Big Data Analytics 简明教程
Big Data Analytics - Summarizing Data
报告在大数据分析中非常重要。每个组织都必须定期提供信息以支持其决策过程。此任务通常由具有 SQL 和 ETL（提取、传输和加载）经验的数据分析师处理。
Reporting is very important in big data analytics. Every organization must have a regular provision of information to support its decision making process. This task is normally handled by data analysts with SQL and ETL (extract, transfer, and load) experience.
负责此任务的团队有责任将大数据分析部门生成的信息传播到组织的不同领域。
The team in charge of this task has the responsibility of spreading the information produced in the big data analytics department to different areas of the organization.
以下示例演示了数据摘要的含义。导航到文件夹 bda/part1/summarize_data ，然后在文件夹中双击 summarize_data.Rproj 文件以将其打开。然后，打开 summarize_data.R 脚本并查看代码，并按照提供的说明进行操作。
The following example demonstrates what summarization of data means. Navigate to the folder bda/part1/summarize_data and inside the folder, open the summarize_data.Rproj file by double clicking it. Then, open the summarize_data.R script and take a look at the code, and follow the explanations presented.
# Install the following packages by running the following code in R.
pkgs = c('data.table', 'ggplot2', 'nycflights13', 'reshape2')
install.packages(pkgs)ggplot2 软件包非常适合数据可视化。 data.table 软件包是 R 中进行快速且节省内存的摘要的绝佳选择。最近的基准测试表明，它甚至比 pandas （用于类似任务的 Python 库）还要快。
The ggplot2 package is great for data visualization. The data.table package is a great option to do fast and memory efficient summarization in R. A recent benchmark shows it is even faster than pandas, the python library used for similar tasks.
使用以下代码查看数据。此代码也可在 bda/part1/summarize_data/summarize_data.Rproj 文件中找到。
Take a look at the data using the following code. This code is also available in bda/part1/summarize_data/summarize_data.Rproj file.
library(nycflights13)
library(ggplot2)
library(data.table)
library(reshape2)
# Convert the flights data.frame to a data.table object and call it DT
DT <- as.data.table(flights)
# The data has 336776 rows and 16 columns
dim(DT)
# Take a look at the first rows
head(DT)
# year month day dep_time dep_delay arr_time arr_delay carrier
# 1: 2013 1 1 517 2 830 11 UA
# 2: 2013 1 1 533 4 850 20 UA
# 3: 2013 1 1 542 2 923 33 AA
# 4: 2013 1 1 544 -1 1004 -18 B6
# 5: 2013 1 1 554 -6 812 -25 DL
# 6: 2013 1 1 554 -4 740 12 UA
# tailnum flight origin dest air_time distance hour minute
# 1: N14228 1545 EWR IAH 227 1400 5 17
# 2: N24211 1714 LGA IAH 227 1416 5 33
# 3: N619AA 1141 JFK MIA 160 1089 5 42
# 4: N804JB 725 JFK BQN 183 1576 5 44
# 5: N668DN 461 LGA ATL 116 762 5 54
# 6: N39463 1696 EWR ORD 150 719 5 54以下代码有一个数据摘要的示例。
The following code has an example of data summarization.
### Data Summarization
# Compute the mean arrival delay
DT[, list(mean_arrival_delay = mean(arr_delay, na.rm = TRUE))]
# mean_arrival_delay
# 1: 6.895377
# Now, we compute the same value but for each carrier
mean1 = DT[, list(mean_arrival_delay = mean(arr_delay, na.rm = TRUE)),
by = carrier]
print(mean1)
# carrier mean_arrival_delay
# 1: UA 3.5580111
# 2: AA 0.3642909
# 3: B6 9.4579733
# 4: DL 1.6443409
# 5: EV 15.7964311
# 6: MQ 10.7747334
# 7: US 2.1295951
# 8: WN 9.6491199
# 9: VX 1.7644644
# 10: FL 20.1159055
# 11: AS -9.9308886
# 12: 9E 7.3796692
# 13: F9 21.9207048
# 14: HA -6.9152047
# 15: YV 15.5569853
# 16: OO 11.9310345
# Now let’s compute to means in the same line of code
mean2 = DT[, list(mean_departure_delay = mean(dep_delay, na.rm = TRUE),
mean_arrival_delay = mean(arr_delay, na.rm = TRUE)),
by = carrier]
print(mean2)
# carrier mean_departure_delay mean_arrival_delay
# 1: UA 12.106073 3.5580111
# 2: AA 8.586016 0.3642909
# 3: B6 13.022522 9.4579733
# 4: DL 9.264505 1.6443409
# 5: EV 19.955390 15.7964311
# 6: MQ 10.552041 10.7747334
# 7: US 3.782418 2.1295951
# 8: WN 17.711744 9.6491199
# 9: VX 12.869421 1.7644644
# 10: FL 18.726075 20.1159055
# 11: AS 5.804775 -9.9308886
# 12: 9E 16.725769 7.3796692
# 13: F9 20.215543 21.9207048
# 14: HA 4.900585 -6.9152047
# 15: YV 18.996330 15.5569853
# 16: OO 12.586207 11.9310345
### Create a new variable called gain
# this is the difference between arrival delay and departure delay
DT[, gain:= arr_delay - dep_delay]
# Compute the median gain per carrier
median_gain = DT[, median(gain, na.rm = TRUE), by = carrier]
print(median_gain)