plot-comparisons.r

library('tidyverse')

setwd('C:\\Users\\nwessel\\Documents\\GitHub\\tt_app\\analysis')

purple = '#9e7cbd'
grey = '#4f4c4c'

# compare monthly data for two periods by direction of travel
read_csv('sample-data/results-monthly.csv') %>%
    group_by( dateRange, direction) %>%
    mutate(ttt = mean_travel_time_minutes) %>%
    extract(
        dateRange,
        into=c('fromDate'),
        regex='From ([0-9]{4}-[0-9]{2}-[0-9]{2})'
    ) %>%
    mutate( fromDate= ymd(fromDate) + days(15) ) %>%
    ggplot( aes( x=fromDate, y=ttt, lty=direction ) ) +
        geom_line( size = 1.2, color=grey ) +
        geom_ribbon(
            aes(
                ymin = moe_lower_p95 / 60,
                ymax = moe_upper_p95 / 60,
                fill = direction
            ), 
            alpha = 0.25
        ) +
        geom_point( color=grey, size=2 ) +
        annotate( # approximate start/end dates of complete-street installation
            'rect',
            xmin = ymd('2023-09-11'),
            xmax = ymd('2023-10-15'),
            ymin = -Inf,
            ymax = Inf,
            fill = grey,
            alpha = 0.2
        ) +
        scale_y_continuous(
            breaks = c(0,2,4,6,8,10,12),
            limits = c(0, NA)
        ) +
        scale_x_date(
            breaks = '1 month',
            minor_breaks = '1 month',
            labels = scales::label_date_short()
        ) +
        labs( title = 'Bloor West weekday travel times between Runnymede & Aberfoyle (PM Peak)' ) + 
        ylab( 'Monthly Average Travel Time (minutes)' ) +
        xlab( 'Month' ) +
        theme_bw()


# compare two date ranges plotted by hour of day
read_csv('sample-data/results-ToD.csv') %>%
    extract(
        timeRange,
        into=c('hour'),
        regex='From ([0-9]+):00'
    ) %>%
    mutate(
        hour = as.numeric(hour),
        period = factor(case_match(
            dateRange,
            'From 2022-11-01 to 2023-04-01' ~ 'before',
            'From 2023-11-01 to 2024-04-01' ~ 'after'
        ), levels=c('before','after')),
    ) %>%
    ggplot( aes( x=0.5+hour, y=mean_travel_time_seconds, lty=direction ) ) +
        geom_line( aes(color=period,), size = 1 ) +
        geom_ribbon(
            aes(
                ymin = moe_lower_p95,
                ymax = moe_upper_p95,
                fill = period
            ), 
            alpha = 0.25
        ) +
        scale_color_manual(values=c('before'=grey,'after'=purple)) +
        labs( title = 'Bloor West travel times between Runnymede & Aberfoyle' ) + 
        ylab( 'Travel Time (seconds)' ) +
        xlab( 'Hour of day' ) + 
        scale_y_continuous(
            breaks = 60 * seq(0,14,1),
            minor_breaks = NULL,
            limits = c(0, NA)
        ) +
        scale_x_continuous(
            breaks = seq(0,24),
            minor_breaks = c(),
        ) +
        theme_bw()