cs代写：Analysing London using Open Data

下面为大家整理一篇优秀的cs代写范文- Analysing London using Open Data，供大家参考学习，这篇论文讨论了如何使用R语言进行数据分析。

Analysing London using Open Data

1. Correlation Analysis

Calculate the Pearson, Spearman and Kendall correlation between the “Average GCSE score” and “Turnout

at Mayoral election 2012” per wards, and “Employment rate” and “Happiness score” per borough.

# reading files

ward <- read.csv(“ward-profiles-excel-version.csv”, fileEncoding = “iso-8859-1”,

header = T, sep = “,”, stringsAsFactors=F, check.names=T)

borough <- read.csv(“london-borough-profiles.csv”, fileEncoding = “iso-8859-1”,

header = T, sep = “,”, stringsAsFactors=F, check.names=T)

Ward dataset

Column 54: “Average.GCSE.capped.point.scores. . . 2014”

Column 67: “Turnout.at.Mayoral.election. . . 2012”

Borough dataset

Column 29: “Employment.rate. . . . . . 2014.”

Column 75: “Happiness.score.2011.14..out.of.10.”

cor(as.numeric(ward[,54]), as.numeric(ward[,67]), method = “pearson”, use = “complete.obs”)

## Warning in is.data.frame(y): NAs introduced by coercion

## [1] 0.5410463

cor(as.numeric(ward[,54]), as.numeric(ward[,67]), method = “spearman”, use = “complete.obs”)

## Warning in is.data.frame(y): NAs introduced by coercion

## [1] 0.5240463

1

cor(as.numeric(ward[,54]), as.numeric(ward[,67]), method = “kendall”, use = “complete.obs”)

## Warning in is.data.frame(y): NAs introduced by coercion

## [1] 0.3706765

cor(as.numeric(borough[,29]), as.numeric(borough[,75]), method = “pearson”, use = “complete.obs”)

## [1] 0.3498277

cor(as.numeric(borough[,29]), as.numeric(borough[,75]), method = “spearman”, use = “complete.obs”)

## [1] 0.4452778

cor(as.numeric(borough[,29]), as.numeric(borough[,75]), method = “kendall”, use = “complete.obs”)

## [1] 0.3376605

2. Regression Analysis

Perform regression analysis between the same variables (as used in exercise 1) per ward and per borough.

fit_ward <- lm(as.numeric(ward[,54]) ~ as.numeric(ward[,67]))

## Warning: NAs introduced by coercion

fit_borough <- lm(as.numeric(borough[,29]) ~ as.numeric(borough[,75]))

3. Plotting

Plot the results of the regression analysis using the ggplot2 command discuss during the lecture.

library(“ggplot2”)

## Warning: package ‘ggplot2’ was built under R version 3.2.4

ggplot(ward, aes(x = as.numeric(ward$Average.GCSE.capped.point.scores…2014),

y = as.numeric(ward$Turnout.at.Mayoral.election…2012))) +

geom_point(shape=1) + geom_smooth(method=lm) + xlab(“Average GCSE score”) +

ylab(“Turnout at Mayoral election 2012”)

## Warning: NAs introduced by coercion

## Warning: NAs introduced by coercion

## Warning: NAs introduced by coercion

2

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

20

30

40

50

275 300 325 350 375 400

Average GCSE score

Turnout at Mayoral election 2012

ggplot(borough, aes(x = as.numeric(borough$Employment.rate……2014.),

y = as.numeric(borough$Happiness.score.2011.14..out.of.10.))) +

geom_point(shape=1) + geom_smooth(method=lm) +

xlab(“Employment rate”) + ylab(“Happiness score”)

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

3

6.0

6.5

7.0

7.5

60 65 70 75 80

Employment rate

Happiness score

4. Discussion of the Results

Starting from the results briefly discuss your findings. In particular think about the problem of having only correlation and not causation in the results you are observing.

51due留学教育原创版权郑重声明：原创assignment代写范文源自编辑创作，未经官方许可，网站谢绝转载。对于侵权行为，未经同意的情况下，51Due有权追究法律责任。主要业务有assignment代写、essay代写、paper代写、cs代写服务。

51due为留学生提供最好的assignment代写服务，亲们可以进入主页了解和获取更多assignment代写范文 提供作业代写服务，详情可以咨询我们的客服QQ：800020041。