group_data <- data %>%
filter(mode == "Cyclist", intersection == "No") # Filter data for Cyclist MidBlock
# Define the group_vars
group_vars <- c("oneway", "twoways", "clearzone", "onstreetparking",
"parkinglotgarage", "concretebarrier", "laneofparkedcars",
"bikelanewithbarrier", "offroadbikepathcycletrack", "raisedbikelane",
"twowayprotectedbicyclelane", "onewayprotectedbicyclelane",
"bufferedbicyclelaneadjacenttocurb", "bufferedbicyclelaneoffsetfromcurb",
"paintedbicyclelaneadjacenttocurb", "paintedbicyclelaneoffsetfromcurb",
"bikeaccessibleshoulder", "sharedlanewithmarkings", "onelane", "twolanes",
"threelanes", "fourlanes", "fivelanes", "sixlanes")
# Grouping the variables by category
categories <- list(
"Road Direction" = c("oneway", "twoways"),
"Number of Lanes" = c("onelane", "twolanes", "threelanes", "fourlanes", "fivelanes", "sixlanes"),
"Cyclist Infrastructure" = c("bikelanewithbarrier", "offroadbikepathcycletrack",
"raisedbikelane", "twowayprotectedbicyclelane",
"onewayprotectedbicyclelane", "bufferedbicyclelaneadjacenttocurb",
"bufferedbicyclelaneoffsetfromcurb",
"paintedbicyclelaneadjacenttocurb",
"paintedbicyclelaneoffsetfromcurb", "bikeaccessibleshoulder",
"sharedlanewithmarkings"),
"Other Variables" = setdiff(group_vars, c("oneway", "twoways", "onelane", "twolanes", "threelanes", "fourlanes", "fivelanes", "sixlanes", "bikelanewithbarrier", "offroadbikepathcycletrack",
"raisedbikelane", "twowayprotectedbicyclelane",
"onewayprotectedbicyclelane", "bufferedbicyclelaneadjacenttocurb",
"bufferedbicyclelaneoffsetfromcurb",
"paintedbicyclelaneadjacenttocurb",
"paintedbicyclelaneoffsetfromcurb", "bikeaccessibleshoulder",
"sharedlanewithmarkings"))
)
odds_ratios_list <- list()
standard_columns <- c("Category", "Variable", "Level", "Injury", "Injury_Percent", "Non-Injury", "Non-Injury_Percent", "OddsRatio", "Lower", "Upper", "PValue")
for (category in names(categories)) {
for (var in categories[[category]]) {
if (var %in% names(group_data)) {
odds_ratio_result <- tryCatch({
if (n_distinct(group_data[[var]]) == 2) {
odds_data <- group_data %>%
select(all_of(var), crashseverity) %>%
table() %>%
as.matrix()
if (all(dim(odds_data) == c(2, 2))) { # Ensure the table has the correct dimensions
# Use epitab to calculate odds ratio
odds_ratio_table <- as.data.frame(epitab(odds_data, method = "oddsratio")$tab) %>%
mutate(across(where(is.numeric), round, 4))
odds_ratio_table <- cbind(Category = category, Variable = var, Level = rownames(odds_ratio_table), odds_ratio_table)
names(odds_ratio_table) <- standard_columns
odds_ratio_table
} else {
data.frame(
Category = category, Variable = var, Level = NA, Injury = "Not applicable",
Injury_Percent = NA, NonInjury = NA, Non_Injury_Percent = NA, OddsRatio = NA,
Lower = NA, Upper = NA, PValue = NA, stringsAsFactors = FALSE
)
}
} else {
data.frame(
Category = category, Variable = var, Level = NA, Injury = "Not applicable",
Injury_Percent = NA, NonInjury = NA, Non_Injury_Percent = NA, OddsRatio = NA,
Lower = NA, Upper = NA, PValue = NA, stringsAsFactors = FALSE
)
}
}, error = function(e) {
data.frame(
Category = category, Variable = var, Level = NA, Injury = "Not applicable",
Injury_Percent = NA, NonInjury = NA, Non_Injury_Percent = NA, OddsRatio = NA,
Lower = NA, Upper = NA, PValue = NA, stringsAsFactors = FALSE
)
})
odds_ratios_list[[var]] <- odds_ratio_result
}
}
}Warning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectodds_ratios_data <- do.call(rbind, odds_ratios_list) # Combine results into a single data frame
odds_ratios_data$OddsRatio_CI <- ifelse(is.na(odds_ratios_data$OddsRatio), "-",
paste0(odds_ratios_data$OddsRatio, " (", odds_ratios_data$Lower, " - ", odds_ratios_data$Upper, ")"))
odds_ratios_data <- odds_ratios_data %>% select(-Lower, -Upper) # Drop Lower and Upper columns
row_colors <- rep(c("#f9f9f9", "#ffffff"), length.out = nrow(odds_ratios_data))
odds_ratios_data$Variable <- ifelse(duplicated(odds_ratios_data$Variable), "", odds_ratios_data$Variable)
odds_ratios_data$Category <- ifelse(duplicated(odds_ratios_data$Category), "", odds_ratios_data$Category)
final_table_html <- htmlTable(
odds_ratios_data %>%
mutate(Injury_Percent_Combined = paste0(Injury, " - (", Injury_Percent, ")")) %>% # Combine into a single column
mutate(Non_Injury_Percent_Combined = paste0(`Non-Injury`, " - (", `Non-Injury_Percent`, ")")) %>%
select("Category", "Variable", "Level", "Injury_Percent_Combined", "Non_Injury_Percent_Combined", "PValue", "OddsRatio_CI") %>%
rename("Injury (%)" = Injury_Percent_Combined, "Non-Injury (%)" = Non_Injury_Percent_Combined) %>%
mutate(across(everything(), ~ ifelse(is.na(.) | . %in% c("NA", "NaN", "Inf", "-Inf"), "", .))),
header = c("Category", "Variable", "Level", "Injury (%)", "Non-Injury (%)", "P-Value", "Odds Ratio (95% CI)"),
align = 'lcccccc',
rnames = FALSE,
css.cell = "text-align: center; padding: 12px 15px; font-size: 12px; white-space: nowrap;",
css.row = sprintf("background-color: %s;", row_colors),
header.css = "background-color: #333333; font-size: 14px; text-align: center; color: white;",
caption = "<h3 style='text-align: center;'>Analysis for Cyclist MidBlock</h3>
<p style='text-align: center;'><em>This analysis focuses on mode: cyclist | intersection: no</em></p>"
)
# Display the final HTML table
print(HTML(final_table_html))
Analysis for Cyclist MidBlockThis analysis focuses on mode: cyclist | intersection: no | ||||||
| Category | Variable | Level | Injury (%) | Non-Injury (%) | P-Value | Odds Ratio (95% CI) |
|---|---|---|---|---|---|---|
| Road Direction | oneway | No | 167 - (0.9598) | 72 - (0.9863) | 1 (NA - NA) | |
| Yes | 7 - (0.0402) | 1 - (0.0137) | 0.4424 | 0.3313 (0.04 - 2.7424) | ||
| twoways | No | 6 - (0.0345) | 0 - (0) | 1 (NA - NA) | ||
| Yes | 168 - (0.9655) | 73 - (1) | 0.1837 | Inf (NaN - Inf) | ||
| Number of Lanes | onelane | No | 174 - (1) | 72 - (0.9863) | 1 (NA - NA) | |
| Yes | 0 - (0) | 1 - (0.0137) | 0.2955 | Inf (NaN - Inf) | ||
| twolanes | No | 73 - (0.4195) | 20 - (0.274) | 1 (NA - NA) | ||
| Yes | 101 - (0.5805) | 53 - (0.726) | 0.0321 | 1.9153 (1.0554 - 3.476) | ||
| threelanes | No | 166 - (0.954) | 72 - (0.9863) | 1 (NA - NA) | ||
| Yes | 8 - (0.046) | 1 - (0.0137) | 0.2884 | 0.2882 (0.0354 - 2.3468) | ||
| fourlanes | No | 120 - (0.6897) | 56 - (0.7671) | 1 (NA - NA) | ||
| Yes | 54 - (0.3103) | 17 - (0.2329) | 0.2808 | 0.6746 (0.3591 - 1.2675) | ||
| fivelanes | No | 167 - (0.9598) | 73 - (1) | 1 (NA - NA) | ||
| Yes | 7 - (0.0402) | 0 - (0) | 0.1082 | 0 (0 - NaN) | ||
| sixlanes | No | 165 - (0.9483) | 71 - (0.9726) | 1 (NA - NA) | ||
| Yes | 9 - (0.0517) | 2 - (0.0274) | 0.515 | 0.5164 (0.1088 - 2.4508) | ||
| Cyclist Infrastructure | bikelanewithbarrier | No | 174 - (1) | 71 - (0.9726) | 1 (NA - NA) | |
| Yes | 0 - (0) | 2 - (0.0274) | 0.0865 | Inf (NaN - Inf) | ||
| offroadbikepathcycletrack | No | 172 - (0.9885) | 73 - (1) | 1 (NA - NA) | ||
| Yes | 2 - (0.0115) | 0 - (0) | 1 | 0 (0 - NaN) | ||
| raisedbikelane | No | 171 - (0.9828) | 72 - (0.9863) | 1 (NA - NA) | ||
| Yes | 3 - (0.0172) | 1 - (0.0137) | 1 | 0.7917 (0.081 - 7.7388) | ||
| twowayprotectedbicyclelane | No | 171 - (0.9828) | 72 - (0.9863) | 1 (NA - NA) | ||
| Yes | 3 - (0.0172) | 1 - (0.0137) | 1 | 0.7917 (0.081 - 7.7388) | ||
| onewayprotectedbicyclelane | No | 174 - (1) | 72 - (0.9863) | 1 (NA - NA) | ||
| Yes | 0 - (0) | 1 - (0.0137) | 0.2955 | Inf (NaN - Inf) | ||
| bufferedbicyclelaneadjacenttocurb | No | 169 - (0.9713) | 72 - (0.9863) | 1 (NA - NA) | ||
| Yes | 5 - (0.0287) | 1 - (0.0137) | 0.6732 | 0.4694 (0.0539 - 4.0897) | ||
| bufferedbicyclelaneoffsetfromcurb | No | 174 - (1) | 72 - (0.9863) | 1 (NA - NA) | ||
| Yes | 0 - (0) | 1 - (0.0137) | 0.2955 | Inf (NaN - Inf) | ||
| paintedbicyclelaneadjacenttocurb | No | 138 - (0.7931) | 57 - (0.7808) | 1 (NA - NA) | ||
| Yes | 36 - (0.2069) | 16 - (0.2192) | 0.8648 | 1.076 (0.5535 - 2.092) | ||
| paintedbicyclelaneoffsetfromcurb | No | 165 - (0.9483) | 68 - (0.9315) | 1 (NA - NA) | ||
| Yes | 9 - (0.0517) | 5 - (0.0685) | 0.5621 | 1.348 (0.4358 - 4.1694) | ||
| bikeaccessibleshoulder | No | 162 - (0.931) | 68 - (0.9315) | 1 (NA - NA) | ||
| Yes | 12 - (0.069) | 5 - (0.0685) | 1 | 0.9926 (0.3368 - 2.926) | ||
| sharedlanewithmarkings | No | 166 - (0.954) | 72 - (0.9863) | 1 (NA - NA) | ||
| Yes | 8 - (0.046) | 1 - (0.0137) | 0.2884 | 0.2882 (0.0354 - 2.3468) | ||
| Other Variables | clearzone | No | 174 - (1) | 71 - (0.9726) | 1 (NA - NA) | |
| Yes | 0 - (0) | 2 - (0.0274) | 0.0865 | Inf (NaN - Inf) | ||
| onstreetparking | No | 97 - (0.5575) | 33 - (0.4521) | 1 (NA - NA) | ||
| Yes | 77 - (0.4425) | 40 - (0.5479) | 0.1624 | 1.527 (0.8814 - 2.6452) | ||
| parkinglotgarage | No | 57 - (0.3276) | 31 - (0.4247) | 1 (NA - NA) | ||
| Yes | 117 - (0.6724) | 42 - (0.5753) | 0.1491 | 0.66 (0.3764 - 1.1576) | ||
| concretebarrier | No | 172 - (0.9885) | 73 - (1) | 1 (NA - NA) | ||
| Yes | 2 - (0.0115) | 0 - (0) | 1 | 0 (0 - NaN) | ||
| laneofparkedcars | No | 170 - (0.977) | 73 - (1) | 1 (NA - NA) | ||
| Yes | 4 - (0.023) | 0 - (0) | 0.3224 | 0 (0 - NaN) | ||
NULL
cyclist_intersection_data <- data %>%
filter(mode == "Cyclist", intersection == "Yes") # Filter data for Cyclist Intersection
group_vars <- c("oneway", "twoways", "onelane", "twolanes", "threelanes", "fourlanes",
"fivelanes", "sixlanes", "signalized", "stopsigns", "roundabout",
"dedicatedsignalforcyclists", "bikeboxes", "twostageturnbox",
"protectedordedicatedintersection", "medianislanddiverter",
"combinedbikelaneturnlane", "throughbikelane")
categories <- list(
"Road Direction" = c("oneway", "twoways"),
"Number of Lanes" = c("onelane", "twolanes", "threelanes", "fourlanes", "fivelanes", "sixlanes"),
"Intersection Infrastructure" = c("signalized", "stopsigns", "roundabout"),
"Cyclist Infrastructure" = c("dedicatedsignalforcyclists", "bikeboxes", "twostageturnbox",
"protectedordedicatedintersection", "medianislanddiverter",
"combinedbikelaneturnlane", "throughbikelane")
)
odds_ratios_list <- list()
standard_columns <- c("Category", "Variable", "Level", "Injury", "Injury_Percent",
"Non-Injury", "Non-Injury_Percent", "OddsRatio", "Lower", "Upper", "PValue")
for (category in names(categories)) {
for (var in categories[[category]]) {
if (var %in% names(cyclist_intersection_data)) {
odds_ratio_result <- tryCatch({
if (n_distinct(cyclist_intersection_data[[var]]) == 2) {
odds_data <- cyclist_intersection_data %>%
select(all_of(var), crashseverity) %>%
table() %>%
as.matrix()
if (all(dim(odds_data) == c(2, 2))) {
odds_ratio_table <- as.data.frame(epitab(odds_data, method = "oddsratio")$tab) %>%
mutate(across(where(is.numeric), round, 4))
odds_ratio_table <- cbind(Category = category, Variable = var, Level = rownames(odds_ratio_table), odds_ratio_table)
setNames(odds_ratio_table, standard_columns)
} else {
setNames(data.frame(
Category = category, Variable = var, Level = NA, Injury = "Not applicable",
Injury_Percent = NA, NonInjury = NA, Non_Injury_Percent = NA, OddsRatio = NA,
Lower = NA, Upper = NA, PValue = NA, stringsAsFactors = FALSE
), standard_columns)
}
} else {
setNames(data.frame(
Category = category, Variable = var, Level = NA, Injury = "Not applicable",
Injury_Percent = NA, NonInjury = NA, Non_Injury_Percent = NA, OddsRatio = NA,
Lower = NA, Upper = NA, PValue = NA, stringsAsFactors = FALSE
), standard_columns)
}
}, error = function(e) {
setNames(data.frame(
Category = category, Variable = var, Level = NA, Injury = "Not applicable",
Injury_Percent = NA, NonInjury = NA, Non_Injury_Percent = NA, OddsRatio = NA,
Lower = NA, Upper = NA, PValue = NA, stringsAsFactors = FALSE
), standard_columns)
})
odds_ratios_list[[var]] <- odds_ratio_result
}
}
}Warning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectodds_ratios_data <- do.call(rbind, odds_ratios_list)
odds_ratios_data$OddsRatio_CI <- ifelse(is.na(odds_ratios_data$OddsRatio), "-",
paste0(odds_ratios_data$OddsRatio, " (", odds_ratios_data$Lower, " - ", odds_ratios_data$Upper, ")"))
odds_ratios_data <- odds_ratios_data %>% select(-Lower, -Upper)
row_colors <- rep(c("#f9f9f9", "#ffffff"), length.out = nrow(odds_ratios_data))
odds_ratios_data$Variable <- ifelse(duplicated(odds_ratios_data$Variable), "", odds_ratios_data$Variable)
odds_ratios_data$Category <- ifelse(duplicated(odds_ratios_data$Category), "", odds_ratios_data$Category)
final_table_html <- htmlTable(
odds_ratios_data %>%
mutate(Injury_Percent_Combined = paste0(Injury, " - (", Injury_Percent, ")")) %>%
mutate(Non_Injury_Percent_Combined = paste0(`Non-Injury`, " - (", `Non-Injury_Percent`, ")")) %>%
select("Category", "Variable", "Level", "Injury_Percent_Combined", "Non_Injury_Percent_Combined", "PValue", "OddsRatio_CI") %>%
rename("Injury (%)" = Injury_Percent_Combined, "Non-Injury (%)" = Non_Injury_Percent_Combined) %>%
mutate(across(everything(), ~ ifelse(is.na(.) | . %in% c("NA", "NaN", "Inf", "-Inf"), "", .))),
header = c("Category", "Variable", "Level", "Injury (%)", "Non-Injury (%)", "P-Value", "Odds Ratio (95% CI)"),
align = 'lcccccc',
rnames = FALSE,
css.cell = "text-align: center; padding: 12px 15px; font-size: 12px; white-space: nowrap;",
css.row = sprintf("background-color: %s;", row_colors),
header.css = "background-color: #333333; font-size: 14px; text-align: center; color: white;",
caption = "<h3 style='text-align: center;'>Analysis for Cyclist Intersection</h3>
<p style='text-align: center;'><em>This analysis focuses on mode: cyclist | intersection: yes</em></p>"
)
print(HTML(final_table_html))
Analysis for Cyclist IntersectionThis analysis focuses on mode: cyclist | intersection: yes | ||||||
| Category | Variable | Level | Injury (%) | Non-Injury (%) | P-Value | Odds Ratio (95% CI) |
|---|---|---|---|---|---|---|
| Road Direction | oneway | No | 293 - (0.8906) | 62 - (0.9254) | 1 (NA - NA) | |
| Yes | 36 - (0.1094) | 5 - (0.0746) | 0.5114 | 0.6564 (0.2476 - 1.7396) | ||
| twoways | No | 5 - (0.0152) | 1 - (0.0149) | 1 (NA - NA) | ||
| Yes | 324 - (0.9848) | 66 - (0.9851) | 1 | 1.0185 (0.1171 - 8.8608) | ||
| Number of Lanes | onelane | No | 289 - (0.8784) | 62 - (0.9254) | 1 (NA - NA) | |
| Yes | 40 - (0.1216) | 5 - (0.0746) | 0.3969 | 0.5827 (0.221 - 1.536) | ||
| twolanes | No | 53 - (0.1611) | 10 - (0.1493) | 1 (NA - NA) | ||
| Yes | 276 - (0.8389) | 57 - (0.8507) | 1 | 1.0946 (0.5257 - 2.2791) | ||
| threelanes | No | 305 - (0.9271) | 61 - (0.9104) | 1 (NA - NA) | ||
| Yes | 24 - (0.0729) | 6 - (0.0896) | 0.6153 | 1.25 (0.4903 - 3.1868) | ||
| fourlanes | No | 190 - (0.5775) | 39 - (0.5821) | 1 (NA - NA) | ||
| Yes | 139 - (0.4225) | 28 - (0.4179) | 1 | 0.9814 (0.5762 - 1.6714) | ||
| fivelanes | No | 316 - (0.9605) | 63 - (0.9403) | 1 (NA - NA) | ||
| Yes | 13 - (0.0395) | 4 - (0.0597) | 0.5049 | 1.5433 (0.4873 - 4.8879) | ||
| sixlanes | No | 306 - (0.9301) | 64 - (0.9552) | 1 (NA - NA) | ||
| Yes | 23 - (0.0699) | 3 - (0.0448) | 0.5937 | 0.6236 (0.1818 - 2.1399) | ||
| Intersection Infrastructure | signalized | No | 176 - (0.535) | 31 - (0.4627) | 1 (NA - NA) | |
| Yes | 153 - (0.465) | 36 - (0.5373) | 0.2868 | 1.3359 (0.7888 - 2.2624) | ||
| stopsigns | No | 153 - (0.465) | 31 - (0.4627) | 1 (NA - NA) | ||
| Yes | 176 - (0.535) | 36 - (0.5373) | 1 | 1.0095 (0.5961 - 1.7097) | ||
| roundabout | No | 323 - (0.9818) | 67 - (1) | 1 (NA - NA) | ||
| Yes | 6 - (0.0182) | 0 - (0) | 0.5951 | 0 (0 - NaN) | ||
| Cyclist Infrastructure | dedicatedsignalforcyclists | No | 319 - (0.9696) | 67 - (1) | 1 (NA - NA) | |
| Yes | 10 - (0.0304) | 0 - (0) | 0.2236 | 0 (0 - NaN) | ||
| bikeboxes | No | 323 - (0.9818) | 64 - (0.9552) | 1 (NA - NA) | ||
| Yes | 6 - (0.0182) | 3 - (0.0448) | 0.1822 | 2.5234 (0.6151 - 10.3526) | ||
| twostageturnbox | No | 323 - (0.9818) | 66 - (0.9851) | 1 (NA - NA) | ||
| Yes | 6 - (0.0182) | 1 - (0.0149) | 1 | 0.8157 (0.0966 - 6.8877) | ||
| protectedordedicatedintersection | No | 315 - (0.9574) | 63 - (0.9403) | 1 (NA - NA) | ||
| Yes | 14 - (0.0426) | 4 - (0.0597) | 0.5221 | 1.4286 (0.4552 - 4.4831) | ||
| medianislanddiverter | No | 320 - (0.9726) | 65 - (0.9701) | 1 (NA - NA) | ||
| Yes | 9 - (0.0274) | 2 - (0.0299) | 1 | 1.094 (0.231 - 5.1813) | ||
| combinedbikelaneturnlane | No | 322 - (0.9787) | 65 - (0.9701) | 1 (NA - NA) | ||
| Yes | 7 - (0.0213) | 2 - (0.0299) | 0.6523 | 1.4154 (0.2875 - 6.9679) | ||
| throughbikelane | No | 303 - (0.921) | 61 - (0.9104) | 1 (NA - NA) | ||
| Yes | 26 - (0.079) | 6 - (0.0896) | 0.8056 | 1.1463 (0.4526 - 2.9033) | ||
NULL
pedestrian_midblock_data <- data %>%
filter(mode == "Pedestrian", intersection == "No") # Filter data for Pedestrian MidBlock
group_vars <- c("oneway", "twoways", "onelane", "twolanes", "threelanes", "fourlanes",
"fivelanes", "sixlanes", "clearzone", "onstreetparking", "standardpaint",
"solidpaint", "striped", "offroadmultiusepathway", "speedhump",
"accessibleshoulder", "sidewalks")
categories <- list(
"Road Direction" = c("oneway", "twoways"),
"Number of Lanes" = c("onelane", "twolanes", "threelanes", "fourlanes", "fivelanes", "sixlanes"),
"Pedestrian Infrastructure" = c("accessibleshoulder", "sidewalks", "standardpaint", "solidpaint", "striped"),
"Other Infrastructure" = c( "clearzone", "onstreetparking", "offroadmultiusepathway", "speedhump")
)
odds_ratios_list <- list()
standard_columns <- c("Category", "Variable", "Level", "Injury", "Injury_Percent",
"Non-Injury", "Non-Injury_Percent", "OddsRatio", "Lower", "Upper", "PValue")
for (category in names(categories)) {
for (var in categories[[category]]) {
if (var %in% names(pedestrian_midblock_data)) {
odds_ratio_result <- tryCatch({
if (n_distinct(pedestrian_midblock_data[[var]]) == 2) {
odds_data <- pedestrian_midblock_data %>%
select(all_of(var), crashseverity) %>%
table() %>%
as.matrix()
if (all(dim(odds_data) == c(2, 2))) {
odds_ratio_table <- as.data.frame(epitab(odds_data, method = "oddsratio")$tab) %>%
mutate(across(where(is.numeric), round, 4))
odds_ratio_table <- cbind(Category = category, Variable = var, Level = rownames(odds_ratio_table), odds_ratio_table)
setNames(odds_ratio_table, standard_columns)
} else {
setNames(data.frame(
Category = category, Variable = var, Level = NA, Injury = "Not applicable",
Injury_Percent = NA, NonInjury = NA, Non_Injury_Percent = NA, OddsRatio = NA,
Lower = NA, Upper = NA, PValue = NA, stringsAsFactors = FALSE
), standard_columns)
}
} else {
setNames(data.frame(
Category = category, Variable = var, Level = NA, Injury = "Not applicable",
Injury_Percent = NA, NonInjury = NA, Non_Injury_Percent = NA, OddsRatio = NA,
Lower = NA, Upper = NA, PValue = NA, stringsAsFactors = FALSE
), standard_columns)
}
}, error = function(e) {
setNames(data.frame(
Category = category, Variable = var, Level = NA, Injury = "Not applicable",
Injury_Percent = NA, NonInjury = NA, Non_Injury_Percent = NA, OddsRatio = NA,
Lower = NA, Upper = NA, PValue = NA, stringsAsFactors = FALSE
), standard_columns)
})
odds_ratios_list[[var]] <- odds_ratio_result
}
}
}Warning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectodds_ratios_data <- do.call(rbind, odds_ratios_list)
odds_ratios_data$OddsRatio_CI <- ifelse(is.na(odds_ratios_data$OddsRatio), "-",
paste0(odds_ratios_data$OddsRatio, " (", odds_ratios_data$Lower, " - ", odds_ratios_data$Upper, ")"))
odds_ratios_data <- odds_ratios_data %>% select(-Lower, -Upper)
row_colors <- rep(c("#f9f9f9", "#ffffff"), length.out = nrow(odds_ratios_data))
odds_ratios_data$Variable <- ifelse(duplicated(odds_ratios_data$Variable), "", odds_ratios_data$Variable)
odds_ratios_data$Category <- ifelse(duplicated(odds_ratios_data$Category), "", odds_ratios_data$Category)
final_table_html <- htmlTable(
odds_ratios_data %>%
mutate(Injury_Percent_Combined = paste0(Injury, " - (", Injury_Percent, ")")) %>%
mutate(Non_Injury_Percent_Combined = paste0(`Non-Injury`, " - (", `Non-Injury_Percent`, ")")) %>%
select("Category", "Variable", "Level", "Injury_Percent_Combined", "Non_Injury_Percent_Combined", "PValue", "OddsRatio_CI") %>%
rename("Injury (%)" = Injury_Percent_Combined, "Non-Injury (%)" = Non_Injury_Percent_Combined) %>%
mutate(across(everything(), ~ ifelse(is.na(.) | . %in% c("NA", "NaN", "Inf", "-Inf"), "", .))),
header = c("Category", "Variable", "Level", "Injury (%)", "Non-Injury (%)", "P-Value", "Odds Ratio (95% CI)"),
align = 'lcccccc',
rnames = FALSE,
css.cell = "text-align: center; padding: 12px 15px; font-size: 12px; white-space: nowrap;",
css.row = sprintf("background-color: %s;", row_colors),
header.css = "background-color: #333333; font-size: 14px; text-align: center; color: white;",
caption = "<h3 style='text-align: center;'>Analysis for Pedestrian MidBlock</h3>
<p style='text-align: center;'><em>This analysis focuses on mode: pedestrian | intersection: no</em></p>"
)
print(HTML(final_table_html))
Analysis for Pedestrian MidBlockThis analysis focuses on mode: pedestrian | intersection: no | ||||||
| Category | Variable | Level | Injury (%) | Non-Injury (%) | P-Value | Odds Ratio (95% CI) |
|---|---|---|---|---|---|---|
| Road Direction | oneway | No | 303 - (0.987) | 69 - (1) | 1 (NA - NA) | |
| Yes | 4 - (0.013) | 0 - (0) | 1 | 0 (0 - NaN) | ||
| twoways | No | 3 - (0.0098) | 0 - (0) | 1 (NA - NA) | ||
| Yes | 304 - (0.9902) | 69 - (1) | 1 | Inf (NaN - Inf) | ||
| Number of Lanes | onelane | No | 302 - (0.9837) | 67 - (0.971) | 1 (NA - NA) | |
| Yes | 5 - (0.0163) | 2 - (0.029) | 0.6167 | 1.803 (0.3425 - 9.4924) | ||
| twolanes | No | 141 - (0.4593) | 31 - (0.4493) | 1 (NA - NA) | ||
| Yes | 166 - (0.5407) | 38 - (0.5507) | 0.8945 | 1.0412 (0.6161 - 1.7597) | ||
| threelanes | No | 294 - (0.9577) | 65 - (0.942) | 1 (NA - NA) | ||
| Yes | 13 - (0.0423) | 4 - (0.058) | 0.5286 | 1.3917 (0.4396 - 4.4059) | ||
| fourlanes | No | 198 - (0.645) | 48 - (0.6957) | 1 (NA - NA) | ||
| Yes | 109 - (0.355) | 21 - (0.3043) | 0.4847 | 0.7947 (0.4523 - 1.3963) | ||
| fivelanes | No | 297 - (0.9674) | 67 - (0.971) | 1 (NA - NA) | ||
| Yes | 10 - (0.0326) | 2 - (0.029) | 1 | 0.8866 (0.1898 - 4.1402) | ||
| sixlanes | No | 290 - (0.9446) | 65 - (0.942) | 1 (NA - NA) | ||
| Yes | 17 - (0.0554) | 4 - (0.058) | 1 | 1.0498 (0.3419 - 3.2235) | ||
| Pedestrian Infrastructure | accessibleshoulder | No | 282 - (0.9186) | 61 - (0.8841) | 1 (NA - NA) | |
| Yes | 25 - (0.0814) | 8 - (0.1159) | 0.3509 | 1.4793 (0.6368 - 3.4366) | ||
| sidewalks | No | 54 - (0.1759) | 13 - (0.1884) | 1 (NA - NA) | ||
| Yes | 253 - (0.8241) | 56 - (0.8116) | 0.8618 | 0.9194 (0.4699 - 1.7988) | ||
| standardpaint | No | 276 - (0.899) | 62 - (0.8986) | 1 (NA - NA) | ||
| Yes | 31 - (0.101) | 7 - (0.1014) | 1 | 1.0052 (0.4232 - 2.3878) | ||
| solidpaint | No | 303 - (0.987) | 69 - (1) | 1 (NA - NA) | ||
| Yes | 4 - (0.013) | 0 - (0) | 1 | 0 (0 - NaN) | ||
| striped | No | 286 - (0.9316) | 63 - (0.913) | 1 (NA - NA) | ||
| Yes | 21 - (0.0684) | 6 - (0.087) | 0.6062 | 1.2971 (0.5029 - 3.3451) | ||
| Other Infrastructure | clearzone | No | 305 - (0.9935) | 69 - (1) | 1 (NA - NA) | |
| Yes | 2 - (0.0065) | 0 - (0) | 1 | 0 (0 - NaN) | ||
| onstreetparking | No | 175 - (0.57) | 34 - (0.4928) | 1 (NA - NA) | ||
| Yes | 132 - (0.43) | 35 - (0.5072) | 0.2837 | 1.3648 (0.8087 - 2.303) | ||
| offroadmultiusepathway | Not applicable - (NA) | NA - (NA) | - | |||
| speedhump | No | 304 - (0.9902) | 67 - (0.971) | 1 (NA - NA) | ||
| Yes | 3 - (0.0098) | 2 - (0.029) | 0.2286 | 3.0249 (0.4957 - 18.4583) | ||
NULL
pedestrian_intersection_data <- data %>%
filter(mode == "Pedestrian", intersection == "Yes") # Filter data for Pedestrian Intersection
group_vars <- c("oneway", "twoways", "onelane", "twolanes", "threelanes", "fourlanes",
"fivelanes", "sixlanes", "signalized", "stopsigns", "roundabout",
"numberoflegsintheintersection", "threelegs", "fourlegs", "fourmlegs",
"unsignalizedcrosswalk", "signalizedcrosswalkstreet", "standardpaint",
"solidpaint", "striped", "pedestrianrefugestriped",
"curbextensionpinchpoint", "rapidrectangularflashingbeacon",
"pedestriancrossedsignal")
categories <- list(
"Road Direction" = c("oneway", "twoways"),
"Number of Legs" = c("threelegs", "fourlegs", "fourmlegs"),
"Number of Lanes" = c("onelane", "twolanes", "threelanes", "fourlanes", "fivelanes", "sixlanes"),
"Pedestrian Infrastructure" = c("pedestrianrefugestriped",
"curbextensionpinchpoint",
"rapidrectangularflashingbeacon",
"pedestriancrossedsignal"),
"Other Variables" = c("signalized", "stopsigns", "roundabout",
"numberoflegsintheintersection",
"unsignalizedcrosswalk", "signalizedcrosswalkstreet", "standardpaint",
"solidpaint", "striped")
)
odds_ratios_list <- list()
standard_columns <- c("Category", "Variable", "Level", "Injury", "Injury_Percent",
"Non-Injury", "Non-Injury_Percent", "OddsRatio", "Lower", "Upper", "PValue")
for (category in names(categories)) {
for (var in categories[[category]]) {
if (var %in% names(pedestrian_intersection_data)) {
odds_ratio_result <- tryCatch({
if (n_distinct(pedestrian_intersection_data[[var]]) == 2) {
odds_data <- pedestrian_intersection_data %>%
select(all_of(var), crashseverity) %>%
table() %>%
as.matrix()
if (all(dim(odds_data) == c(2, 2))) {
odds_ratio_table <- as.data.frame(epitab(odds_data, method = "oddsratio")$tab) %>%
mutate(across(where(is.numeric), round, 4))
odds_ratio_table <- cbind(Category = category, Variable = var, Level = rownames(odds_ratio_table), odds_ratio_table)
setNames(odds_ratio_table, standard_columns)
} else {
setNames(data.frame(
Category = category, Variable = var, Level = NA, Injury = "Not applicable",
Injury_Percent = NA, NonInjury = NA, Non_Injury_Percent = NA, OddsRatio = NA,
Lower = NA, Upper = NA, PValue = NA, stringsAsFactors = FALSE
), standard_columns)
}
} else {
setNames(data.frame(
Category = category, Variable = var, Level = NA, Injury = "Not applicable",
Injury_Percent = NA, NonInjury = NA, Non_Injury_Percent = NA, OddsRatio = NA,
Lower = NA, Upper = NA, PValue = NA, stringsAsFactors = FALSE
), standard_columns)
}
}, error = function(e) {
setNames(data.frame(
Category = category, Variable = var, Level = NA, Injury = "Not applicable",
Injury_Percent = NA, NonInjury = NA, Non_Injury_Percent = NA, OddsRatio = NA,
Lower = NA, Upper = NA, PValue = NA, stringsAsFactors = FALSE
), standard_columns)
})
odds_ratios_list[[var]] <- odds_ratio_result
} else {
odds_ratios_list[[var]] <- setNames(data.frame(
Category = category, Variable = var, Level = NA, Injury = "Variable not available",
Injury_Percent = NA, NonInjury = NA, Non_Injury_Percent = NA, OddsRatio = NA,
Lower = NA, Upper = NA, PValue = NA, stringsAsFactors = FALSE
), standard_columns)
}
}
}Warning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectWarning: Chi-squared approximation may be incorrectodds_ratios_data <- do.call(rbind, odds_ratios_list)
odds_ratios_data$OddsRatio_CI <- ifelse(is.na(odds_ratios_data$OddsRatio), "-",
paste0(odds_ratios_data$OddsRatio, " (", odds_ratios_data$Lower, " - ", odds_ratios_data$Upper, ")"))
odds_ratios_data <- odds_ratios_data %>% select(-Lower, -Upper)
row_colors <- rep(c("#f9f9f9", "#ffffff"), length.out = nrow(odds_ratios_data))
odds_ratios_data$Variable <- ifelse(duplicated(odds_ratios_data$Variable), "", odds_ratios_data$Variable)
odds_ratios_data$Category <- ifelse(duplicated(odds_ratios_data$Category), "", odds_ratios_data$Category)
final_table_html <- htmlTable(
odds_ratios_data %>%
mutate(Injury_Percent_Combined = paste0(Injury, " - (", Injury_Percent, ")")) %>%
mutate(Non_Injury_Percent_Combined = paste0(`Non-Injury`, " - (", `Non-Injury_Percent`, ")")) %>%
select("Category", "Variable", "Level", "Injury_Percent_Combined", "Non_Injury_Percent_Combined", "PValue", "OddsRatio_CI") %>%
rename("Injury (%)" = Injury_Percent_Combined, "Non-Injury (%)" = Non_Injury_Percent_Combined) %>%
mutate(across(everything(), ~ ifelse(is.na(.) | . %in% c("NA", "NaN", "Inf", "-Inf"), "", .))),
header = c("Category", "Variable", "Level", "Injury (%)", "Non-Injury (%)", "P-Value", "Odds Ratio (95% CI)"),
align = 'lcccccc',
rnames = FALSE,
css.cell = "text-align: center; padding: 12px 15px; font-size: 12px; white-space: nowrap;",
css.row = sprintf("background-color: %s;", row_colors),
header.css = "background-color: #333333; font-size: 14px; text-align: center; color: white;",
caption = "<h3 style='text-align: center;'>Analysis for Pedestrian Intersection</h3>
<p style='text-align: center;'><em>This analysis focuses on mode: pedestrian | intersection: yes</em></p>"
)
print(HTML(final_table_html))
Analysis for Pedestrian IntersectionThis analysis focuses on mode: pedestrian | intersection: yes | ||||||
| Category | Variable | Level | Injury (%) | Non-Injury (%) | P-Value | Odds Ratio (95% CI) |
|---|---|---|---|---|---|---|
| Road Direction | oneway | No | 398 - (0.9365) | 67 - (0.9306) | 1 (NA - NA) | |
| Yes | 27 - (0.0635) | 5 - (0.0694) | 0.7968 | 1.1001 (0.4093 - 2.9567) | ||
| twoways | No | 1 - (0.0024) | 1 - (0.0139) | 1 (NA - NA) | ||
| Yes | 424 - (0.9976) | 71 - (0.9861) | 0.269 | 0.1675 (0.0104 - 2.7078) | ||
| Number of Legs | threelegs | No | 338 - (0.7953) | 53 - (0.7361) | 1 (NA - NA) | |
| Yes | 87 - (0.2047) | 19 - (0.2639) | 0.2766 | 1.3928 (0.784 - 2.4742) | ||
| fourlegs | No | 94 - (0.2212) | 19 - (0.2639) | 1 (NA - NA) | ||
| Yes | 331 - (0.7788) | 53 - (0.7361) | 0.4478 | 0.7922 (0.4471 - 1.4035) | ||
| fourmlegs | No | 418 - (0.9835) | 72 - (1) | 1 (NA - NA) | ||
| Yes | 7 - (0.0165) | 0 - (0) | 0.6007 | 0 (0 - NaN) | ||
| Number of Lanes | onelane | No | 401 - (0.9435) | 69 - (0.9583) | 1 (NA - NA) | |
| Yes | 24 - (0.0565) | 3 - (0.0417) | 0.7826 | 0.7264 (0.213 - 2.4782) | ||
| twolanes | No | 94 - (0.2212) | 18 - (0.25) | 1 (NA - NA) | ||
| Yes | 331 - (0.7788) | 54 - (0.75) | 0.6472 | 0.852 (0.4768 - 1.5225) | ||
| threelanes | No | 368 - (0.8659) | 62 - (0.8611) | 1 (NA - NA) | ||
| Yes | 57 - (0.1341) | 10 - (0.1389) | 0.8538 | 1.0413 (0.5049 - 2.1475) | ||
| fourlanes | No | 177 - (0.4165) | 32 - (0.4444) | 1 (NA - NA) | ||
| Yes | 248 - (0.5835) | 40 - (0.5556) | 0.6992 | 0.8921 (0.5393 - 1.4757) | ||
| fivelanes | No | 394 - (0.9271) | 67 - (0.9306) | 1 (NA - NA) | ||
| Yes | 31 - (0.0729) | 5 - (0.0694) | 1 | 0.9485 (0.3562 - 2.5258) | ||
| sixlanes | No | 388 - (0.9129) | 65 - (0.9028) | 1 (NA - NA) | ||
| Yes | 37 - (0.0871) | 7 - (0.0972) | 0.822 | 1.1293 (0.4829 - 2.6408) | ||
| Pedestrian Infrastructure | pedestrianrefugestriped | No | 419 - (0.9859) | 71 - (0.9861) | 1 (NA - NA) | |
| Yes | 6 - (0.0141) | 1 - (0.0139) | 1 | 0.9836 (0.1167 - 8.2923) | ||
| curbextensionpinchpoint | Variable not available - (NA) | NA - (NA) | - | |||
| rapidrectangularflashingbeacon | No | 422 - (0.9929) | 72 - (1) | 1 (NA - NA) | ||
| Yes | 3 - (0.0071) | 0 - (0) | 1 | 0 (0 - NaN) | ||
| pedestriancrossedsignal | No | 400 - (0.9412) | 72 - (1) | 1 (NA - NA) | ||
| Yes | 25 - (0.0588) | 0 - (0) | 0.0361 | 0 (0 - NaN) | ||
| Other Variables | signalized | No | 137 - (0.3224) | 25 - (0.3472) | 1 (NA - NA) | |
| Yes | 288 - (0.6776) | 47 - (0.6528) | 0.6849 | 0.8943 (0.5285 - 1.5134) | ||
| stopsigns | No | 283 - (0.6659) | 43 - (0.5972) | 1 (NA - NA) | ||
| Yes | 142 - (0.3341) | 29 - (0.4028) | 0.2837 | 1.3441 (0.8053 - 2.2434) | ||
| roundabout | No | 421 - (0.9906) | 72 - (1) | 1 (NA - NA) | ||
| Yes | 4 - (0.0094) | 0 - (0) | 1 | 0 (0 - NaN) | ||
| numberoflegsintheintersection | Not applicable - (NA) | NA - (NA) | - | |||
| unsignalizedcrosswalk | No | 334 - (0.7859) | 55 - (0.7639) | 1 (NA - NA) | ||
| Yes | 91 - (0.2141) | 17 - (0.2361) | 0.6462 | 1.1345 (0.6281 - 2.049) | ||
| signalizedcrosswalkstreet | No | 144 - (0.3388) | 28 - (0.3889) | 1 (NA - NA) | ||
| Yes | 281 - (0.6612) | 44 - (0.6111) | 0.4234 | 0.8053 (0.4813 - 1.3473) | ||
| standardpaint | No | 109 - (0.2565) | 22 - (0.3056) | 1 (NA - NA) | ||
| Yes | 316 - (0.7435) | 50 - (0.6944) | 0.3877 | 0.7839 (0.4538 - 1.3543) | ||
| solidpaint | No | 412 - (0.9694) | 72 - (1) | 1 (NA - NA) | ||
| Yes | 13 - (0.0306) | 0 - (0) | 0.2313 | 0 (0 - NaN) | ||
| striped | No | 343 - (0.8071) | 60 - (0.8333) | 1 (NA - NA) | ||
| Yes | 82 - (0.1929) | 12 - (0.1667) | 0.7448 | 0.8366 (0.4302 - 1.6267) | ||
NULL