Big Data Analytics 简明教程

Big Data Analytics - Data Visualization

为了理解数据，通常有用可视化方式。通常在大型数据应用中，乐趣在于发现洞察，而不仅仅是创建美观的图表。以下是使用图表理解数据的不同方法的示例。

为了开始分析航班数据，我们可以首先检查数值变量之间是否存在相关性。此代码也可用在 bda/part1/data_visualization/data_visualization.R 文件中。

# 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 −

correlation

我们可以在图表中看到，数据集中某些变量之间存在很强的相关性。例如，到达延迟和出发延迟似乎高度相关。我们可以看到这一点，因为椭圆显示了两个变量之间几乎线性关系；然而，从这个结果中发现因果关系并不容易。

我们不能说因为两个变量相关，一个变量就会影响另一个变量。我们还在图表中发现了飞行时间和距离之间的强相关性，这是相当合理的，因为随着距离的增加，飞行时间应该增加。

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

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()