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Week 1-1. Numeric and graphic summary

Before we start … let’s install R packages first!

install.packages(c("tidyverse", "gapminder"))

library(tidyverse)
library(gapminder)

1. Data types

e.g.

• Numeric-Continuous: height, weight, age, etc.
• Numeric-Discrete: number of hospitalization to hospital A
• Categorical-Ordinal: satisfaction score on a scale of 1-not at all happy to 5-very happy
• Categorical-Nominal: 1-male/0-female, 1-smoker/0-non-smoker

2. Histogram

• A histogram plots the distribution of a numeric variable’s values using bars
• Cannot plot a histogram with a categorical variable!
• Histogram is about only one single variable. Histogram does not show the relationship of two variables.
• Each bar represents a range of numeric values called “bin” or “class”
• A bar’s height = frequency of data points with a value within the corresponding bin.

[R code]

# Load the 'gapminder' dataset 
df <- gapminder

# Print first 6 rows
head(df)

## # A tibble: 6 x 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

# Plot a histogram of life expectancy
hist(df$lifeExp, 
     main = "Life expectancy", # plot title,
     xlab = "life exp.", # x-axis label
     ylab = "frequency", # y-axis label
     breaks = "FD" # number of bins
     )

• For more methods on how to determine the number of bins, please click HERE.

3. Multiple histograms in one screen

• par(mfrow = c(1, 2)): makes side-by-side plots
• Here, c(1, 2) indicates the number of rows and columns.

par(mfrow = c(1, 2))
hist(df$pop, main = "Gapminder: population", xlab = "Population")
hist(df$lifeExp, main = "Gapminder: life expectancy", xlab = "Life expectancy")

4. Summary statistics

• mean: mean()
• median: median()
• standard deviation: sd()
• variance: var()
• quantile: quantile()
• minimum: min()
• maximum: max()
• summary() can show these numeric summaries at once

mean(df$lifeExp)

## [1] 59.47444

median(df$lifeExp)

## [1] 60.7125

sd(df$lifeExp)

## [1] 12.91711

var(df$lifeExp)

## [1] 166.8517

quantile(df$lifeExp)

##      0%     25%     50%     75%    100% 
## 23.5990 48.1980 60.7125 70.8455 82.6030

min(df$lifeExp)

## [1] 23.599

max(df$lifeExp)

## [1] 82.603

summary(df$lifeExp)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   23.60   48.20   60.71   59.47   70.85   82.60

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