Apply 1: Transit Costs
Olivia Pendergast
2024-02-13
R Markdown
Goal: To predict transit costs Click here for the data
transit_cost <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-01-05/transit_cost.csv')
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 | ▇▁▁▁▁ |
data1 <- transit_cost %>%
# Treat missing values
select(-line, -source1, -currency, -source2, -reference, -country) %>%
# log transform variables with pos-skewed distribution
mutate(cost = log(cost + 1)) %>%
mutate(real_cost = as.numeric(real_cost)) %>%
mutate(tunnel_per = tunnel_per %>% str_remove("%") %>% as.numeric()) %>%
mutate(start_year = as.numeric(start_year)) %>%
mutate(end_year = as.numeric(end_year)) %>%
na.omit()## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `real_cost = as.numeric(real_cost)`.
## Caused by warning:
## ! NAs introduced by coercion## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `start_year = as.numeric(start_year)`.
## Caused by warning:
## ! NAs introduced by coercion## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `end_year = as.numeric(end_year)`.
## Caused by warning:
## ! NAs introduced by coercionIdentify good predictors
Year
data1 %>%
ggplot(aes(cost, year)) +
scale_y_log10() +
geom_point()
Explore Data
Correlation between city and Cost
transit_cost <- data1 %>%
count(e, sort = TRUE) %>%
head(20) %>%
pull(e)
transit_cost## [1] 9459 9460 7136 7137 7138 7139 7144 7145 7146 7147 7152 7153 7154 7155 7160
## [16] 7161 7162 7163 7168 7169data1 %>%
#Filter for top 20 transit costs by city
# filter(city %in% transit_cost) %>%
ggplot(aes(cost, fct_reorder(city, cost))) +
geom_boxplot() 
EDA Shortcut
EDA Shortcut
# Step 1: Prepare the data
data_binarized_tbl1 <- data1 %>%
binarize()
data_binarized_tbl1 %>% glimpse(.)## Rows: 436
## Columns: 76
## $ `e__-Inf_7354.75` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ e__7354.75_7589.5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ e__7589.5_7923.5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ e__7923.5_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Bangkok <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Barcelona <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Beijing <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Changchun <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Changsha <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Chengdu <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Chongqing <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Dongguan <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Guangzhou <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Guiyang <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Hangzhou <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Istanbul <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Madrid <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Mumbai <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Nanjing <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__New_York <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Paris <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Riyadh <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Seoul <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Shanghai <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Shenzhen <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Sofia <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Taipei <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Tianjin <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Tokyo <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ city__Toronto <dbl> 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, …
## $ city__Wuhan <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `city__-OTHER` <dbl> 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, …
## $ `start_year__-Inf_2009` <dbl> 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, …
## $ start_year__2009_2015 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ start_year__2015_2018 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ start_year__2018_Inf <dbl> 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, …
## $ `end_year__-Inf_2016` <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ end_year__2016_2020 <dbl> 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ end_year__2020_2023 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ end_year__2023_Inf <dbl> 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, …
## $ rr__0 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ rr__1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `length__-Inf_6.1` <dbl> 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, …
## $ length__6.1_14.75 <dbl> 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, …
## $ length__14.75_28.2 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
## $ length__28.2_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `tunnel_per__-Inf_49.09` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tunnel_per__49.09_Inf <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ `tunnel__-Inf_3.275` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tunnel__3.275_8.4 <dbl> 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, …
## $ tunnel__8.4_20 <dbl> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, …
## $ tunnel__20_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `stations__-Inf_4` <dbl> 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, …
## $ stations__4_10 <dbl> 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, …
## $ stations__10_20 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
## $ stations__20_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `cost__-Inf_7.4713630881871` <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ cost__7.4713630881871_9.15883647922657 <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, …
## $ cost__9.15883647922657_10.2633708195977 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ cost__10.2633708195977_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `year__-Inf_2012` <dbl> 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, …
## $ year__2012_2016 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ year__2016_2018 <dbl> 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, …
## $ year__2018_Inf <dbl> 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, …
## $ `ppp_rate__-Inf_0.2379` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ ppp_rate__0.2379_0.266 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ ppp_rate__0.266_1.25 <dbl> 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, …
## $ ppp_rate__1.25_Inf <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ `real_cost__-Inf_1168.71` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ real_cost__1168.71_2977.92 <dbl> 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, …
## $ real_cost__2977.92_5540.5525 <dbl> 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, …
## $ real_cost__5540.5525_Inf <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
## $ `cost_km_millions__-Inf_132.9840103` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ cost_km_millions__132.9840103_184.085 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ cost_km_millions__184.085_243.2825 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ cost_km_millions__243.2825_Inf <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …# Step 2 Correlate
data_corr_tbl <- data_binarized_tbl1 %>%
correlate(cost__10.2633708195977_Inf)
data_corr_tbl## # A tibble: 76 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 cost 10.2633708195977_Inf 1
## 2 ppp_rate -Inf_0.2379 0.684
## 3 real_cost 5540.5525_Inf 0.376
## 4 cost -Inf_7.4713630881871 -0.335
## 5 cost 9.15883647922657_10.2633708195977 -0.333
## 6 cost 7.4713630881871_9.15883647922657 -0.331
## 7 ppp_rate 1.25_Inf -0.315
## 8 tunnel_per -Inf_49.09 0.291
## 9 tunnel_per 49.09_Inf -0.291
## 10 ppp_rate 0.266_1.25 -0.287
## # ℹ 66 more rows# Step 3 Plot
data_corr_tbl %>%
plot_correlation_funnel()## Warning: ggrepel: 26 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps