Module 7: Apply it to your data 6

Import Data

exped <- read_excel("../00_data/expedData.xlsx")

Variation

Visualizing distributions

ggplot(data = exped) +
    geom_bar(mapping = aes(x = SEASON_FACTOR))

ggplot(data = exped) +
        geom_histogram(mapping = aes(x = TOTMEMBERS), binwidth = 1)

exped %>% count(YEAR)

## # A tibble: 5 × 2
##    YEAR     n
##   <dbl> <int>
## 1  2020    22
## 2  2021   207
## 3  2022   279
## 4  2023   274
## 5  2024   100

ggplot(data = exped, mapping = aes(x = YEAR, color = HOST_FACTOR)) +
    geom_freqpoly(binwidth = 1)

Typical values

ggplot(data = exped, mapping = aes(x = SMTDAYS)) +
    geom_histogram(binwidth = 0.5)

Unusual values

ggplot(exped) +
    geom_histogram(mapping = aes(x = SMTDAYS), binwidth = 0.5) +
    coord_cartesian(ylim = c(0, 25))

Covariation

A categorical and continuous variable

ggplot(data = exped, mapping = aes(x = HIGHPOINT)) +
    geom_freqpoly(mapping = aes(color = HOST_FACTOR), binwidth = 100) +
    coord_cartesian(xlim = c(5000, 9000))

Two categorical variables

ggplot(data = exped) +
    geom_count(mapping = aes(x = HIGHPOINT, y = MDEATHS))

Two continous variables

ggplot(data = exped) +
    geom_point(mapping = aes(x = HIGHPOINT, y = TOTMEMBERS))

Patterns and models

ggplot(data = exped) +
    geom_boxplot(mapping = aes(x = O2USED, y = HIGHPOINT))