pulled 1
muscle stiffness 1
muscle strain 1
muscoskeletal injuries 1
nail detached 1
nail partially 1
nausea chest 1
nausea diagnosed 1
nausea dizzyness 1
nausea extensive 1
nausea irregular 1
nausea neck 1
nausea paramedics 1
nausea park 1
nausea sweating 1
nausea transported 1
nausea uncontrollable 1
nausea weakness 1
nauseated tingling 1
nauseous dizzy 1
nauseous low 1
nauseous sweating 1
nauseous vomiting 1
nbsp alcohol 1
nbsp diagnosed 1
nbsp medical 1
nbsp muscle 1
nbsp oxygen 1
nbsp patron 1
nbsp spent 1
nearby hospital 1
nebulizer treatments 1
neck amp 1
neck arm 1
neck broken 1
neck feeling 1
neck fracture 1
neck fractures 1
neck head 1
neck hip 1
neck left 1
neck red 1
neck shoulder 1
neck shoulders 1
neck spasm 1
neck tachycardic 1
neck tingling 1
neck transported 1
neuro deficits 1
nickle sized 1
nitro pill 1
normal range 1
nose bleeds 1
nose broken 1
nose complained 1
nose cut 1
nose facial 1
nose impact 1
nose laceration 1
nose neck 1
nose seizure 1
nose sore 1
noticable swelling 1
numb arm 1
numbness afterward 1
numbness slurred 1
numbness transported 1
nurse detected 1
observed nbsp 1
obvious bump 1
obvious deformity 1
office nbsp 1
office requesting 1
operational failure 1
overnight 4yo 1
pack applied 1
painful knee 1
pale clammy 1
palm required 1
paramedics advised 1
paramedics assessment 1
paramedics examined 1
parent’s request 1
parents declined 1
park medic 1
partially severed 1
particle fell 1
patient hospitalization 1
patron bumped 1
patron claims 1
patron drove 1
patron experienced 1
patron feels 1
patron landed 1
patron reports 1
patron stated 1
patron suffered 1
patron unable 1
patron’s eye 1
patron’s hand 1
patron’s spine 1
patrons 10yo 1
patrons stated 1
perforated bowel 1
persistent low 1
person transported 1
personal health 1
personal illness 1
personal information 1
personal medical 1
phone call 1
photophobia transported 1
physical scrapes 1
piercing headache 1
pinched arm 1
pinched fingers 1
pinched foot 1
pinched hand 1
pinched thumb 1
pinched toe 1
pink eye 1
popped blood 1
popping bruising 1
possibel concussion 1
possibly due 1
possibly fractures 1
post surgery 1
potassium transported 1
potential asthma 1
potential eye 1
precautionary reasons 1
preexisting medical 1
prescribed nitro 1
pressure dizziness 1
pressure patron 1
previous head 1
previous health 1
previous hernia 1
previous history 1
private physician 1
private vehicles 1
probable seizure 1
profuse bleeding 1
profuse sweating 1
prolonged nausea 1
provide medical 1
pubic bone 1
punctured ear 1
punctured lung 1
puntured skin 1
purple eye 1
quot 119lbs 1
quot cut 1
quot head 1
quot scalp 1
racing heartbeat 1
rapid breathing 1
ray ruled 1
rayed results 1
reaction patron 1
reactrive nbsp 1
received breathing 1
received cortisone 1
received laceration 1
received medications 1
recent surgery 1
reconstructive surgery 1
red line 1
redness bruising 1
regional medical 1
repeated nose 1
repetetive questions 1
report stated 1
reported bladder 1
reported injuries 1
reported low 1
reported stomach 1
reports history 1
requested transport 1
requiring dermabond 1
requiring liquid 1
requiring root 1
requring surgery 1
respiratory failure 1
respiratory issue 1
responds slowly 1
responsive shaking 1
retina correction 1
reveal fracture 1
rib contusion 1
rib fracture 1
rib transported 1
ribs breast 1
ribs difficulty 1
ribs knee 1
ribs trunk 1
ride inspector 1
ride minor 1
ride tingling 1
rider suffered 1
riding ems 1
ring fingers 1
root canal 1
round swollen 1
rt fore 1
ruptured appendix 1
ruptured carotid 1
scalp hip 1
scalp torn 1
scapula shoulder 1
school due 1
scraped knee 1
scraped knees 1
scraped left 1
scrapes brusing 1
scrapes buises 1
scratch abrasion 1
seatbelt strap 1
seizure admitted 1
seizure ambulance 1
seizure aura 1
seizure focus 1
seizure head 1
seizure medical 1
seizure mother 1
seizure patron 1
seizure severe 1
seizure shortness 1
seizures ambulance 1
severe abdominal 1
severe bleeding 1
severe brain 1
severe burn 1
severe burns 1
severe cramping 1
severe migraine 1
severe motion 1
severed bicep 1
severely injured 1
shaking seizure 1
shaky dizzy 1
shaky passed 1
shin bruising 1
shin possibly 1
shins requiring 1
shock hands 1
short period 1
shoulder 47yo 1
shoulder armpit 1
shoulder blade 1
shoulder concussion 1
shoulder hand 1
shoulder increasing 1
shoulder nbsp 1
shoulder pulled 1
shoulder skinned 1
shoulder tightness 1
shoulder treated 1
sided facial 1
significant injuries 1
sized bruise 1
sized lump 1
skin graft 1
skin infection 1
skin irritation 1
skin rash 1
skin tear 1
skin torn 1
skinned knee 1
skull fracture 1
skull laceration 1
skull scalp 1
sleepy nauseated 1
sleepy paramedics 1
slight bruising 1
slight headache 1
slightest touch 1
slipped disk 1
smoke inhalation 1
sore arm 1
sore bruised 1
sore tailbone 1
spasms difficulty 1
spinal cord 1
spine difficulty 1
spine tenderness 1
spine transported 1
spiral fracture 1
splinter embedded 1
split thumb 1
sprain refused 1
sprain swelling 1
sprained calf 1
sprained foot 1
sprained hand 1
sprained lower 1
stabilized child’s 1
stable condition 1
staph infection 1
staples mild 1
stated multiple 1
stiffness spasms 1
stiffness stress 1
stitches hospitalized 1
stitches required 1
stomach lower 1
stomach nbsp 1
stomach surgery 1
stopped breathing 1
strained ankle 1
strained left 1
strained muscle 1
strained muscles 1
strained neck 1
strap mid 1
struck head 1
subdural hematoma 1
subdural hematomas 1
subject complained 1
subungual hematoma 1
sudden severe 1
suffered brain 1
suffered extensive 1
suffered fall 1
suffered neck 1
sunburn broke 1
superficial abrasion 1
surgery abdominal 1
surgical incision 1
surgical site 1
suspected syncope 1
sustained fractured 1
sweating disorientation 1
sweating dizziness 1
sweating jaw 1
sweating numbness 1
sweating pale 1
swelling deformity 1
swollen arm 1
swollen hands 1
swollen history 1
swollen nose 1
swollen painful 1
swollen purple 1
swollen wrist 1
symptoms loss 1
symptoms size 1
t1 vertebrae 1
tachycardic due 1
takes naporsin 1
taking child 1
tear stated 1
teeth abrasion 1
temple nbsp 1
temporary visual 1
tenderness deformity 1
tenderness photophobia 1
tendons exposed 1
term blindness 1
therapy required 1
thigh bruised 1
thigh stomach 1
thoracic strain 1
thumb popped 1
thumb requiring 1
tib fib 1
tibia left 1
tingling inher 1
tingly red 1
tire tracks 1
tiredness transported 1
tissue injuries 1
titanium rod 1
toe nail 1
toes nbsp 1
toes patron 1
tooth bleeding 1
tooth broke 1
tooth chipped 1
tooth complained 1
tooth dental 1
tooth incisor 1
tooth knocked 1
tooth nbsp 1
tooth rebuilt 1
tooth repaired 1
tore thumb 1
torn carotid 1
torn cartilage 1
torn esophagus 1
torn halfway 1
torn ligament 1
torn retina 1
torn tendons 1
transported patron’s 1
trauma injuries 1
trolley paramedics 1
trouble swallowing 1
truck suffered 1
trunk transported 1
twisted swollen 1
twisted wrist 1
uncontrollable vomiting 1
underarm region 1
unspecified minor 1
unverified reports 1
unwell elevated 1
upper arms 1
upper eyelid 1
upper rib 1
upper teeth 1
uterine cramps 1
vertebra femur 1
vertigo nausea 1
visible injuries 1
visible trauma 1
vision concussion 1
vision nausea 1
vitals blood 1
vomited reported 1
vomiting diarrhea 1
vomiting dizzy 1
vomiting headache 1
vomiting intoxication 1
vomiting lethargic 1
vomiting shortness 1
vomiting sleepiness 1
walk medics 1
walk parents 1
water exposure 1
water squirted 1
weak transported 1
weakness cannabis 1
weakness collapse 1
weakness fatigue 1
weakness hands 1
weakness left 1
weakness nausea 1
weakness slurred 1
weakness vomiting 1
weaknesss low 1
week 13yo 1
week hospitalization 1
weeks earlier 1
weight transported 1
whiplash lasting 1
wide lacerations 1
workers suffered 1
wrist abrasion 1
wrist foot 1
wrist hand 1
wrist head 1
wrist patron 1
wrist scraped 1
wrist transported 1
xray recommended 1


We can also visually look at how these words relate (cluster) together through the use of a bi-gram graph using the {igraph} and {ggraph} packages.

injury_bigrams_filtered %>% 
  count(word1, word2, sort = TRUE) %>% 
  filter(n > 5)  %>% 
  visualize_bigrams()


We see how these words tend to cluster together. If we had a specific research question, we could dig deeper into these.

Types of Injury by Manufacturer

Q: What injuries are most often associated with what manufacturers?

We can organize these data to look at the most common injuries reported by manufacturer as well.

injury_bigrams_unite <- injury_bigrams_filtered %>% 
  unite(bigram, word1, word2, sep = " ")

manuf_of_interest <- injury_bigrams_unite %>% 
  count(manufacturer, sort = TRUE) %>% 
  filter(manufacturer != "Unknown") %>% 
  slice(1:5)

injury_bigrams_unite %>% 
  filter(manufacturer %in% manuf_of_interest$manufacturer) %>% 
  count(manufacturer, bigram, sort = TRUE) %>% 
  arrange(manufacturer, desc(n)) %>% 
  group_by(manufacturer) %>% 
  slice(1:5) %>%
  ungroup() %>% 
  mutate(manufacturer = as.factor(manufacturer),
         bigram = reorder_within(bigram, n, manufacturer)) %>% 
  ggplot(aes(bigram, n, order = -n, fill = manufacturer)) + 
  geom_col(show.legend = FALSE) +
  scale_x_reordered() +
  labs(x = NULL, y = "Number of Incidents", 
       title = "Most common injuries reported by manufacturer",
       subtitle = "Analyzing manufacturers with five most reported incidents") +   facet_wrap(~manufacturer, ncol = 2, scales = "free") + 
  coord_flip() 

Types of Incidents by Ride Category

Q: Are certain types of rides more dangerous?

We can look at the meta groupings (7 groups in these data). The figure below shows the number of incidents according to the amusement ride category.

dat %>% 
  count(device_category) %>% 
  mutate(device_category = fct_reorder(device_category, n)) %>% 
  ggplot(aes(device_category, n, fill = device_category)) + 
  geom_col(show.legend = FALSE) + 
  coord_flip() + 
  scale_fill_grey(start = 0.8, end = 0.3) + 
  labs(x = NULL, y = "Number of Incidents",
       title = "Number of reported incidents by amusement ride category")

Clearly, there are more incidents that involve Coaster rides. We could also expand the device categories to see what device types are creating the most number of incidents.

Types of Injury by Ride Type

We can look at the more granular ride types (52 types in these data). The table below shows the number of incidents according to the ride type.

dat %>% 
  count(`Ride Type` = device_type, name = "Number of Injuries", sort = TRUE) %>% 
  kable() %>%
  kable_styling(bootstrap_options = "striped", full_width = FALSE) %>% 
  scroll_box (height = "400px")
Ride Type Number of Injuries
Coaster - steel 860
Track ride 302
Coaster - wooden 208
Flume ride 170
Boat ride 156
Carousel 101
Rafting ride 95
Coaster - family/kiddie 92
Drop tower 90
Bumper car 87
Car ride 84
Shoot the chute 82
Train/tram 81
Simulator 80
Spinning cups/tubs 73
Swing ride 69
Walk-through 68
Ferris/gondola wheel 55
Roundabout 55
Sizzler/scrambler 54
Himalaya-type 52
Spinning pendulum 46
Plane/jet/helicopter 45
Tilt-a-whirl/waltzer 38
Wheel w/spinning cars 36
Whip 31
Orbiter/octopus-type 30
Pirate ship 26
Hanglider ride 25
Robotic ride 25
Rotor/gravitron 23
Flying carpet ride 20
Trabant 19
Claw-type 18
Sky ride/ski lift 18
Kamikaze/inverter-type 16
Enterprise 13
Spinning tower ride 13
Spinning track ride 13
Paratrooper-type 12
Flipping platform 11
Giant swing 9
Spinning ride - misc. 9
Roll-o-plane/booster/speed 7
Tornado-type 6
Bumper boat 4
Reverse bungee 3
Bungee jump 2
Coaster - unidentified 2
gyro loop 2
Looper 1
Non-motorized spinners 1
dat %>% 
  count(device_category, device_type, sort = TRUE) %>% 
  arrange(device_category, desc(n)) %>% 
  group_by(device_category) %>% 
  slice(1:5) %>% 
  ungroup() %>% 
  mutate(device_category = as.factor(device_category),
         device_type = reorder_within(device_type, n, device_category)) %>% 
  ggplot(aes(device_type, n, order = -n, fill = device_category)) + 
  geom_col(show.legend = FALSE) + 
  scale_x_reordered() +
  labs(x = NULL, y = "Number of incidents",
       title = "Most common incidents according to ride type",
       subtitle = "Categorized by ride category") + 
  facet_wrap(~device_category, ncol = 2, scales = "free") + 
  coord_flip()

Types of Injury by Ride Category.

Finally, we can look at the most common injuries according to ride category.

injury_bigrams_unite %>% 
  count(device_category, bigram, sort = TRUE) %>% 
  arrange(device_category, desc(n)) %>% 
  group_by(device_category) %>% 
  slice(1:6) %>%
  ungroup() %>% 
  mutate(device_category = as.factor(device_category),
         bigram = reorder_within(bigram, n, device_category)) %>% 
  ggplot(aes(bigram, n, order = -n, fill = device_category)) + 
  geom_col(show.legend = FALSE) +
  scale_x_reordered() +
  labs(x = NULL, y = "Number of incidents", 
       title = "Most common injuries reported by ride category",
       subtitle = "Analyzing device categories with six most reported bi-grams") + 
  facet_wrap(~device_category, ncol = 2, scales = "free") + 
  coord_flip() 

These make sense, it is interesting to see that the majority of the injuries occur on the left side of individuals. This could be due to the reporting practices (in fact, no ‘right’ side injuries were reported at all) or due to some other commonality of rides (perhaps they all swing in the same direction).

Accident Text Analysis

Q: What types of accidents occur most frequently?

We will use the same text analysis process that we used previously to see if we can find patterns in the accident descriptions according to incident.

acc_tokens <- dat %>% 
  select(acc_id, acc_desc) %>% 
  unnest_tokens(word, acc_desc) %>% 
  filter(is.na(as.numeric(word)))

data(stop_words) 
my_stop_words <- c("patron", "ride", "stated", "riding", "pain", "attraction")

acc_tokens <- acc_tokens %>% anti_join(stop_words) %>% filter(!word %in% my_stop_words)
Bar-plot

First, we will look at the common words used when reporting accidents in these data.

acc_tokens %>% count(word, sort = TRUE) %>% 
  mutate(word = fct_reorder(word, n)) %>% 
  slice(1:20) %>% 
  ggplot(aes(x = word, y = n, fill = word)) + 
  geom_col(show.legend = FALSE) +
  coord_flip() +
  scale_fill_grey(start = 0.8, end = 0.3) + 
  labs(x = "", y = "Number of incidents",
       title = "Twenty most common reported words in the accident description")

Word-cloud

Second, we will use a word-cloud to create an alternate visual

acc_tokens %>% count(word) %>% with(wordcloud(word, n, max.words = 100, colors =  c( "#56B4E9", "#E69F00")))


Falling seems to be a contributing factor to many of these incidents.

Bi-gram graph

Finally, we will use bi-grams to understand how accidents are described.

acc_bigrams <- dat %>%  
  unnest_tokens(bigram, acc_desc, token = "ngrams", n = 2)
  
acc_bigrams_sep <- acc_bigrams %>% 
  separate(bigram, c("word1", "word2"), sep = " ")

acc_bigrams_filtered <- acc_bigrams_sep %>% 
  filter(!word1 %in% c(stop_words$word, my_stop_words),
         !word2 %in% c(stop_words$word, my_stop_words),
         !is.na(word1),
         is.na(as.numeric(word1)),
         is.na(as.numeric(word2)))

acc_bigrams_filtered %>% 
  unite(bigram, word1, word2, sep = " ") %>% 
  count(bigram, name = "number_of_incidents", sort = TRUE) %>% 
  kable() %>% 
  kable_styling(bootstrap_options = "striped", full_width = FALSE) %>% 
  scroll_box(height = "400px")
bigram number_of_incidents
lap bar 181
pre existing 68
left knee 60
blood pressure 49
left shoulder 35
existing medical 33
medical condition 31
left arm 29
left ankle 27
left hand 27
fell forward 25
hit head 24
left leg 22
restraint bar 22
seat belt 22
child fell 21
experienced neck 21
motion sickness 21
steering wheel 21
owner operator 20
patron’s mother 19
existing condition 18
chest pains 17
final drop 17
safety bar 17
fell backwards 16
late report 16
left wrist 16
personal medical 16
experienced chest 15
left foot 15
male received 15
difficulty breathing 14
left eye 13
shoulder restraint 13
sudden stop 13
bumper car 12
child hit 12
female received 12
low blood 12
medical issue 12
car causing 11
experiencing chest 11
feeling dizzy 11
investigators found 11
left elbow 11
medical history 11
metal bar 11
mother called 11
age unknown 10
cell phone 10
exit gate 10
exit ramp 10
experienced dizziness 10
queue line 10
roller coaster 10
splash pool 10
abrupt stop 9
bumper cars 9
causing injury 9
fell hitting 9
foot slipped 9
leaned forward 9
left hip 9
operator started 9
patron’s hand 9
patron’s head 9
reported neck 9
requested transport 9
slid forward 9
wing seat 9
asthma attack 8
bumped head 8
fell exiting 8
flip flops 8
front seat 8
lap bars 8
lift hill 8
light headed 8
lost consciousness 8
medical care 8
nose bleed 8
panic attack 8
ring finger 8
anxiety attack 7
chest pressure 7
ferris wheel 7
foot caught 7
forward hitting 7
front teeth 7
front tooth 7
grab bar 7
hit mouth 7
index finger 7
severe headache 7
sought medical 7
wearing flip 7
car moved 6
experienced nausea 6
experienced severe 6
experienced shortness 6
experiencing neck 6
falling forward 6
female complained 6
front car 6
fun house 6
guest hit 6
hand caught 6
hand rail 6
head hit 6
hit tooth 6
log flume 6
male complained 6
medical conditions 6
middle finger 6
multiple times 6
paramedics transported 6
patron’s car 6
patron’s friend 6
patron’s left 6
tea cup 6
train car 6
unknown object 6
3d glasses 5
3rd row 5
aid station 5
bar hit 5
beach ball 5
car nbsp 5
carousel horse 5
child bumped 5
child slipped 5
children ages 5
complete stop 5
exit steps 5
feeling weak 5
fell backward 5
fire department 5
front row 5
gates closed 5
head nbsp 5
head rest 5
heart attack 5
hitting head 5
home position 5
left eyebrow 5
left rib 5
left shin 5
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strained muscle 1
strap failed 1
stress due 1
striking nose 1
striking tree 1
strobe light 1
strong santa 1
struck child 1
struck chin 1
struck directly 1
struck female 1
struck finger 1
struck left 1
struck lower 1
struck metal 1
struck mouth 1
struck platform 1
structure physical 1
structure preliminary 1
stuck causing 1
stuck hand 1
stuck restraint 1
stuck underneath 1
stumbled sat 1
style earring 1
subdural hematoma 1
subdural hematomas 1
subject’s family 1
subjects mother 1
subsequently complained 1
sucker bar 1
sudden jolt 1
sudden loud 1
sudden low 1
sudden movement 1
sudden severe 1
suddenly changed 1
suddenly dislodged 1
suddenly inertia 1
suddenly shifted 1
suddenly started 1
suddenly stopped 1
suffered cardiac 1
suffered chest 1
suffered crushing 1
suffered damage 1
suffered foot 1
suffered injury 1
suffered laceration 1
suffered neck 1
suffered seizure 1
suffered whiplash 1
summoned emts 1
sunglasses due 1
super ventricular 1
supervising companion 1
support broke 1
support chains 1
support legs 1
support shaft 1
support structure 1
supposedly moved 1
surrounding fence 1
suspected foul 1
suspended coaster 1
sustain whiplash 1
sustained injury 1
sustained laceration 1
sustained minor 1
swayed causing 1
sweated profusely 1
sweep arm’s 1
sweep bar 1
sweep broke 1
sweep causing 1
sweep continued 1
sweep dropped 1
sweep fell 1
sweep hit 1
sweep pole 1
sweep rodes 1
sweeps additional 1
sweeps broke 1
sweeps nbsp 1
swing arm 1
swing buggy 1
swing chair 1
swing struck 1
swinging cabin 1
swinging light 1
swings nbsp 1
switch causing 1
switched directions 1
swollen ankle 1
swollen lip 1
swung backwards 1
symptoms continued 1
symptoms days 1
symptoms increased 1
symptoms persisting 1
system causing 1
system detected 1
t1 vertebrae 1
table located 1
tachychardia syndrome 1
tail flipped 1
tailbone hurt 1
takeoff jerked 1
takes prescription 1
takes seizure 1
taking antibiotics 1
taking ecstasy 1
taking medication 1
taking photo 1
taking picture 1
taking prozac 1
taking vicodin 1
tall 174lbs 1
tall connection 1
tall guy 1
tantrum dropped 1
team conducted 1
team evacuated 1
technical delay 1
technician figured 1
technician manually 1
techno jump 1
teenage boy 1
teenage girls 1
teens boarded 1
teeth reported 1
teeth swollen 1
temples father 1
temporary visual 1
ten minutes 1
tether safety 1
threads appeared 1
threads connected 1
thrown approximately 1
thrown observing 1
thrust forward 1
thumb caught 1
thumb cutting 1
thumb stuck 1
thumb treated 1
tied sneakers 1
tight mother 1
tightened lapbar 1
tightened properly 1
tightly secure 1
tiki masks 1
tilt causing 1
tilt lt 1
tilted position 1
time catching 1
time condition 1
time cpsc 1
time dial 1
time quot 1
times ems 1
times prior 1
tingling inher 1
tip nail 1
tipped backward 1
tipped backwards 1
tipped suffered 1
tire blew 1
tire bumped 1
tire grabbed 1
tire pressure 1
tire struck 1
tissue injuries 1
titanium rod 1
toe makes 1
toe nail 1
toe shoe 1
told caregivers 1
told ems 1
told park 1
told police 1
tooth broken 1
top center 1
top deck 1
top row 1
top turntable 1
torn meniscus 1
torso slid 1
torso telephone 1
total lap 1
touched flood 1
tow strap 1
tower bent 1
tower striking 1
town lost 1
track caused 1
track disabled 1
track injuring 1
track joint 1
track landing 1
track local 1
track nbsp 1
track paramedics 1
track position 1
track stand 1
track support 1
track switch 1
track trolley 1
tracks causing 1
trailer truck 1
train arrived 1
train coaster 1
train contacted 1
train daughter 1
train decoupled 1
train descended 1
train due 1
train employees 1
train guest 1
train hit 1
train normal 1
train passengers 1
train possibly 1
train prior 1
train reached 1
train roller 1
train station 1
train unloaded 1
trained properly 1
trains bumped 1
trains colliding 1
trains rear 1
trains update 1
tram exited 1
tram jerked 1
tram left 1
tram nbsp 1
tram unit 1
transfer vehicle 1
translators explain 1
transport wheelchair 1
trapping knee 1
trapping patron’s 1
trauma center 1
treatment late 1
treatment mother 1
tree falling 1
trian preparing 1
trim brakes 1
trim break 1
trim piece 1
tripped entering 1
tripped hitting 1
tripping falling 1
trouble swallowing 1
truck flipped 1
truck ran 1
truck stopped 1
trucks door 1
tub cage 1
tub carrying 1
tub hit 1
tub passed 1
tub rode 1
tub spinning 1
tub tilt 1
tubs broke 1
tubs tip 1
tunnel resulting 1
turbo lights 1
turntable ring 1
twelve patrons 1
twin brother 1
twisted causing 1
twisted knee 1
unauthorized modification 1
unbalanced load 1
unbuckled seat 1
uncle reports 1
uncle slid 1
unconscious mother 1
unconscious nbsp 1
uncontrollable blood 1
underarm region 1
underdeveloped neck 1
underneath car 1
underneath lap 1
underspeed vehicle 1
uneven cement 1
uneven terrain 1
unexpected sudden 1
unidentified substance 1
unknown experienced 1
unknown fainted 1
unknown happened 1
unknown lost 1
unknown male 1
unknown ran 1
unknown reason 1
unload belt 1
unload deck 1
unload dock 1
unload guests 1
unload injuring 1
unload location 1
unload lost 1
unload operators 1
unload platforms 1
unload process 1
unload resulting 1
unloaded stuck 1
unloading car 1
unloading causing 1
unloading floor 1
unloading platform 1
unloading sending 1
unloading station 1
unmedicated cardiac 1
unrelated guests 1
unrestrained child 1
unscheduled stop 1
unsecured gate 1
unspecified er 1
unusual incident 1
unusual noise 1
unusually strong 1
update evacuation 1
update included 1
upload landing 1
upper abdominal 1
upper bar 1
upper eyelid 1
upper gondola 1
upper left 1
upper lip 1
upper lower 1
upper scenery 1
upper sheave 1
upper stomach 1
upper sweep 1
upright posture 1
upset stomach 1
upward position 1
urgent care 1
valve timer 1
vehicle accident 1
vehicle advanced 1
vehicle appeared 1
vehicle boat 1
vehicle caught 1
vehicle cessation 1
vehicle chassis 1
vehicle conscious 1
vehicle directly 1
vehicle falling 1
vehicle fell 1
vehicle floor 1
vehicle fumes 1
vehicle hand 1
vehicle hit 1
vehicle launched 1
vehicle lost 1
vehicle lurched 1
vehicle nbsp 1
vehicle paramedics 1
vehicle park 1
vehicle patron’s 1
vehicle reached 1
vehicle restraints 1
vehicle rolled 1
vehicle rounded 1
vehicle spun 1
vehicle starting 1
vehicle stopped 1
vehicle striking 1
vehicle struck 1
vehicle swung 1
vehicle video 1
vehicle wall 1
vehicle’s restraint 1
vehicles collided 1
vehicles struck 1
velcro lap 1
veneer teeth 1
ventricular breathing 1
ventricular tachycardia 1
verbal autism 1
verbal daughter 1
verbally abusive 1
verbally instruct 1
verify correct 1
vertical element 1
vertical position 1
vertical stretch 1
vertical support 1
vertigo nausea 1
vertigo takes 1
vessels pop 1
vibrated causing 1
vibration brace 1
victim leaned 1
victim met 1
victim slipped 1
victim’s head 1
victims climbed 1
video family 1
violent stop 1
visible injury 1
vision difficulty 1
visual disability 1
visual evidence 1
visually check 1
vital signs 1
volt shock 1
vomited numerous 1
vomited uncontrollably 1
vomiting immediately 1
wake uer 1
walked backwards 1
walked forward 1
walked infront 1
walked past 1
walked sideways 1
walking amp 1
walking backward 1
walking inside 1
walking quickly 1
walking surface 1
walking symptoms 1
walkway adjacent 1
walkway injuring 1
walkway nbsp 1
walkway platform 1
wall hit 1
wall immediately 1
wall seated 1
warning information 1
warning jerking 1
water basin 1
water geyser 1
water intake 1
water moisture 1
water struck 1
wave hit 1
wave knocked 1
wave swinger 1
weak vomited 1
weakness fatigued 1
weakness slurred 1
wearing glasses 1
wearing knee 1
wearing rubber 1
wearing seat 1
wearing tied 1
wearing wet 1
weather rainy 1
weather service 1
wedding ring 1
week ago 1
weeks previously 1
weight distribution 1
weight restriction 1
weights regularly 1
weld insufficient 1
weldment causing 1
werdnig hoffman 1
wet chain 1
wet clothing 1
wet ramp 1
wet surface 1
wet table 1
wheel broke 1
wheel car 1
wheel caught 1
wheel cut 1
wheel drive 1
wheel housing 1
wheel inspector 1
wheel nuts 1
wheel patron’s 1
wheel walked 1
wheel worker 1
wheelchair caught 1
wheelchair fell 1
wheelchair reported 1
wheelchair tipped 1
wheelhouse ladder 1
wheels broke 1
whiplash neck 1
whirl guest 1
white bird 1
white center 1
wide step 1
wife experienced 1
wife reports 1
wife suffered 1
wife’s seat 1
wild claw 1
wile exiting 1
winch drum 1
wind blew 1
wind conditions 1
wind event 1
wind speeds 1
winds overturned 1
wing seats 1
wire jumper 1
wire rope 1
witness laid 1
witness noted 1
witness redacted 1
witness statements 1
witness yelled 1
witnessed grandmother 1
witnesses corroborated 1
witnesses nbsp 1
witnesses reported 1
woman leaning 1
woman moved 1
woman slipped 1
woman struck 1
woman unbuckled 1
woman’s hand 1
wood decking 1
wood hit 1
wooden box 1
wooden coaster 1
worm coaster 1
worn fasteners 1
worn mechanism 1
worn restraints 1
worse due 1
worse history 1
worsened paramedics 1
worsened radiating 1
wrist arm 1
wrist bent 1
wrist caught 1
wrist hand 1
wrist previuosly 1
wrist reported 1
wrist struck 1
written documentation 1
written report 1
wrong direction 1
wrong left 1
wsa pushing 1
yellow box 1
yellow plane 1
yellow truck 1
yo boy 1
yo injured 1
yoke handle 1
zipper cage 1
zipper car 1
zipper flew 1
zone section 1
acc_bigrams_filtered %>% 
  count(word1, word2, sort = TRUE) %>% 
  filter(n > 5)  %>% 
  visualize_bigrams()

We can again see that words tend to cluster together. If we had a specific research question, we could dig deeper into these.

Accident Description by Ride Category.

The last exploratory analysis we will do is to look at the associations between common bi-grams in the accident descriptions and the ride groups.

acc_bigrams_unite <- acc_bigrams_filtered %>% 
  unite(bigram, word1, word2, sep = " ")

manuf_of_interest <- acc_bigrams_unite %>% 
  count(manufacturer, sort = TRUE) %>% 
  filter(manufacturer != "Unknown") %>% 
  slice(1:5)

acc_bigrams_unite %>% 
  count(device_category, bigram, sort = TRUE) %>% 
  arrange(device_category, desc(n)) %>% 
  group_by(device_category) %>% 
  slice(1:6) %>%
  ungroup() %>% 
  mutate(device_category = as.factor(device_category),
         bigram = reorder_within(bigram, n, device_category)) %>% 
  ggplot(aes(bigram, n, order = -n, fill = device_category)) + 
  geom_col(show.legend = FALSE) +
  scale_x_reordered() +
  labs(x = NULL, y = "Number of Incidents", 
       title = "Most common accidents reported by device types",
       subtitle = "Analyzing ride categories with six most reported incidents") + 
  facet_wrap(~device_category, ncol = 2, scales = "free") + 
  coord_flip() 

It appears that the lap bar tends to be the biggest cause of accidents among all of the ride categories (except Other Attraction).

We now have a broader understanding about the SaferParks Database. If we were working with a particular company or amusement park, we could conduct further analysis into the common accidents and injuries associated with manufacturer, ride type, and ride category.

Setup Script for this Rmarkdown document:

knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(message = FALSE)
knitr::opts_chunk$set(warning = FALSE)

library(kableExtra)

library(tidyverse)
library(scales)
library(stringr)
library(lubridate)
library(patchwork)

library(tidytext)
library(wordcloud)
library(ggraph)
library(igraph)

ggplot2::theme_set(theme_light())

# Function to add colored text to the document

colorize <- function(x, color) {
  if (knitr::is_latex_output()) {
    sprintf("\\textcolor{%s}{%s}", color, x)
  } else if (knitr::is_html_output()) {
    sprintf("<span style='color: %s;'>%s</span>", color, 
      x)
  } else x
}

# Function for visualizing Bi-grams

visualize_bigrams <- function(bigrams) {
  set.seed(2020)
  a <- grid::arrow(type = "closed", length = unit(.15, "inches"))
  
  bigrams %>%
    graph_from_data_frame() %>%
    ggraph(layout = "fr") +
    geom_node_point(color = "lightblue", size = 3) +
    geom_edge_link(aes(edge_alpha = n), show.legend = FALSE, arrow = a) +
    geom_node_text(aes(label = name), vjust = 1, hjust = 1) +
    theme_void()
}
Andrew G Farina
Andrew G Farina
Academy Professor

My research interests include leadership (risk-taking propensity | appraisal) and character (intentional self-regulation) development.

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