Assignment #1: Getting Started with R & Public Health Data

1 Instructions

• Knit to HTML or PDF.
• Show your code and outputs for each task.
• Keep your narrative answers brief and clear (1–3 sentences).
• Save as Lastname_Firstname_Week1.html (or .pdf) and upload.

2 Learning goals

• Practice basic R operations and objects.
• Load and explore a real dataset.
• Produce simple descriptive summaries.

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3 Part A — R as a Calculator (warm-up)

Task A1. Run the following commands and briefly describe what each does.

5 + 7

## [1] 12

sqrt(144)

## [1] 12

log(10)

## [1] 2.302585

Your notes (1–2 sentences):

• 5 + 7 → 12, This is a simple summation adding 5 and 7.
• sqrt(144) → 12, This operation is the principal square root of a perfect square
• log(10) → 2.302585, This operation is the natural logarithm of ten

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4 Part B — Vectors & Simple Objects

Task B1. Create a numeric vector of five values (any values) and compute the mean, min, and max.

# Your code here
vec <- c(2,5,8,10,12)
mean(vec)

## [1] 7.4

min(vec)

## [1] 2

max(vec)

## [1] 12

Task B2. Create a character vector of three country names and return the second element.

# Your code here
countries <- c("Persia", "Yugoslavia", "Zanzibar")
countries[2]

## [1] "Yugoslavia"

Task B3. Create a logical vector of five values and count how many are TRUE.

# Your code here
flags <- c(TRUE, FALSE, TRUE, FALSE, TRUE)
sum(flags)   # TRUE counts as 1

## [1] 3

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5 Part C — Explore Gapminder (health context)

We’ll use a lightweight dataset:

• Gapminder CSV: https://raw.githubusercontent.com/resbaz/r-novice-gapminder-files/master/data/gapminder-FiveYearData.csv

Task C1. Import the CSV with readr::read_csv() and display the first rows and the structure.

library(readr)

Gapminder <- read_csv("https://raw.githubusercontent.com/resbaz/r-novice-gapminder-files/master/data/gapminder-FiveYearData.csv")

head(Gapminder);str(Gapminder)

## spc_tbl_ [1,704 × 6] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ country  : chr [1:1704] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
##  $ year     : num [1:1704] 1952 1957 1962 1967 1972 ...
##  $ pop      : num [1:1704] 8425333 9240934 10267083 11537966 13079460 ...
##  $ continent: chr [1:1704] "Asia" "Asia" "Asia" "Asia" ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   country = col_character(),
##   ..   year = col_double(),
##   ..   pop = col_double(),
##   ..   continent = col_character(),
##   ..   lifeExp = col_double(),
##   ..   gdpPercap = col_double()
##   .. )
##  - attr(*, "problems")=<externalptr>

Task C2. How many rows and columns are there?

# Add your code
#Tibble description shows: "spc_tbl_ [1,704 × 6]"
#This indicates there are 1,704 rows (Observations) and 6 columns (Variables)
#We can confirm this with the following code:
nrow(Gapminder)

## [1] 1704

ncol(Gapminder)

## [1] 6

Task C3. List the continents represented (unique values).

# Add your code
length(unique(Gapminder$continent))

## [1] 5

Task C4. Create a quick frequency table of countries by continent (counts only).

# Add your code
table(Gapminder$continent)

## 
##   Africa Americas     Asia   Europe  Oceania 
##      624      300      396      360       24

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6 Part D — A First Public Health Summary

Task D1. Filter to the year 2007 and compute the mean life expectancy across all countries.

# Add your code
mean(Gapminder$lifeExp[Gapminder$year == "2007"], na.rm = TRUE)

## [1] 67.00742

Task D2. Show a small summary table of mean life expectancy by continent (year 2007).

# Add your code
aggregate(lifeExp ~ continent, data = Gapminder[Gapminder$year == 2007, ], FUN = mean)

Task D3. In 2–3 sentences, interpret the 2007 results in a public health context.
What patterns (inequities, regional differences) do you notice?

We observe the African continent to have the lowest life expectancy (54.81) in 2007 while Oceania enjoys the highest (80.72 years). The continents between the maximum and minimum are closer within ranges with Asia (70.73), Americas (73.61), and Europe (77.69). We can add supplemental variables that may help clarify this disparity: GDP per Capita, population We notice a similar trend: increase in GDP per Capita appears to follow a similar trend to life expectancy, with the exception of Americas and Asia. We can also notice a smaller population following a inverse pattern with life expectancy. Individual similarities in pattern of pop and gdp to life expectancy can be determined with statistical testing, and combined contributions can be evaluated with a regression.

# Add your code
aggregate(cbind(lifeExp, gdpPercap, pop) ~ continent, data = Gapminder[Gapminder$year == 2007, ], FUN = mean)

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