Although my query shows me values in descending order, ggplot then displays them alphabetically instead of ascending order.
Known solutions to this problem haven't seem to work. They suggest using Reorder or factor for values, which didn't work in this case
This is my code:
boxoffice %>%
group_by(studio) %>%
summarise(movies_made = n()) %>%
arrange(desc(movies_made)) %>%
top_n(10) %>%
arrange(desc(movies_made)) %>%
ggplot(aes(x = studio, y = movies_made, fill = studio, label = as.character(movies_made))) +
geom_bar(stat = 'identity') +
geom_label(label.size = 1, size = 5, color = "white") +
theme(legend.position = "none") +
ylab("Movies Made") +
xlab("Studio")
for those wanting a more complete example, here's where I got:
library(dplyr)
library(ggplot2)
# get some dummy data
boxoffice = boxoffice::boxoffice(dates=as.Date("2017-1-1"))
df <- (
boxoffice %>%
group_by(distributor) %>%
summarise(movies_made = n()) %>%
mutate(studio=reorder(distributor, -movies_made)) %>%
top_n(10))
ggplot(df, aes(x=distributor, y=movies_made)) + geom_col()
You'll need to convert boxoffice$studio to an ordered factor. ggplot will then respect the order of rows in the data set, rather than alphabetizing. Your dplyr chain will look like this:
boxoffice %>%
group_by(studio) %>%
summarise(movies_made = n()) %>%
arrange(desc(movies_made)) %>%
ungroup() %>% # ungroup
mutate(studio = factor(studio, studio, ordered = T)) %>% # convert variable
top_n(10) %>%
arrange(desc(movies_made)) %>%
ggplot(aes(x = studio, y... (rest of plotting code)
Related
I'm am looking at an R Tidy Tuesday dataset (European Energy) . I have wrangled the Imports and Exports as proportions and am looking to arrange the ggplot with an ascend on the Imports values. Just looking to make it look tidy, but can't seem to control the order to see each subsequent country with the next biggest import value.
I have left a couple of attempts in the code but commented out. Thnx in advance.
library(tidyverse)
country_totals <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-08-04/country_totals.csv')
country_totals %>%
filter(!is.na(country_name)) %>%
filter(type %in% c("Imports","Exports")) %>%
group_by(country_name) %>%
mutate(country_type_ttl = sum(`2018`)) %>%
mutate(country_type_pct = `2018`/country_type_ttl) %>%
ungroup() %>%
mutate(type_hold = type) %>%
pivot_wider(names_from = type_hold, values_from = `2018`) %>%
# ggplot(aes(country_name, country_type_pct, fill = type)) +
# ggplot(aes(reorder(country_name, Imports), country_type_pct, fill = type)) +
ggplot(aes(fct_reorder(country_name, Imports), country_type_pct, fill = type)) +
geom_bar(stat = "identity") +
coord_flip()
This could be achieved by adding a column with the value by which you want to reorder, i.e. the percentage share of imports in 2018 using e.g. imports_2018 = country_type_pct[type == "Imports"]. Then reorder the counters according to this column:
`
library(tidyverse)
country_totals <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-08-04/country_totals.csv')
country_totals %>%
filter(!is.na(country_name)) %>%
filter(type %in% c("Imports","Exports")) %>%
group_by(country_name) %>%
mutate(country_type_ttl = sum(`2018`)) %>%
mutate(country_type_pct = `2018`/country_type_ttl,
imports_2018 = country_type_pct[type == "Imports"]) %>%
ungroup() %>%
mutate(type_hold = type) %>%
ggplot(aes(fct_reorder(country_name, imports_2018), country_type_pct, fill = type)) +
geom_bar(stat = "identity") +
coord_flip()
#> Warning: Removed 2 rows containing missing values (position_stack).
I am trying to fix the limits of a bar chart so the horizontal bar doesn't go over the plot area. I could set the limit manually using limits=c(0,3000000)but I guess there is a way to make it automatically scalable. The code
corona.conf <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",header = TRUE,check.names=FALSE)
corona.conf %>% .[,c(-1,-3,-4)] %>% melt(.,variable.name="day") %>%
group_by(`Country/Region`,day) %>% summarize(value=sum(value)) %>%
mutate(day=as.Date(day,format='%m/%d/%y')) %>% mutate(count=value-lag(value)) %>%
replace(is.na(.),0) %>% group_by(`Country/Region`) %>% summarize(count=sum(count)) %>%
top_n(20) %>% arrange(desc(count)) %>% ggplot(.,aes(x=reorder(`Country/Region`,count),y=count,fill=count)) +
geom_bar(stat = "identity") + coord_flip() + geom_text(aes(label=format(count,big.mark = ",")),hjust=-0.1,size=4) +
scale_y_continuous(expand = c(0,0))
I thought something like:
scale_y_continuous(expand = c(0,0),limits=c(0,max(count))
Appreciate any suggestions on the fix.
I think it would be easier to read an run the code by splitting it into several parts.
We can use layer_data to get the information from a ggplot object, and the calculate the maximum from that. Based on your example, I would also suggest you multiply the maximum by 1.7 to include the text.
library(tidyverse)
library(data.table)
corona.conf <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",header = TRUE,check.names=FALSE)
dat <- corona.conf %>% .[,c(-1,-3,-4)] %>% melt(.,variable.name="day") %>%
group_by(`Country/Region`,day) %>% summarize(value=sum(value)) %>%
mutate(day=as.Date(day,format='%m/%d/%y')) %>% mutate(count=value-lag(value)) %>%
replace(is.na(.),0) %>% group_by(`Country/Region`) %>% summarize(count=sum(count)) %>%
top_n(20) %>% arrange(desc(count))
p <- ggplot(dat, aes(x=reorder(`Country/Region`,count),y=count,fill=count)) +
geom_bar(stat = "identity") +
coord_flip() +
geom_text(aes(label=format(count,big.mark = ",")),hjust=-0.1,size=4)
p +
scale_y_continuous(expand = c(0,1), limits = c(0, max(layer_data(p)$y) * 1.7))
I would like to see the y-axis (in the plot is flipped) starting at some arbitrary value, like 7.5
After a little bit of researching, I came across ylim, but in this case is giving me some
errors:
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Warning message:
Removed 10 rows containing missing values (geom_col).
This is my code, and a way to download the data I'm using:
install.packages("remotes")
remotes::install_github("tweed1e/werfriends")
library(werfriends)
friends_raw <- werfriends::friends_episodes
library(tidytext)
library(tidyverse)
#"best" writers with at least 10 episodes
friends_raw %>%
unnest(writers) %>%
group_by(writers) %>%
summarize(mean_rating = mean(rating),
n = n()) %>%
arrange(desc(mean_rating)) %>%
filter(n > 10) %>%
head(10) %>%
mutate(writers = fct_reorder(writers, mean_rating)) %>%
ggplot(aes(x = writers, y = mean_rating, fill = writers)) + geom_col() +
coord_flip() + theme(legend.position = "None") + scale_y_continuous(breaks = seq(7.5,10,0.5)) +
ylim(7.5,10)
You should use coord_cartesian for zoom in a particular location (here the official documentation: https://ggplot2.tidyverse.org/reference/coord_cartesian.html).
With your example, your code should be something like that:
friends_raw %>%
unnest(writers) %>%
group_by(writers) %>%
summarize(mean_rating = mean(rating),
n = n()) %>%
arrange(desc(mean_rating)) %>%
filter(n > 10) %>%
head(10) %>%
mutate(writers = fct_reorder(writers, mean_rating)) %>%
ggplot(aes(x = writers, y = mean_rating, fill = writers)) + geom_col() +
coord_flip() + theme(legend.position = "None") + scale_y_continuous(breaks = seq(7.5,10,0.5)) +
coord_cartesian(ylim = c(7.5,10))
If this is not working please provide a reproducible example of your dataset (see: How to make a great R reproducible example)
I found out the solution. With my actual plot, the answer submitted by #dc37 didn't work because coord_flip() and coord_cartesian() exclude each other. So the way to do this is:
friends_raw %>%
unnest(writers) %>%
group_by(writers) %>%
summarize(mean_rating = mean(rating),
n = n()) %>%
arrange(mean_rating) %>%
filter(n > 10) %>%
head(10) %>%
mutate(writers = fct_reorder(writers, mean_rating)) %>%
ggplot(aes(x = writers, y = mean_rating, fill = writers)) + geom_col() +
theme(legend.position = "None") +
coord_flip(ylim = c(8,8.8))
Let's say I'm working with proportions, I have two main variables (sex and pain_level). It's not difficult to plot them:
With tidyverse and broom (and thanks for this link here: Calling prop.test function in R with dplyr) I can compare if the proportions are statistically different.
Now comes the question!
I want to add to the plot, the error bar. I know it's not as difficult as I'm thinking, but I could not find a way to do it. I've tried to replicate this link here (http://www.andrew.cmu.edu/user/achoulde/94842/labs/lab07_solution.html) but I'm trying to stay at tidyverse environment.
The desired output should be something like that:
Please feel free to use the script/syntax below that simulate the original dataset.
library(tidyverse)
ds <- data.frame(sex = rep(c("M","F"), 18),
pain_level = c("High","Moderate","low"))
#plot
ds %>%
group_by(pain_level, sex) %>%
summarise(n=n()) %>%
mutate(prop = n/sum(n)*100) %>%
ggplot(., aes(x = sex, fill = pain_level, y = prop)) +
geom_bar(stat = "summary") +
facet_wrap( ~ pain_level) +
theme(legend.position = "none")
#p values of proportion test
ds %>%
rowwise %>%
group_by(pain_level, sex) %>%
summarise(cases = n()) %>%
mutate(pop = sum(cases)) %>% #compute totals
distinct(., pain_level, .keep_all= TRUE) %>% #keep only one value of the row
mutate(tst = list(broom::tidy(prop.test(cases, pop, conf.level=0.95)))) %>%
tidyr::unnest(tst)
I think the following might roughly resemble your desired output:
ds %>%
group_by(pain_level, sex) %>%
summarise(cases = n()) %>%
mutate(pop = sum(cases)) %>%
rowwise() %>%
mutate(tst = list(broom::tidy(prop.test(cases, pop, conf.level=0.95)))) %>%
tidyr::unnest(tst) %>%
ggplot(aes(sex, estimate, group = pain_level)) +
geom_col(aes(fill = pain_level)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high)) +
facet_wrap(~ pain_level)
I have the following data:
dataf <- read.table(text = "index,group,taxa1,taxa2,taxa3,total
s1,g1,2,5,3,10
s2,g1,3,4,3,10
s3,g2,1,2,7,10
s4,g2,0,4,6,10", header = T, sep = ",")
I'm trying to make a stacked bar plot of the frequences of the data so that it counts across the row (not down a column) for each index (s1,s2,s3,s4) and then for each group (g1,g2) of each taxa. I'm only able to figure out how to graph the species of one taxa but not all three stacked on each other.
Here are some examples of what I'm trying to make:
These were made on google sheets so they don't look like ggplot but it would be easier to make in r with ggplot2 because the real data set is larger.
You would need to reshape the data.
Here is my solution (broken down by plot)
For first plot
library(tidyverse)
##For first plot
prepare_data_1 <- dataf %>% select(index, taxa1:taxa3) %>%
gather(taxa,value, -index) %>%
mutate(index = str_trim(index)) %>%
group_by(index) %>% mutate(prop = value/sum(value))
##Plot 1
prepare_data_1 %>%
ggplot(aes(x = index, y = prop, fill = fct_rev(taxa))) + geom_col()
For second plot
##For second plot
prepare_data_2 <- dataf %>% select(group, taxa1:taxa3) %>%
gather(taxa,value, -group) %>%
mutate(group = str_trim(group)) %>%
group_by(group) %>% mutate(prop = value/sum(value))
##Plot 2
prepare_data_2 %>%
ggplot(aes(x = group, y = prop, fill = fct_rev(taxa))) + geom_col()
##You need to reshape data before doing that.
dfm = melt(dataf, id.vars=c("index","group"),
measure.vars=c("taxa1","taxa2","taxa3"),
variable.name="variable", value.name="values")
ggplot(dfm, aes(x = index, y = values, group = variable)) +
geom_col(aes(fill=variable)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.25)) +
geom_text(aes(label = values), position = position_stack(vjust = .5), size = 3) + theme_gray()