Archit Rao 9 November 2017
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## [1] 1.0 -0.8 -1.0 0.2 0.8
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 37.36 60.71 67.56 68.58 74.53 95.89
stock<-read.csv("C:/Users/Administrator/Desktop/Data visualisation/nasdaq.csv")
#separtae date month and year
Stock_new<-stock%>%mutate(Month=format(as.Date(Date,format="%d-%m-%Y"),"%m"),Day=format(as.Date(Date,format="%d-%m-%Y"),"%d"))%>%head(173)
#Create a bar chart
gl<-ggplot(Stock_new,aes(x=Date,y=Google))+geom_line(group=1)
plot(gl)
#create a heatmap
Stock_new%>%group_by()## # A tibble: 173 x 9
## Date Amazon Google Facebook Apple Tesla Infosys Month Day
## * <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 03-01-2017 754. 808. 117. 116. 217. 14.7 01 03
## 2 04-01-2017 757. 808. 119. 116. 227. 15.1 01 04
## 3 05-01-2017 780. 813. 121. 117. 227. 15.0 01 05
## 4 06-01-2017 796. 825. 123. 118. 229. 14.8 01 06
## 5 09-01-2017 797. 827. 125. 119. 231. 15.0 01 09
## 6 10-01-2017 796. 826. 124. 119. 230. 14.8 01 10
## 7 11-01-2017 799. 830. 126. 120. 230. 15.2 01 11
## 8 12-01-2017 814. 830. 127. 119. 230. 15.2 01 12
## 9 13-01-2017 817. 831. 128. 119. 238. 14.5 01 13
## 10 17-01-2017 810. 827. 128. 120. 236. 14.5 01 17
## # ... with 163 more rows
ggplot(Stock_new,aes(y=Month,x=Day,fill=-Google))+geom_tile()
#corellation matrix
#install.packages("corrplot")
library(corrplot)## corrplot 0.84 loaded
#coreation matrix
cor_values<-cor(select(head(stock,173),-Date))
#corrlation bw google and amazon
ggplot(Stock_new,aes(x=Google,y=Amazon))+geom_point()+geom_smooth()## `geom_smooth()` using method = 'loess'
corrplot(cor_values,method="color",order="hclust")