Week 7: Apply it to your data 6

Import Data

tuition_cost <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-03-10/tuition_cost.csv')

## Rows: 2973 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): name, state, state_code, type, degree_length
## dbl (5): room_and_board, in_state_tuition, in_state_total, out_of_state_tuit...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

salary_potential <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-03-10/salary_potential.csv')

## Rows: 935 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): name, state_name
## dbl (5): rank, early_career_pay, mid_career_pay, make_world_better_percent, ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

diversity_school <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-03-10/diversity_school.csv')

## Rows: 50655 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): name, state, category
## dbl (2): total_enrollment, enrollment
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Introduction

Questions

Variation

Visualizing distributions

ggplot(data = tuition_cost) +
  geom_bar(mapping = aes(x = type))

Typical values

ggplot(data = salary_potential, mapping = aes(x = early_career_pay)) +
  geom_histogram()

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

Unusual values

Not Applicable

Missing Values

ggplot(data = diversity_school, mapping = aes(x = state, y = total_enrollment)) + 
  geom_point(na.rm = TRUE)

Covariation

A categorical and continuous variable

ggplot(data = salary_potential, mapping = aes(x = stem_percent)) + 
  geom_freqpoly(mapping = aes(colour = state_name), binwidth =300)

Two categorical variables

Not applicable to my data

Two continous variables

ggplot(data = salary_potential) +
  geom_point(mapping = aes(x = rank, y = early_career_pay))

Patterns and models

ggplot(data = tuition_cost) + 
  geom_point(mapping = aes(x = type, y = out_of_state_total))