Grouped barplot with R

Grouped barplot with R

By : Sherrie Campbell
Date : December 01 2020, 05:00 PM
hop of those help? I am trying to draw a grouped bar plot with the following data , This would solve your problem.
code :
     df.long$groups <- factor(df.long$groups, levels=unique(df.long$groups))
     ggplot(df.long,aes(x=groups,y=value,fill=variable))+ labs(x = "groups", y = "connections") + geom_bar(stat="identity",position="dodge")

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Grouped bar Graph using barplot

Grouped bar Graph using barplot

By : Jan
Date : March 29 2020, 07:55 AM
Hope this helps First, do not double post! If necessary edit your previous question, rather than posting a new question.
Second, the error message that you mentioned in your earlier post is pretty self-explanatory. Here's the error message:
code :
mymat <- t(mydf[-1])
colnames(mymat) <- mydf[, 1]
barplot(mymat, beside = TRUE)
Grouped barplot with cut y axis

Grouped barplot with cut y axis

By : Manju
Date : March 29 2020, 07:55 AM
it helps some times You could do this manually. Like barplot, ?gap.barplot returns the center positions of the bars. Use these to add the labels.
Using space for spacing between groups as in regular barplot does not seem to work. We can use a row of NAs to hack a space.
code :
d = t(matrix( c(7,3,2,3,2,2,852,268,128,150,
                            nrow=6, ncol=4 ))
colnames(d)=c("A", "B", "C", "D", "E", "F")

# add row of NAs for spacing

# install.packages('plotrix', dependencies = TRUE)

# create barplot and store returned value in 'a'
a = gap.barplot(as.matrix(d), 
                xaxt='n') # disable the default x-axis

# calculate mean x-position for each group, omitting the first row 
# first row (NAs) is only there for spacing between groups
aa = matrix(a, nrow=nrow(d))
xticks = colMeans(aa[2:nrow(d),])

# add axis labels at mean position
axis(1, at=xticks, lab=LETTERS[1:6])
How can I do a grouped barplot?

How can I do a grouped barplot?

By : user3274755
Date : March 29 2020, 07:55 AM
should help you out ggplot needs an object of type data.frame, not a matrix or table. You can create it this way:
code :
 identification <- data.frame(Party=c("CDU/CSU", "CDU/CSU", ...),
                              Type=c("Neg", "Pos", ...),
                              Resultat=c(meanNegCDUCSU, meanPosCDUCSU, ...))
Grouped scatterplot over grouped barplot

Grouped scatterplot over grouped barplot

By : user3473759
Date : March 29 2020, 07:55 AM
seems to work fine The problem you are having, I think, is that some geometries, such as geom_point and geom_jitter do not use or allow the factor aesthetic for grouping along the x-axis.
Thus when you plot other geoms on top of a chart where factor is not ignored, such as geom_bar, the factor setting is ignored for some layers but not for bar, and you don't get year-resolved columns of points.
code :
quartz(height=6, width=12)

ggplot(Males, aes(x=year, y=wage)) +
  facet_grid(.~ethn) +
  stat_summary(mapping=aes(fill=year), fun.y=mean, geom='bar') +
  stat_summary(fun.data = mean_sdl, geom='errorbar', color='yellow') +
              position=position_jitter(width=0.2), alpha=0.8)

How to create grouped barplot from dataframe grouped by two columns with intervals

How to create grouped barplot from dataframe grouped by two columns with intervals

By : BeardedSloth
Date : March 29 2020, 07:55 AM
I hope this helps you . I grouped the data by two columns ('a' and 'b'). And for each column, I cut it into bins. For each crossed bin, I would like to have the sum of 'c'. Then I want to create a grouped plot for the sum of 'c'. However, when I use .plot(kind='bar') after the grouped data, the bars were not grouped together. , Given the following example data
code :
df = pd.DataFrame({'a': [8, 49, 81, 52, 22, 24, 32, 67, 24, 21, 78, 16, 86, 19, 4, 88, 4, 20, 20, 1],
                   'b': [2, 49, 49, 70, 31, 47, 98, 26, 55, 36, 17, 39, 56, 80, 52, 36, 42, 69, 73, 70],
                   'c': [22, 99, 31, 95, 16, 32, 81, 20, 58, 23, 53, 5, 0, 81, 8, 68, 16, 36, 35, 54]})
grouped = df.groupby([pd.cut(df['a'], [16, 30, 67, 86]),
                      pd.cut(df['b'], [0, 26, 49, 100])])
grouped = grouped['c'].sum()
        .pivot('a', 'b')
grouped = grouped.unstack()
grouped.columns = grouped.columns.categories
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