0. Setup - API Key, Load [R] Packages

Use the 'cloud' version of jupyter notebook with R


You Need a free Census API "Key" - Signup


Install [R] Packages that we will use latter

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# install.packages(c('dplyr','httr','jsonlite','tidyr','ggplot2','foreign',
#                   'reshape','haven','packrat'),
#                   lib='/usr/local/lib/R/site-library',dependencies=TRUE)

Load [R] Packages that we will use latter

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# below aren't necessary, they're alternative 'Paths to Rome'
# library(reshape)
# library(haven)
# library(packrat)
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Mental Exercise: Reflect on how the Jupyter or Rstudio workflow can help transparency

Answer in the jupyter markdown block below

1. Obtain - Data Stream

Application Programming Interface - US Census API

In a nutshell, an API is a set of well-defined 'rules of engagement' to request items from a supplier.


Most commonly, you glue together 3 items as a web URL

1. a standardized "base" web url 
    (doesn't change for anyone or any request)

2. your specific request items 
    (can change, up to the user)

3. your user-specific API Key 
    (fixed for individual user, but different across individuals)

We will get our requested data from the US Census Bureau API

A one stop shop table of contents that shows the currently available Census datasets


2015 Census Planning Database: Tract Level

We will use the US Census Bureau's API, to request the Census Planning Database: Tract Level



API Syntax:


NOTE: clicking the above URL will NOT work NOTE: the two "FOO1" and "FOO2" entries, which are placeholders, to be specified by YOU

Example - Glue: API base url + Request + ...

Michael Tzen Requests:

Scope - LA County (state:06+county:037)
Entity - Tract Level
Attributes - County, State, Total Population in 2010 Census, Tract Land Area, Females in Census 2010

Using the 'API rules of engagement' I shoehorn my requests into the WEB URL below:


NOTE: clicking the above URL will NOT work

NOTE: "YOUR_KEY_GOES_HERE" ("FOO2"), my user-specific API Key, is still NOT specified by me

[R] Example - Glue: API base url + Request + API Key

[R] 'dplyr' package https://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html

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# put key inside quotes
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# library(dplyr)

url_base = "http://api.census.gov/data/2015/pdb/tract?"

request = "County_name,State_name,Tot_Population_CEN_2010,LAND_AREA,Females_CEN_2010&for=tract:*&in=state:06+county:037"

# put key inside quotes

# pasted output has quotes

# paste output, THEN, noquotes
paste0(url_base,'get=',request,'&key=',YOUR_KEY_GOES_HERE) %>% noquote()

NOTE: Census API returns your requested data in "JSON" format


JSON in a nutshell: a list of key-value pairs. The current standard format since it is BOTH machine readable and has human readable layout (looks like a shopping list)

Modify your request - All Tracts in CA

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# add measure name (from Table of Contents), drop off '+county:037" in 'for=tract:*&in=state:06+county:037'

request_more = "County_name,State_name,Tot_Population_CEN_2010,LAND_AREA,Females_CEN_2010&for=tract:*&in=state:06"


# paste output, THEN, noquotes
paste0(url_base,'get=',request_more,'&key=',YOUR_KEY_GOES_HERE) %>% noquote()

On Your Own: assemble an API call for New York State

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url_base = ""

request = ""


Mental Exercise: Reflect on how the software steps make data obtainment a transparent process

Answer in the jupyter markdown block below

2. Input - Data into Software

Using [R] software, read and store the API returned JSON Data

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# library(httr)
# library(jsonlite)

# url_fin = paste0(url_base,'get=',request,'&key=',YOUR_KEY_GOES_HERE)  # pasted output has quotes
# url_fin

url_fin_more = paste0(url_base,'get=',request_more,'&key=',YOUR_KEY_GOES_HERE)  # pasted output has quotes
url_fin = url_fin_more

# ?GET
req_url_fin = httr::GET(url_fin)

# ?content
# extract (json) content from a request

json_req = httr::content(req_url_fin, as = "text")

# agrees with 'point and click' return
# json_req %>% cat()

# human readable, "prettify" like a shopping list
# prettify(json_req,indent=1)

Null Values

["Los Angeles County","California",null,null,null,"06","037","137000"]

Convert JSON Data into Tabular Form - Characters and Numerics are Fundamental

Note: here, we manually copy+paste .json data returned from Census API

the cell above using json_req = httr::content() is the programatic [R] way of getting the API content

  • caveat... we need network access for R and Jupyter (denied)

'Error in curl::curl_fetch_memory(url, handle = handle): Couldn't resolve host name'

So... let's just get the content manually. what we are doing here below.

workaround: just copy and paste returned json (in browser tab) after 'clicking' the r assembled API call

  • 'patchwork' but students can see that .json is nothing more than a format like .csv
  • only used [R] to assemble the URL for API call
  • couldn't use R to 'navigate' the URL via curl::curl_fetch_memory()
  • BEAUTY of jupyter being in-browser, the assembled URL can be clicked (this is completley from jupyter / markdown)
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json_req = '[["County_name","State_name","Tot_Population_CEN_2010","LAND_AREA","Females_CEN_2010","state","county","tract"],
["Alameda County","California","2937","2.657","1476","06","001","400100"]]'
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# ?fromJSON
# https://cran.r-project.org/web/packages/jsonlite/vignettes/json-apis.html

# NEEDS quotes around final url
# url_fin = url_fin_more


data_pdb_raw = json_req %>%
# url_fin %>% 
# noquote() %>%


# json 'null' is correctly encoded as r matrix 'NA'

# r matrix shows 1 NA, which agrees with single null in json source

which(is.na(data_pdb_raw), arr.ind = TRUE)
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data_pdb_raw[unique(which(is.na(data_pdb_raw), arr.ind = TRUE)[,1]),]
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# NA in row 1475
# data_pdb_raw = data_pdb_raw[-1475,]

data_pdb_raw[unique(which(is.na(data_pdb_raw), arr.ind = TRUE)[,1]),]

data_pdb_raw = data_pdb_raw[-unique(which(is.na(data_pdb_raw), arr.ind = TRUE)[,1]),]



# <8b> is a jupyter idiom for '...' shortening output

Hurdle: Immediate Matrix without Header (Variable Names)

BUT, It IS there in row 1. EVERYTHING is currently read as 'character' strings of text stored in a tabular MATRIX

I view character strings as a huge benefit. Keeping everything as characters (at least initially) allows for consistent behavior when you merge, filter, apply logic to your data. It Prevents you from shooting yourself in the foot (like adding two strings, say "State Code" + "County Code"). This is Tomatoe = Potatoe. Later on, we explicitly convert intended numeric numbers into numeric data (Tomatoe = Tomatoe).

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Hurdle: Humans recognize that first row represents variable names, Need Computer to recognize it too (as column names)

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# first row of matrix - humans understand as the intended variable names

# column names (header) of matrix - computer understands as empty

# make the assignment: column names of matrix = first row of matrix
colnames(data_pdb_raw) = data_pdb_raw[1,]

# assignment reflected but first row still present (redundant)
data_pdb_raw %>% head()

# data_pdb_mat: a new object distinguishing it from _raw source
# throw away first row

data_pdb_mat = data_pdb_raw[-1,] 


Hurdle: Matrix to Dataframe, but [R] default converts characters, we want to keep everything as Fundamental 'Character' string

options(stringsAsFactors = FALSE)

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# set options
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# NOTE: default of [R]: automaticaly converting 'character' to 'factor' 
# this is an artifact leftover from oldtime convenience

data_pdb_mat %>% as.data.frame() %>% str()

# turn that default option OFF
options(stringsAsFactors = FALSE)

data_pdb_mat %>% as.data.frame() %>% str()

# store as data frame
data_pdb_df = data_pdb_mat %>% as.data.frame()

Hurdle: Humans recognize that 'Tot_Population_CEN_2010' represents a count, Need Computer to recognize it too (as 'Numeric' number)

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data_pdb_df %>%

# want numeric numbers for
# Tot_Population_CEN_2010
# Females_CEN_2010

# more data
data_pdb_df = data_pdb_df %>% 

data_pdb_df %>%

data_pdb_df %>%

On Your Own: convert 'Females_CEN_2010' to a numeric number

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data_pdb_df = data_pdb_df %>% 

# check structure

Clear Hurdle? Basic Assertions - Check some Snapple Facts

Can Population be Negative?

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data_pdb_df %>% 
filter(Tot_Population_CEN_2010 < 0)

data_pdb_df %>% 
filter(Tot_Population_CEN_2010 < 0) %>%

Basic Assertion: How ManyTracts have Positive Population?

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data_pdb_df %>% 
filter(Tot_Population_CEN_2010 > 0) %>% nrow()

Basic Assertion: How Many Tracts have Positive OR Zero Population? (Should be all Tracts in LA County)

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# apply filter where Total Population Count >=0

data_pdb_df %>% 
filter((Tot_Population_CEN_2010 > 0) || (Tot_Population_CEN_2010 == 0)) %>% 

# number of rows in census returned dataset (raw)
# no filter
data_pdb_df %>% nrow()

On Your Own: How Many Tracts have Zero Population?

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data_pdb_df %>% 
filter() %>% 

Basic Assertion: Does our sum of LA County population agree with Independent Source?

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# is.na(data_pdb_df)
# summary(data_pdb_df)

data_pdb_df %>%
group_by(County_name) %>%
summarise(Tot_Pop_County = sum(Tot_Population_CEN_2010))


Mental Exercise: Reflect on how the software steps make reading in data a transparent process

Answer in the jupyter markdown block below

Start with Tidy data, then Wrangle to Coordinate

The API returned census data is already in "Tidy" format (each row is an entity, each column is a measure)

Example: Tidy

Name Year Age Sex
Arnold Schwarzenegger 2030 34 M
Arnold Schwarzenegger 2040 44 M
Sofia Vergara 2030 30 F
Sofia Vergara 2040 40 F

Example: Coordinate

Name Year Variable Name Variable Value
Arnold Schwarzenegger 2030 Age 34
Arnold Schwarzenegger 2030 Sex M
Arnold Schwarzenegger 2040 Age 44
Arnold Schwarzenegger 2040 Sex M
Sofia Vergara 2030 Age 30
Sofia Vergara 2030 Sex F
Sofia Vergara 2040 Age 40
Sofia Vergara 2040 Sex F

Create Primary Key / Entity ID by gluing raw entity variables via 'tidyr' and unite()

In [ ]:
# library(tidyr)

data_pdb_df %>% 

# [State,County,Tract] are the three variables, when viewed together, define the entity id
# County_name, State_name, Tot_Population_CEN_2010, are the three variables that are measurements of the entitiy

# ?tidyr::unite() to glue together variables into single entity id

# primary key := create a single entity id, by uniting the three entity variables
data_pdb_df_pk = data_pdb_df %>% 

data_pdb_df_pk %>% head()

# note: unite() default behavior drops original entity variables, and glues in underscore "_"

On Your Own: ?separate() the 'state_county_tract' variable back into 3 separate variables for state, county, tract

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data_pdb_df_pk %>%

Many ways to Rome: Create Primary Key / Entity ID

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# Alternative way of creating entity id by gluing entity variables
# using mutate(paste0())

data_pdb_df %>% 
mutate(state_county_tract_v2 = paste0(state,county,tract)) %>% 

Tidy to Coordinate via 'tidyr' and gather()

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# Except for our entity id "state_county_tract"
# gather all columns (several measures) into two seperate columns [variable name,variable value]

data_pdb_df_pk %>%
gather(var_name,var_val,-state_county_tract) %>%

data_pdb_df_pk %>%
gather(var_name,var_val,-state_county_tract) %>%

data_pdb_df_coord = data_pdb_df_pk %>%

Many ways to Rome - Tidy to Coordinate via 'reshape' and melt()

NOTE: 'reshape' more powerful, sometimes overkill. Allows more control of "melting" and "casting" into various tabular shapes

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# library(reshape)

# Many ways to Rome: alternative method
# ?reshape2::melt() to coordinate format

# data_pdb_df_pk %>%
# reshape::melt("state_county_tract") %>%
# head()

# data_pdb_df_pk %>%
# reshape::melt("state_county_tract") %>%
# tail()

Mental Exercise: Reflect on how the wrangled data structures can help transparency

Answer in the jupyter markdown block below

4. Analyze - Plot, Summary, Model

Plot a Graphic

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data_pdb_df_pk %>% head()

# library(ggplot2)

# convert 
## Relevel the cars by mpg
## this allows the plot to sort from most to least

# histogram of population count (each entity is a tract)

# p = ggplot(data_pdb_df_pk, aes(x=reorder(state_county_tract,-Tot_Population_CEN_2010), y=Tot_Population_CEN_2010)) + 
#     geom_point(stat="identity")

On Your Own: Request Tract Size Variable, Make scatterplot of Population by Tract Size


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ggplot(data_pdb_df_pk, aes(x=(LAND_AREA),y=(Tot_Population_CEN_2010))) + 

ggplot(data_pdb_df_pk, aes(x=log(LAND_AREA),y=log(Tot_Population_CEN_2010))) + 

Summarize a Table

Assume A Table is Our Desired Analysis

County Level Count of Males and Females, sorted in decreasing order by Total Population Count

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data_pdb_df_pk %>%
mutate(Num_Males_2010 = Tot_Population_CEN_2010 - Females_CEN_2010) %>%
group_by(State_name,County_name) %>%
         ) %>%

tab_sex_county = data_pdb_df_pk %>%
mutate(Num_Males_2010 = Tot_Population_CEN_2010 - Females_CEN_2010) %>%
group_by(State_name,County_name) %>%
         ) %>%

Fit a Regression Model

Regress Population Count on Land Area

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data_pdb_df_pk %>%

# intercept allowed to be estimated when land area is 0
lm(Tot_Population_CEN_2010 ~ LAND_AREA,data=data_pdb_df_pk) %>%

# force intercept to be 0
# 0 population when 0 land area
lm(Tot_Population_CEN_2010 ~ -1 + LAND_AREA, data=data_pdb_df_pk) %>%

# log-outcome and log-predictor, estimate the intercept
lm(log(Tot_Population_CEN_2010 + 0.01) ~ 1 + log(LAND_AREA + 0.01), data=data_pdb_df_pk) %>%

Mental Exercise: Reflect on how you got to this "final" analysis stage. Compare the amount of code in 'Analyze Step 4' with Steps 1-3.

Answer in the jupyter markdown block below


Organization is a fundamental part of Transparency, hence step 0. Further, Organization is crucial during handoff migrations.

Transfer your wrangled data to a new environment

Output: 'data_pdb_df_pk'

In .csv format (Universal), Output Table to Computer File

Note: your_output_dir = '/home/foo' needs to represent your output directory

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# your_output_dir = 'Z:\\projects\\workshop_ccpr_stat\\workshop_data_workflow\\data_proc\\'

your_output_dir = '/home/jovyan/work/test/'

# ?write.csv

# write.csv(tab_sex_county,paste0(your_output_dir,'tab_sex_county.csv'))

# drop row number

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# list.files('/home/jovyan/work/test/')

In .dta format (Stata), Output Table to Computer File

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# library(haven)

# ?write_dta
# write_dta(data_pdb_df_pk, paste0(your_output_dir,'data_pdb_df_pk_haven.dta'))
# ?read_dta

# ?read.dta

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Transfer your software versions. What versions are you using!?

Archive your software versions!


type in the function yourself in the Code box below

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# sessionInfo()

Software Versions... big whoop... So What? How do I make use of this info?

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# library(devtools)
# devtools::install_version("tidyr", version = "0.3.1", repos = "http://cran.us.r-project.org")

Bundle the packages, export, and then unbundle in another environment

Even easier than manually installing specific version 1-by-1


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# ?packrat::bundle

# ?packrat::unbundle
bundle_path = "/foo_path/foo.tar.gz"

Transfer your Jupyter File (from tmpnb.org) to your personal computer

Download as a .ipynb file or an .r



Fit a regression model in Stata

read in data

use "Z:\projects\workshop_ccpr_stat\workshop_data_workflow\data_proc\data_pdb_df_pk_foreign.dta", clear

estimate intercept

reg Tot_Population_CEN_2010 LAND_AREA

force intercept to be 0

reg Tot_Population_CEN_2010 LAND_AREA, nocon

log-outcome and log-predictor, estimate the intercept

generate log_Tot_Pop = log(Tot_Population_CEN_2010+0.01)
generate log_Land_Area = log(LAND_AREA+0.01)

reg log_Tot_Pop log_Land_Area