The metrosp package provides access to the Metro de São Paulo public transportation data. The datasets are compact and the sources publish irregularly, so the package ships the data in a “lazy” format. All data comes prepackaged and is called directly, with no download or import step.
The bundled data is a fixed snapshot, current through July 2026. Data newer than the snapshot is published to the data-latest GitHub release.
library(metrosp)
library(dplyr)There are four main datasets:
passengers_entrance: monthly passengers entering the metro system, by linepassengers_transported: monthly passengers transported by the metro system, by linestation_averages: monthly average of weekday passengers per stationstation_daily: daily passengers per stationFor convenience, metrosp also provides information on stations and lines of the metro system (lines and stations). The lines dataset is a spatial dataset and requires the sf package to work properly; it carries the line numbers and Portuguese/English line names for the full network (including planned and CPTM lines).
library(sf)
lines
#> Simple feature collection with 55 features and 6 fields
#> Geometry type: GEOMETRY
#> Dimension: XY
#> Bounding box: xmin: -46.98358 ymin: -23.77875 xmax: -46.18294 ymax: -23.19513
#> Geodetic CRS: WGS 84
#> First 10 features:
#> status company_name line_number type line_name_pt line_name
#> 1 current Metrô 1 metro Azul Blue
#> 2 current Metrô 2 metro Verde Green
#> 3 current Metrô 3 metro Vermelha Red
#> 4 current Metrô 5 metro Lilás Lilac
#> 5 current Metrô 15 metro Prata Silver
#> 6 current ViaQuatro 4 metro Amarela Yellow
#> 7 future Metrô 2 metro Verde Green
#> 8 future Metrô 2 metro Verde Green
#> 9 future Metrô 2 metro Verde Green
#> 10 future Metrô 15 metro Prata Silver
#> geom
#> 1 LINESTRING (-46.60291 -23.4...
#> 2 LINESTRING (-46.69089 -23.5...
#> 3 LINESTRING (-46.66754 -23.5...
#> 4 LINESTRING (-46.63049 -23.5...
#> 5 LINESTRING (-46.5838 -23.58...
#> 6 LINESTRING (-46.63449 -23.5...
#> 7 LINESTRING (-46.54846 -23.4...
#> 8 LINESTRING (-46.54283 -23.5...
#> 9 LINESTRING (-46.7015 -23.54...
#> 10 LINESTRING (-46.46899 -23.5...The package also provides a named vector of colors for each line of the metro system (metro_colors), station opening dates (station_inauguration), and a São Paulo holiday and business-day calendar (calendar_spo).
metro_colors
#> Blue Green Red Yellow Lilac Silver
#> "#171796" "#007A5E" "#ED2E38" "#FFD525" "#874ABF" "#8F8F8C"Using the datasets is straightforward, just call the dataset name.
glimpse(passengers_entrance)
#> Rows: 4,620
#> Columns: 9
#> $ date <date> 2012-01-01, 2012-01-01, 2012-01-01, 2012-01-01, 2012-01-…
#> $ line_number <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, …
#> $ metric_abb <chr> "max", "mdo", "mdu", "msa", "total", "max", "mdo", "mdu",…
#> $ value <dbl> 122637.00, 24663.40, 99339.64, 48876.25, 2504294.00, 1314…
#> $ metric <chr> "Daily Peak", "Average on Sundays", "Average on Business …
#> $ metric_pt <chr> "Máxima Diária", "Média dos Domingos", "Média dos Dias Út…
#> $ line_name <chr> "Yellow", "Yellow", "Yellow", "Yellow", "Yellow", "Yellow…
#> $ line_name_pt <chr> "Amarela", "Amarela", "Amarela", "Amarela", "Amarela", "A…
#> $ year <dbl> 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 201…All datasets are returned as tibble so using the dplyr package is recommended.
This tutorial will briefly introduce the main datasets and how to use them by making simple visualizations with the data. To better replicate the visualization, use the ggplot2 package and the custom theme below.
library(ggplot2)
theme_series <- theme_minimal(base_family = "Avenir", base_size = 10) +
theme(
panel.background = element_rect(fill = "#f5f5f5"),
plot.background = element_rect(fill = "#f5f5f5"),
plot.margin = margin(20, 10, 20, 10),
plot.title = element_text(family = "Lora", size = 14),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(color = "gray90", linewidth = 0.25),
axis.title.x = element_blank(),
axis.line.x = element_line(color = "gray10", linewidth = 0.5),
axis.ticks.x = element_line(color = "gray10", linewidth = 0.5),
strip.background = element_rect(fill = "#0D1B2A"),
strip.text = element_text(color = "#ffffff"),
legend.position = "bottom"
)Both passengers_entrance and passengers_transported are monthly series by line. An entry is a passenger crossing a turnstile; a transported passenger is a turnstile entry plus a transfer between lines at an interchange station, so transported counts run above entry counts for the same line and month.
The data is aggregated into metrics:
max: maximum number of passengers (daily peak)mdu: average number of passengers on business daysmdo: average number of passengers on Sundaysmsa: average number of passengers on Saturdaystotal: total number of passengersThis dataset is identified by month (date), line (line_number, line_name), and metric (metric_abb, metric). The data is in tidy format and values are in individual passengers.
glimpse(passengers_entrance)
#> Rows: 4,620
#> Columns: 9
#> $ date <date> 2012-01-01, 2012-01-01, 2012-01-01, 2012-01-01, 2012-01-…
#> $ line_number <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, …
#> $ metric_abb <chr> "max", "mdo", "mdu", "msa", "total", "max", "mdo", "mdu",…
#> $ value <dbl> 122637.00, 24663.40, 99339.64, 48876.25, 2504294.00, 1314…
#> $ metric <chr> "Daily Peak", "Average on Sundays", "Average on Business …
#> $ metric_pt <chr> "Máxima Diária", "Média dos Domingos", "Média dos Dias Út…
#> $ line_name <chr> "Yellow", "Yellow", "Yellow", "Yellow", "Yellow", "Yellow…
#> $ line_name_pt <chr> "Amarela", "Amarela", "Amarela", "Amarela", "Amarela", "A…
#> $ year <dbl> 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 201…Note that a special line was defined to aggregate the total of the METRÔ system (line_name = "METRO System" or line_number = 99). For most uses, it’s best to filter out this line.
total_entrance <- passengers_entrance |>
filter(metric_abb == "total", line_number != 99)The plot shows the total monthly passenger entrances by metro line. Line 4 starts in 2012 and the other lines in January 2016. July 2017 is the one gap, a month METRO never published an entrance table for.
ggplot(total_entrance, aes(x = date, y = value, color = line_name)) +
geom_line(lwd = 0.8) +
facet_wrap(vars(line_name), scales = "free_y") +
scale_color_manual(values = metro_colors) +
guides(color = "none") +
labs(
title = "Total Entrance by Line",
subtitle = "Total monthly passenger entrances by metro line",
x = NULL,
y = "Total Entrance"
) +
theme_series
This dataset has the same columns as passengers_entrance, but values are in thousands of passengers, as METRÔ publishes them. Multiply by 1000 before comparing the two datasets.
Coverage is limited to the METRÔ-operated system. Line 4 never appears, and Line 5 stops in August 2018, when the line passed to ViaMobilidade: the Dataverse source that covers both lines does not publish transported counts.
glimpse(passengers_transported)
#> Rows: 3,335
#> Columns: 9
#> $ date <date> 2016-01-01, 2016-01-01, 2016-01-01, 2016-01-01, 2016-01-…
#> $ line_number <dbl> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 5, 5, 5, 5, …
#> $ metric_abb <chr> "max", "mdo", "mdu", "msa", "total", "max", "mdo", "mdu",…
#> $ value <dbl> 1301, 453, 1202, 691, 29345, 622, 173, 574, 251, 13328, 1…
#> $ metric <chr> "Daily Peak", "Average on Sundays", "Average on Business …
#> $ metric_pt <chr> "Máxima Diária", "Média dos Domingos", "Média dos Dias Út…
#> $ line_name <chr> "Blue", "Blue", "Blue", "Blue", "Blue", "Green", "Green",…
#> $ line_name_pt <chr> "Azul", "Azul", "Azul", "Azul", "Azul", "Verde", "Verde",…
#> $ year <dbl> 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 201…Note that a special line was defined to aggregate the total of the METRÔ system (line_name = "METRO System" or line_number = 99). For most uses, it’s best to filter out this line.
daily_avg <- passengers_transported |>
filter(metric_abb == "mdu", line_number != 99)The plot below shows the daily average (business days) passengers transported by metro line. The Lilac panel stops in 2018, when Line 5 left the METRÔ reports.
ggplot(daily_avg, aes(x = date, y = value, color = line_name)) +
geom_line(lwd = 0.8) +
facet_wrap(vars(line_name), scales = "free_y") +
scale_color_manual(values = metro_colors) +
labs(
title = "Daily Average Passenger Transported by Line",
subtitle = "Monthly averages across business days (thousands)",
x = NULL,
y = "Daily Average"
) +
guides(color = "none") +
theme_series
This dataset is identified by month (date), line (line_number, line_name), and station (station_name). The only value column available is avg_passenger, which is the daily average (business days) of passengers entering the station.
glimpse(station_averages)
#> Rows: 11,281
#> Columns: 7
#> $ date <date> 2012-01-01, 2012-01-01, 2012-01-01, 2012-01-01, 2012-01…
#> $ line_number <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,…
#> $ station_name <chr> "Butantã", "Faria Lima", "Luz", "Paulista", "Pinheiros",…
#> $ avg_passenger <dbl> 37066.82, 31989.09, 100889.32, 127844.59, 97537.45, 9919…
#> $ line_name <chr> "Yellow", "Yellow", "Yellow", "Yellow", "Yellow", "Yello…
#> $ line_name_pt <chr> "Amarela", "Amarela", "Amarela", "Amarela", "Amarela", "…
#> $ year <dbl> 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 20…The plot below shows the daily average (business days) passengers entering each station of line 4. Note that the temporal range of the data is unequal across stations, since not all of them were inaugurated at the same time.
line4st <- station_averages |>
filter(line_number == 4)
ggplot(line4st, aes(x = date, y = avg_passenger)) +
geom_line(lwd = 0.8, color = metro_colors["Yellow"]) +
facet_wrap(vars(station_name), scales = "free_y") +
labs(
x = NULL,
y = "Average Passengers",
title = "Passengers per Station (line 4)"
) +
theme_series
This dataset is identified by day (date), line (line_number, line_name), and station (station_name). The only value column available is passengers, which is the daily number of passengers entering the station. Additionally, the column station_code contains three-letter abbreviations for stations, but only for METRÔ-operated lines.
glimpse(station_daily)
#> Rows: 244,174
#> Columns: 8
#> $ date <date> 2012-01-01, 2012-01-01, 2012-01-01, 2012-01-01, 2012-01-…
#> $ line_number <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, …
#> $ station_name <chr> "Butantã", "Faria Lima", "Luz", "Paulista", "Pinheiros", …
#> $ passengers <dbl> 7742, 4737, 695, 2277, 332, 25317, 21930, 3923, 14356, 39…
#> $ line_name <chr> "Yellow", "Yellow", "Yellow", "Yellow", "Yellow", "Yellow…
#> $ line_name_pt <chr> "Amarela", "Amarela", "Amarela", "Amarela", "Amarela", "A…
#> $ station_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ year <dbl> 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 201…The plot below shows the trend of daily passengers entering each station of line 4 in 2023.
line4st_daily <- station_daily |>
filter(line_number == 4, year == 2023)
ggplot(line4st_daily, aes(x = date, y = passengers)) +
geom_smooth(
lwd = 0.8,
color = metro_colors["Yellow"],
method = "loess",
span = 0.65
) +
facet_wrap(vars(station_name), scales = "free_y", ncol = 3) +
scale_x_date(date_breaks = "1 month", date_labels = "%b") +
labs(
title = "Passengers per Station (line 4, 2023)",
subtitle = "LOESS smoothed trend",
x = NULL,
y = "Average Passengers"
) +
theme_series