Public Libraries Survey (PLS)
A comprehensive compilation of administrative information on all public libraries in the United States.
Two tables, with one record per library system and one record per library building or bookmobile.
Released annually since 1992.
Conducted by the Institute of Museum and Library Services (IMLS), collected by the Census Bureau.
Please skim before you begin:
README FY #### PLS PUD.txtincluded in each zipped fileA haiku regarding this microdata:
Download, Import, Preparation
Download and import the most recent administrative entity csv file:
this_tf <- tempfile()
csv_url <- "https://www.imls.gov/sites/default/files/2023-06/pls_fy2021_csv.zip"
download.file( csv_url , this_tf, mode = 'wb' )
unzipped_files <- unzip( this_tf , exdir = tempdir() )
administrative_entity_csv_fn <-
unzipped_files[ grepl( 'AE(.*)csv$' , basename( unzipped_files ) ) ]
pls_df <- read.csv( administrative_entity_csv_fn )
names( pls_df ) <- tolower( names( pls_df ) )
pls_df[ , 'one' ] <- 1Recode missing values as described in the readme included with each zipped file:
for( this_col in names( pls_df ) ){
if( class( pls_df[ , this_col ] ) == 'character' ){
pls_df[ pls_df[ , this_col ] %in% 'M' , this_col ] <- NA
}
if(
( class( pls_df[ , this_col ] ) == 'numeric' ) |
( this_col %in% c( 'phone' , 'startdat' , 'enddate' ) )
){
pls_df[ pls_df[ , this_col ] %in% c( -1 , -3 , -4 , -9 ) , this_col ] <- NA
}
}Save Locally
Save the object at any point:
# pls_fn <- file.path( path.expand( "~" ) , "PLS" , "this_file.rds" )
# saveRDS( pls_df , file = pls_fn , compress = FALSE )Load the same object:
Variable Recoding
Add new columns to the data set:
pls_df <-
transform(
pls_df ,
c_relatn =
factor( c_relatn , levels = c( "HQ" , "ME" , "NO" ) ,
c( "HQ-Headquarters of a federation or cooperative" ,
"ME-Member of a federation or cooperative" ,
"NO-Not a member of a federation or cooperative" )
) ,
more_than_one_librarian = as.numeric( libraria > 1 )
) Analysis Examples with base R
Descriptive Statistics
Calculate the mean (average) of a linear variable, overall and by groups:
mean( pls_df[ , "popu_lsa" ] , na.rm = TRUE )
tapply(
pls_df[ , "popu_lsa" ] ,
pls_df[ , "stabr" ] ,
mean ,
na.rm = TRUE
)Calculate the distribution of a categorical variable, overall and by groups:
prop.table( table( pls_df[ , "c_relatn" ] ) )
prop.table(
table( pls_df[ , c( "c_relatn" , "stabr" ) ] ) ,
margin = 2
)Calculate the sum of a linear variable, overall and by groups:
sum( pls_df[ , "popu_lsa" ] , na.rm = TRUE )
tapply(
pls_df[ , "popu_lsa" ] ,
pls_df[ , "stabr" ] ,
sum ,
na.rm = TRUE
)Calculate the median (50th percentile) of a linear variable, overall and by groups:
Subsetting
Limit your data.frame to more than one million annual visits:
Calculate the mean (average) of this subset:
Regression Models and Tests of Association
Perform a t-test:
Perform a chi-squared test of association:
this_table <- table( pls_df[ , c( "more_than_one_librarian" , "c_relatn" ) ] )
chisq.test( this_table )Perform a generalized linear model:
glm_result <-
glm(
popu_lsa ~ more_than_one_librarian + c_relatn ,
data = pls_df
)
summary( glm_result )Replication Example
This example matches Interlibrary Relationship Frequencies on PDF page 169 of the User’s Guide:
# remove closed and temporarily closed libraries
results <- table( pls_df[ !( pls_df[ , 'statstru' ] %in% c( 3 , 23 ) ) , 'c_relatn' ] )
stopifnot( results[ "HQ-Headquarters of a federation or cooperative" ] == 112 )
stopifnot( results[ "ME-Member of a federation or cooperative" ] == 6859 )
stopifnot( results[ "NO-Not a member of a federation or cooperative" ] == 2236 )Analysis Examples with dplyr
The R dplyr library offers an alternative grammar of data manipulation to base R and SQL syntax. dplyr offers many verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions, and the tidyverse style of non-standard evaluation. This vignette details the available features. As a starting point for PLS users, this code replicates previously-presented examples:
Calculate the mean (average) of a linear variable, overall and by groups:
pls_tbl %>%
summarize( mean = mean( popu_lsa , na.rm = TRUE ) )
pls_tbl %>%
group_by( stabr ) %>%
summarize( mean = mean( popu_lsa , na.rm = TRUE ) )Analysis Examples with data.table
The R data.table library provides a high-performance version of base R’s data.frame with syntax and feature enhancements for ease of use, convenience and programming speed. data.table offers concise syntax: fast to type, fast to read, fast speed, memory efficiency, a careful API lifecycle management, an active community, and a rich set of features. This vignette details the available features. As a starting point for PLS users, this code replicates previously-presented examples:
Calculate the mean (average) of a linear variable, overall and by groups:
pls_dt[ , mean( popu_lsa , na.rm = TRUE ) ]
pls_dt[ , mean( popu_lsa , na.rm = TRUE ) , by = stabr ]Analysis Examples with duckdb
The R duckdb library provides an embedded analytical data management system with support for the Structured Query Language (SQL). duckdb offers a simple, feature-rich, fast, and free SQL OLAP management system. This vignette details the available features. As a starting point for PLS users, this code replicates previously-presented examples:
library(duckdb)
con <- dbConnect( duckdb::duckdb() , dbdir = 'my-db.duckdb' )
dbWriteTable( con , 'pls' , pls_df )Calculate the mean (average) of a linear variable, overall and by groups: