I need to make a boxplot with groups and variables using the data below :
df<-as.data.frame(cbind(c(1,2,3),
c(0.4,-0.11,-0.07),
c(0.31,0.07,0),
c(0.45,-0.23,0.02)))
names(df)<-c('cat','var1','var2','var3')
I need to make a barplot with the cat1 on the abscissa and the measurements of each variables on the ordinate.
For example concerning the cat=1, I need in the abscissa the number of cat1 with 3 barplots representing the value of (var1,..var3).
library(tidyverse)
df <- df %>%
gather(var, val, -cat)
ggplot(df, aes(cat, val, fill=var)) +
geom_col(position="dodge")
Related
I want to generate a point plot which shows equal number of rows in each frame (can be facets if nothing else works) & in alphabetical order (A1,A2,B2,B2 etc.) since the plot length is too high to see the axis labels clearly. I want to break this plot into 4 windows with the same number of rows i.e. 13 each. (preferably tidyverse & not hard coded # of rows)
library(tidyverse)
df <- data.frame(names=c(paste0(LETTERS,1),paste0(LETTERS,2)),value=1:52)
df %>%
arrange(desc(names)) %>%
ggplot(aes(y=names,x=value))+
geom_point()+
scale_y_discrete(limits=rev)
We can create a grouping column with gl and use facet_wrap
library(dplyr)
library(ggplot2)
df %>%
arrange(desc(names)) %>%
mutate(grp = as.integer(gl(n(), ceiling(n()/4), n()))) %>%
ggplot(aes(y=names,x=value))+
geom_point() +
facet_wrap(~ grp, scales = 'free_y')
-output
Suppose I have the following dataset
set.seed(85)
a <- data.frame(replicate(10,sample(0:3,5,rep=TRUE)))
and I plot it in the following way:
library(ggplot2)
ggplot(stack(a), aes(x = values)) +
geom_bar()
From the graph I can read that there are a little less than 1250 occurrences of '3' in the dataset, but is there a way to output frequency of each x-axis value in the dataset as an independent list (i.e. not as numbers on the barplot)? I am looking for a list of how many occurrences of '3' there are in the dataset (and also for the values, 0, 1, & 2).
output:
0: 1249
1: 1200
2: ...
3: ...
Any help is much appreciated
We can convert to 'long' format and then do the count
library(dplyr)
library(tidyr)
a %>%
pivot_longer(everything()) %>%
count(value)
To get the barplot
library(ggplot2)
a %>%
pivot_longer(everything()) %>%
count(value) %>%
ggplot(aes(x = value, y = n)) +
geom_bar(stat = 'identity')
In base R, unlist and get the table
table(unlist(a))
or for plotting
barplot(table(unlist(a)))
I'm trying to do a plot with facets with some data from a previous model. As a simple example:
t=1:10;
x1=t^2;
x2=sqrt(t);
y1=sin(t);
y2=cos(t);
How can I plot this data in a 2x2 grid, being the rows one factor (levels x and y, plotted with different colors) and the columns another factor (levels 1 and 2, plotted with different linetypes)?
Note: t is the common variable for the X axis of all subplots.
ggplot will be more helpful if the data can be first put into tidy form. df is your data, df_tidy is that data in tidy form, where the series is identified in one column that can be mapped in ggplot -- in this case to the facet.
library(tidyverse)
df <- tibble(
t=1:10,
x1=t^2,
x2=sqrt(t),
y1=sin(t),
y2=cos(t),
)
df_tidy <- df %>%
gather(series, value, -t)
ggplot(df_tidy, aes(t, value)) +
geom_line() +
facet_wrap(~series, scales = "free_y")
I have a task and i need to plot graph using ggplot2.
I have a vector of rating (Samsung S4 ratings from its users)
I generate this data using this:
TestRate<- data.frame (rating=sample (x =1:5, size=100, replace=T ), month= sample(x=1:12,size=100,rep=T) )
And now I need to plot a graph, where on X axis will be dates (monthes in our example data) and 5 different lines grouped by 5 different ratings (1,2,3,4,5). Each line shows count of its ratings for corresponding month
How can I plot this in ggplot2?
You need first to count the number of elements per couple of (rating, month):
library(data.table)
setDT(TestRate)[,count:=.N,by=list(month, rating)]
And then you can plot the result:
ggplot(TestRate, aes(month, count, color=as.factor(rating))) + geom_line()
If your data.table is not set (so to speak), you can use dplyr (and rename the legend while you are at it).
df <- TestRate %>% group_by(rating, month) %>% summarise(count = n())
ggplot(df, aes(x=month, y=count, color=as.factor(rating))) + geom_line() + labs(color = "Rating")
I am trying to make a grouped barplot and I am running into trouble. For example, if I was using the mtcars dataset and I wanted to group everything by the 'vs' column (col #8), find the average of all remaining columns, and then plot them by group.
Below is a very poor example of what I am trying to do and I know it is incorrect.
Ideally, mpg for vs=1 & vs=0 would be side by side, followed by cyl's means side by side, etc. I don't care if aggregate is skipped for dyplr or if ggplot is used or even if the aggregate step is not needed...just looking for a way to do this since it is driving me crazy.
df = mtcars
agg = aggregate(df[,-8], by=list(df$vs), FUN=mean)
agg
barplot(t(agg), beside=TRUE, col=df$vs))
Try
library(ggplot2)
library(dplyr)
library(tidyr)
df %>%
group_by(vs=factor(vs)) %>%
summarise_each(funs(mean)) %>%
gather(Var, Val, -vs) %>%
ggplot(., aes(x=Var, y=Val, fill=vs))+
geom_bar(stat='identity', position='dodge')
Or using base R
m1 <- as.matrix(agg[-1])
row.names(m1) <- agg[,1]
barplot(m1, beside=TRUE, col=c('red', 'blue'), legend=row.names(m1))