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When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document.

We will be using our housing_data example to produce this report.

You can embed an R code chunk like this:

url<-"https://data.gov.in/sites/default/files/datafile/housing_price_index_2010-11_100.csv"

download.file(url,destfile = "housing_data.csv",method = "curl")

housing_data <- read.csv("housing_data.csv")
head(housing_data)
##   Particulars X06.2011 X09.2011 X12.2011 X03.2012 X06.2012 X09.2012
## 1   All India    116.0    119.4    125.5    134.1    142.6    147.1
## 2   Ahmedabad    121.3    130.4    137.1    141.0    140.8    146.4
## 3   Bangalore    110.7    107.8    138.6    133.3    133.3    136.6
## 4     Chennai    101.2    110.4    110.7    108.2    119.2    117.8
## 5       Delhi    126.8    124.8    136.7    158.2    177.3    183.2
## 6     Kolkata    103.0    105.0    103.2    106.1    135.2    149.1
##   X12.2012 X03.2013 X06.2013 X09.2013
## 1    157.0    160.8    162.3    169.2
## 2    150.6    155.0    161.9    171.7
## 3    141.2    141.9    142.3    150.4
## 4    137.6    137.4    138.3    150.0
## 5    200.7    213.1    214.8    215.7
## 6    162.5    169.4    171.8    173.5

We have also learnt to use read.table() function to read data

housing_data_2<-read.table("housing_data.csv", header = TRUE, sep = ",")

Manipulation

Then we manipulated data a little bit

housing_data_2012 <- housing_data[,c(1,5:11)]
col_names<-housing_data_2012$Particulars
row_names<-colnames(housing_data_2012)

housing_data_transpose <- as.data.frame(t(housing_data_2012[,-1]),row.names = F)
colnames(housing_data_transpose) <- col_names
housing_data_transpose$quarter<-row_names[-1]
housing_data_transpose<-housing_data_transpose[,c(8,1:7)]

#Finding number of occurrences with Housing Index more than 150

housing_data_transpose$count_150<-apply(housing_data_transpose,MARGIN=1,FUN=function(x) length(which(x[c(-1,-2)]>150)))

Plots

Plots are beautiful and easy to visualize or communicate through data

#Box Plot
boxplot(housing_data_2012[-1])