library(ggeconodist)
ggplot(mammogram_costs, aes(x = city)) +
geom_econodist(
aes(ymin = tenth, median = median, ymax = ninetieth), stat = "identity"
) +
scale_y_continuous(expand = c(0,0), position = "right", limits = range(0, 800)) +
coord_flip() +
labs(
x = NULL, y = NULL,
title = "Mammoscams",
subtitle = "United States, prices for a mammogram*\nBy metro area, 2016, $",
caption = "*For three large insurance companies\nSource: Health Care Cost Institute"
) +
theme_econodist() -> gg
grid.newpage()
left_align(gg, c("subtitle", "title", "caption")) %>%
add_econodist_legend(econodist_legend_grob(), below = "subtitle") %>%
grid.draw()
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## Warning: package 'hrbrthemes' was built under R version 3.5.3
## Warning: package 'dplyr' was built under R version 3.5.3
library(waffle)
data.frame(
parts = factor(rep(month.abb[1:3], 3), levels=month.abb[1:3]),
vals = c(10, 20, 30, 6, 14, 40, 30, 20, 10),
col = rep(c("blue", "black", "red"), 3),
fct = c(rep("Thing 1", 3),
rep("Thing 2", 3),
rep("Thing 3", 3))
) -> xdf
xdf %>%
count(parts, wt = vals) %>%
ggplot(aes(fill = parts, values = n)) +
geom_waffle(n_rows = 20, size = 0.33, colour = "white", flip = TRUE) +
scale_fill_manual(
name = NULL,
values = c("#a40000", "#c68958", "#ae6056"),
labels = c("Fruit", "Sammich", "Pizza")
) +
coord_equal() +
theme_ipsum_rc(grid="") +
theme_enhance_waffle()
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## # A tibble: 18 x 3
## year status n
## <dbl> <chr> <int>
## 1 2010 hurricane 138
## 2 2010 tropical depression 71
## 3 2010 tropical storm 193
## 4 2011 hurricane 79
## 5 2011 tropical depression 41
## 6 2011 tropical storm 203
## 7 2012 hurricane 122
## 8 2012 tropical depression 56
## 9 2012 tropical storm 276
## 10 2013 hurricane 13
## 11 2013 tropical depression 47
## 12 2013 tropical storm 142
## 13 2014 hurricane 75
## 14 2014 tropical depression 24
## 15 2014 tropical storm 40
## 16 2015 hurricane 50
## 17 2015 tropical depression 58
## 18 2015 tropical storm 112
ggplot(storms_df, aes(fill = status, values = n)) +
geom_waffle(color = "white", size = .25, n_rows = 10, flip = TRUE) +
facet_wrap(~year, nrow = 1, strip.position = "bottom") +
scale_x_discrete() +
scale_y_continuous(labels = function(x) x * 10, # make this multiplyer the same as n_rows
expand = c(0,0)) +
ggthemes::scale_fill_tableau(name=NULL) +
coord_equal() +
labs(
title = "Faceted Waffle Bar Chart",
subtitle = "{dplyr} storms data",
x = "Year",
y = "Count"
) +
theme_minimal(base_family = "Roboto Condensed") +
theme(panel.grid = element_blank(), axis.ticks.y = element_line()) +
guides(fill = guide_legend(reverse = TRUE))
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## names vals
## 1 A 80
## 2 B 30
## 3 C 20
## 4 D 10
pain.adult.1997 <- c(`YOY (406)` = 406, `Adult (24)` = 24)
waffle(
pain.adult.1997 / 2, rows = 7, size = 0.5,
colors = c("#c7d4b6", "#a3aabd"),
title = "Paine Run Brook Trout Abundance (1997)",
xlab = "1 square = 2 fish", pad = 3
) -> A
pine.adult.1997 <- c(`YOY (221)` = 221, `Adult (143)` = 143)
waffle(
pine.adult.1997 / 2, rows = 7, size = 0.5,
colors = c("#c7d4b6", "#a3aabd"),
title = "Piney River Brook Trout Abundance (1997)",
xlab = "1 square = 2 fish", pad = 8
) -> B
stan.adult.1997 <- c(`YOY (270)` = 270, `Adult (197)` = 197)
waffle(
stan.adult.1997 / 2, rows = 7, size = 0.5,
colors = c("#c7d4b6", "#a3aabd"),
title = "Staunton River Trout Abundance (1997)",
xlab = "1 square = 2 fish"
) -> C
iron(A, B, C)
## -- Attaching packages ----------------------------------------------------------------- tidyverse 1.2.1 --
## v tibble 2.1.3 v purrr 0.2.5
## v tidyr 0.8.2 v stringr 1.3.1
## v readr 1.1.1 v forcats 0.3.0
## Warning: package 'tibble' was built under R version 3.5.3
## -- Conflicts -------------------------------------------------------------------- tidyverse_conflicts() --
## x tidyr::extract() masks magrittr::extract()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
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library(ggchicklet)
data("debates2019")
###debates2019
debates2019 %>%
mutate(speaker = fct_reorder(speaker, elapsed, sum, .desc=FALSE)) %>%
mutate(topic = fct_other(
topic,
c("Immigration", "Economy", "Climate Change", "Gun Control", "Healthcare", "Foreign Policy"))
) %>%
ggplot(aes(speaker, elapsed, group = timestamp, fill = topic)) +
geom_chicklet(width = 0.75) +
scale_y_continuous(
expand = c(0, 0.0625),
position = "right",
breaks = seq(0, 14, 2),
labels = c(0, sprintf("%d min.", seq(2, 14, 2)))
) +
scale_fill_manual(
name = NULL,
values = c(
"Immigration" = "#ae4544",
"Economy" = "#d8cb98",
"Climate Change" = "#a4ad6f",
"Gun Control" = "#cc7c3a",
"Healthcare" = "#436f82",
"Foreign Policy" = "#7c5981",
"Other" = "#cccccc"
),
breaks = setdiff(unique(debates2019$topic), "Other")
) +
guides(
fill = guide_legend(nrow = 1)
) +
coord_flip() +
labs(
x = NULL, y = NULL, fill = NULL,
title = "How Long Each Candidate Spoke",
subtitle = "Nights 1 & 2 of the June 2019 Democratic Debates",
caption = "Each bar segment represents the length of a candidate’s response to a question.\n\nOriginals <https://www.nytimes.com/interactive/2019/admin/100000006581096.embedded.html?>\n<https://www.nytimes.com/interactive/2019/admin/100000006584572.embedded.html?>\nby @nytimes Weiyi Cai, Jason Kao, Jasmine C. Lee, Alicia Parlapiano and Jugal K. Patel\n\n#rstats reproduction by @hrbrmstr"
) +
theme_ipsum_rc(grid="X") +
theme(axis.text.x = element_text(color = "gray60", size = 10)) +
theme(legend.position = "top")
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## # A tibble: 28 x 3
## # Groups: Year [4]
## Year DOW n
## <int> <fct> <int>
## 1 2015 Friday 181
## 2 2015 Monday 135
## 3 2015 Saturday 213
## 4 2015 Sunday 256
## 5 2015 Thursday 143
## 6 2015 Tuesday 140
## 7 2015 Wednesday 142
## 8 2016 Friday 257
## 9 2016 Monday 221
## 10 2016 Saturday 322
## # ... with 18 more rows
library(ggplot2)
library(waffle)
ggplot(dat2, aes(fill = DOW, values = n)) +
geom_waffle(color = "white", size = 0.5, n_rows = 20, flip = TRUE) +
facet_wrap(~Year, nrow = 1, strip.position = "bottom") +
scale_x_discrete() +
ggthemes::scale_fill_tableau(name=NULL) +
coord_equal() +
labs(
title = "Faceted Waffle Bar Chart",
x = "Year",
y = "Count"
) +
theme_minimal(base_family = "Roboto Condensed") +
theme(panel.grid = element_blank(), axis.ticks.y = element_line()) +
guides(fill = guide_legend(reverse = TRUE))
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