R/my_history.R
In EvaMaeRey/ggmemory: What the Package Does (One Line, Title Case)
library(tidyverse)
read_csv(
"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-05/nurses.csv"
)
nurses <-
read_csv(
"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-05/nurses.csv"
)
nurses
nurses_raw <-
read_csv(
"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-05/nurses.csv"
)
nurses_raw
nurses_raw %>%
janitor::clean_names()
nurses_raw %>%
janitor::clean_names() ->
nurses
nurses
nurses %>%
ggplot()
nurses %>%
ggplot() +
aes(x = year)
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn)
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(size = 4)
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(size = 4,
alpha = .4)
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(size = 4,
alpha = .4) +
scale_y_log10()
option(scipen = 10)
options(scipen = 10)
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(size = 4,
alpha = .4) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(size = 4,
alpha = .4,
shape = 21) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(
size = 4,
alpha = .4,
shape = 21,
fill = "magenta"
) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(
size = 4,
alpha = .4,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_line() +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
aes(group = state) +
geom_line() +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
aes(group = state) +
geom_line(color = "magenta") +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
aes(group = state) +
geom_line(color = "magenta") +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10() +
facet_wrap(facets = state)
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
aes(group = state) +
geom_line(color = "magenta") +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10() +
facet_wrap(facets = vars(state))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
aes(group = state) +
geom_line(color = "magenta") +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10() +
facet_wrap(facets = vars(state))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
ggplot() +
aes(x = year) +
aes(y = hourly_wage_avg) +
aes(group = state) +
geom_line(color = "magenta") +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10() +
facet_wrap(facets = vars(state))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
ggplot() +
aes(x = year) +
aes(y = hourly_wage_avg) +
aes(group = state) +
geom_line(color = "magenta") +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
facet_wrap(facets = vars(state))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = 1998, y = 2020) +
geom_point()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1, intercept = 0)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum2")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum2") +
geom_segment(aes(xend = `2020`))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum2") +
geom_segment(aes(xend = `2020`, yend = state))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum2") +
geom_segment(aes(xend = `2020`, yend = state,
color = "plum2"))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum2") +
geom_segment(aes(xend = `2020`, yend = state), color = "plum2")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum2") +
geom_segment(aes(xend = `2020`, yend = state), color = "plum2") +
geom_point(aes(x = `2020`))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum2") +
geom_segment(aes(xend = `2020`, yend = state), color = "plum2") +
geom_point(aes(x = `2020`),
color = "plum4")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum3") +
geom_segment(aes(xend = `2020`, yend = state), color = "plum3") +
geom_point(aes(x = `2020`),
color = "plum4")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
mutate(state = fct)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum3") +
geom_segment(aes(xend = `2020`, yend = state), color = "plum3") +
geom_point(aes(x = `2020`),
color = "plum4")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
arrange(`1998`) %>%
mutate(state = fct_inorder(state)) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum3") +
geom_segment(aes(xend = `2020`, yend = state), color = "plum3") +
geom_point(aes(x = `2020`),
color = "plum4")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(xend = `1998`, yend = 0)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "darkgrey")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
ggxmean::geom_lm()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 60)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
ggxmean::geom_lm()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
ggxmean::geom_lm()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US") +
ggpmisc::stat_dens2d_filter(geom = "text_repel",
keep.fraction = 0.25)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US") +
ggrepel::geom_text_repel()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US") +
ggrepel::geom_text_repel(aes(label = state))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US") +
ggrepel::geom_text_repel(aes(label = state))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US") +
ggpmisc::stat_dens2d_filter(geom = "text_repel", keep.fraction = 0.5)
install.packages("ggpmisc")
install.packages("ggpmisc")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US") +
ggpmisc::stat_dens2d_filter(geom = "text_repel", keep.fraction = 0.5)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US") +
ggrepel::geom_text_repel(aes(label = state),
data = . %>% sample_frac(.2))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state),
data = . %>% sample_frac(.2))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12)),
data = . %>% sample_frac(.2))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2))
state
state.abb
state.name
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format())
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_continuous(labels = scales::dollar_format())
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_continuous(labels = scales::dollar_format()) +
theme_minimal()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_continuous(labels = scales::dollar_format()) +
theme_minimal() +
ggstamp::stamp_arrow(x = 35,
y = 35,
xend = 35,
yend = 50)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_continuous(labels = scales::dollar_format()) +
theme_minimal() +
ggstamp::stamp_arrow(
x = 35,
y = 35,
xend = 35,
yend = 60,
size = 1
)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_continuous(labels = scales::dollar_format()) +
theme_minimal() +
ggstamp::stamp_arrow(
x = 35,
y = 35,
xend = 35,
yend = 60,
size = .5
) +
ggstamp::stamp_text(
x = 38,
y = 50,
size = 4,
label = "Gain median wage",
angle = 90
)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_continuous(labels = scales::dollar_format()) +
theme_minimal() +
ggstamp::stamp_arrow(
x = 35,
y = 35,
xend = 35,
yend = 60,
size = .5
) +
ggstamp::stamp_text(
x = 38,
y = 50,
char_width = 12,
size = 4,
label = "Gain median wage",
angle = -90
)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020",
subtitle = "50 US States and District of Columbia") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_continuous(labels = scales::dollar_format()) +
theme_minimal() +
ggstamp::stamp_arrow(
x = 35,
y = 35,
xend = 35,
yend = 60,
size = .75
) +
ggstamp::stamp_text(
x = 38,
y = 50,
char_width = 12,
size = 4,
label = "Gain median wage",
angle = -90
)
EvaMaeRey/ggmemory documentation built on Dec. 17, 2021, 7:25 p.m.
R Package Documentation
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library(tidyverse)
read_csv(
"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-05/nurses.csv"
)
nurses <-
read_csv(
"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-05/nurses.csv"
)
nurses
nurses_raw <-
read_csv(
"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-05/nurses.csv"
)
nurses_raw
nurses_raw %>%
janitor::clean_names()
nurses_raw %>%
janitor::clean_names() ->
nurses
nurses
nurses %>%
ggplot()
nurses %>%
ggplot() +
aes(x = year)
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn)
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(size = 4)
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(size = 4,
alpha = .4)
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(size = 4,
alpha = .4) +
scale_y_log10()
option(scipen = 10)
options(scipen = 10)
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(size = 4,
alpha = .4) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(size = 4,
alpha = .4,
shape = 21) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(
size = 4,
alpha = .4,
shape = 21,
fill = "magenta"
) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(
size = 4,
alpha = .4,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
geom_line() +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
aes(group = state) +
geom_line() +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
aes(group = state) +
geom_line(color = "magenta") +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10()
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
aes(group = state) +
geom_line(color = "magenta") +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10() +
facet_wrap(facets = state)
nurses %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
aes(group = state) +
geom_line(color = "magenta") +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10() +
facet_wrap(facets = vars(state))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
ggplot() +
aes(x = year) +
aes(y = total_employed_rn) +
aes(group = state) +
geom_line(color = "magenta") +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10() +
facet_wrap(facets = vars(state))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
ggplot() +
aes(x = year) +
aes(y = hourly_wage_avg) +
aes(group = state) +
geom_line(color = "magenta") +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
scale_y_log10() +
facet_wrap(facets = vars(state))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
ggplot() +
aes(x = year) +
aes(y = hourly_wage_avg) +
aes(group = state) +
geom_line(color = "magenta") +
geom_point(
size = 4,
alpha = .7,
shape = 21,
fill = "magenta",
color = "white"
) +
facet_wrap(facets = vars(state))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = 1998, y = 2020) +
geom_point()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1, intercept = 0)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum2")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum2") +
geom_segment(aes(xend = `2020`))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum2") +
geom_segment(aes(xend = `2020`, yend = state))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum2") +
geom_segment(aes(xend = `2020`, yend = state,
color = "plum2"))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum2") +
geom_segment(aes(xend = `2020`, yend = state), color = "plum2")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum2") +
geom_segment(aes(xend = `2020`, yend = state), color = "plum2") +
geom_point(aes(x = `2020`))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum2") +
geom_segment(aes(xend = `2020`, yend = state), color = "plum2") +
geom_point(aes(x = `2020`),
color = "plum4")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum3") +
geom_segment(aes(xend = `2020`, yend = state), color = "plum3") +
geom_point(aes(x = `2020`),
color = "plum4")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
mutate(state = fct)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum3") +
geom_segment(aes(xend = `2020`, yend = state), color = "plum3") +
geom_point(aes(x = `2020`),
color = "plum4")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
arrange(`1998`) %>%
mutate(state = fct_inorder(state)) %>%
ggplot() +
aes(x = `1998`, y = state) +
geom_point(color = "plum3") +
geom_segment(aes(xend = `2020`, yend = state), color = "plum3") +
geom_point(aes(x = `2020`),
color = "plum4")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(xend = `1998`, yend = 0)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "darkgrey")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
ggxmean::geom_lm()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 60)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
ggxmean::geom_lm()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_point() +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
ggxmean::geom_lm()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 30)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US") +
ggpmisc::stat_dens2d_filter(geom = "text_repel",
keep.fraction = 0.25)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US") +
ggrepel::geom_text_repel()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US") +
ggrepel::geom_text_repel(aes(label = state))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US") +
ggrepel::geom_text_repel(aes(label = state))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US") +
ggpmisc::stat_dens2d_filter(geom = "text_repel", keep.fraction = 0.5)
install.packages("ggpmisc")
install.packages("ggpmisc")
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US") +
ggpmisc::stat_dens2d_filter(geom = "text_repel", keep.fraction = 0.5)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 1998, $US") +
ggrepel::geom_text_repel(aes(label = state),
data = . %>% sample_frac(.2))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state),
data = . %>% sample_frac(.2))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12)),
data = . %>% sample_frac(.2))
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2))
state
state.abb
state.name
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format())
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_continuous(labels = scales::dollar_format())
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_continuous(labels = scales::dollar_format()) +
theme_minimal()
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_continuous(labels = scales::dollar_format()) +
theme_minimal() +
ggstamp::stamp_arrow(x = 35,
y = 35,
xend = 35,
yend = 50)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_continuous(labels = scales::dollar_format()) +
theme_minimal() +
ggstamp::stamp_arrow(
x = 35,
y = 35,
xend = 35,
yend = 60,
size = 1
)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_continuous(labels = scales::dollar_format()) +
theme_minimal() +
ggstamp::stamp_arrow(
x = 35,
y = 35,
xend = 35,
yend = 60,
size = .5
) +
ggstamp::stamp_text(
x = 38,
y = 50,
size = 4,
label = "Gain median wage",
angle = 90
)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_continuous(labels = scales::dollar_format()) +
theme_minimal() +
ggstamp::stamp_arrow(
x = 35,
y = 35,
xend = 35,
yend = 60,
size = .5
) +
ggstamp::stamp_text(
x = 38,
y = 50,
char_width = 12,
size = 4,
label = "Gain median wage",
angle = -90
)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median)
nurses %>%
filter(state != "Guam" &
state != "Virgin Islands" &
state != "Puerto Rico") %>%
filter(year %in% range(year)) %>%
select(state, year, hourly_wage_median) %>%
pivot_wider(names_from = year, values_from = hourly_wage_median) %>%
ggplot() +
aes(x = `1998`, y = `2020`) +
geom_abline(slope = 1,
intercept = 0,
linetype = "dashed") +
coord_equal(ylim = c(0, 60),
xlim = c(0, 40)) +
labs(title = "Median hourly wage, 1998 and 2020",
subtitle = "50 US States and District of Columbia") +
geom_segment(aes(xend = `1998`, yend = 0),
color = "grey") +
geom_segment(aes(xend = `1998`, yend = `1998`),
color = "grey35") +
geom_point() +
ggxmean::geom_lm() +
labs(x = "Hourly wage in 1998, $US") +
labs(y = "Hourly wage in 2020, $US") +
ggrepel::geom_text_repel(aes(label = state %>%
str_wrap(12),
lineheight = .8),
data = . %>% sample_frac(.2)) +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_continuous(labels = scales::dollar_format()) +
theme_minimal() +
ggstamp::stamp_arrow(
x = 35,
y = 35,
xend = 35,
yend = 60,
size = .75
) +
ggstamp::stamp_text(
x = 38,
y = 50,
char_width = 12,
size = 4,
label = "Gain median wage",
angle = -90
)
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