Apply 2
Brady Martin
2025-02-13
Goal: Build a regression model to predict the cost (cost_km_millions) of the transit project. Click here for the data.
Import Data
transit_cost <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2021/2021-01-05/transit_cost.csv')## Rows: 544 Columns: 20
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): country, city, line, start_year, end_year, tunnel_per, source1, cu...
## dbl (9): e, rr, length, tunnel, stations, cost, year, ppp_rate, cost_km_mil...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.skimr::skim(transit_cost)| Name | transit_cost |
| Number of rows | 544 |
| Number of columns | 20 |
| _______________________ | |
| Column type frequency: | |
| character | 11 |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| country | 7 | 0.99 | 2 | 2 | 0 | 56 | 0 |
| city | 7 | 0.99 | 4 | 16 | 0 | 140 | 0 |
| line | 7 | 0.99 | 2 | 46 | 0 | 366 | 0 |
| start_year | 53 | 0.90 | 4 | 9 | 0 | 40 | 0 |
| end_year | 71 | 0.87 | 1 | 4 | 0 | 36 | 0 |
| tunnel_per | 32 | 0.94 | 5 | 7 | 0 | 134 | 0 |
| source1 | 12 | 0.98 | 4 | 54 | 0 | 17 | 0 |
| currency | 7 | 0.99 | 2 | 3 | 0 | 39 | 0 |
| real_cost | 0 | 1.00 | 1 | 10 | 0 | 534 | 0 |
| source2 | 10 | 0.98 | 3 | 16 | 0 | 12 | 0 |
| reference | 19 | 0.97 | 3 | 302 | 0 | 350 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| e | 7 | 0.99 | 7738.76 | 463.23 | 7136.00 | 7403.00 | 7705.00 | 7977.00 | 9510.00 | ▇▇▂▁▁ |
| rr | 8 | 0.99 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| length | 5 | 0.99 | 58.34 | 621.20 | 0.60 | 6.50 | 15.77 | 29.08 | 12256.98 | ▇▁▁▁▁ |
| tunnel | 32 | 0.94 | 29.38 | 344.04 | 0.00 | 3.40 | 8.91 | 21.52 | 7790.78 | ▇▁▁▁▁ |
| stations | 15 | 0.97 | 13.81 | 13.70 | 0.00 | 4.00 | 10.00 | 20.00 | 128.00 | ▇▁▁▁▁ |
| cost | 7 | 0.99 | 805438.12 | 6708033.07 | 0.00 | 2289.00 | 11000.00 | 27000.00 | 90000000.00 | ▇▁▁▁▁ |
| year | 7 | 0.99 | 2014.91 | 5.64 | 1987.00 | 2012.00 | 2016.00 | 2019.00 | 2027.00 | ▁▁▂▇▂ |
| ppp_rate | 9 | 0.98 | 0.66 | 0.87 | 0.00 | 0.24 | 0.26 | 1.00 | 5.00 | ▇▂▁▁▁ |
| cost_km_millions | 2 | 1.00 | 232.98 | 257.22 | 7.79 | 134.86 | 181.25 | 241.43 | 3928.57 | ▇▁▁▁▁ |
data <- transit_cost %>%
# Treat missing values
select(-cost, -currency, -source2, -source1, -reference, -line, -real_cost) %>%
mutate(tunnel_per = tunnel_per %>% parse_number()) %>%
mutate(across(c(start_year, end_year, tunnel_per), as.numeric)) %>%
# Convert character columns to factors
mutate(across(where(is.character), as.factor)) %>%
# Convert rr to a factor
mutate(rr = factor(rr)) %>%
# Remove missing values
na.omit() %>%
# log transform variables with pos-skewed distributions
mutate(cost_km_millions = log(cost_km_millions))## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `across(c(start_year, end_year, tunnel_per), as.numeric)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.skimr::skim(data)| Name | data |
| Number of rows | 436 |
| Number of columns | 13 |
| _______________________ | |
| Column type frequency: | |
| factor | 3 |
| numeric | 10 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| country | 0 | 1 | FALSE | 55 | CN: 169, IN: 27, TR: 20, ES: 15 |
| city | 0 | 1 | FALSE | 135 | Bei: 21, Sha: 18, Ist: 15, Mum: 13 |
| rr | 0 | 1 | FALSE | 2 | 0: 404, 1: 32 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| e | 0 | 1 | 7694.72 | 482.20 | 7136.00 | 7354.75 | 7589.50 | 7923.50 | 9510.00 | ▇▅▂▁▁ |
| start_year | 0 | 1 | 2012.99 | 6.89 | 1982.00 | 2009.00 | 2015.00 | 2018.00 | 2025.00 | ▁▁▂▇▇ |
| end_year | 0 | 1 | 2018.88 | 6.49 | 1987.00 | 2016.00 | 2020.00 | 2023.00 | 2030.00 | ▁▁▂▇▆ |
| length | 0 | 1 | 20.59 | 21.93 | 0.60 | 6.10 | 14.75 | 28.20 | 200.00 | ▇▁▁▁▁ |
| tunnel_per | 0 | 1 | 74.13 | 37.00 | 0.00 | 49.09 | 100.00 | 100.00 | 100.00 | ▂▁▁▁▇ |
| tunnel | 0 | 1 | 13.64 | 15.50 | 0.00 | 3.27 | 8.40 | 20.00 | 160.00 | ▇▁▁▁▁ |
| stations | 0 | 1 | 13.77 | 14.03 | 0.00 | 4.00 | 10.00 | 20.00 | 128.00 | ▇▁▁▁▁ |
| year | 0 | 1 | 2014.51 | 5.91 | 1987.00 | 2012.00 | 2016.00 | 2018.00 | 2027.00 | ▁▁▂▇▂ |
| ppp_rate | 0 | 1 | 0.71 | 0.88 | 0.00 | 0.24 | 0.27 | 1.25 | 5.00 | ▇▂▁▁▁ |
| cost_km_millions | 0 | 1 | 5.23 | 0.63 | 2.05 | 4.89 | 5.22 | 5.49 | 8.28 | ▁▁▇▁▁ |
Explore Data
Identify good predictors.
Length
data %>%
ggplot(aes(cost_km_millions, length)) +
scale_y_log10() +
geom_point()
Tunnel
data %>%
ggplot(aes(cost_km_millions, tunnel)) +
scale_y_log10() +
geom_point()## Warning in scale_y_log10(): log-10 transformation introduced infinite values.
Stations
data %>%
ggplot(aes(cost_km_millions, as.factor(year))) +
geom_boxplot()
city
data %>%
group_by(city) %>%
filter(n() > 5) %>%
ungroup() %>%
# Plot
ggplot(aes(cost_km_millions, fct_reorder(city, cost_km_millions))) +
geom_point() +
labs(y = "City of Transit Project")
EDA shortcut
# Step 1: Prepare data
data_binarized_tbl <- data %>%
select(-e) %>%
binarize()
data_binarized_tbl %>% glimpse()## Rows: 436
## Columns: 83
## $ country__BG <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__CA <dbl> 1, 1, 1, 1, 1, 0, …
## $ country__CN <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__DE <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__ES <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__FR <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__IN <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__IT <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__JP <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__KR <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__RU <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__SA <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__SE <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__TH <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__TR <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__TW <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__US <dbl> 0, 0, 0, 0, 0, 0, …
## $ country__VN <dbl> 0, 0, 0, 0, 0, 0, …
## $ `country__-OTHER` <dbl> 0, 0, 0, 0, 0, 1, …
## $ city__Bangkok <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Barcelona <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Beijing <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Changchun <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Changsha <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Chengdu <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Chongqing <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Dongguan <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Guangzhou <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Guiyang <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Hangzhou <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Istanbul <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Madrid <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Mumbai <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Nanjing <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__New_York <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Paris <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Riyadh <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Seoul <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Shanghai <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Shenzhen <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Sofia <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Taipei <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Tianjin <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Tokyo <dbl> 0, 0, 0, 0, 0, 0, …
## $ city__Toronto <dbl> 0, 1, 1, 1, 1, 0, …
## $ city__Wuhan <dbl> 0, 0, 0, 0, 0, 0, …
## $ `city__-OTHER` <dbl> 1, 0, 0, 0, 0, 1, …
## $ `start_year__-Inf_2009` <dbl> 0, 1, 0, 0, 0, 1, …
## $ start_year__2009_2015 <dbl> 0, 0, 0, 0, 0, 0, …
## $ start_year__2015_2018 <dbl> 0, 0, 0, 0, 0, 0, …
## $ start_year__2018_Inf <dbl> 1, 0, 1, 1, 1, 0, …
## $ `end_year__-Inf_2016` <dbl> 0, 0, 0, 0, 0, 0, …
## $ end_year__2016_2020 <dbl> 0, 1, 0, 0, 0, 1, …
## $ end_year__2020_2023 <dbl> 0, 0, 0, 0, 0, 0, …
## $ end_year__2023_Inf <dbl> 1, 0, 1, 1, 1, 0, …
## $ rr__0 <dbl> 1, 1, 1, 1, 1, 1, …
## $ rr__1 <dbl> 0, 0, 0, 0, 0, 0, …
## $ `length__-Inf_6.1` <dbl> 1, 0, 0, 0, 0, 0, …
## $ length__6.1_14.75 <dbl> 0, 1, 1, 0, 1, 1, …
## $ length__14.75_28.2 <dbl> 0, 0, 0, 1, 0, 0, …
## $ length__28.2_Inf <dbl> 0, 0, 0, 0, 0, 0, …
## $ `tunnel_per__-Inf_49.09` <dbl> 0, 0, 0, 0, 0, 0, …
## $ tunnel_per__49.09_Inf <dbl> 1, 1, 1, 1, 1, 1, …
## $ `tunnel__-Inf_3.275` <dbl> 0, 0, 0, 0, 0, 0, …
## $ tunnel__3.275_8.4 <dbl> 1, 0, 1, 0, 1, 1, …
## $ tunnel__8.4_20 <dbl> 0, 1, 0, 1, 0, 0, …
## $ tunnel__20_Inf <dbl> 0, 0, 0, 0, 0, 0, …
## $ `stations__-Inf_4` <dbl> 0, 0, 1, 0, 0, 0, …
## $ stations__4_10 <dbl> 1, 1, 0, 0, 1, 1, …
## $ stations__10_20 <dbl> 0, 0, 0, 1, 0, 0, …
## $ stations__20_Inf <dbl> 0, 0, 0, 0, 0, 0, …
## $ `year__-Inf_2012` <dbl> 0, 0, 0, 0, 0, 1, …
## $ year__2012_2016 <dbl> 0, 1, 0, 0, 0, 0, …
## $ year__2016_2018 <dbl> 1, 0, 1, 0, 0, 0, …
## $ year__2018_Inf <dbl> 0, 0, 0, 1, 1, 0, …
## $ `ppp_rate__-Inf_0.2379` <dbl> 0, 0, 0, 0, 0, 0, …
## $ ppp_rate__0.2379_0.266 <dbl> 0, 0, 0, 0, 0, 0, …
## $ ppp_rate__0.266_1.25 <dbl> 1, 1, 1, 1, 1, 0, …
## $ ppp_rate__1.25_Inf <dbl> 0, 0, 0, 0, 0, 1, …
## $ `cost_km_millions__-Inf_4.89022561868544` <dbl> 0, 0, 0, 0, 0, 0, …
## $ cost_km_millions__4.89022561868544_5.21539741232945 <dbl> 0, 0, 0, 0, 0, 0, …
## $ cost_km_millions__5.21539741232945_5.49422035213027 <dbl> 0, 0, 0, 0, 0, 0, …
## $ cost_km_millions__5.49422035213027_Inf <dbl> 1, 1, 1, 1, 1, 1, …# Step 2: Correlate
data_corr_tbl <- data_binarized_tbl %>%
correlate(cost_km_millions__5.49422035213027_Inf)
data_corr_tbl## # A tibble: 83 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 cost_km_millions 5.49422035213027_Inf 1
## 2 cost_km_millions -Inf_4.89022561868544 -0.333
## 3 cost_km_millions 4.89022561868544_5.21539741232945 -0.333
## 4 cost_km_millions 5.21539741232945_5.49422035213027 -0.333
## 5 country US 0.304
## 6 country CN -0.264
## 7 country -OTHER 0.196
## 8 city New_York 0.187
## 9 city -OTHER 0.160
## 10 year 2018_Inf 0.156
## # ℹ 73 more rows# Step 3: Plot
data_corr_tbl %>%
plot_correlation_funnel()## Warning: ggrepel: 44 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Build Models
Split data
data <- sample_n(data, 200)
# Split into training and testing dataset
set.seed(1234)
data_split <- rsample::initial_split(data)
data_train <- training(data_split)
data_test <- testing(data_split)
# Further split training dataset for cross-validation
set.seed(2345)
data_cv <- rsample::vfold_cv(data_train)
data_cv## # 10-fold cross-validation
## # A tibble: 10 × 2
## splits id
## <list> <chr>
## 1 <split [135/15]> Fold01
## 2 <split [135/15]> Fold02
## 3 <split [135/15]> Fold03
## 4 <split [135/15]> Fold04
## 5 <split [135/15]> Fold05
## 6 <split [135/15]> Fold06
## 7 <split [135/15]> Fold07
## 8 <split [135/15]> Fold08
## 9 <split [135/15]> Fold09
## 10 <split [135/15]> Fold10Evaluate Models
usemodels::use_xgboost(cost_km_millions ~ ., data = data_train)## xgboost_recipe <-
## recipe(formula = cost_km_millions ~ ., data = data_train) %>%
## step_zv(all_predictors())
##
## xgboost_spec <-
## boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(),
## loss_reduction = tune(), sample_size = tune()) %>%
## set_mode("classification") %>%
## set_engine("xgboost")
##
## xgboost_workflow <-
## workflow() %>%
## add_recipe(xgboost_recipe) %>%
## add_model(xgboost_spec)
##
## set.seed(57769)
## xgboost_tune <-
## tune_grid(xgboost_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))# Specify recipe
xgboost_recipe <-
recipe(formula = cost_km_millions ~ ., data = data_train) %>%
recipes::update_role(e, new_role = "id variable") %>%
step_other(country, city) %>%
step_dummy(country, city, rr, one_hot = TRUE) %>%
step_YeoJohnson(ppp_rate, stations, tunnel, length)
xgboost_recipe %>% prep() %>% juice() %>% glimpse()## Rows: 150
## Columns: 18
## $ e <dbl> 7569, 7624, 7443, 7418, 7882, 7969, 7410, 8112, 7306,…
## $ start_year <dbl> 2014, 2011, 2001, 2014, 2019, 2017, 2019, 2009, 2020,…
## $ end_year <dbl> 2024, 2019, 2008, 2021, 2024, 2021, 2029, 2014, 2024,…
## $ length <dbl> 2.876866, 4.565869, 1.090173, 1.053419, 2.418344, 3.5…
## $ tunnel_per <dbl> 0.00, 0.00, 100.00, 100.00, 100.00, 95.93, 100.00, 66…
## $ tunnel <dbl> 0.0000000, 0.0000000, 1.1127810, 1.0745293, 2.5293074…
## $ stations <dbl> 3.751365, 4.632381, 1.182723, 1.182723, 2.394627, 3.7…
## $ year <dbl> 2016, 2015, 2004, 2011, 2018, 2016, 2024, 2010, 2018,…
## $ ppp_rate <dbl> 0.06142028, 0.57227321, 0.47986718, 0.48664798, 0.183…
## $ cost_km_millions <dbl> 4.284493, 3.858411, 4.598570, 5.614749, 5.249705, 5.0…
## $ country_CN <dbl> 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,…
## $ country_ES <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,…
## $ country_TR <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ country_other <dbl> 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0,…
## $ city_Changsha <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
## $ city_other <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,…
## $ rr_X0 <dbl> 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ rr_X1 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…# Specify model
xgboost_spec <-
boost_tree(trees = tune(), min_n = tune(), mtry = tune(), learn_rate = tune()) %>%
set_mode("regression") %>%
set_engine("xgboost")
# Combine recipe and model using workflow
xgboost_workflow <-
workflow() %>%
add_recipe(xgboost_recipe) %>%
add_model(xgboost_spec)
# Tune hyperparmeters
set.seed(234)
xgboost_tune <-
tune_grid(xgboost_workflow,
resamples = data_cv,
grid = 5)## i Creating pre-processing data to finalize unknown parameter: mtry