Did the 1984 Massachusetts ban on the promotion of alcohol sales via a happy hour affect deadly driving accidents in the state?
data_80 <- data_80[c("STATE","COUNTY", "MONTH", "DAY", "YEAR", "HOUR", "DRUNK_DR", "FATALS", "VE_FORMS")]
data_81 <- data_81[c("STATE","COUNTY", "MONTH", "DAY", "YEAR", "HOUR", "DRUNK_DR", "FATALS", "VE_FORMS")]
data_82 <- data_82[c("STATE","COUNTY", "MONTH", "DAY", "YEAR", "HOUR", "DRUNK_DR", "FATALS", "VE_FORMS")]
data_83 <- data_83[c("STATE","COUNTY", "MONTH", "DAY", "YEAR", "HOUR", "DRUNK_DR", "FATALS", "VE_FORMS")]
data_84 <- data_84[c("STATE","COUNTY", "MONTH", "DAY", "YEAR", "HOUR", "DRUNK_DR", "FATALS", "VE_FORMS")]
data_85 <- data_85[c("STATE","COUNTY", "MONTH", "DAY", "YEAR", "HOUR", "DRUNK_DR", "FATALS", "VE_FORMS")]
data_86 <- data_86[c("STATE","COUNTY", "MONTH", "DAY", "YEAR", "HOUR", "DRUNK_DR", "FATALS", "VE_FORMS")]
data_87 <- data_87[c("STATE","COUNTY", "MONTH", "DAY", "YEAR", "HOUR", "DRUNK_DR", "FATALS", "VE_FORMS")]
data_88 <- data_88[c("STATE","COUNTY", "MONTH", "DAY", "YEAR", "HOUR", "DRUNK_DR", "FATALS", "VE_FORMS")]
data_89 <- data_89[c("STATE","COUNTY", "MONTH", "DAY", "YEAR", "HOUR", "DRUNK_DR", "FATALS", "VE_FORMS")]
totaldata <- rbind(data_80,data_81, data_82, data_83, data_84, data_85, data_86, data_87, data_88, data_89)
rm(data_80,data_81, data_82, data_83, data_84, data_85, data_86, data_87, data_88, data_89)totaldata$YEAR <- sprintf("19%d", totaldata$YEAR)
totaldata$YEAR <- as.integer(totaldata$YEAR)
totaldata$MONTH <- sprintf("%02d", totaldata$MONTH )
totaldata <- mutate(totaldata, dates2 = paste(YEAR, MONTH, sep = "-"))
totaldata$date <- as.Date(with(totaldata, paste(YEAR, MONTH, DAY, sep ="-"), "%Y-%m-%d"))
# Create FIPS
totaldata$STATE <- sprintf("%02d", totaldata$STATE)
totaldata$COUNTY <- sprintf("%03d", totaldata$COUNTY)
totaldata$FIPS <- paste(totaldata$STATE,totaldata$COUNTY, sep="")
# Only looking at alcohol related incidents
data_drunk <- subset(totaldata, DRUNK_DR >0 )
data_drunk$posttreatment <- ifelse(data_drunk$date > "1984-12-11" | data_drunk$date == "1984-12-11" , 1, 0)
# Create new data set
newdf <- data_drunk %>% group_by(STATE, FIPS, YEAR, posttreatment) %>%
count()
newdf <- rename(newdf, fatalaccidents = n)Census Data has population data by county
census$FIPS <- sprintf("%05d",census$FIPS )
mergeddata <- merge(newdf, census, by="FIPS")head(mergeddata, 10)## FIPS STATE YEAR posttreatment fatalaccidents Area.Name Census1980
## 1 01001 01 1982 0 4 Autauga Co. 32259
## 2 01001 01 1986 1 7 Autauga Co. 32259
## 3 01001 01 1985 1 2 Autauga Co. 32259
## 4 01001 01 1987 1 4 Autauga Co. 32259
## 5 01001 01 1988 1 6 Autauga Co. 32259
## 6 01001 01 1983 0 6 Autauga Co. 32259
## 7 01001 01 1989 1 1 Autauga Co. 32259
## 8 01001 01 1981 0 2 Autauga Co. 32259
## 9 01003 01 1984 1 3 Baldwin Co. 78556
## 10 01003 01 1987 1 22 Baldwin Co. 78556
## Estimate1981 Estimate1982 Estimate1983 Estimate1984 Estimate1985
## 1 31985 32036 32054 32134 32245
## 2 31985 32036 32054 32134 32245
## 3 31985 32036 32054 32134 32245
## 4 31985 32036 32054 32134 32245
## 5 31985 32036 32054 32134 32245
## 6 31985 32036 32054 32134 32245
## 7 31985 32036 32054 32134 32245
## 8 31985 32036 32054 32134 32245
## 9 80287 82331 83978 86752 89401
## 10 80287 82331 83978 86752 89401
## Estimate1986 Estimate1987 Estimate1988 Estimate1989
## 1 32893 33268 33636 33996
## 2 32893 33268 33636 33996
## 3 32893 33268 33636 33996
## 4 32893 33268 33636 33996
## 5 32893 33268 33636 33996
## 6 32893 33268 33636 33996
## 7 32893 33268 33636 33996
## 8 32893 33268 33636 33996
## 9 91311 93214 94649 96198
## 10 91311 93214 94649 96198 syntheticcontrol <-
mergeddata %>%
synthetic_control(outcome= fatalaccidents,
unit = STATE,
time = YEAR,
i_unit = "26",
i_time = 1984,
generate_placebos = TRUE) %>%
generate_predictor(time_window = 1980,
fatalaccidents_1980 = fatalaccidents) %>%
generate_predictor(time_window = 1981,
fatalaccidents_1981 = fatalaccidents) %>%
generate_predictor(time_window = 1982,
fatalaccidents_1982 = fatalaccidents) %>%
generate_predictor(time_window = 1983,
fatalaccidents_1983 = fatalaccidents) %>%
generate_predictor(time_window = 1984,
fatalaccidents_1984 = fatalaccidents) %>%
generate_weights(optimization_window =1980:1984,
Margin.ipop=.02,Sigf.ipop=7,Bound.ipop=6) %>%
generate_control()## Warning: Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)
## Values from `fatalaccidents` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = {summary_fun}` to summarise duplicates.
## * Use the following dplyr code to identify duplicates.
## {data} %>%
## dplyr::group_by(YEAR, STATE) %>%
## dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
## dplyr::filter(n > 1L)## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
## always returns an ungrouped data frame and adjust accordingly.
## ℹ The deprecated feature was likely used in the tidysynth package.
## Please report the issue to the authors.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Error in `tidyr::spread()`:
## ! Each row of output must be identified by a unique combination of keys.
## Keys are shared for 78 rows:
## * 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78