###Libraries I used###

library(tidyverse)
library(readr)
library(janitor)
library(here)
library(plyr)
library(dir)
library(lubridate)
library(tidyr)
library(readtext)

q1_2020 <- data.frame(read_csv(file.path( "~/Documents/cyclistic_data/divvy_trips/Divvy_Trips_2020_Q1/", "Divvy_Trips_2020_Q1.csv"))) %>%
  select(everything())

##establish naming convention throughout all datasets##

q1_2020 <- as.data.frame(q1_2020) 

q1_2020 <- q1_2020 %>%
  select(-c(end_lng,
            start_lat,
            start_lng,
            end_lat))

q4_2019 <- read_csv(file.path( "~/Documents/cyclistic_data/divvy_trips/Divvy_Trips_2019_Q4/", "Divvy_Trips_2019_Q4.csv")) %>%
  select(everything())

q4_2019<- as.data.frame(q4_2019)
  
names(q4_2019)[names(q4_2019) == "trip_id"] <- "ride_id"
names(q4_2019)[names(q4_2019) == "bikeid" ] <- "rideable_type"
names(q4_2019)[names(q4_2019) == "start_time" ] <- "started_at"
names(q4_2019)[names(q4_2019) == "end_time" ] <- "ended_at"
names(q4_2019)[names(q4_2019) == "from_station_name" ] <- "start_station_name"
names(q4_2019)[names(q4_2019) == "from_station_id" ] <- "start_station_id"
names(q4_2019)[names(q4_2019) == "to_station_name" ] <- "end_station_name"
names(q4_2019)[names(q4_2019) == "to_station_id" ] <- "end_station_id"
names(q4_2019)[names(q4_2019) == "usertype" ] <- "member_casual"


##Convert these columns to character so that they can stack correctly###


q4_2019 <-  mutate(q4_2019, ride_id = as.character(ride_id)
                   ,rideable_type = as.character(rideable_type)) 

##drop columns that are not part of q1_2020##
q4_2019 <- q4_2019 %>%
  select(-c(birthyear,
            gender,
            tripduration))

q3_2019<- read_csv(file.path("~/Documents/cyclistic_data/divvy_trips/Divvy_Trips_2019_Q3/", "Divvy_Trips_2019_Q3.csv")) %>%
  select(everything())

q3_2019<- as.data.frame(q3_2019) 
  
names(q3_2019)[names(q3_2019) == "trip_id"] <- "ride_id"
names(q3_2019)[names(q3_2019) == "bikeid" ] <- "rideable_type"
names(q3_2019)[names(q3_2019) == "start_time" ] <- "started_at"
names(q3_2019)[names(q3_2019) == "end_time" ] <- "ended_at"
names(q3_2019)[names(q3_2019) == "from_station_name" ] <- "start_station_name"
names(q3_2019)[names(q3_2019) == "from_station_id" ] <- "start_station_id"
names(q3_2019)[names(q3_2019) == "to_station_name" ] <- "end_station_name"
names(q3_2019)[names(q3_2019) == "to_station_id" ] <- "end_station_id"
names(q3_2019)[names(q3_2019) == "usertype" ] <- "member_casual"

##Convert these colums to character so that they can stack correctly###

q3_2019 <-  mutate(q3_2019, ride_id = as.character(ride_id)
                   ,rideable_type = as.character(rideable_type)) 

##drop columns that are not part of q1_2020##
q3_2019 <- q3_2019 %>%
  select(-c(tripduration,
            birthyear,
            gender))

q2_2019 <- read.delim(file.path("~/Documents/cyclistic_data/divvy_trips/Divvy_Trips_2019_Q2/", "Divvy_Trips_2019_Q2.txt"), header = TRUE, sep = ",") %>%
  select(everything())

q2_2019 <- as.data.frame(q2_2019) 

names(q2_2019)[names(q2_2019) ==  "X01...Rental.Details.Rental.ID" ] <- "ride_id"
names(q2_2019)[names(q2_2019) ==  "X01...Rental.Details.Bike.ID" ] <- "rideable_type"
names(q2_2019)[names(q2_2019) ==  "X01...Rental.Details.Local.Start.Time"] <- "started_at"
names(q2_2019)[names(q2_2019) ==  "X01...Rental.Details.Local.End.Time"] <- "ended_at"
names(q2_2019)[names(q2_2019) ==  "X03...Rental.Start.Station.Name"] <- "start_station_name"
names(q2_2019)[names(q2_2019) ==  "X03...Rental.Start.Station.ID"] <- "start_station_id"
names(q2_2019)[names(q2_2019) ==  "X02...Rental.End.Station.Name"] <- "end_station_name"
names(q2_2019)[names(q2_2019) ==  "X02...Rental.End.Station.ID"] <- "end_station_id"
names(q2_2019)[names(q2_2019) ==  "User.Type"] <- "member_casual"

##convert these colums to character so that they can stack correctly###

q2_2019 <-  mutate(q2_2019, ride_id = as.character(ride_id)
                   ,rideable_type = as.character(rideable_type)) 

##drop columns that are not part of q1_2020##

q2_2019 <- q2_2019 %>%
  select(-c(X01...Rental.Details.Duration.In.Seconds.Uncapped,
            Member.Gender,
            X05...Member.Details.Member.Birthday.Year))

##Check the dataframes for incongruencies###

str(q1_2020)
str(q4_2019)
str(q3_2019)
str(q2_2019)

##Merge dataframes##

merge_data1 <- rbind(q1_2020, q4_2019, q3_2019, q2_2019, fill = NULL)

str(merge_data1)
summary(merge_data1)

#####set merge_data1$member_casual from 4 categories to 2####

merge_data1$member_casual<- as.character(merge_data1$member_casual)


merge_data1$member_casual[merge_data1$member_casual == "member"] <-"Member"
merge_data1$member_casual[merge_data1$member_casual == "Customer"] <-"Member"
merge_data1$member_casual[merge_data1$member_casual == "casual"] <-"Casual"
merge_data1$member_casual[merge_data1$member_casual == "Subscriber"] <-"Casual"


##check for na, empty, and null cells##

sum(is.na(merge_data1))
sum(is_empty(merge_data1))
sum(is.null(merge_data1))

##[1] 2
## sum(is_empty(merge_data1))
##[1] 0
##> sum(is.null(merge_data1))
##[1] 0
##> View(merge_data1 = NULL)

###create day, month, and year columns###

merge_data1 <- merge_data1%>%
  add_column(day =weekdays(merge_data1$started_at),
             month = month(merge_data1$started_at),
            year = year(merge_data1$started_at),
            ride_duration = difftime(merge_data1$ended_at, merge_data1$started_at,
                                     units = "mins"))
######Some int are negative in ride_duration because some bikes did not finish their trips because of mechanical issues.####
###drop rows where ride_duration < 0 ###
###because data is being deleted, create a new data frame####

merge_data1$ride_duration[merge_data1$ride_duration < 0] <- NA


sum(is.na(merge_data1$ride_duration))

##NA = 131##
###drop na rows and create new data frame###


trips <- data_frame(merge_data1)

trips<- trips %>%
  na.omit(trips)