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00003_informalexample_2.1_of_section_2.1.2.R
# informalexample 2.1 of section 2.1.2
# (informalexample 2.1 of section 2.1.2) : Starting with R and data : Starting with R : R programming
print(seq_len(25))## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24 25
# [1] 1 2 3 4 5 6 7 8 9 10 11 12
# [13] 13 14 15 16 17 18 19 20 21 22 23 24
# [25] 2500004_informalexample_2.2_of_section_2.1.2.R
# informalexample 2.2 of section 2.1.2
# (informalexample 2.2 of section 2.1.2) : Starting with R and data : Starting with R : R programming
example_vector <- c(10, 20, 30) # Note: 1
example_list <- list(a = 10, b = 20, c = 30) # Note: 2
example_vector[1] # Note: 3 ## [1] 10
## [1] 10
example_list[1]## $a
## [1] 10
## $a
## [1] 10
example_vector[[2]] # Note: 4 ## [1] 20
## [1] 20
example_list[[2]]## [1] 20
## [1] 20
example_vector[c(FALSE, TRUE, TRUE)] # Note: 5 ## [1] 20 30
## [1] 20 30
example_list[c(FALSE, TRUE, TRUE)]## $b
## [1] 20
##
## $c
## [1] 30
## $b
## [1] 20
##
## $c
## [1] 30
example_list$b # Note: 6 ## [1] 20
## [1] 20
example_list[["b"]]## [1] 20
## [1] 20
# Note 1:
# Build an example vector. c() is R’s concatenate operator—it builds longer vectors and lists
# from shorter ones without nesting. For example
# c(1) is just the number 1, and c(1, c(2, 3)) is
# equivalent to c(1, 2, 3), which in turn is the
# integers 1 through 3 (though stored in a floating
# point format).
# Note 2:
# Build an example list.
# Note 3:
# Demonstrate vector and list use of []. Notice for the list [] returns a new short list, not the item.
# Note 4:
# Demonstrate vector and list use of [[]]. In common cases [[]] forces a single item to be returned, though for nested lists of complex type this item itself could be a list.
# Note 5:
# Vectors and lists be indexed by vectors of logicals, integers, and (if the vector or list has names) characters.
# Note 6:
# For named examples, the syntax example_list$b is essentially a short-hand for
# example_list[["b"]] (the same is true for named
# vectors). 00005_informalexample_2.3_of_section_2.1.2.R
# informalexample 2.3 of section 2.1.2
# (informalexample 2.3 of section 2.1.2) : Starting with R and data : Starting with R : R programming
x <- 1:5
print(x) # Note: 1 ## [1] 1 2 3 4 5
# [1] 1 2 3 4 5
# x <- cumsumMISSPELLED(x) # Note: 2
# Error in cumsumMISSPELLED(x) : could not find function "cumsumMISSPELLED"
print(x) # Note: 3 ## [1] 1 2 3 4 5
# [1] 1 2 3 4 5
x <- cumsum(x) # Note: 4
print(x)## [1] 1 3 6 10 15
# [1] 1 3 6 10 15
# Note 1:
# Define a value we are interested in and store it in the variable x.
# Note 2:
# Attempt, and fail, to assign a new result to x.
# Note 3:
# Notice in addition to supply a useful error message, R preserved the original value of x.
# Note 4:
# Try the operation again, using the correct spelling of cumsum(). cumsum(), short for cumulative sum, is a useful function that computes running totals quickly. 00006_informalexample_2.4_of_section_2.1.2.R
# informalexample 2.4 of section 2.1.2
# (informalexample 2.4 of section 2.1.2) : Starting with R and data : Starting with R : R programming
nchar("a string")## [1] 8
# [1] 8
nchar(c("a", "aa", "aaa", "aaaa"))## [1] 1 2 3 4
# [1] 1 2 3 400007_informalexample_2.5_of_section_2.1.2.R
# informalexample 2.5 of section 2.1.2
# (informalexample 2.5 of section 2.1.2) : Starting with R and data : Starting with R : R programming
1 +
2## [1] 3
00008_informalexample_2.6_of_section_2.1.2.R
# informalexample 2.6 of section 2.1.2
# (informalexample 2.6 of section 2.1.2) : Starting with R and data : Starting with R : R programming
1## [1] 1
+ 2## [1] 2
00009_informalexample_2.7_of_section_2.1.2.R
# informalexample 2.7 of section 2.1.2
# (informalexample 2.7 of section 2.1.2) : Starting with R and data : Starting with R : R programming
d <- data.frame(x = c(1, NA, 3)) # Note: 1
print(d)## x
## 1 1
## 2 NA
## 3 3
# x
# 1 1
# 2 NA
# 3 3 # Note: 2
d$x[is.na(d$x)] <- 0 # Note: 3
print(d)## x
## 1 1
## 2 0
## 3 3
# x
# 1 1
# 2 0
# 3 3
# Note 1:
# “data.frame” is R’s tabular data type, and the most important data type in R. A data.frame
# holds data organized in rows and columns.
# Note 2:
# When printing data.frames, row numbers are shown in the fist (unnamed) column, and column
# values are shown under their matching column
# names.
# Note 3:
# We can place a slice or selection of the x column of d on the left-hand side of an assignment to easily replace all NA values with zero. 00010_informalexample_2.8_of_section_2.1.2.R
# informalexample 2.8 of section 2.1.2
# (informalexample 2.8 of section 2.1.2) : Starting with R and data : Starting with R : R programming
d <- data.frame(x = 1, y = 2) # Note: 1
d2 <- d # Note: 2
d$x <- 5 # Note: 3
print(d)## x y
## 1 5 2
# x y
# 1 5 2
print(d2)## x y
## 1 1 2
# x y
# 1 1 2
# Note 1:
# Create some example data and refer to it by the name d.
# Note 2:
# Create an additional reference d2 to the same data.
# Note 3:
# Alter the value referred to by d. 00011_informalexample_2.9_of_section_2.1.2.R
# informalexample 2.9 of section 2.1.2
# (informalexample 2.9 of section 2.1.2) : Starting with R and data : Starting with R : R programming
data <- data.frame(revenue = c(2, 1, 2), # Note: 1
sort_key = c("b", "c", "a"),
stringsAsFactors = FALSE)
print(data)## revenue sort_key
## 1 2 b
## 2 1 c
## 3 2 a
# revenue sort_key
# 1 2 b
# 2 1 c
# 3 2 a
. <- data # Note: 2
. <- .[order(.$sort_key), , drop = FALSE] # Note: 3
.$ordered_sum_revenue <- cumsum(.$revenue)
.$fraction_revenue_seen <- .$ordered_sum_revenue/sum(.$revenue)
result <- . # Note: 4
print(result)## revenue sort_key ordered_sum_revenue fraction_revenue_seen
## 3 2 a 2 0.4
## 1 2 b 4 0.8
## 2 1 c 5 1.0
# revenue sort_key ordered_sum_revenue fraction_revenue_seen
# 3 2 a 2 0.4
# 1 2 b 4 0.8
# 2 1 c 5 1.0
# Note 1:
# Our notional, or example data.
# Note 2:
# Assign our data to a temporary variable named “.”. The original values will remain available
# in the “data” variable, making it easy to re-start
# the calculation from the beginning if
# necessary.
# Note 3:
# Use the order command to sort the rows. The “drop = FALSE” is not strictly needed, but it is
# good to get in the habit of including it. For
# single column data.frames without the “drop =
# FALSE” argument, the “[,]” indexing operator will
# convert the result to a vector, which is almost
# never the R user's true intent. The “drop = FALSE”
# argument turns off this conversion, and it is a
# good idea to include it “just in case” and a
# definite requirement when either the data.frame
# has a single column or when we don’t know if the
# data.frame has more than one column (as the
# data.frame comes from somewhere else).
# Note 4:
# Assign the result away from “.” to a more memorable variable name. 00012_informalexample_2.10_of_section_2.1.2.R
# informalexample 2.10 of section 2.1.2
# (informalexample 2.10 of section 2.1.2) : Starting with R and data : Starting with R : R programming
library("dplyr")##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
result <- data %>%
arrange(., sort_key) %>%
mutate(., ordered_sum_revenue = cumsum(revenue)) %>%
mutate(., fraction_revenue_seen = ordered_sum_revenue/sum(revenue))00013_informalexample_2.11_of_section_2.1.2.R
# informalexample 2.11 of section 2.1.2
# (informalexample 2.11 of section 2.1.2) : Starting with R and data : Starting with R : R programming
d <- data.frame(col1 = c(1, 2, 3), col2 = c(-1, 0, 1))
d$col3 <- d$col1 + d$col2
print(d)## col1 col2 col3
## 1 1 -1 0
## 2 2 0 2
## 3 3 1 4
# col1 col2 col3
# 1 1 -1 0
# 2 2 0 2
# 3 3 1 400015_example_2.1_of_section_2.2.1.R
# example 2.1 of section 2.2.1
# (example 2.1 of section 2.2.1) : Starting with R and data : Working with data from files : Working with well-structured data from files or URLs
# Title: Reading the UCI car data
uciCar <- read.table( # Note: 1
'../UCICar/car.data.csv', # Note: 2
sep = ',', # Note: 3
header = TRUE, # Note: 4
stringsAsFactor = TRUE # Note: 5
)
View(uciCar) # Note: 6
# Note 1:
# Command to read from a file or URL and store the result in a new data frame object
# called
# uciCar.
# Note 2:
# Filename or URL to get the data from.
# Note 3:
# Specify the column or field separator as a
# comma.
# Note 4:
# Tell R to expect a header line that defines
# the data column names.
# Note 5:
# Tell R to convert string values to factors. This is the default behavior, so we are just using this argument to document intent.
# Note 6:
# Examine the data with R’s built-in
# table viewer. 00016_example_2.2_of_section_2.2.1.R
# example 2.2 of section 2.2.1
# (example 2.2 of section 2.2.1) : Starting with R and data : Working with data from files : Working with well-structured data from files or URLs
# Title: Exploring the car data
class(uciCar)## [1] "data.frame"
## [1] "data.frame" # Note: 1
summary(uciCar)## buying maint doors persons lug_boot safety
## high :432 high :432 2 :432 2 :576 big :576 high:576
## low :432 low :432 3 :432 4 :576 med :576 low :576
## med :432 med :432 4 :432 more:576 small:576 med :576
## vhigh:432 vhigh:432 5more:432
## rating
## acc : 384
## good : 69
## unacc:1210
## vgood: 65
## buying maint doors
## high :432 high :432 2 :432
## low :432 low :432 3 :432
## med :432 med :432 4 :432
## vhigh:432 vhigh:432 5more:432
##
## persons lug_boot safety
## 2 :576 big :576 high:576
## 4 :576 med :576 low :576
## more:576 small:576 med :576
##
## rating
## acc : 384
## good : 69
## unacc:1210
## vgood: 65
dim(uciCar)## [1] 1728 7
## [1] 1728 7 # Note: 2
# Note 1:
# The loaded object uciCar is of type data.frame.
# Note 2:
# The “[1]” is just an output sequence
# marker. The actual information is this: uciCar has
# 1728 rows and 7 columns. Always try to confirm you
# got a good parse by at least checking that the
# number of rows is exactly one fewer than the
# number of lines of text in the original file. The
# difference of one is because the column header
# counts as a line of text, but not as a data row. 00018_example_2.3_of_section_2.2.2.R
# example 2.3 of section 2.2.2
# (example 2.3 of section 2.2.2) : Starting with R and data : Working with data from files : Using R with less-structured data
# Title: Loading the credit dataset
setwd("../../PDSwR2/Statlog") # Note: 1
d <- read.table('../Statlog/german.data', sep=' ',
stringsAsFactors = FALSE, header = FALSE)
# Note 1:
# Replace this path with the actual path where you have saved PDSwR2. 00019_example_2.4_of_section_2.2.2.R
# example 2.4 of section 2.2.2
# (example 2.4 of section 2.2.2) : Starting with R and data : Working with data from files : Using R with less-structured data
# Title: Setting column names
d <- read.table('../Statlog/german.data',
sep = " ",
stringsAsFactors = FALSE, header = FALSE)
colnames(d) <- c('Status_of_existing_checking_account', 'Duration_in_month',
'Credit_history', 'Purpose', 'Credit_amount', 'Savings_account_bonds',
'Present_employment_since',
'Installment_rate_in_percentage_of_disposable_income',
'Personal_status_and_sex', 'Other_debtors_guarantors',
'Present_residence_since', 'Property', 'Age_in_years',
'Other_installment_plans', 'Housing',
'Number_of_existing_credits_at_this_bank', 'Job',
'Number_of_people_being_liable_to_provide_maintenance_for',
'Telephone', 'foreign_worker', 'Good_Loan')
str(d)## 'data.frame': 1000 obs. of 21 variables:
## $ Status_of_existing_checking_account : chr "A11" "A12" "A14" "A11" ...
## $ Duration_in_month : int 6 48 12 42 24 36 24 36 12 30 ...
## $ Credit_history : chr "A34" "A32" "A34" "A32" ...
## $ Purpose : chr "A43" "A43" "A46" "A42" ...
## $ Credit_amount : int 1169 5951 2096 7882 4870 9055 2835 6948 3059 5234 ...
## $ Savings_account_bonds : chr "A65" "A61" "A61" "A61" ...
## $ Present_employment_since : chr "A75" "A73" "A74" "A74" ...
## $ Installment_rate_in_percentage_of_disposable_income : int 4 2 2 2 3 2 3 2 2 4 ...
## $ Personal_status_and_sex : chr "A93" "A92" "A93" "A93" ...
## $ Other_debtors_guarantors : chr "A101" "A101" "A101" "A103" ...
## $ Present_residence_since : int 4 2 3 4 4 4 4 2 4 2 ...
## $ Property : chr "A121" "A121" "A121" "A122" ...
## $ Age_in_years : int 67 22 49 45 53 35 53 35 61 28 ...
## $ Other_installment_plans : chr "A143" "A143" "A143" "A143" ...
## $ Housing : chr "A152" "A152" "A152" "A153" ...
## $ Number_of_existing_credits_at_this_bank : int 2 1 1 1 2 1 1 1 1 2 ...
## $ Job : chr "A173" "A173" "A172" "A173" ...
## $ Number_of_people_being_liable_to_provide_maintenance_for: int 1 1 2 2 2 2 1 1 1 1 ...
## $ Telephone : chr "A192" "A191" "A191" "A191" ...
## $ foreign_worker : chr "A201" "A201" "A201" "A201" ...
## $ Good_Loan : int 1 2 1 1 2 1 1 1 1 2 ...
## 'data.frame': 1000 obs. of 21 variables:
## $ Status_of_existing_checking_account : chr "A11" "A12" "A14" "A11" ...
## $ Duration_in_month : int 6 48 12 42 24 36 24 36 12 30 ...
## $ Credit_history : chr "A34" "A32" "A34" "A32" ...
## $ Purpose : chr "A43" "A43" "A46" "A42" ...
## $ Credit_amount : int 1169 5951 2096 7882 4870 9055 2835 6948 3059 5234 ...
## $ Savings_account_bonds : chr "A65" "A61" "A61" "A61" ...
## $ Present_employment_since : chr "A75" "A73" "A74" "A74" ...
## $ Installment_rate_in_percentage_of_disposable_income : int 4 2 2 2 3 2 3 2 2 4 ...
## $ Personal_status_and_sex : chr "A93" "A92" "A93" "A93" ...
## $ Other_debtors_guarantors : chr "A101" "A101" "A101" "A103" ...
## $ Present_residence_since : int 4 2 3 4 4 4 4 2 4 2 ...
## $ Property : chr "A121" "A121" "A121" "A122" ...
## $ Age_in_years : int 67 22 49 45 53 35 53 35 61 28 ...
## $ Other_installment_plans : chr "A143" "A143" "A143" "A143" ...
## $ Housing : chr "A152" "A152" "A152" "A153" ...
## $ Number_of_existing_credits_at_this_bank : int 2 1 1 1 2 1 1 1 1 2 ...
## $ Job : chr "A173" "A173" "A172" "A173" ...
## $ Number_of_people_being_liable_to_provide_maintenance_for: int 1 1 2 2 2 2 1 1 1 1 ...
## $ Telephone : chr "A192" "A191" "A191" "A191" ...
## $ foreign_worker : chr "A201" "A201" "A201" "A201" ...
## $ Good_Loan : int 1 2 1 1 2 1 1 1 1 2 ...00021_example_2.5_of_section_2.2.2.R
# example 2.5 of section 2.2.2
# (example 2.5 of section 2.2.2) : Starting with R and data : Working with data from files : Using R with less-structured data
# Title: Transforming the car data
source("../Statlog/mapping.R") # Note: 1
for(ci in colnames(d)) { # Note: 2
if(is.character(d[[ci]])) {
d[[ci]] <- as.factor(mapping[d[[ci]]]) # Note: 3
}
}
# Note 1:
# This file can be found at https://github.com/WinVector/PDSwR2/blob/master/Statlog/../Statlog/mapping.R .
# Note 2:
# Prefer using column names to column indices.
# Note 3:
# The [[]] notation is using the fact
# that data.frames are named lists of columns. So we
# are working on each column in turn. Notice the
# mapping lookup is vectorized: i.e. it is applied
# to all elements in the column in one step. 00022_example_2.6_of_section_2.2.2.R
# example 2.6 of section 2.2.2
# (example 2.6 of section 2.2.2) : Starting with R and data : Working with data from files : Using R with less-structured data
# Title: Summary of Good_Loan and Purpose
setwd("../../PDSwR2/Statlog") # Note: 1
d <- readRDS("creditdata.RDS") # Note: 2
table(d$Purpose, d$Good_Loan) ##
## BadLoan GoodLoan
## business 34 63
## car (new) 89 145
## car (used) 17 86
## domestic appliances 4 8
## education 22 28
## furniture/equipment 58 123
## others 5 7
## radio/television 62 218
## repairs 8 14
## retraining 1 8
## BadLoan GoodLoan
## business 34 63
## car (new) 89 145
## car (used) 17 86
## domestic appliances 4 8
## education 22 28
## furniture/equipment 58 123
## others 5 7
## radio/television 62 218
## repairs 8 14
## retraining 1 8
# Note 1:
# Set the working directory. You will have to replace ../../PDSwR2/Statlog with the actual full path to Statlog on your machine.
# Note 2:
# Read the prepared statlog data. 00024_example_2.8_of_section_2.3.1.R
# example 2.8 of section 2.3.1
# (example 2.8 of section 2.3.1) : Starting with R and data : Working with relational databases : A production-size example
# Title: Loading data into R from a relational database
library("DBI")
library("dplyr") # Note: 1
library("rquery")
dlist <- readRDS("../PUMS/PUMSsample.RDS") # Note: 2
db <- dbConnect(RSQLite::SQLite(), ":memory:") # Note: 3
dbWriteTable(db, "dpus", as.data.frame(dlist$ss16pus)) # Note: 4
dbWriteTable(db, "dhus", as.data.frame(dlist$ss16hus))
rm(list = "dlist") # Note: 5
dbGetQuery(db, "SELECT * FROM dpus LIMIT 5") # Note: 6 ## RT SERIALNO SPORDER PUMA ST ADJINC AGEP CIT
## 1 P 000000338 03 02701 Alabama/AL 1007588 06 Born in the U.S.
## 2 P 000000338 05 02701 Alabama/AL 1007588 08 Born in the U.S.
## 3 P 000000343 03 01400 Alabama/AL 1007588 12 Born in the U.S.
## 4 P 000000539 04 01400 Alabama/AL 1007588 11 Born in the U.S.
## 5 P 000002284 02 00600 Alabama/AL 1007588 08 Born in the U.S.
## CITWP COW DDRS DEAR DEYE DOUT DPHY DRAT DRATX DREM ENG FER GCL GCM
## 1 <NA> <NA> No No No <NA> No <NA> <NA> No <NA> <NA> <NA> <NA>
## 2 <NA> <NA> No No No <NA> No <NA> <NA> No <NA> <NA> <NA> <NA>
## 3 <NA> <NA> No No No <NA> No <NA> <NA> Yes <NA> <NA> <NA> <NA>
## 4 <NA> <NA> No No No <NA> No <NA> <NA> Yes <NA> <NA> <NA> <NA>
## 5 <NA> <NA> No No No <NA> No <NA> <NA> No <NA> <NA> <NA> <NA>
## GCR HINS1 HINS2 HINS3 HINS4 HINS5 HINS6 HINS7 INTP JWMNP JWRIP JWTR
## 1 <NA> No No No Yes No No No <NA> <NA> <NA> <NA>
## 2 <NA> No No No Yes No No No <NA> <NA> <NA> <NA>
## 3 <NA> No No No Yes No No No <NA> <NA> <NA> <NA>
## 4 <NA> No No No Yes No No No <NA> <NA> <NA> <NA>
## 5 <NA> Yes No No No No No No <NA> <NA> <NA> <NA>
## LANP LANX MAR MARHD
## 1 <NA> No, speaks only English Never married or under 15 years old <NA>
## 2 <NA> No, speaks only English Never married or under 15 years old <NA>
## 3 <NA> No, speaks only English Never married or under 15 years old <NA>
## 4 <NA> No, speaks only English Never married or under 15 years old <NA>
## 5 <NA> No, speaks only English Never married or under 15 years old <NA>
## MARHM MARHT MARHW MARHYP MIG MIL MLPA MLPB
## 1 <NA> <NA> <NA> <NA> Yes, same house (nonmovers) <NA> <NA> <NA>
## 2 <NA> <NA> <NA> <NA> Yes, same house (nonmovers) <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA> Yes, same house (nonmovers) <NA> <NA> <NA>
## 4 <NA> <NA> <NA> <NA> Yes, same house (nonmovers) <NA> <NA> <NA>
## 5 <NA> <NA> <NA> <NA> Yes, same house (nonmovers) <NA> <NA> <NA>
## MLPCD MLPE MLPFG MLPH MLPI MLPJ MLPK NWAB NWAV NWLA NWLK NWRE OIP PAP
## 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## RELP RETP SCH
## 1 Biological son or daughter <NA> Yes, public school or public college
## 2 Stepson or stepdaughter <NA> Yes, public school or public college
## 3 Biological son or daughter <NA> Yes, public school or public college
## 4 Biological son or daughter <NA> Yes, public school or public college
## 5 Other relative <NA> Yes, public school or public college
## SCHG SCHL SEMP SEX SSIP SSP WAGP WKHP WKL WKW WRK YOEP
## 1 Grade 1 Kindergarten <NA> Female <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 Grade 2 Grade 1 <NA> Female <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 Grade 6 Grade 5 <NA> Female <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 Grade 4 Grade 4 <NA> Male <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 Grade 1 Kindergarten <NA> Male <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## ANC ANC1P ANC2P DECADE DIS
## 1 Single African American Not reported <NA> Without a disability
## 2 Single African American Not reported <NA> Without a disability
## 3 Multiple African American Irish <NA> With a disability
## 4 Not reported Not reported Not reported <NA> With a disability
## 5 Not reported Not reported Not reported <NA> Without a disability
## DRIVESP ESP ESR FHICOVP FOD1P FOD2P
## 1 <NA> Both parents in labor force <NA> No <NA> <NA>
## 2 <NA> Both parents in labor force <NA> No <NA> <NA>
## 3 <NA> Mother in the labor force <NA> No <NA> <NA>
## 4 <NA> Both parents in labor force <NA> No <NA> <NA>
## 5 <NA> <NA> <NA> Yes <NA> <NA>
## HICOV HISP INDP JWAP
## 1 With health insurance coverage Not Spanish/Hispanic/Latino <NA> <NA>
## 2 With health insurance coverage Not Spanish/Hispanic/Latino <NA> <NA>
## 3 With health insurance coverage Not Spanish/Hispanic/Latino <NA> <NA>
## 4 With health insurance coverage Not Spanish/Hispanic/Latino <NA> <NA>
## 5 With health insurance coverage Not Spanish/Hispanic/Latino <NA> <NA>
## JWDP MIGPUMA MIGSP MSP NAICSP NATIVITY
## 1 <NA> <NA> <NA> <NA> <NA> Native
## 2 <NA> <NA> <NA> <NA> <NA> Native
## 3 <NA> <NA> <NA> <NA> <NA> Native
## 4 <NA> <NA> <NA> <NA> <NA> Native
## 5 <NA> <NA> <NA> <NA> <NA> Native
## NOP OC OCCP PAOC
## 1 Living with two parents: Both parents NATIVE Yes <NA> <NA>
## 2 Living with two parents: Both parents NATIVE Yes <NA> <NA>
## 3 Living with mother only: Mother NATIVE Yes <NA> <NA>
## 4 Living with two parents: Both parents NATIVE Yes <NA> <NA>
## 5 <NA> No (includes GQ) <NA> <NA>
## PERNP PINCP POBP POVPIP POWPUMA POWSP
## 1 <NA> <NA> Alabama/AL 158 <NA> <NA>
## 2 <NA> <NA> Alabama/AL 158 <NA> <NA>
## 3 <NA> <NA> Alabama/AL 072 <NA> <NA>
## 4 <NA> <NA> Alabama/AL 003 <NA> <NA>
## 5 <NA> <NA> Alabama/AL 079 <NA> <NA>
## PRIVCOV PUBCOV
## 1 Without private health insurance coverage With public health coverage
## 2 Without private health insurance coverage With public health coverage
## 3 Without private health insurance coverage With public health coverage
## 4 Without private health insurance coverage With public health coverage
## 5 With private health insurance coverage Without public health coverage
## QTRBIR RAC1P
## 1 April through June Black or African American alone
## 2 January through March Black or African American alone
## 3 April through June Two or More Races
## 4 January through March White alone
## 5 April through June White alone
## RAC2P RAC3P RACAIAN
## 1 Black or African American alone Black or African American alone No
## 2 Black or African American alone Black or African American alone No
## 3 Two or More Races White; Black or African American No
## 4 White alone White alone No
## 5 White alone White alone No
## RACASN RACBLK RACNH RACNUM RACPI RACSOR RACWHT RC SCIENGP SCIENGRLP
## 1 No Yes No 1 No No No Yes <NA> <NA>
## 2 No Yes No 1 No No No Yes <NA> <NA>
## 3 No Yes No 2 No No Yes Yes <NA> <NA>
## 4 No No No 1 No No Yes Yes <NA> <NA>
## 5 No No No 1 No No Yes Yes <NA> <NA>
## SFN SFR SOCP VPS WAOB FAGEP FANCP FCITP FCITWP
## 1 <NA> <NA> <NA> <NA> US state (POB = 001-059) No No No No
## 2 <NA> <NA> <NA> <NA> US state (POB = 001-059) No No No No
## 3 <NA> <NA> <NA> <NA> US state (POB = 001-059) No No No No
## 4 <NA> <NA> <NA> <NA> US state (POB = 001-059) No No No No
## 5 <NA> <NA> <NA> <NA> US state (POB = 001-059) No No No No
## FCOWP FDDRSP FDEARP FDEYEP FDISP FDOUTP FDPHYP FDRATP FDRATXP FDREMP
## 1 No No No No No No No No No No
## 2 No No No No No No No No No No
## 3 No No No No No No No No No No
## 4 No No No No No No No No No No
## 5 No Yes Yes Yes Yes No Yes No No Yes
## FENGP FESRP FFERP FFODP FGCLP FGCMP FGCRP FHINS1P FHINS2P FHINS3C
## 1 No No No No No No No No No <NA>
## 2 No No No No No No No No No <NA>
## 3 No No No No No No No No No <NA>
## 4 No No No No No No No No No <NA>
## 5 No No No No No No No Yes Yes <NA>
## FHINS3P FHINS4C FHINS4P FHINS5C FHINS5P FHINS6P FHINS7P FHISP FINDP
## 1 No No No <NA> No No No No No
## 2 No No No <NA> No No No No No
## 3 No No No <NA> No No No No No
## 4 No No No <NA> No No No No No
## 5 Yes <NA> Yes <NA> Yes Yes Yes No No
## FINTP FJWDP FJWMNP FJWRIP FJWTRP FLANP FLANXP FMARHDP FMARHMP FMARHTP
## 1 No No No No No No No No No No
## 2 No No No No No No No No No No
## 3 No No No No No No No No No No
## 4 No No No No No No No No No No
## 5 No No No No No No No No No No
## FMARHWP FMARHYP FMARP FMIGP FMIGSP FMILPP FMILSP FOCCP FOIP FPAP FPERNP
## 1 No No No No No No No No No No No
## 2 No No No No No No No No No No No
## 3 No No No No No No No No No No No
## 4 No No No No No No No No No No No
## 5 No No No No No No No No No No No
## FPINCP FPOBP FPOWSP FPRIVCOVP FPUBCOVP FRACP FRELP FRETP FSCHGP FSCHLP
## 1 No No No No No No No No No No
## 2 No No No No No No No No No No
## 3 No No No No No No No No No No
## 4 No No No No No No No No No No
## 5 No No No Yes Yes No No No No No
## FSCHP FSEMP FSEXP FSSIP FSSP FWAGP FWKHP FWKLP FWKWP FWRKP FYOEP
## 1 No No No No No No No No No No No
## 2 No No No No No No No No No No No
## 3 No No No No No No No No No No No
## 4 No No No No No No No No No No No
## 5 No No No No No No No No No No No
dpus <- tbl(db, "dpus") # Note: 7
dhus <- tbl(db, "dhus")
print(dpus) # Note: 8 ## # Source: table<dpus> [?? x 203]
## # Database: sqlite 3.29.0 [:memory:]
## RT SERIALNO SPORDER PUMA ST ADJINC AGEP CIT CITWP COW DDRS
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 P 0000003… 03 02701 Alab… 10075… 06 Born… <NA> <NA> No
## 2 P 0000003… 05 02701 Alab… 10075… 08 Born… <NA> <NA> No
## 3 P 0000003… 03 01400 Alab… 10075… 12 Born… <NA> <NA> No
## 4 P 0000005… 04 01400 Alab… 10075… 11 Born… <NA> <NA> No
## 5 P 0000022… 02 00600 Alab… 10075… 08 Born… <NA> <NA> No
## 6 P 0000062… 03 02500 Alab… 10075… Unde… Born… <NA> <NA> <NA>
## 7 P 0000066… 03 02400 Alab… 10075… 24 Born… <NA> Empl… No
## 8 P 0000078… 01 00302 Alab… 10075… 82 Born… <NA> <NA> No
## 9 P 0000083… 04 02702 Alab… 10075… 31 Born… <NA> Empl… No
## 10 P 0000085… 01 02701 Alab… 10075… 93 Born… <NA> <NA> No
## # … with more rows, and 192 more variables: DEAR <chr>, DEYE <chr>,
## # DOUT <chr>, DPHY <chr>, DRAT <chr>, DRATX <chr>, DREM <chr>,
## # ENG <chr>, FER <chr>, GCL <chr>, GCM <chr>, GCR <chr>, HINS1 <chr>,
## # HINS2 <chr>, HINS3 <chr>, HINS4 <chr>, HINS5 <chr>, HINS6 <chr>,
## # HINS7 <chr>, INTP <chr>, JWMNP <chr>, JWRIP <chr>, JWTR <chr>,
## # LANP <chr>, LANX <chr>, MAR <chr>, MARHD <chr>, MARHM <chr>,
## # MARHT <chr>, MARHW <chr>, MARHYP <chr>, MIG <chr>, MIL <chr>,
## # MLPA <chr>, MLPB <chr>, MLPCD <chr>, MLPE <chr>, MLPFG <chr>,
## # MLPH <chr>, MLPI <chr>, MLPJ <chr>, MLPK <chr>, NWAB <chr>,
## # NWAV <chr>, NWLA <chr>, NWLK <chr>, NWRE <chr>, OIP <chr>, PAP <chr>,
## # RELP <chr>, RETP <chr>, SCH <chr>, SCHG <chr>, SCHL <chr>, SEMP <chr>,
## # SEX <chr>, SSIP <chr>, SSP <chr>, WAGP <chr>, WKHP <chr>, WKL <chr>,
## # WKW <chr>, WRK <chr>, YOEP <chr>, ANC <chr>, ANC1P <chr>, ANC2P <chr>,
## # DECADE <chr>, DIS <chr>, DRIVESP <chr>, ESP <chr>, ESR <chr>,
## # FHICOVP <chr>, FOD1P <chr>, FOD2P <chr>, HICOV <chr>, HISP <chr>,
## # INDP <chr>, JWAP <chr>, JWDP <chr>, MIGPUMA <chr>, MIGSP <chr>,
## # MSP <chr>, NAICSP <chr>, NATIVITY <chr>, NOP <chr>, OC <chr>,
## # OCCP <chr>, PAOC <chr>, PERNP <chr>, PINCP <chr>, POBP <chr>,
## # POVPIP <chr>, POWPUMA <chr>, POWSP <chr>, PRIVCOV <chr>, PUBCOV <chr>,
## # QTRBIR <chr>, RAC1P <chr>, RAC2P <chr>, …
glimpse(dpus)## Observations: ??
## Variables: 203
## Database: sqlite 3.29.0 [:memory:]
## $ RT <chr> "P", "P", "P", "P", "P", "P", "P", "P", "P", "P", "P",…
## $ SERIALNO <chr> "000000338", "000000338", "000000343", "000000539", "0…
## $ SPORDER <chr> "03", "05", "03", "04", "02", "03", "03", "01", "04", …
## $ PUMA <chr> "02701", "02701", "01400", "01400", "00600", "02500", …
## $ ST <chr> "Alabama/AL", "Alabama/AL", "Alabama/AL", "Alabama/AL"…
## $ ADJINC <chr> "1007588", "1007588", "1007588", "1007588", "1007588",…
## $ AGEP <chr> "06", "08", "12", "11", "08", "Under 1 year", "24", "8…
## $ CIT <chr> "Born in the U.S.", "Born in the U.S.", "Born in the U…
## $ CITWP <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ COW <chr> NA, NA, NA, NA, NA, NA, "Employee of a private for-pro…
## $ DDRS <chr> "No", "No", "No", "No", "No", NA, "No", "No", "No", "N…
## $ DEAR <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ DEYE <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ DOUT <chr> NA, NA, NA, NA, NA, NA, "No", "No", "No", "Yes", "No",…
## $ DPHY <chr> "No", "No", "No", "No", "No", NA, "No", "Yes", "No", "…
## $ DRAT <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ DRATX <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "No", NA, NA, NA, …
## $ DREM <chr> "No", "No", "Yes", "Yes", "No", NA, "No", "No", "No", …
## $ ENG <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ FER <chr> NA, NA, NA, NA, NA, NA, NA, NA, "No", NA, NA, NA, NA, …
## $ GCL <chr> NA, NA, NA, NA, NA, NA, NA, "No", "No", "No", "No", NA…
## $ GCM <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ GCR <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ HINS1 <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "Yes…
## $ HINS2 <chr> "No", "No", "No", "No", "No", "No", "No", "Yes", "No",…
## $ HINS3 <chr> "No", "No", "No", "No", "No", "No", "No", "Yes", "No",…
## $ HINS4 <chr> "Yes", "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "…
## $ HINS5 <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ HINS6 <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ HINS7 <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ INTP <chr> NA, NA, NA, NA, NA, NA, "0000000", "0000900", "0000000…
## $ JWMNP <chr> NA, NA, NA, NA, NA, NA, "005", NA, "030", NA, "005", N…
## $ JWRIP <chr> NA, NA, NA, NA, NA, NA, "Drove alone", NA, "Drove alon…
## $ JWTR <chr> NA, NA, NA, NA, NA, NA, "Car, truck, or van", NA, "Car…
## $ LANP <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ LANX <chr> "No, speaks only English", "No, speaks only English", …
## $ MAR <chr> "Never married or under 15 years old", "Never married …
## $ MARHD <chr> NA, NA, NA, NA, NA, NA, NA, "No", NA, "No", "No", NA, …
## $ MARHM <chr> NA, NA, NA, NA, NA, NA, NA, "No", NA, "No", "No", NA, …
## $ MARHT <chr> NA, NA, NA, NA, NA, NA, NA, "One time", NA, "One time"…
## $ MARHW <chr> NA, NA, NA, NA, NA, NA, NA, "No", NA, "No", "No", NA, …
## $ MARHYP <chr> NA, NA, NA, NA, NA, NA, NA, "1952", NA, "1952", "2009"…
## $ MIG <chr> "Yes, same house (nonmovers)", "Yes, same house (nonmo…
## $ MIL <chr> NA, NA, NA, NA, NA, NA, "Never served in the military"…
## $ MLPA <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Did not serve thi…
## $ MLPB <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Did not serve thi…
## $ MLPCD <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Did not serve thi…
## $ MLPE <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Did not serve thi…
## $ MLPFG <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Did not serve thi…
## $ MLPH <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Did not serve thi…
## $ MLPI <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Did not serve thi…
## $ MLPJ <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Served this perio…
## $ MLPK <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Did not serve thi…
## $ NWAB <chr> NA, NA, NA, NA, NA, NA, "Did not report", "No", "Did n…
## $ NWAV <chr> NA, NA, NA, NA, NA, NA, "Did not report", "Did not rep…
## $ NWLA <chr> NA, NA, NA, NA, NA, NA, "Did not report", "No", "Did n…
## $ NWLK <chr> NA, NA, NA, NA, NA, NA, "Did not report", "No", "Did n…
## $ NWRE <chr> NA, NA, NA, NA, NA, NA, "Did not report", "Did not rep…
## $ OIP <chr> NA, NA, NA, NA, NA, NA, "None", "None", "None", "None"…
## $ PAP <chr> NA, NA, NA, NA, NA, NA, "None", "None", "None", "None"…
## $ RELP <chr> "Biological son or daughter", "Stepson or stepdaughter…
## $ RETP <chr> NA, NA, NA, NA, NA, NA, "None", "005800", "None", "024…
## $ SCH <chr> "Yes, public school or public college", "Yes, public s…
## $ SCHG <chr> "Grade 1", "Grade 2", "Grade 6", "Grade 4", "Grade 1",…
## $ SCHL <chr> "Kindergarten", "Grade 1", "Grade 5", "Grade 4", "Kind…
## $ SEMP <chr> NA, NA, NA, NA, NA, NA, "0000000", "0000000", "0000000…
## $ SEX <chr> "Female", "Female", "Female", "Male", "Male", "Female"…
## $ SSIP <chr> NA, NA, NA, NA, NA, NA, "None", "None", "None", "None"…
## $ SSP <chr> NA, NA, NA, NA, NA, NA, "None", "16500", "None", "1350…
## $ WAGP <chr> NA, NA, NA, NA, NA, NA, "0022000", "0000000", "0021000…
## $ WKHP <chr> NA, NA, NA, NA, NA, NA, "40", NA, "40", NA, "40", NA, …
## $ WKL <chr> NA, NA, NA, NA, NA, NA, "Within the past 12 months", "…
## $ WKW <chr> NA, NA, NA, NA, NA, NA, "50 to 52 weeks worked during …
## $ WRK <chr> NA, NA, NA, NA, NA, NA, NA, "Did not work", "Worked", …
## $ YOEP <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ANC <chr> "Single", "Single", "Multiple", "Not reported", "Not r…
## $ ANC1P <chr> "African American", "African American", "African Ameri…
## $ ANC2P <chr> "Not reported", "Not reported", "Irish", "Not reported…
## $ DECADE <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ DIS <chr> "Without a disability", "Without a disability", "With …
## $ DRIVESP <chr> NA, NA, NA, NA, NA, NA, "1.000 vehicles (Drove alone)"…
## $ ESP <chr> "Both parents in labor force", "Both parents in labor …
## $ ESR <chr> NA, NA, NA, NA, NA, NA, "Civilian employed, at work", …
## $ FHICOVP <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FOD1P <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "FINE ARTS", "ACCO…
## $ FOD2P <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ HICOV <chr> "With health insurance coverage", "With health insuran…
## $ HISP <chr> "Not Spanish/Hispanic/Latino", "Not Spanish/Hispanic/L…
## $ INDP <chr> NA, NA, NA, NA, NA, NA, "TRN-TRUCK TRANSPORTATION", NA…
## $ JWAP <chr> NA, NA, NA, NA, NA, NA, "6:35 a.m. to 6:39 a.m.", NA, …
## $ JWDP <chr> NA, NA, NA, NA, NA, NA, "6:30 a.m. to 6:34 a.m.", NA, …
## $ MIGPUMA <chr> NA, NA, NA, NA, NA, NA, NA, "00290", NA, NA, NA, NA, N…
## $ MIGSP <chr> NA, NA, NA, NA, NA, NA, NA, "Alabama/AL", NA, NA, NA, …
## $ MSP <chr> NA, NA, NA, NA, NA, NA, "Never married", "Divorced", "…
## $ NAICSP <chr> NA, NA, NA, NA, NA, NA, "TRN-TRUCK TRANSPORTATION", NA…
## $ NATIVITY <chr> "Native", "Native", "Native", "Native", "Native", "Nat…
## $ NOP <chr> "Living with two parents: Both parents NATIVE", "Livin…
## $ OC <chr> "Yes", "Yes", "Yes", "Yes", "No (includes GQ)", "Yes",…
## $ OCCP <chr> NA, NA, NA, NA, NA, NA, "TRN-DRIVER/SALES WORKERS AND …
## $ PAOC <chr> NA, NA, NA, NA, NA, NA, NA, "Females with no own child…
## $ PERNP <chr> NA, NA, NA, NA, NA, NA, "000022000", "000000000", "000…
## $ PINCP <chr> NA, NA, NA, NA, NA, NA, "000022000", "000023200", "000…
## $ POBP <chr> "Alabama/AL", "Alabama/AL", "Alabama/AL", "Alabama/AL"…
## $ POVPIP <chr> "158", "158", "072", "003", "079", "109", "346", "202"…
## $ POWPUMA <chr> NA, NA, NA, NA, NA, NA, "02400", NA, "02700", NA, "007…
## $ POWSP <chr> NA, NA, NA, NA, NA, NA, "Alabama/AL", NA, "Alabama/AL"…
## $ PRIVCOV <chr> "Without private health insurance coverage", "Without …
## $ PUBCOV <chr> "With public health coverage", "With public health cov…
## $ QTRBIR <chr> "April through June", "January through March", "April …
## $ RAC1P <chr> "Black or African American alone", "Black or African A…
## $ RAC2P <chr> "Black or African American alone", "Black or African A…
## $ RAC3P <chr> "Black or African American alone", "Black or African A…
## $ RACAIAN <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ RACASN <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ RACBLK <chr> "Yes", "Yes", "Yes", "No", "No", "No", "Yes", "No", "Y…
## $ RACNH <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ RACNUM <chr> "1", "1", "2", "1", "1", "1", "1", "1", "1", "1", "1",…
## $ RACPI <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ RACSOR <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ RACWHT <chr> "No", "No", "Yes", "Yes", "Yes", "Yes", "No", "Yes", "…
## $ RC <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No (include…
## $ SCIENGP <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "No", "No", NA, NA…
## $ SCIENGRLP <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "No", "No", NA, NA…
## $ SFN <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ SFR <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ SOCP <chr> NA, NA, NA, NA, NA, NA, "TRN-DRIVER/SALES WORKERS AND …
## $ VPS <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "WWII", NA, NA, NA…
## $ WAOB <chr> "US state (POB = 001-059)", "US state (POB = 001-059)"…
## $ FAGEP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FANCP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FCITP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FCITWP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FCOWP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FDDRSP <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FDEARP <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FDEYEP <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FDISP <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FDOUTP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FDPHYP <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FDRATP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FDRATXP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FDREMP <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FENGP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FESRP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FFERP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FFODP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FGCLP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FGCMP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FGCRP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FHINS1P <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FHINS2P <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FHINS3C <chr> NA, NA, NA, NA, NA, NA, NA, "No", NA, "No", "No", NA, …
## $ FHINS3P <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FHINS4C <chr> "No", "No", "No", "No", NA, "No", NA, NA, NA, NA, NA, …
## $ FHINS4P <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FHINS5C <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ FHINS5P <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FHINS6P <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FHINS7P <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FHISP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FINDP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FINTP <chr> "No", "No", "No", "No", "No", "No", "Yes", "Yes", "No"…
## $ FJWDP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FJWMNP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FJWRIP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FJWTRP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FLANP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FLANXP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FMARHDP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FMARHMP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FMARHTP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FMARHWP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FMARHYP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FMARP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FMIGP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FMIGSP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FMILPP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FMILSP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FOCCP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FOIP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FPAP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FPERNP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "Yes"…
## $ FPINCP <chr> "No", "No", "No", "No", "No", "No", "Yes", "Yes", "Yes…
## $ FPOBP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FPOWSP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FPRIVCOVP <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FPUBCOVP <chr> "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No"…
## $ FRACP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FRELP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FRETP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FSCHGP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FSCHLP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FSCHP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FSEMP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FSEXP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FSSIP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FSSP <chr> "No", "No", "No", "No", "No", "No", "Yes", "Yes", "No"…
## $ FWAGP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "Yes"…
## $ FWKHP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FWKLP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FWKWP <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No",…
## $ FWRKP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
## $ FYOEP <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", …
View(rsummary(db, "dpus")) # Note: 9
# Note 1:
# Attach some packages we wish to use commands
# and functions from.
# Note 2:
# Load the data from the compressed RDS disk
# format into R memory. Note: you will need to
# change the path PUMSsample to where you have saved
# the contents of PDSwR2/PUMS.
# Note 3:
# Connect to a new RSQLite in-memory database.
# We are going to use RSQLite for our examples. In
# practice you would connect to a pre-existing
# database such as PostgreSQL or Spark with
# pre-existing tables.
# Note 4:
# Copy the data from the in-memory structure
# dlist into the database.
# Note 5:
# Remove our local copy of the data, as we are simulating having found the data in the database.
# Note 6:
# Use the SQL query language for a quick look at up to 5 rows of our data.
# Note 7:
# Build dplyr handles that refer to the remote database data.
# Note 8:
# Use dplyr to examine and work with the remote data.
# Note 9:
# Use the rquery package to get a summary of the remote data. 00025_example_2.9_of_section_2.3.1.R
# example 2.9 of section 2.3.1
# (example 2.9 of section 2.3.1) : Starting with R and data : Working with relational databases : A production-size example
# Title: Loading data from a database
dpus <- dbReadTable(db, "dpus") # Note: 1
dpus <- dpus[, c("AGEP", "COW", "ESR", "PERNP",
"PINCP","SCHL", "SEX", "WKHP")] # Note: 2
for(ci in c("AGEP", "PERNP", "PINCP", "WKHP")) { # Note: 3
dpus[[ci]] <- as.numeric(dpus[[ci]])
}## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion
dpus$COW <- strtrim(dpus$COW, 50) # Note: 4
str(dpus) # Note: 5## 'data.frame': 100000 obs. of 8 variables:
## $ AGEP : num 6 8 12 11 8 NA 24 82 31 93 ...
## $ COW : chr NA NA NA NA ...
## $ ESR : chr NA NA NA NA ...
## $ PERNP: num NA NA NA NA NA NA 22000 0 21000 0 ...
## $ PINCP: num NA NA NA NA NA ...
## $ SCHL : chr "Kindergarten" "Grade 1" "Grade 5" "Grade 4" ...
## $ SEX : chr "Female" "Female" "Female" "Male" ...
## $ WKHP : num NA NA NA NA NA NA 40 NA 40 NA ...
# Note 1:
# Copy data from the database into R memory. This assumes we are continuing from the previous
# example, so the packages we have attached are
# still available and the database handle
# db is still valid.
# Note 2:
# Select a subset of columns we want to work with. Restricting columns is not required, but
# improves legibility of later printing.
# Note 3:
# All of the columns in this copy of PUMS data are stored as character type to preserve features such as leading zeros
# from the original data. Here we are converting columns we wish to treat as numeric to the numeric type. Non-numeric values, often missing entries,
# get coded with the symbol NA which stands for not available.
# Note 4:
# The PUMS level names are very long (which is one of the reasons these columns are distributed
# as integers), so for this dataset that has level
# names instead of level codes we are shortening the
# employment codes to no more than 50
# characters.
# Note 5:
# Look at the first few rows of data in a column orientation. 00026_example_2.10_of_section_2.3.1.R
# example 2.10 of section 2.3.1
# (example 2.10 of section 2.3.1) : Starting with R and data : Working with relational databases : A production-size example
# Title: Re-mapping values and selecting rows from data
target_emp_levs <- c( # Note: 1
"Employee of a private for-profit company or busine",
"Employee of a private not-for-profit, tax-exempt, ",
"Federal government employee",
"Local government employee (city, county, etc.)",
"Self-employed in own incorporated business, profes",
"Self-employed in own not incorporated business, pr",
"State government employee")
complete <- complete.cases(dpus) # Note: 2
stdworker <- with(dpus, # Note: 3
(PINCP>1000) &
(ESR=="Civilian employed, at work") &
(PINCP<=250000) &
(PERNP>1000) & (PERNP<=250000) &
(WKHP>=30) &
(AGEP>=18) & (AGEP<=65) &
(COW %in% target_emp_levs))
dpus <- dpus[complete & stdworker, , drop = FALSE] # Note: 4
no_advanced_degree <- is.na(dpus$SCHL) | # Note: 5
(!(dpus$SCHL %in% c("Associate's degree",
"Bachelor's degree",
"Doctorate degree",
"Master's degree",
"Professional degree beyond a bachelor's degree")))
dpus$SCHL[no_advanced_degree] <- "No Advanced Degree"
dpus$SCHL <- relevel(factor(dpus$SCHL), # Note: 6
"No Advanced Degree")
dpus$COW <- relevel(factor(dpus$COW),
target_emp_levs[[1]])
dpus$ESR <- relevel(factor(dpus$ESR),
"Civilian employed, at work")
dpus$SEX <- relevel(factor(dpus$SEX),
"Male")
saveRDS(dpus, "dpus_std_employee.RDS") # Note: 7
summary(dpus) # Note: 8## AGEP
## Min. :18.00
## 1st Qu.:31.00
## Median :41.00
## Mean :41.34
## 3rd Qu.:52.00
## Max. :65.00
##
## COW
## Employee of a private for-profit company or busine:26101
## Employee of a private not-for-profit, tax-exempt, : 2877
## Federal government employee : 978
## Local government employee (city, county, etc.) : 2589
## Self-employed in own incorporated business, profes: 1180
## Self-employed in own not incorporated business, pr: 1643
## State government employee : 1743
## ESR PERNP PINCP
## Civilian employed, at work:37111 Min. : 1100 Min. : 1100
## 1st Qu.: 24000 1st Qu.: 25000
## Median : 40000 Median : 40000
## Mean : 50268 Mean : 51584
## 3rd Qu.: 65000 3rd Qu.: 66000
## Max. :250000 Max. :250000
##
## SCHL SEX
## No Advanced Degree :20461 Male :20125
## Associate's degree : 3670 Female:16986
## Bachelor's degree : 8407
## Doctorate degree : 478
## Master's degree : 3386
## Professional degree beyond a bachelor's degree: 709
##
## WKHP
## Min. :30.00
## 1st Qu.:40.00
## Median :40.00
## Mean :42.66
## 3rd Qu.:45.00
## Max. :98.00
##
# Note 1:
# Define a vector of employment definitions we consider “standard.”
# Note 2:
# Build a new logical vector indicating which rows have valid values in all of our columns of
# interest. In real applications, dealing with
# missing values is important and cannot always be
# handled by skipping incomplete rows. We will
# return to the issue of properly dealing with
# missing values when we discuss managing
# data.
# Note 3:
# Build a new logical vector indicating which workers we consider typical full-time employees.
# Each of these column names are the ones we
# discussed earlier. The results of any analysis
# will be heavily influenced by this definition, so
# in a real task we would spend a lot of time
# researching the choices in this step. It literally
# controls who and what we are studying. Notice to
# keep things simple and homogeneous we restricted
# this study to civilians, which would be an
# unacceptable limitation in a complete work.
# Note 4:
# Restrict only to rows or examples that meet or definition of typical worker.
# Note 5:
# Re-code education, merging the less-than-bachelor’s-degree levels to the single level “No
# Advanced Degree”.
# Note 6:
# Convert our string-valued columns to factors, picking the reference level with the re-level()
# function.
# Note 7:
# Save this data to a file so we can use it in later examples. This file is also already
# available at the path
# PDSwR2/PUMS/dpus_std_employee.RDS
# Note 8:
# Take a look at our data. One of the advantages of factors is summary() builds up useful counts
# for them. However, it was best to delay having
# string codes as factors until after we finished
# with re-mapping level codes. 00027_informalexample_2.15_of_section_2.3.1.R
# informalexample 2.15 of section 2.3.1
# (informalexample 2.15 of section 2.3.1) : Starting with R and data : Working with relational databases : A production-size example
levels(dpus$SCHL) # Note: 1 ## [1] "No Advanced Degree"
## [2] "Associate's degree"
## [3] "Bachelor's degree"
## [4] "Doctorate degree"
## [5] "Master's degree"
## [6] "Professional degree beyond a bachelor's degree"
## [1] "No Advanced Degree" "Associate's degree"
## [3] "Bachelor's degree" "Doctorate degree"
## [5] "Master's degree" "Professional degree beyond a bachelor's degree"
head(dpus$SCHL) # Note: 2 ## [1] No Advanced Degree No Advanced Degree No Advanced Degree
## [4] Bachelor's degree Associate's degree Associate's degree
## 6 Levels: No Advanced Degree Associate's degree ... Professional degree beyond a bachelor's degree
## [1] Associate's degree Associate's degree Associate's degree No Advanced Degree Doctorate degree Associate's degree
## 6 Levels: No Advanced Degree Associate's degree Bachelor's degree Doctorate degree ... Professional degree beyond a bachelor's degree
str(dpus$SCHL) # Note: 3 ## Factor w/ 6 levels "No Advanced Degree",..: 1 1 1 3 2 2 1 1 1 3 ...
## Factor w/ 6 levels "No Advanced Degree",..: 2 2 2 1 4 2 1 5 1 1 ...
# Note 1:
# Show the possible levels for SCHL.
# Note 2:
# Show the first few string values for SCHL.
# Note 3:
# Show how the first few levels are represented as codes. 00028_informalexample_2.16_of_section_2.3.1.R
# informalexample 2.16 of section 2.3.1
# (informalexample 2.16 of section 2.3.1) : Starting with R and data : Working with relational databases : A production-size example
d <- cbind( # Note: 1
data.frame(SCHL = as.character(dpus$SCHL), # Note: 2
stringsAsFactors = FALSE),
model.matrix(~SCHL, dpus) # Note: 3
)
d$'(Intercept)' <- NULL # Note: 4
str(d) # Note: 5 ## 'data.frame': 37111 obs. of 6 variables:
## $ SCHL : chr "No Advanced Degree" "No Advanced Degree" "No Advanced Degree" "Bachelor's degree" ...
## $ SCHLAssociate's degree : num 0 0 0 0 1 1 0 0 0 0 ...
## $ SCHLBachelor's degree : num 0 0 0 1 0 0 0 0 0 1 ...
## $ SCHLDoctorate degree : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SCHLMaster's degree : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SCHLProfessional degree beyond a bachelor's degree: num 0 0 0 0 0 0 0 0 0 0 ...
## 'data.frame': 41305 obs. of 6 variables:
## $ SCHL : chr "Associate's degree" "Associate's degree" "Associate's degree" "No Advanced Degree" ...
## $ SCHLAssociate's degree : num 1 1 1 0 0 1 0 0 0 0 ...
## $ SCHLBachelor's degree : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SCHLDoctorate degree : num 0 0 0 0 1 0 0 0 0 0 ...
## $ SCHLMaster's degree : num 0 0 0 0 0 0 0 1 0 0 ...
## $ SCHLProfessional degree beyond a bachelor's degree: num 0 0 0 0 0 0 0 0 0 0 ...
# Note 1:
# The cbind operator combines two data frames by columns, or each row is built by matching columns from rows in each data frame.
# Note 2:
# Build a data.frame with the SCHL column re-coded as character strings instead of as a factor.
# Note 3:
# Build a matrix with dummy variables generated from the SCHL factor column.
# Note 4:
# Remove a column named "(Intercept)" from the data.frame, as it is a side-effect of model.matrix we are not interested in at this time.
# Note 5:
# Show the structure that presents the original SCHL string form along with the indicators.
# str() presents the first few rows in transpose format (flipped so rows are now up and down and columns are across). 00029_informalexample_2.17_of_section_2.3.1.R
# informalexample 2.17 of section 2.3.1
# (informalexample 2.17 of section 2.3.1) : Starting with R and data : Working with relational databases : A production-size example
table(schooling = dpus$SCHL, sex = dpus$SEX) # Note: 1 ## sex
## schooling Male Female
## No Advanced Degree 12012 8449
## Associate's degree 1719 1951
## Bachelor's degree 4290 4117
## Doctorate degree 267 211
## Master's degree 1489 1897
## Professional degree beyond a bachelor's degree 348 361
## sex
## schooling Male Female
## No Advanced Degree 13178 9350
## Associate's degree 1796 2088
## Bachelor's degree 4927 4519
## Doctorate degree 361 269
## Master's degree 1792 2225
## Professional degree beyond a bachelor's degree 421 379
tapply( # Note: 2
dpus$PINCP, # Note: 3
list(dpus$SCHL, dpus$SEX), # Note: 4
FUN = mean # Note: 5
)## Male Female
## No Advanced Degree 43273.61 32053.49
## Associate's degree 56579.48 41005.83
## Bachelor's degree 76160.76 56549.53
## Doctorate degree 96943.95 84251.07
## Master's degree 94119.23 69048.59
## Professional degree beyond a bachelor's degree 107741.94 95951.54
## Male Female
## No Advanced Degree 44304.21 33117.37
## Associate's degree 56971.93 42002.06
## Bachelor's degree 76111.84 57260.44
## Doctorate degree 104943.33 89336.99
## Master's degree 94663.41 69104.54
## Professional degree beyond a bachelor's degree 111047.26 92071.56
# Note 1:
# Use the table command to count how even each pair of SCHL and SEX occurs.
# Note 2:
# Use tapply to tally how often each pair of SCHL of SEX occurs
# Note 3:
# This argument is the vector of data we are aggregating or summarizing in the tapply.
# Note 4:
# This argument list specifies how we are grouping the data, in this case simultaneously by SCHL and SEX.
# Note 5:
# The argument specifies how we are aggregating values, in this case we are taking the mean or average using the mean function. 00030_informalexample_2.18_of_section_2.3.1.R
# informalexample 2.18 of section 2.3.1
# (informalexample 2.18 of section 2.3.1) : Starting with R and data : Working with relational databases : A production-size example
library("dplyr")
dpus %>%
group_by(., SCHL, SEX) %>%
summarize(.,
count = n(),
mean_income = mean(PINCP)) %>%
ungroup(.) %>%
arrange(., SCHL, SEX)## # A tibble: 12 x 4
## SCHL SEX count mean_income
## <fct> <fct> <int> <dbl>
## 1 No Advanced Degree Male 12012 43274.
## 2 No Advanced Degree Female 8449 32053.
## 3 Associate's degree Male 1719 56579.
## 4 Associate's degree Female 1951 41006.
## 5 Bachelor's degree Male 4290 76161.
## 6 Bachelor's degree Female 4117 56550.
## 7 Doctorate degree Male 267 96944.
## 8 Doctorate degree Female 211 84251.
## 9 Master's degree Male 1489 94119.
## 10 Master's degree Female 1897 69049.
## 11 Professional degree beyond a bachelor's degree Male 348 107742.
## 12 Professional degree beyond a bachelor's degree Female 361 95952.
## # A tibble: 12 x 4
## SCHL SEX count mean_income
## <fct> <fct> <int> <dbl>
## 1 No Advanced Degree Male 13178 44304.
## 2 No Advanced Degree Female 9350 33117.
## 3 Associate's degree Male 1796 56972.
## 4 Associate's degree Female 2088 42002.
## 5 Bachelor's degree Male 4927 76112.
## 6 Bachelor's degree Female 4519 57260.
## 7 Doctorate degree Male 361 104943.
## 8 Doctorate degree Female 269 89337.
## 9 Master's degree Male 1792 94663.
## 10 Master's degree Female 2225 69105.
## 11 Professional degree beyond a bachelor's degree Male 421 111047.
## 12 Professional degree beyond a bachelor's degree Female 379 92072.00031_example_2.11_of_section_2.3.1.R
# example 2.11 of section 2.3.1
# (example 2.11 of section 2.3.1) : Starting with R and data : Working with relational databases : A production-size example
# Title: Plotting the data
WVPlots::ScatterHist(
dpus, "AGEP", "PINCP",
"Expected income (PINCP) as function age (AGEP)",
smoothmethod = "lm",
point_alpha = 0.025)