大数据分析 - 数据可视化

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  简述

  为了理解数据，将其可视化通常很有用。通常在大数据应用程序中，兴趣在于发现洞察力，而不仅仅是制作漂亮的图。以下是使用绘图理解数据的不同方法的示例。

  要开始分析航班数据，我们可以从检查数值变量之间是否存在相关性开始。此代码也可在bda/part1/data_visualization/data_visualization.R文件。

  
  # Install the package corrplot by running
  install.packages('corrplot')  
  # then load the library 
  library(corrplot)  
  # Load the following libraries  
  library(nycflights13) 
  library(ggplot2) 
  library(data.table) 
  library(reshape2)  
  # We will continue working with the flights data 
  DT <- as.data.table(flights)  
  head(DT) # take a look  
  # We select the numeric variables after inspecting the first rows. 
  numeric_variables = c('dep_time', 'dep_delay',  
     'arr_time', 'arr_delay', 'air_time', 'distance')
  # Select numeric variables from the DT data.table 
  dt_num = DT[, numeric_variables, with = FALSE]  
  # Compute the correlation matrix of dt_num 
  cor_mat = cor(dt_num, use = "complete.obs")  
  print(cor_mat) 
  ### Here is the correlation matrix 
  #              dep_time   dep_delay   arr_time   arr_delay    air_time    distance 
  # dep_time   1.00000000  0.25961272 0.66250900  0.23230573 -0.01461948 -0.01413373 
  # dep_delay  0.25961272  1.00000000 0.02942101  0.91480276 -0.02240508 -0.02168090 
  # arr_time   0.66250900  0.02942101 1.00000000  0.02448214  0.05429603  0.04718917 
  # arr_delay  0.23230573  0.91480276 0.02448214  1.00000000 -0.03529709 -0.06186776 
  # air_time  -0.01461948 -0.02240508 0.05429603 -0.03529709  1.00000000  0.99064965 
  # distance  -0.01413373 -0.02168090 0.04718917 -0.06186776  0.99064965  1.00000000  
  # We can display it visually to get a better understanding of the data 
  corrplot.mixed(cor_mat, lower = "circle", upper = "ellipse")  
  # save it to disk 
  png('corrplot.png') 
  print(corrplot.mixed(cor_mat, lower = "circle", upper = "ellipse")) 
  dev.off()
  

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  此代码生成以下相关矩阵可视化 -

  

  我们可以在图中看到，数据集中的一些变量之间存在很强的相关性。例如，到达延迟和离开延迟似乎高度相关。我们可以看到这一点，因为椭圆显示了两个变量之间的几乎线性关系，但是，从这个结果中找到因果关系并不容易。

  我们不能说因为两个变量是相关的，所以一个变量对另一个变量有影响。此外，我们在图中发现飞行时间和距离之间存在很强的相关性，这是相当合理的预期，因为距离越远，飞行时间应该会增加。

  我们还可以对数据进行单变量分析。可视化分布的一种简单有效的方法是box-plots. 以下代码演示了如何使用 ggplot2 库生成箱线图和格子图。此代码也可在bda/part1/data_visualization/boxplots.R文件。

  
  source('data_visualization.R') 
  ### Analyzing Distributions using box-plots  
  # The following shows the distance as a function of the carrier 
  p = ggplot(DT, aes(x = carrier, y = distance, fill = carrier)) + # Define the carrier 
     in the x axis and distance in the y axis 
     geom_box-plot() + # Use the box-plot geom 
     theme_bw() + # Leave a white background - More in line with tufte's 
        principles than the default 
     guides(fill = FALSE) + # Remove legend 
     labs(list(title = 'Distance as a function of carrier', # Add labels 
        x = 'Carrier', y = 'Distance')) 
  p   
  # Save to disk 
  png(‘boxplot_carrier.png’) 
  print(p) 
  dev.off()   
  # Let's add now another variable, the month of each flight 
  # We will be using facet_wrap for this 
  p = ggplot(DT, aes(carrier, distance, fill = carrier)) + 
     geom_box-plot() + 
     theme_bw() + 
     guides(fill = FALSE) +  
     facet_wrap(~month) + # This creates the trellis plot with the by month variable
     labs(list(title = 'Distance as a function of carrier by month', 
        x = 'Carrier', y = 'Distance')) 
  p   
  # The plot shows there aren't clear differences between distance in different months  
  # Save to disk 
  png('boxplot_carrier_by_month.png') 
  print(p) 
  dev.off()
  

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