Madrid Traffic Accidents Dataset

descriptive
traffic accidents
Author

Kazuharu Yanagimoto

Published

March 24, 2024

This is an analysis of the Madrid Traffic Accidents dataset. You can find a code for downloading and cleaning the data in code/00_download/ and code/01_cleaning/.

Note

I wrote this code for the workshop “An Advanced Introduction to R” at CEMFI in January 2022. You can find the slides and the materials in the GitHub repository

Code 
accident_bike |>
  ggplot(aes(x = fct_rev(type_person), fill = fct_rev(gender))) +
  geom_bar(position = "dodge") +
  coord_flip() +
  scale_fill_gender() +
  labs(x = NULL, y = NULL, fill = NULL, title = "Number of Accidents") +
  theme(legend.position = c(0.85, 0.1), panel.grid.major.y = element_blank()) +
  guides(fill = guide_legend(reverse = TRUE))

Logit Regression of Hospitalization and Death

Code 
models <- list(
  "(1)" = feglm(
    is_hospitalized ~
      type_person + positive_alcohol + positive_drug | age_c + gender,
    family = binomial(logit),
    data = accident_bike
  ),
  "(2)" = feglm(
    is_hospitalized ~
      type_person +
        positive_alcohol +
        positive_drug |
        age_c + gender + type_vehicle,
    family = binomial(logit),
    data = accident_bike
  ),
  "(3)" = feglm(
    is_hospitalized ~
      type_person +
        positive_alcohol +
        positive_drug |
        age_c + gender + type_vehicle + weather,
    family = binomial(logit),
    data = accident_bike
  ),
  "(4)" = feglm(
    is_died ~ type_person + positive_alcohol + positive_drug | age_c + gender,
    family = binomial(logit),
    data = accident_bike
  ),
  "(5)" = feglm(
    is_died ~
      type_person +
        positive_alcohol +
        positive_drug |
        age_c + gender + type_vehicle,
    family = binomial(logit),
    data = accident_bike
  ),
  "(6)" = feglm(
    is_died ~
      type_person +
        positive_alcohol +
        positive_drug |
        age_c + gender + type_vehicle + weather,
    family = binomial(logit),
    data = accident_bike
  )
)

cm <- c(
  "type_personPassenger" = "Passenger",
  "type_personPedestrian" = "Pedestrian",
  "positive_alcoholTRUE" = "Positive Alcohol"
)

gm <- tibble(
  raw = c("nobs", "FE: age_c", "FE: gender", "FE: type_vehicle", "FE: weather"),
  clean = c(
    "Observations",
    "FE: Age Group",
    "FE: Gender",
    "FE: Type of Vehicle",
    "FE: Weather"
  ),
  fmt = c(0, 0, 0, 0, 0)
)

modelsummary(
  models,
  stars = c("+" = .1, "*" = .05, "**" = .01),
  coef_map = cm,
  gof_map = gm
) |>
  group_tt(j = list("Hospitalization" = 2:4, "Died within 24 hours" = 5:7))
Hospitalization Died within 24 hours
(1) (2) (3) (4) (5) (6)
+ p < 0.1, * p < 0.05, ** p < 0.01
Passenger 0.026 0.501** 0.475** -1.697** -1.445* -1.440*
(0.100) (0.069) (0.068) (0.616) (0.631) (0.631)
Pedestrian 2.117** 2.381** 2.298** 2.190** 2.326** 2.327**
(0.114) (0.061) (0.058) (0.260) (0.248) (0.246)
Positive Alcohol 0.007 0.355** 0.381** -13.685** -13.421** -13.460**
(0.072) (0.081) (0.081) (0.059) (0.055) (0.054)
Observations 197477 197369 176133 118462 116344 112659
FE: Age Group X X X X X X
FE: Gender X X X X X X
FE: Type of Vehicle X X X X
FE: Weather X X