使用R中的基本概率分析R

Question

我不是统计学家，但我确实希望使用基本概率来理解我的数据发生了什么。

我创建的看着我使用直方图，然后比较不同群体我感兴趣的分析，以集团整体特定箱数据的繁琐，但非常有用的方法。它向我们展示了我们公司的一些令人难以置信的见解，并且很容易解释图中发生的事情。尽管这样说很乏味，但这种类型的分析非常有用，其他人可能已经为它创建了一个函数。

下面是我的代码如下。这种类型的分析是否已经存在于一个函数中？另外我使用了logi.hist.plot（），它做了类似的事情，但它可能有问题，我更喜欢使用这个数据的“原始视图”。

library(dplyr) 
library(ggplot2) 

#Create the data 
set.seed(84102) 
daba <- data.frame(YES_NO = c(0,0,1,1,1,1,0,0,0,1,0,1,0,1,0,1,0,0,0,1)) 
daba$UserCount <-  c(23,43,45,65,32,10,34,68,65,75,43,24,37,54,73,29,87,32,21,12) 

#Create the bins using hist(), clean up bins and make them integers 
hist_breaks <- cut(daba$UserCount, breaks = hist(daba$UserCount, breaks =  20)$breaks) 
daba$Breaks <- hist_breaks 
daba$Breaks <- sub(".*,","",daba$Breaks) 
daba$Breaks <- sub("]","",daba$Breaks) 
daba$Breaks[is.na(daba$Breaks)] <- 0 
daba$Breaks <- as.integer(daba$Breaks) 

#Create two data groups to be compared 
daba_NO <- filter(daba, daba$YES_NO == 0) 
daba_YES <- filter(daba, daba$YES_NO == 1) 

#Aggregate user count into histogram bins using aggregate() 
daba_NOAgg <- aggregate(data = daba_NO, daba_NO$Breaks~daba_NO$UserCount, sum) 
daba_YESAgg <- aggregate(data = daba_YES, daba_YES$Breaks~daba_YES$UserCount, sum) 

#Rename the columns to clean it up 
colnames(daba_NOAgg) <- c("UserCountNo", "Breaks") 
colnames(daba_YESAgg) <- c("UserCountYes", "Breaks") 

#Merge the two groups back together 
daba_SUMAgg <- merge(x = daba_NOAgg, y = daba_YESAgg, by.x = "Breaks", by.y = "Breaks") 

#Generate basic probability for Yes group of users 
daba_SUMAgg$Probability <-  (daba_SUMAgg$UserCountYes/(daba_SUMAgg$UserCountNo+daba_SUMAgg$UserCountYes))*100 

#Graph the data 
ggplot(data = daba_SUMAgg)+ 
    geom_point(alpha = 0.4, mapping = aes(y = daba_SUMAgg$Probability, x =  daba_SUMAgg$Breaks))+ 
    labs(x = "BINS", y = "PROBABILITY", title = "PROBABILITY ANALYSIS USING  BINS") 


daba_SUMAgg 

Answer 1

没有必要当你有dplyrgroup_by分裂您的数据集。无需从您的范围创建数值来绘图。我认为你的过程错过了一些东西（请参阅我上面的评论）。

我建议使用

library(dplyr) 
library(ggplot2) 

#Create the data 
set.seed(84102) 
daba <- data.frame(YES_NO = c(0,0,1,1,1,1,0,0,0,1,0,1,0,1,0,1,0,0,0,1)) 
daba$UserCount <-  c(23,43,45,65,32,10,34,68,65,75,43,24,37,54,73,29,87,32,21,12) 

daba %>% 
    mutate(Breaks = cut(UserCount, breaks = hist(UserCount, breaks = 20)$breaks, right = F)) %>% # create your breaks (use right = F other wise you miss the value 10) 
    group_by(Breaks, YES_NO) %>%        # for every range and YES_NO value 
    summarise(UserCount = sum(UserCount)) %>%    # get sum of counts 
    mutate(Prc = UserCount/sum(UserCount)) %>%    # get the probability/percentage 
    ungroup() %>%           # forget the grouping 
    mutate(YES_NO = factor(YES_NO)) %>%      # change this to factor for the plot 
    ggplot(aes(Breaks, Prc, col=YES_NO, group=YES_NO)) +  # plot 
    geom_point() + 
    geom_line() 

一步运行管道工艺步骤，看看数据处理工作和数据集的外观它被绘制之前。