library(stringr)
library(RMySQL)
## Loading required package: DBI
library(ggplot2)
MySQL_Username <- "root"
MySQL_Password <- "Coolyo123!"
JA_Data <- read.csv("https://raw.githubusercontent.com/juddanderman/cuny-data-607/master/Project3/linkedin-profiles-skills.csv", encoding="UTF-8", na.strings=c("","NA"), stringsAsFactors = F)
JA_Data <- cbind("LinkedIn", JA_Data[ , c(10,3,4,2,5,6)], NA)
JA_Data[ , 2] <- tolower(JA_Data[ , 2])
JA_Data[ , 2] <- iconv(JA_Data[ , 2], from = "latin1", to = "UTF-8")
JA_Data <- unique(JA_Data)
JA_Data$ID <- seq.int(nrow(JA_Data))
colnames(JA_Data) <- c("Source","Skill","Title","Location","Name","School","Degree","Company","Record_ID")
t(head(JA_Data, 1))
## 1
## Source "LinkedIn"
## Skill "talent management"
## Title "Principal and Founder, Bersin by Deloitte"
## Location "Oakland, California"
## Name "Josh Bersin"
## School "University of California, Berkeley - Walter A. Haas School of Business"
## Degree "MBA, 1988"
## Company NA
## Record_ID "1"
KC_Data <- read.csv("https://raw.githubusercontent.com/cunyauthor/Project3/master/API_Job.csv", encoding="UTF-8", na.strings=c("","NA"), stringsAsFactors = F)
KC_Data <- KC_Data[KC_Data[ , 1] != "count",] # Remove heading rows
KC_Data <- KC_Data[!is.na(KC_Data[ , 5]),] # Remove rows with blank skills
KC_Data <- cbind(Source = "KDnuggets+Dice", KC_Data[ , c(5,7,9)], NA, NA, NA, KC_Data[ , 8])
KC_Data[ , 2] <- as.character(str_extract_all(KC_Data[ , 2] , "l\\=\\S+\\&c"))
KC_Data[ , 2] <- str_replace_all(KC_Data[ , 2] , "(l\\=|\\&c)", "")
KC_Data[ , 2] <- str_replace_all(KC_Data[ , 2] , "\\+", " ")
KC_Data$ID <- seq.int(nrow(KC_Data))
colnames(KC_Data) <- c("Source","Skill","Title","Location","Name","School","Degree","Company","Record_ID")
t(head(KC_Data, 1))
## 1
## Source "KDnuggets+Dice"
## Skill "Owning Up To The Title"
## Title "Sr Sitecore Web Developer"
## Location "Milford"
## Name NA
## School NA
## Degree NA
## Company "UROOJ Corporation"
## Record_ID "1"
Dice_Freq <- read.csv("https://raw.githubusercontent.com/juddanderman/cuny-data-607/master/Project3/dice-listings-skills.csv", encoding="UTF-8", na.strings=c("","NA"), stringsAsFactors = F, row.names = 1)
Dice_Freq[ , 1] <- iconv(Dice_Freq[ , 1], from = "latin1", to = "UTF-8")
colnames(Dice_Freq) <- c("Skill","Count","Frequency")
t(head(Dice_Freq, 1))
## 1
## Skill "agile project management"
## Count "24647"
## Frequency "0.3071162"
Skill_class <- read.csv("https://raw.githubusercontent.com/scottogden10/607-Assignment2/master/skill_dic.csv", encoding="UTF-8", na.strings=c("","NA"), stringsAsFactors = F)
Skill_class[ , 2] <- iconv(Skill_class[ , 2], from = "latin1", to = "UTF-8")
colnames(Skill_class) <- c("Skill","Family","Category")
t(head(Skill_class, 1))
## 1
## Skill "access"
## Family "t"
## Category NA
connection <- dbConnect(MySQL(), user=MySQL_Username, password=MySQL_Password)
dbSendQuery(connection, 'CREATE SCHEMA IF NOT EXISTS Skills;')
## <MySQLResult:300136848,0,0>
dbSendQuery(connection, 'USE Skills;')
## <MySQLResult:2035296,0,1>
dbSendQuery(connection, 'DROP TABLE IF EXISTS tbl_LinkedIn;')
## <MySQLResult:386114240,0,2>
dbSendQuery(connection, 'DROP TABLE IF EXISTS tbl_KDnuggets_Dice;')
## <MySQLResult:1,0,3>
dbSendQuery(connection, 'DROP TABLE IF EXISTS tbl_Skill_Freq;')
## <MySQLResult:0,0,4>
dbSendQuery(connection, 'DROP TABLE IF EXISTS tbl_Skill_Class;')
## <MySQLResult:0,0,5>
dbWriteTable(connection, "tbl_LinkedIn", JA_Data, append = TRUE, row.names = FALSE)
## [1] TRUE
dbSendQuery(connection, "ALTER TABLE tbl_LinkedIn
MODIFY COLUMN Record_id MEDIUMINT NOT NULL,
MODIFY COLUMN Source VARCHAR(25) NOT NULL,
MODIFY COLUMN Skill VARCHAR(50) NOT NULL,
MODIFY COLUMN Title VARCHAR(250) NULL,
MODIFY COLUMN Location VARCHAR(50) NULL,
MODIFY COLUMN Name VARCHAR(50) NULL,
MODIFY COLUMN School VARCHAR(75) NULL,
MODIFY COLUMN Degree VARCHAR(100) NULL,
MODIFY COLUMN Company VARCHAR(50) NULL,
ADD PRIMARY KEY (Record_id);")
## <MySQLResult:300270568,0,9>
dbWriteTable(connection, "tbl_KDnuggets_Dice", KC_Data, append = TRUE, row.names = FALSE)
## [1] TRUE
dbSendQuery(connection, "ALTER TABLE tbl_KDnuggets_Dice
MODIFY COLUMN Record_id MEDIUMINT NOT NULL,
MODIFY COLUMN Source VARCHAR(25) NOT NULL,
MODIFY COLUMN Skill VARCHAR(50) NOT NULL,
MODIFY COLUMN Title VARCHAR(250) NULL,
MODIFY COLUMN Location VARCHAR(50) NULL,
MODIFY COLUMN Name VARCHAR(50) NULL,
MODIFY COLUMN School VARCHAR(75) NULL,
MODIFY COLUMN Degree VARCHAR(100) NULL,
MODIFY COLUMN Company VARCHAR(50) NULL,
ADD PRIMARY KEY (Record_id);")
## <MySQLResult:401873832,0,13>
dbWriteTable(connection, "tbl_Skill_Freq", Dice_Freq, append = TRUE, row.names = FALSE)
## [1] TRUE
dbSendQuery(connection, "ALTER TABLE tbl_Skill_Freq
MODIFY COLUMN Skill VARCHAR(50) NOT NULL,
MODIFY COLUMN Count INT NOT NULL,
MODIFY COLUMN Frequency DOUBLE NOT NULL,
ADD PRIMARY KEY (Skill);")
## <MySQLResult:7602280,0,17>
dbWriteTable(connection, "tbl_Skill_Class", Skill_class, append = TRUE, row.names = FALSE)
## [1] TRUE
dbSendQuery(connection, "ALTER TABLE tbl_Skill_Class
MODIFY COLUMN Skill VARCHAR(50) NOT NULL,
MODIFY COLUMN Family varchar(50) NULL,
MODIFY COLUMN Category varchar(50) NULL,
ADD PRIMARY KEY (Skill);")
## <MySQLResult:0,0,21>
SC<-dbGetQuery(connection,'select * from tbl_Skill_class')
All_Data <- dbGetQuery(connection, "SELECT Source, A.Skill, Title, Location, Name, School, Company, Record_id, Count, Frequency, Family, Category
FROM
(SELECT * FROM tbl_linkedin UNION
SELECT * FROM tbl_KDnuggets_Dice) AS A
LEFT JOIN tbl_Skill_Freq AS B
ON A.Skill = B.Skill
Left join tbl_skill_class C
ON A.Skill=C.Skill
ORDER BY A.Source, A.Skill, A.Title")
##Geography
geo<-dbGetQuery(connection,"SELECT Location,count(Location) as Count
FROM tbl_KDnuggets_Dice group by Location
having count(location)>0 order by count(location) desc")
head(geo,15)
## Location Count
## 1 New York 428
## 2 San Francisco 188
## 3 Atlanta 170
## 4 Charlotte 152
## 5 Jersey City 144
## 6 Seattle 135
## 7 Boston 117
## 8 San Diego 113
## 9 Austin 106
## 10 Washington 105
## 11 Los Angeles 99
## 12 Dallas 93
## 13 Chicago 91
## 14 Reston 84
## 15 Sunnyvale 80
geo1<-head(geo,15)
ggplot(geo1, aes(x=reorder(geo1$Location,-geo1$Count),y=geo1$Count))+geom_bar(stat="identity",fill="pink",colour="black")+theme(axis.text.x = element_text(angle = 90, hjust = 1))+xlab("Location")+ ylab("Count")+labs(title="Top 15 Most Common Locations on Dice")
library(zipcode)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
data(zipcode)
y<-left_join(geo1 ,zipcode, by=c("Location" = "city"))
head(y)
## Location Count zip state latitude longitude
## 1 New York 428 10001 NY 40.75074 -73.99653
## 2 New York 428 10002 NY 40.71704 -73.98700
## 3 New York 428 10003 NY 40.73251 -73.98935
## 4 New York 428 10004 NJ 40.69923 -74.04118
## 5 New York 428 10005 NY 40.70602 -74.00858
## 6 New York 428 10006 NY 40.70790 -74.01342
coords<-aggregate(y[, 2:6], list(y$Location), mean)
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
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## returning NA
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## returning NA
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## returning NA
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## returning NA
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## returning NA
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## returning NA
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## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
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## returning NA
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## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
head(coords)
## Group.1 Count zip state latitude longitude
## 1 Atlanta 170 NA NA 34.28355 -85.08409
## 2 Austin 106 NA NA 30.99552 -97.49090
## 3 Boston 117 NA NA 41.97371 -71.94011
## 4 Charlotte 152 NA NA 35.45512 -81.29719
## 5 Chicago 91 NA NA 41.85240 -87.67876
## 6 Dallas 93 NA NA 33.31553 -96.22423
library(ggmap)
usa_center <- as.numeric(geocode("United States"))
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=United%20States&sensor=false
USAMap <- ggmap(get_googlemap(center=usa_center, scale=2, zoom=4), extent="normal")
## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=37.09024,-95.712891&zoom=4&size=640x640&scale=2&maptype=terrain&sensor=false
USAMap+geom_point(aes(x=longitude, y=latitude), data=coords, col="red", alpha=0.5, size=coords$Count*.05) +
scale_size_continuous(range=range(coords$Count))
##Top Jobs on Dice with KD Nuggets Skills
KDAnalysis2<-dbGetQuery(connection, "Select Lower(Title) as Title, count(lower(title)) As Freq from
tbl_KDnuggets_Dice group by lower(title) having count(lower(title))>10
order by count(lower(title)) desc limit 20
")
ggplot(KDAnalysis2, aes(x=reorder(KDAnalysis2$Title,-KDAnalysis2$Freq),y=KDAnalysis2$Freq))+geom_bar(stat="identity")+theme(axis.text.x = element_text(angle = 90, hjust = 1))+xlab("Title")+ ylab("Count")+labs(title="Top Dice Jobs Associated with KD Skills")
##All Jobs like Data Sci
KDAnalysis3<-dbGetQuery(connection, "Select Lower(Title) as Title, count(lower(title)) As Freq from
tbl_KDnuggets_Dice where Title like '%data sci%' group by lower(title) having count(lower(title))>10
order by count(lower(title)) desc
")
head(KDAnalysis2,15)
## Title Freq
## 1 business analyst 117
## 2 data scientist 112
## 3 cybercoders 70
## 4 business/systems analyst 66
## 5 senior software engineer 58
## 6 software engineer 53
## 7 project manager 51
## 8 .net developer 45
## 9 network engineer 45
## 10 business intelligence analyst 42
## 11 full stack developer 40
## 12 data engineer 39
## 13 sr. hadoop developer (19199) 37
## 14 data scientist - 50% client site- 50% work from home! 37
## 15 data analyst 37
KDAnalysis3
## Title Freq
## 1 data scientist 112
## 2 data scientist - 50% client site- 50% work from home! 37
## 3 data scientist - machine learning 18
## 4 data scientist - junior-senior levels! 15
## 5 lead data scientist 14
## 6 sr data scientist 13
## 7 data scientist - sales platform - relo 12
## 8 senior data scientist - machine learning 11
ggplot(KDAnalysis3, aes(x=KDAnalysis3$Title,y=KDAnalysis3$Freq))+geom_bar(stat="identity",fill="purple",colour="black")+theme(axis.text.x = element_text(angle = 90, hjust = 1))+xlab("Title")+ ylab("Count")+labs(title="Data Science Jobs on Dice Associated with KD")
sum(KDAnalysis3$Freq)/9750*100
## [1] 2.379487
##2.3 percent of jobs are data science. It is one of the largest categories.
LiAnalysis<-dbGetQuery(connection, "Select A.Skill,LiFreq, B.Count, Frequency as DiceFreq
from (Select Skill, count(Skill) as LiFreq from
tbl_linkedin group by Skill ) as A
left join tbl_Skill_Freq B on A.Skill=B.Skill
where LiFreq > 10 order by LiFreq desc limit 15
")
head(LiAnalysis,15)
## Skill LiFreq Count DiceFreq
## 1 data mining 26 5994 0.074688797
## 2 analytics 25 572 0.007127459
## 3 big data 24 6054 0.075436432
## 4 data analysis 23 12727 0.158585972
## 5 machine learning 23 326 0.004062153
## 6 python 21 4526 0.056396646
## 7 r 19 311 0.003875245
## 8 management 18 17762 0.221325059
## 9 statistics 18 95 0.001183756
## 10 business intelligence 16 5631 0.070165601
## 11 predictive analytics 14 599 0.007463895
## 12 sql 14 12067 0.150361980
## 13 algorithms 14 1437 0.017905873
## 14 hadoop 13 1571 0.019575592
## 15 data science 13 6350 0.079124768
ggplot(LiAnalysis, aes(x=reorder(LiAnalysis$Skill, -LiAnalysis$LiFreq),y=LiAnalysis$LiFreq))+geom_bar(stat="identity",fill="blue",colour="black")+theme(axis.text.x = element_text(angle = 90, hjust = 1))+xlab("Skills")+ ylab("Count")+labs(title="Top Linked In Skills")
##Groups linked in skills by Family, Technical or not
LiAnalysis2<-dbGetQuery(connection, " select Family, COunt(skill) as Count from (Select A.Skill, B.Count, Frequency as DiceFreq, Family, Category
from
tbl_linkedin as A
left join tbl_Skill_Freq B on A.Skill=B.Skill
left join tbl_skill_class C on A.Skill=C.Skill ) F where family is not null
Group by Family
")
LiAnalysis2<-data.frame(LiAnalysis2,"TechFam"=c("Non-Tech","Technical"))
LiAnalysis2
## Family Count TechFam
## 1 nt 306 Non-Tech
## 2 t 1152 Technical
ggplot(LiAnalysis2, aes(x=LiAnalysis2$TechFam,y=LiAnalysis2$Count))+geom_bar(stat="identity",fill="green",colour="black")+theme(axis.text.x = element_text(angle = 90, hjust = 1))+xlab("Technical N/Y")+ ylab("Count")+labs(title="Linked in Skill Category")
##Groups based on Soft skill type
LiAnalysis3<-dbGetQuery(connection, " select Category, COunt(skill) as Count from (Select A.Skill, B.Count, Frequency as DiceFreq, Family, Category
from
tbl_linkedin as A
left join tbl_Skill_Freq B on A.Skill=B.Skill
left join tbl_skill_class C on A.Skill=C.Skill ) F where Category is not null
Group by Category
")
LiAnalysis3<-data.frame(LiAnalysis3,"Cat"=c("Communication","Human Resources","Management","Other ie French","Soft Skill"))
LiAnalysis3
## Category Count Cat
## 1 co 19 Communication
## 2 hr 36 Human Resources
## 3 mn 192 Management
## 4 ot 48 Other ie French
## 5 ss 10 Soft Skill
ggplot(LiAnalysis3, aes(x=reorder(LiAnalysis3$Cat,-LiAnalysis3$Count),y=LiAnalysis3$Count))+geom_bar(stat="identity",fill="red",colour="black")+theme(axis.text.x = element_text(angle = 90, hjust = 1))+xlab("Non Technical Skill Category")+ ylab("Count") +labs(title="Linked In Non Tech Skills")
##Frequncy in the Top Data Scientists vs frequency in All Dice Jobs
plot(x=LiAnalysis$LiFreq/26,y=LiAnalysis$DiceFreq,xlab="LinkedIn Frequency (top 15)",ylab="Dice Frecency", main="Relationship of Linked in and Dice",col="dark red")
cor.test(LiAnalysis$LiFreq,LiAnalysis$DiceFreq)
##
## Pearson's product-moment correlation
##
## data: LiAnalysis$LiFreq and LiAnalysis$DiceFreq
## t = 0.045168, df = 13, p-value = 0.9647
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5029638 0.5214433
## sample estimates:
## cor
## 0.01252645
##No Strong relationship between Dice Frequency and Frequency in top data scientists.
###Word clouds
library(wordcloud)
## Loading required package: RColorBrewer
library(tm)
## Loading required package: NLP
##
## Attaching package: 'NLP'
## The following object is masked from 'package:ggplot2':
##
## annotate
##All skills
z<-left_join(JA_Data ,Skill_class, by="Skill")
JA_Datatec<-subset(z, Family=="t")
names<-(JA_Datatec$Skill)
tb<-table(names)
set.seed(1234)
wordcloud(names(tb), as.numeric(tb), min.freq = 1,
max.words=150, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
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##Technical
z<-left_join(JA_Data ,subset(Skill_class,Skill_class$Family=="t"), by="Skill")
JA_Datatec<-subset(z, Family=="t")
names<-(JA_Datatec$Skill)
tb<-table(names)
set.seed(1234)
wordcloud(names(tb), as.numeric(tb), min.freq = 1,
max.words=150, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
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##Not-Technical
z<-left_join(JA_Data ,subset(Skill_class,Skill_class$Family=="nt"), by="Skill")
JA_Datatec<-subset(z, Family=="nt")
names<-(JA_Datatec$Skill)
tb<-table(names)
set.seed(1234)
wordcloud(names(tb), as.numeric(tb), min.freq = 1,
max.words=150, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
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dbSendQuery(connection, 'DROP TABLE tbl_LinkedIn;')
## <MySQLResult:318203888,0,30>
dbSendQuery(connection, 'DROP TABLE tbl_KDnuggets_Dice;')
## <MySQLResult:318203888,0,31>
dbSendQuery(connection, 'DROP TABLE tbl_Skill_Freq;')
## <MySQLResult:318203888,0,32>
dbSendQuery(connection, 'DROP SCHEMA Skills;')
## <MySQLResult:318203888,0,33>
connection <- dbConnect(MySQL(), user=MySQL_Username, password=MySQL_Password)
dbDisconnect(connection)
## [1] TRUE