Nothing
## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 5, # ancho en pulgadas
fig.height = 2.5, # alto en pulgadas
dpi = 120 # resolution
)
## ----setup--------------------------------------------------------------------
library(f1pits)
## ----message=TRUE, warning=TRUE-----------------------------------------------
# Accessing the data, for example, round 1, Australian GP 2026:
pits(1, 2026) -> pitdata
pitdata
## ----message=TRUE, warning=TRUE-----------------------------------------------
pitelo(pitdata)
## ----message=TRUE, warning=TRUE-----------------------------------------------
pitelo(pitdata, stat_fun = 3, calc = 2, k = 40, c = 20, d = 1000)
## ----message=TRUE, warning=TRUE-----------------------------------------------
pits(1, 2024) -> pitdata24
pits(1, 2025) -> pitdata25
# Join datasets:
pitdata_multiple <- dplyr::bind_rows(pitdata, pitdata24, pitdata25)
# Show all teams in dataset:
unique(pitdata_multiple$Team)
pitelo(pitdata_multiple, fml = TRUE)
pitelo(pitdata_multiple, fml = FALSE)
## ----message=TRUE, warning=TRUE-----------------------------------------------
# Create an ELO tibble with a starting value of 1000 for all teams, except Cadillac.
# As a new team it will be slightly penalized
# because its team structure is completely new.
elo_data <- tibble::tibble(
Team = c("Ferrari", "Red Bull", "Mercedes", "Racing Bulls", "McLaren",
"Haas", "Alpine", "Williams", "Audi", "Aston Martin", "Cadillac"),
Rating = c(1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 950))
elo_data
str(elo_data)
pitelo(pitdata, elo = elo_data)
## ----message=TRUE, warning=TRUE-----------------------------------------------
# Plotting the data:
pitplot(pitdata, 2) -> pitplot_pitdata
pitplot_pitdata
## ----message=TRUE, warning=TRUE, fig.height=4---------------------------------
pitplot(pitdata, 2, title_text = paste0(pitart(3), " Pit Stop data")) -> pitplot_pitdata_title_edit
pitplot_pitdata_title_edit
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