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cs代写:Analysing London using Open Data

2018-03-03 来源: 51due教员组 类别: 更多范文

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。

Open Data,R语言数据分析,assignment代写,cs代写,作业代写

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.

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