5.2 Data prepration
There are a lot of columns, and a lot of weird names, so let’s clean things up a little bit. Let’s keep only the rows for which Fuel.Type.Code is ELEC, so that we are dealing with only electric vehicle charging stations. This data set also has ethanol, hydrogen, etc., but we don’t want to focus on those. Let’s also rename some columns, and transform the Status.Code into something more human readable. Since there are some NA values in the lev2 and lev3 columns, we’ll replace each NA with 0. We’ll also clean up some network names,
Code
library(leaflet)
library(htmltools)
library(tidyverse)
library(pubtheme)
d = readRDS('data/EVstations.rds')
colnames(d) = tolower(colnames(d))
d = d %>%
select(-matches('cng|lng|lpg|hydrogen|e85|bd[.]blends|french|^ng[.]|plus4|level1')) %>%
filter(fuel.type.code == 'ELEC') %>%
rename(lev2 = ev.level2.evse.num,
lev3 = ev.dc.fast.count,
network = ev.network,
lat = latitude,
lon = longitude) %>%
filter(lat > 20,
lon < -60,
lat < 55,
lon >-130) %>%
mutate(status = case_when(status.code == 'E' ~ 'avail',
status.code == 'P' ~ 'planned',
status.code == 'T' ~ 'temp.unavail',
TRUE ~ ''),
lev2 = ifelse(is.na(lev2), 0, lev2),
lev3 = ifelse(is.na(lev3), 0, lev3),
lev23 = lev2 + lev3) %>%
mutate(network = ifelse(network == 'Tesla Destination',
'Tesla',
network),
network = gsub('é', 'E', network),
network = gsub('É', 'E', network),
network = gsub('SemaCharge', 'Blink', network), ## https://www.semaconnect.com/
network = gsub(' Network', '', network))
head(d,2) fuel.type.code station.name street.address
1 ELEC LADWP - Truesdale Center 11797 Truesdale St
2 ELEC LADWP - West LA District Office 1394 S Sepulveda Blvd
intersection.directions city state zip station.phone status.code
1 Sun Valley CA 91352 E
2 Los Angeles CA 90024 E
expected.date groups.with.access.code access.days.time cards.accepted lev2
1 Private Fleet use only 39
2 Private 4
lev3 ev.other.info network ev.network.web geocode.status lat
1 3 Non-Networked GPS 34.24832
2 0 Non-Networked 200-8 34.05254
lon date.last.confirmed id updated.at owner.type.code
1 -118.3880 2023-01-10 1517 2023-02-15 22:45:41 UTC LG
2 -118.4485 2023-01-10 1519 2023-02-15 22:45:41 UTC LG
federal.agency.id federal.agency.name open.date ev.connector.types
1 NA 1999-10-15 CHADEMO J1772 J1772COMBO
2 NA 2020-02-28 J1772
country access.code access.detail.code federal.agency.code facility.type
1 US private UTILITY
2 US private UTILITY
ev.pricing ev.on.site.renewable.source restricted.access rd.blends
1 <NA>
2 Free <NA>
rd.blended.with.biodiesel rd.maximum.biodiesel.level nps.unit.name
1 <NA> NA
2 <NA> NA
maximum.vehicle.class src status lev23
1 csv avail 42
2 csv avail 4Which are the biggest networks?
Code
# A tibble: 43 × 4
network lev2 lev3 tot
<chr> <dbl> <dbl> <dbl>
1 ChargePoint 58662 2168 60830
2 Non-Networked 23879 1479 25358
3 Tesla 14931 21812 36743
4 Blink 11075 88 11163
5 FLO 6180 453 6633
6 EV Connect 3863 487 4350
7 Circuit électrique 3631 839 4470
8 Volta 2922 104 3026
9 AMPUP 2879 5 2884
10 POWERFLEX 2473 28 2501
# ℹ 33 more rows
# A tibble: 43 × 4
network lev2 lev3 tot
<chr> <dbl> <dbl> <dbl>
1 Tesla 14931 21812 36743
2 Electrify America 166 3542 3708
3 eVgo 362 2618 2980
4 ChargePoint 58662 2168 60830
5 Non-Networked 23879 1479 25358
6 Circuit électrique 3631 839 4470
7 SHELL_RECHARGE 1979 576 2555
8 FCN 120 546 666
9 EV Connect 3863 487 4350
10 FLO 6180 453 6633
# ℹ 33 more rows
# A tibble: 43 × 4
network lev2 lev3 tot
<chr> <dbl> <dbl> <dbl>
1 ChargePoint 58662 2168 60830
2 Tesla 14931 21812 36743
3 Non-Networked 23879 1479 25358
4 Blink 11075 88 11163
5 FLO 6180 453 6633
6 Circuit électrique 3631 839 4470
7 EV Connect 3863 487 4350
8 Electrify America 166 3542 3708
9 Volta 2922 104 3026
10 eVgo 362 2618 2980
# ℹ 33 more rowsLet’s create a new network column where all networks other than the top 5 networks with Level 2 charging stations a grouped together as an “Other” category. Likewise with Level 3 charging stations.
Code
top5lev2 = c('ChargePoint',
'Non-Networked',
'Tesla',
'Blink',
'FLO')
top5lev3 = c('Tesla',
'Electrify America',
'eVgo',
'ChargePoint',
'Non-Networked')
d = d %>%
mutate(network2 = ifelse(network %in% top5lev2, network, 'Other'),
network3 = ifelse(network %in% top5lev3, network, 'Other'),
network2 = factor(network2, levels = c(top5lev2, 'Other')),
network3 = factor(network3, levels = c(top5lev3, 'Other')))
head(d,2) fuel.type.code station.name street.address
1 ELEC LADWP - Truesdale Center 11797 Truesdale St
2 ELEC LADWP - West LA District Office 1394 S Sepulveda Blvd
intersection.directions city state zip station.phone status.code
1 Sun Valley CA 91352 E
2 Los Angeles CA 90024 E
expected.date groups.with.access.code access.days.time cards.accepted lev2
1 Private Fleet use only 39
2 Private 4
lev3 ev.other.info network ev.network.web geocode.status lat
1 3 Non-Networked GPS 34.24832
2 0 Non-Networked 200-8 34.05254
lon date.last.confirmed id updated.at owner.type.code
1 -118.3880 2023-01-10 1517 2023-02-15 22:45:41 UTC LG
2 -118.4485 2023-01-10 1519 2023-02-15 22:45:41 UTC LG
federal.agency.id federal.agency.name open.date ev.connector.types
1 NA 1999-10-15 CHADEMO J1772 J1772COMBO
2 NA 2020-02-28 J1772
country access.code access.detail.code federal.agency.code facility.type
1 US private UTILITY
2 US private UTILITY
ev.pricing ev.on.site.renewable.source restricted.access rd.blends
1 <NA>
2 Free <NA>
rd.blended.with.biodiesel rd.maximum.biodiesel.level nps.unit.name
1 <NA> NA
2 <NA> NA
maximum.vehicle.class src status lev23 network2 network3
1 csv avail 42 Non-Networked Non-Networked
2 csv avail 4 Non-Networked Non-Networked