I am trying to get a boxplot with 3 different tools in each dataset size like the one below:
ggplot(data1, aes(x = dataset, y = time, color = tool)) + geom_boxplot() +
labs(x = 'Datasets', y = 'Seconds', title = 'Time') +
scale_y_log10() + theme_bw()
But I need to transform x-axis to log scale. For that, I need to numericize each dataset to be able to transform them to log scale. Even without transforming them, they look like the one below:
ggplot(data2, aes(x = dataset, y = time, color = tool)) + geom_boxplot() +
labs(x = 'Datasets', y = 'Seconds', title = 'Time') +
scale_y_log10() + theme_bw()
I checked boxplot parameters and grouping parameters of aes, but could not resolve my problem. At first, I thought this problem is caused by scaling to log, but removing those elements did not resolve the problem.
What am I missing exactly? Thanks...
Files are in this link. "data2" is the numericized version of "data1".
Your question was a tough cookie, but I learned something new from it!
Just using group = dataset is not sufficient because you also have the tool variable to look out for. After digging around a bit, I found this post which made use of the interaction() function.
This is the trick that was missing. You want to use group because you are not using a factor for the x values, but you need to include tool in the separation of your data (hence using interaction() which will compute the possible crosses between the 2 variables).
# This is for pretty-printing the axis labels
my_labs <- function(x){
paste0(x/1000, "k")
}
levs <- unique(data2$dataset)
ggplot(data2, aes(x = dataset, y = time, color = tool,
group = interaction(dataset, tool))) +
geom_boxplot() + labs(x = 'Datasets', y = 'Seconds', title = 'Time') +
scale_x_log10(breaks = levs, labels = my_labs) + # define a log scale with your axis ticks
scale_y_log10() + theme_bw()
This plots
Related
I'd like to have some labels stacked on top of a geom_bar graph. Here's an example:
df <- data.frame(x=factor(c(TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE)))
ggplot(df) + geom_bar(aes(x,fill=x)) + opts(axis.text.x=theme_blank(),axis.ticks=theme_blank(),axis.title.x=theme_blank(),legend.title=theme_blank(),axis.title.y=theme_blank())
Now
table(df$x)
FALSE TRUE
3 5
I'd like to have the 3 and 5 on top of the two bars. Even better if I could have the percent values as well. E.g. 3 (37.5%) and 5 (62.5%). Like so:
(source: skitch.com)
Is this possible? If so, how?
To plot text on a ggplot you use the geom_text. But I find it helpful to summarise the data first using ddply
dfl <- ddply(df, .(x), summarize, y=length(x))
str(dfl)
Since the data is pre-summarized, you need to remember to change add the stat="identity" parameter to geom_bar:
ggplot(dfl, aes(x, y=y, fill=x)) + geom_bar(stat="identity") +
geom_text(aes(label=y), vjust=0) +
opts(axis.text.x=theme_blank(),
axis.ticks=theme_blank(),
axis.title.x=theme_blank(),
legend.title=theme_blank(),
axis.title.y=theme_blank()
)
As with many tasks in ggplot, the general strategy is to put what you'd like to add to the plot into a data frame in a way such that the variables match up with the variables and aesthetics in your plot. So for example, you'd create a new data frame like this:
dfTab <- as.data.frame(table(df))
colnames(dfTab)[1] <- "x"
dfTab$lab <- as.character(100 * dfTab$Freq / sum(dfTab$Freq))
So that the x variable matches the corresponding variable in df, and so on. Then you simply include it using geom_text:
ggplot(df) + geom_bar(aes(x,fill=x)) +
geom_text(data=dfTab,aes(x=x,y=Freq,label=lab),vjust=0) +
opts(axis.text.x=theme_blank(),axis.ticks=theme_blank(),
axis.title.x=theme_blank(),legend.title=theme_blank(),
axis.title.y=theme_blank())
This example will plot just the percentages, but you can paste together the counts as well via something like this:
dfTab$lab <- paste(dfTab$Freq,paste("(",dfTab$lab,"%)",sep=""),sep=" ")
Note that in the current version of ggplot2, opts is deprecated, so we would use theme and element_blank now.
Another solution is to use stat_count() when dealing with discrete variables (and stat_bin() with continuous ones).
ggplot(data = df, aes(x = x)) +
geom_bar(stat = "count") +
stat_count(geom = "text", colour = "white", size = 3.5,
aes(label = ..count..),position=position_stack(vjust=0.5))
So, this is our initial plot↓
library(ggplot2)
df <- data.frame(x=factor(c(TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE)))
p <- ggplot(df, aes(x = x, fill = x)) +
geom_bar()
p
As suggested by yuan-ning, we can use stat_count().
geom_bar() uses stat_count() by default. As mentioned in the ggplot2 reference, stat_count() returns two values: count for number of points in bin and prop for groupwise proportion. Since our groups match the x values, both props are 1 and aren’t useful. But we can use count (referred to as “..count..”) that actually denotes bar heights, in our geom_text(). Note that we must include “stat = 'count'” into our geom_text() call as well.
Since we want both counts and percentages in our labels, we’ll need some calculations and string pasting in our “label” aesthetic instead of just “..count..”. I prefer to add a line of code to create a wrapper percent formatting function from the “scales” package (ships along with “ggplot2”).
pct_format = scales::percent_format(accuracy = .1)
p <- p + geom_text(
aes(
label = sprintf(
'%d (%s)',
..count..,
pct_format(..count.. / sum(..count..))
)
),
stat = 'count',
nudge_y = .2,
colour = 'royalblue',
size = 5
)
p
Of course, you can further edit the labels with colour, size, nudges, adjustments etc.
I'd like to have some labels stacked on top of a geom_bar graph. Here's an example:
df <- data.frame(x=factor(c(TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE)))
ggplot(df) + geom_bar(aes(x,fill=x)) + opts(axis.text.x=theme_blank(),axis.ticks=theme_blank(),axis.title.x=theme_blank(),legend.title=theme_blank(),axis.title.y=theme_blank())
Now
table(df$x)
FALSE TRUE
3 5
I'd like to have the 3 and 5 on top of the two bars. Even better if I could have the percent values as well. E.g. 3 (37.5%) and 5 (62.5%). Like so:
(source: skitch.com)
Is this possible? If so, how?
To plot text on a ggplot you use the geom_text. But I find it helpful to summarise the data first using ddply
dfl <- ddply(df, .(x), summarize, y=length(x))
str(dfl)
Since the data is pre-summarized, you need to remember to change add the stat="identity" parameter to geom_bar:
ggplot(dfl, aes(x, y=y, fill=x)) + geom_bar(stat="identity") +
geom_text(aes(label=y), vjust=0) +
opts(axis.text.x=theme_blank(),
axis.ticks=theme_blank(),
axis.title.x=theme_blank(),
legend.title=theme_blank(),
axis.title.y=theme_blank()
)
As with many tasks in ggplot, the general strategy is to put what you'd like to add to the plot into a data frame in a way such that the variables match up with the variables and aesthetics in your plot. So for example, you'd create a new data frame like this:
dfTab <- as.data.frame(table(df))
colnames(dfTab)[1] <- "x"
dfTab$lab <- as.character(100 * dfTab$Freq / sum(dfTab$Freq))
So that the x variable matches the corresponding variable in df, and so on. Then you simply include it using geom_text:
ggplot(df) + geom_bar(aes(x,fill=x)) +
geom_text(data=dfTab,aes(x=x,y=Freq,label=lab),vjust=0) +
opts(axis.text.x=theme_blank(),axis.ticks=theme_blank(),
axis.title.x=theme_blank(),legend.title=theme_blank(),
axis.title.y=theme_blank())
This example will plot just the percentages, but you can paste together the counts as well via something like this:
dfTab$lab <- paste(dfTab$Freq,paste("(",dfTab$lab,"%)",sep=""),sep=" ")
Note that in the current version of ggplot2, opts is deprecated, so we would use theme and element_blank now.
Another solution is to use stat_count() when dealing with discrete variables (and stat_bin() with continuous ones).
ggplot(data = df, aes(x = x)) +
geom_bar(stat = "count") +
stat_count(geom = "text", colour = "white", size = 3.5,
aes(label = ..count..),position=position_stack(vjust=0.5))
So, this is our initial plot↓
library(ggplot2)
df <- data.frame(x=factor(c(TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE)))
p <- ggplot(df, aes(x = x, fill = x)) +
geom_bar()
p
As suggested by yuan-ning, we can use stat_count().
geom_bar() uses stat_count() by default. As mentioned in the ggplot2 reference, stat_count() returns two values: count for number of points in bin and prop for groupwise proportion. Since our groups match the x values, both props are 1 and aren’t useful. But we can use count (referred to as “..count..”) that actually denotes bar heights, in our geom_text(). Note that we must include “stat = 'count'” into our geom_text() call as well.
Since we want both counts and percentages in our labels, we’ll need some calculations and string pasting in our “label” aesthetic instead of just “..count..”. I prefer to add a line of code to create a wrapper percent formatting function from the “scales” package (ships along with “ggplot2”).
pct_format = scales::percent_format(accuracy = .1)
p <- p + geom_text(
aes(
label = sprintf(
'%d (%s)',
..count..,
pct_format(..count.. / sum(..count..))
)
),
stat = 'count',
nudge_y = .2,
colour = 'royalblue',
size = 5
)
p
Of course, you can further edit the labels with colour, size, nudges, adjustments etc.
Hi I am trying to code for a scatter plot for three variables in R:
Race= [0,1]
YOI= [90,92,94]
ASB_mean = [1.56, 1.59, 1.74]
Antisocial <- read.csv(file = 'Antisocial.csv')
Table_1 <- ddply(Antisocial, "YOI", summarise, ASB_mean = mean(ASB))
Table_1
Race <- unique(Antisocial$Race)
Race
ggplot(data = Table_1, aes(x = YOI, y = ASB_mean, group_by(Race))) +
geom_point(colour = "Black", size = 2) + geom_line(data = Table_1, aes(YOI,
ASB_mean), colour = "orange", size = 1)
Image of plot: https://drive.google.com/file/d/1E-ePt9DZJaEr49m8fguHVS0thlVIodu9/view?usp=sharing
Data file: https://drive.google.com/file/d/1UeVTJ1M_eKQDNtvyUHRB77VDpSF1ASli/view?usp=sharing
Can someone help me understand where I am making mistake? I want to plot mean ASB vs YOI grouped by Race. Thanks.
I am not sure what is your desidered output. Maybe, if I well understood your question I Think that you want somthing like this.
g_Antisocial <- Antisocial %>%
group_by(Race) %>%
summarise(ASB = mean(ASB),
YOI = mean(YOI))
Antisocial %>%
ggplot(aes(x = YOI, y = ASB, color = as_factor(Race), shape = as_factor(Race))) +
geom_point(alpha = .4) +
geom_point(data = g_Antisocial, size = 4) +
theme_bw() +
guides(color = guide_legend("Race"), shape = guide_legend("Race"))
and this is the output:
#Maninder: there are a few things you need to look at.
First of all: The grammar of graphics of ggplot() works with layers. You can add layers with different data (frames) for the different geoms you want to plot.
The reason why your code is not working is that you mix the layer call and or do not really specify (and even mix) what is the scatter and line visualisation you want.
(I) Use ggplot() + geom_point() for a scatter plot
The ultimate first layer is: ggplot(). Think of this as your drawing canvas.
You then speak about adding a scatter plot layer, but you actually do not do it.
For example:
# plotting antisocal data set
ggplot() +
geom_point(data = Antisocial, aes(x = YOI, y = ASB, colour = as.factor(Race)))
will plot your Antiscoial data set using the scatter, i.e. geom_point() layer.
Note that I put Race as a factor to have a categorical colour scheme otherwise you might end up with a continous palette.
(II) line plot
In analogy to above, you would get for the line plot the following:
# plotting Table_1
ggplot() +
geom_line(data = Table_1, aes(x = YOI, y = ASB_mean))
I save showing the plot of the line.
(III) combining different layers
# putting both together
ggplot() +
geom_point(data = Antisocial, aes(x = YOI, y = ASB, colour = as.factor(Race))) +
geom_line(data = Table_1, aes(x = YOI, y = ASB_mean)) +
## this is to set the legend title and have a nice(r) name in your colour legend
labs(colour = "Race")
This yields:
That should explain how ggplot-layering works. Keep an eye on the datasets and geoms that you want to use. Before working with inheritance in aes, I recommend to keep the data= and aes() call in the geom_xxxx. This avoids confustion.
You may want to explore with geom_jitter() instead of geom_point() to get a bit of a better presentation of your dataset. The "few" points plotted are the result of many datapoints in the same position (and overplotted).
Moving away from plotting to your question "I want to plot mean ASB vs YOI grouped by Race."
I know too little about your research to fully comprehend what you mean with that.
I take it that the mean ASB you calculated over the whole population is your reference (aka your Table_1), and you would like to see how the Race groups feature vs this population mean.
One option is to group your race data points and show them as boxplots for each YOI.
This might be what you want. The boxplot gives you the median and quartiles, and you can compare this per group against the calculated ASB mean.
For presentation purposes, I highlighted the line by increasing its size and linetype. You can play around with the colours, etc. to give you the aesthetics you aim for.
Please note, that for the grouped boxplot, you also have to treat your integer variable YOI, I coerced into a categorical factor. Boxplot works with fill for the body (colour sets only the outer line). In this setup, you also need to supply a group value to geom_line() (I just assigned it to 1, but that is arbitrary - in other contexts you can assign another variable here).
ggplot() +
geom_boxplot(data = Antisocial, aes(x = as.factor(YOI), y = ASB, fill = as.factor(Race))) +
geom_line(data = Table_1, aes(x = as.factor(YOI), y = ASB_mean, group = 1)
, size = 2, linetype = "dashed") +
labs(x = "YOI", fill = "Race")
Hope this gets you going!
When I combine geom_vline() with facet_grid() like so:
DATA <- data.frame(x = 1:6,y = 1:6, f = rep(letters[1:2],3))
ggplot(DATA,aes(x = x,y = y)) +
geom_point() +
facet_grid(f~.) +
geom_vline(xintercept = 2:3,
colour =c("goldenrod3","dodgerblue3"))
I get an error message stating Error: Aesthetics must be either length 1 or the same as the data (4): colour because there are two lines in each facet and there are two facets. One way to get around this is to use rep(c("goldenrod3","dodgerblue3"),2), but this requires that every time I change the faceting variables, I also have to calculate the number of facets and replace the magic number (2) in the call to rep(), which makes re-using ggplot code so much less nimble.
Is there a way to get the number of facets directly from ggplot for use in this situation?
You could put the xintercept and colour info into a data.frame to pass to geom_vline and then use scale_color_identity.
ggplot(DATA, aes(x = x, y = y)) +
geom_point() +
facet_grid(f~.) +
geom_vline(data = data.frame(xintercept = 2:3,
colour = c("goldenrod3","dodgerblue3") ),
aes(xintercept = xintercept, color = colour) ) +
scale_color_identity()
This side-steps the issue of figuring out the number of facets, although that could be done by pulling out the number of unique values in the faceting variable with something like length(unique(DATA$f)).
I'd like to have some labels stacked on top of a geom_bar graph. Here's an example:
df <- data.frame(x=factor(c(TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE)))
ggplot(df) + geom_bar(aes(x,fill=x)) + opts(axis.text.x=theme_blank(),axis.ticks=theme_blank(),axis.title.x=theme_blank(),legend.title=theme_blank(),axis.title.y=theme_blank())
Now
table(df$x)
FALSE TRUE
3 5
I'd like to have the 3 and 5 on top of the two bars. Even better if I could have the percent values as well. E.g. 3 (37.5%) and 5 (62.5%). Like so:
(source: skitch.com)
Is this possible? If so, how?
To plot text on a ggplot you use the geom_text. But I find it helpful to summarise the data first using ddply
dfl <- ddply(df, .(x), summarize, y=length(x))
str(dfl)
Since the data is pre-summarized, you need to remember to change add the stat="identity" parameter to geom_bar:
ggplot(dfl, aes(x, y=y, fill=x)) + geom_bar(stat="identity") +
geom_text(aes(label=y), vjust=0) +
opts(axis.text.x=theme_blank(),
axis.ticks=theme_blank(),
axis.title.x=theme_blank(),
legend.title=theme_blank(),
axis.title.y=theme_blank()
)
As with many tasks in ggplot, the general strategy is to put what you'd like to add to the plot into a data frame in a way such that the variables match up with the variables and aesthetics in your plot. So for example, you'd create a new data frame like this:
dfTab <- as.data.frame(table(df))
colnames(dfTab)[1] <- "x"
dfTab$lab <- as.character(100 * dfTab$Freq / sum(dfTab$Freq))
So that the x variable matches the corresponding variable in df, and so on. Then you simply include it using geom_text:
ggplot(df) + geom_bar(aes(x,fill=x)) +
geom_text(data=dfTab,aes(x=x,y=Freq,label=lab),vjust=0) +
opts(axis.text.x=theme_blank(),axis.ticks=theme_blank(),
axis.title.x=theme_blank(),legend.title=theme_blank(),
axis.title.y=theme_blank())
This example will plot just the percentages, but you can paste together the counts as well via something like this:
dfTab$lab <- paste(dfTab$Freq,paste("(",dfTab$lab,"%)",sep=""),sep=" ")
Note that in the current version of ggplot2, opts is deprecated, so we would use theme and element_blank now.
Another solution is to use stat_count() when dealing with discrete variables (and stat_bin() with continuous ones).
ggplot(data = df, aes(x = x)) +
geom_bar(stat = "count") +
stat_count(geom = "text", colour = "white", size = 3.5,
aes(label = ..count..),position=position_stack(vjust=0.5))
So, this is our initial plot↓
library(ggplot2)
df <- data.frame(x=factor(c(TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE)))
p <- ggplot(df, aes(x = x, fill = x)) +
geom_bar()
p
As suggested by yuan-ning, we can use stat_count().
geom_bar() uses stat_count() by default. As mentioned in the ggplot2 reference, stat_count() returns two values: count for number of points in bin and prop for groupwise proportion. Since our groups match the x values, both props are 1 and aren’t useful. But we can use count (referred to as “..count..”) that actually denotes bar heights, in our geom_text(). Note that we must include “stat = 'count'” into our geom_text() call as well.
Since we want both counts and percentages in our labels, we’ll need some calculations and string pasting in our “label” aesthetic instead of just “..count..”. I prefer to add a line of code to create a wrapper percent formatting function from the “scales” package (ships along with “ggplot2”).
pct_format = scales::percent_format(accuracy = .1)
p <- p + geom_text(
aes(
label = sprintf(
'%d (%s)',
..count..,
pct_format(..count.. / sum(..count..))
)
),
stat = 'count',
nudge_y = .2,
colour = 'royalblue',
size = 5
)
p
Of course, you can further edit the labels with colour, size, nudges, adjustments etc.