Excess Rentals in TfL Bike Sharing

Importing and Cleaning Data

url <- "https://data.london.gov.uk/download/number-bicycle-hires/ac29363e-e0cb-47cc-a97a-e216d900a6b0/tfl-daily-cycle-hires.xlsx"

# Download TFL data to temporary file
httr::GET(url, write_disk(bike.temp <- tempfile(fileext = ".xlsx")))

## Response [https://airdrive-secure.s3-eu-west-1.amazonaws.com/london/dataset/number-bicycle-hires/2022-09-06T12%3A41%3A48/tfl-daily-cycle-hires.xlsx?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAJJDIMAIVZJDICKHA%2F20220919%2Feu-west-1%2Fs3%2Faws4_request&X-Amz-Date=20220919T235157Z&X-Amz-Expires=300&X-Amz-Signature=7e3b67ab58fc087b3c5b5a3ccc7142fc3064e6e0490896d9071cc43ff43c1eea&X-Amz-SignedHeaders=host]
##   Date: 2022-09-19 23:51
##   Status: 200
##   Content-Type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
##   Size: 180 kB
## <ON DISK>  /var/folders/vt/lh0htjnn5j185nv7cw_x47200000gn/T//RtmpTQ9V8v/file4b039a2a23c.xlsx

# Use read_excel to read it as dataframe
bike0 <- read_excel(bike.temp,
                   sheet = "Data",
                   range = cell_cols("A:B"))

# change dates to get year, month, and week
bike <- bike0 %>% 
  clean_names() %>% 
  rename (bikes_hired = number_of_bicycle_hires) %>% 
  mutate (year = year(day),
          month = lubridate::month(day, label = TRUE),
          week = isoweek(day))

Monthly Changes in in TfL Bike Rentals

Weekly Changes in TfL Bike Rentals

The second one looks at percentage changes from the expected level of weekly rentals. The two grey shaded rectangles correspond to Q2 (weeks 14-26) and Q4 (weeks 40-52).