Vincent Houghton Elementary School (Grade 5)

The chart below describes the grades of fourteen students from 5th grade of Vincent Houghton Elementary school. It contains the student’s grade from five subjects and the number of days they were present in school.

• Part 1: Create a .CSV file that includes all of the information above:
  • Install all the packages that will be used in this project.
    
  • Created a .CSV file using Excel and uploaded it to GitHub.
    
• Part 2: Read the information from your .CSV file into R, and use tidyr and dplyr as needed to tidy and transform your “data”:
  • Read the .CSV file form GitHub.
    
  • Used dplyr and tidyr packages to transform and tidy the data.
    
  • More tiding and transforming data.
    
• Part 3: Perform analysis on the “data”:
  • Found the correlation between the data as a whole and graphed it using ggplot2.
    
  • Found the correlation between the data by subject.
  • Found the correlation between average grade and days present.

Part 1:

• Installing all the necessary packages needed for this data analysis.

library(curl)
library(tidyr)

## Warning: package 'tidyr' was built under R version 3.2.2

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.2.2

## 
## 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

library(stringr)
library(ggplot2)
library(ggthemes)

## Warning: package 'ggthemes' was built under R version 3.2.2

library(knitr)

Up

• I created a .CSV file using Microsoft Excel with the data of grade of 5th grade students in Vincent Houghton Elementary School in my local GitHub repository.

Part 2:

• Using the library curl I uploaded the table from my online GitHub repository.

Vincent_Grade_5 <- read.csv(file="https://raw.githubusercontent.com/nabilahossain/Class-IS607/master/Project%202/Vincent%20Houghton%20Elementary%20School/Vincent%20Houghton%20(Grade%205).csv", header=TRUE, sep=",")
Vincent_Grade_5

##           Student English Math Science Social.Studies Technology
## 1     Alicia Cruz      46   47      94             96         84
## 2   Solomon Tunde      64   84      43             60         83
## 3   Daniel Zainea      47   82      52             58         96
## 4    Thomas Brown      96   50      58             71        100
## 5   Lisa Giamonco      40   76      50             85         78
## 6  Cheryl Harldat      75   78      98             41         88
## 7   Winston Cadiz      90   73      90             75         45
## 8    Pablo Romero      91   50      85             68         72
## 9   Jennifer Weil      98   63      43             58         40
## 10 Ronald Rosario      98   40      98             75         81
## 11  Martha Carpio      69   60      52             60         47
## 12 Jasmine Carpio      99   86      49             89         93
## 13    Raul Martin      42   59      54             48         80
## 14    Desio Trump      41   42      69             92         55
##    Days.Present
## 1           169
## 2           121
## 3           146
## 4            95
## 5           118
## 6           159
## 7           175
## 8           101
## 9           107
## 10           94
## 11          171
## 12          140
## 13          174
## 14          167

Up

• Using libraries dplyrand tidyr I transformed and tidied the data. I created a new table by transforming my original table from “wide” structure to “long” structure. I also separated the last and first names of the students.

VES <- Vincent_Grade_5 %>%  separate(Student, c("First_Name", "Last_Name")) %>%  arrange(Last_Name)
VES1 <- VES  %>% gather("Subject", "Scores", 3:7) %>% select(Last_Name, First_Name, Subject, Scores, Days_Present = Days.Present)
VES1$Subject <- str_replace_all(VES1$Subject, pattern = "\\.", replacement = " ") 
head(VES1, 15)

##    Last_Name First_Name Subject Scores Days_Present
## 1      Brown     Thomas English     96           95
## 2      Cadiz    Winston English     90          175
## 3     Carpio     Martha English     69          171
## 4     Carpio    Jasmine English     99          140
## 5       Cruz     Alicia English     46          169
## 6   Giamonco       Lisa English     40          118
## 7    Harldat     Cheryl English     75          159
## 8     Martin       Raul English     42          174
## 9     Romero      Pablo English     91          101
## 10   Rosario     Ronald English     98           94
## 11     Trump      Desio English     41          167
## 12     Tunde    Solomon English     64          121
## 13      Weil   Jennifer English     98          107
## 14    Zainea     Daniel English     47          146
## 15     Brown     Thomas    Math     50           95

Up

• I summarized the data in a given class (by subject), by finding the average grade of students and the days they were present in school. The table VES2 (Vincent Elementary School 2), holds the summary of each class. I also found the average grades of each student by combining their five classes (table VES3).

VES2 <- VES1 %>% group_by(Subject) %>% summarise(Average_Grade=mean(Scores), Average_Days_Present=mean(Days_Present)) %>% data.frame()
VES2

##          Subject Average_Grade Average_Days_Present
## 1        English      71.14286             138.3571
## 2           Math      63.57143             138.3571
## 3        Science      66.78571             138.3571
## 4 Social Studies      69.71429             138.3571
## 5     Technology      74.42857             138.3571

VES3 <- VES1 %>% group_by(Last_Name, First_Name) %>% summarise(Average_Grade=mean(Scores), Days_Present=mean(Days_Present)) %>% data.frame()
VES3

##    Last_Name First_Name Average_Grade Days_Present
## 1      Brown     Thomas          75.0           95
## 2      Cadiz    Winston          74.6          175
## 3     Carpio    Jasmine          83.2          140
## 4     Carpio     Martha          57.6          171
## 5       Cruz     Alicia          73.4          169
## 6   Giamonco       Lisa          65.8          118
## 7    Harldat     Cheryl          76.0          159
## 8     Martin       Raul          56.6          174
## 9     Romero      Pablo          73.2          101
## 10   Rosario     Ronald          78.4           94
## 11     Trump      Desio          59.8          167
## 12     Tunde    Solomon          66.8          121
## 13      Weil   Jennifer          60.4          107
## 14    Zainea     Daniel          67.0          146

Up

Part 3:

y <- cor(VES1$Scores, VES1$Days_Present)
y

## [1] -0.1263216

• Using the build in correlation function I found that the correlation between the student’s grades and the amount of days they were present in school. I then created two scatter plot with the best fit line. The first graph shows a scatter plot with the grades versus attendance, by student’s first name. The second graph is by subject. If we look at the graph below we see that the data is vastly scattered. The correlation is -0.1263216. The correlation is close to 0, therefore there is no relationship between the student’s grade and their attendance.

ggplot(data = VES1, aes(x = Scores, y = Days_Present, color=First_Name)) +  geom_point(aes(shape = First_Name)) + theme_igray() + geom_smooth(method = "lm", se=FALSE, color="black", formula = y ~ x) + scale_shape_manual(values = c(1:14)) + ggtitle("Graph 1: Grades Vs Days Present (by student)") + ylab("Days Present") + xlab("Grades")

ggplot(data = VES1, aes(x = Scores, y = Days_Present, color=Subject)) +  geom_point() + scale_colour_tableau()  + theme_solarized(light=FALSE) + ggtitle("Graph 2: Grades Vs. Days Present (by subject)") + geom_smooth(method = "lm", se=FALSE, color="yellow", formula = y ~ x) + ylab("Days Present") + xlab("Grades")

Up

• I found the correlation between students’ grade and attendance, by each subject. I created a table and graph with the summary of each subject. In The table we see that English is the only subject where there is a moderate relationship between grades and attendance. It has a negative relationship, meaning that student who have a low attendance rate seem to have higher grades.

z <- VES1 %>% select(Subject, Scores, Days_Present) %>% group_by(Subject) %>% summarize(Correlation = cor(Scores, Days_Present)) %>% data.frame()
VES4 <- VES2 %>% left_join(z, by = "Subject")
kable(VES4, digits = 2, align = "c", caption = "Table 1: Summary of each subject.")

Table 1: Summary of each subject.

Subject	Average_Grade	Average_Days_Present	Correlation
English	71.14	138.36	-0.53
Math	63.57	138.36	0.15
Science	66.79	138.36	0.13
Social Studies	69.71	138.36	-0.02
Technology	74.43	138.36	-0.25

ggplot(data = VES1, aes(x = Scores, y = Days_Present, color=First_Name)) +  geom_point() + geom_smooth(method = "lm", se=FALSE, color="black", formula = y ~ x) + facet_wrap(~Subject) + theme_igray() + ggtitle("Graph 3: Grades Vs. Days Present in Each subject (by students)") + ylab("Days Present") + xlab("Grades")   

Up

kable(VES3, align = "c", caption = "Table 2: Summary of each student.")   

Table 2: Summary of each student.

Last_Name	First_Name	Average_Grade	Days_Present
Brown	Thomas	75.0	95
Cadiz	Winston	74.6	175
Carpio	Jasmine	83.2	140
Carpio	Martha	57.6	171
Cruz	Alicia	73.4	169
Giamonco	Lisa	65.8	118
Harldat	Cheryl	76.0	159
Martin	Raul	56.6	174
Romero	Pablo	73.2	101
Rosario	Ronald	78.4	94
Trump	Desio	59.8	167
Tunde	Solomon	66.8	121
Weil	Jennifer	60.4	107
Zainea	Daniel	67.0	146

• Lastly I try to find the correlation between each student’s average grade and their attendance. The table above shows each student’s name, their average grade and the number of days they were present. Then I find the correlation between the average grade and the attendance. Below I graph their relationship. The correlation between the average grade and attendance is -0.31. Meaning they share a weak relationship; students’ with higher average seems to have more absence then other students.

x <- cor(VES3$Average_Grade, VES3$Days_Present)
x

## [1] -0.3066169

ggplot(data = VES3, aes(y = Days_Present, x = Average_Grade, color=First_Name)) +  geom_point() + theme_solarized(light=FALSE) + geom_smooth(method = "lm", se=FALSE, color="red", formula = y ~ x) + ggtitle("Graph 4: Average Grade Vs. Days Present")+ ylab("Days Present") + xlab("Average Grades") 

Up