An example script for census data in R

census_cleanup.R

library(tidyverse)
library(tidycensus)

# My recommendation is to use the tidycensus library to make getting this data
# easier than reading in the data from the Census website. 
#
# Before you can begin, you'll need to get an API key from the Census Bureau.
# You can acquire one here: 
#
# Once you have the API key, run the following in RStudio: 
# usethis::edit_r_environ()
#
# This will open your .Renviron file. Here, you'll add the following line, replacing
# YOUR_API_KEY with the key sent to you by the Census Bureau: 
# CENSUS_API_KEY = YOUR_API_KEY
#
# Restart R for the changes to take effect.

# Finding Census Data -----------------------------------------------------

# First, we'll need to know the variable ID from the Census or ACS -- since there
# are thousands of these IDs accross different Census files, we'll use the 
# load_variables function to find the information. It takes two arguments: 
# 1. the year,  and 2. the dataset.

variables <- load_variables(2018, "acs5")

# We'll now open up the data frame and look for the button in the upper left
# called 'Filter.' From here, start typing "attainment" to find the set of
# variables related to that data. Here we can see the list of IDs that are
# associated with that dataset.
variables %>% View()

# We'll use that list of IDs to build a data frame.
attainment <- get_acs(geography = "us",
                      variables = c("B15001_001",
                                    "B15001_002",
                                    "B15001_003",
                                    "B15001_004",
                                    "B15001_005",
                                    "B15001_006",
                                    "B15001_007",
                                    "B15001_008",
                                    "B15001_009",
                                    "B15001_010",
                                    "B15001_011",
                                    "B15001_012",
                                    "B15001_013",
                                    "B15001_014",
                                    "B15001_015",
                                    "B15001_016",
                                    "B15001_017",
                                    "B15001_018",
                                    "B15001_019",
                                    "B15001_020",
                                    "B15001_021",
                                    "B15001_022",
                                    "B15001_023",
                                    "B15001_024",
                                    "B15001_025",
                                    "B15001_026",
                                    "B15001_027",
                                    "B15001_028",
                                    "B15001_029",
                                    "B15001_030",
                                    "B15001_031",
                                    "B15001_032",
                                    "B15001_033",
                                    "B15001_034",
                                    "B15001_035",
                                    "B15001_036",
                                    "B15001_037",
                                    "B15001_038",
                                    "B15001_039",
                                    "B15001_040",
                                    "B15001_041",
                                    "B15001_042",
                                    "B15001_043",
                                    "B15001_044",
                                    "B15001_045",
                                    "B15001_046",
                                    "B15001_047",
                                    "B15001_048",
                                    "B15001_049",
                                    "B15001_050",
                                    "B15001_051",
                                    "B15001_052",
                                    "B15001_053",
                                    "B15001_054",
                                    "B15001_055",
                                    "B15001_056",
                                    "B15001_057",
                                    "B15001_058",
                                    "B15001_059",
                                    "B15001_060",
                                    "B15001_061",
                                    "B15001_062",
                                    "B15001_063",
                                    "B15001_064",
                                    "B15001_065",
                                    "B15001_066",
                                    "B15001_067",
                                    "B15001_068",
                                    "B15001_069",
                                    "B15001_070",
                                    "B15001_071",
                                    "B15001_072",
                                    "B15001_073",
                                    "B15001_074",
                                    "B15001_075",
                                    "B15001_076",
                                    "B15001_077",
                                    "B15001_078",
                                    "B15001_079",
                                    "B15001_080",
                                    "B15001_081",
                                    "B15001_082",
                                    "B15001_083"),
                      year = 2018)

# Now we can do things like chart the data. For example:
ggplot(attainment, aes(variable, estimate)) +
  geom_bar(stat="identity")

# Using your export ------------------------------------------------------

# Just as a note: the data set you sent me would take quite a bit of work to get
# it into a tidy data format. As a starting point, you could do something like
# the code below (but I can't do the work for you): 

# First, we read in our data. We use the skip argument to tell read_csv to 
# ignore the first three rows of the spreadsheet. 
data <- read_csv("export (3).csv", skip = 3)

# Then, we use the names() function to manually rename our columns. 
names(data) <- c("age","type","demographic","completed","total_estimate","total_moe","percent_estimate","percent_moe","male_estimate","male_estimate_moe","male_percent","male_percent_moe","female_estimate","female_estimate_moe","female_percent","female_percent_moe")

# Finally, we use gather() to get our data into a tidy format. As an example:  
data2 <- data %>% 
  select(age, demographic, total_estimate, male_estimate, female_estimate) %>% 
  gather(estimate_type, estimate_value, total_estimate, male_estimate, female_estimate)

# Now we could chart things, for example:
ggplot(data2, aes(demographic, estimate_value)) +
  geom_bar(stat="identity")