Chhabra_Ridhi_BANA4137_Homework9
Ridhi Chhabra
2024-04-01
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.3.2## Warning: package 'ggplot2' was built under R version 4.3.2## Warning: package 'readr' was built under R version 4.3.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## âś” dplyr 1.1.3 âś” readr 2.1.4
## âś” forcats 1.0.0 âś” stringr 1.5.0
## âś” ggplot2 3.4.4 âś” tibble 3.2.1
## âś” lubridate 1.9.2 âś” tidyr 1.3.0
## âś” purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## âś– dplyr::filter() masks stats::filter()
## âś– dplyr::lag() masks stats::lag()
## â„ą Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# Load the dataset
country = read_csv("country_stat.csv")## Rows: 10545 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): country, continent, region
## dbl (6): year, infant_mortality, life_expectancy, fertility, population, gdp
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.head(country)## # A tibble: 6 Ă— 9
## country year infant_mortality life_expectancy fertility population gdp
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Albania 1960 115. 62.9 6.19 1636054 NA
## 2 Algeria 1960 148. 47.5 7.65 11124892 1.38e10
## 3 Angola 1960 208 36.0 7.32 5270844 NA
## 4 Antigua … 1960 NA 63.0 4.43 54681 NA
## 5 Argentina 1960 59.9 65.4 3.11 20619075 1.08e11
## 6 Armenia 1960 NA 66.9 4.55 1867396 NA
## # â„ą 2 more variables: continent <chr>, region <chr>QUESTION 1 : Are there missing values in the data? If so, can you show how data is missing? (open-ended question)
Objective
Check for missing values and visualize missingness pattern.
summary(country)## country year infant_mortality life_expectancy
## Length:10545 Min. :1960 Min. : 1.50 Min. :13.20
## Class :character 1st Qu.:1974 1st Qu.: 16.00 1st Qu.:57.50
## Mode :character Median :1988 Median : 41.50 Median :67.54
## Mean :1988 Mean : 55.31 Mean :64.81
## 3rd Qu.:2002 3rd Qu.: 85.10 3rd Qu.:73.00
## Max. :2016 Max. :276.90 Max. :83.90
## NA's :1453
## fertility population gdp continent
## Min. :0.840 Min. :3.124e+04 Min. :4.040e+07 Length:10545
## 1st Qu.:2.200 1st Qu.:1.333e+06 1st Qu.:1.846e+09 Class :character
## Median :3.750 Median :5.009e+06 Median :7.794e+09 Mode :character
## Mean :4.084 Mean :2.701e+07 Mean :1.480e+11
## 3rd Qu.:6.000 3rd Qu.:1.523e+07 3rd Qu.:5.540e+10
## Max. :9.220 Max. :1.376e+09 Max. :1.174e+13
## NA's :187 NA's :185 NA's :2972
## region
## Length:10545
## Class :character
## Mode :character
##
##
##
## Interpretation
There is 1453 NA’s in infant_mortality, 187 NA’s in fertility, 185 NA’s in population and 2972 NA’s in gdp
QUESTION 2 : How many unique countries are included in the data? How many years of observations are included in the data?
Objective
Determine the number of unique countries and years of observations.
length(unique(country$country))## [1] 185diff(range(country$year)) + 1## [1] 57Interpretation
There are 185 unique countries and 57 years.
QUESTION 3 : In the data, create a new variable called GDP_per_capita which equals to GDP/population.
Objective
To create a new variable for GDP per capita.
country_per <- country %>%
mutate(GDP_per_capita = gdp/population)QUESTION 4 : Propose four questions you would like to know about this data. At least one question needs to be related to time series and be answered using time series data visualization. Some example questions can be: Does the developing countries grow slower than the developed countries? Is Africa catching up with world or left behind? Is the world more divided now than it was 50 years ago? Please propose your own questions and do not use the exactly same questions listed above.
QUESTION 4.1 :What has been the Infant Mortality Rate by Region Over Time
Objective
To analyze the trend of infant mortality rate over time for different regions.
infant_mortality_by_region_plot <- ggplot(country, aes(x = year, y = infant_mortality, color = region)) +
geom_line() +
labs(title = "Infant Mortality Rate by Region Over Time", x = "Year", y = "Infant Mortality Rate") +
theme_minimal()
print(infant_mortality_by_region_plot)## Warning: Removed 249 rows containing missing values (`geom_line()`).
### Interpretation
This visualization depicts the variations in infant mortality rates across different regions over time, aiding in the recognition of regional patterns and discrepancies in infant mortality rates throughout the years.
QUESTION 4.2 : which Asian countries have found financial success in recent years?
Objective
(Part 1) To find and analyze asian country that has been financial successful and (Part 2) has GPD per capita more than 2000.
country_per %>%
filter(continent == ("Asia")) %>%
ggplot() +
geom_line(aes(x = year, y = GDP_per_capita, color = country)) +
theme(legend.text=element_text(size=7), legend.title=element_text(size=7))## Warning: Removed 959 rows containing missing values (`geom_line()`).
# Now looking at Asian countries which have GPD Per Capita > 2000
country_per %>%
filter(continent == ("Asia")) %>%
filter(GDP_per_capita > 2000) %>%
ggplot() +
geom_line(aes(x = year, y = GDP_per_capita, color = country))
Interpretation
From this, we can see United Arab Emirates’ financial success going up in the last couple of years while other countries are also doing well in terms of financial success.
QUESTION 4.3: What is the distribution of life expectancy across all countries
Objective
To visually explore the distribution of life expectancy across all countries in the dataset.
# Histogram of life expectancy
life_expectancy_histogram <- ggplot(country, aes(x = life_expectancy)) +
geom_histogram(binwidth = 2, fill = "skyblue", color = "black") +
labs(title = "Distribution of Life Expectancy", x = "Life Expectancy", y = "Frequency") +
theme_minimal()
print(life_expectancy_histogram)
### Interpretation
The histogram shows the frequency distribution of life expectancy values. We can observe the range of life expectancy values and identify any noticeable patterns, such as whether the distribution is skewed or symmetric. This visualization provides insights into the overall life expectancy distribution among the countries in the dataset.
QUESTION 4.4: How does infant mortality vary by continent?
Objective
To compare the variation in infant mortality rates across different continents.
# Box plot of infant mortality by continent
infant_mortality_boxplot <- ggplot(country, aes(x = continent, y = infant_mortality, fill = continent)) +
geom_boxplot() +
labs(title = "Infant Mortality Rate by Continent", x = "Continent", y = "Infant Mortality Rate") +
theme_minimal() +
scale_fill_brewer(palette = "Set3")
print(infant_mortality_boxplot)## Warning: Removed 1453 rows containing non-finite values (`stat_boxplot()`).
Interpretation
The box plot compares the distribution of infant mortality rates across continents. By examining the box plots for each continent, we can identify differences in the central tendency and spread of infant mortality rates. This visualization helps us understand how infant mortality varies geographically and provides insights into potential disparities in healthcare and socio-economic conditions among continents. Additionally, differences in the whiskers’ lengths and outliers can indicate variations in the severity of infant mortality rates among continents.