RStudio-Project

Under the supervision of instructors Andrew Dickinson and Colleen O’Briant, I completed RStudio projects for EC 320 at the University of Oregon. These projects were completed by the programming language for statistical computing and graphics - RStudio.

Below is the overview of the projects:

Tibble

• Intro to tidyverse table functions
• Hold & create data set in Tibbles
• Form data into a Tibbles

Example:

tibble(
  sex = c("male", "female", "female"),
  study_time = c(8, 4, 4),
  grade = c(78, 74, 86)
)

	sex	study_time	grade
1	male	8	78
2	female	4	74
3	female	4	86

lm

• Using lm() to estimate models with: log transformations, categorical variables using dummies, and interactions between variables, etc
• broom:tidy and broom:glance()

Example broom:tidy:

lm(lifeExp ~ gdpPercap, data = gapminder) %>%
  broom::tidy(conf.int = TRUE)

# A tibble: 2 × 7
  term         estimate std.error statistic   p.value  conf.low conf.high
  <chr>           <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
1 (Intercept) 54.0      0.315         171.  0         53.3      54.6     
2 gdpPercap    0.000765 0.0000258      29.7 3.57e-156  0.000714  0.000815

line of best fit

• Express the relationship in the given scatter plot of different data points

Example:

students %>%
  ggplot(aes(x = grade1, y = final_grade)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)

dplyr

• dplyr functions such as summarize() and group_by()
• Analyze data using filter(), arrange(), and mutate(), etc
• Combine two or more Tibbles by bind_rows(), bind_cols(), and left_join(), etc

EX: What percentage of A students study for more than 10 hours per week?

students %>%
  filter(final_grade >= 90) %>%
  group_by(study_time) %>%
  summarize(n = n()) %>%
  mutate(percent = n / sum(n))

Outcome table:

# A tibble: 4 × 3
  study_time        n percent
  <fct>         <int>   <dbl>
1 less than 2H     10   0.294
2 2 - 5H           13   0.382
3 5 - 10H           7   0.206
4 more than 10H     4   0.118

ggplot2

• Visualization of data such as Bar plot, Histogram, Box plot, and scatter plot, etc
• Add a line of best fit in scatterplot

Example plots:

ggplot - aes, geom

• Aesthetic mappings: map variables in the plot to different axes or different colors
• Geom: Added the plot + as cayers

Examples plots: