vignettes-source/overview.md
In kjhealy/gssr: US General Social Survey (GSS) Data for R
title: "An Overview of gssr"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{An Overview of gssr}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
Loading the gssr package and data
library(gssr)
#> Package loaded. To attach the GSS data, type data(gss_all) at the console.
#> For the codebook, type data(gss_doc). The gss_all and gss_doc objects will then be available to use.
As the startup message notes, the data objects are not automatically loaded. That is, we do not use R's "lazy loading" functionality. This is because the main GSS dataset is rather large. Instead we load it manually with data(). We'll also load the tibble that contains the codebook.
data(gss_all)
data(gss_doc)
gss_all
#> # A tibble: 64,814 x 6,108
#> year id wrkstat hrs1 hrs2 evwork occ prestige wrkslf wrkgovt
#> <dbl> <dbl> <dbl+l> <dbl> <dbl> <dbl+lb> <dbl> <dbl+lb> <dbl+l> <dbl+l>
#> 1 1972 1 1 [WOR… NA NA NA 205 50 2 [SOM… NA
#> 2 1972 2 5 [RET… NA NA 1 [YES] 441 45 2 [SOM… NA
#> 3 1972 3 2 [WOR… NA NA NA 270 44 2 [SOM… NA
#> 4 1972 4 1 [WOR… NA NA NA 1 57 2 [SOM… NA
#> 5 1972 5 7 [KEE… NA NA 1 [YES] 385 40 2 [SOM… NA
#> 6 1972 6 1 [WOR… NA NA NA 281 49 2 [SOM… NA
#> 7 1972 7 1 [WOR… NA NA NA 522 41 2 [SOM… NA
#> 8 1972 8 1 [WOR… NA NA NA 314 36 2 [SOM… NA
#> 9 1972 9 2 [WOR… NA NA NA 912 26 2 [SOM… NA
#> 10 1972 10 1 [WOR… NA NA NA 984 18 2 [SOM… NA
#> # … with 64,804 more rows, and 6,098 more variables: commute <dbl+lbl>,
#> # industry <dbl+lbl>, occ80 <dbl+lbl>, prestg80 <dbl+lbl>,
#> # indus80 <dbl+lbl>, indus07 <dbl+lbl>, occonet <dbl+lbl>,
#> # found <dbl+lbl>, occ10 <dbl+lbl>, occindv <dbl+lbl>,
#> # occstatus <dbl+lbl>, occtag <dbl+lbl>, prestg10 <dbl+lbl>,
#> # prestg105plus <dbl+lbl>, indus10 <dbl+lbl>, indstatus <dbl+lbl>,
#> # indtag <dbl+lbl>, marital <dbl+lbl>, martype <dbl+lbl>,
#> # agewed <dbl+lbl>, divorce <dbl+lbl>, widowed <dbl+lbl>,
#> # spwrksta <dbl+lbl>, sphrs1 <dbl+lbl>, sphrs2 <dbl+lbl>,
#> # spevwork <dbl+lbl>, cowrksta <dbl+lbl>, cowrkslf <dbl+lbl>,
#> # coevwork <dbl+lbl>, cohrs1 <dbl+lbl>, cohrs2 <dbl+lbl>,
#> # spocc <dbl+lbl>, sppres <dbl+lbl>, spwrkslf <dbl+lbl>,
#> # spind <dbl+lbl>, spocc80 <dbl+lbl>, sppres80 <dbl+lbl>,
#> # spind80 <dbl+lbl>, spocc10 <dbl+lbl>, spoccindv <dbl+lbl>,
#> # spoccstatus <dbl+lbl>, spocctag <dbl+lbl>, sppres10 <dbl+lbl>,
#> # sppres105plus <dbl+lbl>, spind10 <dbl+lbl>, spindstatus <dbl+lbl>,
#> # spindtag <dbl+lbl>, coocc10 <dbl+lbl>, coind10 <dbl+lbl>,
#> # paocc16 <dbl+lbl>, papres16 <dbl+lbl>, pawrkslf <dbl+lbl>,
#> # paind16 <dbl+lbl>, paocc80 <dbl+lbl>, papres80 <dbl+lbl>,
#> # paind80 <dbl+lbl>, paocc10 <dbl+lbl>, paoccindv <dbl+lbl>,
#> # paoccstatus <dbl+lbl>, paocctag <dbl+lbl>, papres10 <dbl+lbl>,
#> # papres105plus <dbl+lbl>, paind10 <dbl+lbl>, paindstatus <dbl+lbl>,
#> # paindtag <dbl+lbl>, maocc80 <dbl+lbl>, mapres80 <dbl+lbl>,
#> # mawrkslf <dbl+lbl>, maind80 <dbl+lbl>, maocc10 <dbl+lbl>,
#> # maoccindv <dbl+lbl>, maoccstatus <dbl+lbl>, maocctag <dbl+lbl>,
#> # mapres10 <dbl+lbl>, mapres105plus <dbl+lbl>, maind10 <dbl+lbl>,
#> # maindstatus <dbl+lbl>, maindtag <dbl+lbl>, sibs <dbl+lbl>,
#> # childs <dbl+lbl>, age <dbl+lbl>, agekdbrn <dbl+lbl>, educ <dbl+lbl>,
#> # paeduc <dbl+lbl>, maeduc <dbl+lbl>, speduc <dbl+lbl>,
#> # coeduc <dbl+lbl>, codeg <dbl+lbl>, degree <dbl+lbl>, padeg <dbl+lbl>,
#> # madeg <dbl+lbl>, spdeg <dbl+lbl>, major1 <dbl+lbl>, major2 <dbl+lbl>,
#> # dipged <dbl+lbl>, spdipged <dbl+lbl>, codipged <dbl+lbl>,
#> # cosector <dbl+lbl>, whenhs <dbl+lbl>, whencol <dbl+lbl>, …
gss_doc
#> # A tibble: 6,144 x 5
#> id description properties marginals text
#> <chr> <chr> <list> <list> <chr>
#> 1 caseid YEAR + Responde… <tibble [2 … <tibble [1… None
#> 2 year GSS year for th… <tibble [2 … <tibble [3… None
#> 3 id Respondent ID n… <tibble [2 … <tibble [1… None
#> 4 age Age of responde… <tibble [3 … <tibble [1… 13. Respondent's age
#> 5 sex Respondents sex <tibble [3 … <tibble [3… 23. Code respondent's …
#> 6 race Race of respond… <tibble [3 … <tibble [4… 24. What race do you c…
#> 7 racec… What Is R's rac… <tibble [3 … <tibble [2… 1602. What is your rac…
#> 8 racec… What Is R's rac… <tibble [3 … <tibble [2… 1602. What is your rac…
#> 9 racec… What Is R's rac… <tibble [3 … <tibble [2… 1602. What is your rac…
#> 10 hispa… Hispanic specif… <tibble [3 … <tibble [3… 1601. IF R IS FEMALE, …
#> # … with 6,134 more rows
Descriptive analysis of the data: an example
The GSS is a complex survey. When working with it, we need to take its structure into account in order to properly calculate statistics such as the population mean for a variable in some year, its standard error, and so on. For this we use the survey and srvyr packages. For details on survey, see Lumley (2010). We will also do some recoding, so we load several additional tidyverse packages to assist us.
Packages
library(dplyr)
library(ggplot2)
library(survey)
#> Loading required package: Matrix
#>
#> Attaching package: 'Matrix'
#> The following objects are masked from 'package:tidyr':
#>
#> expand, pack, unpack
#> Loading required package: survival
#>
#> Attaching package: 'survey'
#> The following object is masked from 'package:graphics':
#>
#> dotchart
library(srvyr)
#>
#> Attaching package: 'srvyr'
#> The following object is masked from 'package:stats':
#>
#> filter
Functions
Three quick-and-dirty functions, one to help clean some labels, the other to define some custom colors.
convert_agegrp <- function(x){
x <- gsub("\\(", "", x)
x <- gsub("\\[", "", x)
x <- gsub("\\]", "", x)
x <- gsub(",", "-", x)
x <- gsub("-89", "+", x)
regex <- "^(.*$)"
x <- gsub(regex, "Age \\1", x)
x
}
my_colors <- function (palette = "cb")
{
cb.palette <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7")
rcb.palette <- rev(cb.palette)
bly.palette <- c("#E69F00", "#0072B2", "#000000", "#56B4E9",
"#009E73", "#F0E442", "#D55E00", "#CC79A7")
if (palette == "cb")
return(cb.palette)
else if (palette == "rcb")
return(rcb.palette)
else if (palette == "bly")
return(bly.palette)
else stop("Choose cb, rcb, or bly only.")
}
# from help(chartr)
capwords <- function(x, strict = FALSE) {
cap <- function(x) paste(toupper(substring(x, 1, 1)),
{x <- substring(x, 2); if(strict) tolower(x) else x},
sep = "", collapse = " " )
sapply(strsplit(x, split = " "), cap, USE.NAMES = !is.null(names(x)))
}
Subsetting the Dataset
The GSS data retains labeling information (as it was originally imported via the haven package). When working with the data in an analysis, we will probably want to convert the labeled variables to data types such as factors. This should be done with care (and not on the whole dataset all at once). Typically, we will want to focus on some relatively small subset of variables and examine those. For example, let's say we want to explore the fefam question.
cont_vars <- c("year", "id", "ballot", "age")
cat_vars <- c("race", "sex", "fefam")
wt_vars <- c("vpsu",
"vstrat",
"oversamp",
"formwt", # weight to deal with experimental randomization
"wtssall", # weight variable
"sampcode", # sampling error code
"sample") # sampling frame and method
vars <- c(cont_vars, cat_vars, wt_vars)
gss_fam <- gss_all %>%
select(c(cont_vars, cat_vars, wt_vars))
gss_fam
#> # A tibble: 64,814 x 14
#> year id ballot age race sex fefam vpsu vstrat oversamp
#> <dbl> <dbl> <dbl+> <dbl> <dbl+l> <dbl+l> <dbl> <dbl> <dbl+> <dbl>
#> 1 1972 1 NA 23 1 [WHI… 2 [FEM… NA NA NA 1
#> 2 1972 2 NA 70 1 [WHI… 1 [MAL… NA NA NA 1
#> 3 1972 3 NA 48 1 [WHI… 2 [FEM… NA NA NA 1
#> 4 1972 4 NA 27 1 [WHI… 2 [FEM… NA NA NA 1
#> 5 1972 5 NA 61 1 [WHI… 2 [FEM… NA NA NA 1
#> 6 1972 6 NA 26 1 [WHI… 1 [MAL… NA NA NA 1
#> 7 1972 7 NA 28 1 [WHI… 1 [MAL… NA NA NA 1
#> 8 1972 8 NA 27 1 [WHI… 1 [MAL… NA NA NA 1
#> 9 1972 9 NA 21 2 [BLA… 2 [FEM… NA NA NA 1
#> 10 1972 10 NA 30 2 [BLA… 2 [FEM… NA NA NA 1
#> # … with 64,804 more rows, and 4 more variables: formwt <dbl>,
#> # wtssall <dbl+lbl>, sampcode <dbl+lbl>, sample <dbl+lbl>
Recode the Subsetted Data
Next, some recoding, along with creating some new variables.
qrts <- quantile(as.numeric(gss_fam$age),
na.rm = TRUE)
qrts
#> 0% 25% 50% 75% 100%
#> 18 31 44 59 89
quintiles <- quantile(as.numeric(gss_fam$age),
probs = seq(0, 1, 0.2), na.rm = TRUE)
quintiles
#> 0% 20% 40% 60% 80% 100%
#> 18 29 38 49 63 89
## Recoding
gss_fam <- gss_fam %>%
purrr::modify_at(vars(), haven::zap_missing) %>%
purrr::modify_at(wt_vars, as.numeric) %>%
purrr::modify_at(cat_vars, as_factor) %>%
purrr::modify_at(cat_vars, forcats::fct_relabel, capwords, strict = TRUE) %>%
mutate(ageq = cut(x = age, breaks = unique(qrts), include.lowest = TRUE),
ageq = forcats::fct_relabel(ageq, convert_agegrp),
agequint = cut(x = age, breaks = unique(quintiles), include.lowest = TRUE),
agequint = forcats::fct_relabel(agequint, convert_agegrp),
year_f = droplevels(factor(year)),
young = ifelse(age < 26, "Yes", "No"),
fefam = forcats::fct_recode(fefam, NULL = "IAP", NULL = "DK", NULL = "NA"),
fefam_d = forcats::fct_recode(fefam,
Agree = "Strongly Agree",
Disagree = "Strongly Disagree"),
fefam_n = recode(fefam_d, "Agree" = 0, "Disagree" = 1))
gss_fam <- gss_fam %>%
mutate(compwt = oversamp * formwt * wtssall,
samplerc = case_when(sample %in% c(3:4) ~ 3,
sample %in% c(6:7) ~ 6,
TRUE ~ sample))
gss_fam
#> # A tibble: 64,814 x 22
#> year id ballot age race sex fefam vpsu vstrat oversamp formwt
#> <dbl> <dbl> <dbl+> <dbl> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 1972 1 NA 23 White Fema… <NA> NA NA 1 1
#> 2 1972 2 NA 70 White Male <NA> NA NA 1 1
#> 3 1972 3 NA 48 White Fema… <NA> NA NA 1 1
#> 4 1972 4 NA 27 White Fema… <NA> NA NA 1 1
#> 5 1972 5 NA 61 White Fema… <NA> NA NA 1 1
#> 6 1972 6 NA 26 White Male <NA> NA NA 1 1
#> 7 1972 7 NA 28 White Male <NA> NA NA 1 1
#> 8 1972 8 NA 27 White Male <NA> NA NA 1 1
#> 9 1972 9 NA 21 Black Fema… <NA> NA NA 1 1
#> 10 1972 10 NA 30 Black Fema… <NA> NA NA 1 1
#> # … with 64,804 more rows, and 11 more variables: wtssall <dbl>,
#> # sampcode <dbl>, sample <dbl>, ageq <fct>, agequint <fct>,
#> # year_f <fct>, young <chr>, fefam_d <fct>, fefam_n <dbl>, compwt <dbl>,
#> # samplerc <dbl>
Integrate the Survey Weights
Now set up the survey object.
options(survey.lonely.psu = "adjust")
options(na.action="na.pass")
gss_svy <- gss_fam %>%
filter(year > 1974) %>%
tidyr::drop_na(fefam_d, young) %>%
mutate(stratvar = interaction(year, vstrat)) %>%
as_survey_design(id = vpsu,
strata = stratvar,
weights = wtssall,
nest = TRUE)
Calculate the survey-weighted means and confidence intervals for the question of interest for every year in the data
We're now in a position to calculate some properly-weighted summary statistics for the variables we're interested in.
## Get the breakdown for every year
out_ff <- gss_svy %>%
group_by(year, sex, young, fefam_d) %>%
summarize(prop = survey_mean(na.rm = TRUE, vartype = "ci"))
out_ff
#> # A tibble: 168 x 7
#> year sex young fefam_d prop prop_low prop_upp
#> <dbl> <fct> <chr> <fct> <dbl> <dbl> <dbl>
#> 1 1977 Male No Agree 0.726 0.685 0.766
#> 2 1977 Male No Disagree 0.274 0.234 0.315
#> 3 1977 Male Yes Agree 0.551 0.469 0.633
#> 4 1977 Male Yes Disagree 0.449 0.367 0.531
#> 5 1977 Female No Agree 0.674 0.639 0.709
#> 6 1977 Female No Disagree 0.326 0.291 0.361
#> 7 1977 Female Yes Agree 0.415 0.316 0.514
#> 8 1977 Female Yes Disagree 0.585 0.486 0.684
#> 9 1985 Male No Agree 0.542 0.496 0.587
#> 10 1985 Male No Disagree 0.458 0.413 0.504
#> # … with 158 more rows
Plot the Results
We finish with a polished plot of the trends in fefam over time, for men and women in two (recoded) age groups over time.
theme_set(theme_minimal())
facet_names <- c("No" = "Age Over 25 when surveyed",
"Yes" = "Age 18-25 when surveyed")
fefam_txt <- "Disagreement with the statement, ‘It is much better for\neveryone involved if the man is the achiever outside the\nhome and the woman takes care of the home and family’"
out_ff %>%
filter(fefam_d == "Disagree") %>%
ggplot(mapping =
aes(x = year, y = prop,
ymin = prop_low,
ymax = prop_upp,
color = sex,
group = sex,
fill = sex)) +
geom_line(size = 1.2) +
geom_ribbon(alpha = 0.3, color = NA) +
scale_x_continuous(breaks = seq(1978, 2018, 4)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = my_colors("bly")[2:1],
labels = c("Men", "Women"),
guide = guide_legend(title=NULL)) +
scale_fill_manual(values = my_colors("bly")[2:1],
labels = c("Men", "Women"),
guide = guide_legend(title=NULL)) +
facet_wrap(~ young, labeller = as_labeller(facet_names),
ncol = 1) +
coord_cartesian(xlim = c(1977, 2017)) +
labs(x = "Year",
y = "Percent Disagreeing",
subtitle = fefam_txt,
caption = "Kieran Healy http://socviz.co.\n
Data source: General Social Survey") +
theme(legend.position = "bottom")
References
Lumley, Thomas (2010). Complex Surveys: A Guide to Analysis Using R. New York: Wiley.
kjhealy/gssr documentation built on April 21, 2024, 2:35 a.m.
R Package Documentation
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title: "An Overview of gssr" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{An Overview of gssr} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}
Loading the gssr package and data
library(gssr)
#> Package loaded. To attach the GSS data, type data(gss_all) at the console.
#> For the codebook, type data(gss_doc). The gss_all and gss_doc objects will then be available to use.
As the startup message notes, the data objects are not automatically loaded. That is, we do not use R's "lazy loading" functionality. This is because the main GSS dataset is rather large. Instead we load it manually with data(). We'll also load the tibble that contains the codebook.
data(gss_all)
data(gss_doc)
gss_all
#> # A tibble: 64,814 x 6,108
#> year id wrkstat hrs1 hrs2 evwork occ prestige wrkslf wrkgovt
#> <dbl> <dbl> <dbl+l> <dbl> <dbl> <dbl+lb> <dbl> <dbl+lb> <dbl+l> <dbl+l>
#> 1 1972 1 1 [WOR… NA NA NA 205 50 2 [SOM… NA
#> 2 1972 2 5 [RET… NA NA 1 [YES] 441 45 2 [SOM… NA
#> 3 1972 3 2 [WOR… NA NA NA 270 44 2 [SOM… NA
#> 4 1972 4 1 [WOR… NA NA NA 1 57 2 [SOM… NA
#> 5 1972 5 7 [KEE… NA NA 1 [YES] 385 40 2 [SOM… NA
#> 6 1972 6 1 [WOR… NA NA NA 281 49 2 [SOM… NA
#> 7 1972 7 1 [WOR… NA NA NA 522 41 2 [SOM… NA
#> 8 1972 8 1 [WOR… NA NA NA 314 36 2 [SOM… NA
#> 9 1972 9 2 [WOR… NA NA NA 912 26 2 [SOM… NA
#> 10 1972 10 1 [WOR… NA NA NA 984 18 2 [SOM… NA
#> # … with 64,804 more rows, and 6,098 more variables: commute <dbl+lbl>,
#> # industry <dbl+lbl>, occ80 <dbl+lbl>, prestg80 <dbl+lbl>,
#> # indus80 <dbl+lbl>, indus07 <dbl+lbl>, occonet <dbl+lbl>,
#> # found <dbl+lbl>, occ10 <dbl+lbl>, occindv <dbl+lbl>,
#> # occstatus <dbl+lbl>, occtag <dbl+lbl>, prestg10 <dbl+lbl>,
#> # prestg105plus <dbl+lbl>, indus10 <dbl+lbl>, indstatus <dbl+lbl>,
#> # indtag <dbl+lbl>, marital <dbl+lbl>, martype <dbl+lbl>,
#> # agewed <dbl+lbl>, divorce <dbl+lbl>, widowed <dbl+lbl>,
#> # spwrksta <dbl+lbl>, sphrs1 <dbl+lbl>, sphrs2 <dbl+lbl>,
#> # spevwork <dbl+lbl>, cowrksta <dbl+lbl>, cowrkslf <dbl+lbl>,
#> # coevwork <dbl+lbl>, cohrs1 <dbl+lbl>, cohrs2 <dbl+lbl>,
#> # spocc <dbl+lbl>, sppres <dbl+lbl>, spwrkslf <dbl+lbl>,
#> # spind <dbl+lbl>, spocc80 <dbl+lbl>, sppres80 <dbl+lbl>,
#> # spind80 <dbl+lbl>, spocc10 <dbl+lbl>, spoccindv <dbl+lbl>,
#> # spoccstatus <dbl+lbl>, spocctag <dbl+lbl>, sppres10 <dbl+lbl>,
#> # sppres105plus <dbl+lbl>, spind10 <dbl+lbl>, spindstatus <dbl+lbl>,
#> # spindtag <dbl+lbl>, coocc10 <dbl+lbl>, coind10 <dbl+lbl>,
#> # paocc16 <dbl+lbl>, papres16 <dbl+lbl>, pawrkslf <dbl+lbl>,
#> # paind16 <dbl+lbl>, paocc80 <dbl+lbl>, papres80 <dbl+lbl>,
#> # paind80 <dbl+lbl>, paocc10 <dbl+lbl>, paoccindv <dbl+lbl>,
#> # paoccstatus <dbl+lbl>, paocctag <dbl+lbl>, papres10 <dbl+lbl>,
#> # papres105plus <dbl+lbl>, paind10 <dbl+lbl>, paindstatus <dbl+lbl>,
#> # paindtag <dbl+lbl>, maocc80 <dbl+lbl>, mapres80 <dbl+lbl>,
#> # mawrkslf <dbl+lbl>, maind80 <dbl+lbl>, maocc10 <dbl+lbl>,
#> # maoccindv <dbl+lbl>, maoccstatus <dbl+lbl>, maocctag <dbl+lbl>,
#> # mapres10 <dbl+lbl>, mapres105plus <dbl+lbl>, maind10 <dbl+lbl>,
#> # maindstatus <dbl+lbl>, maindtag <dbl+lbl>, sibs <dbl+lbl>,
#> # childs <dbl+lbl>, age <dbl+lbl>, agekdbrn <dbl+lbl>, educ <dbl+lbl>,
#> # paeduc <dbl+lbl>, maeduc <dbl+lbl>, speduc <dbl+lbl>,
#> # coeduc <dbl+lbl>, codeg <dbl+lbl>, degree <dbl+lbl>, padeg <dbl+lbl>,
#> # madeg <dbl+lbl>, spdeg <dbl+lbl>, major1 <dbl+lbl>, major2 <dbl+lbl>,
#> # dipged <dbl+lbl>, spdipged <dbl+lbl>, codipged <dbl+lbl>,
#> # cosector <dbl+lbl>, whenhs <dbl+lbl>, whencol <dbl+lbl>, …
gss_doc
#> # A tibble: 6,144 x 5
#> id description properties marginals text
#> <chr> <chr> <list> <list> <chr>
#> 1 caseid YEAR + Responde… <tibble [2 … <tibble [1… None
#> 2 year GSS year for th… <tibble [2 … <tibble [3… None
#> 3 id Respondent ID n… <tibble [2 … <tibble [1… None
#> 4 age Age of responde… <tibble [3 … <tibble [1… 13. Respondent's age
#> 5 sex Respondents sex <tibble [3 … <tibble [3… 23. Code respondent's …
#> 6 race Race of respond… <tibble [3 … <tibble [4… 24. What race do you c…
#> 7 racec… What Is R's rac… <tibble [3 … <tibble [2… 1602. What is your rac…
#> 8 racec… What Is R's rac… <tibble [3 … <tibble [2… 1602. What is your rac…
#> 9 racec… What Is R's rac… <tibble [3 … <tibble [2… 1602. What is your rac…
#> 10 hispa… Hispanic specif… <tibble [3 … <tibble [3… 1601. IF R IS FEMALE, …
#> # … with 6,134 more rows
Descriptive analysis of the data: an example
The GSS is a complex survey. When working with it, we need to take its structure into account in order to properly calculate statistics such as the population mean for a variable in some year, its standard error, and so on. For this we use the survey and srvyr packages. For details on survey, see Lumley (2010). We will also do some recoding, so we load several additional tidyverse packages to assist us.
Packages
library(dplyr)
library(ggplot2)
library(survey)
#> Loading required package: Matrix
#>
#> Attaching package: 'Matrix'
#> The following objects are masked from 'package:tidyr':
#>
#> expand, pack, unpack
#> Loading required package: survival
#>
#> Attaching package: 'survey'
#> The following object is masked from 'package:graphics':
#>
#> dotchart
library(srvyr)
#>
#> Attaching package: 'srvyr'
#> The following object is masked from 'package:stats':
#>
#> filter
Functions
Three quick-and-dirty functions, one to help clean some labels, the other to define some custom colors.
convert_agegrp <- function(x){
x <- gsub("\\(", "", x)
x <- gsub("\\[", "", x)
x <- gsub("\\]", "", x)
x <- gsub(",", "-", x)
x <- gsub("-89", "+", x)
regex <- "^(.*$)"
x <- gsub(regex, "Age \\1", x)
x
}
my_colors <- function (palette = "cb")
{
cb.palette <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7")
rcb.palette <- rev(cb.palette)
bly.palette <- c("#E69F00", "#0072B2", "#000000", "#56B4E9",
"#009E73", "#F0E442", "#D55E00", "#CC79A7")
if (palette == "cb")
return(cb.palette)
else if (palette == "rcb")
return(rcb.palette)
else if (palette == "bly")
return(bly.palette)
else stop("Choose cb, rcb, or bly only.")
}
# from help(chartr)
capwords <- function(x, strict = FALSE) {
cap <- function(x) paste(toupper(substring(x, 1, 1)),
{x <- substring(x, 2); if(strict) tolower(x) else x},
sep = "", collapse = " " )
sapply(strsplit(x, split = " "), cap, USE.NAMES = !is.null(names(x)))
}
Subsetting the Dataset
The GSS data retains labeling information (as it was originally imported via the haven package). When working with the data in an analysis, we will probably want to convert the labeled variables to data types such as factors. This should be done with care (and not on the whole dataset all at once). Typically, we will want to focus on some relatively small subset of variables and examine those. For example, let's say we want to explore the fefam question.
cont_vars <- c("year", "id", "ballot", "age")
cat_vars <- c("race", "sex", "fefam")
wt_vars <- c("vpsu",
"vstrat",
"oversamp",
"formwt", # weight to deal with experimental randomization
"wtssall", # weight variable
"sampcode", # sampling error code
"sample") # sampling frame and method
vars <- c(cont_vars, cat_vars, wt_vars)
gss_fam <- gss_all %>%
select(c(cont_vars, cat_vars, wt_vars))
gss_fam
#> # A tibble: 64,814 x 14
#> year id ballot age race sex fefam vpsu vstrat oversamp
#> <dbl> <dbl> <dbl+> <dbl> <dbl+l> <dbl+l> <dbl> <dbl> <dbl+> <dbl>
#> 1 1972 1 NA 23 1 [WHI… 2 [FEM… NA NA NA 1
#> 2 1972 2 NA 70 1 [WHI… 1 [MAL… NA NA NA 1
#> 3 1972 3 NA 48 1 [WHI… 2 [FEM… NA NA NA 1
#> 4 1972 4 NA 27 1 [WHI… 2 [FEM… NA NA NA 1
#> 5 1972 5 NA 61 1 [WHI… 2 [FEM… NA NA NA 1
#> 6 1972 6 NA 26 1 [WHI… 1 [MAL… NA NA NA 1
#> 7 1972 7 NA 28 1 [WHI… 1 [MAL… NA NA NA 1
#> 8 1972 8 NA 27 1 [WHI… 1 [MAL… NA NA NA 1
#> 9 1972 9 NA 21 2 [BLA… 2 [FEM… NA NA NA 1
#> 10 1972 10 NA 30 2 [BLA… 2 [FEM… NA NA NA 1
#> # … with 64,804 more rows, and 4 more variables: formwt <dbl>,
#> # wtssall <dbl+lbl>, sampcode <dbl+lbl>, sample <dbl+lbl>
Recode the Subsetted Data
Next, some recoding, along with creating some new variables.
qrts <- quantile(as.numeric(gss_fam$age),
na.rm = TRUE)
qrts
#> 0% 25% 50% 75% 100%
#> 18 31 44 59 89
quintiles <- quantile(as.numeric(gss_fam$age),
probs = seq(0, 1, 0.2), na.rm = TRUE)
quintiles
#> 0% 20% 40% 60% 80% 100%
#> 18 29 38 49 63 89
## Recoding
gss_fam <- gss_fam %>%
purrr::modify_at(vars(), haven::zap_missing) %>%
purrr::modify_at(wt_vars, as.numeric) %>%
purrr::modify_at(cat_vars, as_factor) %>%
purrr::modify_at(cat_vars, forcats::fct_relabel, capwords, strict = TRUE) %>%
mutate(ageq = cut(x = age, breaks = unique(qrts), include.lowest = TRUE),
ageq = forcats::fct_relabel(ageq, convert_agegrp),
agequint = cut(x = age, breaks = unique(quintiles), include.lowest = TRUE),
agequint = forcats::fct_relabel(agequint, convert_agegrp),
year_f = droplevels(factor(year)),
young = ifelse(age < 26, "Yes", "No"),
fefam = forcats::fct_recode(fefam, NULL = "IAP", NULL = "DK", NULL = "NA"),
fefam_d = forcats::fct_recode(fefam,
Agree = "Strongly Agree",
Disagree = "Strongly Disagree"),
fefam_n = recode(fefam_d, "Agree" = 0, "Disagree" = 1))
gss_fam <- gss_fam %>%
mutate(compwt = oversamp * formwt * wtssall,
samplerc = case_when(sample %in% c(3:4) ~ 3,
sample %in% c(6:7) ~ 6,
TRUE ~ sample))
gss_fam
#> # A tibble: 64,814 x 22
#> year id ballot age race sex fefam vpsu vstrat oversamp formwt
#> <dbl> <dbl> <dbl+> <dbl> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 1972 1 NA 23 White Fema… <NA> NA NA 1 1
#> 2 1972 2 NA 70 White Male <NA> NA NA 1 1
#> 3 1972 3 NA 48 White Fema… <NA> NA NA 1 1
#> 4 1972 4 NA 27 White Fema… <NA> NA NA 1 1
#> 5 1972 5 NA 61 White Fema… <NA> NA NA 1 1
#> 6 1972 6 NA 26 White Male <NA> NA NA 1 1
#> 7 1972 7 NA 28 White Male <NA> NA NA 1 1
#> 8 1972 8 NA 27 White Male <NA> NA NA 1 1
#> 9 1972 9 NA 21 Black Fema… <NA> NA NA 1 1
#> 10 1972 10 NA 30 Black Fema… <NA> NA NA 1 1
#> # … with 64,804 more rows, and 11 more variables: wtssall <dbl>,
#> # sampcode <dbl>, sample <dbl>, ageq <fct>, agequint <fct>,
#> # year_f <fct>, young <chr>, fefam_d <fct>, fefam_n <dbl>, compwt <dbl>,
#> # samplerc <dbl>
Integrate the Survey Weights
Now set up the survey object.
options(survey.lonely.psu = "adjust")
options(na.action="na.pass")
gss_svy <- gss_fam %>%
filter(year > 1974) %>%
tidyr::drop_na(fefam_d, young) %>%
mutate(stratvar = interaction(year, vstrat)) %>%
as_survey_design(id = vpsu,
strata = stratvar,
weights = wtssall,
nest = TRUE)
Calculate the survey-weighted means and confidence intervals for the question of interest for every year in the data
We're now in a position to calculate some properly-weighted summary statistics for the variables we're interested in.
## Get the breakdown for every year
out_ff <- gss_svy %>%
group_by(year, sex, young, fefam_d) %>%
summarize(prop = survey_mean(na.rm = TRUE, vartype = "ci"))
out_ff
#> # A tibble: 168 x 7
#> year sex young fefam_d prop prop_low prop_upp
#> <dbl> <fct> <chr> <fct> <dbl> <dbl> <dbl>
#> 1 1977 Male No Agree 0.726 0.685 0.766
#> 2 1977 Male No Disagree 0.274 0.234 0.315
#> 3 1977 Male Yes Agree 0.551 0.469 0.633
#> 4 1977 Male Yes Disagree 0.449 0.367 0.531
#> 5 1977 Female No Agree 0.674 0.639 0.709
#> 6 1977 Female No Disagree 0.326 0.291 0.361
#> 7 1977 Female Yes Agree 0.415 0.316 0.514
#> 8 1977 Female Yes Disagree 0.585 0.486 0.684
#> 9 1985 Male No Agree 0.542 0.496 0.587
#> 10 1985 Male No Disagree 0.458 0.413 0.504
#> # … with 158 more rows
Plot the Results
We finish with a polished plot of the trends in fefam over time, for men and women in two (recoded) age groups over time.
theme_set(theme_minimal())
facet_names <- c("No" = "Age Over 25 when surveyed",
"Yes" = "Age 18-25 when surveyed")
fefam_txt <- "Disagreement with the statement, ‘It is much better for\neveryone involved if the man is the achiever outside the\nhome and the woman takes care of the home and family’"
out_ff %>%
filter(fefam_d == "Disagree") %>%
ggplot(mapping =
aes(x = year, y = prop,
ymin = prop_low,
ymax = prop_upp,
color = sex,
group = sex,
fill = sex)) +
geom_line(size = 1.2) +
geom_ribbon(alpha = 0.3, color = NA) +
scale_x_continuous(breaks = seq(1978, 2018, 4)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = my_colors("bly")[2:1],
labels = c("Men", "Women"),
guide = guide_legend(title=NULL)) +
scale_fill_manual(values = my_colors("bly")[2:1],
labels = c("Men", "Women"),
guide = guide_legend(title=NULL)) +
facet_wrap(~ young, labeller = as_labeller(facet_names),
ncol = 1) +
coord_cartesian(xlim = c(1977, 2017)) +
labs(x = "Year",
y = "Percent Disagreeing",
subtitle = fefam_txt,
caption = "Kieran Healy http://socviz.co.\n
Data source: General Social Survey") +
theme(legend.position = "bottom")
References
Lumley, Thomas (2010). Complex Surveys: A Guide to Analysis Using R. New York: Wiley.
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