R语言大数据分析纽约市的311万条投诉统计可视化与时间序列分析

介绍

本文并不表示R在数据分析方面比Python更好或更快速，我本人每天都使用两种语言。这篇文章只是提供了比较这两种语言的机会。

本文中的数据每天都会更新，我的文件版本更大，为4.63 GB。

CSV文件包含纽约市的311条投诉。它是纽约市开放数据门户网站中最受欢迎的数据集。

数据工作流程

install.packages("devtools")
library("devtools")
install_github("ropensci/plotly")

library(plotly)

需要创建一个帐户以连接到plotly API。或者，可以只使用默认的ggplot2图形。

set_credentials_file("DemoAccount", "lr1c37zw81") ## Replace contents with your API Key

使用dplyr在R中进行分析

假设已安装sqlite3（因此可通过终端访问）。

$ sqlite3 data.db # Create your database
$.databases       # Show databases to make sure it works
$.mode csv
$.import <filename> <tablename>
# Where filename is the name of the csv & tablename is the name of the new database table
$.quit

将数据加载到内存中。

library(readr)
# data.table, selecting a subset of columns
time_data.table <- system.time(fread('/users/ryankelly/NYC_data.csv', select = c('Agency', 'Created Date','Closed Date', 'Complaint Type', 'Descriptor', 'City'), showProgress = T))

kable(data.frame(rbind(time_data.table, time_data.table_full, time_readr)))

	user.self	sys.self	elapsed
time_data.table	63.588	1.952	65.633
time_data.table_full	205.571	3.124	208.880
time_readr	277.720	5.018	283.029

我将使用data.table读取数据。该 fread 函数大大提高了读取速度。

关于dplyr

默认情况下，dplyr查询只会从数据库中提取前10行。

library(dplyr)      ## Will be used for pandas replacement# Connect to the database
db <- src_sqlite('/users/ryankelly/data.db')
db

数据处理的两个最佳选择（除了R之外）是：

数据表
dplyr

预览数据

# Wrapped in a function for display purposes
head_ <- function(x, n = 5) kable(head(x, n))head_(data)

Agency	CreatedDate	ComplaintType	Descriptor	City
NYPD	04/11/2015 02:13:04 AM	Noise - Street/Sidewalk	Loud Music/Party	BROOKLYN
DFTA	04/11/2015 02:12:05 AM	Senior Center Complaint	N/A	ELMHURST
NYPD	04/11/2015 02:11:46 AM	Noise - Commercial	Loud Music/Party	JAMAICA
NYPD	04/11/2015 02:11:02 AM	Noise - Street/Sidewalk	Loud Talking	BROOKLYN
NYPD	04/11/2015 02:10:45 AM	Noise - Street/Sidewalk	Loud Music/Party	NEW YORK

选择几列

ComplaintType	Descriptor	Agency
Noise - Street/Sidewalk	Loud Music/Party	NYPD
Senior Center Complaint	N/A	DFTA
Noise - Commercial	Loud Music/Party	NYPD
Noise - Street/Sidewalk	Loud Talking	NYPD
Noise - Street/Sidewalk	Loud Music/Party	NYPD

ComplaintType	Descriptor	Agency
Noise - Street/Sidewalk	Loud Music/Party	NYPD
Senior Center Complaint	N/A	DFTA
Noise - Commercial	Loud Music/Party	NYPD
Noise - Street/Sidewalk	Loud Talking	NYPD
Noise - Street/Sidewalk	Loud Music/Party	NYPD
Noise - Street/Sidewalk	Loud Talking	NYPD
Noise - Commercial	Loud Music/Party	NYPD
HPD Literature Request	The ABCs of Housing - Spanish	HPD
Noise - Street/Sidewalk	Loud Talking	NYPD
Street Condition	Plate Condition - Noisy	DOT

使用WHERE过滤行

ComplaintType	Descriptor	Agency
Noise - Street/Sidewalk	Loud Music/Party	NYPD
Noise - Commercial	Loud Music/Party	NYPD
Noise - Street/Sidewalk	Loud Talking	NYPD
Noise - Street/Sidewalk	Loud Music/Party	NYPD
Noise - Street/Sidewalk	Loud Talking	NYPD

使用WHERE和IN过滤列中的多个值

ComplaintType	Descriptor	Agency
Noise - Street/Sidewalk	Loud Music/Party	NYPD
Noise - Commercial	Loud Music/Party	NYPD
Noise - Street/Sidewalk	Loud Talking	NYPD
Noise - Street/Sidewalk	Loud Music/Party	NYPD
Noise - Street/Sidewalk	Loud Talking	NYPD

在DISTINCT列中查找唯一值

##       City
## 1 BROOKLYN
## 2 ELMHURST
## 3  JAMAICA
## 4 NEW YORK
## 5
## 6  BAYSIDE

使用COUNT（*）和GROUP BY查询值计数

# dt[, .(No.Complaints = .N), Agency]
#setkey(dt, No.Complaints) # setkey index's the dataq <- data %>% select(Agency) %>% group_by(Agency) %>% summarise(No.Complaints = n())
head_(q)

Agency	No.Complaints
3-1-1	22499
ACS	3
AJC	7
ART	3
CAU	8

使用ORDER和-排序结果

数据库中有多少个城市？

# dt[, unique(City)]q <- data %>% select(City) %>% distinct() %>% summarise(Number.of.Cities = n())
head(q)

##   Number.of.Cities
## 1             1818

让我们来绘制10个最受关注的城市

City	No.Complaints
BROOKLYN	2671085
NEW YORK	1692514
BRONX	1624292
	766378
STATEN ISLAND	437395
JAMAICA	147133
FLUSHING	117669
ASTORIA	90570
Jamaica	67083
RIDGEWOOD	66411

用 UPPER 转换CITY格式。

CITY	No.Complaints
BROOKLYN	2671085
NEW YORK	1692514
BRONX	1624292
	766378
STATEN ISLAND	437395
JAMAICA	147133
FLUSHING	117669
ASTORIA	90570
JAMAICA	67083
RIDGEWOOD	66411

投诉类型（按城市）


# Plot result
plt <- ggplot(q_f, aes(ComplaintType, No.Complaints, fill = CITY)) + geom_bar(stat = 'identity') + theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1))plt

第2部分时间序列运算

提供的数据不适合SQLite的标准日期格式。

在SQL数据库中创建一个新列，然后使用格式化的date语句重新插入数据创建一个新表并将格式化日期插入原始列名。

使用时间戳字符串过滤SQLite行：YYYY-MM-DD hh：mm：ss

# dt[CreatedDate < '2014-11-26 23:47:00' & CreatedDate > '2014-09-16 23:45:00',
#      .(ComplaintType, CreatedDate, City)]q <- data %>% filter(CreatedDate < "2014-11-26 23:47:00",   CreatedDate > "2014-09-16 23:45:00") %>%select(ComplaintType, CreatedDate, City)head_(q)

ComplaintType	CreatedDate	City
Noise - Street/Sidewalk	2014-11-12 11:59:56	BRONX
Taxi Complaint	2014-11-12 11:59:40	BROOKLYN
Noise - Commercial	2014-11-12 11:58:53	BROOKLYN
Noise - Commercial	2014-11-12 11:58:26	NEW YORK
Noise - Street/Sidewalk	2014-11-12 11:58:14	NEW YORK

使用strftime从时间戳中拉出小时单位

# dt[, hour := strftime('%H', CreatedDate), .(ComplaintType, CreatedDate, City)]q <- data %>% mutate(hour = strftime('%H', CreatedDate)) %>% select(ComplaintType, CreatedDate, City, hour)head_(q)

ComplaintType	CreatedDate	City	hour
Noise - Street/Sidewalk	2015-11-04 02:13:04	BROOKLYN	02
Senior Center Complaint	2015-11-04 02:12:05	ELMHURST	02
Noise - Commercial	2015-11-04 02:11:46	JAMAICA	02
Noise - Street/Sidewalk	2015-11-04 02:11:02	BROOKLYN	02
Noise - Street/Sidewalk	2015-11-04 02:10:45	NEW YORK	02

汇总时间序列

首先，创建一个时间戳记四舍五入到前15分钟间隔的新列

# Using lubridate::new_period()
# dt[, interval := CreatedDate - new_period(900, 'seconds')][, .(CreatedDate, interval)]q <- data %>% mutate(interval = sql("datetime((strftime('%s', CreatedDate) / 900) * 900, 'unixepoch')")) %>%                     select(CreatedDate, interval)head_(q, 10)

CreatedDate	interval
2015-11-04 02:13:04	2015-11-04 02:00:00
2015-11-04 02:12:05	2015-11-04 02:00:00
2015-11-04 02:11:46	2015-11-04 02:00:00
2015-11-04 02:11:02	2015-11-04 02:00:00
2015-11-04 02:10:45	2015-11-04 02:00:00
2015-11-04 02:09:07	2015-11-04 02:00:00
2015-11-04 02:05:47	2015-11-04 02:00:00
2015-11-04 02:03:43	2015-11-04 02:00:00
2015-11-04 02:03:29	2015-11-04 02:00:00
2015-11-04 02:02:17	2015-11-04 02:00:00

绘制2003年的结果