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
原创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
原创
原文链接：http://tecdat.cn/?p=9800
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 columnstime_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 | user.child | sys.child | |
|---|---|---|---|---|---|
time_data.table | 63.588 | 1.952 | 65.633 | 0 | 0 |
time_data.table_full | 205.571 | 3.124 | 208.880 | 0 | 0 |
time_readr | 277.720 | 5.018 | 283.029 | 0 | 0 |
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 databasedb <- 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 purposeshead_ <- function(x, n = 5) kable(head(x, n))head_(data)Agency | CreatedDate | ClosedDate | 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 resultplt <- 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
?
?
原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。
原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。