I am trying to show different growing season lengths by displaying crop planting and harvest dates at multiple regions.
My final goal is a graph that looks like this:
which was taken from an answer to this question. Note that the dates are in julian days (day of year).
My first attempt to reproduce a similar plot is:
library(data.table)
library(ggplot2)
mydat <- "Region\tCrop\tPlanting.Begin\tPlanting.End\tHarvest.Begin\tHarvest.End\nCenter-West\tSoybean\t245\t275\t1\t92\nCenter-West\tCorn\t245\t336\t32\t153\nSouth\tSoybean\t245\t1\t1\t122\nSouth\tCorn\t183\t336\t1\t153\nSoutheast\tSoybean\t275\t336\t1\t122\nSoutheast\tCorn\t214\t336\t32\t122"
# read data as data table
mydat <- setDT(read.table(textConnection(mydat), sep = "\t", header=T))
# melt data table
m <- melt(mydat, id.vars=c("Region","Crop"), variable.name="Period", value.name="value")
# plot stacked bars
ggplot(m, aes(x=Crop, y=value, fill=Period, colour=Period)) +
geom_bar(stat="identity") +
facet_wrap(~Region, nrow=3) +
coord_flip() +
theme_bw(base_size=18) +
scale_colour_manual(values = c("Planting.Begin" = "black", "Planting.End" = "black",
"Harvest.Begin" = "black", "Harvest.End" = "black"), guide = "none")
However, there's a few issues with this plot:
Because the bars are stacked, the values on the x-axis are aggregated and end up too high - out of the 1-365 scale that represents day of year.
I need to combine Planting.Begin and Planting.End in the same color, and do the same to Harvest.Begin and Harvest.End.
Also, a "void" (or a completely uncolored bar) needs to be created between Planting.Begin and Harvest.End.
Perhaps the graph could be achieved with geom_rect or geom_segment, but I really want to stick to geom_bar since it's more customizable (for example, it accepts scale_colour_manual in order to add black borders to the bars).
Any hints on how to create such graph?
I don't think this is something you can do with a geom_bar or geom_col. A more general approach would be to use geom_rect to draw rectangles. To do this, we need to reshape the data a bit
plotdata <- mydat %>%
dplyr::mutate(Crop = factor(Crop)) %>%
tidyr::pivot_longer(Planting.Begin:Harvest.End, names_to="period") %>%
tidyr::separate(period, c("Type","Event")) %>%
tidyr::pivot_wider(names_from=Event, values_from=value)
# Region Crop Type Begin End
# <chr> <fct> <chr> <int> <int>
# 1 Center-West Soybean Planting 245 275
# 2 Center-West Soybean Harvest 1 92
# 3 Center-West Corn Planting 245 336
# 4 Center-West Corn Harvest 32 153
# 5 South Soybean Planting 245 1
# ...
We've used tidyr to reshape the data so we have one row per rectangle that we want to draw and we've also make Crop a factor. We can then plot it like this
ggplot(plotdata) +
aes(ymin=as.numeric(Crop)-.45, ymax=as.numeric(Crop)+.45, xmin=Begin, xmax=End, fill=Type) +
geom_rect(color="black") +
facet_wrap(~Region, nrow=3) +
theme_bw(base_size=18) +
scale_y_continuous(breaks=seq_along(levels(plotdata$Crop)), labels=levels(plotdata$Crop))
The part that's a bit messy here that we are using a discrete scale for y but geom_rect prefers numeric values, so since the values are factors now, we use the numeric values for the factors to create ymin and ymax positions. Then we need to replace the y axis with the names of the levels of the factor.
If you also wanted to get the month names on the x axis you could do something like
dateticks <- seq.Date(as.Date("2020-01-01"), as.Date("2020-12-01"),by="month")
# then add this to you plot
... +
scale_x_continuous(breaks=lubridate::yday(dateticks),
labels=lubridate::month(dateticks, label=TRUE, abbr=TRUE))
Related
As an R-beginner, there's one hurdle that I just can't find the answer to. I have a table where I can see the amount of responses to a question according to gender.
Response
Gender
n
1
1
84
1
2
79
2
1
42
2
2
74
3
1
84
3
2
79
etc.
I want to plot these in a column chart: on the y I want the n (or its proportions), and on the x I want to have two seperate bars: one for gender 1, and one for gender 2. It should look like the following example that I was given:
The example that I want to emulate
However, when I try to filter the columns according to gender inside aes(), it returns an error! Could anyone tell me why my approach is not working? And is there another practical way to filter the columns of the table that I have?
ggplot(table) +
geom_col(aes(x = select(filter(table, gender == 1), Q),
y = select(filter(table, gender == 1), n),
fill = select(filter(table, gender == 2), n), position = "dodge")
Maybe something like this:
library(RColorBrewer)
library(ggplot2)
df %>%
ggplot(aes(x=factor(Response), y=n, fill=factor(Gender)))+
geom_col(position=position_dodge())+
scale_fill_brewer(palette = "Set1")
theme_light()
Your answer does not work, because you are assigning the x and y variables as if it was two different datasets (one for x and one for y). In line with the solution from TarJae, you need to think of it as the axis in a diagram - so you need for your x axis to assign the categorical variables you are comparing, and you want for the y axis to assign the numerical variables which determines the height of the bars. Finally, you want to compare them by colors, so each group will have a different color - that is where you include your grouping variable (here, I use fill).
library(dplyr) ## For piping
library(ggplot2) ## For plotting
df %>%
ggplot(aes(x = Response, y = n, fill = as.character(Gender))) +
geom_bar(stat = "Identity", position = "Dodge")
I am adding "Identity" because the default in geom_bar is to count the occurences in you data (i.e., if you data was not aggregated). I am adding "Dodge" to avoid the bars to be stacked. I will recommend you, to look at this resource for more information: https://r4ds.had.co.nz/index.html
I collected seven different samples containing varying concentrations of the Kunjin Virus.
3 samples are from the 24 hour time point: 667, 1330, 1670
2 samples are from the 48 hour time point: 323000, 590000
2 samples are from the 72 hour time point: 3430000, 4670000
How do I create a dotplot reflecting this data including error bars in R? I'm using ggplot2.
My code so far is:
data1 <-data.frame(hours, titer)
ggplot(data1, aes(x=hours, y=titer, colour = hours)) + geom_point()
I would suggest you next approach. If you want error bars you can compute it based on mean and standard deviation. In the next code is sketched the way to do that. I have used one standard deviation but you can set any other value. Also as you want to see different samples, I have used facet_wrap(). Here the code:
library(ggplot2)
library(dplyr)
#Data
df <- data.frame(sample=c(rep('24 hour',3),rep('48 hour',2),rep('72 hour',2)),
titer=c(667, 1330, 1670,323000, 590000,3430000, 4670000),
stringsAsFactors = F)
#Compute error bars
df <- df %>% group_by(sample) %>% mutate(Mean=mean(titer),SD=sd(titer))
#Plot
ggplot(df,aes(x=sample,y=titer,color=sample,group=sample))+
geom_errorbar(aes(ymin=Mean-SD,ymax=Mean+SD),color='black')+
geom_point()+
scale_y_continuous(labels = scales::comma)+
facet_wrap(.~sample,scales='free')
Output:
If you have a common y-axis scale, you can try this:
#Plot 2
ggplot(df,aes(x=sample,y=titer,color=sample,group=sample))+
geom_errorbar(aes(ymin=Mean-SD,ymax=Mean+SD),color='black')+
geom_point()+
scale_y_continuous(labels = scales::comma)+
facet_wrap(.~sample,scales = 'free_x')
Output:
I'm currently trying to develop a surface plot that examines the results of the below data frame. I want to plot the increasing values of noise on the x-axis and the increasing values of mu on the y-axis, with the point estimate values on the z-axis. After looking at ggplot2 and ggplotly, it's not clear how I would plot each of these columns in surface or 3D plot.
df <- "mu noise0 noise1 noise2 noise3 noise4 noise5
1 1 0.000000 0.9549526 0.8908646 0.919630 1.034607
2 2 1.952901 1.9622004 2.0317115 1.919011 1.645479
3 3 2.997467 0.5292921 2.8592976 3.034377 3.014647
4 4 3.998339 4.0042379 3.9938346 4.013196 3.977212
5 5 5.001337 4.9939060 4.9917115 4.997186 5.009082
6 6 6.001987 5.9929932 5.9882173 6.015318 6.007156
7 7 6.997924 6.9962483 7.0118066 6.182577 7.009172
8 8 8.000022 7.9981131 8.0010066 8.005220 8.024569
9 9 9.004437 9.0066182 8.9667536 8.978415 8.988935
10 10 10.006595 9.9987245 9.9949733 9.993018 10.000646"
Thanks in advance.
Here's one way using geom_tile(). First, you will want to get your data frame into more of a Tidy format, where the goal is to have columns:
mu: nothing changes here
noise: need to combine your "noise0", "noise1", ... columns together, and
z: serves as the value of the noise and we will apply the fill= aesthetic using this column.
To do that, I'm using dplyr and gather(), but there are other ways (melt(), or pivot_longer() gets you that too). I'm also adding some code to pull out just the number portion of the "noise" columns and then reformatting that as an integer to ensure that you have x and y axes as numeric/integers:
# assumes that df is your data as data.frame
df <- df %>% gather(key="noise", value="z", -mu)
df <- df %>% separate(col = "noise", into=c('x', "noise"), sep=5) %>% select(-x)
df$noise <- as.integer(df$noise)
Here's an example of how you could plot it, but aesthetics are up to you. I decided to also include geom_text() to show the actual values of df$z so that we can see better what's going on. Also, I'm using rainbow because "it's pretty" - you may want to choose a more appropriate quantitative comparison scale from the RColorBrewer package.
ggplot(df, aes(x=noise, y=mu, fill=z)) + theme_bw() +
geom_tile() +
geom_text(aes(label=round(z, 2))) +
scale_fill_gradientn(colors = rainbow(5))
EDIT: To answer OP's follow up, yes, you can also showcase this via plotly. Here's a direct transition:
p <- plot_ly(
df, x= ~noise, y= ~mu, z= ~z,
type='mesh3d', intensity = ~z,
colors= colorRamp(rainbow(5))
)
p
Static image here:
A much more informative way to show this particular set of information is to see the variation of df$z as it relates to df$mu by creating df$delta_z and then using that to plot. (you can also plot via ggplot() + geom_tile() as above):
df$delta_z <- df$z - df$mu
p1 <- plot_ly(
df, x= ~noise, y= ~mu, z= ~delta_z,
type='mesh3d', intensity = ~delta_z,
colors= colorRamp(rainbow(5))
)
Giving you this (static image here):
ggplot accepts data in the long format, which means that you need to melt your dataset using, for example, a function from the reshape2 package:
dfLong = melt(df,
id.vars = "mu",
variable.name = "noise",
value.name = "meas")
The resulting column noise contains entries such as noise0, noise1, etc. You can extract the numbers and convert to a numeric column:
dfLong$noise = with(dfLong, as.numeric(gsub("noise", "", noise)))
This converts your data to:
mu noise meas
1 1 0 1.0000000
2 2 0 2.0000000
3 3 0 3.0000000
...
As per ggplot documentation:
ggplot2 can not draw true 3D surfaces, but you can use geom_contour(), geom_contour_filled(), and geom_tile() to visualise 3D surfaces in 2D.
So, for example:
ggplot(dfLong,
aes(x = noise
y = mu,
fill = meas)) +
geom_tile() +
scale_fill_gradientn(colours = terrain.colors(10))
Produces:
I have a data frame (100 x 4). The first column is a set of "bins" 0-100, the remaining columns are the counts for each variable of events within each bin (0 to the maximum number of events).
What I'm trying to do is to plot each of the three columns of data (2:4), alongside each other. Because the counts in each of the bins for each of the data sets is close to identical, the data are overlapped in the histogram/barplots I've created, despite my use of beside=true, and position = dodge.
I've set the first column as both numeric and character, but the results are identical- the bars are overlayed on top of each other. (semi-transparent density plots don't work because I want counts not the distribution densities).
The attached code, based on both R and other documentation produced the attached chart.
barplot(BinCntDF$preT,main=NewMain_Trigger, plot=TRUE,
xlab="sample frequency interval counts (0-100 msec bins)",
names.arg=BinCntDF$dT, las=0,
ylab="bin counts", axes=TRUE, xlim=c(0,100),
ylim=c(0,1000), col="red")
geom_bar(position="dodge")
barplot(BinCntDF$postT, beside=TRUE, add=TRUE)
geom_bar()
The goal is to be able to compare the two (or more) data sets side by side on the same axes, without either overlapping the other(s).
I think you have confused barplot with ggplot2. ggplot2 is a library where the function geom_bar comes from and isn't compatible with barplot which comes with Base R.
Simply compare ?barplot and ?geom_bar, and you will see that geom_bar is from the ggplot2 library. To achieve what you're after I have used the ggplot2 library and reshape2.
Step 1
Based on your description, I have assumed that your data looks roughly like this:
df <- data.frame(x = 1:10,
c1 = sample(0:100, replace=TRUE, size=10),
c2 = sample(0:50, replace=TRUE, size=10),
c3 = sample(0:70, replace=TRUE, size=10))
To plot it using ggplot2 you first have to transform the data to a long format instead of a wide format. You can do this using melt function from reshape2.
library(reshape2)
a <- melt(df, id=c("x"))
The output would look something like this
> head(a)
x variable value
1 1 c1 62
2 2 c1 47
3 3 c1 20
4 4 c1 64
5 5 c1 4
6 6 c1 52
Step 2
There are plenty of tutorials online to what ggplot2 does and the arguments. I would recommend you Google, or search through the many threads in SO to understand.
ggplot(a, aes(x=x, y=value, group=variable, fill=variable)) +
geom_bar(stat='identity', position='dodge')
Which gives you the output:
In a nutshell:
group groups the variables of interest
stat=identity ensures that no additional aggregations are made on your data
With that many bins (100) and groups (3) the plot will look messy, but try this:
set.seed(123)
myDF <- data.frame(bins=1:100, x=sample(1:100, replace=T), y=sample(1:100, replace=T), z=sample(1:100, replace=T))
myDF.m <- melt(myDF, id.vars='bins')
ggplot(myDF.m, aes(x=bins, y=value, fill=variable)) + geom_bar(stat='identity', position='dodge')
You could also try plotting w/ facets:
ggplot(myDF.m, aes(x=bins, y=value, fill=variable)) + geom_bar(stat='identity') + facet_wrap(~ variable)
i've decadal time series from 1700 to 1900 (21 time slices) and for each decade i've got 7 categories that represent a quantity; see here
As you can see, only 5 of the decades actually have data.
I can plot a nice little stacked area chart in R, with the help of this very nice example, which retains only the 5 time slices that have data.
My problem is that i want an x-axis that retains all 21 times slices but still plots a stacked area chart using only the 5 time slices. The idea is that the stacked areas will still only be plotted against the correct year but simply connect up to the next point, 10 ticks down the x-axis, ignoring the no-data in between. i can achieve something in excel but i dont like it.
My reasoning is i want to plot lines on the top of the stacked area that are much more complete, for example from 1700 to 1850, or 1800 to 1900, for visual comparison purposes.
This post suggests how to connect dots in a line chart when you want to ignore NAs but it doesnt work for me in this instance.
a <- 1700:1900
b <- a[seq(1, length(a), 10)]
df <- data.frame("Year"=b,replicate(7,sample(1:21)))
rows <- c(2:10,11:15,17,19,21)
df[rows,2:8] <- NA
df
thanks a lot
If you wish to transform your year to factor, on the lines of the code below:
# Transform the data to long
library(reshape2)
df <- melt(data = df, na.rm = FALSE, id.vars = "Year")
df$Year <- as.factor(df$Year)
# Chart
require(ggplot2)
ggplot(df, aes(Year, value)) +
geom_area(aes(colour = variable, fill= variable), position = 'stack')
It will generate the chart below:
I wasn't sure if you are interested in mapping all of the X variables. I was thinking that this is the case so I reshaped your data. Presumably, it is wiser not to change the Year to factor. The code below:
a <- 1700:1900
b <- a[seq(1, length(a), 10)]
df <- data.frame("Year"=b,replicate(7,sample(1:21)))
rows <- c(2:10,11:15,17,19,21)
df[rows,2:8] <- NA
# Transform the data to long
library(reshape2)
df <- melt(data = df, na.rm = FALSE, id.vars = "Year")
# Leave it as int.
# df$Year <- as.factor(df$Year)
# Chart
require(ggplot2)
ggplot(df, aes(Year, value)) +
geom_area(aes(colour = variable, fill= variable), position = 'stack')
would generate much more meaningful chart:
Potentially, if you decide to use years as factors you may group them and have one category for a number of missing years so the x-axis is more readable. I would say it's a matter of presentation to great extent.