Análise de dados de trânsito Bid/Waze https://www.iadb.org/en/topics-effectiveness-improving-lives/coronavirus-impact-dashboard

analise_trafego_brasil_bidwaze.R

library(tidyverse)
library(clipr)
library(zoo)

bid %>% filter(region_type == "city") %>% distinct(region_slug) %>% count()

bid %>% filter(region_type == "city") %>% distinct(region_slug, population) %>% summarise(total = sum(population))

# BRASIL
brasil <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "country" ) %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# BRASIL, apenas cidades
brasil_metro <- bid %>% 
  group_by(region_name, data, week_number) %>%
  filter(week_number != 46 & region_type == "city" ) %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# SAO PAULO, cidade
sao_paulo <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "city" & region_name == "São Paulo") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# SAO PAULO, UF
sp <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "state" & region_name == "Sao Paulo") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# RIO DE JANEIRO, cidade
rio_de_janeiro <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "city" & region_name == "Rio de Janeiro") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# RIO DE JANEIRO, UF
rj <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "state" & region_name == "Rio De Janeiro") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# Belo Horizonte, cidade
bh <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "city" & region_name == "Belo Horizonte") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# Recife, cidade
recife <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "city" & region_name == "Recife") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# Fortaleza, cidade
fortaleza <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "city" & region_name == "Fortaleza") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# Natal, cidade
natal <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "city" & region_name == "Natal") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# Brasília, cidade
salvador <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "city" & region_name == "Salvador") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# Goiania, cidade
goiania <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "city" & region_name == "Goiânia") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# Brasília, cidade
bsb <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "city" & region_name == "Brasília") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# Belém, cidade
belem <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "city" & region_name == "Belém") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# Brasília, cidade
manaus <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "city" & region_name == "Manaus") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# São José dos Campos, cidade
sjc <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "city" & region_name == "São José dos Campos") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

# Curitiba, cidade
curitiba <- bid %>% 
  group_by(data, week_number) %>%
  filter(week_number != 46 & region_type == "city" & region_name == "Curitiba") %>%
  summarise(media = mean((ratio_20 - 1) * 100), mediana = median((ratio_20 - 1) * 100)) %>%
  ggplot() + geom_bar(aes(data, media), stat = "identity", position = "dodge")

get_data_bidwaze.R

library(readr)
library(tidyverse)

# DADOS DE MOBILIDADE BID/WAZE
dia <- read_csv('http://tiny.cc/idb-traffic-daily')
semana <- read_csv('http://tiny.cc/idb-traffic-weekly')
metadata <- read_csv('http://tiny.cc/idb-traffic-metadata')

# agrega datas
bid$data <- as.Date(paste0(2020,"-", bid$max_month,"-", bid$max_day))