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race_and_poverty_graphing.R
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race_and_poverty_graphing.R
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##### ORGIN POINTS: income#####
#ALL scooters
library(tidyverse)
points_geog <- read_csv("/Users/tlim/Desktop/points_with_geog_info.csv")
income_scooter_df <- points_geog %>%
filter(!is.na(poverty_Q_min) & is_trip) %>%
group_by(poverty_Q_min, day_O, hour_O) %>%
mutate(
count = n()
) %>%
ungroup() %>%
mutate(
pov_Q = as.character(poverty_Q_min)
) %>%
distinct(count, .keep_all = TRUE) %>%
arrange(day_O, hour_O)
ggplot(income_scooter_df, mapping = aes(x = hour_O, y = count, color = pov_Q)) +
geom_point(size = 3) +
geom_path(mapping = aes(group = pov_Q), size = 1) +
facet_wrap("day_O", nrow = 3, labeller = labeller(day_O = labels)) +
scale_color_discrete() +
theme(text = element_text(size=18, family = "serif"))
labels = c(`17` = "Wednesday 7/17", `18` = "Thursday 7/18", `19` = "Friday 7/19", `20` = "Saturday 7/20", `21` = "Sunday 7/21", `22` = "Monday 7/22", `23` = "Tuesday 7/23", `24` = "Wednesday 7/24")
#just relocation trips
income_scooter_df <- points_geog %>%
filter(!is.na(poverty_Q_min) & (is_relocation | is_recharge)) %>%
group_by(poverty_Q_min, day_O, hour_O) %>%
mutate(
count = n()
) %>%
ungroup() %>%
mutate(
poverty_Q = as.character(poverty_Q_min)
) %>%
distinct(count, .keep_all = TRUE) %>%
arrange(day_O, hour_O)
ggplot(income_scooter_df, mapping = aes(x = hour_O, y = count, color = poverty_Q)) +
geom_point() +
geom_path(mapping = aes(group = poverty_Q)) +
facet_wrap("day_O", nrow = 2) +
scale_color_discrete()
#just recharge trips
income_scooter_df <- points_geog %>%
filter(!is.na(poverty_Q_min) & is_recharge) %>%
group_by(poverty_Q_min, day_O, hour_O) %>%
mutate(
count = n()
) %>%
ungroup() %>%
mutate(
poverty_Q = as.character(poverty_Q_min)
) %>%
distinct(count, .keep_all = TRUE) %>%
arrange(day_O, hour_O)
ggplot(income_scooter_df, mapping = aes(x = hour_O, y = count, color = poverty_Q)) +
geom_point() +
geom_path(mapping = aes(group = poverty_Q)) +
facet_wrap("day_O", nrow = 2) +
scale_color_discrete()
##### ORGIN POINTS: race#####
#ALL scooters
income_scooter_df <- points_geog %>%
filter(!is.na(AA_Q_min) & is_trip) %>%
group_by(AA_Q_min, day_O, hour_O) %>%
mutate(
count = n()
) %>%
ungroup() %>%
mutate(
AA_Q = as.character(AA_Q_min)
) %>%
distinct(count, .keep_all = TRUE) %>%
arrange(day_O, hour_O)
ggplot(income_scooter_df, mapping = aes(x = hour_O, y = count, color = AA_Q)) +
geom_point(size = 3) +
geom_path(mapping = aes(group = AA_Q), size = 1) +
facet_wrap("day_O", nrow = 3, labeller = labeller(day_O = labels)) +
scale_color_discrete() +
theme(text = element_text(size=18, family = "serif"))
labels = c(`17` = "Wednesday 7/17", `18` = "Thursday 7/18", `19` = "Friday 7/19", `20` = "Saturday 7/20", `21` = "Sunday 7/21", `22` = "Monday 7/22", `23` = "Tuesday 7/23", `24` = "Wednesday 7/24")
#just relocation trips
income_scooter_df <- points_geog %>%
filter(!is.na(AA_Q_min) & is_relocation) %>%
group_by(AA_Q_min, day_O, hour_O) %>%
mutate(
count = n()
) %>%
ungroup() %>%
mutate(
AA_Q = as.character(AA_Q_min)
) %>%
distinct(count, .keep_all = TRUE) %>%
arrange(day_O, hour_O)
ggplot(income_scooter_df, mapping = aes(x = hour_O, y = count, color = AA_Q)) +
geom_point() +
geom_path(mapping = aes(group = AA_Q)) +
facet_wrap("day_O", nrow = 2) +
scale_color_discrete() +
theme(text = element_text(size=20))
#just recharge trips
income_scooter_df <- points_geog %>%
filter(!is.na(AA_Q_min) & is_recharge) %>%
group_by(AA_Q_min, day_O, hour_O) %>%
mutate(
count = n()
) %>%
ungroup() %>%
mutate(
AA_Q = as.character(AA_Q_min)
) %>%
distinct(count, .keep_all = TRUE) %>%
arrange(day_O, hour_O)
ggplot(income_scooter_df, mapping = aes(x = hour_O, y = count, color = AA_Q)) +
geom_point() +
geom_path(mapping = aes(group = AA_Q)) +
facet_wrap("day_O", nrow = 2) +
scale_color_discrete()
##### DEST POINTS: income#####
#ALL scooters
library(tidyverse)
points_geog <- read_csv("/Users/tlim/Desktop/DEST_points_with_geog_info.csv")
income_scooter_df <- points_geog %>%
filter(!is.na(poverty_Q_min)) %>%
group_by(poverty_Q_min, day_O, hour_O) %>%
mutate(
count = n()
) %>%
ungroup() %>%
mutate(
poverty_Q = as.character(poverty_Q_min)
) %>%
distinct(count, .keep_all = TRUE) %>%
arrange(day_O, hour_O)
ggplot(income_scooter_df, mapping = aes(x = hour_O, y = count, color = poverty_Q)) +
geom_point() +
geom_path(mapping = aes(group = poverty_Q)) +
facet_wrap("day_O", nrow = 2) +
scale_color_discrete()
#just relocation trips
income_scooter_df <- points_geog %>%
filter(!is.na(poverty_Q_min) & (is_relocation | is_recharge)) %>%
group_by(poverty_Q_min, day_O, hour_O) %>%
mutate(
count = n()
) %>%
ungroup() %>%
mutate(
poverty_Q = as.character(poverty_Q_min)
) %>%
distinct(count, .keep_all = TRUE) %>%
arrange(day_O, hour_O)
ggplot(income_scooter_df, mapping = aes(x = hour_O, y = count, color = poverty_Q)) +
geom_point() +
geom_path(mapping = aes(group = poverty_Q)) +
facet_wrap("day_O", nrow = 2) +
scale_color_discrete()
#just recharge trips
income_scooter_df <- points_geog %>%
filter(!is.na(poverty_Q_min) & is_recharge) %>%
group_by(poverty_Q_min, day_O, hour_O) %>%
mutate(
count = n()
) %>%
ungroup() %>%
mutate(
poverty_Q = as.character(poverty_Q_min)
) %>%
distinct(count, .keep_all = TRUE) %>%
arrange(day_O, hour_O)
ggplot(income_scooter_df, mapping = aes(x = hour_O, y = count, color = poverty_Q)) +
geom_point() +
geom_path(mapping = aes(group = poverty_Q)) +
facet_wrap("day_O", nrow = 2) +
scale_color_discrete()
##### DEST POINTS: race#####
#ALL scooters
income_scooter_df <- points_geog %>%
filter(!is.na(AA_Q_min) & is_trip) %>%
group_by(AA_Q_min, day_O, hour_O) %>%
mutate(
count = n()
) %>%
ungroup() %>%
mutate(
AA_Q = as.character(AA_Q_min)
) %>%
distinct(count, .keep_all = TRUE) %>%
arrange(day_O, hour_O)
ggplot(income_scooter_df, mapping = aes(x = hour_O, y = count, color = AA_Q)) +
geom_point() +
geom_path(mapping = aes(group = AA_Q)) +
facet_wrap("day_O", nrow = 2) +
scale_color_discrete()
#just relocation trips
income_scooter_df <- points_geog %>%
filter(!is.na(AA_Q_min) & is_relocation) %>%
group_by(AA_Q_min, day_O, hour_O) %>%
mutate(
count = n()
) %>%
ungroup() %>%
mutate(
AA_Q = as.character(AA_Q_min)
) %>%
distinct(count, .keep_all = TRUE) %>%
arrange(day_O, hour_O)
ggplot(income_scooter_df, mapping = aes(x = hour_O, y = count, color = AA_Q)) +
geom_point() +
geom_path(mapping = aes(group = AA_Q)) +
facet_wrap("day_O", nrow = 2) +
scale_color_discrete()
# reloc + recharge trips
income_scooter_df <- points_geog %>%
filter(!is.na(AA_Q_min) & is_recharge) %>%
group_by(AA_Q_min, day_O, hour_O) %>%
mutate(
count = n()
) %>%
ungroup() %>%
mutate(
AA_Q = as.character(AA_Q_min)
) %>%
distinct(count, .keep_all = TRUE) %>%
arrange(day_O, hour_O)
ggplot(income_scooter_df, mapping = aes(x = hour_O, y = count, color = AA_Q)) +
geom_point() +
geom_path(mapping = aes(group = AA_Q)) +
facet_wrap("day_O", nrow = 2) +
scale_color_discrete()
#Presentation plots
income_scooter_df <- points_geog %>%
filter(!is.na(poverty_Q_min) & is_trip) %>%
group_by(poverty_Q_min, day_O, hour_O) %>%
mutate(
count = n()
) %>%
ungroup() %>%
mutate(
pov_Q = as.character(poverty_Q_min)
) %>%
distinct(count, .keep_all = TRUE) %>%
arrange(day_O, hour_O)
ggplot(income_scooter_df, mapping = aes(x = hour_O, y = count, color = pov_Q)) +
geom_point(size = 15) +
geom_path(mapping = aes(group = pov_Q), size = 6) +
facet_wrap("day_O", nrow = 2, labeller = labeller(day_O = labels)) +
scale_color_discrete() +
theme(text = element_text(size=80, family = "sans", face = "bold"))
income_scooter_df <- points_geog %>%
filter(!is.na(AA_Q_min) & is_trip) %>%
group_by(AA_Q_min, day_O, hour_O) %>%
mutate(
count = n()
) %>%
ungroup() %>%
mutate(
AA_Q = as.character(AA_Q_min)
) %>%
distinct(count, .keep_all = TRUE) %>%
arrange(day_O, hour_O)
ggplot(income_scooter_df, mapping = aes(x = hour_O, y = count, color = AA_Q)) +
geom_point(size = 15) +
geom_path(mapping = aes(group = AA_Q), size = 7) +
facet_wrap("day_O", nrow = 2, labeller = labeller(day_O = labels)) +
scale_color_discrete() +
theme(text = element_text(size=80, family = "sans", face = "bold"))