In r4ds/bookclub-dsieur: Data Science in Education Using R Book Club

knitr::opts_chunk$set(echo = FALSE)

Useful Links

Data Science in Education Using R Ryan A. Estrellado, Emily A. Bovee, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez https://datascienceineducation.com/

Bookclub GitHub Repo https://github.com/r4ds/bookclub-dsieur

Chapters and Topics

• Ch 5 Getting Started with R and RStudio
• Ch 6 Foundational Skills

5.6.2 Writing Code in an R Script

print("We're going to use R as a calculator.")
print("First up, addition!")
12 + 8
632 + 41
print("Next, subtraction!")
48 - 6
0.65 - 1.42

R Script returned results

print("We're going to use R as a calculator.")
print("First up, addition!")
12 + 8
632 + 41
print("Next, subtraction!")
48 - 6
0.65 - 1.42

5.7 Installing the {dataedu} Package

• The recommended method for downloading dataedu kept giving me an error message
• install.packages("devtools", repos = "http://cran.us.r-project.org")
• devtools::install_github("data-edu/dataedu")
  
  
• So I went to the repo https://github.com/data-edu/dataedu, where it recommended using the "remotes" package, which worked for me.
• install.packages("remotes")
• remotes::install_github("data-edu/dataedu")

6.5 Foundational Skills Framework

• Projects
• Functions
• Packages
• Data

Writing a function:

'# we've named the function "addition" and asked for two arguments, "number_1" and "number_2"
addition <- function(number_1, number_2) {
number_1 + number_2
}

addition(number_1 = 3, number_2 = 1)

addition(0.921, 12.01)

addition(62, 34)

Function output

#' writing our function
#' we've named the function "addition"
#' and asked for two arguments, "number_1" and "number_2"
addition <- function(number_1, number_2) {
    number_1 + number_2
}

#' using our function
#' below are 3 separate examples of utilizing our new function called "addition"
#' note that we provide each argument separated by commas
addition(number_1 = 3, number_2 = 1)

addition(0.921, 12.01)

addition(62, 34)

Interpretting code

library(tidyverse)
library(janitor)

roster <- roster_raw %>%
clean_names() %>%
remove_empty(c("rows", "cols")) %>%
mutate(hire_date = excel_numeric_to_date(hire_date),
cert = coalesce(certification, certification_1)) %>%
select(-certification, -certification_1)

apply filter function

`# using the filter() function from the stats package
x <- 1:100

stats::filter(x, rep(1, 3))

`# using the filter() function from the dplyr package
starwars %>%
dplyr::filter(mass > 85)

Loading data

dataedu::ma_data_init

dataedu::ma_data_init -> ma_data

ma_data_init <- dataedu::ma_data_init

Exploring and manipulating your data

names(ma_data_init)

glimpse(ma_data_init)

summary(ma_data_init)

glimpse(ma_data_init$Town)

summary(ma_data_init$Town)

glimpse(ma_data_init$AP_Test Takers)

summary(ma_data_init$AP_Test Takers)

6.12.4 Exploring Our Data with the Pipe Operator

ma_data_init %>%
group_by(District Name) %>%
count()

ma_data_init %>% group_by(District Name) %>%
count() %>%
filter(n > 10)

Exploring Our Data with the Pipe Operator Output (cont.)

ma_data_init %>%
group_by(District Name) %>%
count() %>%
filter(n > 10) %>%
arrange(desc(n))

r4ds/bookclub-dsieur documentation built on May 20, 2022, 6:24 p.m.