Phase 1.Rmd

---
title: "Phase I: Data Exploration"
author: "Shreya and Francis"
date: "October 10, 2017"
output: html_document
---

Content of the RMarkdown
1. Read data
2. Gre data
3. Message data
4. Login data
5. School data (still need to be merged)
6. Event data (still need to be merged)
7. App decision data (still need to be merged)
8. App base data
9. Merge data
10. Final Data Visualizations


```{r}
install.packages("readxl")
install.packages("psych")
install.packages("ggplot2")
install.packages("dplyr")
install.packages("tidyr")
```


# Read Data
```{r}
library(readxl)
appbase <- read_excel("C:/Users/Shreya/Documents/GITHUB/Capstone/ApplicationBase.xlsx")
```

```{r}
library(readxl)
appdecision <- read_excel("C:/Users/Shreya/Documents/GITHUB/Capstone/ApplicationDecisions.xlsx")
```

```{r}
library(readxl)
eventattendance <- read_excel("C:/Users/Shreya/Documents/GITHUB/Capstone/PersonEventAttendance.xlsx")
```

```{r}
library(readxl)
gre <- read_excel("C:/Users/Shreya/Documents/GITHUB/Capstone/PersonGRE.xlsx")
```

```{r}
library(readxl)
login <- read_excel("C:/Users/Shreya/Documents/GITHUB/Capstone/PersonLogins.xlsx")
```

```{r}
library(readxl)
message <- read_excel("C:/Users/Shreya/Documents/GITHUB/Capstone/PersonMessages.xlsx")
```

```{r}
library(readxl)
school <- read_excel("C:/Users/Shreya/Documents/GITHUB/Capstone/PersonSchools.xlsx")
```


# GRE Data
GRE: Gre Scores of applicants with 3 main categories in these scores:
Verbal (Range 0-170), 
Quantitative (Range 0-170)
Writing (Range 0-6)

```{r}
hist(gre$Verbal)
```
This histogram of verbal score shows that there are 2 gre scale that are currently being used in the data
one is current scale: 0-170 (increments of 1 point) (maximum applicant population has this score range)
other is prior / traditional scale: 0 - 800 (increments of 10 points) (changed on August 2011)

```{r}
hist(gre$Quantitative)
```

```{r}
gre$Writing <- as.numeric(gre$Writing)
hist(gre$Writing)
```

Maximum admitted applicants have 3 or more score level on gre writing scale of 0-6.

GRE_score conversion from traditional to new scale (not needed but added)
```{r}
gre$Quantitative_new <- 
  ifelse(gre$Quantitative >=760, 170,
         ifelse(gre$Quantitative ==750, 169,
                ifelse(gre$Quantitative ==740, 169,
                       ifelse(gre$Quantitative == 730, 168,
                              ifelse(gre$Quantitative == 720, 168,
                                     ifelse(gre$Quantitative == 710, 167,
                                            ifelse(gre$Quantitative == 700, 166,
                                                   ifelse(gre$Quantitative == 690, 165,
                                                          ifelse(gre$Quantitative == 680, 165, 
                                                                ifelse(gre$Quantitative ==670, 164,
         ifelse(gre$Quantitative ==660, 164,
                ifelse(gre$Quantitative ==650, 163,
                       ifelse(gre$Quantitative == 640, 162,
                              ifelse(gre$Quantitative == 630, 162,
                                     ifelse(gre$Quantitative == 620, 161,
                                            ifelse(gre$Quantitative == 610, 160,
                                                   ifelse(gre$Quantitative == 600, 160,
                                                          ifelse(gre$Quantitative == 590, 159, 
                                                                 ifelse(gre$Quantitative ==580, 158,
         ifelse(gre$Quantitative ==570, 158,
                ifelse(gre$Quantitative ==560, 157,
                       ifelse(gre$Quantitative == 550, 156,
                              ifelse(gre$Quantitative == 540, 156,
                                     ifelse(gre$Quantitative == 530, 155,
                                            ifelse(gre$Quantitative == 520, 154,
                                                   ifelse(gre$Quantitative == 510, 154,
                                                          ifelse(gre$Quantitative == 500, 153,
                                                                 ifelse(gre$Quantitative ==490, 152,
         ifelse(gre$Quantitative ==480, 152,
                ifelse(gre$Quantitative ==470, 151,
                       ifelse(gre$Quantitative == 460, 151,
                              ifelse(gre$Quantitative == 450, 150,
                                     ifelse(gre$Quantitative == 440, 149,
                                            ifelse(gre$Quantitative == 430, 149,
                                                   ifelse(gre$Quantitative == 420, 148,
                                                          ifelse(gre$Quantitative == 410, 147,
                                                                 ifelse(gre$Quantitative ==400, 146,
         ifelse(gre$Quantitative ==390, 146,
                ifelse(gre$Quantitative ==380, 145,
                       ifelse(gre$Quantitative == 370, 144,
                              ifelse(gre$Quantitative == 360, 143,
                                     ifelse(gre$Quantitative == 350, 143,
                                            ifelse(gre$Quantitative == 340, 142,
                                                   ifelse(gre$Quantitative == 330, 141,
                                                          ifelse(gre$Quantitative == 320, 140, 
         ifelse(gre$Quantitative ==310, 139,
                ifelse(gre$Quantitative ==300, 138,
                       ifelse(gre$Quantitative == 290, 137,
                              ifelse(gre$Quantitative == 280, 135,
                                     ifelse(gre$Quantitative ==759, 170, 
                                            gre$Quantitative))))))))))))))))))))))))))))))))))))))))))))))))))
gre$Quantitative_new <- as.numeric(gre$Quantitative_new)
```

```{r}
gre$Verbal_new <- 
  ifelse(gre$Verbal >=760, 170,
         ifelse(gre$Verbal ==750, 169,
                ifelse(gre$Verbal ==740, 169,
                       ifelse(gre$Verbal == 730, 168,
                              ifelse(gre$Verbal == 720, 168,
                                     ifelse(gre$Verbal == 710, 167,
                                            ifelse(gre$Verbal == 700, 166,
                                                   ifelse(gre$Verbal == 690, 165,
                                                          ifelse(gre$Verbal == 680, 165, 
                                                                ifelse(gre$Verbal ==670, 164,
         ifelse(gre$Verbal ==660, 164,
                ifelse(gre$Verbal ==650, 163,
                       ifelse(gre$Verbal == 640, 162,
                              ifelse(gre$Verbal == 630, 162,
                                     ifelse(gre$Verbal == 620, 161,
                                            ifelse(gre$Verbal == 610, 160,
                                                   ifelse(gre$Verbal == 600, 160,
                                                          ifelse(gre$Verbal == 590, 159, 
                                                                 ifelse(gre$Verbal ==580, 158,
         ifelse(gre$Verbal ==570, 158,
                ifelse(gre$Verbal ==560, 157,
                       ifelse(gre$Verbal == 550, 156,
                              ifelse(gre$Verbal == 540, 156,
                                     ifelse(gre$Verbal == 530, 155,
                                            ifelse(gre$Verbal == 520, 154,
                                                   ifelse(gre$Verbal == 510, 154,
                                                          ifelse(gre$Verbal == 500, 153,
                                                                 ifelse(gre$Verbal ==490, 152,
         ifelse(gre$Verbal ==480, 152,
                ifelse(gre$Verbal ==470, 151,
                       ifelse(gre$Verbal == 460, 151,
                              ifelse(gre$Verbal == 450, 150,
                                     ifelse(gre$Verbal == 440, 149,
                                            ifelse(gre$Verbal == 430, 149,
                                                   ifelse(gre$Verbal == 420, 148,
                                                          ifelse(gre$Verbal == 410, 147,
                                                                 ifelse(gre$Verbal ==400, 146,
         ifelse(gre$Verbal ==390, 146,
                ifelse(gre$Verbal ==380, 145,
                       ifelse(gre$Verbal == 370, 144,
                              ifelse(gre$Verbal == 360, 143,
                                     ifelse(gre$Verbal == 350, 143,
                                            ifelse(gre$Verbal == 340, 142,
                                                   ifelse(gre$Verbal == 330, 141,
                                                          ifelse(gre$Verbal == 320, 140, 
         ifelse(gre$Verbal ==310, 139,
                ifelse(gre$Verbal ==300, 138,
                       ifelse(gre$Verbal == 290, 137,
                              ifelse(gre$Verbal == 280, 135,
                                     ifelse(gre$Verbal ==759, 170, 
                                            gre$Verbal))))))))))))))))))))))))))))))))))))))))))))))))))
gre$Verbal_new <- as.numeric(gre$Verbal_new)
```

```{r}
gre$total <- gre$Verbal_new+gre$Quantitative_new
gre$total <- as.numeric(gre$total)
```

```{r}
range(gre$total, na.rm=TRUE)
```

```{r}
gre <- setNames(gre, c("identity", "gre_verbal", "gre_quantitative", "gre_writing",  "gre_quantitative_new", "gre_verbal_new", "gre_total_new"))
```

Thanks for the following Charles
```{r}
#z scores for scaling on same scale, irrespective of scale
gre$gre_scale_quantitative <- scale(gre$gre_quantitative)
gre$gre_scale_verbal <- scale(gre$gre_verbal)
```

```{r}
gre1 <- gre[-c(1:3,5:6)]
```

```{r}
plot(gre1)
```


# Message Data
```{r}
table(message$opened_or_url)
#table(message$identity, message$opened_or_url)
```

```{r}
message$opened_or_url_1 <- ifelse(message$opened_or_url == "NULL", "0", 1)
message$opened_or_url_1 <-as.numeric(message$opened_or_url_1)
```

```{r}
library(dplyr)
library(tidyr)
message$count <- 1
message2 <- message %>% group_by(identity) %>% summarize(sum(opened_or_url_1))
message3 <- message %>% group_by(identity) %>% summarize(sum(count))
```

```{r}
message2 <- setNames(message2, c("identity", "sum_open_or_url_message"))
message3 <- setNames(message3, c("identity", "sum_num_of_message"))
```

```{r}
library(dplyr)
message_main <- dplyr::full_join(message3, message2, by="identity")
```


# Login Data
```{r}
library(dplyr)
library(tidyr)
login$count <- 1
login1 <- login %>% group_by(identity) %>% summarize(sum(count))
```

```{r}
login1 <- setNames(login1, c("identity", "sum_login_count"))
```


# School Data
```{r}
school$gpa_numeric <- ifelse(school$gpa == "NULL", NA, school$gpa)
school$gpa_numeric <- as.numeric(school$gpa_numeric)
range(school$gpa_numeric, na.rm = TRUE)
```

```{r}
school$gpa_numeric <- ifelse(school$gpa >= "5", NA, school$gpa)
school$gpa_numeric <- as.numeric(school$gpa_numeric)
range(school$gpa_numeric, na.rm = TRUE)
```

```{r}
hist(school$gpa_numeric, col="chartreuse3")
```

```{r}
table(school$earned_degree)
```

```{r}
school$earned_degree_num <- ifelse(school$earned_degree == "N", "No", ifelse(school$earned_degree == "Y", "Yes", school$earned_degree))
```


# Event Data
```{r}
table(eventattendance$EventType)
```

```{r}
eventattendance.unique <- unique(eventattendance$identity)
```


# Application Decision Data
```{r}
table(appdecision$decision_name)
```


# Application Base Data
```{r}
appbase$sex_1 <- ifelse(appbase$sex == "NULL", "Not_Stated", appbase$sex)
appbase$PrevApply_1 <- ifelse(appbase$PrevApply == "0", "No", "Yes")
appbase$Scholarship_app_1 <- ifelse(appbase$Scholarship_app == "0", "No", "Yes")
```

```{r}
range(appbase$age_at_app)
```

```{r}
appbase$age_at_app_new <- ifelse(appbase$age_at_app <=16, NA, appbase$age_at_app)
appbase$age_at_app_new <- as.numeric(appbase$age_at_app_new)
```

```{r}
appbase$credits_req <- appbase$`Credits Req`
appbase$credits_req_new <- 
  ifelse(appbase$credits_req ==32, "32",
         
  ifelse(appbase$credits_req ==33, "33-36",
  ifelse(appbase$credits_req ==34, "33-36",
  ifelse(appbase$credits_req == 36, "33-36",
         
  ifelse(appbase$credits_req == 38, "38-45",
  ifelse(appbase$credits_req == 40, "38-45",
  ifelse(appbase$credits_req == 41, "38-45",
  ifelse(appbase$credits_req == 42, "38-45",
  ifelse(appbase$credits_req == 43, "38-45",
  ifelse(appbase$credits_req == 44, "38-45",
  ifelse(appbase$credits_req == 45, "38-45",
         
  ifelse(appbase$credits_req == 47, "47-60",
  ifelse(appbase$credits_req == 48, "47-60",                    
  ifelse(appbase$credits_req == 52, "47-60",
  ifelse(appbase$credits_req == 57, "47-60",
  ifelse(appbase$credits_req == 60, "47-60",
         
  ifelse(appbase$credits_req == 62, "62-75",
  ifelse(appbase$credits_req == 69, "62-75",
  ifelse(appbase$credits_req == 74, "62-75",
  ifelse(appbase$credits_req == 75, "62-75",             
         
  ifelse(appbase$credits_req == 86, "86-95",
  ifelse(appbase$credits_req == 90, "86-95",
  ifelse(appbase$credits_req == 95, "86-95",
         appbase$credits_req)))))))))))))))))))))))
table(appbase$credits_req_new)
```


# Merging Data
```{r}
Data <- dplyr::full_join(appbase, gre, by="identity", all=TRUE)
Data <- dplyr::full_join(Data, login1, by="identity", all=TRUE)
Data <- dplyr::full_join(Data, message_main, by="identity", all=TRUE)
```










# Final Data Visualizations
1
```{r}
library(ggplot2)
ggplot(data=school, aes(school$gpa_numeric)) + 
  geom_histogram(breaks=seq(0, 4.4, by = 0.2), 
                 col="red", 
                 aes(fill=..count..)) +
  scale_fill_gradient("Count", low = "green", high = "red") +
  labs(title="Previous School GPA Records", x="GPA Score Reported", y="Number of Applicants")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```
2
```{r}
library(ggplot2)
ggplot(data=school,aes(x=earned_degree_num)) + 
  geom_bar(fill="chartreuse3") + 
  geom_text(stat='count',aes(label=..count..),vjust=-1, col="darkslateblue") + 
  ylim(-200, 7000) + 
  labs(title="Priviously: Degree Earned or Not (dropped)", x="Yes/No", y="Number of Cases reported in total")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```
3,4,5,6,7
```{r}
library(ggplot2)
ggplot(data=gre, aes(gre$gre_verbal_new)) + 
  geom_histogram(breaks=seq(0, 170, by = 2), 
                 col="chartreuse4", 
                 aes(fill=..count..)) +
  scale_fill_gradient("Count", low = "yellow", high = "chartreuse4")+
  labs(title="Histogram for Verbal Scores recorded in the GRE", x="Verbal Score (Max = 170)", y="Number of recorded applicant cases")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```

```{r}
library(ggplot2)
ggplot(data=gre, aes(gre$gre_quantitative_new)) + 
  geom_histogram(breaks=seq(0, 170, by = 2), 
                 col="blue", 
                 aes(fill=..count..)) +
  scale_fill_gradient("Count", low = "green", high = "blue")+
  labs(title="Histogram for Quantitative Scores recorded in the GRE", x="Quantitative Score (Max = 170)", y="Number of recorded applicant cases")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```

```{r}
library(ggplot2)
ggplot(data=gre, aes(gre$gre_writing)) + 
  geom_histogram(breaks=seq(0, 6, by = 0.5), 
                 col="purple4", 
                 aes(fill=..count..)) +
  scale_fill_gradient("Count", low = "orchid", high = "purple4")+
  labs(title="Histogram for Writing Scores recorded in the GRE", x="Quantitative Score (Scale 0-6)", y="Number of recorded applicant cases")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```

```{r}
library(ggplot2)
ggplot(data=gre, aes(gre$gre_total_new)) + 
  geom_histogram(breaks=seq(140, 340, by = 5), 
                 col="mediumblue", 
                 aes(fill=..count..)) +
  scale_fill_gradient("Count", low = "darkorange", high = "mediumblue")+
  labs(title="Histogram for Verbal and Quants Scores recorded in the GRE", x="Quantitative (Max = 340)", y="Number of recorded applicant cases")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```

```{r}
library(psych)
pairs.panels(gre1[c(1:4)], gap = 0)
```
8
```{r}
counts <- table(eventattendance$EventType)
#barplot(counts, main="Event Audience Barplot", ylab="Number of Audience", xlab="Event Type")
#try to look for a way to write the xlab labels vertically
par(las=2)
par(mar=c(5,15,4,2)) 
#(space on xlab side, space on ylab side, spacing between different intervals on x lab and the ylab)
barplot(counts, main="Event Attendance Barplot", horiz=TRUE, xlab="Number of Attendees", col="chartreuse3")
```
9
```{r}
counts <- table(appdecision$decision_name)
par(las=2)
par(mar=c(5,16,4,2)) 
#(space on xlab side, space on ylab side, spacing between different intervals on x lab and the ylab)
barplot(counts, main="Decision Name Barplot", horiz=TRUE, xlab="Number of Applicants in each decision type", col="chartreuse3")
```
10
```{r}
library(ggplot2)
ggplot(data=appbase,aes(x=credits_req_new)) + 
  geom_bar(fill="blueviolet") + 
  geom_text(stat='count',aes(label=..count..),vjust=-1, col="darkslateblue") + 
  ylim(-50, 1500) + 
  labs(title="Degree Credits Required", x="Number of Credits", y="Number of Applicants")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```
11
```{r}
library(ggplot2)
ggplot(data=appbase,aes(x=app_number)) + 
  geom_bar(fill=c("chocolate4","chocolate", "chocolate2", "chocolate1", "chocolate1")) + 
  geom_text(stat='count',aes(label=..count..),vjust=-1, col="darkslateblue") + 
  ylim(-50, 4500) + 
  labs(title="Number of App_Number", x="Number of Apps", y="Number of Applicants")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```
12
```{r}
library(ggplot2)
ggplot(data=appbase,aes(x=sex_1)) + 
  geom_bar(fill=115) + 
  geom_text(stat='count',aes(label=..count..),vjust=-1, col="darkslateblue") + 
  ylim(-50, 4200) + 
  labs(title="Gender Dominance amongst Applicants", x="Gender", y="Number of Applicants")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```
13
```{r}
library(ggplot2)
ggplot(data=appbase,aes(x=PrevApply_1)) + 
  geom_bar(fill="violetred") + 
  geom_text(stat='count',aes(label=..count..),vjust=-1, col="darkslateblue") + 
  ylim(-50, 5000) + 
  labs(title="Previous_Applicant or not", x="Previous Applicant or Not", y="Number of Applicants")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```
14
```{r}
library(ggplot2)
ggplot(data=appbase,aes(x=EverDeposited)) + 
  geom_bar(fill="olivedrab") + 
  geom_text(stat='count',aes(label=..count..),vjust=-1, col="darkslateblue") + 
  ylim(-50, 4300) + 
  labs(title="Applicant_Deposited_Entry_Fee_or_Not", x="Applicant_Deposited_Entry_Fee_or_Not", y="Number_of_Applicants")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```
15
```{r}
library(ggplot2)
ggplot(data=appbase,aes(x=Scholarship_app_1)) + 
  geom_bar(fill="hotpink4") + 
  geom_text(stat='count',aes(label=..count..),vjust=-1, col="darkslateblue") + 
  ylim(-50, 4700) + 
  labs(title="Scholarship Applications", x="Applied_or_Not", y="Number of Applicants")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```
16
```{r}
library(ggplot2)
ggplot(data=appbase, aes(appbase$age_at_app_new)) + 
  geom_histogram(breaks=seq(19, 65, by = 1), 
                 col="orangered", 
                 aes(fill=..count..)) +
  scale_fill_gradient("Count", low = "yellow", high = "orangered")+
  labs(title="Applicant's Age", x="Age", y="Number of Applicants")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```
17
```{r}
library(ggplot2)
ggplot(data=Data, aes(Data$sum_login_count)) + 
  geom_histogram(breaks=seq(-5, 650, by = 5), 
                 col="darkblue", 
                 aes(fill=..count..)) +
  scale_fill_gradient("Count", low = "cyan", high = "darkblue")+
  labs(title="Number of Logins per Applicant", x="Number of Logins", y="Number of Applicants")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```
18
```{r}
library(ggplot2)
ggplot(data=Data, aes(Data$sum_num_of_message)) + 
  geom_histogram(breaks=seq(-5, 450, by = 5), 
                 col="turquoise4", 
                 aes(fill=..count..)) +
  scale_fill_gradient("Count", low = "turquoise", high = "turquoise4")+
  labs(title="Number of Messages SENT", x="Number of Messages", y="Number of Applicants")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```
19
```{r}
library(ggplot2)
ggplot(data=Data, aes(Data$sum_open_or_url_message)) + 
  geom_histogram(breaks=seq(-10, 450, by = 5), 
                 col="darkblue", 
                 aes(fill=..count..)) +
  scale_fill_gradient("Count", low = "palegreen", high = "royalblue")+
  labs(title="Number of messages OPEN/URL", x="NUmber of Messages", y="Number of Applicants")+ 
  theme(plot.title = element_text(face = "bold", size=15))
```
20
```{r}
appbase<-dplyr::select(appbase,identity,app_number,age_at_app,'Credits Req',Scholarship_app)
regression<-lm(appbase$app_number~appbase$age_at_app+appbase$`Credits Req`+appbase$Scholarship_app)
summary(regression)
appbase<-dplyr::select(appbase,-identity)
appbase<-na.omit(appbase) 
appbase2<-appbase[c("app_number","age_at_app","Credits Req","Scholarship_app")]
CORb <- cor(appbase2)
corrplot(CORb, order="AOE", method="circle", tl.pos="lt", type="upper", tl.col="black", tl.cex=0.6, tl.srt=45,addCoef.col="black", addCoefasPercent = TRUE,sig.level=0.50, insig = "blank")
```
21
```{r}
appage<-dplyr::select(appbase,app_number,age_at_app)
regression<-lm(appbase$age_at_app~appbase$app_number)
summary(regression)
appage<-na.omit(appage) 
appage2<-appage[c("app_number","age_at_app")]
CORa <- cor(appage2)
corrplot(CORa, order="AOE", method="circle", tl.pos="lt", type="upper", tl.col="black", tl.cex=0.6, tl.srt=45,addCoef.col="black", addCoefasPercent = TRUE,sig.level=0.50, insig = "blank")
```
22.
```{r}
applar<-dplyr::select(appbase,app_number,Scholarship_app)
regression<-lm(appbase$app_number~appbase$Scholarship_app)
summary(regression)
applar<-na.omit(applar) 
applar2<-applar[c("app_number","Scholarship_app")]
CORs <- cor(applar2)
corrplot(CORs, order="AOE", method="circle", tl.pos="lt", type="upper", tl.col="black", tl.cex=0.6, tl.srt=45,addCoef.col="black", addCoefasPercent = TRUE,sig.level=0.50, insig = "blank")
```
23.
```{r}
appbase<-dplyr::select(appbase,identity,app_number,age_at_app,Scholarship_app)
gpa<-dplyr::select(PersonSchools,identity,gpa)
gre<-dplyr::select(PersonGRE,identity,Verbal,Quantitative,Writing)
appgg<-dplyr::full_join(appbase,gpa,by="identity", all=TRUE)
appgg<-dplyr::select(appgg,-identity)
appgg<-na.omit(appgg) 
regression<-lm(appgg$app_number~appgg$gpa)
summary(regression)
appgg2<-appgg[c("app_number","age_at_app","Scholarship_app","gpa")]
gpa1<-dplyr::select(PersonSchools,gpa)

is.NullOb <- function(x) is.null(x) | all(sapply(x, is.null))

## Recursively step down into list, removing all such objects 
rmNullObs <- function(x) {
   x <- Filter(Negate(is.NullOb), x)
   lapply(x, function(x) if (is.list(x)) rmNullObs(x) else x)
}

rmNullObs(gpa1)
gpa2 <- list(gpa1)
gpa3<-rmNullObs(gpa2)
```
```{r}
non_null_names <- which(!sapply(gpa1, is.null))
gpa2 <- gpa1[non_null_names]
gpa3<-names(gpa2) <- non_null_names
appgg2<-appgg[c("app_number","age_at_app","Scholarship_app","gpa3")]

CORgg <- cor(appgg2)
corrplot(CORgg, order="AOE", method="circle", tl.pos="lt", type="upper", tl.col="black", tl.cex=0.6, tl.srt=45,addCoef.col="black", addCoefasPercent = TRUE,sig.level=0.50, insig = "blank")
appgg<-dplyr::full_join(appbase,gre,by="identity", all=TRUE)
appgg<-na.omit(appgg)
appgg2<-appgg[c("app_number","Verbal","Quantitative")]
regression<-lm(appgg2$app_number~appgg2$Verbal+appgg2$Quantitative)
summary(regression)
cor(appgg2)
corrplot(CORgg, order="AOE", method="circle", tl.pos="lt", type="upper", tl.col="black", tl.cex=0.6, tl.srt=45,addCoef.col="black", addCoefasPercent = TRUE,sig.level=0.50, insig = "blank")
greh<-subset(gre,"Verbel">= 150)
```
24.
```{r}
install.packages("rJava")
library(rJava)
installed.packages("iplots")
library(iplots)
install.packages(c("devtools","jsonlite", "knitr", "shiny", "httpuv"))
library(devtools)
install_github("mages/googleVis")
library(googleVis)

install.packages("googleVis")
library(googleVis)

country<-table(appbase$permanent_country)
install.packages('googleVis')
df<-as.data.frame(country)
View(df)
library(ggplot2)
library(maps)
WorldData <- map_data('world')
head(WorldData, 100)
df <-data.frame(region=c('Hungary','Lithuania','Argentina'),value=c(4,10???11))

Total <- WorldData[WorldData$region %in% df$region, ]
Total$value <- df$value[match(Total$region, df$region)]

ggplot(Total, aes(x=long, y=lat, group = group, fill = value)) + 
  geom_polygon(colour = "white") +
  scale_fill_continuous(low = "thistle2", 
                        high = "darkred", 
                        guide="colorbar") +
  theme_bw()  + 
  labs(fill = "legend" ,title = "Title", x="", y="") +
  scale_y_continuous(breaks=c()) + 
  scale_x_continuous(breaks=c()) + 
  theme(panel.border =  element_blank())
library(plotGoogleMaps)
polygons.plot <- df[,c("Freq","Var1")]
polygons.plot <- polygons.plot[polygons.plot$NAME!="Antarctica",]
names(polygons.plot) <- c("Freq","Var1")

plot(polygons.plot)
```