scale_x_reordered does not work in facet_grid - r
I am a newbie in R and would like to seek your advice regarding visualization using reorder_within, and scale_x_reordered (library: tidytext).
I want to show the data (ordered by max to min) by states for each year. This is sample data for illustrative purposes.
test <- data.frame(stateabb = rep(state.abb, each = 5, times = 1),
year = seq(2001,2005,1),
value = sample(1:100, 250, replace = TRUE))
I successfully created the simple chart by state and year by using the following code.
ggplot(test, aes(x = stateabb, y = value)) +
geom_bar(stat = "identity") +
facet_grid(year ~ ., scales = "free_x")
Looking at this chart, it is very hard to see which State is the best in each year. So, I decided to order the value each year by using reorder_within.
ggplot(test, aes(x = reorder_within(stateabb, -value, year), y = value)) +
geom_bar(stat = "identity") +
facet_grid(year ~ ., scales = "free_x") +
scale_x_reordered()
However, I could not show it as I did in the first picture. I thought scale_x_reordered could solve it, but it did not turn out as I expected. I also understand that I need to set the x-axis free in order to show the order of states in each year. But doing that does not get me anywhere. What did I do wrong here? Is there any other appropriate way to show the order of these states by year? Any suggestions or advice to show this chart properly would be appreciated. Thank you so much ahead of time!
This can't work, because facet_grid would only have one shared x-axis. But the orders are different in every facet. You want facet_wrap. For example like this:
library(ggplot); library(tidytext)
ggplot(test, aes(x = reorder_within(stateabb, -value, year), y = value)) +
geom_bar(stat = "identity") + scale_x_reordered() +
facet_wrap(year ~ ., ncol = 1, scales = "free_x", strip.position = "right")
Related
Can't make a ggplot with multiple lines, geom_line()
I'm trying to plot two lines using flight data I gathered. My problem is that after trying different formulas, R is still only showing one line. I've separated my data according to regions (see image below). Can someone help me out with my formula? If you need any additional information don't hesitate to ask, this is my first time posting on this channel. ggplot(ica.vs.total, aes(x = Year, y = flights)) + geom_line(aes(color = region, group = region), size = 1) + theme_minimal()
When I enter :
library(ggplot2)
ica.vs.total = data.frame(flights=c(215947,197757,185782,201023,279218,261045,213343,205609),
region=c('TotalFlights','TotalFlights','TotalFlights','TotalFlights',
'TotalFlightsICA','TotalFlightsICA','TotalFlightsICA','TotalFlightsICA'),
Year=c(2008,2009,2010,2011,2000,2001,2002,2003))
g = ggplot(ica.vs.total, aes(x = Year, y = flights)) +
geom_line(aes(color = region, group = region), size = 1)+
theme_minimal()
print(g)
I get the expected result :
Double check your code.
How do I correctly connect data points ggplot
I am making a stratigraphic plot but somehow, my data points don't connect correctly.
The purpose of this plot is that the values on the x-axis are connected so you get an overview of the change in d18O throughout time (age, ma).
I've used the following script:
library(readxl)
R_pliocene_tot <- read_excel("Desktop/R_d18o.xlsx")
View(R_pliocene_tot)
install.packages("analogue")
install.packages("gridExtra")
library(tidyverse)
R_pliocene_Rtot <- R_pliocene_tot %>%
gather(key=param, value=value, -age_ma)
R_pliocene_Rtot
R_pliocene_Rtot %>%
ggplot(aes(x=value, y=age_ma)) +
geom_path() +
geom_point() +
facet_wrap(~param, scales = "free_x") +
scale_y_reverse() +
labs(x = NULL, y = "Age (ma)")
which leads to the following figure:
Something is wrong with the geom_path function, I guess, but I can't figure out what it is.
Though the comment seem solve the problem I don't think the question asked was answered. So here is some introduction about ggplot2 library regard geom_path
library(dplyr)
library(ggplot2)
# This dataset contain two group with random value for y and x run from 1->20
# The param is just to replicate the question param variable.
df <- tibble(x = rep(seq(1, 20, by = 1), 2),
y = runif(40, min = 1, max = 100),
group = c(rep("group 1", 20), rep("group 2", 20)),
param = rep("a param", 40))
df %>%
ggplot(aes(x = x, y = y)) +
# In geom_path there is group aesthetics which help the function to know
# which data point should is in which path.
# The one in the same group will be connected together.
# here I use the color to help distinct the path a bit more.
geom_path(aes(group = group, color = group)) +
geom_point() +
facet_wrap(~param, scales = "free_x") +
scale_y_reverse() +
labs(x = NULL, y = "Age (ma)")
In your data which work well with group = 1 I guessed all data points belong to one group and you just want to draw a line connect all those data point. So take my data example above and draw with aesthetics group = 1, you can see the result that have two line similar to the above example but now the end point of group 1 is now connected with the starting point of group 2.
So all data point is now on one path but the order of how they draw is depend on the order they appear in the data. (I keep the color just to help see it a bit clearer)
df %>%
ggplot(aes(x = x, y = y)) +
geom_path(aes(group = 1, color = group)) +
geom_point() +
facet_wrap(~param, scales = "free_x") +
scale_y_reverse() +
labs(x = NULL, y = "Age (ma)")
Hope this give you better understanding of ggplot2::geom_path
Plotting a bar chart with years grouped together
I am using the fivethirtyeight bechdel dataset, located here https://github.com/rudeboybert/fivethirtyeight, and am attempting to recreate the first plot shown in the article here https://fivethirtyeight.com/features/the-dollar-and-cents-case-against-hollywoods-exclusion-of-women/. I am having trouble getting the years to group together similarly to how they did in the article.
This is the current code I have:
ggplot(data = bechdel, aes(year)) +
geom_histogram(aes(fill = clean_test), binwidth = 5, position = "fill") +
scale_fill_manual(breaks = c("ok", "dubious", "men", "notalk", "nowomen"),
values=c("red", "salmon", "lightpink", "dodgerblue",
"blue")) +
theme_fivethirtyeight()
I see where you were going with using the histogram geom but this really looks more like a categorical bar chart. Once you take that approach it's easier, after a bit of ugly code to get the correct labels on the year columns. The bars are stacked in the wrong order on this one, and there needs to be some formatting applied to look like the 538 chart, but I'll leave that for you. library(fivethirtyeight) library(tidyverse) library(ggthemes) library(scales) # Create date range column bechdel_summary <- bechdel %>% mutate(date.range = ((year %/% 10)* 10) + ((year %% 10) %/% 5 * 5)) %>% mutate(date.range = paste0(date.range," - '",substr(date.range + 5,3,5))) ggplot(data = bechdel_summary, aes(x = date.range, fill = clean_test)) + geom_bar(position = "fill", width = 0.95) + scale_y_continuous(labels = percent) + theme_fivethirtyeight() ggplot
dodge columns in ggplot2
I am trying to create a picture that summarises my data. Data is about prevalence of drug use obtained from different practices form different countries. Each practice has contributed with a different amount of data and I want to show all of this in my picture.
Here is a subset of the data to work on:
gr<-data.frame(matrix(0,36))
gr$drug<-c("a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b")
gr$practice<-c("a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r")
gr$country<-c("c1","c1","c1","c1","c1","c1","c1","c1","c1","c1","c2","c2","c2","c2","c2","c2","c3","c3","c1","c1","c1","c1","c1","c1","c1","c1","c1","c1","c2","c2","c2","c2","c2","c2","c3","c3")
gr$prevalence<-c(9.14,5.53,16.74,1.93,8.51,14.96,18.90,11.18,15.00,20.10,24.56,22.29,19.41,20.25,25.01,25.87,29.33,20.76,18.94,24.60,26.51,13.37,23.84,21.82,23.69,20.56,30.53,16.66,28.71,23.83,21.16,24.66,26.42,27.38,32.46,25.34)
gr$prop<-c(0.027,0.023,0.002,0.500,0.011,0.185,0.097,0.067,0.066,0.023,0.433,0.117,0.053,0.199,0.098,0.100,0.594,0.406,0.027,0.023,0.002,0.500,0.011,0.185,0.097,0.067,0.066,0.023,0.433,0.117,0.053,0.199,0.098,0.100,0.594,0.406)
gr$low.CI<-c(8.27,4.80,12.35,1.83,7.22,14.53,18.25,10.56,14.28,18.76,24.25,21.72,18.62,19.83,24.36,25.22,28.80,20.20,17.73,23.15,21.06,13.12,21.79,21.32,22.99,19.76,29.60,15.41,28.39,23.25,20.34,24.20,25.76,26.72,31.92,24.73)
gr$high.CI<-c(10.10,6.37,22.31,2.04,10.00,15.40,19.56,11.83,15.74,21.52,24.87,22.86,20.23,20.68,25.67,26.53,29.86,21.34,20.21,26.10,32.79,13.63,26.02,22.33,24.41,21.39,31.48,17.98,29.04,24.43,22.01,25.12,27.09,28.05,33.01,25.95)
The code I wrote is this
p<-ggplot(data=gr, aes(x=factor(drug), y=as.numeric(gr$prevalence), ymax=max(high.CI),position="dodge",fill=practice,width=prop))
colour<-c(rep("gray79",10),rep("gray60",6),rep("gray39",2))
p + theme_bw()+
geom_bar(stat="identity",position = position_dodge(0.9)) +
labs(x="Drug",y="Prevalence") +
geom_errorbar(ymax=gr$high.CI,ymin=gr$low.CI,position=position_dodge(0.9),width=0.25,size=0.25,colour="black",aes(x=factor(drug), y=as.numeric(gr$prevalence), fill=practice)) +
ggtitle("Drug usage by country and practice") +
scale_fill_manual(values = colour)+ guides(fill=F)
The figure I obtain is this one where bars are all on top of each other while I want them "dodge".
I also obtain the following warning:
ymax not defined: adjusting position using y instead
Warning message:
position_dodge requires non-overlapping x intervals
Ideally I would get each bar near one another, with their error bars in the middle of its bar, all organised by country.
Also should I be concerned about the warning (which I clearly do not fully understand)?
I hope this makes sense. I hope I am close enough, but I don't seem to be going anywhere, some help would be greatly appreciated.
Thank you
ggplot's geom_bar() accepts the width parameter, but doesn't line them up neatly against one another in dodged position by default. The following workaround references the solution here:
library(dplyr)
# calculate x-axis position for bars of varying width
gr <- gr %>%
group_by(drug) %>%
arrange(practice) %>%
mutate(pos = 0.5 * (cumsum(prop) + cumsum(c(0, prop[-length(prop)])))) %>%
ungroup()
x.labels <- gr$practice[gr$drug == "a"]
x.pos <- gr$pos[gr$drug == "a"]
ggplot(gr,
aes(x = pos, y = prevalence,
fill = country, width = prop,
ymin = low.CI, ymax = high.CI)) +
geom_col(col = "black") +
geom_errorbar(size = 0.25, colour = "black") +
facet_wrap(~drug) +
scale_fill_manual(values = c("c1" = "gray79",
"c2" = "gray60",
"c3" = "gray39"),
guide = F) +
scale_x_continuous(name = "Drug",
labels = x.labels,
breaks = x.pos) +
labs(title = "Drug usage by country and practice", y = "Prevalence") +
theme_classic()
There is a lot of information you are trying to convey here - to contrast drug A and drug B across countries using the barplots and accounting for proportions, you might use the facet_grid function. Try this:
colour<-c(rep("gray79",10),rep("gray60",6),rep("gray39",2))
gr$drug <- paste("Drug", gr$drug)
p<-ggplot(data=gr, aes(x=factor(practice), y=as.numeric(prevalence),
ymax=high.CI,ymin = low.CI,
position="dodge",fill=practice, width=prop))
p + theme_bw()+ facet_grid(drug~country, scales="free") +
geom_bar(stat="identity") +
labs(x="Practice",y="Prevalence") +
geom_errorbar(position=position_dodge(0.9), width=0.25,size=0.25,colour="black") +
ggtitle("Drug usage by country and practice") +
scale_fill_manual(values = colour)+ guides(fill=F)
The width is too small in the C1 country and as you indicated the one clinic is quite influential.
Also, you can specify your aesthetics with the ggplot(aes(...)) and not have to reset it and it is not needed to include the dataframe objects name in the aes function within the ggplot call.
How to use percentage as label in stacked bar plot?
I'm trying to display percentage numbers as labels inside the bars of a stacked bar plot in ggplot2. I found some other post from 3 years ago but I'm not able to reproduce it: How to draw stacked bars in ggplot2 that show percentages based on group?
The answer to that post is almost exactly what I'm trying to do.
Here is a simple example of my data:
df = data.frame('sample' = c('cond1','cond1','cond1','cond2','cond2','cond2','cond3','cond3','cond3','cond4','cond4','cond4'),
'class' = c('class1','class2','class3','class1','class2','class3','class1','class2','class3','class1','class2','class3'))
ggplot(data=df, aes(x=sample, fill=class)) +
coord_flip() +
geom_bar(position=position_fill(reverse=TRUE), width=0.7)
I'd like for every bar to show the percentage/fraction, so in this case they would all be 33%. In reality it would be nice if the values would be calculated on the fly, but I can also hand the percentages manually if necessary. Can anybody help?
Side question: How can I reduce the space between the bars? I found many answers to that as well but they suggest using the width parameter in position_fill(), which doesn't seem to exist anymore.
Thanks so much!
EDIT:
So far, there are two examples that show exactly what I was asking for (big thanks for responding so quickly), however they fail when applying it to my real data. Here is the example data with just another element added to show what happens:
df = data.frame('sample' = c('cond1','cond1','cond1','cond2','cond2','cond2','cond3','cond3','cond3','cond4','cond4','cond4','cond1'),
'class' = c('class1','class2','class3','class1','class2','class3','class1','class2','class3','class1','class2','class3','class2'))
Essentially, I'd like to have only one label per class/condition combination.
I think what OP wanted was labels on the actual sections of the bars. We can do this using data.table to get the count percentages and the formatted percentages and then plot using ggplot: library(data.table) library(scales) dt <- setDT(df)[,list(count = .N), by = .(sample,class)][,list(class = class, count = count, percent_fmt = paste0(formatC(count*100/sum(count), digits = 2), "%"), percent_num = count/sum(count) ), by = sample] ggplot(data=dt, aes(x=sample, y= percent_num, fill=class)) + geom_bar(position=position_fill(reverse=TRUE), stat = "identity", width=0.7) + geom_text(aes(label = percent_fmt),position = position_stack(vjust = 0.5)) + coord_flip() Edit: Another solution where you calculate the y-value of your label in the aggregate. This is so we don't have to rely on position_stack(vjust = 0.5): dt <- setDT(df)[,list(count = .N), by = .(sample,class)][,list(class = class, count = count, percent_fmt = paste0(formatC(count*100/sum(count), digits = 2), "%"), percent_num = count/sum(count), cum_pct = cumsum(count/sum(count)), label_y = (cumsum(count/sum(count)) + cumsum(ifelse(is.na(shift(count/sum(count))),0,shift(count/sum(count))))) / 2 ), by = sample] ggplot(data=dt, aes(x=sample, y= percent_num, fill=class)) + geom_bar(position=position_fill(reverse=TRUE), stat = "identity", width=0.7) + geom_text(aes(label = percent_fmt, y = label_y)) + coord_flip()
Here is a solution where you first calculate the percentages using dplyr and then plot them:
UPDATED:
options(stringsAsFactors = F)
df = data.frame(sample = c('cond1','cond1','cond1','cond2','cond2','cond2','cond3','cond3','cond3','cond4','cond4','cond4'),
class = c('class1','class2','class3','class1','class2','class3','class1','class2','class3','class1','class2','class3'))
library(dplyr)
library(scales)
df%>%
# count how often each class occurs in each sample.
count(sample, class)%>%
group_by(sample)%>%
mutate(pct = n / sum(n))%>%
ggplot(aes(x = sample, y = pct, fill = class)) +
coord_flip() +
geom_col(width=0.7)+
geom_text(aes(label = paste0(round(pct * 100), '%')),
position = position_stack(vjust = 0.5))
Use scales library(scales) ggplot(data=df, aes(x=sample, fill=class)) + coord_flip() + geom_bar(position=position_fill(reverse=TRUE), width=0.7) + scale_y_continuous(labels =percent_format())