Project 2 requires you to perform an exploratory data analysis that compares data sets.

Here is the data to be analyzed. it is actually four short data sets. Use ggplot2 with R. Draw conclusions from your exploratory data analysis. Deliver your code and conclusions in a single R Markdown file on rpubs.com or github

First You will need to install the following packages, and require them:
install.packages(“ggplot2”)
install.packages(“reshape”)
install.packages(“plyr”)
require(ggplot2)
## Loading required package: ggplot2
require(reshape)
## Loading required package: reshape
require(plyr)
## Loading required package: plyr
## 
## Attaching package: 'plyr'
## 
## The following objects are masked from 'package:reshape':
## 
##     rename, round_any
You could either use the following command to read data entered in a CSV file called PRoject2 saved in your Active Directory using the command df = read.csv(file=“project2.csv”,head=TRUE,sep=“,”) or you can just type in the small data sets:
df <-     data.frame(x1 = c(10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0), 
                     y1 = c(8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68),
                     x2 = c(10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0),
                     y2 = c(9.14, 8.14, 8.74, 8.77, 9.26, 8.1, 6.13, 3.1, 9.13, 7.26, 4.74),
                     x3 = c(10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0),
                     y3 = c(7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73),
                     x4 = c(8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 19.0, 8.0, 8.0, 8.0),
                     y4 = c(6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 12.5, 5.56, 7.91, 6.89)
                     )

df
##    x1    y1 x2   y2 x3    y3 x4    y4
## 1  10  8.04 10 9.14 10  7.46  8  6.58
## 2   8  6.95  8 8.14  8  6.77  8  5.76
## 3  13  7.58 13 8.74 13 12.74  8  7.71
## 4   9  8.81  9 8.77  9  7.11  8  8.84
## 5  11  8.33 11 9.26 11  7.81  8  8.47
## 6  14  9.96 14 8.10 14  8.84  8  7.04
## 7   6  7.24  6 6.13  6  6.08  8  5.25
## 8   4  4.26  4 3.10  4  5.39 19 12.50
## 9  12 10.84 12 9.13 12  8.15  8  5.56
## 10  7  4.82  7 7.26  7  6.42  8  7.91
## 11  5  5.68  5 4.74  5  5.73  8  6.89
Summary of the data
summary(df)
##        x1             y1               x2             y2       
##  Min.   : 4.0   Min.   : 4.260   Min.   : 4.0   Min.   :3.100  
##  1st Qu.: 6.5   1st Qu.: 6.315   1st Qu.: 6.5   1st Qu.:6.695  
##  Median : 9.0   Median : 7.580   Median : 9.0   Median :8.140  
##  Mean   : 9.0   Mean   : 7.501   Mean   : 9.0   Mean   :7.501  
##  3rd Qu.:11.5   3rd Qu.: 8.570   3rd Qu.:11.5   3rd Qu.:8.950  
##  Max.   :14.0   Max.   :10.840   Max.   :14.0   Max.   :9.260  
##        x3             y3              x4           y4        
##  Min.   : 4.0   Min.   : 5.39   Min.   : 8   Min.   : 5.250  
##  1st Qu.: 6.5   1st Qu.: 6.25   1st Qu.: 8   1st Qu.: 6.170  
##  Median : 9.0   Median : 7.11   Median : 8   Median : 7.040  
##  Mean   : 9.0   Mean   : 7.50   Mean   : 9   Mean   : 7.501  
##  3rd Qu.:11.5   3rd Qu.: 7.98   3rd Qu.: 8   3rd Qu.: 8.190  
##  Max.   :14.0   Max.   :12.74   Max.   :19   Max.   :12.500
Attributes of the data
attributes(df)
## $names
## [1] "x1" "y1" "x2" "y2" "x3" "y3" "x4" "y4"
## 
## $row.names
##  [1]  1  2  3  4  5  6  7  8  9 10 11
## 
## $class
## [1] "data.frame"
Plotting dataset I
plot(df$x1, df$y1, main = "Data Set I", xlab = "x", ylab = "y")

Plotting dataset II
plot(df$x2, df$y2,  main = "Data Set II", xlab = "x", ylab = "y")

Plotting dataset III
plot(df$x3, df$y3, main = "Data Set III", xlab = "x", ylab = "y")

Plotting dataset IV
plot(df$x4, df$y4,  main = "Data Set IV", xlab = "x", ylab = "y")

To plot all data sets on one graph:
1. Set vectors P with x and y values
p1:
# 1. Set vectors P with x and y values
p1 <- data.frame(x= df$x1, y = df$y1)

p1
##     x     y
## 1  10  8.04
## 2   8  6.95
## 3  13  7.58
## 4   9  8.81
## 5  11  8.33
## 6  14  9.96
## 7   6  7.24
## 8   4  4.26
## 9  12 10.84
## 10  7  4.82
## 11  5  5.68
p2 <- data.frame(x= df$x2, y = df$y2)

p2
##     x    y
## 1  10 9.14
## 2   8 8.14
## 3  13 8.74
## 4   9 8.77
## 5  11 9.26
## 6  14 8.10
## 7   6 6.13
## 8   4 3.10
## 9  12 9.13
## 10  7 7.26
## 11  5 4.74
p3 <- data.frame(x= df$x3, y = df$y3)

p3
##     x     y
## 1  10  7.46
## 2   8  6.77
## 3  13 12.74
## 4   9  7.11
## 5  11  7.81
## 6  14  8.84
## 7   6  6.08
## 8   4  5.39
## 9  12  8.15
## 10  7  6.42
## 11  5  5.73
p4 <- data.frame(x= df$x4, y = df$y4)

p4
##     x     y
## 1   8  6.58
## 2   8  5.76
## 3   8  7.71
## 4   8  8.84
## 5   8  8.47
## 6   8  7.04
## 7   8  5.25
## 8  19 12.50
## 9   8  5.56
## 10  8  7.91
## 11  8  6.89
2. use melt function to have one x , y and dataset variables
zz <- melt(list(dataset1=p1, dataset2=p2, dataset3=p3, dataset4=p4), id.vars="x")

#here is zz melted datapoints
zz
##     x variable value       L1
## 1  10        y  8.04 dataset1
## 2   8        y  6.95 dataset1
## 3  13        y  7.58 dataset1
## 4   9        y  8.81 dataset1
## 5  11        y  8.33 dataset1
## 6  14        y  9.96 dataset1
## 7   6        y  7.24 dataset1
## 8   4        y  4.26 dataset1
## 9  12        y 10.84 dataset1
## 10  7        y  4.82 dataset1
## 11  5        y  5.68 dataset1
## 12 10        y  9.14 dataset2
## 13  8        y  8.14 dataset2
## 14 13        y  8.74 dataset2
## 15  9        y  8.77 dataset2
## 16 11        y  9.26 dataset2
## 17 14        y  8.10 dataset2
## 18  6        y  6.13 dataset2
## 19  4        y  3.10 dataset2
## 20 12        y  9.13 dataset2
## 21  7        y  7.26 dataset2
## 22  5        y  4.74 dataset2
## 23 10        y  7.46 dataset3
## 24  8        y  6.77 dataset3
## 25 13        y 12.74 dataset3
## 26  9        y  7.11 dataset3
## 27 11        y  7.81 dataset3
## 28 14        y  8.84 dataset3
## 29  6        y  6.08 dataset3
## 30  4        y  5.39 dataset3
## 31 12        y  8.15 dataset3
## 32  7        y  6.42 dataset3
## 33  5        y  5.73 dataset3
## 34  8        y  6.58 dataset4
## 35  8        y  5.76 dataset4
## 36  8        y  7.71 dataset4
## 37  8        y  8.84 dataset4
## 38  8        y  8.47 dataset4
## 39  8        y  7.04 dataset4
## 40  8        y  5.25 dataset4
## 41 19        y 12.50 dataset4
## 42  8        y  5.56 dataset4
## 43  8        y  7.91 dataset4
## 44  8        y  6.89 dataset4
#cleaning up ZZ
zz$variable <- NULL

rename(zz, c("value" = "y", "L1" = "DataSet"))
##     x     y  DataSet
## 1  10  8.04 dataset1
## 2   8  6.95 dataset1
## 3  13  7.58 dataset1
## 4   9  8.81 dataset1
## 5  11  8.33 dataset1
## 6  14  9.96 dataset1
## 7   6  7.24 dataset1
## 8   4  4.26 dataset1
## 9  12 10.84 dataset1
## 10  7  4.82 dataset1
## 11  5  5.68 dataset1
## 12 10  9.14 dataset2
## 13  8  8.14 dataset2
## 14 13  8.74 dataset2
## 15  9  8.77 dataset2
## 16 11  9.26 dataset2
## 17 14  8.10 dataset2
## 18  6  6.13 dataset2
## 19  4  3.10 dataset2
## 20 12  9.13 dataset2
## 21  7  7.26 dataset2
## 22  5  4.74 dataset2
## 23 10  7.46 dataset3
## 24  8  6.77 dataset3
## 25 13 12.74 dataset3
## 26  9  7.11 dataset3
## 27 11  7.81 dataset3
## 28 14  8.84 dataset3
## 29  6  6.08 dataset3
## 30  4  5.39 dataset3
## 31 12  8.15 dataset3
## 32  7  6.42 dataset3
## 33  5  5.73 dataset3
## 34  8  6.58 dataset4
## 35  8  5.76 dataset4
## 36  8  7.71 dataset4
## 37  8  8.84 dataset4
## 38  8  8.47 dataset4
## 39  8  7.04 dataset4
## 40  8  5.25 dataset4
## 41 19 12.50 dataset4
## 42  8  5.56 dataset4
## 43  8  7.91 dataset4
## 44  8  6.89 dataset4
Sum of all x values are equal and sum of all y values are equal in each dataset
xsum <- aggregate(zz$x, by=list(Category=zz$L1), FUN=sum)
xsum
##   Category  x
## 1 dataset1 99
## 2 dataset2 99
## 3 dataset3 99
## 4 dataset4 99
ysum <- aggregate(zz$value, by=list(Category=zz$L1), FUN=sum)
ysum
##   Category     x
## 1 dataset1 82.51
## 2 dataset2 82.51
## 3 dataset3 82.50
## 4 dataset4 82.51
3. Use ggplot function to graph all 4 data sets into 1 graph
ggplot(zz, aes(x, y=value, color=L1)) + 
  geom_point() + 
  geom_line() +
  scale_color_manual("Dataset", 
       values = c("dataset1" = "darkgreen", "dataset2" = "blue", "dataset3" = "red", "dataset4" = "orange"))

The conclustions from comparing the 4 data set graphs:
1) It was interesting to find out that the sum of all x values for each dataset is the same (99) and the sum of all y values in each dataset is also same rounded to one decimal place.(82.5)
2) All the graphe seem to be going from left bottom quadrant to top rith quadrant y value is increasing as the x value is increasing. Only Dataset IV was a little different to have only one point as an outlier.