1 more graph about needs

1-wrangling.R

@@ -31,6 +31,11 @@ gojek$vehicle <- content %>%
   str_remove("go-?") %>% 
   str_to_title()
 
+# order id
+gojek$order_id <- content %>% 
+  str_extract("RB-.+\\d+") %>% 
+  str_squish()
+
 # distance
 gojek$distance <- content %>% 
   str_extract("[D|J]\\w+.+\\skm") %>% 
@@ -162,11 +167,17 @@ gojek <- gojek %>%
 gojek$price - gojek$discount + gojek$fee == gojek$paid
 
 # total consumption
-colSums(gojek[,c(3:8)])
+colSums(gojek[,c(4:9)])
 
 # sort by datetime
 gojek <- gojek %>% arrange(datetime)
 
+# check: double invoice
+n_distinct(select(gojek, order_id)) == nrow(gojek)
+
+# handle double record
+# ???
+
 # save to RDS
 saveRDS(gojek, file = "output/gojek.rds")
 

2-clock.R

@@ -74,6 +74,7 @@ ggplot(data = clock) +
     panel.grid.minor = element_blank(),
     plot.subtitle = element_text(colour = "gray40"),
     plot.caption = element_text(colour = "gray60"),
+    plot.title.position = "plot",
     plot.background = element_rect(fill = "white", size = 0)
   )
 

3-distance.R

@@ -1,7 +1,7 @@
 # Title:           Distance
 # Author:          Andi Herlan
 # Email:           andi.herlan@protonmail.com
-# Data Used:       gojek.rds
+# Data Used:       gojek.rds, java.rds
 # Packages Used:   dplyr, mapboxapi, ggplot2, ggrepel
 # Output File:     distance.png
 # Data Output:     -
@@ -98,6 +98,7 @@ p1 <- ggplot(data = distn) +
     text = element_text(family = "Sans"),
     legend.position = c(0.85, 0.28),
     panel.grid.minor = element_blank(),
+    plot.title.position = "plot",
     plot.subtitle = element_text(colour = "gray40"),
     plot.caption = element_text(colour = "gray60"),
     axis.title = element_text(colour = "gray50"),

4-cost.R

@@ -100,10 +100,10 @@ ggplot() +
             size = 3.4, colour = "#005400") +
   # percent transaction done in app
   geom_text(data = filter(cost, cat == 1 & payment == "Cash"), 
-            aes(x = 0.5, y = y_trans + 170, label = paste0(n_trans$percent[2], "%")), 
+            aes(x = 0.5, y = y_trans + 150, label = paste0(n_trans$percent[2], "%")), 
             size = 12, colour = "gray60") +
   geom_text(data = filter(cost, cat == 1 & payment == "Cash"), 
-            aes(x = 0.5, y = y_trans - 350, 
+            aes(x = 0.5, y = y_trans - 365, 
                 label = "of transactions\nwere completed\nwithin the app"), 
             size = 5, colour = "gray60") +
   # graph's labels
@@ -122,13 +122,15 @@ ggplot() +
     axis.title = element_text(colour = "gray50"),
     plot.subtitle = element_text(colour = "gray40"),
     plot.caption = element_text(colour = "gray60"),
+    plot.title.position = "plot",
     strip.text = element_text(face = "bold", size = 11),
     panel.grid.major.x = element_blank(),
     panel.grid.minor.x = element_blank(),
     panel.grid.minor.y = element_blank(),
     panel.spacing = unit(0, "cm"),
-    legend.position = "top",
-    legend.justification = c(0, 1),
+    legend.position = c(0.25, 0.85),
+    legend.direction = "horizontal",
+    legend.justification = "center",
     plot.background = element_rect(fill = "white", size = 0)
   )
 

5-needs.R

@@ -0,0 +1,201 @@
+# Title:           Needs
+# Author:          Andi Herlan
+# Email:           andi.herlan@protonmail.com
+# Data Used:       gojek.rds
+# Packages Used:   dplyr, tidyr, stringr, ggplot2, ggalluvial
+# Output File:     needs.png
+# Data Output:     od.rds
+# Reference:       https://corybrunson.github.io/ggalluvial
+
+
+# clear environment
+rm(list = ls())
+
+library(dplyr)
+library(tidyr)
+library(stringr)
+library(ggplot2)
+library(ggalluvial)
+
+# main data
+gojek <- readRDS("output/gojek.rds")
+
+# origin destination
+od <- gojek %>% 
+  select(datetime, origin = pickup, destination) %>% 
+  pivot_longer(cols = c(origin, destination), 
+               names_to = "pin",
+               values_to = "address") %>% 
+  separate(address, sep = ",", into = c("location", "drop"), remove = FALSE) %>% 
+  mutate(location = ifelse(is.na(location), address, location)) %>% 
+  select(-address, -drop)
+
+# better for string
+od$location <- tolower(od$location)
+od$location <- str_replace_all(od$location, "-", " ")
+
+# location categories, OMG my code! $#^$#@%
+od <- od %>% 
+  mutate(
+    # detect station
+    station = ifelse(str_detect(location, "stasiun"), TRUE, NA),
+    station = ifelse(str_detect(location, "station"),
+                    TRUE, station),
+    # detect bus stop
+    bus = ifelse(str_detect(location, "halte"), TRUE, NA),
+    bus = ifelse(str_detect(location, "kelapa dua sasak rt.2/rw.2"), 
+                    TRUE, bus),
+    # detect office
+    office = ifelse(str_detect(location, "tempo"), TRUE, NA),
+    office = ifelse(str_detect(location, "palmerah barat no.22"),
+                       TRUE, office),
+    office = ifelse(str_detect(location, "kementerian pekerjaan umum"),
+                       TRUE, office),
+    office = ifelse(str_detect(location, "ministry of public works"),
+                       TRUE, office),
+    office = ifelse(str_detect(location, "pattimura no.20"),
+                       TRUE, office),
+    office = ifelse(str_detect(location, "raden patah"),
+                       TRUE, office),
+    office = ifelse(str_detect(location, "al azhar"),
+                       TRUE, office),
+    # detect home
+    home = ifelse(str_detect(location, "jalan taman indah"), TRUE, NA), # h. ranto
+    home = ifelse(str_detect(location, "al makmur"), # bu lastri
+                     TRUE, home),
+    home = ifelse(str_detect(location, "alfamidi raya rtm"), # h. ranto
+                     TRUE, home),
+    home = ifelse(str_detect(location, "tugu kp areman rt 04/ rw 05 no. 38"),
+                     TRUE, home),
+    home = ifelse(str_detect(location, "sasak i no.39"), # bu mamay
+                     TRUE, home),
+    home = ifelse(str_detect(location, "srengseng raya no.45"), # h. nasir
+                     TRUE, home),
+    home = ifelse(str_detect(location, "lap. tenis"), # h. nasir
+                     TRUE, home),
+    home = ifelse(str_detect(location, "rumbut no.4"), # h. ranto
+                     TRUE, home),
+    home = ifelse(str_detect(location, "h. moat"), # bu lastri
+                     TRUE, home),
+    home = ifelse(str_detect(location, "wisma srikandi"), # bu lastri
+                     TRUE, home),
+    home = ifelse(str_detect(location, "rtm lampu merah"), # bu lastri
+                     TRUE, home),
+    home = ifelse(str_detect(location, "bu lastri"), # bu lastri
+                     TRUE, home),
+    home = ifelse(str_detect(location, "srengseng raya no.7b"), # h. nasir
+                     TRUE, home),
+    # other places
+    place = ifelse(!is.na(station), NA, ifelse(
+                     !is.na(bus), NA, ifelse(
+                       !is.na(office), NA, ifelse(
+                         !is.na(home), NA, TRUE))))
+  )
+
+# empty category
+nrow(od) - colSums(!is.na(od[,4:8])) %>% as.matrix() %>% sum()
+
+# odplot <- od %>% 
+#   select(-datetime) %>%
+#   mutate(count = 1) %>% 
+#   group_by(origin, destination, payment) %>% 
+#   summarise(freq = sum(count), .groups = "drop")
+
+# enrichment
+od <- od %>% 
+  select(-location) %>% 
+  pivot_longer(cols = c("station", "bus", "office", "home", "place"),
+               names_to = "category",
+               values_to = "value") %>% 
+  filter(value == TRUE) %>% 
+  select(-value) %>% 
+  pivot_wider(id_cols = "datetime", 
+              names_from = "pin",
+              values_from = "category")
+
+# save od data
+saveRDS(od, "output/od.rds")
+
+# add payment
+od <- gojek %>% 
+  select(datetime, payment) %>% 
+  left_join(od, by = "datetime")
+
+# data for needs
+odp <- od %>% 
+  select(-datetime) %>% 
+  mutate(destination = ifelse(destination == "home", "Go Home",
+                              ifelse(
+                                destination == "bus", "Transit",
+                                ifelse(
+                                  destination == "station", "Transit",
+                                  ifelse(
+                                    destination == "office", "Work",
+                                    "Business")))),
+         origin = ifelse(origin %in% c("station", "bus"),
+                         "stop", origin),
+         origin = str_to_title(origin), 
+         count = 1) %>% 
+  group_by(origin, destination, payment) %>% 
+  summarise(freq = sum(count), .groups = "drop")
+
+odp$origin <- odp$origin %>% 
+  factor(levels = c("Home", "Office", "Place", "Stop"))
+
+odp$destination <- odp$destination %>% 
+  factor(levels = c("Go Home", "Work", "Business", "Transit"))
+
+# plot
+ggplot(data = odp,
+       aes(axis1 = origin, axis2 = destination, y = freq)) +
+  scale_x_discrete(limits = c("Origin", "Needs"), 
+                   expand = c(0, 0.2, 0, 0.7)) +
+  geom_alluvium(aes(fill = payment)) +
+  geom_stratum() +
+  geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
+  scale_fill_manual(values = c("orange", "#00BBE0"),
+                    label = c("Cash", "GoPay")) +
+  annotate(geom = "text", x = 2.25, y = 190, 
+           label = paste("The most frequence trip", 
+                         "was from stop (a station",
+                         "or other transit node)",
+                         "to come back home.",
+                         " ",
+                         "The second was a trip",
+                         "for go home after doing",
+                         "some activities.",
+                         " ",
+                         "There was a strong relation",
+                         "between office and transit.",
+                         " ",
+                         "If I had to pay by cash,",
+                         "maybe GoPay balance was 0",
+                         "when doing activity outside.",
+                         sep = "\n"),
+           hjust = "left", vjust = "top",
+           size = 3.2, col = "gray50") +
+  labs(title = "Effort with GOJEK",
+       subtitle = "I even want to come home from home #SelaluAdaJalan #DiRumahAja",
+       caption = "Github: akherlan | Data: GOJEK", 
+       fill = "Payment") +
+  theme_minimal() +
+  theme(
+    text = element_text(family = "Sans"),
+    axis.title = element_blank(),
+    axis.text.x = element_text(face = "bold", size = 10),
+    axis.text.y = element_blank(),
+    panel.grid = element_blank(),
+    legend.position = c(0.75, 0.85),
+    legend.direction = "vertical",
+    legend.justification = "left",
+    # panel.border = element_rect(colour = "black", size = 1, fill = "transparent", linetype = 2),
+    plot.title.position = "plot",
+    plot.subtitle = element_text(colour = "gray40"),
+    plot.caption = element_text(colour = "gray60"),
+    plot.background = element_rect(fill = "white", size = 0)
+  )
+
+# save PNG
+ggsave("needs.png", path = "figs", dpi = 150, units = "px",
+       width = 2*760, height = 2*462)
+

README.md

@@ -1,4 +1,4 @@
-## GOJEK
+## Travel with Gojek
 
 **Mengolah data invoice dari Gojek**
 
@@ -14,10 +14,22 @@ Saya tidak terlalu rutin menggunakan moda transporasi ini. Namun totalnya sudah
 
 ![movement with gojek](figs/distance_map.png)
 
-Dengan GOJEK saya sudah melaju seolah-olah mulai dari ujung Barat pulau Jawa hingga ke ujung Timurnya.
+Dengan Gojek saya sudah melaju seolah-olah mulai dari ujung Barat pulau Jawa hingga ke ujung Timurnya.
 
-### Costs
+### Cost
 
 ![expenses with gojek](figs/cost.png)
 
 Biaya ini belum termasuk dengan penggunaan GoBills untuk BPJS, pulsa, dll. apalagi dengan GoFood. Belum.
+
+### Needs
+
+![effort with gojek](figs/needs.png)
+
+Perjalanan terbanyak ketika ng-Gojek adalah dari stasiun (atau halte) untuk pulang, disusul oleh perjalanan pulang selepas beraktivitas di luar selain kerja.
+
+Ada hubungan yang erat antara kantor dengan aktivitas transit.
+
+Jika saya harus menggunakan uang tunai kemungkinan karena saya kehabisan GoPay saat berada di luar rumah.
+
+Pertanyaan berikutnya adalah kenapa ada perjalanan dari rumah ke rumah? Punya rumah lebih dari satu! Hahahaha... Saya pindah-pindah kost sebetulnya.