New undergraduate students annually

Education in Vietnam

I first created a chart to visualize the number of new undergraduate students every year from 2017 to 2019 in Vietnam.

The decrease in the number of new undergraduate students from 2017-2018 to 2018-2019 is an interesting insight. In 2018, the Vietnamese Department of Education devised a completely new version for the university entrance examination; as a result, there were barely any review materials for the students, which may have led to more students failing the exam compared to the previous years.

Total number of New Undergraduate Students from 2017 to 2019 in Vietnam

Then I broke them down into different categories of students: Full-time, Part-time, and Remote study

My code is as follows

# Install libraries
install.packages("RColorBrewer")

# Import libraries
library(readxl) # to read excel files
library(dplyr) # to carry out data wrangling functions
library(magrittr) # to write pipes (denoted as %>%)
library(ggplot2) # to draw graphs
library(stringr) # to wrangle strings
library(tidyr) # to tidy data
library(RColorBrewer) # for color palette

# Import dataset
data_2017_2019 <- read_excel("data_2017_2019.xlsx", sheet = "Final clean data")

My data is in this form, which contains 6 rows and 35 columns:

From there, I carried out 5 main steps:

• Step 1: As there are 3 categories of schools in this dataset (total, public, non-public), I want to focus only on school_type == total. I use filter here

new_undergraduate_student <- data_2017_2019 %>% 
  filter(school_type == 'total') # filter out school type = 'total'

• As there are 35 columns in this dataset, I just want to keep the relevant columns for my visualization. Therefore, I use select to pick out the columns I need.

new_undergraduate_student <- data_2017_2019 %>% 
  filter(school_type == 'total') %>% # filter out school type = 'total'
  select(academic_year, `2_new_undergraduate_students`, `2a_Full-time`, `2b_Part-time`, `2c_Remote-study`) 
  # select columns relevant to our topic, which is undergraduatr student here

• After this step, in order to draw a bar chart, I have to transform the current table from wide to long form. I use pivot_longer to do this.

new_undergraduate_student <- data_2017_2019 %>% 
  filter(school_type == 'total') %>% # filter out school type = 'total'
  select(academic_year, `2_new_undergraduate_students`, `2a_Full-time`, `2b_Part-time`, `2c_Remote-study`) %>% 
  # select columns relevant to our topic, which is undergraduatr student here
  pivot_longer(!academic_year, names_to = "categories", values_to = "values") 
  # change table structure from wide to long to visualize

• In order to make the output category names cleaner (e.g. from 2_new_undergraduate_students to 2. New undergraduate students), I create a new column and use case_when to update the new name

new_undergraduate_student$edited_categories <- case_when(
  new_undergraduate_student$categories == "2_new_undergraduate_students" ~ "2. New undergraduate students",
  new_undergraduate_student$categories == "2a_Full-time" ~ "2a. Full-time",
  new_undergraduate_student$categories == "2b_Part-time" ~ "2b. Part-time",
  new_undergraduate_student$categories == "2c_Remote-study" ~ "2c. Remote study",
  TRUE ~ as.character(new_undergraduate_student$categories)
)

• Then, I use ggplot to visualize my data

For the first bar chart with the total number of new undergraduate students, I pick out the relevant columns to visualize it. I also carry out similar steps with my second bar chart.

# All new undergraduate students from 2017 to 2019 in Vietnam

all_new_undergrad <- subset(new_undergraduate_student, edited_categories == "2. New undergraduate students")

all_new_undergrad %>% 
  ggplot(aes(x=academic_year, y = values)) + 
  geom_col(fill = 'Light Blue') +
  labs(x='Academic Year', y='Number of New Undergraduate Students') + 
  geom_text(aes(label = values), vjust=-0.3, size = 2.5) + 
  theme_classic()

# Breakdown of new undergraduate students from 2017 to 2019 in Vietnam

undergrad_breakdown <- subset(new_undergraduate_student, edited_categories != "2. New undergraduate students")

undergrad_breakdown %>% 
  ggplot(aes(x=academic_year, y = values, fill=edited_categories)) + 
  geom_col(position = "dodge") +
  scale_fill_brewer(palette="Pastel2") +
  labs(x='Academic Year', y='Number of New Undergraduate Students', fill = 'Type of Students') + 
  geom_text(aes(label = values), position=position_dodge(0.9), vjust=-0.3, size = 2.5) + 
  theme_classic()