Getting started with DSjobtracker

The package contains two datasets

  1. DSraw : Raw dataset with 551 rows and 152 columns
  2. DStidy : Cleaned tidy dataset with 1172 rows and 109 columns

Both of these datasets contain information about job vacancies related to data science, which were collected for the span of a month, by searching for specific Search_Term and then following the search results to gather data manually.

Usage

  1. Install the library from github
# install devtools if not already installed
# install.packages("devtools")
devtools::install_github("thiyangt/DSjobtracker")
  1. Load the library
  1. Load the dataset into your environment
data("DStidy_2020")

Overview of columns

tibble::glimpse(DStidy_2020)
#> Rows: 430
#> Columns: 115
#> $ ID                                 <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, …
#> $ Consultant                         <chr> "Thiyanga", "Jayani", "Jayani", "Ja…
#> $ DateRetrieved                      <date> 2020-08-05, 2020-08-07, 2020-08-07…
#> $ DatePublished                      <date> NA, 2020-07-31, 2020-08-06, 2020-0…
#> $ Job_title                          <chr> NA, "Junior Data Scientist", "Engin…
#> $ Company                            <chr> NA, "Dialog Axiata PLC", "London St…
#> $ R                                  <fct> 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0,…
#> $ SAS                                <fct> 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,…
#> $ SPSS                               <fct> 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,…
#> $ Python                             <fct> 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,…
#> $ MAtlab                             <fct> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Scala                              <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ C_Sharp                            <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ MS_Word                            <fct> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1,…
#> $ Ms_Excel                           <fct> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1,…
#> $ OLE_DB                             <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Ms_Access                          <fct> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,…
#> $ Ms_PowerPoint                      <fct> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,…
#> $ Spreadsheets                       <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Data_visualization                 <fct> 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
#> $ Presentation_Skills                <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Communication                      <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ BigData                            <fct> 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0,…
#> $ Data_warehouse                     <fct> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ cloud_storage                      <fct> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Google_Cloud                       <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ AWS                                <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Machine_Learning                   <fct> 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0,…
#> $ Deep_Learning                      <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Computer_vision                    <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Java                               <fct> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,…
#> $ Cpp                                <fct> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
#> $ C                                  <fct> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
#> $ Linux_Unix                         <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ SQL                                <fct> 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0,…
#> $ NoSQL                              <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ RDBMS                              <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Oracle                             <fct> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,…
#> $ MySQL                              <fct> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,…
#> $ PHP                                <fct> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
#> $ Flash_Actionscript                 <fct> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
#> $ SPL                                <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ web_design_and_development_tools   <fct> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
#> $ Wordpress                          <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ AI                                 <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ `Natural_Language_Processing(NLP)` <fct> 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,…
#> $ Microsoft_Power_BI                 <fct> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Google_Analytics                   <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ graphics_and_design_skills         <fct> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
#> $ Data_marketing                     <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ SEO                                <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Content_Management                 <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Tableau                            <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
#> $ D3                                 <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
#> $ Alteryx                            <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ KNIME                              <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Spotfire                           <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Spark                              <fct> 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,…
#> $ S3                                 <fct> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
#> $ Redshift                           <fct> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
#> $ DigitalOcean                       <fct> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
#> $ Javascript                         <fct> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
#> $ Kafka                              <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Storm                              <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Bash                               <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Hadoop                             <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,…
#> $ Data_Pipelines                     <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ MPP_Platforms                      <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Qlik                               <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Pig                                <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Hive                               <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,…
#> $ Tensorflow                         <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Map_Reduce                         <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Impala                             <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Solr                               <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Teradata                           <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ MongoDB                            <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Elasticsearch                      <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ YOLO                               <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ agile_execution                    <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,…
#> $ Data_management                    <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ pyspark                            <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Data_mining                        <fct> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
#> $ Data_science                       <fct> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
#> $ Web_Analytic_tools                 <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ IOT                                <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Numerical_Analysis                 <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Economic                           <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Finance_Knowledge                  <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Investment_Knowledge               <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Problem_Solving                    <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Korean_language                    <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Bash_Linux_Scripting               <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Team_Handling                      <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Debtor_reconcilation               <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Payroll_management                 <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Bayesian                           <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Optimization                       <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Bahasa_Malaysia                    <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ Knowledge_in                       <chr> NA, NA, "Elasticsearch, Logstash, K…
#> $ City                               <chr> NA, "Colombo", "Colombo", "Colombo"…
#> $ Location                           <chr> "NY", "LK", "LK", "LK", "LK", "Mala…
#> $ Educational_qualifications         <chr> NA, "Degree in Engineering / IT or …
#> $ Salary                             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ English_proficiency                <chr> NA, NA, NA, NA, NA, NA, "1", NA, NA…
#> $ URL                                <chr> NA, "https://www.google.com/search?…
#> $ Search_Term                        <chr> NA, "Data Analysis Jobs in Sri Lank…
#> $ Job_Category                       <fct> Unimportant, Data Science, Data Sci…
#> $ Minimum_Years_of_experience        <dbl> 4, 2, 1, 2, 0, 5, 0, 0, 1, 7, 5, 2,…
#> $ Experience                         <chr> "4+", "2-3", "1-2", "2+", "0 years_…
#> $ Experience_Category                <fct> More than 2 and less than 5 years, …
#> $ Job_Country                        <chr> NA, "Sri Lanka", "Sri Lanka", "Sri …
#> $ Edu_Category                       <fct> NA, Some Degree, Some Degree, Some …
#> $ Minimum_Salary                     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ Salary_Basis                       <fct> unspecified, unspecified, unspecifi…

More information on the meanings of the column names can be accessed through the help

?DStidy_2020

Examples

Barplot of top twenty skills required for data science jobs

library(tidyr)
library(magrittr)
library(dplyr)
library(ggplot2)
library(wordcloud2)
library(viridis)
library(forcats)

theme_set(theme_minimal())

skills_long <- DStidy_2020 %>%
  select(c(R:Bahasa_Malaysia)) %>%
  pivot_longer(c(R:Bahasa_Malaysia), values_to = "Value", names_to = "Name") %>%
  mutate(Value = as.numeric(levels(Value))[Value]) %>%
  group_by(Name) %>%
  summarize(Total = sum(Value)) %>%
  arrange(Total)

skills_long %>%
  mutate(Name = factor(Name, levels = .$Name)) %>%
  top_n(20) %>%
  ggplot(aes(x = Name, y = Total)) +
  geom_bar(stat = "identity") +
  geom_label(aes(label = Total),
    nudge_y = -10, size = 3.25,
    label.padding = unit(0.125, "lines")
  ) +
  coord_flip() +
  labs(
    title = "Top twenty skills required for data science jobs",
    x = "Skill Required", y = "No of job vacancies"
  )

Wordcloud of software skills

not_software_columns <- c(
  "Presentation_Skills", "Data_visualization",
  "Spreadsheets", "BigData",
  "Communication", "BigData",
  "Data_warehouse", "cloud_storage",
  "Google_Cloud", "Machine_Learning",
  "Computer_vision", "Deep_Learning", "RDBMS",
  "web_design_and_development_tools", "AI",
  "Natural_Language_Processing(NLP)",
  "graphics_and_design_skills", "Data_marketing",
  "SEO", "Content_Management",
  "Data_Pipelines", "MPP_Platforms", "agile_execution",
  "Data_management", "Data_mining", "Data_science",
  "Web_Analytic_tools", "IOT",
  "Numerical_Analysis", "Finance_Knowledge", "Economic",
  "Investment_Knowledge", "Problem_Solving",
  "Korean_language", "Team_Handling",
  "Debtor_reconcilation", "Payroll_management",
  "Bayesian", "Optimization", "Bahasa_Malaysia"
)
indicators <- DStidy_2020 %>%
  select(c(R:Bahasa_Malaysia))
software_indicators <- indicators %>%
  select(colnames(.)[!colnames(.) %in% not_software_columns])

software_indicators_long <- software_indicators %>%
  pivot_longer(colnames(.), values_to = "Value", names_to = "Name") %>%
  mutate(Value = as.numeric(levels(Value))[Value]) %>%
  group_by(Name) %>%
  summarize(Total = sum(Value)) %>%
  arrange(Total)

wordcloud2(software_indicators_long %>%
  transmute(word = Name, freq = log(Total)),
size = 0.35,
minRotation = pi / 2, maxRotation = pi / 2,
color = viridis(nrow(software_indicators_long)),
fontFamily = "Montserrat"
)

The log of the counts were used to visualize them better

Required experience and the salary

count_data <- DStidy_2020 %>%
  select(Experience_Category, Edu_Category) %>%
  filter(!is.na(Edu_Category)) %>%
  count(Experience_Category, Edu_Category)

max_vacancies <- max(count_data$n)

count_data %>%
  ggplot(aes(x = fct_rev(Experience_Category), y = n, color = Edu_Category, size = n)) +
  geom_point() +
  geom_curve(
    data = tibble(
      x1 = c(4.4), x2 = c(4),
      y1 = c(62.5), y2 = c(58)
    ),
    aes(x = x1, y = y1, xend = x2, yend = y2),
    arrow = arrow(length = unit(0.07, "inch")), size = 0.4,
    color = "gray20", curvature = -0.3
  ) +
  coord_flip() +
  lims(y = c(0, 100)) +
  annotate("text",
    x = 4.65, y = 60, size = 2.8,
    label = paste("With less than 2 years of experience\n and a BSc degree \n you can still apply for", max_vacancies, "job vacancies")
  ) +
  scale_color_brewer(type = "qual", palette = "Set1") +
  scale_size(guide = "none") +
  labs(
    x = "Years of Experience needed",
    y = "Number of job vacancies",
    color = "Minimum education qualification"
  ) +
  theme(legend.position = "bottom") +
  guides(color = guide_legend(nrow = 2, title.position = "top"))

Software Skills needed for each Job Category

# radar plot with job category and skills in a radar
job_skill_data <- DStidy_2020 %>% 
  select(R:Bahasa_Malaysia,Job_Category) %>% 
  filter(Job_Category != "Unimportant") %>% 
  pivot_longer(c(R:Bahasa_Malaysia),names_to="Name",values_to = "Value") %>%
  mutate(Value = as.numeric(levels(Value))[Value]) %>%
  group_by(Job_Category,Name) %>% 
  summarize(Total = sum(Value)) %>% 
  ungroup() %>% 
  filter(Total > 0) %>% 
  mutate(logTotal = log(Total)) %>%
  ungroup()

common_skills <- job_skill_data %>% 
  count(Name) %>% 
  filter(n == 3 & !(Name %in% not_software_columns)) %>% 
  .$Name

plot_data <- job_skill_data %>% 
  filter(Name %in% common_skills) %>% 
  mutate(Name = as.numeric(factor(Name,labels = common_skills)))

  plot_data %>% 
  ggplot(aes(x = Name,y = logTotal,fill = Job_Category,color = Job_Category))+
  geom_area(size = 0,position = position_dodge(width=0.9),alpha=0.1) + 
  geom_point(size=0.5) +
  geom_segment(aes(xend = Name,yend = logTotal,alpha = logTotal),
               y = 0,size = 1.25)+
  scale_x_continuous(labels = common_skills,breaks = 1:length(common_skills)) +
  theme(axis.text.y = element_blank(),
        legend.position = "none") + 
  labs(x = NULL,
       y = NULL) +
  scale_fill_brewer(palette = "Set1",type = "qual") + 
  coord_polar() + 
  facet_wrap(~ Job_Category,ncol=1)