大数据分析 – 数据可视化

大数据分析 – 数据可视化


为了理解数据,将数据可视化通常很有用。通常在大数据应用程序中,兴趣依赖于发现洞察力,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 –

相关性

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,从这个结果中找出因果关系并不简单。

我们不能说由于两个变量是相关的,所以一个对另一个有影响。我们还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,因为距离越远,飞行时间就会增加。

我们还可以对数据进行单变量分析。一种简单而有效的可视化分布的方法是箱线图以下代码演示了如何使用 ggplot2 库生成箱线图和格状图。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()

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