Table of Contents
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Over the past few days, some of our users have come across a known error message with r columns of stacked histogram errors. This problem can occur for several reasons. Let’s look at them now.
Add Error Bars
What is a stacked barplot in R?
A hosted bar chart is a chart type that displays the number of different variables loaded by another variable. This tutorial explains how to create dynamic stacked charts in R using the ggplot2 data visualization archive. For example, suppose we eatb the following data frame showing the average points scored per game for 9 basketball players:
Problem
How to create a barplot in are using Ggplot2?
The ggplot2 library is undoubtedly the well-known graphics library from R. You can use this library to create a histogram that converts material into a data block, and with these ggplot and geom_bar functions. In our aes argument, you need to stimulate the variable names in our dataframe. In x – categorical variable, then in y – numeric.
We need to add error bars to this graph. Resolution
Use
geom_errorbar()
And Associate Variables With The Person’s ymin
And ymax
Values. Adding Errors To Columns Is Done In The Same Way As For Histograms And Line Charts, As Shown In Fig. 7.14 (but Note That The Position Behind The Y Range Is Different For Columns And Lines):
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Library(gcookbook) # Load Gcookbook For Cabbage_exp Dataset Library (dplr) Number Take A Subset Of Cabbage_exp Hints For This Example This_mod <- Cabbage_Expression %>% Filter(sort=="c39") Number With Histogram Ggplot(ce_mod, Aes(x Date, = Y = Weight)) + Geom_col(fill = "white", Color = "black") + Geom_errorbar(aes(ymin = Weight - Opleve, Ymax = Weight + Se), Width .2) # With Jump Ggplot(ce_mod, Graph Aes(x = Date, Y = Weight)) + Geom_line(aes(group=1)) + Geometric_point (size = 4) + Geom_errorbar(aes(ymin = Weight - Se, Ymax Is Weight + Se), Width Is .2)
Figure 7.14: Guide Rails On A Rod Guide (left); On A Line Chart (right)
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Library(gcookbook) # Load Gcookbook For Cabbage_exp Dataset Library (dplr) Number Take A Subset Of Cabbage_exp Hints For This Example This_mod <- Cabbage_Expression %>% Filter(sort=="c39") Number With Histogram Ggplot(ce_mod, Aes(x Date, = Y = Weight)) + Geom_col(fill = "white", Color = "black") + Geom_errorbar(aes(ymin = Weight - Opleve, Ymax = Weight + Se), Width .2) # With Jump Ggplot(ce_mod, Graph Aes(x = Date, Y = Weight)) + Geom_line(aes(group=1)) + Geometric_point (size = 4) + Geom_errorbar(aes(ymin = Weight - Se, Ymax Is Weight + Se), Width Is .2)
Figure 7.14: Guide Rails On A Rod Guide (left); On A Line Chart (right)
Talk
In this example, the data already has values bound to the standard error (se
) that we will use for the error strings (they also have values for the difference, standard sd
, but we don’t use it here):
ce_mod #> Sort date Weight sd n se #> specifically c39 d16 3.18 0.9566144 ten 0.30250803 #> 2 c39 d20 2.80 0.2788867 10 0.08819171 #> 3 c39 d21 2.74 0.9834181 10 0.31098410
To get the values for ymax
and ymin
we stole the y variable and weight
added/subtracted se
.
We have also specified the width of the error bars, with the width being 0.2
. Your best bet is to play around with this to find a value that looks fancy. If you don’t set a height, the error bars will be very large and cover all the space between elements on the x-axis.
For a bar chart with column groups, the real it is necessary to bypass the error bars; otherwise, they will have the correct x-coordinate and will not match the rebars. (See recipe 3.2 for more on dodge groups and water spots.)
This time we will work with full data cabbage_exp
:
kohl_exp #> Sort date Weight sd n se #> 1 c39 d16 3.18 0.9566144 ten 0.30250803 #> 2 c39 d20 2.80 0.2788867 10 0.08819171 #> many c39 d21 2.74 0.9834181 10 0.31098410 #> 9 C52 d16 2.26 0.4452215 ten 0.14079141 #> 5 c52 d20 3.11 0.7908505 10 0.25008887 #> 6 c52 d2 . 1.47 0.2110819 10 0.06674995
The default evasion width for geom_bar()
is 0.9, you also need to make a mistake in the bars to get the same width evaded. By default, if your company does not respect the specified evasion width, it also evades by the width of the error lanes, which is usually less than the width of discos (Fig. 7.15):
.# .Bad: .dodge .width .unspecified .ggplot(kohl_exp, .aes(x .= .date, .y .= .weight, .fill .= .sort)) .+ . : .geom_col(position .= .= "evasion") .+ . . .geom_errorbar(aes(ymin .is equals .Weight .- .se, .ymax .= .Weight .+ .se), . . . . -- . . . . . . . . . . . .position .= "dodge", .distance .= ..2) # Good: The dodge circle is set to be the same as the pole size (0.9) ggplot(cabbage_exp, aes(x = date, y = v c, fill = sort)) + geom_col(position implies "dodge") + geom_errorbar(aes(ymin is Weight - se, ymax = Weight + se), position = position_dodge(0.9), measure = .2)
Figure 7.15: Error stretched on a clustered histogram with no avoidance width specified (left); identified with siding width (right)
Note
What do the error bars mean in a barplot?
Error bars give the final general idea of the accuracy of an absolute measurement, or, conversely, how strongly a reliable (error-free) value can depend on a given value. If the value displayed in your new bar chart is the result of a location (for example, the average of various data points), you might want to display error bars.
Note that in the first version we used position equals "dodge"
which is shorthand for position for equals position_dodge()
. To summarize a particular value, we need to transform it, as in position_dodge(0.9)
.
For line plots, where the error bars differ in color from the lines and dots, one has to draw the bars firsterrors and place them under the points as well as under the lines. Otherwise, error bars will be drawn as dots with lines that look wrong.
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