I am wondering what is the idiomatic approach for constructing a ggplot2 boxplot given precomputated values for each boxplot
> df
base p10 p90 lower_quartile mean median upper_quartile
1 1 32 35 33 33.63740 34 34
2 2 32 35 33 33.77753 34 35
3 3 32 36 33 33.89361 34 35
4 4 33 36 33 33.89691 34 35
5 5 32 35 33 33.85145 34 35
6 6 35 37 37 36.48259 37 37
Attempting to draw these plots with
ggplot(df, aes(base)) +
geom_boxplot(aes(ymin = p10,
lower = lower_quartile,
middle = median,
upper = upper_quartile,
ymax = p90),
stat = "identity")
does not give the desired plots. What am I missing?
I don't know what base represents in your data.frame but in order to do it correctly your x-axis is supposed to be discrete (to show the different boxplots). Then for the y axis you need a ymin, a lower, a middle, an upper and a ymax which you have provided. The x-axis is the variable that is used to plot the different boxplots. So, if you turn it into a factor then it works:
library(ggplot2)
#I have added base as factor
ggplot(df, aes(factor(base))) +
geom_boxplot(aes(ymin = p10,
lower = lower_quartile,
middle = median,
upper = upper_quartile,
ymax = p90),
stat = "identity")
Output:
And this way it works.
Related
I have the following data
Input Rtime Rcost Rsolutions Btime Bcost
1 12 proc. 1 36 614425 40 36
2 15 proc. 1 51 534037 50 51
3 18-proc 5 62 1843820 66 66
4 20-proc 4 68 1645581 104400 73
5 20-proc(l) 4 64 1658509 14400 65
6 21-proc 10 78 3923623 453600 82
I want to create a grouped bar chart from this data such that x-axis contains Input field (as groups) and y axis represent the log scale for the Rtime and Btime fields (the two bars).
All solutions/examples I checked online had similar data put into a three column layout. I do not know how to use the data I have to generate the grouped bar-chart. Or if there is a way to convert this data (manually converting is not an options because it is a huge file with a lot of rows) into a R and ggplot compatible data format.
Edit :
Graph generated using gncs solution
As requested, a ggplot2 solution that also uses reshape2:
library(reshape2)
df <- read.table(text = " Input Rtime Rcost Rsolutions Btime Bcost
1 12-proc. 1 36 614425 40 36
2 15-proc. 1 51 534037 50 51
3 18-proc 5 62 1843820 66 66
4 20-proc 4 68 1645581 104400 73
5 20-proc(l) 4 64 1658509 14400 65
6 21-proc 10 78 3923623 453600 82",header = TRUE,sep = "")
dfm <- melt(df[,c('Input','Rtime','Btime')],id.vars = 1)
ggplot(dfm,aes(x = Input,y = value)) +
geom_bar(aes(fill = variable),stat = "identity",position = "dodge") +
scale_y_log10()
Note a style difference here, where since log(1) = 0, ggplot2 treats that as a bar of zero height and doesn't plot anything, whereas barplot plots a little stub (which in my opinion is a little misleading).
I think I understand the problem and this is what I would suggest (short run - option):
data <- read.table("data.txt", header=TRUE)
subset <- t(data.frame(data$Rtime, data$Btime))
barplot(subset, legend = c("Rtime", "Btime"), names.arg=data$Input, log="y", beside=TRUE)
Is that what you want? It is kind of dirty, but it does the job.
Update: code corrected.
As requested, a ggplot2 solution that also uses pivot_longer() https://tidyr.tidyverse.org/reference/pivot_longer.html to transform the data into a format that geom_bar() can easily plot.
library(dplyr)
library(ggplot2)
df <- read.table(text = " Input Rtime Rcost Rsolutions Btime Bcost
1 12-proc. 1 36 614425 40 36
2 15-proc. 1 51 534037 50 51
3 18-proc 5 62 1843820 66 66
4 20-proc 4 68 1645581 104400 73
5 20-proc(l) 4 64 1658509 14400 65
6 21-proc 10 78 3923623 453600 82",
header = TRUE,sep = "")
dfm <- pivot_longer(df, -Input, names_to="variable", values_to="value")
## pivot_longer takes the input data frame, excludes the Input field from the transformation, turns the remaining column names into the variable "variable" (often called the "key"), and assigns the values to the variable "value".
ggplot(dfm,aes(x = Input,y = value)) +
geom_bar(aes(fill = variable),stat = "identity",position = "dodge") +
scale_y_log10()
joran's answer helped me a lot, but I had to use stat="identity" in the ggplot statement like that:
ggplot(dfm, aes(x = Input,y = value)) +
geom_bar(aes(fill = variable), position = "dodge", stat="identity") +
scale_y_log10()
My version of R is 3.2.2 and ggplot2 version 1.0.1
Thanks.
I want to create a 2 by 2 faceted plot with a vertical line shared by the four facets. However, because the facets on top have the same date information as the facets at the bottom, I only want to have the vline annotated twice: in this case in the two facets at the bottom.
I looked a.o. here, which does not work for me. (In addition I have my doubts whether this is still valid code, today.) I also looked here. I also looked up how to influence the font size in geom_text: according to the help pages this is size. In the case below it doesn't work out well.
This is my code:
library(ggplot2)
library(tidyr)
my_df <- read.table(header = TRUE, text =
"Date AM_PM First_Second Systolic Diastolic Pulse
01/12/2017 AM 1 134 83 68
01/12/2017 PM 1 129 84 76
02/12/2017 AM 1 144 88 56
02/12/2017 AM 2 148 93 65
02/12/2017 PM 1 131 85 59
02/12/2017 PM 2 129 83 58
03/12/2017 AM 1 153 90 62
03/12/2017 AM 2 143 92 59
03/12/2017 PM 1 139 89 56
03/12/2017 PM 2 141 86 56
04/12/2017 AM 1 140 87 58
04/12/2017 AM 2 135 85 55
04/12/2017 PM 1 140 89 67
04/12/2017 PM 2 128 88 69
05/12/2017 AM 1 134 99 67
05/12/2017 AM 2 128 90 63
05/12/2017 PM 1 136 88 63
05/12/2017 PM 2 123 83 61
")
# setting the classes right
my_df$Date <- as.Date(as.character(my_df$Date), format = "%d/%m/%Y")
my_df$First_Second <- as.factor(my_df$First_Second)
# to tidy format
my_df2 <- gather(data = my_df, key = Measure, value = Value,
-c(Date, AM_PM, First_Second), factor_key = TRUE)
# Measures in 1 facet, facets split over AM_PM and First_Second
## add anntotations column for geom_text
my_df2$Annotations <- rep("", 54)
my_df2$Annotations[c(4,6)] <- "Start"
p2 <- ggplot(data = my_df2) +
ggtitle("Blood Pressure and Pulse as a function of AM/PM,\n Repetition, and date") +
geom_line(aes(x = Date, y = Value, col= Measure, group = Measure), size = 1.) +
geom_point(aes(x = Date, y = Value, col= Measure, group = Measure), size= 1.5) +
facet_grid(First_Second ~ AM_PM) +
geom_vline(aes(xintercept = as.Date("2017/12/02")), linetype = "dashed",
colour = "darkgray") +
theme(axis.text.x=element_text(angle = -90))
p2
yields this graph:
This is the basic plot from which I start. Now we try to annotate it.
p2 + annotate(geom="text", x = as.Date("2017/12/02"), y= 110, label="start", size= 3)
yielding this plot:
This plot has the problem that the annotation occurs 4 times, while we only want it in the bottom parts of the graph.
Now we use geom_text which will use the "Annotations" column in our dataframe, in line with this SO Question. Be carefull, the column added to the dataframe must be present when you create "p2", the first time (that is why we added the column supra)
p2 + geom_text(aes(x=as.Date("2017/12/02"), y=100, label = Annotations, size = .6))
yielding this plot:
Yes, we succeeded in getting the annotation only in the bottom two parts of the graph. But the font is too big ( ... and ugly) and when we try to correct it with size, two things are interesting: (1) the font size is not changed (although you would expect that from the help pages) and (2) a legend is added.
I have been clicking around a lot and have been unable to solve this after hours and hours. Any help would be appreciated.
I have a problem with drawing stacked barplot with ggplot. My data looks like this:
timeInterval TotalWilling TotalAccepted SimID
1 16 12 Sim1
1 23 23 Sim2
1 63 60 Sim3
1 69 60 Sim4
1 61 60 Sim5
1 60 54 Sim6
2 16 8 Sim1
2 23 21 Sim2
2 63 52 Sim3
2 69 64 Sim4
2 61 45 Sim5
2 60 32 Sim6
3 16 14 Sim1
3 23 11 Sim2
3 63 59 Sim3
3 69 69 Sim4
3 61 28 Sim5
3 60 36 Sim6
I would like to draw a stacked barplot for each simID over a timeInterval, and Willing and Accepted should be stacked. I achieved the barplot with the following simple code:
dat <- read.csv("myDat.csv")
meltedDat <- melt(dat,id.vars = c("SimID", "timeInterval"))
ggplot(meltedDat, aes(timeInterval, value, fill = variable)) + facet_wrap(~ SimID) +
geom_bar(stat="identity", position = "stack")
I get the following graph:
Here my problem is that I would like to put percentages on each stack. Which means, I want to put percentage as for Willing label: (Willing/(Willing+Accepted)) and for Accepted part, ((Accepted/(Accepted+Willing)) so that I can see how many percent is willing how many is accepted such as 45 on red part of stack to 55 on blue part for each stack. I cannot seem to achieve this kind of labeling.
Any hint is appreciated.
applied from Showing data values on stacked bar chart in ggplot2
meltedDat <- melt(dat,id.vars = c("SimID", "timeInterval"))
meltedDat$normvalue <- meltedDat$value
meltedDat$valuestr <- sprintf("%.2f%%", meltedDat$value, meltedDat$normvalue*100)
meltedDat <- ddply(meltedDat, .(timeInterval, SimID), transform, pos = cumsum(normvalue) - (0.5 * normvalue))
ggplot(meltedDat, aes(timeInterval, value, fill = variable)) + facet_wrap(~ SimID) + geom_bar(stat="identity", position = "stack") + geom_text(aes(x=timeInterval, y=pos, label=valuestr), size=2)
also, it looks like you may have some of your variables coded as factors.
I have a df like this.
> te1.m.comb
temp variable value
1 35 Light.180.1.x.MAX1 10.398333
3 35 Dark.180.1.x.MAX1 -4.337142
5 35 Light.288.5.x.MAX3 17.825376
7 35 Dark.288.5.x.MAX3 -4.331998
9 35 Light.D125.x.K1 15.150205
11 35 Dark.D125.x.K1 -4.376553
13 35 Light.SO443WL.x.SO479WL 11.003542
15 35 Dark.SO443WL.x.SO479WL -3.216878
17 35 Light.SO450WL.x.SO465WL 15.970640
19 35 Dark.SO450WL.x.SO465WL -3.109330
21 35 Light.SO459WL.x.SO469WL 11.393617
23 35 Dark.SO459WL.x.SO469WL -3.857454
2 40 Light.180.1.x.MAX1 8.589651
4 40 Dark.180.1.x.MAX1 -5.569157
6 40 Light.288.5.x.MAX3 15.977499
8 40 Dark.288.5.x.MAX3 -5.582502
10 40 Light.D125.x.K1 13.651815
12 40 Dark.D125.x.K1 -5.243391
14 40 Light.SO443WL.x.SO479WL 8.518077
16 40 Dark.SO443WL.x.SO479WL -4.861841
18 40 Light.SO450WL.x.SO465WL 13.691814
20 40 Dark.SO450WL.x.SO465WL -4.514559
22 40 Light.SO459WL.x.SO469WL 9.262019
24 40 Dark.SO459WL.x.SO469WL -5.138836
I would like to plot the relative proportions using ggplot. For example, instead of plotting each of the variable and its value, i would like to plot the ratio value of Light.180.1.x.MAX1 / Dark.180.1.x.MAX1 i.e 10.398333/-4.337142 and so on. How can i do that in ggplot?
Here is my boxplot code which just plots each of the variable and its value..
ggplot(te1.m.comb, aes(variable, value)) + geom_boxplot() + facet_grid(temp ~.)
I renamed your data.frame df so that the reading can be easy and added the ratio column:
df$ratio = with(df, c(value/c(value[-1],NA)))
Here is the plot:
library(ggplot2)
ggplot(df, aes(variable, ratio)) +
geom_bar(stat = "identity") +
facet_grid(temp~.) + ยจ
scale_y_reverse()
I have the following data
Input Rtime Rcost Rsolutions Btime Bcost
1 12 proc. 1 36 614425 40 36
2 15 proc. 1 51 534037 50 51
3 18-proc 5 62 1843820 66 66
4 20-proc 4 68 1645581 104400 73
5 20-proc(l) 4 64 1658509 14400 65
6 21-proc 10 78 3923623 453600 82
I want to create a grouped bar chart from this data such that x-axis contains Input field (as groups) and y axis represent the log scale for the Rtime and Btime fields (the two bars).
All solutions/examples I checked online had similar data put into a three column layout. I do not know how to use the data I have to generate the grouped bar-chart. Or if there is a way to convert this data (manually converting is not an options because it is a huge file with a lot of rows) into a R and ggplot compatible data format.
Edit :
Graph generated using gncs solution
As requested, a ggplot2 solution that also uses reshape2:
library(reshape2)
df <- read.table(text = " Input Rtime Rcost Rsolutions Btime Bcost
1 12-proc. 1 36 614425 40 36
2 15-proc. 1 51 534037 50 51
3 18-proc 5 62 1843820 66 66
4 20-proc 4 68 1645581 104400 73
5 20-proc(l) 4 64 1658509 14400 65
6 21-proc 10 78 3923623 453600 82",header = TRUE,sep = "")
dfm <- melt(df[,c('Input','Rtime','Btime')],id.vars = 1)
ggplot(dfm,aes(x = Input,y = value)) +
geom_bar(aes(fill = variable),stat = "identity",position = "dodge") +
scale_y_log10()
Note a style difference here, where since log(1) = 0, ggplot2 treats that as a bar of zero height and doesn't plot anything, whereas barplot plots a little stub (which in my opinion is a little misleading).
I think I understand the problem and this is what I would suggest (short run - option):
data <- read.table("data.txt", header=TRUE)
subset <- t(data.frame(data$Rtime, data$Btime))
barplot(subset, legend = c("Rtime", "Btime"), names.arg=data$Input, log="y", beside=TRUE)
Is that what you want? It is kind of dirty, but it does the job.
Update: code corrected.
As requested, a ggplot2 solution that also uses pivot_longer() https://tidyr.tidyverse.org/reference/pivot_longer.html to transform the data into a format that geom_bar() can easily plot.
library(dplyr)
library(ggplot2)
df <- read.table(text = " Input Rtime Rcost Rsolutions Btime Bcost
1 12-proc. 1 36 614425 40 36
2 15-proc. 1 51 534037 50 51
3 18-proc 5 62 1843820 66 66
4 20-proc 4 68 1645581 104400 73
5 20-proc(l) 4 64 1658509 14400 65
6 21-proc 10 78 3923623 453600 82",
header = TRUE,sep = "")
dfm <- pivot_longer(df, -Input, names_to="variable", values_to="value")
## pivot_longer takes the input data frame, excludes the Input field from the transformation, turns the remaining column names into the variable "variable" (often called the "key"), and assigns the values to the variable "value".
ggplot(dfm,aes(x = Input,y = value)) +
geom_bar(aes(fill = variable),stat = "identity",position = "dodge") +
scale_y_log10()
joran's answer helped me a lot, but I had to use stat="identity" in the ggplot statement like that:
ggplot(dfm, aes(x = Input,y = value)) +
geom_bar(aes(fill = variable), position = "dodge", stat="identity") +
scale_y_log10()
My version of R is 3.2.2 and ggplot2 version 1.0.1
Thanks.