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.Rhistory
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library(tidyverse)
library(lubridate)
library(googledrive)
TIME.SLOT <- 10
df <- read.csv('speed-cam (5).csv',stringsAsFactors = FALSE)
colnames(df)<- make.names( c("date","hour","minute","speed","Unit","Speed Photo Path",
"X","Y","W","H","Area","Direction"))
df$datetime <- ymd_hm(paste(df$date,df$hour,df$minute,sep='-'))
df <- df[!is.na(df$datetime),]
df$interval.minutes <- df$minute %/%TIME.SLOT *TIME.SLOT
df$interval.datetime <- ymd_hm(paste(df$date,df$hour,df$interval.minutes,sep='-'))
datetime.index <- data.frame(datetime.index = seq(min(df$datetime),max(df$datetime),by=60))
df <- merge(datetime.index,df,by.x='datetime.index',by.y='datetime',all.x = TRUE)
groupy <-
df %>%
group_by(interval.datetime, Direction) %>%
summarise(vehicles = n(),max.speed = max(speed),av.speed = mean(speed))
gato <- groupy %>% gather(key,value,-interval.datetime,-Direction)
gato$key.direction <- paste(gato$key,gato$Direction)
ggplot(gato %>% filter(!key=='vehicles'),aes(interval.datetime,value,color=key.direction))+geom_line()
ggplot(gato %>% filter(key=='vehicles'),aes(interval.datetime,value))+geom_bar(stat='identity', bin=300)
df %>% top_n(10,speed) %>% select(Speed.Photo.Path,speed) %>% arrange(desc(speed))
ggplot(gato %>% filter(!key=='vehicles'),aes(interval.datetime,value,color=key))+geom_line()
groupy <-
df %>%
group_by(interval.datetime, Direction) %>%
summarise(vehicles = n(),max.speed = max(speed),av.speed = mean(speed))
gato <- groupy %>% gather(key,value,-interval.datetime,-Direction)
gato$key.direction <- paste(gato$key,gato$Direction)
ggplot(gato %>% filter(!key=='vehicles'),aes(interval.datetime,value,color=key))+geom_line()
ggplot(gato %>% filter(key=='vehicles'),aes(interval.datetime,value))+geom_bar(stat='identity', bin=300)
df %>% top_n(10,speed) %>% select(Speed.Photo.Path,speed) %>% arrange(desc(speed))
library(tidyverse)
library(lubridate)
library(googledrive)
TIME.SLOT <- 10
df <- read.csv('speed-cam (6).csv',stringsAsFactors = FALSE)
colnames(df)<- make.names( c("date","hour","minute","speed","Unit","Speed Photo Path",
"X","Y","W","H","Area","Direction"))
df$datetime <- ymd_hm(paste(df$date,df$hour,df$minute,sep='-'))
df <- df[!is.na(df$datetime),]
df$interval.minutes <- df$minute %/%TIME.SLOT *TIME.SLOT
df$interval.datetime <- ymd_hm(paste(df$date,df$hour,df$interval.minutes,sep='-'))
datetime.index <- data.frame(datetime.index = seq(min(df$datetime),max(df$datetime),by=60))
df <- merge(datetime.index,df,by.x='datetime.index',by.y='datetime',all.x = TRUE)
groupy <-
df %>%
group_by(interval.datetime, Direction) %>%
summarise(vehicles = n(),max.speed = max(speed),av.speed = mean(speed))
gato <- groupy %>% gather(key,value,-interval.datetime,-Direction)
gato$key.direction <- paste(gato$key,gato$Direction)
ggplot(gato %>% filter(!key=='vehicles'),aes(interval.datetime,value,color=key))+geom_line()
ggplot(gato %>% filter(key=='vehicles'),aes(interval.datetime,value))+geom_bar(stat='identity', bin=300)
df %>% top_n(10,speed) %>% select(Speed.Photo.Path,speed) %>% arrange(desc(speed))
library(tidyverse)
library(lubridate)
library(googledrive)
TIME.SLOT <- 10
df <- read.csv('speed-cam (6).csv',stringsAsFactors = FALSE)
colnames(df)<- make.names( c("date","hour","minute","speed","Unit","Speed Photo Path",
"X","Y","W","H","Area","Direction"))
df$datetime <- ymd_hm(paste(df$date,df$hour,df$minute,sep='-'))
df <- df[!is.na(df$datetime),]
df$interval.minutes <- df$minute %/%TIME.SLOT *TIME.SLOT
df$interval.datetime <- ymd_hm(paste(df$date,df$hour,df$interval.minutes,sep='-'))
datetime.index <- data.frame(datetime.index = seq(min(df$datetime),max(df$datetime),by=60))
df <- merge(datetime.index,df,by.x='datetime.index',by.y='datetime',all.x = TRUE)
groupy <-
df %>%
group_by(interval.datetime, Direction) %>%
summarise(vehicles = n(),max.speed = max(speed),av.speed = mean(speed))
gato <- groupy %>% gather(key,value,-interval.datetime,-Direction)
gato$key.direction <- paste(gato$key,gato$Direction)
ggplot(gato %>% filter(!key=='vehicles'),aes(interval.datetime,value,color=key))+geom_line()
ggplot(gato %>% filter(key=='vehicles'),aes(interval.datetime,value))+geom_bar(stat='identity', bin=300)
df %>% top_n(10,speed) %>% select(Speed.Photo.Path,speed) %>% arrange(desc(speed))
groupy <-
df %>%
group_by(interval.datetime, Direction) %>%
summarise(vehicles = n(),max.speed = max(speed),av.speed = mean(speed))
gato <- groupy %>% gather(key,value,-interval.datetime,-Direction)
gato$key.direction <- paste(gato$key,gato$Direction)
ggplot(gato %>% filter(!key=='vehicles'),aes(interval.datetime,value,color=key))+geom_line()
ggplot(gato %>% filter(key=='vehicles'),aes(interval.datetime,value))+geom_bar(stat='identity')
library(tidyverse)
library(lubridate)
library(googledrive)
TIME.SLOT <- 10
df <- read.csv('speed-cam (6).csv',stringsAsFactors = FALSE)
colnames(df)<- make.names( c("date","hour","minute","speed","Unit","Speed Photo Path",
"X","Y","W","H","Area","Direction"))
df$datetime <- ymd_hm(paste(df$date,df$hour,df$minute,sep='-'))
df <- df[!is.na(df$datetime),]
df$interval.minutes <- df$minute %/%TIME.SLOT *TIME.SLOT
df$interval.datetime <- ymd_hm(paste(df$date,df$hour,df$interval.minutes,sep='-'))
datetime.index <- data.frame(datetime.index = seq(min(df$datetime),max(df$datetime),by=60))
df <- merge(datetime.index,df,by.x='datetime.index',by.y='datetime',all.x = TRUE)
View(df)
library(tidyverse)
library(lubridate)
library(googledrive)
TIME.SLOT <- 10
df <- read.csv('speed-cam (6).csv',stringsAsFactors = FALSE)
colnames(df)<- make.names( c("date","hour","minute","speed","Unit","Speed Photo Path",
"X","Y","W","H","Area","Direction"))
df$datetime <- ymd_hm(paste(df$date,df$hour,df$minute,sep='-'))
df <- df[!is.na(df$datetime),]
df$interval.minutes <- df$minute %/%TIME.SLOT *TIME.SLOT
df$interval.datetime <- ymd_hm(paste(df$date,df$hour,df$interval.minutes,sep='-'))
datetime.index <- data.frame(datetime.index = seq(min(df$datetime),max(df$datetime),by=60))
df <- merge(datetime.index,df,by.x='datetime.index',by.y='datetime',all.x = TRUE)
groupy <-
df %>%
group_by(interval.datetime, Direction) %>%
summarise(vehicles = n(),max.speed = max(speed),av.speed = mean(speed))
gato <- groupy %>% gather(key,value,-interval.datetime,-Direction)
gato$key.direction <- paste(gato$key,gato$Direction)
ggplot(gato %>% filter(!key=='vehicles'),aes(interval.datetime,value,color=key))+geom_line()
ggplot(gato %>% filter(key=='vehicles'),aes(interval.datetime,value))+geom_bar(stat='identity')
df %>% top_n(10,speed) %>% select(Speed.Photo.Path,speed) %>% arrange(desc(speed))
library(tidyverse)
library(lubridate)
library(googledrive)
TIME.SLOT <- 30
df <- read.csv('speed-cam (6).csv',stringsAsFactors = FALSE)
colnames(df)<- make.names( c("date","hour","minute","speed","Unit","Speed Photo Path",
"X","Y","W","H","Area","Direction"))
df$datetime <- ymd_hm(paste(df$date,df$hour,df$minute,sep='-'))
df <- df[!is.na(df$datetime),]
df$interval.minutes <- df$minute %/%TIME.SLOT *TIME.SLOT
df$interval.datetime <- ymd_hm(paste(df$date,df$hour,df$interval.minutes,sep='-'))
datetime.index <- data.frame(datetime.index = seq(min(df$datetime),max(df$datetime),by=60))
df <- merge(datetime.index,df,by.x='datetime.index',by.y='datetime',all.x = TRUE)
groupy <-
df %>%
group_by(interval.datetime, Direction) %>%
summarise(vehicles = n(),max.speed = max(speed),av.speed = mean(speed))
gato <- groupy %>% gather(key,value,-interval.datetime,-Direction)
gato$key.direction <- paste(gato$key,gato$Direction)
ggplot(gato %>% filter(!key=='vehicles'),aes(interval.datetime,value,color=key))+geom_line()
ggplot(gato %>% filter(key=='vehicles'),aes(interval.datetime,value))+geom_bar(stat='identity')
df %>% top_n(10,speed) %>% select(Speed.Photo.Path,speed) %>% arrange(desc(speed))