Jean-Philippe Boucher, Université du Québec À Montréal (🐦 @J_P_Boucher)
Arthur Charpentier, Université du Québec À Montréal (🐦 @freakonometrics)
Ewen Gallic, Aix-Marseille Université (🐦 @3wen)
As mention in the course, we will use simulated datasets, based on true data from a private Canadian insurance company (The Co-operators General Insurance Company) for this hands-on session on telematics. Each observation corresponds to an insurance policy for a vehicule and a driver (a vehicule may therefore be insured for multiple drivers). The available variables are as follows:
Falsevin: id of the vehiculeRA_GENDER: gender (1 male, 2 female, 3 unknown)RA_MARITALSTATUS: marital status (1 married, 0 other)RA_VEH_USE: vehicule use (1 commute, 2 pleasure, 0 other)RA_EXPOSURE_TIME: exposure time (in years)RA_DISTANCE_DRIVEN: total distance traveled by the driverRA_NBTRIP: number of tripsRA_HOURS_DRIVEN: number of hours drivenRA_ACCIDENT_IND: number of claimsThe dataset is in CSV format (comma-separated values). It has been separated into a training sample (CanadaPanelTrain.csv, with 29,108 observations) and a test sample (CanadaPanelTest.csv, with 19,570 observations).
library(tidyverse)
canada_train <- readr::read_csv("./data/canada_panel/CanadaPanelTrain.csv")
canada_test <- readr::read_csv("./data/canada_panel/CanadaPanelTest.csv")
nrow(canada_train)## [1] 29108## [1] 19570## # A tibble: 29,108 x 9
## Falsevin RA_GENDER RA_MARITALSTATUS RA_VEH_USE RA_EXPOSURE_TIME
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 2 1 2 0.863
## 2 1 2 1 2 0.485
## 3 6 2 1 1 0.329
## 4 6 2 1 1 0.964
## 5 6 2 1 1 1.00
## 6 7 2 0 1 0.573
## 7 7 2 0 1 0.912
## 8 8 1 0 2 0.496
## 9 8 1 0 2 0.501
## 10 8 1 0 2 0.501
## # … with 29,098 more rows, and 4 more variables: RA_DISTANCE_DRIVEN <dbl>,
## # RA_NBTRIP <dbl>, RA_HOURS_DRIVEN <dbl>, RA_ACCIDENT_IND <dbl>## # A tibble: 19,570 x 9
## Falsevin RA_GENDER RA_MARITALSTATUS RA_VEH_USE RA_EXPOSURE_TIME
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 2 0 1 0.496
## 2 2 2 0 1 0.778
## 3 3 1 0 2 1.00
## 4 3 1 0 2 0.414
## 5 4 2 1 1 0.479
## 6 4 2 1 1 0.499
## 7 4 2 1 1 0.414
## 8 5 1 1 1 0.666
## 9 5 1 1 1 0.918
## 10 9 1 1 2 0.392
## # … with 19,560 more rows, and 4 more variables: RA_DISTANCE_DRIVEN <dbl>,
## # RA_NBTRIP <dbl>, RA_HOURS_DRIVEN <dbl>, RA_ACCIDENT_IND <dbl>While R offers a convenient function to display summary statistics of a dataset, it lacks some outputs of interest, especially variance. Let us create our own summary function:
my_summary <- function(x){
results <- tibble(`Average` = mean(x),
`Variance` = var(x),
`Minimum` = min(x),
`Maximum` = max(x),
`25th percentile` = quantile(x, probs = 0.25, names = FALSE),
`50th percentile` = quantile(x, probs = 0.50, names = FALSE),
`75th percentile` = quantile(x, probs = 0.75, names = FALSE))
}We can then apply this function to some desired columns:
summary_var <- c("RA_EXPOSURE_TIME", "RA_NBTRIP", "RA_ACCIDENT_IND")
table <-
canada_train %>%
dplyr::select(!!summary_var) %>% # Only keep the desired columns
dplyr::select_if(is.numeric) %>% # Makes sure to keep only numeric variables
purrr::map(my_summary) %>% # Apply my_summary() to each selected variables
dplyr::bind_rows(.id = "variable_name") # bind the rows in a single tibbleThis table can be displayed in a markdown format. To obtain a prettier output, we can add a label to the variables instead of leaving the name of the variable. To that end, we can create a tibble where the labels can be defined.
labels_cols <-
tibble(
variable_name = c(
"Falsevin", "RA_GENDER", "RA_MARITALSTATUS",
"RA_VEH_USE", "RA_EXPOSURE_TIME", "RA_DISTANCE_DRIVEN",
"RA_NBTRIP", "RA_HOURS_DRIVEN", "RA_ACCIDENT_IND"
),
label = c(
"ID vehicle", "Gender", "Marital Status",
"Vehicle use", "Exposure Time", "Distance Driven",
"# Trips", "Hours Driven", "# Claims"
)
)
formatted_table <-
table %>%
# Add the corresponding label to the variables
dplyr::left_join(labels_cols) %>%
# Put the variable `label` in first position
dplyr::select(label, everything()) %>%
# Remove `variable_name` from the tibble
dplyr::select(-variable_name) %>%
dplyr::rename(Variable = label)
formatted_table## # A tibble: 3 x 8
## Variable Average Variance Minimum Maximum `25th percentil…
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Exposur… 6.52e-1 6.11e-2 0.277 1.08 0.485
## 2 # Trips 1.07e+3 3.90e+5 15 3283 607
## 3 # Claims 3.47e-2 3.61e-2 0 3 0
## # … with 2 more variables: `50th percentile` <dbl>, `75th
## # percentile` <dbl>The output of the table can be in Markdown:
formatted_table %>%
knitr::kable(
format = "pandoc", digits = 3,
format.args = list(big.mark = " ")
)| Variable | Average | Variance | Minimum | Maximum | 25th percentile | 50th percentile | 75th percentile |
|---|---|---|---|---|---|---|---|
| Exposure Time | 0.652 | 0.061 | 0.277 | 1.079 | 0.485 | 0.521 | 0.94 |
| # Trips | 1 074.081 | 390 413.843 | 15.000 | 3 283.000 | 607.000 | 937.000 | 1 415.25 |
| # Claims | 0.035 | 0.036 | 0.000 | 3.000 | 0.000 | 0.000 | 0.00 |
A \(\LaTeX\) format can also be obtained:
formatted_table %>%
knitr::kable(
format = "latex", digits = 3,
format.args = list(big.mark = " ")
) %>%
cat()##
## \begin{tabular}{l|r|r|r|r|r|r|r}
## \hline
## Variable & Average & Variance & Minimum & Maximum & 25th percentile & 50th percentile & 75th percentile\\
## \hline
## Exposure Time & 0.652 & 0.061 & 0.277 & 1.079 & 0.485 & 0.521 & 0.94\\
## \hline
## \# Trips & 1 074.081 & 390 413.843 & 15.000 & 3 283.000 & 607.000 & 937.000 & 1 415.25\\
## \hline
## \# Claims & 0.035 & 0.036 & 0.000 & 3.000 & 0.000 & 0.000 & 0.00\\
## \hline
## \end{tabular}Some scatter plots can be graphes: