Mode folders and files to the parent directory

AV_adoption_interests.Rproj

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX

Analysis/1.clean_data.R

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+### Import, join, and keep the required columns#################################
+################################################################################
+# import the data
+df_main <- read.csv("Data/Raw data/survey_res_main.csv")
+df_pers <- read.csv("Data/Raw data/survey_res_person.csv")
+
+# join two data frames for the respondents' personal details
+df_pers <- df_pers[df_pers$relationship == 8,]
+df <- merge(df_main, df_pers, by.x = "sampno"); rm(df_main); rm(df_pers)
+
+# keep the required columns only
+df <- subset(df, select = c(age_grp,                region, 
+                            future_decision_role,   num_hh_vehicles,
+                            household_members_1,    household_members_2, 
+                            household_members_3,    household_members_4,
+                            modes_used_1,           modes_used_2, 
+                            modes_used_3,           modes_used_4, 
+                            modes_used_5,           modes_used_6, 
+                            modes_used_7,           modes_used_8, 
+                            modes_used_9,           modes_used_10,       
+                            modes_used_11,          modes_used_12,        
+                            autonomous_aware,       autonomous_att_1, 
+                            autonomous_att_2,       autonomous_att_3, 
+                            autonomous_att_4,       autonomous_att_5, 
+                            autonomous_att_7,       autonomous_att_8, 
+                            autonomous_hhveh,       autonomous_rideshare,        
+                            autonomous_pooled,      autonomous_pref, 
+                            housing,                income,        
+                            gender,                 employment, 
+                            student,                education, 
+                            license,                drive_freq,       
+                            transit_freq,           tnc_freq, 
+                            job_type,               work_mode, 
+                            work_distance,          work_days,       
+                            school_mode,            school_distance, 
+                            ethnicity,              race_1, 
+                            race_2,                 race_3,    
+                            race_4,                race_5, 
+                            race_6,                race_7))
+
+#------------------------------------------------------------------------------#
+#------------------------------------------------------------------------------#
+
+
+### Manipulate individual columns###############################################
+################################################################################
+# age_grp
+df$age_grp <- factor(df$age_grp, levels = c(2, 3, 4), labels = c(1, 2, 3))
+
+# modes_used
+df$modes_used_1 <-  factor(df$modes_used_1, levels = c(1, 2, 3, 4), 
+                    labels = c(0, 0, 0, 1))
+df$modes_used_2 <-  factor(df$modes_used_2, levels = c(1, 2, 3, 4), 
+                    labels = c(0, 0, 0, 1))
+df$modes_used_3 <-  factor(df$modes_used_3, levels = c(1, 2, 3, 4), 
+                    labels = c(0, 0, 0, 1))
+df$modes_used_4 <-  factor(df$modes_used_4, levels = c(1, 2, 3, 4), 
+                    labels = c(0, 0, 0, 1))
+df$modes_used_5 <-  factor(df$modes_used_5, levels = c(1, 2, 3, 4), 
+                    labels = c(0, 0, 0, 1))
+df$modes_used_6 <-  factor(df$modes_used_6, levels = c(1, 2, 3, 4), 
+                    labels = c(0, 0, 0, 1))
+df$modes_used_7 <-  factor(df$modes_used_7, levels = c(1, 2, 3, 4), 
+                    labels = c(0, 0, 0, 1))
+df$modes_used_8 <-  factor(df$modes_used_8, levels = c(1, 2, 3, 4), 
+                    labels = c(0, 0, 0, 1))
+df$modes_used_9 <-  factor(df$modes_used_9, levels = c(1, 2, 3, 4), 
+                    labels = c(0, 0, 0, 1))
+df$modes_used_10 <- factor(df$modes_used_10, levels = c(1, 2, 3, 4), 
+                    labels = c(0, 0, 0, 1))
+df$modes_used_11 <- factor(df$modes_used_11, levels = c(1, 2, 3, 4), 
+                    labels = c(0, 0, 0, 1))
+df$modes_used_12 <- factor(df$modes_used_12, levels = c(1, 2, 3, 4),
+                    labels = c(0, 0, 0, 1))
+
+# autonomous_aware
+df$autonomous_aware <- factor(df$autonomous_aware, levels = c(1, 2, 3, 4), 
+                       labels = c(1, 1, 2, 3))
+
+# autonomous_hhveh
+df <- df[!(is.na(df$autonomous_hhveh)),] #delete missing observations
+df$autonomous_hhveh <- 4 - df$autonomous_hhveh #reverse coding
+summary(df$autonomous_hhveh)
+
+# autonomous_rideshare
+df <- df[!(is.na(df$autonomous_rideshare)),] #delete missing observations
+
+# autonomous_pooled
+df <- df[!(is.na(df$autonomous_pooled)),] #delete missing observations
+df$autonomous_pooled <- 5 - df$autonomous_pooled #reverse coding
+
+# autonomous_pref
+df <- df[!(is.na(df$autonomous_pref)),] #delete missing observations
+df$autonomous_pref <- 5 - df$autonomous_pref #reverse coding
+
+# income
+df$income <- factor(df$income, levels = (1:11), 
+             labels = c(1, 1, 2, 2, 2, 3, 3, 4, 4, 4, 5))
+
+# gender
+df$gender <- factor(df$gender, levels = c(1, 2, 3, 4), labels = c(0, 1, 1, 1))
+
+# education
+df$education <- factor(df$education, levels = c(1:8), 
+                labels = c(1, 2, 2, 3, 3, 3, 4, 4))
+
+# license
+df$license <- factor(df$license, levels = c(1, 2), labels = c(0, 1))
+
+# drive_freq
+df$drive_freq <- ifelse(is.na(df$drive_freq), 5, df$drive_freq)
+
+# job_type
+df$job_type <- ifelse(is.na(df$job_type), 5, df$job_type)
+
+# work_mode
+df$work_mode <- ifelse(is.na(df$work_mode), 19, df$work_mode)
+df$work_mode <- factor(df$work_mode, levels = c(1:19), 
+                labels =c(1, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 5, 6))
+
+# work_distance 
+df$work_distance <- ifelse(is.na(df$work_distance), 0, df$work_distance)
+
+# work_days
+df$work_days     <- ifelse(is.na(df$work_days), 0, df$work_days)
+
+# school_mode
+df$school_mode <- ifelse(is.na(df$school_mode), 18, df$school_mode)
+df$school_mode <- factor(df$school_mode, levels = c(1:18), 
+                  labels =c(1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 5, 6))
+
+# school distance
+df$school_distance <- ifelse(is.na(df$school_distance), 0, df$school_distance)
+
+# ethnicity
+df$ethnicity <- factor(df$ethnicity, levels = c(1, 2, 3), labels = c(0, 1, 1))
+#------------------------------------------------------------------------------#
+#------------------------------------------------------------------------------#
+
+
+### Export cleaned data#########################################################
+################################################################################
+write.csv(df, "Data/Cleaned data/cleaned_data.csv")
+saveRDS(df, "Data/Cleaned data/cleaned_data.rds")
+rm(df)
+#------------------------------------------------------------------------------#
+#------------------------------------------------------------------------------#
+