I have been trying to get ggplot in R to map 2 variables side by side in a bar plot against a catergorical Y Value
The data I have been using is the build in mpg in the "carat" Package.
However every time I run my code( which is listed below)
I receive the errorError: Aesthetics must be either length 1 or the same as the data (234): y
my code is :
ggplot(mpg,aes(x=fl,y=c(cty,hwy)))+
geom_bar()
Can someone please help
To summarise I am using the MPG dataset in R and I'm trying to plot cty and why side by side in a barplot against their fuel type(fl)
One way to do this is to put the data into long format.
Not really sure how meaningful this graph is as it gives the sum highway and city miles per gallon. Might be more meaningful to calculate the average highway and city miles per gallon for the different fuel types.
library(ggplot2)
library(tidyr)
mpg %>%
pivot_longer(c(cty,hwy)) %>%
ggplot(aes(x = fl, y=value, fill = name))+
geom_col(position = "dodge")
Created on 2021-04-10 by the reprex package (v2.0.0)
Barcharts makes the height of the bar proportional to the number of cases in each group. It can't have x and y aesthetic at the same time.
From your description I think you want to map categorical data to numeric, in this case use boxplots e.g.
mpg %>%
select(fl, cty, hwy) %>%
pivot_longer(-fl) %>%
ggplot(aes(x = fl, y = value, fill = name)) + geom_boxplot()
Related
I would really appreciate some insight on the zagging when using the following code in R:
tbi_military %>%
ggplot(aes(x = year, y = diagnosed, color = service)) +
geom_line() +
facet_wrap(vars(severity))
The dataset is comprised of 5 variables (3 character, 2 numerical). Any insight would be so appreciated.
enter image description here
This is just an illustration with a standard dataset. Let's say we're interested in plotting the weight of chicks over time depending on a diet. We would attempt to plot this like so:
library(ggplot2)
ggplot(ChickWeight, aes(Time, weight, colour = factor(Diet))) +
geom_line()
You can see the zigzag pattern appear, because per diet/time point, there are multiple observations. Because geom_line sorts the data depending on the x-axis, this shows up as a vertical line spanning the range of datapoints at that time per diet.
The data has an additional variable called 'Chick' that separates out individual chicks. Including that in the grouping resolves the zigzag pattern and every line is the weight over time per individual chick.
ggplot(ChickWeight, aes(Time, weight, colour = factor(Diet))) +
geom_line(aes(group = interaction(Chick, Diet)))
If you don't have an extra variable that separates out individual trends, you could instead choose to summarise the data per timepoint by, for example, taking the mean at every timepoint.
ggplot(ChickWeight, aes(Time, weight, colour = factor(Diet))) +
geom_line(stat = "summary", fun = mean)
Created on 2021-08-30 by the reprex package (v1.0.0)
I am trying to develop an animated plot showing how the rates of three point attempts and assists have changed for NBA teams over time. While the points in my plot are transitioning correctly, I tried to add a vertical and horizontal mean line, however this is staying constant for the overall averages rather than shifting year by year.
p<-ggplot(dataBREFPerPossTeams, aes(astPerPossTeam,fg3aPerPossTeam,col=ptsPerPossTeam))+
geom_point()+
scale_color_gradient(low='yellow',high='red')+
theme_classic()+
xlab("Assists Per 100 Possessions")+
ylab("Threes Attempted Per 100 Possessions")+labs(color="Points Per 100 Possessions")+
geom_hline(aes(yintercept = mean(fg3aPerPossTeam)), color='blue',linetype='dashed')+
geom_vline(aes(xintercept = mean(astPerPossTeam)), color='blue',linetype='dashed')
anim<-p+transition_time(as.integer(yearSeason))+labs(title='Year: {frame_time}')
animate(anim, nframes=300)
Ideally, the two dashed lines would shift as the years progress, however, right now they are staying constant. Any ideas on how to fix this?
I am using datasets::airquality since you have not shared your data. The idea here is that you need to have the values for your other geom (here it is mean) as a variable in your dataset, so gganimate can draw the connection between the values and frame (i.e. transition_time).
So What I did was grouping by frame (here it is month and it will be yearSeason for you) and then mutating a column with the average of my desired variables. Then in geoms I used that appended variable instead of getting the mean inside of the geom. Look below;
library(datasets) #datasets::airquality
library(ggplot2)
library(gganimate)
library(dplyr)
g <- airquality %>%
group_by(Month) %>%
mutate(mean_wind=mean(Wind),
mean_temp=mean(Temp)) %>%
ggplot()+
geom_point(aes(Wind,Temp, col= Solar.R))+
geom_hline(aes(yintercept = mean_temp), color='blue',linetype='dashed')+
geom_vline(aes(xintercept = mean_wind), color='green',linetype='dashed')+
scale_color_gradient(low='yellow',high='red')+
theme_classic()+
xlab("Wind")+
ylab("Temp")+labs(color="Solar.R")
animated_g <- g + transition_time(as.integer(Month))+labs(title='Month: {frame_time}')
animate(animated_g, nframes=18)
Created on 2019-06-09 by the reprex package (v0.3.0)
I am new to R and am struggling to understand how to create a matrix line plot (or plot with line subplots) given a data set with let's say one x and 5 y-columns such that:
-the first subplot is a plot of variables 1 and 2 (function of x)
-the second subplot variables 1 and 3 and so on
The idea is to use one of the variables (in this example number 1) as a reference and pair it with the rest so that they can be easily compared.
Thank you very much for your help.
Here's an example of one way to do that using tidyr and ggplot. tidyr::gather can pull the non-mpg columns into long format, each matched with its respective mpg. Then the data is mapped in ggplot so that x is mpg and y is the other value, and the name of the column it came from is mapped to facets.
library(tidyverse)
mtcars %>%
select(rowname, mpg, cyl, disp, hp) %>%
gather(stat, value, cyl:hp) %>%
ggplot(aes(mpg, value)) +
geom_point() +
facet_grid(stat~., scales = "free")
I have a problem with my density histogram in ggplot2. I am working in RStudio, and I am trying to create density histogram of income, dependent on persons occupation. My problem is, that when I use my code:
data = read.table("http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
sep=",",header=F,col.names=c("age", "type_employer", "fnlwgt", "education",
"education_num","marital", "occupation", "relationship", "race","sex",
"capital_gain", "capital_loss", "hr_per_week","country", "income"),
fill=FALSE,strip.white=T)
ggplot(data=dat, aes(x=income)) +
geom_histogram(stat='count',
aes(x= income, y=stat(count)/sum(stat(count)),
col=occupation, fill=occupation),
position='dodge')
I get in response histogram of each value divided by overall count of all values of all categories, and I would like for example for people earning >50K whom occupation is 'craft repair' divided by overall number of people whos occupation is craft-repair, and the same for <=50K and of the same occupation category, and like that for every other type of occupation
And the second question is, after doing propper density histogram, how can I sort the bars in decreasing order?
This is a situation where it makes sence to re-aggregate your data first, before plotting. Aggregating within the ggplot call works fine for simple aggregations, but when you need to aggregate, then peel off a group for your second calculation, it doesn't work so well. Also, note that because your x axis is discrete, we don't use a histogram here, instead we'll use geom_bar()
First we aggregate by count, then calculate percent of total using occupation as the group.
d2 <- data %>% group_by(income, occupation) %>%
summarize(count= n()) %>%
group_by(occupation) %>%
mutate(percent = count/sum(count))
Then simply plot a bar chart using geom_bar and position = 'dodge' so the bars are side by side, rather than stacked.
d2 %>% ggplot(aes(income, percent, fill = occupation)) +
geom_bar(stat = 'identity', position='dodge')
I have a simple plot of same data from an experiment.
plot(x=sample95$PositionA, y=sample95$AbsA, xlab=expression(position (mm)), ylab=expression(A[260]), type='l')
I would like to shade a particular area under the line, let's say from 35-45mm. From what I've searched so far, I think I need to use the polygon function, but I'm unsure how to assign vertices from a big dataset like this. Every example I've seen so far uses a normal curve.
Any help is appreciated, I am very new to R/RStudio!
Here is a solution using tidyverse tools including ggplot2. I use the built in airquality dataset as an example.
This first part is just to put the data in a format that we can plot by combining the month and the day into a single date. You can just substitute date for PositionA in your data.
library(tidyverse)
df <- airquality %>%
as_tibble() %>%
magrittr::set_colnames(str_to_lower(colnames(.))) %>%
mutate(date = as.Date(str_c("1973-", month, "-", day)))
This is the plot code. In ggplot2, we start with the function ggplot() and add geom functions to it with + to create the plot in layers.
The first function, geom_line, joins up all observations in the order that they appear based on the x variable, so it makes the line that we see. Each geom needs a particular mapping to an aesthetic, so here we want date on the x axis and temp on the y axis, so we write aes(x = date, y = temp).
The second function, geom_ribbon, is designed to plot bands at particular x values between a ymax and a ymin. This lets us shade the area underneath the line by choosing a constant ymin = 55 (a value lower than the minimum temperature) and setting ymax = temp.
We shade a specific part of the chart by specifying the data argument. Normally geom functions act on the dataset inherited from ggplot(), but you can override them by specifying individually. Here we use filter to only plot the points where the date is in June in geom_ribbon.
ggplot(df) +
geom_line(aes(x = date, y = temp)) +
geom_ribbon(
data = filter(df, date < as.Date("1973-07-01") & date > as.Date("1973-06-01")),
mapping = aes(x = date, ymax = temp, ymin = 55)
)
This gives the chart below:
Created on 2018-02-20 by the reprex package (v0.2.0).