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TNC Trip Characteristics 022820.R
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TNC Trip Characteristics 022820.R
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# TNC Trip Characteristics 022820.R
# Analyze TNC trip characteristics for draft (final?) dataset
# March 3, 2020
# Import Libraries
suppressMessages(library(tidyverse))
# Set up working directory
file_location <- "M:/Data/HomeInterview/TNC Survey/2020/Draft022820/"
wd <- "M:/Data/HomeInterview/TNC Survey/2020/Draft022820/Analysis/" # work directory
root <- "M:/Data/HomeInterview/TNC Survey/2020/Draft022820/"
setwd(wd)
# Bring in data and most recent weights
TNC_Trip <- paste0(file_location,"ex_trip.tsv")
TNC_Person <- paste0(file_location,"ex_person.tsv")
TNC_Location <- paste0(file_location,"ex_location.tsv")
TNC_Household <- paste0(file_location,"ex_hh.tsv")
TNC_Vehicle <- paste0(file_location,"ex_vehicle.tsv")
TNC_Day <- paste0(file_location,"ex_day.tsv")
TNC_Trip_Other<- paste0(file_location,"ex_trip_with_purpose_other.tsv")
TNC_Weights <- "M:/Data/HomeInterview/TNC Survey/2020/Draft012220/Weights_update_Feb1/wts_bayarea_alladult4i.csv"
trip <- read_tsv(TNC_Trip,col_names=TRUE)
person <- read_tsv(TNC_Person,col_names=TRUE)
location <- read_tsv(TNC_Location,col_names=TRUE)
household <- read_tsv(TNC_Household,col_names=TRUE)
vehicle <- read_tsv(TNC_Vehicle,col_names=TRUE)
day <- read_tsv(TNC_Day,col_names=TRUE)
trip_other <- read_tsv(TNC_Trip_Other,col_names=TRUE)
weight <- read.csv(TNC_Weights,header = TRUE)
save(trip,file= paste0(root,"TNC_Trip.Rdata"))
save(person,file= paste0(root,"TNC_Person.Rdata"))
save(location,file= paste0(root,"TNC_Location.Rdata"))
save(household,file= paste0(root,"TNC_Household.Rdata"))
save(vehicle,file= paste0(root,"TNC_Vehicle.Rdata"))
save(day,file= paste0(root,"TNC_Day.Rdata"))
save(trip_other,file= paste0(root,"TNC_Trip_Other.Rdata"))
save(weight,file= paste0(root,"TNC_Weight.Rdata"))
household_income <- household %>%
select(hh_id,income_detailed,income_followup,income_aggregate,home_county_fips,num_people)
adults <- left_join(person,weight, by="person_id") %>% # Include only persons with a weight value
filter(!is.na(wt_alladult_WkDay)) %>%
left_join(.,household_income,by="hh_id") %>%
mutate(
incomerc=case_when(
income_imputed == 1 ~"1_less to 25k",
income_imputed == 2 ~"2_25k to 49,999",
income_imputed == 3 ~"3_50k to 74,999",
income_imputed == 4 ~"4_75k to 99,999",
income_imputed == 5 ~"5_100k to 149,999",
income_imputed == 6 ~"6_150k to 199,999",
income_imputed == 7 ~"7_200k to 249,999",
income_imputed == 8 ~"8_250k+",
TRUE ~"Uncoded"),
racerc=case_when(
ethnicity_white == 1 & ethnicity_multi == 0 ~"1_White",
ethnicity_af_am == 1 & ethnicity_multi == 0 ~"2_Black",
ethnicity_asian == 1 & ethnicity_multi == 0 ~"3_Asian",
ethnicity_aiak == 1 & ethnicity_multi == 0 ~"4_Other",
ethnicity_hapi == 1 & ethnicity_multi == 0 ~"4_Other",
ethnicity_mideast == 1 & ethnicity_multi == 0 ~"4_Other",
ethnicity_other == 1 & ethnicity_multi == 0 ~"4_Other",
ethnicity_no_answer %in% c (1,-9998) & raceeth_imputed == 1 ~"1_White",
ethnicity_no_answer %in% c (1,-9998) & raceeth_imputed == 2 ~"2_Black",
ethnicity_no_answer %in% c (1,-9998) & raceeth_imputed == 3 ~"3_Asian", # Note this includes PI in data.
ethnicity_no_answer %in% c (1,-9998) & raceeth_imputed == 4 ~"5_Hispanic",
ethnicity_hisp == 1 ~"5_Hispanic",
ethnicity_multi == 1 ~"4_Other",
TRUE ~"Uncoded"),
agerc=case_when(
age==4 ~"1_between 18 and 24",
age==5 ~"2_between 25 and 34",
age==6 ~"3_between 35 and 44",
age==7 ~"4_between 45 and 54",
age==8 ~"5_between 55 and 64",
age==9 | age==10 ~"6_65+",
TRUE ~"Uncoded"),
num_peoplerc=case_when(
num_people==1 ~"1 Person HH",
num_people==2 ~"2 Person HH",
num_people==3 ~"3 Person HH",
num_people==4 ~"4 Person HH",
num_people>=5 ~"5+ Person HH",
TRUE ~"Uncoded"),
County_Name=case_when(
home_county_fips=="001" ~ "Alameda",
home_county_fips=="013" ~ "Contra Costa",
home_county_fips=="041" ~ "Marin",
home_county_fips=="055" ~ "Napa",
home_county_fips=="075" ~ "San Francisco",
home_county_fips=="081" ~ "San Mateo",
home_county_fips=="085" ~ "Santa Clara",
home_county_fips=="095" ~ "Solano",
home_county_fips=="097" ~ "Sonoma"),
dummy=1
)%>%
select(person_id,hh_id,County_Name, wt_alladult_WkDay,home_county_fips,incomerc,racerc,agerc,num_peoplerc,dummy)
# Summarize income
incomesum <- adults %>%
group_by(County_Name,incomerc) %>%
summarize(total=sum(wt_alladult_WkDay)) %>%
spread (., incomerc, total, fill=0)
# Summarize race
racesum <- adults %>%
group_by(County_Name,racerc) %>%
summarize(total=sum(wt_alladult_WkDay)) %>%
spread (., racerc, total, fill=0)
# Summarize age
agesum <- adults %>%
group_by(County_Name,agerc) %>%
summarize(total=sum(wt_alladult_WkDay)) %>%
spread (., agerc, total, fill=0)
# Summarize persons by HH size
num_peoplesum <- adults %>%
group_by(County_Name,num_peoplerc) %>%
summarize(total=sum(wt_alladult_WkDay)) %>%
spread (., num_peoplerc, total, fill=0)
write.csv(incomesum, "TNC Survey Adults by Income.csv", row.names = FALSE, quote = T)
write.csv(racesum, "TNC SUrvey Adults by Race.csv", row.names = FALSE, quote = T)
write.csv(agesum, "TNC Survey Adults by Age.csv", row.names = FALSE, quote = T)
write.csv(num_peoplesum, "TNC Survey Adults by HH Size.csv", row.names = FALSE, quote = T)
weight_trial <- positive %>%
group_by(County_Name) %>%
summarize(count=n(),minimum=min(wt_alladult_WkDay),maximum=max(wt_alladult_WkDay),median=median(wt_alladult_WkDay),mean=mean(wt_alladult_WkDay))
print(weight_trial)
# Get median distance by mode
# Recode modes
trip2 <- trip %>%
mutate(
mode_recode=case_when(
mode_type==1 ~"Walk",
mode_type==2 ~"Bike",
mode_type==3 ~"Car",
mode_type==4 ~"Taxi",
mode_type==5 ~"Transit",
mode_type==6 ~"Schoolbus",
mode_type==7 ~"Other",
mode_type==8 ~"Shuttle/vanpool",
mode_type==9 ~"TNC",
mode_type==10 ~"Carshare",
mode_type==11 ~"Bikeshare",
mode_type==12 ~"Scooter share",
mode_type==13 ~"Long-distance passenger mode",
TRUE~"Unknown")
)
# Total Median
median_dist_duration_speed <- trip2 %>%
summarize(median_dist=median(distance,na.rm=TRUE), median_duration=median(duration,na.rm=TRUE),
median_speed=median(speed_mph,na.rm=TRUE))
# Median distance, duration, speed by mode
median_dist_duration_speed_mode <- trip2 %>%
group_by(mode_recode) %>%
summarize(median_dist=median(distance, na.rm=TRUE), median_duration=median(duration, na.rm=TRUE),
median_speed=median(speed_mph,na.rm=TRUE))
# Separate out Uber and Lyft trips
tnc_only <- trip %>%
filter(mode_1 %in% c(64,65) | mode_2 %in% c(64,65) | mode_3 %in% c(64,65)) %>% mutate(
fare_type=case_when(
mode_lyft==1 | mode_uber==1 ~ "Pooled",
mode_lyft==2 | mode_uber==2 ~ "Regular or Econo",
mode_lyft==3 | mode_uber==3 ~ "Premium",
TRUE ~ "Unknown"
))
tnc_only2 <- tnc_only %>%
filter(mode_type!=9)
# Join income to trip table
hh_income <- households %>%
select(hh_id,income_detailed)
tnc_only <- left_join(tnc_only,hh_income, by="hh_id")
# Recode income and trip purpose
tnc_only <- tnc_only %>% mutate(
hh_income=case_when(
income_detailed==1 ~ "Under $15,000",
income_detailed==2 ~ "$15,000-$24,999",
income_detailed==3 ~ "$25,000-$34,999",
income_detailed==4 ~ "$35,000-$49,999",
income_detailed==5 ~ "$50,000-$74,999",
income_detailed==6 ~ "$75,000-$99,999",
income_detailed==7 ~ "$100,000-$149,999",
income_detailed==8 ~ "$150,000-$199,999",
income_detailed==9 ~ "$200,000-$249,999",
income_detailed==10 ~ "$250,000 or more",
income_detailed==999 ~ "Prefer not to answer"),
origin_purpose=case_when(
o_purp_cat==1 ~ "Home",
o_purp_cat==2 ~ "Work",
o_purp_cat==3 ~ "Work-related",
o_purp_cat==4 ~ "School",
o_purp_cat==5 ~ "Escort",
o_purp_cat==6 ~ "Shop",
o_purp_cat==7 ~ "Meal",
o_purp_cat==8 ~ "Social/recreation",
o_purp_cat==9 ~ "Errand/other",
o_purp_cat==10 ~ "Change mode"),
destination_purpose=case_when(
d_purp_cat==1 ~ "Home",
d_purp_cat==2 ~ "Work",
d_purp_cat==3 ~ "Work-related",
d_purp_cat==4 ~ "School",
d_purp_cat==5 ~ "Escort",
d_purp_cat==6 ~ "Shop",
d_purp_cat==7 ~ "Meal",
d_purp_cat==8 ~ "Social/recreation",
d_purp_cat==9 ~ "Errand/other",
d_purp_cat==10 ~ "Change mode")
)
# Write out TNC-only trip file
write.csv(tnc_only, "TNC_Pilot_Trips.csv", row.names = FALSE, quote = T)
# Recode modes to get mode share for all trips and output summary
trips2 <- trips %>% mutate(
mode_recode=case_when(
mode_1==36 | mode_2==36 | mode_3==36 ~ "Taxi",
mode_1 %in% c(64,65) | mode_2 %in% c(64,65) | mode_3 %in% c(64,65) ~ "TNC",
TRUE ~ "Other"
),
mode_recode2=case_when(
mode_recode=="TNC" & (mode_uber==1 | mode_lyft==1) ~ "Pooled TNC",
mode_recode=="TNC" & (mode_uber==2 | mode_lyft==2) ~ "Regular or Premium TNC",
mode_recode=="TNC" & (mode_uber==3 | mode_lyft==3) ~ "Regular or Premium TNC",
TRUE ~ mode_recode
)
)
summary <- trips2 %>%
group_by(mode_recode2) %>%
summarize(total=sum(weight))
trial <- trip %>%
group_by(mode_1) %>%
summarize(measure=median(distance))
trial <- trip %>%
filter (hh_id==181027787 & person_num==1 & day_num==1)
trial <- trip %>%
filter (hh_id==181145778 & person_num==2 & day_num==6)
trial2 <- trip %>%
filter (hh_id==193038995 & person_num==1 & day_num==3)
person1 <- person %>%
filter (hh_id==181057950)
other <- trip %>%
filter(!is.na(d_purpose_other)) %>%
select(hh_id,person_num,trip_num,d_purpose_other,d_purpose_category_imputed,d_purpose_imputed,
d_purpose, d_purpose_category)
missing_mode <- trip %>% filter(mode_type==-9998 & mode_1>0)
trial <- adults %>%
filter(home_county_fips=="041")
racesum1 <- adults %>%
filter(wt_alladult_WkDay>0) %>%
group_by(County_Name,racerc) %>%
summarize(total=sum(dummy)) %>%
spread (., racerc, total, fill=0)
write.csv(racesum1, "TNC_Ethnicity_Unweighted.csv", row.names = FALSE, quote = T)