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)