大数据分析 - 汇总数据

简述

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。

负责这项任务的团队负责将大数据分析部门产生的信息传播到组织的不同领域。

以下示例演示了数据汇总的含义。导航到文件夹bda/part1/summarize_data并在文件夹内，打开summarize_data.Rproj双击文件。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。


# Install the following packages by running the following code in R. 
pkgs = c('data.table', 'ggplot2', 'nycflights13', 'reshape2') 
install.packages(pkgs)

ggplot2包非常适合数据可视化。data.table包是在R. 最近的一项基准测试表明它甚至比pandas，用于类似任务的python库。

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。


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

下面的代码有一个数据汇总的例子。


### 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)