大数据分析 - 数据可视化

为了理解数据，将其可视化通常很有用。通常在大数据应用中，人们的兴趣在于寻找洞察力，而不仅仅是制作漂亮的图表。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，我们可以首先检查数值变量之间是否存在相关性。此代码也可在bda/part1/data_visualization/data_visualization.R文件中找到。

# 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()

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，到达延误和出发延误似乎高度相关。我们可以看到这一点，因为椭圆显示两个变量之间几乎呈线性关系，但是，从这个结果中找到因果关系并不容易。

我们不能说，当两个变量相关时，一个变量就会影响另一个变量。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，因为随着距离的增加，飞行时间应该会增加。

我们还可以对数据进行单变量分析。箱形图是可视化分布的一种简单有效的方法。以下代码演示了如何使用 ggplot2 库生成箱线图和网格图。此代码也可在bda/part1/data_visualization/boxplots.R文件中找到。

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()