Week 7: Apply it to your data 6

Import data

data <- read_excel("../00_data/myData.xlsx")

Introduction

Questions

Variation

Visualizing distributions

ggplot(data = data) +
  geom_bar(mapping = aes(x = food_category))

ggplot(data = data) +
  geom_histogram(mapping = aes(x = consumption))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(data = data, mapping = aes(x = consumption, colour = food_category)) +
  geom_freqpoly()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Typical values

ggplot(data = data, mapping = aes(x = consumption)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Unusual values

ggplot(data) + 
  geom_histogram(mapping = aes(x = consumption), binwidth = 0.5) +
  coord_cartesian(ylim = c(0, 50))

Missing Values

data <- data %>% 
  mutate(consumption = ifelse(consumption < 0 | consumption > 400, NA, consumption))

ggplot(data = data, mapping = aes(x = consumption, y = co2_emmission)) + 
  geom_point()

## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).

Covariation

A categorical and continuous variable

ggplot(data = data, mapping = aes(x = co2_emmission)) + 
  geom_freqpoly(mapping = aes(colour = food_category), binwidth = 500)

ggplot(data = data, mapping = aes(x = co2_emmission, y = food_category)) +
  geom_boxplot()

Two categorical variables

ggplot(data = data) +
  geom_count(mapping = aes(x = food_category, y = co2_emmission))

ggplot(data = data) +
  geom_count(mapping = aes(x = food_category, y = consumption))

## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_sum()`).

ggplot(data = data) +
  geom_count(mapping = aes(x = consumption, y = co2_emmission))

## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_sum()`).

Two continous variables

ggplot(data = data) +
  geom_point(mapping = aes(x = consumption, y = co2_emmission))

## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).

ggplot(data = data) +
  geom_bin2d(mapping = aes(x = consumption, y = co2_emmission))

## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_bin2d()`).

ggplot(data = data) +
  geom_point(mapping = aes(x = food_category, y = consumption))

## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).

ggplot(data = data) +
  geom_bin2d(mapping = aes(x = food_category, y = consumption))

## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_bin2d()`).

Patterns and models

ggplot(data = data) + 
  geom_point(mapping = aes(x = consumption, y = co2_emmission))

## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).