Setting up Rstudio

Setting up RMarkdown when opening it enables you to create dynamic, reproducible, and visually appealing reports, presentations, and documents, that can help you communicate your data analysis and research findings more effectively.

knitr::opts_chunk$set(echo = TRUE, warning=FALSE,comment = NA, message=FALSE,
                      fig.height=4, fig.width=6)

Data Importation

job <- read.csv("C:\\Users\\user\\Downloads\\job.csv")

Load the following libraries

library(plyr)
library(ggplot2)
count(job)

Delete the missing observations

job<-na.omit(job)

The available observations after deleting the missing observations

count(job)

Data preparation and cleaning

The Structure of the Dataset

str(job)
'data.frame':   169 obs. of  11 variables:
 $ Gender         : chr  "female" "male" "male" "male" ...
 $ Degree         : chr  "Humans" "Engine " "Humans" "Engine " ...
 $ Job            : chr  "Human Resource" "Information Technology" "Finance " "Finance " ...
 $ Experience     : num  15 10 11 9 6 9 7 13 5 6 ...
 $ Income         : int  77000 87000 91000 76000 56000 72000 66000 105000 48000 59000 ...
 $ Work_confi     : int  7 7 6 6 8 10 9 6 6 7 ...
 $ Social_confi   : int  8 6 6 6 8 8 9 6 7 7 ...
 $ Leisure_confi  : int  10 5 7 6 9 9 9 8 8 7 ...
 $ Interest_MA    : num  4.3 9 8.8 8.7 4.1 1.7 1.5 8.1 7.8 7.8 ...
 $ Interest_PolyU : num  8.2 8 8 8 8 8 8 8 8 8 ...
 $ ApplYes_PolYesU: chr  "Yes" "Yes" "Yes" "No" ...
attach(job)

View the first few observations

head(job,5)

Summary Statistics

summary(job)
    Gender             Degree              Job              Experience    
 Length:169         Length:169         Length:169         Min.   : 1.000  
 Class :character   Class :character   Class :character   1st Qu.: 4.000  
 Mode  :character   Mode  :character   Mode  :character   Median : 7.000  
                                                          Mean   : 7.941  
                                                          3rd Qu.:12.000  
                                                          Max.   :15.000  
     Income         Work_confi      Social_confi    Leisure_confi   
 Min.   : 12000   Min.   : 4.000   Min.   : 3.000   Min.   : 4.000  
 1st Qu.: 38000   1st Qu.: 6.000   1st Qu.: 7.000   1st Qu.: 7.000  
 Median : 68000   Median : 8.000   Median : 8.000   Median : 8.000  
 Mean   : 67231   Mean   : 7.485   Mean   : 7.408   Mean   : 7.663  
 3rd Qu.: 94000   3rd Qu.: 8.000   3rd Qu.: 8.000   3rd Qu.: 9.000  
 Max.   :142000   Max.   :10.000   Max.   :10.000   Max.   :10.000  
  Interest_MA     Interest_PolyU  ApplYes_PolYesU   
 Min.   : 0.500   Min.   :3.300   Length:169        
 1st Qu.: 3.500   1st Qu.:5.000   Class :character  
 Median : 4.900   Median :6.500   Mode  :character  
 Mean   : 5.301   Mean   :6.099                     
 3rd Qu.: 7.500   3rd Qu.:7.000                     
 Max.   :10.000   Max.   :8.200

Converting string variables to factors

if(!require(dplyr)){install.packages('dplyr')} #installing the package if not
library(dplyr) #loading the library
library(gtsummary)

job<- job %>%mutate(Gender = factor(Gender),Degree = factor(Degree),
                                               Job=factor(Job), 
                    ApplYes_PolYesU = factor(ApplYes_PolYesU))

Now check the structure of the dataset

str(job)
'data.frame':   169 obs. of  11 variables:
 $ Gender         : Factor w/ 2 levels "female","male": 1 2 2 2 2 1 1 1 1 1 ...
 $ Degree         : Factor w/ 4 levels "Business","Engine ",..: 3 2 3 2 3 3 3 4 2 2 ...
 $ Job            : Factor w/ 5 levels "Arts","Educationn ",..: 4 5 3 3 3 2 2 2 5 5 ...
 $ Experience     : num  15 10 11 9 6 9 7 13 5 6 ...
 $ Income         : int  77000 87000 91000 76000 56000 72000 66000 105000 48000 59000 ...
 $ Work_confi     : int  7 7 6 6 8 10 9 6 6 7 ...
 $ Social_confi   : int  8 6 6 6 8 8 9 6 7 7 ...
 $ Leisure_confi  : int  10 5 7 6 9 9 9 8 8 7 ...
 $ Interest_MA    : num  4.3 9 8.8 8.7 4.1 1.7 1.5 8.1 7.8 7.8 ...
 $ Interest_PolyU : num  8.2 8 8 8 8 8 8 8 8 8 ...
 $ ApplYes_PolYesU: Factor w/ 2 levels "No","Yes": 2 2 2 1 2 2 1 2 2 2 ...

Summary statistics

job[,c(1, 4, 5, 6, 7, 8)] %>% tbl_summary(by = Gender) %>% add_p()
Characteristic female, N = 981 male, N = 711 p-value2
Experience 9.0 (4.0, 12.8) 7.0 (4.0, 11.0) 0.11
Income 70,500 (40,000, 94,750) 61,000 (35,000, 92,000) 0.4
Work_confi
    4 2 (2.0%) 0 (0%)
    5 4 (4.1%) 10 (14%)
    6 12 (12%) 17 (24%)
    7 20 (20%) 7 (9.9%)
    8 31 (32%) 28 (39%)
    9 22 (22%) 6 (8.5%)
    10 7 (7.1%) 3 (4.2%)
Social_confi 0.008
    3 0 (0%) 1 (1.4%)
    4 0 (0%) 1 (1.4%)
    5 3 (3.1%) 5 (7.0%)
    6 15 (15%) 16 (23%)
    7 22 (22%) 12 (17%)
    8 38 (39%) 33 (46%)
    9 16 (16%) 1 (1.4%)
    10 4 (4.1%) 2 (2.8%)
Leisure_confi 0.016
    4 0 (0%) 1 (1.4%)
    5 3 (3.1%) 9 (13%)
    6 5 (5.1%) 9 (13%)
    7 28 (29%) 24 (34%)
    8 23 (23%) 13 (18%)
    9 32 (33%) 13 (18%)
    10 7 (7.1%) 2 (2.8%)
1 Median (IQR); n (%)
2 Wilcoxon rank sum test; Fisher's exact test 
if(!require(stargazer)){install.packages('stargazer')}
library(stargazer)
stargazer(job[,], type = "text")

=======================================================
Statistic       N     Mean     St. Dev.   Min     Max  
-------------------------------------------------------
Experience     169   7.941      4.302    1.000  15.000 
Income         169 67,230.770 31,101.790 12,000 142,000
Work_confi     169   7.485      1.389      4      10   
Social_confi   169   7.408      1.227      3      10   
Leisure_confi  169   7.663      1.322      4      10   
Interest_MA    169   5.301      2.497    0.500  10.000 
Interest_PolyU 169   6.099      1.372    3.300   8.200 
-------------------------------------------------------
if(!require(gtsummary)){install.packages('gtsummary')}
library(gtsummary)

job %>%
  select(Experience, Income, Work_confi) %>%
  tbl_summary(
    #by = trt,
    label = list(Income ~ "Income"),
    statistic = list(all_continuous() ~ "{min} {median} {mean} {sd} {max}"),
    digits = list(c(Income, Experience, Work_confi) ~ c(0, 0, 2, 2,0))
  )
Characteristic N = 1691
Experience 1 7 7.94 4.30 15
Income 12,000 68,000 67,230.77 31,101.79 142,000
Work_confi
    4 2 (1%)
    5 14 (8%)
    6 29 (17%)
    7 27 (16%)
    8 59 (35%)
    9 28 (17%)
    10 10 (6%)
1 Minimum Median Mean SD Maximum; n (%) 
job[,c(-4,-5, -6, -7, -8, -9, -10)] %>% tbl_summary()
Characteristic N = 1691
Gender
    female 98 (58%)
    male 71 (42%)
Degree
    Business 40 (24%)
    Engine 49 (29%)
    Humans 52 (31%)
    Science 28 (17%)
Job
    Arts 14 (8.3%)
    Educationn 54 (32%)
    Finance 43 (25%)
    Human Resource 29 (17%)
    Information Technology 29 (17%)
ApplYes_PolYesU 79 (47%)
1 n (%)
Pie Chart and Scatter Plot
n <- c(98, 71)

perc <- paste0(n, " = ", round(100 * n/sum(n), 2), "%")
pie(n, labels = perc)

ggplot(job, aes(x=Experience, y=Income)) +
  geom_point()+
  geom_smooth(method=lm, se=FALSE)

##Pie chart of sex

pie(n, labels = perc, main = "Gender pie chart",col = c("blue", "black"))
legend("topleft", c("Male","Female"), cex = 0.8,
       fill = c("blue", "black"))

job[,c(1, 4, 5, 6, 7, 8, 9, 10)] %>% tbl_summary(by = Gender) %>% add_p()
Characteristic female, N = 981 male, N = 711 p-value2
Experience 9.0 (4.0, 12.8) 7.0 (4.0, 11.0) 0.11
Income 70,500 (40,000, 94,750) 61,000 (35,000, 92,000) 0.4
Work_confi
    4 2 (2.0%) 0 (0%)
    5 4 (4.1%) 10 (14%)
    6 12 (12%) 17 (24%)
    7 20 (20%) 7 (9.9%)
    8 31 (32%) 28 (39%)
    9 22 (22%) 6 (8.5%)
    10 7 (7.1%) 3 (4.2%)
Social_confi 0.008
    3 0 (0%) 1 (1.4%)
    4 0 (0%) 1 (1.4%)
    5 3 (3.1%) 5 (7.0%)
    6 15 (15%) 16 (23%)
    7 22 (22%) 12 (17%)
    8 38 (39%) 33 (46%)
    9 16 (16%) 1 (1.4%)
    10 4 (4.1%) 2 (2.8%)
Leisure_confi 0.016
    4 0 (0%) 1 (1.4%)
    5 3 (3.1%) 9 (13%)
    6 5 (5.1%) 9 (13%)
    7 28 (29%) 24 (34%)
    8 23 (23%) 13 (18%)
    9 32 (33%) 13 (18%)
    10 7 (7.1%) 2 (2.8%)
Interest_MA 4.75 (3.23, 7.38) 5.00 (3.90, 8.00) 0.2
Interest_PolyU 5.85 (5.00, 7.00) 6.60 (5.50, 7.20) 0.065
1 Median (IQR); n (%)
2 Wilcoxon rank sum test; Fisher's exact test

gender and Job category

job [,c(1,3)] %>%
  tbl_summary(by = Gender) %>%
  add_p() %>%
  add_overall() %>% 
  bold_labels()
Characteristic Overall, N = 1691 female, N = 981 male, N = 711 p-value2
Job <0.001
    Arts 14 (8.3%) 12 (12%) 2 (2.8%)
    Educationn 54 (32%) 36 (37%) 18 (25%)
    Finance 43 (25%) 28 (29%) 15 (21%)
    Human Resource 29 (17%) 19 (19%) 10 (14%)
    Information Technology 29 (17%) 3 (3.1%) 26 (37%)
1 n (%)
2 Pearson's Chi-squared test

Gender by ApplYes_PolYesU

job [,c(3,11)] %>%
  tbl_summary(by = ApplYes_PolYesU) %>%
  add_p() %>%
  add_overall() %>% 
  bold_labels()
Characteristic Overall, N = 1691 No, N = 901 Yes, N = 791 p-value2
Job 0.6
    Arts 14 (8.3%) 9 (10%) 5 (6.3%)
    Educationn 54 (32%) 32 (36%) 22 (28%)
    Finance 43 (25%) 21 (23%) 22 (28%)
    Human Resource 29 (17%) 15 (17%) 14 (18%)
    Information Technology 29 (17%) 13 (14%) 16 (20%)
1 n (%)
2 Pearson's Chi-squared test

Stacked Bar Graph (gender and species)

library(ggplot2)
ggplot(job, aes(x = Job))+
  geom_bar(aes(fill = Gender), 
           position = position_stack(reverse = FALSE)) +
  geom_text(aes(label = after_stat(count)),  stat='count'
            , color="green", size =3, nudge_y= 8, nudge_x=0,size=9)+
 theme_minimal()

Stacked Bar Graph (gender and island)

ggplot(job, aes(x = Degree))+
  geom_bar(aes(fill = Gender), 
           position = position_stack(reverse = FALSE)) +
  geom_text(aes(label = after_stat(count)),  stat='count'
            , color="green", size =3, nudge_y= 8, nudge_x=0,size=9)+
 theme_minimal()

Stacked Bar Graph (Island and species)

ggplot(job, aes(x = Degree))+
  geom_bar(aes(fill = Job), 
           position = position_stack(reverse = FALSE)) +
  geom_text(aes(label = after_stat(count)),  stat='count'
            , color="blue", size =3, nudge_y= 8, nudge_x=0,size=9)+
 theme_minimal()

Income and Gender

job [,c(1,5)] %>%
  tbl_summary(by = Gender,
              statistic = list(all_continuous() ~ "{mean} {median} {sd}"),
              digits = list(Income ~ 4)) %>%
  add_p() %>%
  add_overall() %>% 
  bold_labels()
Characteristic Overall, N = 1691 female, N = 981 male, N = 711 p-value2
Income 67,230.7692 68,000.0000 31,101.7914 68,867.3469 70,500.0000 30,910.4586 64,971.8310 61,000.0000 31,442.9060 0.4
1 Mean Median SD
2 Wilcoxon rank sum test

Income and Degree

ggplot(job, aes(x = Degree, y = Income))+
  labs(title = "", y = "Income", x = "Degree")+
  geom_boxplot(aes(fill = Degree)) +theme(legend.position="none")

Summaries of body mass against islands

group_mean <- aggregate(Income ~ Degree, data = job, mean)
group_mean

To show (e.g.) means, you need geom_col()

df <- data.frame(Degree = c("Business", "Engine", "Humans", "Science"),
                 mean = c(70025.00,
                          68387.76,
                          69807.69,
                          56428.57))

ggplot(df, aes(x= reorder(Degree, +mean), y = mean)) +
  geom_col(aes(fill="red"))+
  labs(title = "", y = "Mean Income", x = "Degree")+
  theme_minimal() +theme(legend.position="none")

ggplot(data= job, aes(x=Degree, y =Income)) +
  geom_bar(position = "dodge",
           stat = "summary",
           fun = "mean")+
  theme_minimal() +theme(legend.position="none")

Income and Degree Across Gender

ggplot(data= job, aes(x=Degree, y =Income, fill=Gender)) +
  geom_bar(position = "dodge",
           stat = "summary",
           fun = "mean")

  theme_minimal()
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  ..- attr(*, "class")= chr [1:2] "element_blank" "element"
 $ plot.title                :List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : NULL
  ..$ size         : 'rel' num 1.2
  ..$ hjust        : num 0
  ..$ vjust        : num 1
  ..$ angle        : NULL
  ..$ lineheight   : NULL
  ..$ margin       : 'margin' num [1:4] 0points 0points 5.5points 0points
  .. ..- attr(*, "unit")= int 8
  ..$ debug        : NULL
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 $ plot.title.position       : chr "panel"
 $ plot.subtitle             :List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : NULL
  ..$ size         : NULL
  ..$ hjust        : num 0
  ..$ vjust        : num 1
  ..$ angle        : NULL
  ..$ lineheight   : NULL
  ..$ margin       : 'margin' num [1:4] 0points 0points 5.5points 0points
  .. ..- attr(*, "unit")= int 8
  ..$ debug        : NULL
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 $ plot.caption              :List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : NULL
  ..$ size         : 'rel' num 0.8
  ..$ hjust        : num 1
  ..$ vjust        : num 1
  ..$ angle        : NULL
  ..$ lineheight   : NULL
  ..$ margin       : 'margin' num [1:4] 5.5points 0points 0points 0points
  .. ..- attr(*, "unit")= int 8
  ..$ debug        : NULL
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 $ plot.caption.position     : chr "panel"
 $ plot.tag                  :List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : NULL
  ..$ size         : 'rel' num 1.2
  ..$ hjust        : num 0.5
  ..$ vjust        : num 0.5
  ..$ angle        : NULL
  ..$ lineheight   : NULL
  ..$ margin       : NULL
  ..$ debug        : NULL
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 $ plot.tag.position         : chr "topleft"
 $ plot.margin               : 'margin' num [1:4] 5.5points 5.5points 5.5points 5.5points
  ..- attr(*, "unit")= int 8
 $ strip.background          : list()
  ..- attr(*, "class")= chr [1:2] "element_blank" "element"
 $ strip.background.x        : NULL
 $ strip.background.y        : NULL
 $ strip.clip                : chr "inherit"
 $ strip.placement           : chr "inside"
 $ strip.text                :List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : chr "grey10"
  ..$ size         : 'rel' num 0.8
  ..$ hjust        : NULL
  ..$ vjust        : NULL
  ..$ angle        : NULL
  ..$ lineheight   : NULL
  ..$ margin       : 'margin' num [1:4] 4.4points 4.4points 4.4points 4.4points
  .. ..- attr(*, "unit")= int 8
  ..$ debug        : NULL
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 $ strip.text.x              : NULL
 $ strip.text.y              :List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : NULL
  ..$ size         : NULL
  ..$ hjust        : NULL
  ..$ vjust        : NULL
  ..$ angle        : num -90
  ..$ lineheight   : NULL
  ..$ margin       : NULL
  ..$ debug        : NULL
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 $ strip.switch.pad.grid     : 'simpleUnit' num 2.75points
  ..- attr(*, "unit")= int 8
 $ strip.switch.pad.wrap     : 'simpleUnit' num 2.75points
  ..- attr(*, "unit")= int 8
 $ strip.text.y.left         :List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : NULL
  ..$ size         : NULL
  ..$ hjust        : NULL
  ..$ vjust        : NULL
  ..$ angle        : num 90
  ..$ lineheight   : NULL
  ..$ margin       : NULL
  ..$ debug        : NULL
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 - attr(*, "class")= chr [1:2] "theme" "gg"
 - attr(*, "complete")= logi TRUE
 - attr(*, "validate")= logi TRUE