Repeaters, primary, male (% of male enrollment)

The indicator 'Repeaters, primary, male (% of male enrollment)' is a significant metric in evaluating the educational landscape, particularly concerning the retention and progression of male students in primary education. This indicator represents the percentage of students who, having completed a grade, repeat the same grade rather than advancing to the next one. Understanding this indicator illuminates various aspects of an education system, indicating potential issues in educational quality, accessibility, and the socio-economic factors that influence education.

The importance of monitoring the percentage of male repeaters in primary education cannot be understated. High rates of repeaters may indicate underlying challenges, such as inadequate teaching quality, insufficient educational resources, or socio-economic factors influencing students' ability to learn effectively. Consequently, it also provides insights into the educational equity of a system, highlighting disparities that may exist between different demographics, thus guiding policymakers and educators in creating more inclusive strategies.

When we examine the recent data from 2019, the median value of male repeaters in primary education stands at 1.4%. This percentage illustrates a relatively low rate of male students repeating grades generally, indicating a positive trend towards educational advancement. However, this figure alone does not capture the nuances of different regions. For instance, Nepal shows a notable high of 7.86%, suggesting significant barriers preventing male students from progressing through their education smoothly. In contrast, Kazakhstan has one of the lowest rates at 0.05%, implying effective educational systems and policies that encourage grade advancement without the need for repetition. Monaco presents an outlier by having a middle ground of 1.4%, highlighting variations and potential policies influencing these net outcomes.

The interplay between the percentage of repeaters and other educational metrics is noteworthy. For example, this indicator is closely linked with literacy rates, educational attainment, and drop-out rates. High repeater rates often correlate with lower literacy rates, as students who do not progress to higher grades may miss essential learning opportunities and skills necessary for future academic success. Additionally, understanding drop-out rates within the context of repeaters helps paint a fuller picture of educational sustainability. In environments where students frequently repeat grades, frustration and disengagement might lead to higher drop-out rates as students abandon their educational journey.

Several factors can affect the rates of male repeaters in primary education. Economic factors play a pivotal role; in families with limited resources, the costs associated with education can hinder a child's ability to progress, either due to financial stress or the necessity for children to contribute to the family income. Furthermore, cultural attitudes toward education, particularly regarding gender roles, can further influence these rates. In regions where men's education may be prioritized or valued differently, the dynamics could reflect on repeaters. Educational infrastructure, such as teacher availability, school quality, and support systems, also contributes significantly; inadequate resources can lead to poor educational outcomes.

To mitigate high repeater rates among male students, several strategies can be employed. Effective teacher training programs that focus on dynamic teaching styles suited to various learning backgrounds could foster better understanding and retention of core material among students. Additionally, enhancing parental engagement through workshops and community programs can create a supportive home environment encouraging academic perseverance. Investments in educational technology and resources can bridge gaps in infrastructure, particularly in regions facing significant challenges in these areas. Tailoring educational policies to address the socio-economic and cultural factors affecting education will ensure that students are not only enrolled but are also progressing successfully through their primary education.

Despite the positive outlook indicated by a lower median percentage of repeaters, flaws within the educational system should not be overlooked. For instance, while a low percentage might suggest minimal repeating, it may also mask broader systemic issues, such as ineffective evaluation systems that promote students without adequately measuring their understanding and readiness for subsequent educational stages. This scenario can ultimately lead to greater challenges in higher education where students may struggle to catch up with the curriculum. Therefore, a balanced approach that considers both repeater rates and overall educational quality is vital.

In conclusion, the indicator of 'Repeaters, primary, male (% of male enrollment)' is more than just a statistic; it serves as a window into the complexities of educational systems worldwide. By understanding its significance, examining the interrelations with other educational metrics, assessing impacting factors, and exploring targeted strategies for improvement, education stakeholders can work towards achieving equitable and effective education for all children. As we strive for a holistic educational approach, continuous monitoring and evaluation will help in forging pathways toward an improved educational future.

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'SE.PRM.REPT.MA.ZS'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'SE.PRM.REPT.MA.ZS'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))