Week 7: Apply it to your data 6

Introduction

data %>% filter(births > 30000)

## # A tibble: 235 × 3
##     year month births
##    <dbl> <dbl>  <dbl>
##  1  1991     1  32213
##  2  1991     2  30345
##  3  1991     3  34869
##  4  1991     4  35398
##  5  1991     5  36371
##  6  1991     6  34378
##  7  1991     7  35436
##  8  1991     8  34421
##  9  1991     9  34410
## 10  1991    10  33092
## # ℹ 225 more rows

Questions

Variation

Visualizing distributions

diamonds %>%
  ggplot(aes(x=cut))

geom_bar()

## geom_bar: just = 0.5, width = NULL, na.rm = FALSE, orientation = NA
## stat_count: width = NULL, na.rm = FALSE, orientation = NA
## position_stack

diamonds %>%
  ggplot(mapping = aes(x = carat))

geom_histogram(binwidth = 0.5)

## geom_bar: na.rm = FALSE, orientation = NA
## stat_bin: binwidth = 0.5, bins = NULL, na.rm = FALSE, orientation = NA, pad = FALSE
## position_stack

Typical values

ggplot(data = faithful, mapping = aes(x = eruptions)) + 
  geom_histogram(binwidth = 0.25)

Unusual values

ggplot(diamonds) + 
  geom_histogram(mapping = aes(x = y), binwidth = 0.5)

Missing Values

Covariation

A categorical and continuous variable

Two categorical variables

Two continous variables

Patterns and models