Komputasi Statistika (1)

Ujian Akhir Semester

Email: sofianicklaus21@gmail.com
RPubs: https://rpubs.com/sofia3484

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1 Regresi Linear Berganda

Dalam proyek ini saya memberikan anda dataset insurance.csv, informasi lanjut mengenai data ini dapat anda baca di Kaggle.

Tugas kalian adalah sebagai berikut:

1. Meringkas informasi penting yang terkadung data isurance.csv tersebut.
2. Memahami faktor-faktor apa yang mempengaruhi premi asuransi konsumen.
3. Menemukan model terbaik yang dapat memprediksi premi asuransi konsumen.

Jawaban

1.1 Meringkas informasi penting yang terkandung data insurance.csv tersebut

1.1.1 Packages

library(readxl)

## Warning: package 'readxl' was built under R version 3.6.3

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.6.3

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## The following objects are masked from 'package:stats':
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library(ggplot2)

## Warning: package 'ggplot2' was built under R version 3.6.3

library(plotly)

## Warning: package 'plotly' was built under R version 3.6.3

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library(hrbrthemes)

## Warning: package 'hrbrthemes' was built under R version 3.6.3

## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.

##       Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and

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library(corrplot)

## Warning: package 'corrplot' was built under R version 3.6.3

## corrplot 0.84 loaded

library(ggrepel)
library(DT)

## Warning: package 'DT' was built under R version 3.6.3

library(Metrics)

## Warning: package 'Metrics' was built under R version 3.6.3

1.1.2 Pemanggilan data

insurance <- read.csv("C:/Users/user/Downloads/insurance.csv")

Variabel dari asuransi:

• usia: Usia penerima premi

• Jenis kelamin: Jenis kelamin penerima asuransi, yaitu, perempuan dan laki-laki

• bmi: Indeks massa tubuh

• anak-anak: Jumlah anak yang dilindungi oleh asuransi kesehatan / Jumlah tanggungan

• perokok: merokok

• Wilayah: wilayah pemukiman penerima asuransi di AS, timur laut, tenggara, barat daya, barat laut.

• Biaya: Biaya medis individu yang ditagih oleh asuransi kesehatan

1.1.3 Pengecekan Tipe data

str(insurance)

## 'data.frame':    1338 obs. of  7 variables:
##  $ age     : int  19 18 28 33 32 31 46 37 37 60 ...
##  $ sex     : Factor w/ 2 levels "female","male": 1 2 2 2 2 1 1 1 2 1 ...
##  $ bmi     : num  27.9 33.8 33 22.7 28.9 ...
##  $ children: int  0 1 3 0 0 0 1 3 2 0 ...
##  $ smoker  : Factor w/ 2 levels "no","yes": 2 1 1 1 1 1 1 1 1 1 ...
##  $ region  : Factor w/ 4 levels "northeast","northwest",..: 4 3 3 2 2 3 3 2 1 2 ...
##  $ charges : num  16885 1726 4449 21984 3867 ...

Hasil data frame menghasilkan bahwa data age merupakan integer, sex merupakan tipe faktor, bmi merupakan tipe data numerik, children merupakan tipe data integer, smoker merupakan tipe data faktor, region merupakan tipe data faktor, dan charges merupakan tipe data number.

1.1.4 Pemeriksaan data hilang

colSums(is.na(insurance))

##      age      sex      bmi children   smoker   region  charges 
##        0        0        0        0        0        0        0

Tidak ada data yang hilang

1.1.4.1 Perubahan Tipe Data

age        <- select(insurance,age)
sex        <- as.numeric(insurance$sex)
bmi        <- select(insurance, bmi)
children   <- as.numeric(insurance$children)
smoker     <- as.numeric(insurance$smoker)
region     <- as.character(insurance$region)
charges   <- select(insurance, charges)

new_ins <- data.frame(age, sex, bmi, children, smoker, region, charges)

Tipe data sex, children dan smoker diubah menjadi tipe data numerik.

Pada data sex:

* 1 = Wanita (Female)

* 2 = Pria (Male)

Pada data smoker:

• 1 = Tidak merokok (No)

• 2 = Merokok (yes)

1.1.5 Visualisasi Data

1.1.5.1 Age

group_age <- insurance %>%
                group_by(age) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

head(group_age)

## # A tibble: 6 x 2
##     age total
##   <int> <int>
## 1    18    69
## 2    19    68
## 3    20    29
## 4    21    28
## 5    22    28
## 6    23    28

g1 <- ggplot(insurance, aes(age))+ geom_histogram(fill = "#56B4E9",colour="blue",alpha = 0.5, binwidth = 4)+ggtitle("Histogram of Age")
g1 + theme(plot.background = element_rect(fill = "#BFD5E3"))+ theme(panel.background = element_rect(fill = "white"))

Dari histogram di atas, banyaknya individu yang ditagih oleh asuransi kesehatan berumur 18-19 tahun.

1.1.5.2 Sex

group_sex <- insurance %>%
              group_by(sex) %>%
              summarise(total = n()) 

## `summarise()` ungrouping output (override with `.groups` argument)

group_sex

## # A tibble: 2 x 2
##   sex    total
##   <fct>  <int>
## 1 female   662
## 2 male     676

Gender wanita yang ditagih oleh asuransi kesehatan ada sebanyak 662 orang dan gender pria ada sebanyak 676 orang.

# Presentase Sex 
group_sex <- group_sex %>% 
  mutate(prop = total / sum(group_sex$total)) 
group_sex$label <- c("50.5%","49.5%")

mycols <- c("#D291BC", "#FEC8D8")
ggplot(group_sex , aes(x="", y = prop, fill = sex))+
  geom_bar(stat="identity", color="white")+
  coord_polar(theta="y", start=0)+
  geom_text(aes(y = cumsum(prop) - prop/2, label=label), color="white")+
  scale_fill_manual(values = mycols) +
  theme_void() +
  ggtitle("Pie Chart Sex")

Dari pie chart di atas, didapatkan bahwa proporsi wanita yang ditagih oleh asuransi kesehatan ada 49.5%. Sedangkan, proporsi pria ada sebanyak 50.5%.

1.1.5.3 Region

group_region <- insurance %>%
                  group_by(region) %>%
                  summarise(total = n()) 

## `summarise()` ungrouping output (override with `.groups` argument)

group_region <- group_region %>% 
  mutate(prop = total / sum(group_region$total)*100) 
group_region

## # A tibble: 4 x 3
##   region    total  prop
##   <fct>     <int> <dbl>
## 1 northeast   324  24.2
## 2 northwest   325  24.3
## 3 southeast   364  27.2
## 4 southwest   325  24.3

Individu yang ditagih oleh asuransi kesehatan yang berasal dari northeast sebanyak 324 orang, northwest sebanyak 325 orang, southeast sebanyak 364 orang, dan southwest sebanyak 325 orang.

group_region$label <- c("24.290%","27.205%","24.290%","24.215%")
mycols <- c("#0073C2FF", "#EFC000FF", "#868686FF", "#CD534CFF")

ggplot(group_region , aes(x="", y = prop, fill = region))+
  geom_bar(stat="identity", color="white")+
  coord_polar(theta="y", start=0)+
  geom_text_repel(aes(y = cumsum(prop) - prop/2, label=label), color="white")+
  scale_fill_manual(values = mycols) +
  theme_void()+
  ggtitle("Pie Chart Region")

Dari pie chart di atas, individu yang ditagih oleh asuransi kesehatan yang berasal dari northeast ada sebanyak 24.215%, northwest ada sebanyak 24.29%, southeast ada sebanyak 27.20478%, dan southwest ada sebanyak 24,289%. Terlihat distribusi di atas hampir rata.

1.1.5.4 bmi

Pada kali ini, data bmi akan dikategorikan menjadi tiga, yaitu:

• < 18,5 = Underweight

• 18.5 - 24.8 = Normal Weight

• 24.9 - 29.8 = Overweight

• lebih dari sama dengan 29.9 = Obese

new_ins_bmi <- mutate(insurance, Weight_Status = ifelse(insurance$bmi < 18.5, "Underweight", ifelse(insurance$bmi< 24.9,"Normal Weight", ifelse(insurance$bmi< 29.9,"Overweight","Obese"))))
new_ins_bmi <- new_ins_bmi %>%
                group_by(Weight_Status)%>%
                summarise(total=n())

## `summarise()` ungrouping output (override with `.groups` argument)

new_ins_bmi

## # A tibble: 4 x 2
##   Weight_Status total
##   <chr>         <int>
## 1 Normal Weight   222
## 2 Obese           719
## 3 Overweight      377
## 4 Underweight      20

BMI dari individu yang ditagih oleh asuransi kesehatan yaitu normal weight ada sebanyak 222 orang, obese ada sebanyak 719 orang, overweight ada sebanyak 377 orang, dan underweight ada sebanyak 20 orang.

new_ins_bmi <- new_ins_bmi %>% 
  mutate(prop = total / sum(new_ins_bmi$total)) %>%
  arrange(desc(Weight_Status))

new_ins_bmi$label <- scales::percent(new_ins_bmi$prop)
mycols <- c("#770f05", "#35455D", "#F1C5AE", "#ff6961")

ggplot(new_ins_bmi , aes(x="", y = prop, fill = Weight_Status))+
  geom_bar(stat="identity", color="white")+
  coord_polar(theta="y", start=0)+
  geom_text_repel(aes(y = cumsum(prop) - prop/2, label=label), color="white")+
  scale_fill_manual(values = mycols) +
  theme_void()+
  ggtitle("Pie Chart of Status of Weight")

Dari pie chart di atas, bmi dari individu yang ditagih oleh asuransi kesehatan yang underweight ada sebanyak 1%, normal weight ada sebanyak 17%, overweight ada sebanyak 28%, dan obese ada sebanyak 54%.

1.1.5.5 Smoker

group_smoker <- insurance %>%
                  group_by(smoker) %>%
                  summarise(total = n()) 

## `summarise()` ungrouping output (override with `.groups` argument)

group_smoker

## # A tibble: 2 x 2
##   smoker total
##   <fct>  <int>
## 1 no      1064
## 2 yes      274

Individu yang ditagih oleh asuransi kesehatan yang tidak merokok ada sebanyak 1064 orang dan yang merokok ada sebanyak 274 orang.

group_smoker <- group_smoker %>% 
  mutate(prop = total / sum(group_smoker$total)) %>%
  arrange(desc(smoker))
group_smoker$label <- scales::percent(group_smoker$prop)

mycols <- c("#D291BC", "#FEC8D8")
ggplot(group_smoker , aes(x="", y = prop, fill = smoker))+
  geom_bar(stat="identity", color="white")+
  coord_polar(theta="y", start=0)+
  geom_text(aes(y = cumsum(prop) - prop/2, label=label), color="white")+
  scale_fill_manual(values = mycols) +
  theme_void()+
  ggtitle("Pie Chart of Smoker")

DAri pie chart di atas, individu yang ditagih oleh asuransi kesehatan yang tidak merokok ada sebesar 80% dan yang merokok ada sebesar 20%.

1.1.5.6 Children

group_child <- insurance %>%
            group_by(children) %>%
            summarise(total = n()) 

## `summarise()` ungrouping output (override with `.groups` argument)

group_child

## # A tibble: 6 x 2
##   children total
##      <int> <int>
## 1        0   574
## 2        1   324
## 3        2   240
## 4        3   157
## 5        4    25
## 6        5    18

Individu yang yang ditanggung asuransi kesehatan memiliki lima anak ada sebanyak 18 orang, empat anak 25 orang, tiga anak sebanyak 157 anak, 2 anak sebanyak 240 orang, satu nanak sebanyak 324 orang, dan tidak mempunyai anak sebanyak 574 orang.

group_child <- group_child %>% 
  mutate(prop = total / sum(group_child$total)) %>%
  arrange(desc(children))

group_child$label <- scales::percent(group_child$prop)
group_child$children <-as.factor(group_child$children)
mycols <- c("#770f05", "#35455D", "#F1C5AE", "#ff6961","#064F40","#A67E05")

ggplot(group_child , aes(x="", y = prop, fill = children))+
  geom_bar(stat="identity", color="white")+
  coord_polar(theta="y", start=0)+
  geom_text_repel(aes(y = cumsum(prop) - prop/2, label=label), color="white")+
  scale_fill_manual(values = mycols) +
  theme_void()+
  ggtitle("Pie Chart of How Many Children They Have")

Dari pie chart, individu yang yang ditanggung asuransi kesehatan memiliki lima anak sebesar 1.35%, empat anak sebesar 1.87%, tiga anak sebesar 11.73%, dua anak sebesar 17.94%, satu anak sebesar 24.22%, tidak mempunyai anak sebesar 42.90%.

1.1.5.7 Charges

Selain itu , kami juga akan mengkategorikan biaya menjadi dua bagian yaitu:

• lebih dari rata-rata biaya = High Charges

• < rata-rata biaya = Low Charges

new_ins_charges <- mutate(insurance, Type_Charges = ifelse(insurance$charges > mean(insurance$charges), "High Charges", "Low Charges"))
new_ins_charges <- new_ins_charges %>%
                    group_by(Type_Charges) %>%
                    summarise(total=n())

## `summarise()` ungrouping output (override with `.groups` argument)

new_ins_charges

## # A tibble: 2 x 2
##   Type_Charges total
##   <chr>        <int>
## 1 High Charges   420
## 2 Low Charges    918

Individu yang ditanggung oleh asuransi kesehatan yang memiliki tipe high charges ada sebanyak 420 orang dan yang memiliki tipe low charges ada sebanyak 918 orang.

new_ins_charges <- new_ins_charges %>% 
  mutate(prop = total / sum(new_ins_charges$total)) %>%
  arrange(desc(Type_Charges))

new_ins_charges$label <- scales::percent(new_ins_charges$prop)
mycols <- c("#D291BC", "#4D342A")

ggplot(new_ins_charges , aes(x="", y = prop, fill = Type_Charges))+
  geom_bar(stat="identity", color="white")+
  coord_polar(theta="y", start=0)+
  geom_text_repel(aes(y = cumsum(prop) - prop/2, label=label), color="white")+
  scale_fill_manual(values = mycols) +
  theme_void()+
  ggtitle("Pie Chart of Type of Charges")

Dari pie chart di atas, individu yang ditanggung oleh asuransi kesehatan yang memiliki tipe high charges ada sebesar 31% dan yang memiliki tipe low charges ada sebesar 69% .

1.2 Memahami Faktor-faktor apa yang mempengaruhi premi asuransi konsumen

1.2.1 Correlation

Korelasi menggunakan data new_ins dan tidak menggunakan variabel region dikarenakan pada visualisasi pie chart terlihat distribusi region hampir rata.

Ins_cor <-cor(new_ins[-6])
corrplot.mixed(Ins_cor)

Dari korelasi di atas didapatkan bahwa terdapat ikatan kuat antara smoker dan charges sehingga dapat disimpukan bahwa faktor smoker atau faktor apakah orang itu merokok atau tidak , age, dan bmiberpengaruh terhadap biaya premi asuransi konsumen. Faktor sexdan children tidak memiliki pengaruh terhadap variabel charges karena hasilnya hanya 0.06 dan 0.07.

1.2.2 Density of Age and Charges

d_age <-ggplot(insurance, aes(x = charges,fill = age, group=age)) + geom_density(alpha = 0.5, adjust=1.5)+theme_ipsum()+ggtitle("Density of Age")
d_age <-d_age + theme(plot.background = element_rect(fill = "#FEF8DD"))+ theme(panel.background = element_rect(fill = "white"))
ggplotly(d_age)

## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

Density di atas memberikan kita informasi bahwa ada perbedaan dalam densitas umur, maka variabel age mempengaruhi variabel charges.

1.2.3 Density of Sex and Charges

d_sex <-ggplot(insurance, aes(x = charges,fill = sex, group=sex)) + geom_density(alpha = 0.5, adjust=1.5)+theme_ipsum()+ggtitle("Density of Sex")
d_sex <-d_sex + theme(plot.background = element_rect(fill = "#FFE7C7"))+ theme(panel.background = element_rect(fill = "white"))
ggplotly(d_sex)

Density di atas memberikan kita informasi bahwa tidak terlalu terlihat adanya perbedaan dalam densitas sex, maka variabel sex tidak mempengaruhi variabel charges.

1.2.4 Density of Bmi and Charges

d_bmi <- ggplot(insurance, aes(x = charges,fill = bmi, group=bmi)) + geom_density(alpha = 0.5, adjust=1.5)+theme_ipsum()+ggtitle("Density of BMI")
d_bmi <- d_bmi + theme(plot.background = element_rect(fill = "#B7FFBF"))+ theme(panel.background = element_rect(fill = "white"))
ggplotly(d_bmi)

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Density di atas memberikan kita informasi bahwa ada perbedaan dalam densitas bmi, maka variabel bmi mempengaruhi variabel charges.

1.2.5 Density of Children and Charges

d_child <-ggplot(insurance, aes(x = charges,fill = children, group=children)) + geom_density(alpha = 0.5, adjust=1.5)+theme_ipsum()+ggtitle("Density of How Many Children They Have")
d_child <-d_child + theme(plot.background = element_rect(fill = "#cbbf97"))+ theme(panel.background = element_rect(fill = "white"))
ggplotly(d_child)

Density di atas memberikan kita informasi bahwa tidak ada perbedaan antara densitas yang tertanggung yang memiliki jumlah anak 0,1,2,3,dan 4. Ketika yang tertanggung memiliki anak sebanyak 5 orang, maka jumlah anak tersebut mulai mempengaruhi jumlah premi.

1.2.6 Density of Smoker and Charges

d_smoker <-ggplot(insurance, aes(x = charges,fill = smoker)) + geom_density(alpha = 0.5, adjust=1.5)+theme_ipsum()+ggtitle("Density of Smoker")
d_smoker <-d_smoker + theme(plot.background = element_rect(fill = "#B7FFBF"))+ theme(panel.background = element_rect(fill = "white"))
ggplotly(d_smoker)

Density di atas memberikan kita informasi bahwa ada perbedaan dalam densitas orang yang merokok dan tidak merokok, maka variabel smoker mempengaruhi variabel charges.

1.3 Menemukan model terbaik yang dapat memprediksi premi asuransi konsumen

lm(charges~., data=insurance)

## 
## Call:
## lm(formula = charges ~ ., data = insurance)
## 
## Coefficients:
##     (Intercept)              age          sexmale              bmi  
##        -11938.5            256.9           -131.3            339.2  
##        children        smokeryes  regionnorthwest  regionsoutheast  
##           475.5          23848.5           -353.0          -1035.0  
## regionsouthwest  
##          -960.1

Model Regresinya adalah

\[ Y_i=β_0+β_1x_1+β_2x_2+ϵ_i\]

\[ Y_i=-11938.5+256.9 \ x_1-131.3\ x_2+339.2 \ x_3 + 475.5 \ x_4 + 23848.5 \ x_5 -353 \ x_6 -1035 \ x_7 - 960.1 \ x_8+ϵ_i\]

\[ Y_i=-11938.5+256.9 \ age-131.3\ sex \ male +339.2 \ bmi + 475.5 \ children + 23848.5 \ smokeryes -353 \ region\ northwest -1035 \ region \ southeast - 960.1 \ region \ southwest +ϵ_i\]

Dari model regresi didapatkan bahwa, perokok meningkatkan premi asuransinya sebanyak 23,848.5 dollar. Setiap meningkatnya jumlah anak, maka terdapat peningkatan premi asuransi sebanyak $475.5. Setiap bmi mengalami 1 unit peningkatan, maka akan menghasilkan peningkatan premi sebanyak 339.2 dollar. Setiap umur orang yang terikat pembayaran premi asuransi naik satu tahun, maka premi asuransi akan meningkat sebanyak 256.9 dollar.

Berdasarkan hasil korelasi age terhadap charges,smoker terhadap charges, bmi terhadap charges , yaitu 0.3, 0.7, dan 0.2 dimana lebih baik dibandingkan yang lainnya dan berdasarkan hasil densitas yang menunjukkan bahwa adanya pengaruh age, smoker, dan bmi terhadap charges. Maka model regresi untuk tiga variabel tersebut adalah

lm(charges~ age+smoker+bmi, data=insurance)

## 
## Call:
## lm(formula = charges ~ age + smoker + bmi, data = insurance)
## 
## Coefficients:
## (Intercept)          age    smokeryes          bmi  
##    -11676.8        259.5      23823.7        322.6

\[ Yi=-11676.8 +259.5 \ age- 23823.7 \ smoker + 322.6 \ bmi+ϵ_i\] Dari model regresi didapatkan bahwa, perokok meningkatkan premi asuransinya sebanyak 23,823.7 dollar. Setiap meningkatnya umur, maka terdapat peningkatan premi asuransi sebanyak $259.5. Setiap bmi mengalami 1 unit peningkatan, maka akan menghasilkan peningkatan premi sebanyak 322.6 dollar.

1.4 Grouping The Equation

Karena perokok, tanggungan total, dan IMT berpengaruh terhadap asuransi biaya kesehatan, saya mengelompokkan faktor-faktor tersebut ke dalam 8 kategori yang ditunjukkan pada sub-bab di bawah ini. Akhirnya, saya hanya memiliki usia atau dan BMI sebagai variabel input untuk persamaan prediksi saya. Persamaannya akan ditampilkan di sub-bab, jadi Anda perlu memeriksanya.

** cat - 1 **: Perokok, tidak memiliki tanggungan, BMI di bawah 30.

** cat - 2 **: Perokok, tidak memiliki ketergantungan, BMI lebih dari 30.

** cat - 3 **: Perokok, tanggungan, BMI di bawah 30.

** cat - 4 **: Perokok, tanggungan, BMI di atas 30.

** cat - 5 **: Bukan perokok, tidak memiliki tanggungan, BMI di bawah 30.

** cat - 6 **: Bukan perokok, tidak memiliki ketergantungan, BMI lebih dari 30.

** cat - 7 **: Bukan perokok, memiliki tanggungan, BMI di bawah 30.

** cat - 8 **: Bukan perokok, memiliki tanggungan, BMI lebih dari 30.

1.4.1 Cat - 1

Perokok, tidak memiliki tanggungan, BMI di bawah 30.

Dari chart, Age vs Charges dapat didekati dengan menggunakan regresi linier.

BMI vs Charges terlihat memiliki korelasi yang tidak teratur.

## 
## Call:
## lm(formula = charges ~ age, data = ins_smoker_nochild_under30)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5740.7 -2372.9  -776.5   612.0 17119.6 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11970.56    1454.78   8.228 5.55e-11 ***
## age           252.39      36.25   6.962 5.70e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4276 on 52 degrees of freedom
## Multiple R-squared:  0.4824, Adjusted R-squared:  0.4725 
## F-statistic: 48.46 on 1 and 52 DF,  p-value: 5.696e-09

## 
## Call:
## lm(formula = charges ~ age + bmi, data = ins_smoker_nochild_under30)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2539.3 -1754.2 -1059.9  -203.1 15678.0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -956.74    5104.29  -0.187   0.8521    
## age           251.20      34.35   7.312 1.75e-09 ***
## bmi           505.18     192.06   2.630   0.0112 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4051 on 51 degrees of freedom
## Multiple R-squared:  0.5442, Adjusted R-squared:  0.5264 
## F-statistic: 30.45 on 2 and 51 DF,  p-value: 1.985e-09

age dan bmi dipilih karena memiliki r-squared yang lumayan tinggi. Model regresi dari dua variabel age dan bmi akan jadi seperti berikut:

\[Charges = -956.74 + (251.20 * AGE) + (505.18 * BMI)\]

1.4.2 Cat - 2

Smoker, have no dependent, BMI over 30.

Dari plot di atas, Age vs Charges sepertinya dapat didekati dengan model regresi linear

Dari plot di atas, BMI vs Charges memiliki korelasi yang tidak beraturan.

## 
## Call:
## lm(formula = charges ~ age, data = ins_smoker_nochild_over30)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -19722  -2239  -1235    786  19807 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 28983.17    1737.81  16.678  < 2e-16 ***
## age           306.74      43.38   7.071 2.05e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5361 on 59 degrees of freedom
## Multiple R-squared:  0.4587, Adjusted R-squared:  0.4495 
## F-statistic:    50 on 1 and 59 DF,  p-value: 2.05e-09

## 
## Call:
## lm(formula = charges ~ age + bmi, data = ins_smoker_nochild_over30)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -16667.0  -1505.1   -737.2     47.9  22684.4 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8120.10    5338.78   1.521 0.133702    
## age           292.16      38.73   7.544 3.57e-10 ***
## bmi           614.01     150.40   4.082 0.000138 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4766 on 58 degrees of freedom
## Multiple R-squared:  0.5795, Adjusted R-squared:  0.565 
## F-statistic: 39.97 on 2 and 58 DF,  p-value: 1.225e-11

age dan bmi akan digunakan karena memiliki R-squared yang lumayan tinggi. Model regresinya akan jadi sebagai berikut:

\[Charges = 8120.10 + (292.16 * AGE) + (614.01 * BMI)\]

1.4.3 Cat - 3

Perokok, punya anak, BMI dibawah 30

Plot age vs charges sepertinya bisa didekati dengan metode regresi.

BMI vs Charges memiliki korelasi yang tidak teratur.

## 
## Call:
## lm(formula = charges ~ age, data = ins_smoker_child_under30)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4651.4 -1487.2  -352.5   637.6 15865.3 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10842.61    1332.43   8.137 7.77e-12 ***
## age           274.71      33.18   8.280 4.19e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3199 on 73 degrees of freedom
## Multiple R-squared:  0.4843, Adjusted R-squared:  0.4772 
## F-statistic: 68.56 on 1 and 73 DF,  p-value: 4.194e-12

## 
## Call:
## lm(formula = charges ~ age + bmi, data = ins_smoker_child_under30)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2741.8 -1070.1  -608.8    -5.2 15619.8 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2428.48    2769.81   0.877   0.3835    
## age           259.48      31.33   8.282 4.56e-12 ***
## bmi           359.27     105.64   3.401   0.0011 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2989 on 72 degrees of freedom
## Multiple R-squared:  0.5557, Adjusted R-squared:  0.5433 
## F-statistic: 45.02 on 2 and 72 DF,  p-value: 2.075e-13

Model akan menggunakna dua variabel yaitu age dan bmi karena memiliki R-squared yang lumayan tinggi, modelnya adalah sebagai berikut:

\[ charges= 2428.48 + (259.48 * AGE) + (359.27 * BMI)\]

1.4.4 Cat - 4

Perokok, Punya anak, BMI melebihi 30

Dari plot di atas, sepertinya Age vs Charges bisa didekati mendekati metode linear

BMI vs Charges memiliki korelasi yang tidak teratur.

## 
## Call:
## lm(formula = charges ~ age, data = ins_smoker_child_over30)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4354.3 -2271.4  -859.3  1174.1 18445.4 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 32648.20    1361.98  23.971  < 2e-16 ***
## age           241.21      31.86   7.572 4.89e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3723 on 82 degrees of freedom
## Multiple R-squared:  0.4115, Adjusted R-squared:  0.4043 
## F-statistic: 57.34 on 1 and 82 DF,  p-value: 4.889e-11

## 
## Call:
## lm(formula = charges ~ age + bmi, data = ins_smoker_child_over30)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2212.2 -1334.7  -653.6    40.7 17621.6 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16021.03    3341.64   4.794 7.30e-06 ***
## age           253.72      27.69   9.162 3.81e-14 ***
## bmi           447.91      84.22   5.318 9.08e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3225 on 81 degrees of freedom
## Multiple R-squared:  0.5638, Adjusted R-squared:  0.553 
## F-statistic: 52.35 on 2 and 81 DF,  p-value: 2.553e-15

Metode regresi menggunakan dua variabel age dan bmi karena memiliki R-squared yang lumayan tinggi, modelnya adalah sebagai berikut:

\[ charges = 16021.03 + (253.72 * AGE) + (447.91 * BMI)\]

1.4.5 Cat - 5

Bukan perokok, tidak mempunyai anak, BMI dibawah 30

Age vs Charges chart sepertinya bisa didekati dengan metode regresi linear

BMI vs Charges memiliki korealsi yang tidak teratur

## 
## Call:
## lm(formula = charges ~ age, data = ins_nonsmoker_nochild_under30)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2428.1 -1440.0  -886.5  -391.6 21672.8 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3239.15     687.81  -4.709 4.43e-06 ***
## age           277.00      16.79  16.495  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3950 on 217 degrees of freedom
## Multiple R-squared:  0.5563, Adjusted R-squared:  0.5543 
## F-statistic: 272.1 on 1 and 217 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = charges ~ age + bmi, data = ins_nonsmoker_nochild_under30)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2457.4 -1453.0  -852.6  -391.1 21715.4 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3791.02    2191.60  -1.730   0.0851 .  
## age           276.56      16.91  16.354   <2e-16 ***
## bmi            22.40      84.44   0.265   0.7911    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3958 on 216 degrees of freedom
## Multiple R-squared:  0.5565, Adjusted R-squared:  0.5524 
## F-statistic: 135.5 on 2 and 216 DF,  p-value: < 2.2e-16

Metode regresi linear menggunakan variabel age karena memiliki R-Squared yang tinggi, modelnya adalah sebagai berikut:

\[ charges = -3239.15 + (277.00 * AGE)\]

1.4.6 Cat - 6

Bukan Perokok, tidak mempunyai anak, BMI melebihi 30.

Age vs Charges bisa didekati menggunakan metode regresi linear

BMI vs Charges memiliki korelasi yang tidak teratur.

## 
## Call:
## lm(formula = charges ~ age, data = ins_nonsmoker_nochild_over30)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2595.7 -1666.9 -1096.7  -377.7 20412.6 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2155.79     635.34  -3.393 0.000809 ***
## age           254.01      14.66  17.329  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3861 on 238 degrees of freedom
## Multiple R-squared:  0.5579, Adjusted R-squared:  0.556 
## F-statistic: 300.3 on 1 and 238 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = charges ~ age + bmi, data = ins_nonsmoker_nochild_over30)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2811.9 -1632.1 -1099.0  -396.8 20324.2 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -465.01    2319.97  -0.200    0.841    
## age           254.86      14.71  17.321   <2e-16 ***
## bmi           -48.90      64.53  -0.758    0.449    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3864 on 237 degrees of freedom
## Multiple R-squared:  0.5589, Adjusted R-squared:  0.5552 
## F-statistic: 150.2 on 2 and 237 DF,  p-value: < 2.2e-16

Metode regresi linear menggunakan variabel age karena memiliki R-squard yang lumayan tinggi, sehingga model regresinya adalah sebagai berikut:

\[ charges = -2155.79 + (254.01 * AGE)\]

1.4.7 Cat - 7

Bukan Perokok, Mempunyai anak, BMI di bawah 30

Age vs Charges terlihat bisa didekati dengan metode regresi linear.

BMI vs Charges memiliki korelasi yang tidak beraturan

## 
## Call:
## lm(formula = charges ~ age, data = ins_nonsmoker_child_under30)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -3050  -2080  -1578   -808  22413 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -884.08    1029.59  -0.859    0.391    
## age           247.85      25.87   9.581   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4975 on 281 degrees of freedom
## Multiple R-squared:  0.2462, Adjusted R-squared:  0.2436 
## F-statistic:  91.8 on 1 and 281 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = charges ~ age + bmi, data = ins_nonsmoker_child_under30)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3255.8 -2178.9 -1528.4  -664.9 22367.6 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2835.49    2564.04  -1.106    0.270    
## age           245.27      26.07   9.408   <2e-16 ***
## bmi            79.79      96.01   0.831    0.407    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4978 on 280 degrees of freedom
## Multiple R-squared:  0.2481, Adjusted R-squared:  0.2427 
## F-statistic: 46.19 on 2 and 280 DF,  p-value: < 2.2e-16

Model di atas menggunakan variabel age karena mempunyai nilai R-Squared yang lebih baik, sehingga model regresinya adalah sebagai berikut

\[ charges = -884.08 + (247.85 * AGE)\]

1.4.8 Cat - 8

Bukan Perokok, Mempunyai anak, BMI di atas 30.

Age vs Charges sepertinya bisa didekati dengan metode regresi linear.

BMI vs Charges memiliki korelasi yang tidak teratur.

## 
## Call:
## lm(formula = charges ~ age, data = ins_nonsmoker_child_over30)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3138.6 -2283.0 -1647.7  -746.8 24235.0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2161.36    1041.35  -2.076   0.0387 *  
## age           282.54      24.23  11.660   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5250 on 320 degrees of freedom
## Multiple R-squared:  0.2982, Adjusted R-squared:  0.296 
## F-statistic:   136 on 1 and 320 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = charges ~ age + bmi, data = ins_nonsmoker_child_over30)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3320.7 -2278.4 -1636.0  -711.3 24252.0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -923.87    2648.40  -0.349    0.727    
## age           283.07      24.28  11.658   <2e-16 ***
## bmi           -35.83      70.50  -0.508    0.612    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5256 on 319 degrees of freedom
## Multiple R-squared:  0.2988, Adjusted R-squared:  0.2944 
## F-statistic: 67.95 on 2 and 319 DF,  p-value: < 2.2e-16

Model regresi linear menggunakan variabel age karena memiliki R-Squared yang lebih baik dibandingkan yang lainnya, sehingga modelnya adalah sebagai berikut:

\[ charges = -2161.36 + (282.54 * AGE)\]

1.5 Charges Prediction

Fungsi untuk memprediksi data:

predict <- function(x){
               for(i in 1:nrow(x)){
               if(x[i,"smoker"] == "yes" && x[i,"children"] == 0 && x[i,"bmi"] < 30){
               x[i,"result"] = -956.74 + (251.20*x[i,"age"]) + (505.18*x[i,"bmi"])
               } else if(x[i,"smoker"] == "yes" && x[i,"children"] == 0 && x[i,"bmi"] >= 30) {
               x[i,"result"] = 8120.10 + (292.16*x[i,"age"]) + (614.01*x[i,"bmi"])
               } else if(x[i,"smoker"] == "yes" && x[i,"children"] > 0 && x[i,"bmi"] < 30){
               x[i,"result"] = 2428.48 + (259.48*x[i,"age"]) + (359.27*x[i,"bmi"])
               } else if(x[i,"smoker"] == "yes" && x[i,"children"] > 0 && x[i,"bmi"] >= 30){
               x[i,"result"] = 16021.03 + (253.72*x[i,"age"]) + (447.91*x[i,"bmi"])
               } else if(x[i,"smoker"] == "no" && x[i,"children"] == 0 && x[i,"bmi"] < 30){
               x[i,"result"] = -3239.15 + (277.00*x[i,"age"])
               } else if(x[i,"smoker"] == "no" && x[i,"children"] == 0 && x[i,"bmi"] >= 30){
               x[i,"result"] = -2155.79 + (254.01*x[i,"age"])
               } else if(x[i,"smoker"] == "no" && x[i,"children"] > 0 && x[i,"bmi"] < 30){
               x[i,"result"] = -884.08 + (247.85*x[i,"age"])
               } else {
               x[i,"result"] = -2161.36 + (282.54*x[i,"age"])
               }    
               }
          return(x)
          }

Hasil dari prediksi adalah sebagai berikut:

Root Mean Square Error dari hasil prediksi adalah :

## [1] 4437.567

Nilai Mean error dari prediksi adalah plus minus 4437.56

Mean Absolute Percent Error dari prediksi yang dilakukan adalah:

## [1] 0.2672371

Mean percentage error dari prediksi saya adalah 26.7%

1.6 Study Cases

Berapa biaya asuransi untuk orang-orang ini?

1. Kun, 24 tahun, 32.05 BMI, tidak memiliki tanggungan, bukan perokok, dan tinggal di timur laut.

2. Hendery, 21 tahun, 29.545 BMI, 2 tanggungan, perokok, dan tinggal di tenggara.

Gunakanlah fungsi ini untuk menjawab:

predict_charge <- function(age, bmi, children, smoker){
    if(smoker == "yes" && children == 0 && bmi < 30){
        result = -956.74 + (251.20*age) + (505.18*bmi)
    } else if(smoker == "yes" && children == 0 && bmi >= 30) {
        result = 8120.10 + (292.16*age) + (614.01*bmi)
    } else if(smoker == "yes" && children > 0 && bmi < 30){
        result = 2428.48 + (259.48*age) + (359.27*bmi)
    } else if(smoker == "yes" && children > 0 && bmi >= 30){
        result = 16021.03 + (253.72*age) + (447.91*bmi)
    } else if(smoker == "no" && children == 0 && bmi < 30){
        result = -3239.15 + (277.00*age)
    } else if(smoker == "no" && children == 0 && bmi >= 30){
        result = -2155.79 + (254.01*age)
    } else if(smoker == "no" && children > 0 && bmi < 30){
        result = -884.08 + (247.85*age)
    } else {
        result = -2161.36 + (282.54*age)
    }
    return(result)
    }

The medical cost insurance untuk Kun:

predict_charge(24, 32.05, 0, "no")

## [1] 3940.45

The medical cost insurance untuk Hendery:

predict_charge(21, 29.545, 2, "yes")

## [1] 18492.19

2 ANOVA/MANOVA

Suatu perusahaan di Amerika Serikat ingin mempekerjakan seseorang dari luar Amerika Serikat untuk posisi teknis, mereka perlu mengajukan aplikasi ke pemerintah Amerika Serikat untuk mendapatkan kartu hijau atau visa bagi pelamar asing. Untuk menunjukkan ekuitas bagi karyawan AS dan non-AS, perusahaan perlu menyatakan seberapa banyak mereka bersedia membayar karyawan ketika mereka mengajukan permohonan visa atau kartu hijau. Sementara itu, mereka perlu memberikan jumlah rata-rata, yang disebut “prevailing wage” seorang karyawan dengan keterampilan dan latar belakang serupa biasanya dibayar untuk posisi yang sama.

Perbedaan antara upah yang dibayar dan upah yang berlaku dapat menunjukkan apakah perusahaan AS bersedia membayar lebih banyak gaji kepada karyawan non-AS. Gaji lebih banyak untuk calon karyawan asing akan menarik. Selain itu, perlu diperhatikan bahwa untuk area dan pekerjaan yang berbeda, gaji dapat menunjukkan perbedaan. Oleh karena itu perlu untuk mencari tahu hubungan antara gaji, area dan posisi dapat membantu karyawan non-AS untuk memilih pekerjaan di AS.

Berdasarkan klasifikasi VISA yang mereka miliki disimpulkan bahwa ada lima jenis yang berbeda: “green card”, “H-1B”, “H-1B1 Chile”, “H- 1B1 Singapore” dan “E-3 Australia”. Untuk projek ini, silahkan anda memilih kelas VISA “H-1B” untuk melakukan data mentah pelamar yang berpenduduk tetap tahun 2018 atau 2019. Kalian dapat mendwonload Data asli yang dikumpulkan oleh Kantor Sertifikasi Tenaga Kerja Asing Departemen Tenaga Kerja AS

JAWABAN

2.1 Packages

library(readxl)
library(dplyr)
library(ggplot2)
library(plotly)
library(hrbrthemes)
library(corrplot)
library(ggrepel)

2.2 Pemanggilan data

labour <- read.csv("C:/Users/user/Downloads/H-1B_Disclosure_Data_FY2019.csv")

head(labour)

##       ï..CASE_NUMBER CASE_STATUS CASE_SUBMITTED DECISION_DATE
## 1 I-200-16092-327771   WITHDRAWN       4/8/2016     4/30/2019
## 2 I-203-17188-450729   WITHDRAWN      7/14/2017     5/13/2019
## 3 I-203-17229-572307   WITHDRAWN      8/23/2017     4/30/2019
## 4 I-203-17356-299648   WITHDRAWN     12/22/2017     8/20/2019
## 5 I-203-18008-577576   WITHDRAWN      1/10/2018     4/15/2019
## 6 I-200-18150-993604   WITHDRAWN      5/30/2018      8/8/2019
##   ORIGINAL_CERT_DATE     VISA_CLASS                   JOB_TITLE SOC_CODE
## 1                              H-1B ASSOCIATE CREATIVE DIRECTOR  11-2011
## 2                    E-3 Australian ACCOUNT SUPERVISOR (MOTHER)  11-2011
## 3                    E-3 Australian EXECUTIVE CREATIVE DIRECTOR  11-2011
## 4                    E-3 Australian     PROJECT MANAGEMENT LEAD  11-2011
## 5                    E-3 Australian       CREATIVE DIRECTOR, UX  11-2011
## 6                              H-1B     GLOBAL BRAND SUPERVISOR  11-2011
##                             SOC_TITLE FULL_TIME_POSITION
## 1 ADVERTISING AND PROMOTIONS MANAGERS                  Y
## 2 ADVERTISING AND PROMOTIONS MANAGERS                  Y
## 3 ADVERTISING AND PROMOTIONS MANAGERS                  Y
## 4 ADVERTISING AND PROMOTIONS MANAGERS                  Y
## 5 ADVERTISING AND PROMOTIONS MANAGERS                  Y
## 6 ADVERTISING AND PROMOTIONS MANAGERS                  Y
##   PERIOD_OF_EMPLOYMENT_START_DATE PERIOD_OF_EMPLOYMENT_END_DATE
## 1                       10/1/2016                     9/30/2019
## 2                       10/1/2017                     10/1/2019
## 3                       9/11/2017                     9/11/2019
## 4                        1/8/2018                      1/7/2020
## 5                        2/1/2018                      2/1/2020
## 6                       7/13/2018                     7/13/2021
##   TOTAL_WORKER_POSITIONS NEW_EMPLOYMENT CONTINUED_EMPLOYMENT
## 1                      1              0                    1
## 2                      1              0                    1
## 3                      1              0                    0
## 4                      1              0                    0
## 5                      1              0                    0
## 6                      1              0                    1
##   CHANGE_PREVIOUS_EMPLOYMENT NEW_CONCURRENT_EMPLOYMENT CHANGE_EMPLOYER
## 1                          0                         0               0
## 2                          0                         0               0
## 3                          1                         0               0
## 4                          1                         0               0
## 5                          1                         0               0
## 6                          0                         0               0
##   AMENDED_PETITION                   EMPLOYER_NAME EMPLOYER_BUSINESS_DBA
## 1                0          R/GA MEDIA GROUP, INC.                      
## 2                0           MOTHER INDUSTRIES LLC                      
## 3                0          WE ARE UNLIMITED, INC.                      
## 4                0             HELLO ELEPHANT, LLC                      
## 5                0             HELLO ELEPHANT, LLC                      
## 6                0 MCCANN-ERICKSON MARKETING, INC.                      
##                       EMPLOYER_ADDRESS1 EMPLOYER_ADDRESS2 EMPLOYER_CITY
## 1                  450 WEST 33RD STREET                        NEW YORK
## 2                       595 11TH AVENUE                        NEW YORK
## 3 225 NORTH MICHIGAN AVENUE, 21ST FLOOR                         CHICAGO
## 4                        45 MAIN STREET                        BROOKLYN
## 5                        45 MAIN STREET                        BROOKLYN
## 6                      622 THIRD AVENUE                        NEW YORK
##   EMPLOYER_STATE EMPLOYER_POSTAL_CODE EMPLOYER_COUNTRY EMPLOYER_PROVINCE
## 1             NY                10001                                   
## 2             NY                10036                                   
## 3             IL                60601                                   
## 4             NY                11201                                   
## 5             NY                11201                                   
## 6             NY                10017                                   
##   EMPLOYER_PHONE EMPLOYER_PHONE_EXT NAICS_CODE AGENT_REPRESENTING_EMPLOYER
## 1                                       541810                            
## 2                                       541810                            
## 3                                       541810                            
## 4                                       541810                            
## 5                                       541810                            
## 6                                       541810                            
##   AGENT_ATTORNEY_LAW_FIRM_BUSINESS_NAME AGENT_ATTORNEY_ADDRESS1
## 1                                                              
## 2                                                              
## 3                                                              
## 4                                                              
## 5                                                              
## 6                                                              
##   AGENT_ATTORNEY_ADDRESS2 AGENT_ATTORNEY_CITY AGENT_ATTORNEY_STATE
## 1                                                                 
## 2                                                                 
## 3                                                                 
## 4                                                                 
## 5                                                                 
## 6                                                                 
##   AGENT_ATTORNEY_POSTAL_CODE AGENT_ATTORNEY_COUNTRY AGENT_ATTORNEY_PROVINCE
## 1                                                                          
## 2                                                                          
## 3                                                                          
## 4                                                                          
## 5                                                                          
## 6                                                                          
##   AGENT_ATTORNEY_PHONE AGENT_ATTORNEY_PHONE_EXT STATE_OF_HIGHEST_COURT
## 1                                                                     
## 2                                                                     
## 3                                                                     
## 4                                                                     
## 5                                                                     
## 6                                                                     
##   NAME_OF_HIGHEST_STATE_COURT WORKSITE_WORKERS_1 SECONDARY_ENTITY_1
## 1                                             NA                   
## 2                                             NA                   
## 3                                             NA                   
## 4                                             NA                   
## 5                                             NA                   
## 6                                             NA                   
##   SECONDARY_ENTITY_BUSINESS_NAME_1 WORKSITE_ADDRESS1_1 WORKSITE_ADDRESS2_1
## 1                                                                         
## 2                                                                         
## 3                                                                         
## 4                                                                         
## 5                                                                         
## 6                                                                         
##   WORKSITE_CITY_1 WORKSITE_COUNTY_1 WORKSITE_STATE_1 WORKSITE_POSTAL_CODE_1
## 1        NEW YORK          NEW YORK               NY                  10001
## 2        NEW YORK          NEW YORK               NY                  10036
## 3         CHICAGO              COOK               IL                  60601
## 4        BROOKLYN             KINGS               NY                  11201
## 5        BROOKLYN          NEW YORK               NY                  11201
## 6         ATLANTA            FULTON               GA                  30305
##   WAGE_RATE_OF_PAY_FROM_1 WAGE_RATE_OF_PAY_TO_1 WAGE_UNIT_OF_PAY_1
## 1             $179,000.00           $189,000.00               Year
## 2             $110,000.00           $130,000.00               Year
## 3             $275,000.00           $325,000.00               Year
## 4             $140,000.00           $170,000.00               Year
## 5             $180,000.00           $210,000.00               Year
## 6              $84,400.00           $104,400.00               Year
##   PREVAILING_WAGE_1 PW_UNIT_OF_PAY_1 PW_TRACKING_NUMBER_1 PW_WAGE_LEVEL_1
## 1                                                                        
## 2                                                                        
## 3                                                                        
## 4                                                                        
## 5                                                                        
## 6                                                                        
##   PW_OES_YEAR_1 PW_OTHER_SOURCE_1 PW_NON.OES_YEAR_1 PW_SURVEY_PUBLISHER_1
## 1                                                NA                    NA
## 2                                                NA                    NA
## 3                                                NA                    NA
## 4                                                NA                    NA
## 5                                                NA                    NA
## 6                                                NA                    NA
##   PW_SURVEY_NAME_1 WORKSITE_WORKERS_2 SECONDARY_ENTITY_2
## 1                                  NA                   
## 2                                  NA                   
## 3                                  NA                   
## 4                                  NA                   
## 5                                  NA                   
## 6                                  NA                   
##   SECONDARY_ENTITY_BUSINESS_NAME_2 WORKSITE_ADDRESS1_2 WORKSITE_ADDRESS2_2
## 1                                                                         
## 2                                                                         
## 3                                                                         
## 4                                                                         
## 5                                                                         
## 6                                                                         
##   WORKSITE_CITY_2 WORKSITE_COUNTY_2 WORKSITE_STATE_2 WORKSITE_POSTAL_CODE_2
## 1                                                                          
## 2                                                                          
## 3                                                                          
## 4                                                                          
## 5                                                                          
## 6                                                                          
##   WAGE_RATE_OF_PAY_FROM_2 WAGE_RATE_OF_PAY_TO_2 WAGE_UNIT_OF_PAY_2
## 1                      NA                    NA                   
## 2                      NA                    NA                   
## 3                      NA                    NA                   
## 4                      NA                    NA                   
## 5                      NA                    NA                   
## 6                      NA                    NA                   
##   PREVAILING_WAGE_2 PW_UNIT_OF_PAY_2 PW_TRACKING_NUMBER_2 PW_WAGE_LEVEL_2
## 1                NA                                                      
## 2                NA                                                      
## 3                NA                                                      
## 4                NA                                                      
## 5                NA                                                      
## 6                NA                                                      
##   PW_OES_YEAR_2 PW_OTHER_SOURCE_2 PW_NON.OES_YEAR_2 PW_SURVEY_PUBLISHER_2
## 1            NA                                  NA                    NA
## 2            NA                                  NA                    NA
## 3            NA                                  NA                    NA
## 4            NA                                  NA                    NA
## 5            NA                                  NA                    NA
## 6            NA                                  NA                    NA
##   PW_SURVEY_NAME_2 WORKSITE_WORKERS_3 SECONDARY_ENTITY_3
## 1                                  NA                   
## 2                                  NA                   
## 3                                  NA                   
## 4                                  NA                   
## 5                                  NA                   
## 6                                  NA                   
##   SECONDARY_ENTITY_BUSINESS_NAME_3 WORKSITE_ADDRESS1_3 WORKSITE_ADDRESS2_3
## 1                                                                         
## 2                                                                         
## 3                                                                         
## 4                                                                         
## 5                                                                         
## 6                                                                         
##   WORKSITE_CITY_3 WORKSITE_COUNTY_3 WORKSITE_STATE_3 WORKSITE_POSTAL_CODE_3
## 1                                                                          
## 2                                                                          
## 3                                                                          
## 4                                                                          
## 5                                                                          
## 6                                                                          
##   WAGE_RATE_OF_PAY_FROM_3 WAGE_RATE_OF_PAY_TO_3 WAGE_UNIT_OF_PAY_3
## 1                      NA                    NA                   
## 2                      NA                    NA                   
## 3                      NA                    NA                   
## 4                      NA                    NA                   
## 5                      NA                    NA                   
## 6                      NA                    NA                   
##   PREVAILING_WAGE_3 PW_UNIT_OF_PAY_3 PW_TRACKING_NUMBER_3 PW_WAGE_LEVEL_3
## 1                                                                        
## 2                                                                        
## 3                                                                        
## 4                                                                        
## 5                                                                        
## 6                                                                        
##   PW_OES_YEAR_3 PW_OTHER_SOURCE_3 PW_NON.OES_YEAR_3 PW_SURVEY_PUBLISHER_3
## 1            NA                                  NA                    NA
## 2            NA                                  NA                    NA
## 3            NA                                  NA                    NA
## 4            NA                                  NA                    NA
## 5            NA                                  NA                    NA
## 6            NA                                  NA                    NA
##   PW_SURVEY_NAME_3 WORKSITE_WORKERS_4 SECONDARY_ENTITY_4
## 1                                  NA                   
## 2                                  NA                   
## 3                                  NA                   
## 4                                  NA                   
## 5                                  NA                   
## 6                                  NA                   
##   SECONDARY_ENTITY_BUSINESS_NAME_4 WORKSITE_ADDRESS1_4 WORKSITE_ADDRESS2_4
## 1                                                                         
## 2                                                                         
## 3                                                                         
## 4                                                                         
## 5                                                                         
## 6                                                                         
##   WORKSITE_CITY_4 WORKSITE_COUNTY_4 WORKSITE_STATE_4 WORKSITE_POSTAL_CODE_4
## 1                                                                          
## 2                                                                          
## 3                                                                          
## 4                                                                          
## 5                                                                          
## 6                                                                          
##   WAGE_RATE_OF_PAY_FROM_4 WAGE_RATE_OF_PAY_TO_4 WAGE_UNIT_OF_PAY_4
## 1                      NA                    NA                 NA
## 2                      NA                    NA                 NA
## 3                      NA                    NA                 NA
## 4                      NA                    NA                 NA
## 5                      NA                    NA                 NA
## 6                      NA                    NA                 NA
##   PREVAILING_WAGE_4 PW_UNIT_OF_PAY_4 PW_TRACKING_NUMBER_4 PW_WAGE_LEVEL_4
## 1                NA               NA                                     
## 2                NA               NA                                     
## 3                NA               NA                                     
## 4                NA               NA                                     
## 5                NA               NA                                     
## 6                NA               NA                                     
##   PW_OES_YEAR_4 PW_OTHER_SOURCE_4 PW_NON.OES_YEAR_4 PW_SURVEY_PUBLISHER_4
## 1            NA                                  NA                    NA
## 2            NA                                  NA                    NA
## 3            NA                                  NA                    NA
## 4            NA                                  NA                    NA
## 5            NA                                  NA                    NA
## 6            NA                                  NA                    NA
##   PW_SURVEY_NAME_4 WORKSITE_WORKERS_5 SECONDARY_ENTITY_5
## 1                                  NA                   
## 2                                  NA                   
## 3                                  NA                   
## 4                                  NA                   
## 5                                  NA                   
## 6                                  NA                   
##   SECONDARY_ENTITY_BUSINESS_NAME_5 WORKSITE_ADDRESS1_5 WORKSITE_ADDRESS2_5
## 1                                                                         
## 2                                                                         
## 3                                                                         
## 4                                                                         
## 5                                                                         
## 6                                                                         
##   WORKSITE_CITY_5 WORKSITE_COUNTY_5 WORKSITE_STATE_5 WORKSITE_POSTAL_CODE_5
## 1                                                                          
## 2                                                                          
## 3                                                                          
## 4                                                                          
## 5                                                                          
## 6                                                                          
##   WAGE_RATE_OF_PAY_FROM_5 WAGE_RATE_OF_PAY_TO_5 WAGE_UNIT_OF_PAY_5
## 1                      NA                    NA                 NA
## 2                      NA                    NA                 NA
## 3                      NA                    NA                 NA
## 4                      NA                    NA                 NA
## 5                      NA                    NA                 NA
## 6                      NA                    NA                 NA
##   PREVAILING_WAGE_5 PW_UNIT_OF_PAY_5 PW_TRACKING_NUMBER_5 PW_WAGE_LEVEL_5
## 1                NA               NA                                     
## 2                NA               NA                                     
## 3                NA               NA                                     
## 4                NA               NA                                     
## 5                NA               NA                                     
## 6                NA               NA                                     
##   PW_OES_YEAR_5 PW_OTHER_SOURCE_5 PW_NON.OES_YEAR_5 PW_SURVEY_PUBLISHER_5
## 1            NA                                  NA                    NA
## 2            NA                                  NA                    NA
## 3            NA                                  NA                    NA
## 4            NA                                  NA                    NA
## 5            NA                                  NA                    NA
## 6            NA                                  NA                    NA
##   PW_SURVEY_NAME_5 WORKSITE_WORKERS_6 SECONDARY_ENTITY_6
## 1                                  NA                   
## 2                                  NA                   
## 3                                  NA                   
## 4                                  NA                   
## 5                                  NA                   
## 6                                  NA                   
##   SECONDARY_ENTITY_BUSINESS_NAME_6 WORKSITE_ADDRESS1_6 WORKSITE_ADDRESS2_6
## 1                                                                         
## 2                                                                         
## 3                                                                         
## 4                                                                         
## 5                                                                         
## 6                                                                         
##   WORKSITE_CITY_6 WORKSITE_COUNTY_6 WORKSITE_STATE_6 WORKSITE_POSTAL_CODE_6
## 1                                                                          
## 2                                                                          
## 3                                                                          
## 4                                                                          
## 5                                                                          
## 6                                                                          
##   WAGE_RATE_OF_PAY_FROM_6 WAGE_RATE_OF_PAY_TO_6 WAGE_UNIT_OF_PAY_6
## 1                      NA                    NA                   
## 2                      NA                    NA                   
## 3                      NA                    NA                   
## 4                      NA                    NA                   
## 5                      NA                    NA                   
## 6                      NA                    NA                   
##   PREVAILING_WAGE_6 PW_UNIT_OF_PAY_6 PW_TRACKING_NUMBER_6 PW_WAGE_LEVEL_6
## 1                NA                                                      
## 2                NA                                                      
## 3                NA                                                      
## 4                NA                                                      
## 5                NA                                                      
## 6                NA                                                      
##   PW_OES_YEAR_6 PW_OTHER_SOURCE_6 PW_NON.OES_YEAR_6 PW_SURVEY_PUBLISHER_6
## 1            NA                                  NA                    NA
## 2            NA                                  NA                    NA
## 3            NA                                  NA                    NA
## 4            NA                                  NA                    NA
## 5            NA                                  NA                    NA
## 6            NA                                  NA                    NA
##   PW_SURVEY_NAME_6 WORKSITE_WORKERS_7 SECONDARY_ENTITY_7
## 1                                  NA                   
## 2                                  NA                   
## 3                                  NA                   
## 4                                  NA                   
## 5                                  NA                   
## 6                                  NA                   
##   SECONDARY_ENTITY_BUSINESS_NAME_7 WORKSITE_ADDRESS1_7 WORKSITE_ADDRESS2_7
## 1                                                                         
## 2                                                                         
## 3                                                                         
## 4                                                                         
## 5                                                                         
## 6                                                                         
##   WORKSITE_CITY_7 WORKSITE_COUNTY_7 WORKSITE_STATE_7 WORKSITE_POSTAL_CODE_7
## 1                                                                          
## 2                                                                          
## 3                                                                          
## 4                                                                          
## 5                                                                          
## 6                                                                          
##   WAGE_RATE_OF_PAY_FROM_7 WAGE_RATE_OF_PAY_TO_7 WAGE_UNIT_OF_PAY_7
## 1                      NA                    NA                 NA
## 2                      NA                    NA                 NA
## 3                      NA                    NA                 NA
## 4                      NA                    NA                 NA
## 5                      NA                    NA                 NA
## 6                      NA                    NA                 NA
##   PREVAILING_WAGE_7 PW_UNIT_OF_PAY_7 PW_TRACKING_NUMBER_7 PW_WAGE_LEVEL_7
## 1                NA               NA                                     
## 2                NA               NA                                     
## 3                NA               NA                                     
## 4                NA               NA                                     
## 5                NA               NA                                     
## 6                NA               NA                                     
##   PW_OES_YEAR_7 PW_OTHER_SOURCE_7 PW_NON.OES_YEAR_7 PW_SURVEY_PUBLISHER_7
## 1            NA                                  NA                    NA
## 2            NA                                  NA                    NA
## 3            NA                                  NA                    NA
## 4            NA                                  NA                    NA
## 5            NA                                  NA                    NA
## 6            NA                                  NA                    NA
##   PW_SURVEY_NAME_7 WORKSITE_WORKERS_8 SECONDARY_ENTITY_8
## 1                                  NA                   
## 2                                  NA                   
## 3                                  NA                   
## 4                                  NA                   
## 5                                  NA                   
## 6                                  NA                   
##   SECONDARY_ENTITY_BUSINESS_NAME_8 WORKSITE_ADDRESS1_8 WORKSITE_ADDRESS2_8
## 1                                                                         
## 2                                                                         
## 3                                                                         
## 4                                                                         
## 5                                                                         
## 6                                                                         
##   WORKSITE_CITY_8 WORKSITE_COUNTY_8 WORKSITE_STATE_8 WORKSITE_POSTAL_CODE_8
## 1                                                                          
## 2                                                                          
## 3                                                                          
## 4                                                                          
## 5                                                                          
## 6                                                                          
##   WAGE_RATE_OF_PAY_FROM_8 WAGE_RATE_OF_PAY_TO_8 WAGE_UNIT_OF_PAY_8
## 1                      NA                    NA                 NA
## 2                      NA                    NA                 NA
## 3                      NA                    NA                 NA
## 4                      NA                    NA                 NA
## 5                      NA                    NA                 NA
## 6                      NA                    NA                 NA
##   PREVAILING_WAGE_8 PW_UNIT_OF_PAY_8 PW_TRACKING_NUMBER_8 PW_WAGE_LEVEL_8
## 1                NA               NA                                     
## 2                NA               NA                                     
## 3                NA               NA                                     
## 4                NA               NA                                     
## 5                NA               NA                                     
## 6                NA               NA                                     
##   PW_OES_YEAR_8 PW_OTHER_SOURCE_8 PW_NON.OES_YEAR_8 PW_SURVEY_PUBLISHER_8
## 1            NA                                  NA                    NA
## 2            NA                                  NA                    NA
## 3            NA                                  NA                    NA
## 4            NA                                  NA                    NA
## 5            NA                                  NA                    NA
## 6            NA                                  NA                    NA
##   PW_SURVEY_NAME_8 WORKSITE_WORKERS_9 SECONDARY_ENTITY_9
## 1                                  NA                   
## 2                                  NA                   
## 3                                  NA                   
## 4                                  NA                   
## 5                                  NA                   
## 6                                  NA                   
##   SECONDARY_ENTITY_BUSINESS_NAME_9 WORKSITE_ADDRESS1_9 WORKSITE_ADDRESS2_9
## 1                                                                         
## 2                                                                         
## 3                                                                         
## 4                                                                         
## 5                                                                         
## 6                                                                         
##   WORKSITE_CITY_9 WORKSITE_COUNTY_9 WORKSITE_STATE_9 WORKSITE_POSTAL_CODE_9
## 1                                                                          
## 2                                                                          
## 3                                                                          
## 4                                                                          
## 5                                                                          
## 6                                                                          
##   WAGE_RATE_OF_PAY_FROM_9 WAGE_RATE_OF_PAY_TO_9 WAGE_UNIT_OF_PAY_9
## 1                      NA                    NA                   
## 2                      NA                    NA                   
## 3                      NA                    NA                   
## 4                      NA                    NA                   
## 5                      NA                    NA                   
## 6                      NA                    NA                   
##   PREVAILING_WAGE_9 PW_UNIT_OF_PAY_9 PW_TRACKING_NUMBER_9 PW_WAGE_LEVEL_9
## 1                NA                                                      
## 2                NA                                                      
## 3                NA                                                      
## 4                NA                                                      
## 5                NA                                                      
## 6                NA                                                      
##   PW_OES_YEAR_9 PW_OTHER_SOURCE_9 PW_NON.OES_YEAR_9 PW_SURVEY_PUBLISHER_9
## 1            NA                                  NA                    NA
## 2            NA                                  NA                    NA
## 3            NA                                  NA                    NA
## 4            NA                                  NA                    NA
## 5            NA                                  NA                    NA
## 6            NA                                  NA                    NA
##   PW_SURVEY_NAME_9 WORKSITE_WORKERS_10 SECONDARY_ENTITY_10
## 1                                   NA                    
## 2                                   NA                    
## 3                                   NA                    
## 4                                   NA                    
## 5                                   NA                    
## 6                                   NA                    
##   SECONDARY_ENTITY_BUSINESS_NAME_10 WORKSITE_ADDRESS1_10 WORKSITE_ADDRESS2_10
## 1                                                                            
## 2                                                                            
## 3                                                                            
## 4                                                                            
## 5                                                                            
## 6                                                                            
##   WORKSITE_CITY_10 WORKSITE_COUNTY_10 WORKSITE_STATE_10 WORKSITE_POSTAL_CODE_10
## 1                                                                            NA
## 2                                                                            NA
## 3                                                                            NA
## 4                                                                            NA
## 5                                                                            NA
## 6                                                                            NA
##   WAGE_RATE_OF_PAY_FROM_10 WAGE_RATE_OF_PAY_TO_10 WAGE_UNIT_OF_PAY_10
## 1                       NA                     NA                    
## 2                       NA                     NA                    
## 3                       NA                     NA                    
## 4                       NA                     NA                    
## 5                       NA                     NA                    
## 6                       NA                     NA                    
##   PREVAILING_WAGE_10 PW_UNIT_OF_PAY_10 PW_TRACKING_NUMBER_10 PW_WAGE_LEVEL_10
## 1                 NA                                                         
## 2                 NA                                                         
## 3                 NA                                                         
## 4                 NA                                                         
## 5                 NA                                                         
## 6                 NA                                                         
##   PW_OES_YEAR_10 PW_OTHER_SOURCE_10 PW_NON.OES_YEAR_10 PW_SURVEY_PUBLISHER_10
## 1             NA                                    NA                     NA
## 2             NA                                    NA                     NA
## 3             NA                                    NA                     NA
## 4             NA                                    NA                     NA
## 5             NA                                    NA                     NA
## 6             NA                                    NA                     NA
##   PW_SURVEY_NAME_10 H.1B_DEPENDENT WILLFUL_VIOLATOR SUPPORT_H1B STATUTORY_BASIS
## 1                                N                N        <NA>                
## 2                                                                              
## 3                                                                              
## 4                                                                              
## 5                                                                              
## 6                                N                N        <NA>                
##   MASTERS_EXEMPTION PUBLIC_DISCLOSURE
## 1                                    
## 2                                    
## 3                                    
## 4                                    
## 5                                    
## 6

2.3 Pengecekan Tipe data

str(labour)

## 'data.frame':    1048547 obs. of  260 variables:
##  $ ï..CASE_NUMBER                       : Factor w/ 664617 levels "","I-200-09351-355625",..: 2955 651652 651690 651800 651811 24125 49362 134067 161254 235457 ...
##  $ CASE_STATUS                          : Factor w/ 5 levels "","CERTIFIED",..: 5 5 5 5 5 5 5 5 5 5 ...
##  $ CASE_SUBMITTED                       : Factor w/ 251929 levels "","1/10/2017",..: 164186 205934 229401 80339 3 179991 36418 3103 14379 137287 ...
##  $ DECISION_DATE                        : Factor w/ 8084 levels "","1/10/2019",..: 6428 6572 6428 7630 6076 7800 1109 77 308 5643 ...
##  $ ORIGINAL_CERT_DATE                   : Factor w/ 2468 levels "","1/10/2017",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ VISA_CLASS                           : Factor w/ 5 levels "","E-3 Australian",..: 3 2 2 2 2 3 3 3 3 3 ...
##  $ JOB_TITLE                            : Factor w/ 114300 levels "","- ADVISORY ANALYST, ERP.ENTERPRISE SOLUTIONS.US",..: 7954 659 31983 64270 19984 34879 25227 36424 429 61929 ...
##  $ SOC_CODE                             : Factor w/ 572 levels "","11-1011","11-1021",..: 4 4 4 4 4 4 4 4 4 4 ...
##  $ SOC_TITLE                            : Factor w/ 926 levels "","15-1121","15-2031",..: 17 17 17 17 17 17 17 17 17 17 ...
##  $ FULL_TIME_POSITION                   : Factor w/ 3 levels "","N","Y": 3 3 3 3 3 3 3 3 3 3 ...
##  $ PERIOD_OF_EMPLOYMENT_START_DATE      : Factor w/ 1752 levels "","01/01/2017",..: 310 311 1641 292 733 1397 68 1215 808 1634 ...
##  $ PERIOD_OF_EMPLOYMENT_END_DATE        : Factor w/ 2499 levels "","01/01/2019",..: 2471 978 2384 939 1460 2139 220 1953 1532 2377 ...
##  $ TOTAL_WORKER_POSITIONS               : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ NEW_EMPLOYMENT                       : Factor w/ 58 levels "","0","001","01",..: 2 2 2 2 2 2 2 2 2 6 ...
##  $ CONTINUED_EMPLOYMENT                 : Factor w/ 31 levels "","0","00","001",..: 7 7 2 2 2 7 2 7 7 2 ...
##  $ CHANGE_PREVIOUS_EMPLOYMENT           : int  0 0 1 1 1 0 1 0 0 0 ...
##  $ NEW_CONCURRENT_EMPLOYMENT            : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ CHANGE_EMPLOYER                      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ AMENDED_PETITION                     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ EMPLOYER_NAME                        : Factor w/ 70364 levels "","'PROFESSIONAL AND SCIENTIFIC ASSOCIATES, INC.",..: 50507 41422 67857 28075 28075 39125 40129 34611 59207 56652 ...
##  $ EMPLOYER_BUSINESS_DBA                : Factor w/ 11571 levels "","-","--","% NIPPON ELECTRIC GLASS AMERICA INC",..: 1 1 1 1 1 1 5421 1 7068 1 ...
##  $ EMPLOYER_ADDRESS1                    : Factor w/ 63235 levels "","! ATWELL RD.",..: 41988 48766 24575 41823 41823 50504 59544 60989 53798 43146 ...
##  $ EMPLOYER_ADDRESS2                    : Factor w/ 8186 levels "","--","---",..: 1 1 1 1 1 1 458 1 1 1 ...
##  $ EMPLOYER_CITY                        : Factor w/ 5034 levels "",";EHI","10011",..: 3077 3077 792 546 546 3077 1320 3921 2506 1575 ...
##  $ EMPLOYER_STATE                       : Factor w/ 58 levels "","AK","AL","AR",..: 39 39 18 39 39 39 6 6 6 6 ...
##  $ EMPLOYER_POSTAL_CODE                 : Factor w/ 11154 levels "","0","00717-9997",..: 601 693 6450 943 943 650 9581 10259 9525 10340 ...
##  $ EMPLOYER_COUNTRY                     : Factor w/ 9 levels "","AFGHANISTAN",..: 1 1 1 1 1 1 9 9 9 9 ...
##  $ EMPLOYER_PROVINCE                    : Factor w/ 342 levels "","(312) 768-6900",..: 1 1 1 1 1 1 237 1 1 1 ...
##  $ EMPLOYER_PHONE                       : Factor w/ 67761 levels "","(713)3606",..: 1 1 1 1 1 1 13793 21239 17237 28477 ...
##  $ EMPLOYER_PHONE_EXT                   : Factor w/ 1135 levels "","0","0000",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ NAICS_CODE                           : int  541810 541810 541810 541810 541810 541810 454111 541990 541820 334413 ...
##  $ AGENT_REPRESENTING_EMPLOYER          : Factor w/ 3 levels "","N","Y": 1 1 1 1 1 1 3 3 3 3 ...
##  $ AGENT_ATTORNEY_LAW_FIRM_BUSINESS_NAME: Factor w/ 6693 levels "","1101 15TH STREET NW",..: 1 1 1 1 1 1 1568 1130 6166 3844 ...
##  $ AGENT_ATTORNEY_ADDRESS1              : Factor w/ 6373 levels "","#607, 240 SEOCHODAERO",..: 1 1 1 1 1 1 3982 5744 2379 6128 ...
##  $ AGENT_ATTORNEY_ADDRESS2              : Factor w/ 2026 levels "","# 1","# 1101",..: 1 1 1 1 1 1 1731 1632 1604 1 ...
##  $ AGENT_ATTORNEY_CITY                  : Factor w/ 1012 levels "","7 HANOVER SQUARE",..: 1 1 1 1 1 1 496 824 997 579 ...
##  $ AGENT_ATTORNEY_STATE                 : Factor w/ 55 levels "","AK","AL","AR",..: 1 1 1 1 1 1 6 52 6 6 ...
##  $ AGENT_ATTORNEY_POSTAL_CODE           : Factor w/ 2541 levels "","0","00000",..: 1 1 1 1 1 1 2036 2437 2105 2364 ...
##  $ AGENT_ATTORNEY_COUNTRY               : Factor w/ 10 levels "","AUSTRALIA",..: 1 1 1 1 1 1 10 10 10 10 ...
##  $ AGENT_ATTORNEY_PROVINCE              : Factor w/ 163 levels "","(213) 384-1900",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ AGENT_ATTORNEY_PHONE                 : Factor w/ 6051 levels "","1036784000",..: 1 1 1 1 1 1 1681 365 5153 3254 ...
##  $ AGENT_ATTORNEY_PHONE_EXT             : Factor w/ 906 levels "","0","0228",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ STATE_OF_HIGHEST_COURT               : Factor w/ 956 levels "","/COURT OF APPEALS",..: 1 1 1 1 1 1 612 612 606 606 ...
##  $ NAME_OF_HIGHEST_STATE_COURT          : Factor w/ 55 levels "","ALABAMA","ALASKA",..: 1 1 1 1 1 1 6 52 6 6 ...
##  $ WORKSITE_WORKERS_1                   : int  NA NA NA NA NA NA NA 1 1 1 ...
##  $ SECONDARY_ENTITY_1                   : Factor w/ 3 levels "","N","Y": 1 1 1 1 1 1 1 2 2 2 ...
##  $ SECONDARY_ENTITY_BUSINESS_NAME_1     : Factor w/ 51552 levels "","-","- Alabama Department of Public Health State of AL",..: 1 1 1 1 1 1 1 30869 30871 40559 ...
##  $ WORKSITE_ADDRESS1_1                  : Factor w/ 179152 levels "","- 2 Morrissey Blvd,",..: 1 1 1 1 1 1 161416 165122 145693 115809 ...
##  $ WORKSITE_ADDRESS2_1                  : Factor w/ 28067 levels "","--","---",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_CITY_1                      : Factor w/ 12429 levels "","# 19820 Detroit MSA",..: 7519 7519 2002 1394 1394 527 3261 9610 6086 3889 ...
##  $ WORKSITE_COUNTY_1                    : Factor w/ 6246 levels "","--","-MARION",..: 3853 3853 1225 2762 3853 1993 3039 4867 3035 781 ...
##  $ WORKSITE_STATE_1                     : Factor w/ 114 levels "","AK","AL","ALABAMA",..: 76 76 31 76 76 22 10 11 11 11 ...
##  $ WORKSITE_POSTAL_CODE_1               : Factor w/ 15197 levels "","'08889"," 07645",..: 854 974 8887 1263 1263 4595 13097 13962 13030 14068 ...
##  $ WAGE_RATE_OF_PAY_FROM_1              : Factor w/ 68810 levels "","$1,500,000.00",..: 2765 682 3165 2090 2780 7050 8385 23299 68673 22646 ...
##  $ WAGE_RATE_OF_PAY_TO_1                : Factor w/ 39440 levels "","$0.00","$1,000.00",..: 1341 651 1572 1201 1437 85 8380 11623 39362 1 ...
##  $ WAGE_UNIT_OF_PAY_1                   : Factor w/ 6 levels "","Bi-Weekly",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ PREVAILING_WAGE_1                    : Factor w/ 30294 levels "","10","10.02",..: 1 1 1 1 1 1 29697 5630 29696 4945 ...
##  $ PW_UNIT_OF_PAY_1                     : Factor w/ 6 levels "","Bi-Weekly",..: 1 1 1 1 1 1 6 6 6 6 ...
##  $ PW_TRACKING_NUMBER_1                 : Factor w/ 1697 levels "","'N/A'","$21.26 ",..: 1 1 1 1 1 1 16 1 1 1 ...
##  $ PW_WAGE_LEVEL_1                      : Factor w/ 6 levels "","Level I","Level II",..: 1 1 1 1 1 1 3 4 3 4 ...
##  $ PW_OES_YEAR_1                        : Factor w/ 25 levels "","1","18","2001",..: 1 1 1 1 1 1 16 1 1 1 ...
##  $ PW_OTHER_SOURCE_1                    : Factor w/ 523 levels "","'OFLC ONLINE DATA CENTER'",..: 1 1 1 1 1 1 212 1 1 1 ...
##  $ PW_NON.OES_YEAR_1                    : int  NA NA NA NA NA NA NA 2019 2018 2018 ...
##  $ PW_SURVEY_PUBLISHER_1                : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ PW_SURVEY_NAME_1                     : Factor w/ 1198 levels "","2010-2013 CBA EXTENDED BY 2013-2019 MOAs",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_WORKERS_2                   : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ SECONDARY_ENTITY_2                   : Factor w/ 3 levels "","N","Y": 1 1 1 1 1 1 1 1 1 1 ...
##  $ SECONDARY_ENTITY_BUSINESS_NAME_2     : Factor w/ 7390 levels "","- None -",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_ADDRESS1_2                  : Factor w/ 28859 levels "","#1 Pratt Whitney Road",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_ADDRESS2_2                  : Factor w/ 6258 levels "","# 1-1510",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_CITY_2                      : Factor w/ 4730 levels "","# 19804 DETROIT METRO AREA",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_COUNTY_2                    : Factor w/ 2259 levels ""," Mecklenburg County",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_STATE_2                     : Factor w/ 109 levels "","AK","AL","ALABAMA",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_POSTAL_CODE_2               : Factor w/ 6396 levels "","00727","00802",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WAGE_RATE_OF_PAY_FROM_2              : num  NA NA NA NA NA NA 1e+05 NA NA NA ...
##  $ WAGE_RATE_OF_PAY_TO_2                : num  NA NA NA NA NA NA 120000 NA NA NA ...
##  $ WAGE_UNIT_OF_PAY_2                   : Factor w/ 6 levels "","Bi-Weekly",..: 1 1 1 1 1 1 6 1 1 1 ...
##  $ PREVAILING_WAGE_2                    : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ PW_UNIT_OF_PAY_2                     : Factor w/ 6 levels "","Bi-Weekly",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ PW_TRACKING_NUMBER_2                 : Factor w/ 240 levels "","'N/A'","N/A",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ PW_WAGE_LEVEL_2                      : Factor w/ 6 levels "","Level I","Level II",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ PW_OES_YEAR_2                        : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ PW_OTHER_SOURCE_2                    : Factor w/ 122 levels "","2016-2017 AAMC SURVEY OF RESIDENT/FELLOW STIPENDS",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ PW_NON.OES_YEAR_2                    : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ PW_SURVEY_PUBLISHER_2                : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ PW_SURVEY_NAME_2                     : Factor w/ 269 levels "","2016 CNMI PREVAILING WAGE & WORK FORCE ASSESMENTS",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_WORKERS_3                   : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ SECONDARY_ENTITY_3                   : Factor w/ 3 levels "","N","Y": 1 1 1 1 1 1 1 1 1 1 ...
##  $ SECONDARY_ENTITY_BUSINESS_NAME_3     : Factor w/ 2328 levels "","1","103 - North Babylon Day Treatment Program",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_ADDRESS1_3                  : Factor w/ 8263 levels "","#1 Jefferson Barracks Dr.",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_ADDRESS2_3                  : Factor w/ 1774 levels "","# 108","# 201",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_CITY_3                      : Factor w/ 2633 levels "","#19820 Ann Arbor MSA",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_COUNTY_3                    : Factor w/ 1318 levels "","11716","92264",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_STATE_3                     : Factor w/ 100 levels "","AL","ALABAMA",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WORKSITE_POSTAL_CODE_3               : Factor w/ 3607 levels "","`9`002","01020",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WAGE_RATE_OF_PAY_FROM_3              : num  NA NA NA NA NA NA 1e+05 NA NA NA ...
##  $ WAGE_RATE_OF_PAY_TO_3                : num  NA NA NA NA NA NA 120000 NA NA NA ...
##  $ WAGE_UNIT_OF_PAY_3                   : Factor w/ 6 levels "","Bi-Weekly",..: 1 1 1 1 1 1 6 1 1 1 ...
##  $ PREVAILING_WAGE_3                    : Factor w/ 4808 levels "","100","100.00",..: 1 1 1 1 1 1 1 1 1 1 ...
##   [list output truncated]

2.4 Pemeriksaan data hilang

colSums(is.na(labour))

##                        ï..CASE_NUMBER                           CASE_STATUS 
##                                     0                                     0 
##                        CASE_SUBMITTED                         DECISION_DATE 
##                                     0                                     0 
##                    ORIGINAL_CERT_DATE                            VISA_CLASS 
##                                     0                                     0 
##                             JOB_TITLE                              SOC_CODE 
##                                     0                                     0 
##                             SOC_TITLE                    FULL_TIME_POSITION 
##                                     0                                     0 
##       PERIOD_OF_EMPLOYMENT_START_DATE         PERIOD_OF_EMPLOYMENT_END_DATE 
##                                     0                                     0 
##                TOTAL_WORKER_POSITIONS                        NEW_EMPLOYMENT 
##                                383933                                     0 
##                  CONTINUED_EMPLOYMENT            CHANGE_PREVIOUS_EMPLOYMENT 
##                                     0                                383931 
##             NEW_CONCURRENT_EMPLOYMENT                       CHANGE_EMPLOYER 
##                                383932                                383931 
##                      AMENDED_PETITION                         EMPLOYER_NAME 
##                                383931                                     0 
##                 EMPLOYER_BUSINESS_DBA                     EMPLOYER_ADDRESS1 
##                                  1819                                     0 
##                     EMPLOYER_ADDRESS2                         EMPLOYER_CITY 
##                                   343                                     1 
##                        EMPLOYER_STATE                  EMPLOYER_POSTAL_CODE 
##                                     0                                     1 
##                      EMPLOYER_COUNTRY                     EMPLOYER_PROVINCE 
##                                     0                                   673 
##                        EMPLOYER_PHONE                    EMPLOYER_PHONE_EXT 
##                                     0                                     4 
##                            NAICS_CODE           AGENT_REPRESENTING_EMPLOYER 
##                                383933                                     0 
## AGENT_ATTORNEY_LAW_FIRM_BUSINESS_NAME               AGENT_ATTORNEY_ADDRESS1 
##                                     2                                     0 
##               AGENT_ATTORNEY_ADDRESS2                   AGENT_ATTORNEY_CITY 
##                                     7                                     0 
##                  AGENT_ATTORNEY_STATE            AGENT_ATTORNEY_POSTAL_CODE 
##                                     0                                     0 
##                AGENT_ATTORNEY_COUNTRY               AGENT_ATTORNEY_PROVINCE 
##                                     0                                   160 
##                  AGENT_ATTORNEY_PHONE              AGENT_ATTORNEY_PHONE_EXT 
##                                     0                                     0 
##                STATE_OF_HIGHEST_COURT           NAME_OF_HIGHEST_STATE_COURT 
##                                     1                                     0 
##                    WORKSITE_WORKERS_1                    SECONDARY_ENTITY_1 
##                                467647                                     0 
##      SECONDARY_ENTITY_BUSINESS_NAME_1                   WORKSITE_ADDRESS1_1 
##                                   285                                     8 
##                   WORKSITE_ADDRESS2_1                       WORKSITE_CITY_1 
##                                   131                                     8 
##                     WORKSITE_COUNTY_1                      WORKSITE_STATE_1 
##                                    12                                     0 
##                WORKSITE_POSTAL_CODE_1               WAGE_RATE_OF_PAY_FROM_1 
##                                     8                                     0 
##                 WAGE_RATE_OF_PAY_TO_1                    WAGE_UNIT_OF_PAY_1 
##                                     0                                     0 
##                     PREVAILING_WAGE_1                      PW_UNIT_OF_PAY_1 
##                                     0                                     0 
##                  PW_TRACKING_NUMBER_1                       PW_WAGE_LEVEL_1 
##                                    62                                     0 
##                         PW_OES_YEAR_1                     PW_OTHER_SOURCE_1 
##                                     0                                     0 
##                     PW_NON.OES_YEAR_1                 PW_SURVEY_PUBLISHER_1 
##                                494501                               1023620 
##                      PW_SURVEY_NAME_1                    WORKSITE_WORKERS_2 
##                                    13                                997156 
##                    SECONDARY_ENTITY_2      SECONDARY_ENTITY_BUSINESS_NAME_2 
##                                     0                                   112 
##                   WORKSITE_ADDRESS1_2                   WORKSITE_ADDRESS2_2 
##                                     0                                     2 
##                       WORKSITE_CITY_2                     WORKSITE_COUNTY_2 
##                                     0                                     1 
##                      WORKSITE_STATE_2                WORKSITE_POSTAL_CODE_2 
##                                     0                                     0 
##               WAGE_RATE_OF_PAY_FROM_2                 WAGE_RATE_OF_PAY_TO_2 
##                                946736                               1007877 
##                    WAGE_UNIT_OF_PAY_2                     PREVAILING_WAGE_2 
##                                     0                                990606 
##                      PW_UNIT_OF_PAY_2                  PW_TRACKING_NUMBER_2 
##                                     0                                    15 
##                       PW_WAGE_LEVEL_2                         PW_OES_YEAR_2 
##                                     0                               1038461 
##                     PW_OTHER_SOURCE_2                     PW_NON.OES_YEAR_2 
##                                     0                               1001000 
##                 PW_SURVEY_PUBLISHER_2                      PW_SURVEY_NAME_2 
##                               1045011                                     1 
##                    WORKSITE_WORKERS_3                    SECONDARY_ENTITY_3 
##                               1036312                                     0 
##      SECONDARY_ENTITY_BUSINESS_NAME_3                   WORKSITE_ADDRESS1_3 
##                                    56                                     0 
##                   WORKSITE_ADDRESS2_3                       WORKSITE_CITY_3 
##                                     0                                     0 
##                     WORKSITE_COUNTY_3                      WORKSITE_STATE_3 
##                                     0                                     0 
##                WORKSITE_POSTAL_CODE_3               WAGE_RATE_OF_PAY_FROM_3 
##                                     0                                985892 
##                 WAGE_RATE_OF_PAY_TO_3                    WAGE_UNIT_OF_PAY_3 
##                               1030924                                     0 
##                     PREVAILING_WAGE_3                      PW_UNIT_OF_PAY_3 
##                                     0                                     0 
##                  PW_TRACKING_NUMBER_3                       PW_WAGE_LEVEL_3 
##                                     1                                     0 
##                         PW_OES_YEAR_3                     PW_OTHER_SOURCE_3 
##                               1045337                                     0 
##                     PW_NON.OES_YEAR_3                 PW_SURVEY_PUBLISHER_3 
##                               1037713                               1047265 
##                      PW_SURVEY_NAME_3                    WORKSITE_WORKERS_4 
##                                     0                               1044448 
##                    SECONDARY_ENTITY_4      SECONDARY_ENTITY_BUSINESS_NAME_4 
##                                     0                                     0 
##                   WORKSITE_ADDRESS1_4                   WORKSITE_ADDRESS2_4 
##                                     0                                     0 
##                       WORKSITE_CITY_4                     WORKSITE_COUNTY_4 
##                                     0                                     0 
##                      WORKSITE_STATE_4                WORKSITE_POSTAL_CODE_4 
##                                     0                                     0 
##               WAGE_RATE_OF_PAY_FROM_4                 WAGE_RATE_OF_PAY_TO_4 
##                               1044448                               1046479 
##                    WAGE_UNIT_OF_PAY_4                     PREVAILING_WAGE_4 
##                               1048547                               1044448 
##                      PW_UNIT_OF_PAY_4                  PW_TRACKING_NUMBER_4 
##                               1048547                                     0 
##                       PW_WAGE_LEVEL_4                         PW_OES_YEAR_4 
##                                     0                               1047860 
##                     PW_OTHER_SOURCE_4                     PW_NON.OES_YEAR_4 
##                                     0                               1045213 
##                 PW_SURVEY_PUBLISHER_4                      PW_SURVEY_NAME_4 
##                               1047860                                     0 
##                    WORKSITE_WORKERS_5                    SECONDARY_ENTITY_5 
##                               1046503                                     0 
##      SECONDARY_ENTITY_BUSINESS_NAME_5                   WORKSITE_ADDRESS1_5 
##                                     0                                     0 
##                   WORKSITE_ADDRESS2_5                       WORKSITE_CITY_5 
##                                     0                                     0 
##                     WORKSITE_COUNTY_5                      WORKSITE_STATE_5 
##                                     0                                     0 
##                WORKSITE_POSTAL_CODE_5               WAGE_RATE_OF_PAY_FROM_5 
##                                     0                               1046503 
##                 WAGE_RATE_OF_PAY_TO_5                    WAGE_UNIT_OF_PAY_5 
##                               1047561                               1048547 
##                     PREVAILING_WAGE_5                      PW_UNIT_OF_PAY_5 
##                               1046503                               1048547 
##                  PW_TRACKING_NUMBER_5                       PW_WAGE_LEVEL_5 
##                                     0                                     0 
##                         PW_OES_YEAR_5                     PW_OTHER_SOURCE_5 
##                               1048240                                     0 
##                     PW_NON.OES_YEAR_5                 PW_SURVEY_PUBLISHER_5 
##                               1046855                               1048240 
##                      PW_SURVEY_NAME_5                    WORKSITE_WORKERS_6 
##                                     0                               1047340 
##                    SECONDARY_ENTITY_6      SECONDARY_ENTITY_BUSINESS_NAME_6 
##                                     0                                     0 
##                   WORKSITE_ADDRESS1_6                   WORKSITE_ADDRESS2_6 
##                                     0                                     0 
##                       WORKSITE_CITY_6                     WORKSITE_COUNTY_6 
##                                     0                                     0 
##                      WORKSITE_STATE_6                WORKSITE_POSTAL_CODE_6 
##                                     0                                     0 
##               WAGE_RATE_OF_PAY_FROM_6                 WAGE_RATE_OF_PAY_TO_6 
##                               1047340                               1047943 
##                    WAGE_UNIT_OF_PAY_6                     PREVAILING_WAGE_6 
##                                     0                               1047340 
##                      PW_UNIT_OF_PAY_6                  PW_TRACKING_NUMBER_6 
##                                     0                                     0 
##                       PW_WAGE_LEVEL_6                         PW_OES_YEAR_6 
##                                     0                               1048352 
##                     PW_OTHER_SOURCE_6                     PW_NON.OES_YEAR_6 
##                                     0                               1047559 
##                 PW_SURVEY_PUBLISHER_6                      PW_SURVEY_NAME_6 
##                               1048352                                     0 
##                    WORKSITE_WORKERS_7                    SECONDARY_ENTITY_7 
##                               1047703                                     0 
##      SECONDARY_ENTITY_BUSINESS_NAME_7                   WORKSITE_ADDRESS1_7 
##                                     0                                     0 
##                   WORKSITE_ADDRESS2_7                       WORKSITE_CITY_7 
##                                     0                                     0 
##                     WORKSITE_COUNTY_7                      WORKSITE_STATE_7 
##                                     0                                     0 
##                WORKSITE_POSTAL_CODE_7               WAGE_RATE_OF_PAY_FROM_7 
##                                     0                               1047703 
##                 WAGE_RATE_OF_PAY_TO_7                    WAGE_UNIT_OF_PAY_7 
##                               1048114                               1048547 
##                     PREVAILING_WAGE_7                      PW_UNIT_OF_PAY_7 
##                               1047703                               1048547 
##                  PW_TRACKING_NUMBER_7                       PW_WAGE_LEVEL_7 
##                                     0                                     0 
##                         PW_OES_YEAR_7                     PW_OTHER_SOURCE_7 
##                               1048406                                     0 
##                     PW_NON.OES_YEAR_7                 PW_SURVEY_PUBLISHER_7 
##                               1047858                               1048406 
##                      PW_SURVEY_NAME_7                    WORKSITE_WORKERS_8 
##                                     0                               1047960 
##                    SECONDARY_ENTITY_8      SECONDARY_ENTITY_BUSINESS_NAME_8 
##                                     0                                     0 
##                   WORKSITE_ADDRESS1_8                   WORKSITE_ADDRESS2_8 
##                                     0                                     0 
##                       WORKSITE_CITY_8                     WORKSITE_COUNTY_8 
##                                     0                                     0 
##                      WORKSITE_STATE_8                WORKSITE_POSTAL_CODE_8 
##                                     0                                     0 
##               WAGE_RATE_OF_PAY_FROM_8                 WAGE_RATE_OF_PAY_TO_8 
##                               1047960                               1048258 
##                    WAGE_UNIT_OF_PAY_8                     PREVAILING_WAGE_8 
##                               1048547                               1047960 
##                      PW_UNIT_OF_PAY_8                  PW_TRACKING_NUMBER_8 
##                               1048547                                     0 
##                       PW_WAGE_LEVEL_8                         PW_OES_YEAR_8 
##                                     0                               1048437 
##                     PW_OTHER_SOURCE_8                     PW_NON.OES_YEAR_8 
##                                     0                               1048081 
##                 PW_SURVEY_PUBLISHER_8                      PW_SURVEY_NAME_8 
##                               1048437                                     0 
##                    WORKSITE_WORKERS_9                    SECONDARY_ENTITY_9 
##                               1048144                                     0 
##      SECONDARY_ENTITY_BUSINESS_NAME_9                   WORKSITE_ADDRESS1_9 
##                                     0                                     0 
##                   WORKSITE_ADDRESS2_9                       WORKSITE_CITY_9 
##                                     0                                     0 
##                     WORKSITE_COUNTY_9                      WORKSITE_STATE_9 
##                                     0                                     0 
##                WORKSITE_POSTAL_CODE_9               WAGE_RATE_OF_PAY_FROM_9 
##                                     0                               1048144 
##                 WAGE_RATE_OF_PAY_TO_9                    WAGE_UNIT_OF_PAY_9 
##                               1048357                                     0 
##                     PREVAILING_WAGE_9                      PW_UNIT_OF_PAY_9 
##                               1048144                                     0 
##                  PW_TRACKING_NUMBER_9                       PW_WAGE_LEVEL_9 
##                                     0                                     0 
##                         PW_OES_YEAR_9                     PW_OTHER_SOURCE_9 
##                               1048454                                     0 
##                     PW_NON.OES_YEAR_9                 PW_SURVEY_PUBLISHER_9 
##                               1048244                               1048454 
##                      PW_SURVEY_NAME_9                   WORKSITE_WORKERS_10 
##                                     0                               1048259 
##                   SECONDARY_ENTITY_10     SECONDARY_ENTITY_BUSINESS_NAME_10 
##                                     0                                     0 
##                  WORKSITE_ADDRESS1_10                  WORKSITE_ADDRESS2_10 
##                                     0                                     0 
##                      WORKSITE_CITY_10                    WORKSITE_COUNTY_10 
##                                     0                                     0 
##                     WORKSITE_STATE_10               WORKSITE_POSTAL_CODE_10 
##                                     0                               1048259 
##              WAGE_RATE_OF_PAY_FROM_10                WAGE_RATE_OF_PAY_TO_10 
##                               1048259                               1048427 
##                   WAGE_UNIT_OF_PAY_10                    PREVAILING_WAGE_10 
##                                     0                               1048259 
##                     PW_UNIT_OF_PAY_10                 PW_TRACKING_NUMBER_10 
##                                     0                                     0 
##                      PW_WAGE_LEVEL_10                        PW_OES_YEAR_10 
##                                     0                               1048468 
##                    PW_OTHER_SOURCE_10                    PW_NON.OES_YEAR_10 
##                                     0                               1048341 
##                PW_SURVEY_PUBLISHER_10                     PW_SURVEY_NAME_10 
##                               1048468                                     0 
##                        H.1B_DEPENDENT                      WILLFUL_VIOLATOR 
##                                     0                                     0 
##                           SUPPORT_H1B                       STATUTORY_BASIS 
##                                 47863                                     0 
##                     MASTERS_EXEMPTION                     PUBLIC_DISCLOSURE 
##                                     0                                     0

Terlihat banyak sekali data yang hilang

2.5 Deskripsi Variabel

Variabel dari data labour adalah:

• CASE_NUMBER: Pengidentifikasi unik ditetapkan ke setiap aplikasi yang dikirimkan untuk diproses.

• CASE_STATUS: Status terkait dengan keputusan penting terakhir. Nilai yang valid termasuk “Bersertifikat”, “Disertifikasi-Dibatalkan”, “Ditolak”, dan “Dibatalkan”.

• CASE_SUBMITTED: Tanggal dan waktu aplikasi diajukan untuk diproses.

• DECISION_DATE: Tanggal di mana keputusan penting terakhir terjadi.

• ORIGINAL_CERT_DATE: Tanggal Sertifikasi Asli untuk aplikasi “Dibatalkan Tersertifikasi”.

• VISA_CLASS:Menunjukkan jenis aplikasi sementara yang dikirimkan untuk diproses. Nilainya meliputi H-1B, E-3 Australian, H-1B1 Chile, dan H-1B1 Singapura.

• JOB_TITLE: Judul pekerjaan.

• SOC_CODE: Kode pekerjaan yang terkait dengan pekerjaan yang diminta sertifikasi, sebagaimana diklasifikasikan oleh Klasifikasi Pekerjaan Standar (SOC) Sistem.

• SOC_TITLE: Nama pekerjaan yang dikaitkan dengan SOC CODE.

• FULL_TIME_POSITION: Y = Posisi Penuh Waktu; N = Posisi Paruh Waktu.

• PERIOD_OF_EMPLOYMENT_START_DATE Tanggal awal masa kerja untuk Aplikasi Tesertifikasi.

• PERIOD_OF_EMPLOYMENT_END_DATE Tanggal akhir masa kerja untuk Aplikasi Tesertifikasi.

• TOTAL_WORKER_POSITIONS Jumlah total pekerja asing yang diminta untuk sertifikasi.

• NEW_EMPLOYMENT Menunjukkan pekerja yang diminta akan mulai bekerja untuk pemberi kerja baru, sebagaimana didefinisikan oleh USCIS Formulir I-129.

• CONTINUED_EMPLOYMENT Menunjukkan pekerja yang diminta akan melanjutkan pekerjaan dengan yang sama pemberi kerja, sebagaimana didefinisikan oleh USCIS Formulir I-129.

• CHANGE_PREVIOUS_EMPLOYMENT: Menunjukkan pekerja yang diminta akan melanjutkan pekerjaan dengan yang sama pemberi kerja tanpa perubahan material pada tugas pekerjaan, sebagaimana didefinisikan oleh USCIS Formulir I-129.

• NEW_CONCURRENT_EMPLOYMENT: Menunjukkan pekerja yang diminta akan memulai pekerjaan dengan tambahan pemberi kerja, sebagaimana didefinisikan oleh USCIS Formulir I-129.

• CHANGE_EMPLOYER: Menunjukkan pekerja yang diminta akan mulai bekerja untuk majikan baru, menggunakan klasifikasi yang sama saat ini, seperti yang didefinisikan oleh Formulir USCIS I-129.

• AMENDED_PETITION: Menunjukkan pekerja yang diminta akan melanjutkan pekerjaan dengan yang sama pemberi kerja dengan perubahan material untuk tugas pekerjaan, sebagaimana didefinisikan dalam Formulir USCIS I-129.

• EMPLOYER_NAME: Nama majikan yang mengajukan aplikasi kondisi ketenagakerjaan.

• EMPLOYER_BUSINESS_DBA: Nama Dagang atau nama dba majikan yang menyerahkan syarat ketenagakerjaan aplikasi, jika ada.

• EMPLOYER_ADDRESS1,EMPLOYER_ADDRESS2,EMPLOYER_CITY,EMPLOYER_STATE,EMPLOYER_POSTAL_CODE,EMPLOYER_COUNTRY,EMPLOYER_PROVINCE,EMPLOYER_PHONE, EMPLOYER_PHONE_EXT: Informasi kontak Majikan yang meminta tenaga kerja temporer sertifikasi.

• NAICS_CODE:Kode industri yang terkait dengan pemberi kerja yang meminta tenaga kerja permanen kondisi, sebagaimana diklasifikasikan oleh Klasifikasi Industri Amerika Utara Sistem (NAICS).

• AGENT_REPRESENTING_EMPLOYER: Y = Perusahaan diwakili oleh Agen atau Pengacara; N = Majikan adalah tidak diwakili oleh Agen atau Pengacara.

• AGENT_ATTORNEY_LAW_FIRM_BUSINESS_NAMEN Nama Agen / Kantor Pengacara / Nama Bisnis yang mewakili Pemberi kerja meminta sertifikasi tenaga kerja sementara.

  • AGENT_ATTORNEY_ADDRESS1,AGENT_ATTORNEY_ADDRESS2,AGENT_ATTORNEY_CITY,AGENT_ATTORNEY_STATE,AGENT_ATTORNEY_POSTAL_CODE,AGENT_ATTORNEY_COUNTRY,AGENT_ATTORNEY_PROVINCE,AGENT_ATTORNEY_PHONE,AGENT_ATTORNEY_PHONE_EXT,STATE_OF_HIGHEST_COURT,NAME_OF_HIGHEST_STATE_COURT: Informasi kontak Agen / Pengacara yang mewakili Majikan meminta sertifikasi tenaga kerja sementara.

  • WORKSITE_WORKERS_1: Jumlah pekerja yang ditempatkan di lokasi Tempat Kerja Utama.

  • SECONDARY_ENTITY_1: Y = Pekerja akan ditempatkan dengan entitas sekunder; N = Pekerja tidak mau ditempatkan dengan entitas sekunder.

  • SECONDARY_ENTITY_BUSINESS_NAME_1: Nama entitas sekunder tempat pekerja akan ditempatkan (jika berlaku).

  • WORKSITE_ADDRESS1_1, WORKSITE_ADDRESS2_1, WORKSITE_CITY_1, WORKSITE_COUNTY_1, WORKSITE_STATE_1, WORKSITE_POSTAL_CODE_1: Informasi Geografis untuk Lokasi Tempat Kerja Utama.

  • WAGE_RATE_OF_PAY_FROM_1: Tarif upah yang diusulkan oleh perusahaan untuk Lokasi Tempat Kerja Utama.

  • WAGE_RATE_OF_PAY_TO_1: Tingkat upah maksimum yang diusulkan untuk Lokasi Tempat Kerja Utama.

  • WAGE_UNIT_OF_PAY_1: Satuan pembayaran untuk Lokasi Tempat Kerja Utama. Nilai yang valid termasuk “Jam”, “Minggu”, “Dua Mingguan”, “Bulan”, atau “Tahun”.

  • PREVAILING_WAGE_1 Upah yang Berlaku untuk pekerjaan yang diminta untuk Tempat Kerja Utama Lokasi.

  • PW_UNIT_OF_PAY_1: Satuan Pembayaran Upah yang Berlaku untuk Lokasi Tempat Kerja Utama. Sah nilai mencakup “Harian (DAI)”, “Per Jam (HR)”, “Dua mingguan (BI)”, “Mingguan (WK),”" Bulanan (MTH), “dan” Tahunan (YR) ".

  • PW_TRACKING_NUMBER_1: Nomor pelacakan Penetapan Upah yang Berlaku, sebagaimana disediakan pada Formulir ETA-9141, jika Pemberi Kerja menerima Upah yang Berlaku yang dikeluarkan oleh Departemen Tenaga Kerja untuk Lokasi Tempat Kerja Utama.

  • PW_WAGE_LEVEL_1:Tingkat Upah OES, jika Pemberi Kerja menerima Upah yang Berlaku dari OES Program untuk Lokasi Tempat Kerja Utama. Variabel termasuk “I”, “II”,“III”, “IV” atau “T / A.”

  • PW_OES_YEAR_1 Tahun Upah Yang Berlaku OES, jika Majikan menerima Berlaku Upah dari Program OES untuk Lokasi Tempat Kerja Utama.

  • PW_OTHER_SOURCE_1: Sumber Upah Yang Berlaku Non-OES, jika Majikan menerima Yang Berlaku Upah dari sumber sah lain (selain OES) untuk Pratama Lokasi Tempat Kerja. Variabel termasuk “CBA”, “DBA”, “SCA” atau “Other”.

  • PW_NON-OES_YEAR_1: Tahun Upah Yang Berlaku Non-OES, jika Pemberi Kerja menerimanya Upah yang Berlaku dari Sumber Non-OES untuk Lokasi Kerja Utama Lokasi.

  • PW_SURVEY_PUBLISHER_1: Nama produsen atau penerbit survei, jika Pemberi kerja menerima Upah yang Berlaku dari “Survei Lain / PW” untuk Lokasi Kerja Utama Lokasi.

  • PW_SURVEY_NAME_1: Nama survei Upah yang Berlaku, jika Majikan menerima Berlaku Upah dari “Survei Lainnya / PW” untuk Lokasi Kerja Utama.

  • WORKSITE_WORKERS_2: Jumlah pekerja yang ditempatkan di lokasi Tempat Kerja Kedua (jika berlaku).

  • SECONDARY_ENTITY_2: Y = Pekerja akan ditempatkan dengan entitas sekunder untuk Kedua Lokasi Tempat Kerja yang Dilaporkan; N = Pekerja tidak akan ditempatkan dengan entitas sekunder.

  • SECONDARY_ENTITY_BUSINESS_NAME_2: Nama entitas sekunder kedua tempat pekerja akan berada ditempatkan (jika ada).

  • WORKSITE_ADDRESS1_2, WORKSITE_ADDRESS2_2, WORKSITE_CITY_2, WORKSITE_COUNTY_2, WORKSITE_STATE_2, WORKSITE_POSTAL_CODE_2: Informasi Geografis untuk Lokasi Tempat Kerja yang Dilaporkan Kedua (jika berlaku).

  • WAGE_RATE_OF_PAY_FROM_2, WAGE_RATE_OF_PAY_TO_2, WAGE_UNIT_OF_PAY_2: Informasi gaji yang diusulkan pemberi kerja untuk Lokasi Kerja Kedua yang Dilaporkan Lokasi (jika ada).

  • PREVAILING_WAGE_2, PW_UNIT_OF_PAY_2, PW_TRACKING_NUMBER_2, PW_WAGE_LEVEL_2, PW_OES_YEAR_2, PW_OTHER_SOURCE_2, PW_NON-OES_YEAR_2, PW_SURVEY_PUBLISHER_2, PW_SURVEY_NAME_2: Informasi Upah yang Berlaku untuk Lokasi Tempat Kerja Kedua yang Dilaporkan (jika berlaku).

  • WORKSITE_WORKERS_3: Jumlah pekerja yang ditempatkan di lokasi Tempat Kerja Ketiga (jika ada).

  • SECONDARY_ENTITY_3: Y = Pekerja akan ditempatkan dengan entitas sekunder untuk Ketiga Lokasi Tempat Kerja yang Dilaporkan; N = Pekerja tidak akan ditempatkan dengan entitas sekunder.

  • SECONDARY_ENTITY_BUSINESS_NAME_3: Nama entitas sekunder ketiga tempat pekerja akan ditempatkan (jika ada).

  • WORKSITE_ADDRESS1_3, WORKSITE_ADDRESS2_3, WORKSITE_CITY_3, WORKSITE_COUNTY_3, WORKSITE_STATE_3, WORKSITE_POSTAL_CODE_3: Informasi Geografis untuk Lokasi Tempat Kerja yang Dilaporkan Ketiga (jika berlaku).

  • WAGE_RATE_OF_PAY_FROM_3, WAGE_RATE_OF_PAY_TO_3, WAGE_UNIT_OF_PAY_3: Informasi gaji yang diusulkan pemberi kerja untuk Tempat Kerja yang Dilaporkan Ketiga Lokasi (jika ada).

  • PREVAILING_WAGE_3, PW_UNIT_OF_PAY_3, PW_TRACKING_NUMBER_3, PW_WAGE_LEVEL_3, PW_OES_YEAR_3, PW_OTHER_SOURCE_3, PW_NON-OES_YEAR_3, PW_SURVEY_PUBLISHER_3, PW_SURVEY_NAME_3: Informasi Upah yang Berlaku untuk Lokasi Tempat Kerja Ketiga yang Dilaporkan (jika berlaku).

  • WORKSITE_WORKERS_4: Jumlah pekerja yang ditempatkan di lokasi Tempat Kerja Keempat (jika berlaku).

  • SECONDARY_ENTITY_4: Y = Pekerja akan ditempatkan dengan entitas sekunder untuk Keempat Lokasi Tempat Kerja yang Dilaporkan; N = Pekerja tidak akan ditempatkan dengan entitas sekunder.

  • SECONDARY_ENTITY_BUSINESS_NAME_4 Nama entitas sekunder keempat tempat pekerja akan ditempatkan (jika ada).

  • WORKSITE_ADDRESS1_4, WORKSITE_ADDRESS2_4, WORKSITE_CITY_4, WORKSITE_COUNTY_4, WORKSITE_STATE_4, WORKSITE_POSTAL_CODE_4: Informasi Geografis untuk Lokasi Tempat Kerja yang Dilaporkan Keempat (jika berlaku).

  • WAGE_RATE_OF_PAY_FROM_4, WAGE_RATE_OF_PAY_TO_4, WAGE_UNIT_OF_PAY_4: Informasi gaji yang diusulkan pemberi kerja untuk Tempat Kerja yang Dilaporkan Keempat Lokasi (jika ada).

  • PREVAILING_WAGE_4, PW_UNIT_OF_PAY_4, PW_TRACKING_NUMBER_4, PW_WAGE_LEVEL_4, PW_OES_YEAR_4, PW_OTHER_SOURCE_4, PW_NON-OES_YEAR_4, PW_SURVEY_PUBLISHER_4, PW_SURVEY_NAME_4: Informasi Upah yang Berlaku untuk Lokasi Tempat Kerja Keempat yang Dilaporkan (jika berlaku)

  • WORKSITE_WORKERS_5: Jumlah pekerja yang ditempatkan di lokasi Tempat Kerja Kelima (jika ada).

  • SECONDARY_ENTITY_5: Y = Pekerja akan ditempatkan dengan entitas sekunder untuk Kelima Lokasi Tempat Kerja yang Dilaporkan; N = Pekerja tidak akan ditempatkan dengan entitas sekunder.

  • SECONDARY_ENTITY_BUSINESS_NAME_5: Nama entitas sekunder kelima tempat pekerja akan ditempatkan (jika berlaku).

  • WORKSITE_ADDRESS1_5, WORKSITE_ADDRESS2_5, WORKSITE_CITY_5, WORKSITE_COUNTY_5, WORKSITE_STATE_5, WORKSITE_POSTAL_CODE_5: Informasi Geografis untuk Lokasi Tempat Kerja yang Dilaporkan Kelima (jika berlaku).

  • WAGE_RATE_OF_PAY_FROM_5, WAGE_RATE_OF_PAY_TO_5, WAGE_UNIT_OF_PAY_5: Informasi gaji yang diusulkan pemberi kerja untuk Lokasi Kerja yang Dilaporkan Kelima Lokasi (jika ada).

  • PREVAILING_WAGE_5, PW_UNIT_OF_PAY_5,PW_TRACKING_NUMBER_5, PW_WAGE_LEVEL_5, PW_OES_YEAR_5, PW_OTHER_SOURCE_5, PW_NON-OES_YEAR_5, PW_SURVEY_PUBLISHER_5, PW_SURVEY_NAME_5 : Informasi Upah yang Berlaku untuk Lokasi Tempat Kerja yang Dilaporkan Kelima (jika berlaku).

  • WORKSITE_WORKERS_6: Jumlah pekerja yang ditempatkan di lokasi Lokasi Kerja Keenam (jika ada).

  • SECONDARY_ENTITY_6: Y = Pekerja akan ditempatkan dengan entitas sekunder untuk Keenam Lokasi Tempat Kerja yang Dilaporkan; N = Pekerja tidak akan ditempatkan dengan entitas sekunder.

  • SECONDARY_ENTITY_BUSINESS_NAME_6: Nama entitas sekunder keenam tempat pekerja akan ditempatkan (jika ada).

  • WORKSITE_ADDRESS1_6, WORKSITE_ADDRESS2_6, WORKSITE_CITY_6, WORKSITE_COUNTY_6, WORKSITE_STATE_6, WORKSITE_POSTAL_CODE_6: Informasi Geografis untuk Lokasi Tempat Kerja yang Dilaporkan Keenam (jika berlaku).

  • WAGE_RATE_OF_PAY_FROM_6, WAGE_RATE_OF_PAY_TO_6, WAGE_UNIT_OF_PAY_6: Informasi gaji yang diusulkan oleh Perusahaan untuk enam Tempat Kerja yang Dilaporkan di Lokasi(jika ada)

  • PREVAILING_WAGE_6, PW_UNIT_OF_PAY_6, PW_TRACKING_NUMBER_6, PW_WAGE_LEVEL_6, PW_OES_YEAR_6, PW_OTHER_SOURCE_6, PW_NON-OES_YEAR_6, PW_SURVEY_PUBLISHER_6, PW_SURVEY_NAME_6: Informasi Upah yang Berlaku untuk Lokasi Tempat Kerja Keenam yang Dilaporkan (jika berlaku).

  • WORKSITE_WORKERS_7: Jumlah pekerja yang ditempatkan di lokasi Tempat Kerja Ketujuh (jika berlaku).

  • SECONDARY_ENTITY_7: Y = Pekerja akan ditempatkan dengan entitas sekunder untuk Ketujuh Lokasi Tempat Kerja yang Dilaporkan; N = Pekerja tidak akan ditempatkan dengan entitas sekunder.

  • SECONDARY_ENTITY_BUSINESS_NAME_7 Nama entitas sekunder ketujuh tempat pekerja akan berada ditempatkan (jika ada).

  • WORKSITE_ADDRESS1_7, WORKSITE_ADDRESS2_7, WORKSITE_CITY_7, WORKSITE_COUNTY_7, WORKSITE_STATE_7, WORKSITE_POSTAL_CODE_7: Informasi Geografis untuk Lokasi Tempat Kerja yang Dilaporkan Ketujuh (jika berlaku).

  • WAGE_RATE_OF_PAY_FROM_7, WAGE_RATE_OF_PAY_TO_7, WAGE_UNIT_OF_PAY_7: Informasi gaji yang diusulkan pemberi kerja untuk Lokasi Kerja yang Dilaporkan Ketujuh Lokasi (jika ada).

  • PREVAILING_WAGE_7, PW_UNIT_OF_PAY_7, PW_TRACKING_NUMBER_7, PW_WAGE_LEVEL_7, PW_OES_YEAR_7, PW_OTHER_SOURCE_7, PW_NON-OES_YEAR_7, PW_SURVEY_PUBLISHER_7, PW_SURVEY_NAME_7 : Informasi Upah yang Berlaku untuk Lokasi Kerja yang Dilaporkan Ketujuh (jika berlaku).

  • WORKSITE_WORKERS_8: Jumlah pekerja yang ditempatkan di lokasi Tempat Kerja Kedelapan (jika berlaku).

  • SECONDARY_ENTITY_8: Y = Pekerja akan ditempatkan dengan entitas sekunder untuk Kedelapan Lokasi Tempat Kerja yang Dilaporkan; N = Pekerja tidak akan ditempatkan dengan entitas sekunder.

  • SECONDARY_ENTITY_BUSINESS_NAME_8: Nama entitas sekunder kedelapan tempat pekerja akan ditempatkan (jika ada).

  • WORKSITE_ADDRESS1_8, WORKSITE_ADDRESS2_8, WORKSITE_CITY_8, WORKSITE_COUNTY_8,WORKSITE_STATE_8,WORKSITE_POSTAL_CODE_8: Informasi Geografis untuk Lokasi Tempat Kerja yang Dilaporkan Kedelapan (jika berlaku).

  • WAGE_RATE_OF_PAY_FROM_8, WAGE_RATE_OF_PAY_TO_8, WAGE_UNIT_OF_PAY_8: Informasi gaji yang diusulkan pemberi kerja untuk Lokasi Kerja yang Dilaporkan Kedelapan Lokasi (jika ada).

  • PREVAILING_WAGE_8, PW_UNIT_OF_PAY_8, PW_TRACKING_NUMBER_8, PW_WAGE_LEVEL_8, PW_OES_YEAR_8, PW_OTHER_SOURCE_8, PW_NON-OES_YEAR_8, PW_SURVEY_PUBLISHER_8, PW_SURVEY_NAME_8: Informasi Upah yang Berlaku untuk Lokasi Kerja yang Dilaporkan Kedelapan (jika berlaku).

  • WORKSITE_WORKERS_9: Jumlah pekerja yang ditempatkan di lokasi Tempat Kerja Kesembilan (jika ada).

  • SECONDARY_ENTITY_9: Y = Pekerja akan ditempatkan dengan entitas sekunder untuk Kesembilan Lokasi Tempat Kerja yang Dilaporkan; N = Pekerja tidak akan ditempatkan dengan entitas sekunder.

  • SECONDARY_ENTITY_BUSINESS_NAME_9: Nama entitas sekunder kesembilan tempat pekerja akan ditempatkan (jika ada).

  • WORKSITE_ADDRESS1_9, WORKSITE_ADDRESS2_9, WORKSITE_CITY_9, WORKSITE_COUNTY_9, WORKSITE_STATE_9, WORKSITE_POSTAL_CODE_9: Informasi Geografis untuk Lokasi Tempat Kerja yang Dilaporkan Kesembilan (jika berlaku).

  • WAGE_RATE_OF_PAY_FROM_9, WAGE_RATE_OF_PAY_TO_9, WAGE_UNIT_OF_PAY_9, : Informasi gaji yang diusulkan pemberi kerja untuk Lokasi Kerja yang Dilaporkan Kesembilan Lokasi (jika ada).

  • PREVAILING_WAGE_9, PW_UNIT_OF_PAY_9, PW_TRACKING_NUMBER_9, PW_WAGE_LEVEL_9, PW_OES_YEAR_9, PW_OTHER_SOURCE_9, PW_NON-OES_YEAR_9, PW_SURVEY_PUBLISHER_9, PW_SURVEY_NAME_9: Informasi Upah yang Berlaku untuk Lokasi Tempat Kerja yang Dilaporkan Kesembilan (jika berlaku).

  • WORKSITE_WORKERS_10: Jumlah pekerja yang ditempatkan di lokasi Lokasi Kerja Kesepuluh (jika ada).

  • SECONDARY_ENTITY_10: Y = Pekerja akan ditempatkan dengan entitas sekunder untuk Kesepuluh Lokasi Tempat Kerja yang Dilaporkan; N = Pekerja tidak akan ditempatkan dengan entitas sekunder.

  • SECONDARY_ENTITY_BUSINESS_NAME_10: Nama entitas sekunder Kesepuluh tempat pekerja akan ditempatkan (jika ada).

  • WORKSITE_ADDRESS1_10, WORKSITE_ADDRESS2_10,WORKSITE_CITY_10, WORKSITE_COUNTY_10, WORKSITE_STATE_10, WORKSITE_POSTAL_CODE_10: Informasi Geografis untuk Lokasi Tempat Kerja yang Dilaporkan Kesepuluh (jika berlaku).

  • WAGE_RATE_OF_PAY_FROM_10, WAGE_RATE_OF_PAY_TO_10, WAGE_UNIT_OF_PAY_10: Informasi gaji yang diusulkan pemberi kerja untuk Lokasi Kerja yang Dilaporkan Kesepuluh Lokasi (jika ada).

  • PREVAILING_WAGE_10, PW_UNIT_OF_PAY_10, PW_TRACKING_NUMBER_10, PW_WAGE_LEVEL_10, PW_OES_YEAR_10, PW_OTHER_SOURCE_10, PW_NON-OES_YEAR_10, PW_SURVEY_PUBLISHER_10, PW_SURVEY_NAME_10: Informasi Upah yang Berlaku untuk Lokasi Tempat Kerja yang Dilaporkan Kesepuluh (jika berlaku).

  • H-1B_DEPENDENT: Y = Perusahaan Bergantung pada H-1B; N = Pengusaha bukan H-1B Dependen.

  • WILLFUL_VIOLATOR: Y = Perusahaan sebelumnya telah ditemukan sebagai Pelanggar yang Disengaja; N = Pemberi Kerja belum dianggap sebagai Pelanggar yang Disengaja.

  • SUPPORT_H1B: Y = Pemberi kerja akan menggunakan aplikasi kondisi tenaga kerja sementara hanya untuk mendukung petisi H-1B atau perpanjangan status pengecualian H-1B pekerja; N = Pemberi kerja tidak akan menggunakan kondisi tenaga kerja sementara aplikasi untuk mendukung petisi H-1B atau perpanjangan status pengecualian Pekerja H-1B; N / A = tidak dapat diterapkan.

  • STATUTORY_BASIS: Dasar Pembebasan Dukungan H-1B. Nilai yang valid meliputi:

    • “Upah” = Pembebasan berdasarkan $ 60.000 atau lebih tinggi upah tahunan;

    • “Gelar” = Pembebasan berdasarkan Gelar Master atau lebih tinggi terkait khusus; “Keduanya” = Pembebasan berdasarkan “Upah” dan “Gelar”.

  • MASTERS_EXEMPTION: Y = Perusahaan menyelesaikan

    • Lampiran A; N = Pemberi kerja belum selesai

    • Lampiran A; N / A = tidak dapat diterapkan

  • PUBLIC_DISCLOSURE: Lokasi dari informasi pengungkapan publik yang diperlukan. Nilai yang valid sertakan “Tempat Bisnis”, “Tempat Kerja” atau "Keduanya

2.6 Analisa

2.6.1 JOB_TITLE

library(tools)
labour_job <- labour %>%
  filter(!is.na(JOB_TITLE)) %>%
                group_by(JOB_TITLE) %>%
                summarise(total = n())  %>%
  
 arrange(-total) %>% ungroup() -> jt

## `summarise()` ungrouping output (override with `.groups` argument)

jt$JOB_TITLE = toTitleCase(tolower(jt$JOB_TITLE))
jt %>% group_by(JOB_TITLE) %>% summarise(total = sum(total)) %>% 
   arrange(-total) %>% ungroup() -> jt

## `summarise()` ungrouping output (override with `.groups` argument)

labour_job <- labour_job[-1,]
labour_job <- labour_job %>% mutate(prop = total / sum(labour_job$total)*100) 
head(labour_job)

## # A tibble: 6 x 3
##   JOB_TITLE                           total  prop
##   <fct>                               <int> <dbl>
## 1 SOFTWARE ENGINEER                   34691  5.22
## 2 SOFTWARE DEVELOPER                  34366  5.17
## 3 SENIOR SYSTEMS ANALYST JC60         12897  1.94
## 4 SENIOR SOFTWARE ENGINEER             8482  1.28
## 5 MANAGER JC50                         8205  1.23
## 6 TECHNOLOGY LEAD - US - PRACTITIONER  7434  1.12

labour_job_50 <- labour_job %>% top_n(n=50)

## Selecting by prop

labour_job_50

## # A tibble: 50 x 3
##    JOB_TITLE                           total  prop
##    <fct>                               <int> <dbl>
##  1 SOFTWARE ENGINEER                   34691 5.22 
##  2 SOFTWARE DEVELOPER                  34366 5.17 
##  3 SENIOR SYSTEMS ANALYST JC60         12897 1.94 
##  4 SENIOR SOFTWARE ENGINEER             8482 1.28 
##  5 MANAGER JC50                         8205 1.23 
##  6 TECHNOLOGY LEAD - US - PRACTITIONER  7434 1.12 
##  7 ASSISTANT PROFESSOR                  5632 0.847
##  8 PROGRAMMER ANALYST                   4983 0.750
##  9 JAVA DEVELOPER                       4062 0.611
## 10 SENIOR SOFTWARE DEVELOPER            3800 0.572
## # ... with 40 more rows

ggplot(data = labour_job_50, aes(x = reorder(JOB_TITLE,total), y = total)) +  
 geom_bar(stat="identity", fill="green", colour="black") +
coord_flip() + theme_bw(base_size = 10)  +
labs(title="", x ="Top 50 Job Title", y = "Number of applications")

2.6.2 WORKSITE_STATE

group_ws_1 <- labour %>%
                group_by(WORKSITE_STATE_1) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

group_ws_1 <- group_ws_1[-1,]

head(group_ws_1)

## # A tibble: 6 x 2
##   WORKSITE_STATE_1 total
##   <fct>            <int>
## 1 AK                  35
## 2 AL                 239
## 3 ALABAMA           1755
## 4 ALASKA             137
## 5 AR                 496
## 6 ARIZONA           9909

group_ws_2 <- labour%>%
                group_by(WORKSITE_STATE_2) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

group_ws_2 <- group_ws_2[-1,]

head(group_ws_2)

## # A tibble: 6 x 2
##   WORKSITE_STATE_2 total
##   <fct>            <int>
## 1 AK                   1
## 2 AL                  28
## 3 ALABAMA            300
## 4 ALASKA              20
## 5 AR                  52
## 6 ARIZONA            599

group_ws_3 <- labour%>%
                group_by(WORKSITE_STATE_3) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

group_ws_3 <- group_ws_3[-1,]

head(group_ws_3)

## # A tibble: 6 x 2
##   WORKSITE_STATE_3 total
##   <fct>            <int>
## 1 AL                   6
## 2 ALABAMA            130
## 3 ALASKA               2
## 4 AR                  12
## 5 ARIZONA            189
## 6 ARKANSAS            83

group_ws_4 <- labour%>%
                group_by(WORKSITE_STATE_4) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

group_ws_4 <- group_ws_4[-1,]

head(group_ws_4)

## # A tibble: 6 x 2
##   WORKSITE_STATE_4 total
##   <fct>            <int>
## 1 ALABAMA             54
## 2 ALASKA               2
## 3 ARIZONA             66
## 4 ARKANSAS            21
## 5 CALIFORNIA         474
## 6 COLORADO            57

group_ws_5 <- labour%>%
                group_by(WORKSITE_STATE_5) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

group_ws_5 <- group_ws_5[-1,]

head(group_ws_5)

## # A tibble: 6 x 2
##   WORKSITE_STATE_5 total
##   <fct>            <int>
## 1 ALABAMA             29
## 2 ALASKA               2
## 3 ARIZONA             50
## 4 ARKANSAS             8
## 5 CALIFORNIA         196
## 6 COLORADO            26

group_ws_6 <- labour%>%
                group_by(WORKSITE_STATE_6) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

group_ws_6 <- group_ws_6[-1,]

head(group_ws_6)

## # A tibble: 6 x 2
##   WORKSITE_STATE_6 total
##   <fct>            <int>
## 1 ALABAMA             15
## 2 ALASKA               1
## 3 ARIZONA             34
## 4 ARKANSAS             5
## 5 CALIFORNIA         138
## 6 COLORADO            15

group_ws_7 <- labour%>%
                group_by(WORKSITE_STATE_7) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

group_ws_7 <- group_ws_7[-1,]

head(group_ws_7)

## # A tibble: 6 x 2
##   WORKSITE_STATE_7 total
##   <fct>            <int>
## 1 ALABAMA              7
## 2 ARIZONA             35
## 3 ARKANSAS             4
## 4 CALIFORNIA          83
## 5 COLORADO             5
## 6 CONNECTICUT         13

group_ws_8 <- labour%>%
                group_by(WORKSITE_STATE_8) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

group_ws_8 <- group_ws_8[-1,]

head(group_ws_8)

## # A tibble: 6 x 2
##   WORKSITE_STATE_8 total
##   <fct>            <int>
## 1 ALABAMA              5
## 2 ARIZONA             15
## 3 ARKANSAS             1
## 4 CALIFORNIA          54
## 5 COLORADO            10
## 6 CONNECTICUT          9

group_ws_9 <- labour%>%
                group_by(WORKSITE_STATE_9) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

group_ws_9 <- group_ws_9[-1,]

head(group_ws_9)

## # A tibble: 6 x 2
##   WORKSITE_STATE_9 total
##   <fct>            <int>
## 1 ALABAMA              5
## 2 ARIZONA              9
## 3 ARKANSAS             1
## 4 CALIFORNIA          37
## 5 COLORADO             9
## 6 CONNECTICUT          3

group_ws_10 <- labour%>%
                group_by(WORKSITE_STATE_10) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

group_ws_10 <- group_ws_10[-1,]
head(group_ws_10)

## # A tibble: 6 x 2
##   WORKSITE_STATE_10 total
##   <fct>             <int>
## 1 ALABAMA               1
## 2 ARIZONA              13
## 3 ARKANSAS              1
## 4 CALIFORNIA           23
## 5 COLORADO              8
## 6 CONNECTICUT           2

2.6.3 Wage

group_wage_1 <- labour %>%
                group_by(PREVAILING_WAGE_1) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

group_wage_1 <- group_wage_1[-1,]

head(group_wage_1)

## # A tibble: 6 x 2
##   PREVAILING_WAGE_1 total
##   <fct>             <int>
## 1 10                    5
## 2 10.02                 2
## 3 10.08                 1
## 4 10.09                 1
## 5 10.12                 1
## 6 10.13                 1

group_wage_2 <- labour %>%
                group_by(PREVAILING_WAGE_2) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

head(group_wage_2)

## # A tibble: 6 x 2
##   PREVAILING_WAGE_2 total
##               <dbl> <int>
## 1              7.25     1
## 2              8.4      3
## 3              9.53     4
## 4             10.2      1
## 5             10.4      1
## 6             10.5      2

group_wage_3 <- labour %>%
                group_by(PREVAILING_WAGE_3) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

group_wage_3 <- group_ws_3[-1,]

head(group_wage_3)

## # A tibble: 6 x 2
##   WORKSITE_STATE_3 total
##   <fct>            <int>
## 1 ALABAMA            130
## 2 ALASKA               2
## 3 AR                  12
## 4 ARIZONA            189
## 5 ARKANSAS            83
## 6 AZ                  40

group_wage_4 <- labour %>%
                group_by(PREVAILING_WAGE_4) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

head(group_wage_4)

## # A tibble: 6 x 2
##   PREVAILING_WAGE_4 total
##               <dbl> <int>
## 1              9.53     4
## 2             14.1      1
## 3             16.4      2
## 4             16.8      2
## 5             17.7      1
## 6             17.9      1

group_wage_5 <- labour %>%
                group_by(PREVAILING_WAGE_5) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

head(group_wage_5)

## # A tibble: 6 x 2
##   PREVAILING_WAGE_5 total
##               <dbl> <int>
## 1              9.53     4
## 2             15.0      1
## 3             15.9      1
## 4             16.5      1
## 5             16.8      1
## 6             17.0      1

group_wage_6 <- labour %>%
                group_by(PREVAILING_WAGE_6) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

head(group_wage_6)

## # A tibble: 6 x 2
##   PREVAILING_WAGE_6 total
##               <dbl> <int>
## 1              9.53     2
## 2             20.4      1
## 3             24.5      1
## 4             24.6      1
## 5             25.6      1
## 6             25.8      1

group_wage_7 <- labour %>%
                group_by(PREVAILING_WAGE_7) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

head(group_wage_7)

## # A tibble: 6 x 2
##   PREVAILING_WAGE_7 total
##               <dbl> <int>
## 1              9.53     2
## 2             20.4      1
## 3             24.4      1
## 4             24.6      1
## 5             25.8      1
## 6             26.8      1

group_wage_8 <- labour %>%
                group_by(PREVAILING_WAGE_8) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

head(group_wage_8)

## # A tibble: 6 x 2
##   PREVAILING_WAGE_8 total
##               <dbl> <int>
## 1              9.53     2
## 2             20.4      1
## 3             24.6      1
## 4             26.8      1
## 5             27.1      1
## 6             28.2      1

group_wage_9 <- labour %>%
                group_by(PREVAILING_WAGE_9) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

head(group_wage_9)

## # A tibble: 6 x 2
##   PREVAILING_WAGE_9 total
##               <dbl> <int>
## 1              9.53     2
## 2             23.1      1
## 3             24.6      1
## 4             29.2      1
## 5             29.9      1
## 6             30.2      1

group_wage_10 <- labour %>%
                group_by(PREVAILING_WAGE_10) %>%
                summarise(total = n())

## `summarise()` ungrouping output (override with `.groups` argument)

head(group_wage_10)

## # A tibble: 6 x 2
##   PREVAILING_WAGE_10 total
##                <dbl> <int>
## 1               9.53     2
## 2              24.6      1
## 3              31.6      1
## 4              33.5      1
## 5              38.1      1
## 6              44.9      1

2.7 MANOVA

MANOVA dapat digunakan dalam kondisi tertentu:

• Variabel dependen harus didistribusikan secara normal dalam kelompok. Fungsi R mshapiro.test () [dalam paket mvnormtest] dapat digunakan untuk melakukan uji Shapiro-Wilk untuk normalitas multivariat. Ini berguna dalam kasus MANOVA, yang mengasumsikan normalitas multivariat.

• Homogenitas varian di seluruh rentang prediktor.

• Linearitas antara semua pasangan variabel dependen, semua pasangan kovariat, dan semua pasangan variabel-kovariat dependen di setiap sel

Check Univariate Normality Asumption

dependent.vars <- labour$PREVAILING_WAGE_1

Mengubah data kategori menjadi angka

num_JT_1<- as.numeric(labour$JOB_TITLE)
num_JT_1 <- data.frame(num_JT_1= unlist(num_JT_1))

num_STATE_1 <- as.numeric(labour$WORKSITE_STATE_1)
num_STATE_1 <- data.frame(num_STATE_1=unlist(num_STATE_1))
ind_wage <- data.frame(dependent.vars=unlist(dependent.vars))

zz <- cbind(num_JT_1, num_STATE_1, ind_wage)
head(zz)

##   num_JT_1 num_STATE_1 dependent.vars
## 1     7954          76               
## 2      659          76               
## 3    31983          31               
## 4    64270          76               
## 5    19984          76               
## 6    34879          22