How do I duplicate my x-axis (becomes y in type='bar'?) so I have them both at the top and bottom?
Example:
library(dplyr)
library(ggplot2)
library(highcharter)
mpgg <- mpg %>%
filter(class %in% c("suv", "compact", "midsize")) %>%
add_row(manufacturer="loner",class="newClass")%>%
group_by(class, manufacturer) %>%
summarize(count = n())
categories_grouped <- mpgg %>%
group_by(name = class) %>%
do(categories = .$manufacturer) %>%
list_parse()
highchart() %>%
hc_xAxis(categories = categories_grouped) %>%
hc_add_series(data = mpgg, type = "bar", hcaes(y = count, color = manufacturer),
showInLegend = FALSE)
Related
I am creating a grouped bar chart like so:
library(tidyverse)
library(echarts4r)
data("starwars")
starwars %>%
group_by(sex, eye_color) %>%
summarise(height = mean(height, na.rm=TRUE)) %>%
group_by(sex) %>%
e_charts(x = eye_color, timeline = TRUE) %>%
e_bar(height, legend = FALSE)
How do I set the range of the y axis (height) to be the same across groups (sex)?
You could set maximum value for the y axis using e_y_axis(max = XXX), e.g. in the code below I set the max value based on the maximum of height.
library(tidyverse)
library(echarts4r)
data("starwars")
ymax <- max()
dat <- starwars %>%
group_by(sex, eye_color) %>%
summarise(height = mean(height, na.rm=TRUE), .groups = "drop")
ymax <- 50 * ceiling(max(dat$height, na.rm = TRUE) / 50)
dat %>%
group_by(sex) %>%
e_charts(x = eye_color, timeline = TRUE) %>%
e_bar(height, legend = FALSE) %>%
e_y_axis(max = ymax)
I am trying to reorder the geom_col plot by one of the factors pct_female_vacc used below in plot of the variable pct_vacc_GenderType.
df
library(tidyverse)
library(lubridate)
library(scales)
library(gganimate)
file_url1 <- "https://raw.githubusercontent.com/johnsnow09/covid19-df_stack-code/main/cowin_vaccine_data_statewise.csv"
df_vaccination <- read.csv(url(file_url1))
df_vaccination <- df_vaccination %>%
mutate(Updated.On = as.Date(Updated.On))
plot
df_vaccination %>%
filter(State != "India",
Updated.On == max(Updated.On)) %>%
# arrange(desc(Updated.On)) %>%
mutate(pct_female_vacc = Female.Individuals.Vaccinated./Total.Individuals.Vaccinated,
pct_male_vacc = Male.Individuals.Vaccinated./Total.Individuals.Vaccinated,
State = as.factor(State)
) %>%
pivot_longer(cols = c(pct_female_vacc:pct_male_vacc),
names_to = "pct_vacc_GenderType",
values_to = "pct_vacc") %>%
mutate(pct_vacc_GenderType = as.factor(pct_vacc_GenderType)) %>%
na.omit() %>%
ggplot(aes(x = pct_vacc, y = State ,
fill = pct_vacc_GenderType)) +
geom_col()
I am looking to get above plot to be reordered by red color i.e pct_female_vacc factor.
Unable to use reorder_within as I have not used facet_wrap here. Also tried fct_reorder but may be I am not doing it right or even that doesn't work in this case.
What you want to do is simple with forcats::fct_reorder. The only thing you have to be cautious about is that you need to set the factor before pivot_longer. Here you go:
df_vaccination %>%
filter(State != "India",
Updated.On == max(Updated.On) - 1) %>% # the newest date contains only NAs, so I use the second oldest
# arrange(desc(Updated.On)) %>%
mutate(pct_female_vacc = Female.Individuals.Vaccinated./Total.Individuals.Vaccinated,
pct_male_vacc = Male.Individuals.Vaccinated./Total.Individuals.Vaccinated,
State = as.factor(State)
) %>%
mutate(State = forcats::fct_reorder(State, pct_female_vacc)) %>% # since you pivot longer in the next step you have to order your factors here
pivot_longer(cols = c(pct_female_vacc:pct_male_vacc),
names_to = "pct_vacc_GenderType",
values_to = "pct_vacc") %>%
mutate(pct_vacc_GenderType = as.factor(pct_vacc_GenderType)) %>%
filter(!is.na(pct_vacc)) %>% # use this instead of na.omit() to remove NAs
ggplot(aes(x = pct_vacc, y = State ,
fill = pct_vacc_GenderType)) +
geom_col() +
theme(legend.position = "bottom") # I moved the legend to the bottom so it looks better on for stackoverflow
Created on 2021-05-16 by the reprex package (v2.0.0)
arrange the data by pct_female_vacc and change the State to factor based on appearance.
library(tidyverse)
df_vaccination %>%
filter(State != "India",
Updated.On == max(Updated.On)) %>%
mutate(pct_female_vacc = `Female.Individuals.Vaccinated.`/Total.Individuals.Vaccinated,
pct_male_vacc = Male.Individuals.Vaccinated./Total.Individuals.Vaccinated) %>%
arrange(pct_female_vacc) %>%
mutate(State = factor(State, unique(State))) %>%
pivot_longer(cols = c(pct_female_vacc:pct_male_vacc),
names_to = "pct_vacc_GenderType",
values_to = "pct_vacc") %>%
na.omit() %>%
ggplot(aes(x = pct_vacc, y = State ,
fill = pct_vacc_GenderType)) +
geom_col()
plot increase_rate contains abs(increase_rate) > 0.05.
but under the code, discard the data between -0.05 to 0.05.
I also plot data including from -0.05 to 0.05 range.
library(tidyverse)
data(population, package="tidyr")
population %>%
arrange(country, year) %>%
group_by(country) %>%
mutate(population_nextY = lead(population)) %>%
mutate(increase_rate = (population_nextY - population)/population_nextY) %>%
filter(abs(increase_rate) > 0.05) %>%
ungroup %>%
ggplot()+
geom_line(aes(x = year, y = increase_rate, color = country))
I want to get final plot like this.
d <-
population %>%
arrange(country, year) %>%
group_by(country) %>%
mutate(population_nextY = lead(population)) %>%
mutate(increase_rate = (population_nextY - population)/population_nextY) %>%
ungroup
select_country <-
d %>% filter(!between(increase_rate, -0.05, 0.05)) %>%
select(country) %>% distinct %>% unlist
d %>%
filter(country %in% select_country) %>%
ggplot()+
geom_line(aes(x = year, y = increase_rate, color = country))
use between:
filter(!between(increase_rate, -0.05, 0.05))
add column using mutate() function
population %>%
arrange(country, year) %>%
group_by(country) %>%
mutate(population_nextY = lead(population)) %>%
mutate(increase_rate = (population_nextY - population)/population_nextY) %>%
mutate(judge = max(abs(increase_rate), na.rm=T)) %>%
filter(judge > 0.05) %>%
ungroup %>%
ggplot() +
geom_line(aes(x = year, y = increase_rate, color = country))
This works fine:
mtcars %>% ggvis(x = ~wt) %>% layer_lines(y = ~mpg)
But this:
mtcars %>% ggvis(x = ~wt) %>% layer_smooths(y = ~mpg)
gives an error: "Error: Can't find prop y.update."
Maybe this is what you are looking for:
mtcars %>% ggvis(x = ~wt, y = ~mpg) %>% layer_smooths()
require(ggvis)
require(dplyr)
map_data = ggplot2::map_data("world")
map_data %>% select(long, lat, group) %>%
group_by(group) %>%
ggvis(x = ~long, y = ~lat) %>% layer_paths(fill:="#666666") %>%
hide_axis("x") %>% hide_axis("y")
That produces a nice map, but I'd like control over the "ocean" color (e.g. "black"). How to do that?
More generally, the ocean color is the plot default background.
Adding a layer_rects seems to have done it:
require(ggvis)
require(dplyr)
map_data = ggplot2::map_data("world")
minx = min(map_data$long -1)
maxx = max(map_data$long +1)
miny = min(map_data$lat - 1)
maxy = max(map_data$lat + 1)
map_data %>% select(long, lat, group) %>%
group_by(group) %>%
ggvis(x = ~long, y = ~lat) %>%
layer_rects(x=minx, x2=maxx, y=maxy, y2=miny, fill:="#000000") %>%
layer_paths(fill:="#666666") %>%
hide_axis("x") %>% hide_axis("y")