Please note below additional packages installed. The formattable package is used to transform vectors and data frames into more readable and impactful tabular formats. Package ‘conflicted’ solved an issue with the rename func as it pertains to knit/html.
install.packages(“formattable”) install.packages(“conflicted”)
library(tidyverse)## -- Attaching packages ---------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts ------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(RMySQL)## Loading required package: DBIlibrary(formattable)##DBconnect was utilized in this assignment to authenticate/pull in the flight delay information into R
my.database10 = dbConnect(MySQL(), user='root', password = 'Password1', dbname='jobs', host='localhost')
dbListTables(my.database10)## [1] "general_skills" "indeed_jobs"generalskills_list <- fetch(dbSendQuery(my.database10, "SELECT * FROM general_skills"))
dim(generalskills_list)## [1] 30 5colnames(generalskills_list)## [1] "Keyword" "LinkedIn" "Indeed" "SimplyHired" "Monster"summary(generalskills_list)## Keyword LinkedIn Indeed SimplyHired
## Length:30 Length:30 Length:30 Length:30
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Monster
## Length:30
## Class :character
## Mode :character##DBconnect was utilized in this assignment to authenticate/pull in the flight delay information into R
my.database10 = dbConnect(MySQL(), user='root', password = 'Password1', dbname='jobs', host='localhost')
dbListTables(my.database10)## [1] "general_skills" "indeed_jobs"indeed_list <- fetch(dbSendQuery(my.database10, "SELECT * FROM indeed_jobs"))
dim(indeed_list)## [1] 500 43colnames(indeed_list)## [1] "MyUnknownColumn" "Job_Title"
## [3] "Link" "Queried_Salary"
## [5] "Job_Type" "Skill"
## [7] "No_of_Skills" "Company"
## [9] "No_of_Reviews" "No_of_Stars"
## [11] "Date_Since_Posted" "Description"
## [13] "Location" "Company_Revenue"
## [15] "Company_Employees" "Company_Industry"
## [17] "python" "sql"
## [19] "machine learning" "r"
## [21] "hadoop" "tableau"
## [23] "sas" "spark"
## [25] "java" "Others"
## [27] "CA" "NY"
## [29] "VA" "TX"
## [31] "MA" "IL"
## [33] "WA" "MD"
## [35] "DC" "NC"
## [37] "Other_states" "Consulting and Business Services"
## [39] "Internet and Software" "Banks and Financial Services"
## [41] "Health Care" "Insurance"
## [43] "Other_industries"Experimented with the tibble func below for the first time. Tibbles have a refined print method that shows only the first 10 rows, and all the columns that fit on screen. This makes it much easier to work with large data.
library(tidyverse)
library(dplyr)
indeed_listinfo <- as_tibble(indeed_list)
indeed_listinfo## # A tibble: 500 x 43
## MyUnknownColumn Job_Title Link Queried_Salary Job_Type Skill No_of_Skills
## <chr> <chr> <chr> <chr> <chr> <chr> <int>
## 1 1 Data Sci~ http~ <80000 data_sc~ ['Ma~ 5
## 2 2 Data Sci~ http~ <80000 data_sc~ ['Da~ 9
## 3 3 Graduate~ http~ <80000 data_sc~ ['Ce~ 1
## 4 4 Data Sci~ http~ <80000 data_sc~ ['St~ 7
## 5 5 Data Sci~ http~ <80000 data_sc~ ['AI~ 6
## 6 6 Geospati~ http~ <80000 data_sc~ ['St~ 10
## 7 7 Data Sci~ http~ <80000 data_sc~ ['Ma~ 3
## 8 8 Bioinfor~ http~ <80000 data_sc~ ['Li~ 4
## 9 10 Data Sci~ http~ <80000 data_sc~ ['Ma~ 8
## 10 11 Data Sci~ http~ <80000 data_sc~ ['Ma~ 8
## # ... with 490 more rows, and 36 more variables: Company <chr>,
## # No_of_Reviews <chr>, No_of_Stars <chr>, Date_Since_Posted <int>,
## # Description <chr>, Location <chr>, Company_Revenue <chr>,
## # Company_Employees <chr>, Company_Industry <chr>, python <chr>, sql <chr>,
## # `machine learning` <chr>, r <chr>, hadoop <chr>, tableau <chr>, sas <chr>,
## # spark <chr>, java <int>, Others <int>, CA <int>, NY <int>, VA <int>,
## # TX <int>, MA <int>, IL <int>, WA <int>, MD <int>, DC <int>, NC <int>,
## # Other_states <int>, `Consulting and Business Services` <int>, `Internet and
## # Software` <int>, `Banks and Financial Services` <int>, `Health Care` <int>,
## # Insurance <int>, Other_industries <int>generalskills_listinfo <- as_tibble(generalskills_list)
generalskills_listinfo## # A tibble: 30 x 5
## Keyword LinkedIn Indeed SimplyHired Monster
## <chr> <chr> <chr> <chr> <chr>
## 1 machine learning 5,701 3,439 2,561 2,340
## 2 analysis 5,168 3,500 2,668 3,306
## 3 statistics 4,893 2,992 2,308 2,399
## 4 computer science 4,517 2,739 2,093 1,900
## 5 communication 3,404 2,344 1,791 2,053
## 6 mathematics 2,605 1,961 1,497 1,815
## 7 visualization 1,879 1,413 1,153 1,207
## 8 AI composite 1,568 1,125 811 687
## 9 deep learning 1,310 979 675 606
## 10 NLP composite 1,212 910 660 582
## # ... with 20 more rowsindeed_listinfo2 <- data.frame(indeed_listinfo$MyUnknownColumn,
indeed_listinfo$Job_Title,
indeed_listinfo$Job_Type,
indeed_listinfo$Skill,
indeed_listinfo$No_of_Skills,
indeed_listinfo$Description,
indeed_listinfo$Location,
indeed_listinfo$Company_Industry,
indeed_listinfo$python,
indeed_listinfo$sql,
indeed_listinfo$r,
indeed_listinfo$hadoop,
indeed_listinfo$tableau,
indeed_listinfo$sas,
indeed_listinfo$spark,
indeed_listinfo$java,
indeed_listinfo$Others,
indeed_listinfo$CA,
indeed_listinfo$NY,
indeed_listinfo$VA,
indeed_listinfo$TX,
indeed_listinfo$MA,
indeed_listinfo$IL,
indeed_listinfo$WA,
indeed_listinfo$MD,
indeed_listinfo$DC,
indeed_listinfo$NC,
indeed_listinfo$Other_states,
indeed_listinfo$`Consulting and Business Services`,
indeed_listinfo$`Internet and Software`,
indeed_listinfo$`Banks and Financial Services`,
indeed_listinfo$`Health Care`,
indeed_listinfo$Insurance,
indeed_listinfo$Other_industries)
(colnames(indeed_listinfo2))## [1] "indeed_listinfo.MyUnknownColumn"
## [2] "indeed_listinfo.Job_Title"
## [3] "indeed_listinfo.Job_Type"
## [4] "indeed_listinfo.Skill"
## [5] "indeed_listinfo.No_of_Skills"
## [6] "indeed_listinfo.Description"
## [7] "indeed_listinfo.Location"
## [8] "indeed_listinfo.Company_Industry"
## [9] "indeed_listinfo.python"
## [10] "indeed_listinfo.sql"
## [11] "indeed_listinfo.r"
## [12] "indeed_listinfo.hadoop"
## [13] "indeed_listinfo.tableau"
## [14] "indeed_listinfo.sas"
## [15] "indeed_listinfo.spark"
## [16] "indeed_listinfo.java"
## [17] "indeed_listinfo.Others"
## [18] "indeed_listinfo.CA"
## [19] "indeed_listinfo.NY"
## [20] "indeed_listinfo.VA"
## [21] "indeed_listinfo.TX"
## [22] "indeed_listinfo.MA"
## [23] "indeed_listinfo.IL"
## [24] "indeed_listinfo.WA"
## [25] "indeed_listinfo.MD"
## [26] "indeed_listinfo.DC"
## [27] "indeed_listinfo.NC"
## [28] "indeed_listinfo.Other_states"
## [29] "indeed_listinfo..Consulting.and.Business.Services."
## [30] "indeed_listinfo..Internet.and.Software."
## [31] "indeed_listinfo..Banks.and.Financial.Services."
## [32] "indeed_listinfo..Health.Care."
## [33] "indeed_listinfo.Insurance"
## [34] "indeed_listinfo.Other_industries"library(dplyr)
indeed_listinfo3 <- dplyr::rename(indeed_listinfo2,
"ID"="indeed_listinfo.MyUnknownColumn",
"Title" = "indeed_listinfo.Job_Title",
"Type" = "indeed_listinfo.Job_Type",
"Skill" = "indeed_listinfo.Skill",
"Skills" = "indeed_listinfo.No_of_Skills",
"Description" = "indeed_listinfo.Description",
"Location" = "indeed_listinfo.Location",
"Industry" = "indeed_listinfo.Company_Industry",
"Python" = "indeed_listinfo.python",
"SQL" = "indeed_listinfo.sql",
"R" = "indeed_listinfo.r",
"Hadoop" = "indeed_listinfo.hadoop",
"Tableau" = "indeed_listinfo.tableau",
"SAS" = "indeed_listinfo.sas",
"Spark" = "indeed_listinfo.spark",
"Java" = "indeed_listinfo.java",
"Others" = "indeed_listinfo.Others",
"CA" = "indeed_listinfo.CA",
"NY" = "indeed_listinfo.NY",
"VA" = "indeed_listinfo.VA",
"TX" = "indeed_listinfo.TX",
"MA" = "indeed_listinfo.MA",
"IL" = "indeed_listinfo.IL",
"WA" = "indeed_listinfo.WA",
"MD" = "indeed_listinfo.MD",
"DC" = "indeed_listinfo.DC",
"NC" = "indeed_listinfo.NC",
"Other" = "indeed_listinfo.Other_states",
"Consulting" = "indeed_listinfo..Consulting.and.Business.Services.",
"Internet" = "indeed_listinfo..Internet.and.Software.",
"Financial" = "indeed_listinfo..Banks.and.Financial.Services.",
"Healthcare" = "indeed_listinfo..Health.Care.",
"Insurance" = "indeed_listinfo.Insurance",
"Varied" = "indeed_listinfo.Other_industries"
)
table(indeed_listinfo3$Python)##
## 0 1
## 165 335table(indeed_listinfo3$SQL)##
## 0 1
## 253 247table(indeed_listinfo3$R)##
## 0 1
## 206 294table(indeed_listinfo3$Hadoop)##
## 0 1
## 419 81table(indeed_listinfo3$Tableau)##
## 0 1
## 429 71table(indeed_listinfo3$SAS)##
## 0 1
## 388 112table(indeed_listinfo3$Spark)##
## 0 1
## 427 73table(indeed_listinfo3$Java)##
## 0 1
## 408 92table(indeed_listinfo3$Others)##
## 0 1
## 43 457library(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, first, last## The following object is masked from 'package:purrr':
##
## transposesetDT(indeed_listinfo3)[, .N, by=Python]## Python N
## 1: 1 335
## 2: 0 165setDT(indeed_listinfo3)[, .N, by=SQL]## SQL N
## 1: 1 247
## 2: 0 253setDT(indeed_listinfo3)[, .N, by=R]## R N
## 1: 1 294
## 2: 0 206setDT(indeed_listinfo3)[, .N, by=Hadoop]## Hadoop N
## 1: 0 419
## 2: 1 81setDT(indeed_listinfo3)[, .N, by=Tableau]## Tableau N
## 1: 0 429
## 2: 1 71setDT(indeed_listinfo3)[, .N, by=SAS]## SAS N
## 1: 1 112
## 2: 0 388setDT(indeed_listinfo3)[, .N, by=Spark]## Spark N
## 1: 0 427
## 2: 1 73setDT(indeed_listinfo3)[, .N, by=Python]## Python N
## 1: 1 335
## 2: 0 165setDT(indeed_listinfo3)[, .N, by=Java]## Java N
## 1: 0 408
## 2: 1 92