r - How to calculate share per category within a column? - 


df = data.frame(week = as.factor(rep(c(1, 2), times = 5)),                 name = as.factor(rep(letters[1:5], times = 2)),                 count = rpois(n = 10, lambda = 20))      > df      week   name count 1       1         16 2       2      b    14 3       1      c    23 4       2      d    15 5       1      e    12 6       2         15 7       1      b    23 8       2      c    22 9       1      d    22 10      2      e    26

i'd calculate each name's count share per week. @ first going use following method:

transform(df, week1_share = ifelse(week == "1", round((df$count / sum(df$count)  * 100),2), na)) transform(df, week2_share = ifelse(week == "2", round((df$count / sum(df$count)  * 100),2), na))

but making each column merge, put label on bar plot, seemed inefficient. there must type of quick solution dont know of yet.

basically follows add share% may have been calculated above match within each box.

ggplot(df, aes(reorder(week, -count),count, color = "white", group = name, fill = name))+         geom_bar(position = "stack", stat = "identity") +         scale_y_continuous(labels=comma)+         ggthemes::scale_color_tableau()

enter image description here

i don't know why reorder function fails upon me. if have tips sort order in desc, please share.

the data provided has been used:

# loading required data df = data.frame(week = as.factor(rep(c(1, 2), times = 5)),                 name = as.factor(rep(letters[1:5], times = 2)),                 count = rpois(n = 10, lambda = 20))

using plyr package function, percentage , relative positions labelling have been calculated.

#loading required packages     library(plyr) library(ggplot2)  # calculating percentages df = ddply(df, .(week), transform, percent = round(count/sum(count) * 100))  # calculating position plotting df = ddply(df, .(week), transform, pos = cumsum(percent) - (0.5 * percent))

using information calculated above, plotting has been done.

# basic graph p10 <- ggplot() + geom_bar(aes(y = percent, x = week, fill = name),                         data = df, stat="identity")  # adding data labels p10 <- p10 + geom_text(data=df, aes(x = week, y = pos,                                  label = paste0(percent,"%")), size=4) p10

is have been looking ?

enter image description here


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