I would like to create a horizontal bar graph from my data.
The link to my data is here.
The code that I am using
library(ggplot2)
ggplot(data=df , aes(x=fct_inorder(WorkSchedule),y=timing, fill=Value)) + geom_col() + coord_flip()
The output of the plot:
How to change the x-axis to show time from 04:00 till 03:45 (24h)
I tried factor(Source) but it does not work.
UPDATE# How can I change the x axis of this graph?
Many tahnks
With the function lvls_reorder() from library forçats, you can specify the order of the levels of your variable.
library(tidyverse) # forcats is included in tidyverse library
df <- df %>%
mutate(Workschedule = lvls_reorder(Workschedule, c(3,2,4,5,1))
If you transform the variable Source as a factor, you can also determine the order you want.
Related
I need help in creating a scatter plot for each category separately in the same graph for a variable with different colors as shown in the following picture. (Hb is continous variable and NO2 is catagorical variable (quintiles))
We'll need to see a minimum amount of example data to help. Here is one way to approach this type of chart:
library(tidyverse)
iris2<-iris %>% mutate(S2=paste(Species, 1:2, sep="-"))
iris2 %>%
ggplot(aes(x=Sepal.Width, y=Sepal.Length, color=Petal.Length)) +
geom_point() +
facet_wrap(vars(S2), nrow=3)
I find countless examples of reordering X by the corresponding size of Y if the Dataframe for ggplot2 (geom_bar) is read using stat="identity".
I have yet to find an example of stat="count". The reorder function fails as I have no corresponding y.
I have a factored DF of one column, "count" (see below for a poor example), where there are multiple instances of the data as you would expect. However, I expected factored data to be displayed:
ggplot(df, aes(x=df$count)) + geom_bar()
by the order defined from the quantity of each factor, as it is different for unfactored (character) data i.e., will display alphabetically.
Any idea how to reorder?
This is my current awful effort, sadly I figured this out last night, then lost my R command history:
If you start off your project with loading the tidyverse, I suggest you use the built-in tidyverse function: fct_infreq()
ggplot(df, aes(x=fct_infreq(df$count))) + geom_bar()
As your categories are words, consider adding coord_flip() so that your bars run horizontally.
ggplot(df, aes(x=fct_infreq(df$count))) + geom_bar() + coord_flip()
This is what it looks like with some fish species counts: A horzontal bar chart with species on the y axis (but really the flipped x-axis) and counts on horizontal axis (but actually the flipped y-axis). The counts are sorted from least to greatest.
Converting the counts to a factor and then modifying that factor might help accomplish what you need. In the below I'm reversing the order of the counts using fct_rev from the forcats package (part of tidyverse)
library(tidyverse)
iris %>%
count(Sepal.Length) %>%
mutate(n=n %>% as.factor %>% fct_rev) %>%
ggplot(aes(n)) + geom_bar()
Alternatively, if you'd like the bars to be arranged large to small, you can use fct_infreq.
iris %>%
count(Sepal.Length) %>%
mutate(n=n %>% as.factor %>% fct_infreq) %>%
ggplot(aes(n)) + geom_bar()
I'm reading the book by Hadley Wickham about ggplot, but I have trouble to plot certain weights over time in a bar chart. Here is sample data:
dates <- c("20040101","20050101","20060101")
dates.f <- strptime(dates,format="%Y%m%d")
m <- rbind(c(0.2,0.5,0.15,0.1,0.05),c(0.5,0.1,0.1,0.2,0.1),c(0.2,0.2,0.2,0.2,0.2))
m <- cbind(dates.f,as.data.frame(m))
This data.frame has in the first column the dates and each row the corresponding weights. I would like to plot the weights for each year in a bar chart using the "fill" argument.
I'm able to plot the weights as bars using:
p <- ggplot(m,aes(dates.f))
p+geom_bar()
However, this is not exactly what I want. I would like to see in each bar the contribution of each weight. Moreover, I don't understand why I have the strange format on the x-axis, i.e. why there is "2004-07" and "2005-07" displayed.
Thanks for the help
Hope this is what you are looking for:
ggplot2 requires data in a long format.
require(reshape2)
m_molten <- melt(m, "dates.f")
Plotting itself is done by
ggplot(m_molten, aes(x=dates.f, y=value, fill=variable)) +
geom_bar(stat="identity")
You can add position="dodge" to geom_bar if you want then side by side.
EDIT
If you want yearly breaks only: convert m_molten$dates.f to date.
require(scales)
m_molten$dates.f <- as.Date(m_molten$dates.f)
ggplot(m_molten, aes(x=dates.f, y=value, fill=variable)) +
geom_bar(stat="identity") +
scale_x_date(labels = date_format("%y"), breaks = date_breaks("year"))
P.S.: See http://vita.had.co.nz/papers/tidy-data.pdf for Hadley's philosophy of tidy data.
To create the plot you need, you have to reshape your data from "wide" to "tall". There are many ways of doing this, including the reshape() function in base R (not recommended), reshape2 and tidyr.
In the tidyr package you have two functions to reshape data, gather() and spread().
The function gather() transforms from wide to tall. In this case, you have to gather your columns V1:V5.
Try this:
library("tidyr")
tidy_m <- gather(m, var, value, V1:V5)
ggplot(tidy_m,aes(x = dates.f, y=value, fill=var)) +
geom_bar(stat="identity")
I am interested in plotting the results from the following code which produces a frequency distribution table. I would like to graph the Freq column as a bar with the cum.Freq as a line both sharing the interval column as the x-axis.
library("qdap")
x <- c(1,2,3,2,4,2,5,4,6,7,8,9)
dist_tab(x)
I have been able to get the bar chart built using ggplot, but I want to take it further with the cum.Freq added as a secondary axis. I also want to add the percent and cum.percent values added as data labels. Any help is appreciated.
library("ggplot2")
ggplot(dist_tab(x), aes(x=interval)) + geom_bar(aes(y=Freq))
Not sure if I understand your question. Is this what you are looking for?
df <- dist_tab(x)
df.melt <- melt(df, id.vars="interval", measure.vars=c("Freq", "cum.Freq"))
#
ggplot(df.melt, aes(x=interval, y=value, fill=variable)) +
geom_bar(stat="identity", position="dodge")
I have created a graph in ggplot2 using zoo to create month bins. However, I want to be able to modify the graph so it looks like a standard ggplot graph. This means that the bins that aren't used are dropped and the bins that are populate the entire bin space. Here is my code:
library(data.table)
library(ggplot2)
library(scales)
library(zoo)
testset <- data.table(Date=as.Date(c("2013-07-02","2013-08-03","2013-09-04","2013-10-05","2013-11-06","2013-07-03","2013-08-04","2013-09-05","2013-10-06","2013-11-07")),
Action = c("A","B","C","D","E","B","A","B","C","A","B","E","E","C","A"),
rating = runif(30))
The ggplot call is:
ggplot(testset, aes(as.yearmon(Date), fill=Action)) +
geom_bar(position = "dodge") +
scale_x_yearmon()
I'm not sure what I'm missing, but I'd like to find out! Thanks in advance!
To get a "standard-looking" plot, convert the data to a "standard" data type, which is a factor:
ggplot(testset, aes(as.factor(as.yearmon(Date)), fill=Action)) +
geom_bar(position='dodge')