Exporting Data

This lesson is called Exporting Data, part of the Going Deeper with R course. This lesson is called Exporting Data, part of the Going Deeper with R course.

Transcript

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View code shown in video

# Load Packages -----------------------------------------------------------

library(tidyverse)
library(fs)
library(readxl)
library(janitor)

# Create Directories ------------------------------------------------------

dir_create("data-raw")
dir_create("data")

# Download Data -----------------------------------------------------------

# https://www.oregon.gov/ode/educator-resources/assessment/Pages/Assessment-Group-Reports.aspx

# download.file("https://www.oregon.gov/ode/educator-resources/assessment/Documents/TestResults2122/pagr_schools_math_tot_raceethnicity_2122.xlsx",
#               mode = "wb",
#               destfile = "data-raw/pagr_schools_math_tot_raceethnicity_2122.xlsx")
# 
# download.file("https://www.oregon.gov/ode/educator-resources/assessment/Documents/TestResults2122/TestResults2019/pagr_schools_math_tot_raceethnicity_1819.xlsx",
#               mode = "wb",
#               destfile = "data-raw/pagr_schools_math_tot_raceethnicity_1819.xlsx")
# 
# download.file("https://www.oregon.gov/ode/educator-resources/assessment/TestResults2018/pagr_schools_math_raceethnicity_1718.xlsx",
#               mode = "wb",
#               destfile = "data-raw/pagr_schools_math_raceethnicity_1718.xlsx")
# 
# download.file("https://www.oregon.gov/ode/educator-resources/assessment/TestResults2017/pagr_schools_math_raceethnicity_1617.xlsx",
#               mode = "wb",
#               destfile = "data-raw/pagr_schools_math_raceethnicity_1617.xlsx")
# 
# download.file("https://www.oregon.gov/ode/educator-resources/assessment/TestResults2016/pagr_schools_math_raceethnicity_1516.xlsx",
#               mode = "wb",
#               destfile = "data-raw/pagr_schools_math_raceethnicity_1516.xlsx")
#
# download.file("https://github.com/rfortherestofus/going-deeper-v2/raw/main/data-raw/oregon-districts-and-schools.xlsx",
#               mode = "wb",
#               destfile = "data-raw/oregon-districts-and-schools.xlsx")

# Import, Tidy, and Clean Data --------------------------------------------

oregon_districts_and_schools <- 
  read_excel("data-raw/oregon-districts-and-schools.xlsx") |> 
  clean_names() |> 
  rename(school_id = attending_school_institutional_id)

clean_math_proficiency_data <- function(raw_data) {
  
  read_excel(path = raw_data) |> 
    clean_names() |> 
    filter(student_group == "Total Population (All Students)") |> 
    filter(grade_level == "Grade 3") |> 
    select(academic_year, school_id, contains("number_level")) |> 
    pivot_longer(cols = starts_with("number_level"),
                 names_to = "proficiency_level",
                 values_to = "number_of_students") |> 
    mutate(proficiency_level = case_when(
      proficiency_level == "number_level_4" ~ "4",
      proficiency_level == "number_level_3" ~ "3",
      proficiency_level == "number_level_2" ~ "2",
      proficiency_level == "number_level_1" ~ "1"
    )) |> 
    mutate(number_of_students = parse_number(number_of_students)) |> 
    group_by(school_id) |> 
    mutate(pct = number_of_students / sum(number_of_students, na.rm = TRUE)) |> 
    ungroup()
  
}

third_grade_math_proficiency_2021_2022 <-
  clean_math_proficiency_data(raw_data = "data-raw/pagr_schools_math_tot_raceethnicity_2122.xlsx")

third_grade_math_proficiency_2018_2019 <-
  clean_math_proficiency_data(raw_data = "data-raw/pagr_schools_math_tot_raceethnicity_1819.xlsx")


third_grade_math_proficiency <- 
  bind_rows(third_grade_math_proficiency_2018_2019,
            third_grade_math_proficiency_2021_2022) |> 
  left_join(oregon_districts_and_schools,
            join_by(school_id))


# Export ------------------------------------------------------------------

third_grade_math_proficiency |> 
  write_csv(file = "data/third_grade_math_proficiency.csv")

third_grade_math_proficiency |> 
  write_rds(file = "data/third_grade_math_proficiency.rds")

Your Turn

Solution

# Load Packages -----------------------------------------------------------

library(tidyverse)
library(fs)
library(readxl)
library(janitor)

# Create Directories ------------------------------------------------------

dir_create("data-raw")
dir_create("data")

# Download Data -----------------------------------------------------------

# https://www.oregon.gov/ode/reports-and-data/students/Pages/Student-Enrollment-Reports.aspx

# download.file("https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20222023.xlsx",
#               mode = "wb",
#               destfile = "data-raw/fallmembershipreport_20222023.xlsx")
# 
# download.file("https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20212022.xlsx",
#               mode = "wb",
#               destfile = "data-raw/fallmembershipreport_20212022.xlsx")
# 
# download.file("https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20202021.xlsx",
#               mode = "wb",
#               destfile = "data-raw/fallmembershipreport_20202021.xlsx")
# 
# download.file("https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20192020.xlsx",
#               mode = "wb",
#               destfile = "data-raw/fallmembershipreport_20192020.xlsx")
# 
# download.file("https://www.oregon.gov/ode/reports-and-data/students/Documents/fallmembershipreport_20182019.xlsx",
#               mode = "wb",
#               destfile = "data-raw/fallmembershipreport_20182019.xlsx")
#
# download.file("https://github.com/rfortherestofus/going-deeper-v2/raw/main/data-raw/oregon-districts.xlsx",
#               mode = "wb",
#               destfile = "data-raw/oregon-districts.xlsx")


# Import, Tidy, and Clean Data -----------------------------------------------------

oregon_districts <-
  read_excel("data-raw/oregon-districts.xlsx") |> 
  clean_names() |> 
  rename(district_institution_id = attending_district_institutional_id)

clean_enrollment_data <- function(excel_file,
                                  sheet_name) {
  
  read_excel(path = excel_file,
             sheet = sheet_name) |> 
    clean_names() |> 
    select(1, 3, 7:19) |> 
    select(-contains("percent")) |> 
    set_names("district_institution_id",
              "school_institution_id",
              "american_indian_alaska_native",
              "asian",
              "native_hawaiian_pacific_islander",
              "black_african_american",
              "hispanic_latino",
              "white",
              "multi_racial") |> 
    pivot_longer(cols = -c(district_institution_id, school_institution_id),
                 names_to = "race_ethnicity",
                 values_to = "number_of_students") |> 
    mutate(race_ethnicity = case_when(
      race_ethnicity == "american_indian_alaska_native" ~ "American Indian Alaska Native",
      race_ethnicity == "asian" ~ "Asian",
      race_ethnicity == "black_african_american" ~ "Black/African American",
      race_ethnicity == "hispanic_latino" ~ "Hispanic/Latino",
      race_ethnicity == "multiracial" ~ "Multi-Racial",
      race_ethnicity == "native_hawaiian_pacific_islander" ~ "Native Hawaiian Pacific Islander",
      race_ethnicity == "white" ~ "White",
      race_ethnicity == "multi_racial" ~ "Multiracial"
    )) |> 
    mutate(number_of_students = parse_number(number_of_students)) |> 
    group_by(district_institution_id, race_ethnicity) |> 
    summarize(number_of_students = sum(number_of_students, na.rm = TRUE)) |> 
    ungroup() |> 
    group_by(district_institution_id) |> 
    mutate(pct = number_of_students / sum(number_of_students)) |> 
    ungroup() |> 
    mutate(year = sheet_name) 
  
}

enrollment_by_race_ethnicity_2022_2023 <- 
  clean_enrollment_data(excel_file = "data-raw/fallmembershipreport_20222023.xlsx",
                        sheet_name = "School 2022-23") 

enrollment_by_race_ethnicity_2021_2022 <-
  clean_enrollment_data(excel_file = "data-raw/fallmembershipreport_20212022.xlsx",
                        sheet_name = "School 2021-22") 

enrollment_by_race_ethnicity <-
  bind_rows(enrollment_by_race_ethnicity_2021_2022,
            enrollment_by_race_ethnicity_2022_2023) |> 
  left_join(oregon_districts,
            join_by(district_institution_id)) |> 
    select(year, district_institution_id, district, everything())


# Export ------------------------------------------------------------------

write_rds(enrollment_by_race_ethnicity,
          file = "data/enrollment_by_race_ethnicity.rds")

Make a data folder using the dir_create() function from the fs package.
Export the enrollment_by_race_ethnicity data frame as an RDS file in the data folder.

Have any questions? Put them below and we will help you out!

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Mike LeVan • April 25, 2025

Hey. Thinking about when you made the mistake where you used write_csv and accidentally wrote it to an RDS file. I tried to re-create this to see what was happening.

The command created the file "enrollment_by_race_ethnicity.rds" file, but it was the same size as the "enrollment_be_race_ethnicity.csv" file. (165.9kb)

I could open the CSV file and view it as one normally would, but when I clicked on the RDS file and tried to open it I got a dialog box asking me to load the file in an R Object. So I couldn't just view it as a typical CSV file.

I then renamed the RDS file to "enrollment_by_race_ethnicity2.csv" and I could then view it as a CSV file.

So I think the write_csv command was creating a CSV file regardless of the name you gave it, but because you gave it the extension RDS, R Studio treats it as an RDS file instead of a CSV file.

I then used the write_rds command to create "enrollment_by_race_ethnicy.rds". This file was a different size (204.6kb), which makes sense since it contains the extra information not kept by a CSV file.

Am I understanding this correctly?

Gracielle Higino Coach • May 1, 2025

Yes, you're right! The thing is that all these files are plain text files, not binaries. So technically you can also open an RDS file using a text-reading software, such as Excel, Word or NotePad - it'd just be hard to understand because of the encoding. The difference here is that write_rds() adds information, as you said, and it can contain more than one R object. The write_csv() function, on the other hand, just writes delimited text files, one R object at a time, based on the vroom_write() function.