average
Young 0.01921875
Cohoused Young 0.07111951
Old 0.06057224
Cohoused Old 0.12102273
I am using the above data frame to create a histogram or bar and my code is as follows:
C <-ggplot(data=c,aes(x=average))
C + geom_bar()
but the plot is attached here.
I would like the bar heights to reflect my data on the y axis instead of where the bar is placed on the x axis, but I don't know what my problem is in the code.
We can create a column with rownames_to_column
library(dplyr)
library(tibble)
library(ggplot2)
c %>%
rownames_to_column('rn') %>%
ggplot(aes(x = rn, y = average)) +
geom_col()
Or create a column directly in base R
c$rn <- row.names(c)
ggplot(c, aes(x = rn, y = average)) +
geom_col()
Or as #user20650 suggested
ggplot(data=c,aes(x=rownames(c) , y=average))
NOTE: It is better not to name objects with function names (c is a function)
In base R, with barplot, we can directly get the plots
barplot(as.matrix(c))
Related
I made an upset plot using the ggupset package and added a break to the y axis with scale_y_break from the ggbreakpackage.
However, when I add scale_y_break, the combination matrix under the bar plot disappears.
Is there a way to combine the combination matrix of the plot made without scale_y_break with the bar plot portion of a plot made with scale_y_break? I can't seem to be able to access the grobs of these plots or use any other workaround. If anyone could help, I would greatly appreciate it!
Example with scale_x_upset and scale_y_break:
df = tidy_movies %>% distinct(title, year, length, .keep_all=TRUE)
ggplot(df, aes(x=Genres)) + geom_bar() + scale_x_upset(n_intersections = 20)+ scale_y_break(breaks = c(750,1000))
I would like to combine the barplot portion of the plot created with:
df = tidy_movies %>% distinct(title, year, length, .keep_all=TRUE)
ggplot(df, aes(x=Genres)) + geom_bar() + scale_x_upset(n_intersections = 20)+ scale_y_break(breaks = c(750,1000))
with the combination matrix portion of the plot made with:
df = tidy_movies %>% distinct(title, year, length, .keep_all=TRUE)
ggplot(df, aes(x=Genres)) + geom_bar() + scale_x_upset(n_intersections = 20)
Thanks!
Below is a simple ggplot bar plot:
x<-c(1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3)
y<-c(1,2,3,4,5,3,3,3,3,4,5,5,6,7,6,5,4,3,2,3,4,5,3,2,1,1,1,1,1)
d<-cbind(x,y)
ggplot(data=d,aes(x=x,fill=as.factor(y)))+
geom_bar(position = position_dodge())
The issue I'm having is that each value of y is not present in each grouping x. So for example, group 1 along the x-axis only contains groups 1-5 of the y variable, and doesn't have any values for 6 or 7. What I would like is for the plot to leave blank spaces when there is are no values for a y in the given x-grouping, this way it is easier to compare the x-groups.
A solution is to compute the frequencies manually and plot the graph based on that frequencies table.
library(ggplot2)
d1 <- data.frame(table(d))
d1$x <- factor(d1$x)
ggplot(d1, aes(x, Freq, fill = factor(y))) +
geom_bar(stat = "identity", position = position_dodge())
library(tidyverse)
# set factor levels
d2 <- d %>% data.frame() %>% mutate(x=factor(x, levels=c(1:3)),
y=factor(y, levels=c(1:7)))
# count frequencies and send to ggplot2
d2 %>% group_by(x, y, .drop=F) %>% tally() %>%
ggplot(aes(x=x, y=n, fill=y, color=y)) +
geom_bar(position = position_dodge2(),
stat="identity")
Another way to do this using dplyr is to use tally() to count the frequencies, but you need to make sure that you have your variables set as factors first.
Using color=y & fill=y in the aes statement helps to show exactly where on the plot the zero values are. So, now you can see that it is y=6 & y=7 missing from x=1 & x=3, and y=1 missing from x=2
And I chose position_dodge2 for my own personal preferences.
I'm creating a frequency plot using ggplot and the stat_ecdf function. I would like to add the Y-value to the graph for specific X-values, but just can't figure out how. geom_point or geom_text seems likely options, but as stat_ecdf automatically calculates Y, I don't know how to call that value in the geom_point/text mappings.
Sample code for my initial plot is:
x = as.data.frame(rnorm(100))
ggplot(x, aes(x)) +
stat_ecdf()
Now how would I add specific y-x points here, e.g. y-value at x = -1.
The easiest way is to create the ecdf function beforehand using ecdf() from the stats package, then plot it using geom_label().
library(ggplot2)
# create a data.frame with column name
x = data.frame(col1 = rnorm(100))
# create ecdf function
e = ecdf(x$col1)
# plot the result
ggplot(x, aes(col1)) +
stat_ecdf() +
geom_label(aes(x = -1, y = e(-1)),
label = e(-1))
You can try
library(tidyverse)
# data
set.seed(123)
df = data.frame(x=rnorm(100))
# Plot
Values <- c(-1,0.5,2)
df %>%
mutate(gr=FALSE) %>%
bind_rows(data.frame(x=Values,gr=TRUE)) %>%
mutate(y=ecdf(x)(x)) %>%
mutate(xmin=min(x)) %>%
ggplot(aes(x, y)) +
stat_ecdf() +
geom_point(data=. %>% filter(gr), aes(x, y)) +
geom_segment(data=. %>% filter(gr),aes(y=y,x=xmin, xend=x,yend=y), color="red")+
geom_segment(data=. %>% filter(gr),aes(y=0,x=x, xend=x,yend=y), color="red") +
ggrepel::geom_label_repel(data=. %>% filter(gr),
aes(x, y, label=paste("x=",round(x,2),"\ny=",round(y,2))))
The idea is to add the y values in the beginning, together with the index gr specifing which Values you want to show.
Edit:
Since this code adds points to the actual data, which could be wrong for the curve, one should consider to remove these points at least in the ecdf function stat_ecdf(data=. %>% filter(!gr))
i need your help.
I was trying to do a stacked bar plot in R and i m not succeding for the moment. I have read several post but, no succed neither.
Like i am newbie, this is the chart I want (I made it in excel)
And this is how i have the data
Thank you in advance
I would use the package ggplot2 to create this plot as it is easier to position text labels than compared to the basic graphics package:
# First we create a dataframe using the data taken from your excel sheet:
myData <- data.frame(
Q_students = c(1000,1100),
Students_with_activity = c(950, 10000),
Average_debt_per_student = c(800, 850),
Week = c(1,2))
# The data in the dataframe above is in 'wide' format, to use ggplot
# we need to use the tidyr package to convert it to 'long' format.
library(tidyr)
myData <- gather(myData,
Condition,
Value,
Q_students:Average_debt_per_student)
# To add the text labels we calculate the midpoint of each bar and
# add this as a column to our dataframe using the package dplyr:
library(dplyr)
myData <- group_by(myData,Week) %>%
mutate(pos = cumsum(Value) - (0.5 * Value))
#We pass the dataframe to ggplot2 and then add the text labels using the positions which
#we calculated above to place the labels correctly halfway down each
#column using geom_text.
library(ggplot2)
# plot bars and add text
p <- ggplot(myData, aes(x = Week, y = Value)) +
geom_bar(aes(fill = Condition),stat="identity") +
geom_text(aes(label = Value, y = pos), size = 3)
#Add title
p <- p + ggtitle("My Plot")
#Plot p
p
so <- data.frame ( week1= c(1000,950,800), week2=c(1100,10000,850),row.names = c("Q students","students with Activity","average debt per student")
barplot(as.matrix(so))
Here is a snapshot of data:
restaurant_change_sales = c(3330.443, 3122.534)
restaurant_change_labor = c(696.592, 624.841)
restaurant_change_POS = c(155.48, 139.27)
rest_change = data.frame(restaurant_change_sales, restaurant_change_labor, restaurant_change_POS)
I want two bars for each of the columns indicating the change. One graph for each of the columns.
I tried:
ggplot(aes(x = rest_change$restaurant_change_sales), data = rest_change) + geom_bar()
This is not giving the result the way I want. Please help!!
So ... something like:
library(ggplot2)
library(dplyr)
library(tidyr)
restaurant_change_sales = c(3330.443, 3122.534)
restaurant_change_labor = c(696.592, 624.841)
restaurant_change_POS = c(155.48, 139.27)
rest_change = data.frame(restaurant_change_sales,
restaurant_change_labor,
restaurant_change_POS)
cbind(rest_change,
change = c("Before", "After")) %>%
gather(key,value,-change) %>%
ggplot(aes(x = change,
y = value)) +
geom_bar(stat="identity") +
facet_grid(~key)
Which will produce:
Edit:
To be extra fancy e.g. make it so that the order of x-axis labels goes from "Before" to "After", you can add this line: scale_x_discrete(limits = c("Before", "After")) to the end of the ggplot function
Your data are not formatted properly to work well with ggplot2, or really any of the plotting packages in R. So we'll fix your data up first, and then use ggplot2 to plot it.
library(tidyr)
library(dplyr)
library(ggplot2)
# We need to differentiate between the values in the rows for them to make sense.
rest_change$category <- c('first val', 'second val')
# Now we use tidyr to reshape the data to the format that ggplot2 expects.
rc2 <- rest_change %>% gather(variable, value, -category)
rc2
# Now we can plot it.
# The category that we added goes along the x-axis, the values go along the y-axis.
# We want a bar chart and the value column contains absolute values, so no summation
# necessary, hence we use 'identity'.
# facet_grid() gives three miniplots within the image for each of the variables.
ggplot2(rc2, aes(x=category, y=value, facet=variable)) +
geom_bar(stat='identity') +
facet_grid(~variable)
You have to melt your data:
library(reshape2) # or library(data.table)
rest_change$rowN <- 1:nrow(rest_change)
rest_change <- melt(rest_change, id.var = "rowN")
ggplot(rest_change,aes(x = rowN, y = value)) + geom_bar(stat = "identity") + facet_wrap(~ variable)