srvyr
- 0.3.6
+ 0.3.7
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/CODE_OF_CONDUCT.html b/docs/CODE_OF_CONDUCT.html
index 18991e7..b63d6d8 100644
--- a/docs/CODE_OF_CONDUCT.html
+++ b/docs/CODE_OF_CONDUCT.html
@@ -68,7 +68,7 @@
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/articles/extending-srvyr.html b/docs/articles/extending-srvyr.html
index 7c8acf1..3e9b0f9 100644
--- a/docs/articles/extending-srvyr.html
+++ b/docs/articles/extending-srvyr.html
@@ -32,7 +32,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -89,13 +89,13 @@
- vignettes/extending-srvyr.Rmd
extending-srvyr.RmdThat was just a lot of text, but I think it’s probably easiest just to provide an example. The convey package provides several methods for analysis of inequality using survey data. The svygini function calculates the gini coefficient. Here, we’ll write functions that make a srvyr version survey_gini.
To distinguish between ungrouped and grouped survey objects, we’ll make a generic. Also note the use of .svy = current_svy() to get the survey object from the current summarize context.
# S3 generic function
-survey_gini <- function(
- x, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"), .svy = current_svy(), ...
-) {
- UseMethod("survey_gini", .svy)
-}# S3 generic function
+survey_gini <- function(
+ x, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"), .svy = current_svy(), ...
+) {
+ UseMethod("survey_gini", .svy)
+}And here’s the ungrouped version, which uses set_survey_vars(), convey::svygini() and get_var_est().
survey_gini.tbl_svy <- function(
- x, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"), .svy = current_svy(), ...
-) {
- if (missing(vartype)) vartype <- "se"
- vartype <- match.arg(vartype, several.ok = TRUE)
- .svy <- srvyr::set_survey_vars(.svy, x)
-
- out <- convey::svygini(~`__SRVYR_TEMP_VAR__`, na.rm = na.rm, design = .svy)
- out <- srvyr::get_var_est(out, vartype)
- out
-}survey_gini.tbl_svy <- function(
+ x, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"), .svy = current_svy(), ...
+) {
+ if (missing(vartype)) vartype <- "se"
+ vartype <- match.arg(vartype, several.ok = TRUE)
+ .svy <- srvyr::set_survey_vars(.svy, x)
+
+ out <- convey::svygini(~`__SRVYR_TEMP_VAR__`, na.rm = na.rm, design = .svy)
+ out <- srvyr::get_var_est(out, vartype)
+ out
+}Finally, the grouped version which uses the above functions plus survey::svyby().
survey_gini.grouped_svy <- function(
- x, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"), .svy = current_svy(), ...
-) {
- if (missing(vartype)) vartype <- "se"
- vartype <- match.arg(vartype, several.ok = TRUE)
- .svy <- srvyr::set_survey_vars(.svy, x)
- grps_formula <- survey::make.formula(group_vars(.svy))
-
- out <- survey::svyby(
- ~`__SRVYR_TEMP_VAR__`, grps_formula, convey::svygini, na.rm = na.rm, design = .svy
- )
- out <- srvyr::get_var_est(out, vartype, grps = group_vars(.svy))
- out
-}survey_gini.grouped_svy <- function(
+ x, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"), .svy = current_svy(), ...
+) {
+ if (missing(vartype)) vartype <- "se"
+ vartype <- match.arg(vartype, several.ok = TRUE)
+ .svy <- srvyr::set_survey_vars(.svy, x)
+ grps_formula <- survey::make.formula(group_vars(.svy))
+
+ out <- survey::svyby(
+ ~`__SRVYR_TEMP_VAR__`, grps_formula, convey::svygini, na.rm = na.rm, design = .svy
+ )
+ out <- srvyr::get_var_est(out, vartype, grps = group_vars(.svy))
+ out
+}And here’s what these functions look like in practice:
-# Example from ?convey::svygini
-suppressPackageStartupMessages({
- library(srvyr)
- library(survey)
- library(convey)
- library(vardpoor)
-})
-data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )
-
-# Setup for survey package
-des_eusilc <- svydesign(
- ids = ~rb030,
- strata = ~db040,
- weights = ~rb050,
- data = eusilc
-)
-des_eusilc <- convey_prep(des_eusilc)
-
-# Setup for srvyr package
-srvyr_eusilc <- eusilc %>%
- as_survey(
- ids = rb030,
- strata = db040,
- weights = rb050
- ) %>%
- convey_prep()
-
-## Ungrouped
-# Calculate ungrouped for survey package
-svygini(~eqincome, design = des_eusilc)
-#> gini SE
-#> eqincome 0.26497 0.0019
-
-# With our new function
-survey_gini(srvyr_eusilc$variables$eqincome, .svy = srvyr_eusilc)
-#> __SRVYR_COEF__ _se
-#> 1 0.2649652 0.001946982
-
-# And finally, the more typical way through summarize
-srvyr_eusilc %>%
- summarize(eqincome = survey_gini(eqincome))
-#> # A tibble: 1 x 2
-#> eqincome eqincome_se
-#> <dbl> <dbl>
-#> 1 0.265 0.00195
-
-## Groups
-# Calculate by groups for survey
-survey::svyby(~eqincome, ~rb090, des_eusilc, convey::svygini)
-#> rb090 eqincome se
-#> male male 0.2578983 0.002617279
-#> female female 0.2702080 0.002892713
-
-# With our new function
-survey_gini(srvyr_eusilc$variables$eqincome, .svy = group_by(srvyr_eusilc, rb090))
-#> rb090 __SRVYR_COEF__ _se
-#> 1 male 0.2578983 0.002617279
-#> 2 female 0.2702080 0.002892713
-
-# And finally, the more typical way through summarize
-srvyr_eusilc %>%
- group_by(rb090) %>%
- summarize(eqincome = survey_gini(eqincome))
-#> # A tibble: 2 x 3
-#> rb090 eqincome eqincome_se
-#> <fct> <dbl> <dbl>
-#> 1 male 0.258 0.00262
-#> 2 female 0.270 0.00289# Example from ?convey::svygini
+suppressPackageStartupMessages({
+ library(srvyr)
+ library(survey)
+ library(convey)
+ library(vardpoor)
+})
+data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )
+
+# Setup for survey package
+des_eusilc <- svydesign(
+ ids = ~rb030,
+ strata = ~db040,
+ weights = ~rb050,
+ data = eusilc
+)
+des_eusilc <- convey_prep(des_eusilc)
+
+# Setup for srvyr package
+srvyr_eusilc <- eusilc %>%
+ as_survey(
+ ids = rb030,
+ strata = db040,
+ weights = rb050
+ ) %>%
+ convey_prep()
+
+## Ungrouped
+# Calculate ungrouped for survey package
+svygini(~eqincome, design = des_eusilc)
+#> gini SE
+#> eqincome 0.26497 0.0019
+
+# With our new function
+survey_gini(srvyr_eusilc$variables$eqincome, .svy = srvyr_eusilc)
+#> __SRVYR_COEF__ _se
+#> 1 0.2649652 0.001946982
+
+# And finally, the more typical way through summarize
+srvyr_eusilc %>%
+ summarize(eqincome = survey_gini(eqincome))
+#> # A tibble: 1 x 2
+#> eqincome eqincome_se
+#> <dbl> <dbl>
+#> 1 0.265 0.00195
+
+## Groups
+# Calculate by groups for survey
+survey::svyby(~eqincome, ~rb090, des_eusilc, convey::svygini)
+#> rb090 eqincome se
+#> male male 0.2578983 0.002617279
+#> female female 0.2702080 0.002892713
+
+# With our new function
+survey_gini(srvyr_eusilc$variables$eqincome, .svy = group_by(srvyr_eusilc, rb090))
+#> rb090 __SRVYR_COEF__ _se
+#> 1 male 0.2578983 0.002617279
+#> 2 female 0.2702080 0.002892713
+
+# And finally, the more typical way through summarize
+srvyr_eusilc %>%
+ group_by(rb090) %>%
+ summarize(eqincome = survey_gini(eqincome))
+#> # A tibble: 2 x 3
+#> rb090 eqincome eqincome_se
+#> <fct> <dbl> <dbl>
+#> 1 male 0.258 0.00262
+#> 2 female 0.270 0.00289| t |
srvyr
- 0.3.6
+ 0.3.7
srvyr
- 0.3.6
+ 0.3.7
@@ -89,13 +89,13 @@
- vignettes/srvyr-database.Rmd
srvyr-database.RmdSelects only a few variables that will be used in this analysis to save space, but again it could easily be adapted to keep all of the variables.
For more information on the specifics of the American Community Survey, see the asdfree site. Now, our code loads this prepared dataset, initiates a SQLite database, and puts the data into the dataset.
-suppressMessages({
- library(survey)
- library(srvyr)
- library(dplyr)
- library(dbplyr)
- library(RSQLite)
-})
-
-# Load data (Currently only Alaska and Hawaii to keep file size small and with
-# limited variables, butcode that downloaded the files is available here
-# https://github.com/gergness/srvyr/blob/master/vignettes/save_acs_data.R
-# and could easily be adapted to download all states.)
-load("acs_m.Rdata") # acs_m data
-
-# Set up database and table
-db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
-acs_m_db <- copy_to(db, acs_m, "acs_m", temporary = FALSE)
-
-# Or, if the data was already stored in the database, you could do this
-# acs_m_data <- tbl(db, sql("SELECT * FROM acs_m")) suppressMessages({
+ library(survey)
+ library(srvyr)
+ library(dplyr)
+ library(dbplyr)
+ library(RSQLite)
+})
+
+# Load data (Currently only Alaska and Hawaii to keep file size small and with
+# limited variables, butcode that downloaded the files is available here
+# https://github.com/gergness/srvyr/blob/master/vignettes/save_acs_data.R
+# and could easily be adapted to download all states.)
+load("acs_m.Rdata") # acs_m data
+
+# Set up database and table
+db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
+acs_m_db <- copy_to(db, acs_m, "acs_m", temporary = FALSE)
+
+# Or, if the data was already stored in the database, you could do this
+# acs_m_data <- tbl(db, sql("SELECT * FROM acs_m")) Now that we have the data in the database, we can interact with the database directly using sql commands, or we can use dplyr’s functionality to treat it mostly the same as a local data.frame. However, the data is not stored in memory, so we could work with much larger datasets (though in this case, the data is too small for this to be a problem).
## # A tibble: 2 x 2
## sex hicov
## <int> <dbl>
## 1 1 1.14
## 2 2 1.10## Warning: Missing values are always removed in SQL.
## Use `mean(x, na.rm = TRUE)` to silence this warning
## This warning is displayed only once per session.## # Source: lazy query [?? x 2]
-## # Database: sqlite 3.29.0 [:memory:]
+## # Database: sqlite 3.30.1 [:memory:]
## sex hicov
## <int> <dbl>
## 1 1 1.14
## 2 2 1.10# But smaller object size
+object.size(acs_m)## 7777312 bytesobject.size(acs_m_db)## 10376 bytesNote that though many commands behave exactly the same whether on a local data.frame or database, sometimes more advanced / complicated syntax around variable modification allowed in dplyr does not work on a particular database and so it is better to be more explicit. For example, creating a variable inside of a summarize call does not work in some databases. .
- +## # A tibble: 2 x 2
## sex hicov
## <int> <dbl>
## 1 1 0.858
## 2 2 0.895# Works in RSQlite, but didn't in the now defunct MonetDB
-# acs_m_db %>%
-# group_by(sex) %>%
-# summarize(hicov = mean(hicov == 1))
-#
-# > Error in .local(conn, statement, ...) :
-# > Unable to execute statement....
-# > ....
-
-# Creating the variable separately works as an integer works though
-acs_m_db %>%
- group_by(sex) %>%
- mutate(hicov = ifelse(hicov == 1, 1L, 0L)) %>%
- summarize(hicov = mean(hicov))# Works in RSQlite, but didn't in the now defunct MonetDB
+# acs_m_db %>%
+# group_by(sex) %>%
+# summarize(hicov = mean(hicov == 1))
+#
+# > Error in .local(conn, statement, ...) :
+# > Unable to execute statement....
+# > ....
+
+# Creating the variable separately works as an integer works though
+acs_m_db %>%
+ group_by(sex) %>%
+ mutate(hicov = ifelse(hicov == 1, 1L, 0L)) %>%
+ summarize(hicov = mean(hicov))## # Source: lazy query [?? x 2]
-## # Database: sqlite 3.29.0 [:memory:]
+## # Database: sqlite 3.30.1 [:memory:]
## sex hicov
## <int> <dbl>
## 1 1 0.858
## 2 2 0.895Further, sometimes working with variable types can get difficult if you are used to working in R. Notice how in the above, instead of hicov = (hicov == 1), I wrote out the ifelse statement. If I hadn’t RSQLite would be unable to calculate the mean of the boolean variable created.
Finally, a major difference when transitioning from dplyr on local data.frames is that not all R functions are translated to SQL. For example, cut() isn’t implemented in SQL, so you can’t create a new variable in the data.frame using it.
acs_m %>%
- group_by(agecat = cut(agep, c(0, 19, 35, 50, 65, 200))) %>%
- summarize(hicov = mean(hicov == 1))acs_m %>%
+ group_by(agecat = cut(agep, c(0, 19, 35, 50, 65, 200))) %>%
+ summarize(hicov = mean(hicov == 1))## Warning: Factor `agecat` contains implicit NA, consider using
## `forcats::fct_explicit_na`## # A tibble: 6 x 2
@@ -210,24 +210,24 @@
## 4 (50,65] 0.873
## 5 (65,200] 0.992
## 6 <NA> 0.953
# acs_m_db %>%
-# group_by(agecat = cut(agep, c(0, 19, 35, 50, 65, 200))) %>%
-# summarize(hicov = mean(hicov == 1))
-#
-# > Error in .local(conn, statement, ...) :
-# > Unable to execute statement....
-# > ...
-
-acs_m_db %>%
- mutate(agecat = ifelse(agep < 19, "0-18",
- ifelse(agep >= 19 & agep < 35, "19-34",
- ifelse(agep >= 35 & agep < 50, "35-49",
- ifelse(agep >= 50 & agep < 65, "50-64",
- ifelse(agep >= 65, "65+", NA)))))) %>%
- group_by(agecat) %>%
- summarize(hicov = mean(hicov))# acs_m_db %>%
+# group_by(agecat = cut(agep, c(0, 19, 35, 50, 65, 200))) %>%
+# summarize(hicov = mean(hicov == 1))
+#
+# > Error in .local(conn, statement, ...) :
+# > Unable to execute statement....
+# > ...
+
+acs_m_db %>%
+ mutate(agecat = ifelse(agep < 19, "0-18",
+ ifelse(agep >= 19 & agep < 35, "19-34",
+ ifelse(agep >= 35 & agep < 50, "35-49",
+ ifelse(agep >= 50 & agep < 65, "50-64",
+ ifelse(agep >= 65, "65+", NA)))))) %>%
+ group_by(agecat) %>%
+ summarize(hicov = mean(hicov))## # Source: lazy query [?? x 2]
-## # Database: sqlite 3.29.0 [:memory:]
+## # Database: sqlite 3.30.1 [:memory:]
## agecat hicov
## <chr> <dbl>
## 1 0-18 1.09
@@ -241,45 +241,43 @@
Srvyr Setup
Srvyr commands are nearly identical to old. The only difference for setup is that you need a variable that uniquely identifies each row in the database (uid).
-acs_m_db_svy <- acs_m_db %>%
- as_survey_rep(
- weight = pwgtp,
- repweights = matches("pwgtp[0-9]+") ,
- scale = 4 / 80,
- rscales = rep(1 , 80),
- mse = TRUE,
- type = "JK1",
- variables = -c(matches("^pwgtp"))
- )
-
-acs_m_db_svy
+acs_m_db_svy <- acs_m_db %>%
+ as_survey_rep(
+ weight = pwgtp,
+ repweights = matches("pwgtp[0-9]+") ,
+ scale = 4 / 80,
+ rscales = rep(1 , 80),
+ mse = TRUE,
+ type = "JK1",
+ variables = -c(matches("^pwgtp"))
+ )
+
+acs_m_db_svy
## Call: Called via srvyr
## Unstratified cluster jacknife (JK1) with 80 replicates and MSE variances.
## Sampling variables:
## - repweights: `pwgtp1 + pwgtp2 + pwgtp3 + pwgtp4 + pwgtp5 + pwgtp6 + pwgtp7 + pwgtp8 + pwgtp9 + pwgtp10 + pwgtp11 + pwgtp12 + pwgtp13 + pwgtp14 + pwgtp15 + pwgtp16 + pwgtp17 + pwgtp18 + pwgtp19 + pwgtp20 + pwgtp21 + pwgtp22 + pwgtp23 + pwgtp24 + pwgtp25 + pwgtp26 + pwgtp27 + pwgtp28 + pwgtp29 + pwgtp30 + pwgtp31 + pwgtp32 + pwgtp33 + pwgtp34 + pwgtp35 + pwgtp36 + pwgtp37 + pwgtp38 + pwgtp39 + pwgtp40 + pwgtp41 + pwgtp42 + pwgtp43 + pwgtp44 + pwgtp45 + pwgtp46 + pwgtp47 + pwgtp48 + pwgtp49 + pwgtp50 + pwgtp51 + \n pwgtp52 + pwgtp53 + pwgtp54 + pwgtp55 + pwgtp56 + pwgtp57 + pwgtp58 + pwgtp59 + pwgtp60 + pwgtp61 + pwgtp62 + pwgtp63 + pwgtp64 + pwgtp65 + pwgtp66 + pwgtp67 + pwgtp68 + pwgtp69 + pwgtp70 + pwgtp71 + pwgtp72 + pwgtp73 + pwgtp74 + pwgtp75 + pwgtp76 + pwgtp77 + pwgtp78 + pwgtp79 + pwgtp80`
## - weights: pwgtp
## Data variables: serialno (int), sporder (chr), pwgtp (int), pwgtp1 (int),
-## pwgtp2 (int), pwgtp3 (int), pwgtp4 (int), pwgtp5 (int), pwgtp6 (int),
-## pwgtp7 (int), pwgtp8 (int), pwgtp9 (int), pwgtp10 (int), pwgtp11 (int),
-## pwgtp12 (int), pwgtp13 (int), pwgtp14 (int), pwgtp15 (int), pwgtp16
-## (int), pwgtp17 (int), pwgtp18 (int), pwgtp19 (int), pwgtp20 (int),
-## pwgtp21 (int), pwgtp22 (int), pwgtp23 (int), pwgtp24 (int), pwgtp25
-## (int), pwgtp26 (int), pwgtp27 (int), pwgtp28 (int), pwgtp29 (int),
-## pwgtp30 (int), pwgtp31 (int), pwgtp32 (int), pwgtp33 (int), pwgtp34
-## (int), pwgtp35 (int), pwgtp36 (int), pwgtp37 (int), pwgtp38 (int),
-## pwgtp39 (int), pwgtp40 (int), pwgtp41 (int), pwgtp42 (int), pwgtp43
-## (int), pwgtp44 (int), pwgtp45 (int), pwgtp46 (int), pwgtp47 (int),
-## pwgtp48 (int), pwgtp49 (int), pwgtp50 (int), pwgtp51 (int), pwgtp52
-## (int), pwgtp53 (int), pwgtp54 (int), pwgtp55 (int), pwgtp56 (int),
-## pwgtp57 (int), pwgtp58 (int), pwgtp59 (int), pwgtp60 (int), pwgtp61
-## (int), pwgtp62 (int), pwgtp63 (int), pwgtp64 (int), pwgtp65 (int),
-## pwgtp66 (int), pwgtp67 (int), pwgtp68 (int), pwgtp69 (int), pwgtp70
-## (int), pwgtp71 (int), pwgtp72 (int), pwgtp73 (int), pwgtp74 (int),
-## pwgtp75 (int), pwgtp76 (int), pwgtp77 (int), pwgtp78 (int), pwgtp79
-## (int), pwgtp80 (int), agep (int), hicov (int), sex (int), st (chr), rt
-## (chr)
+## pwgtp2 (int), pwgtp3 (int), pwgtp4 (int), pwgtp5 (int), pwgtp6 (int), pwgtp7
+## (int), pwgtp8 (int), pwgtp9 (int), pwgtp10 (int), pwgtp11 (int), pwgtp12
+## (int), pwgtp13 (int), pwgtp14 (int), pwgtp15 (int), pwgtp16 (int), pwgtp17
+## (int), pwgtp18 (int), pwgtp19 (int), pwgtp20 (int), pwgtp21 (int), pwgtp22
+## (int), pwgtp23 (int), pwgtp24 (int), pwgtp25 (int), pwgtp26 (int), pwgtp27
+## (int), pwgtp28 (int), pwgtp29 (int), pwgtp30 (int), pwgtp31 (int), pwgtp32
+## (int), pwgtp33 (int), pwgtp34 (int), pwgtp35 (int), pwgtp36 (int), pwgtp37
+## (int), pwgtp38 (int), pwgtp39 (int), pwgtp40 (int), pwgtp41 (int), pwgtp42
+## (int), pwgtp43 (int), pwgtp44 (int), pwgtp45 (int), pwgtp46 (int), pwgtp47
+## (int), pwgtp48 (int), pwgtp49 (int), pwgtp50 (int), pwgtp51 (int), pwgtp52
+## (int), pwgtp53 (int), pwgtp54 (int), pwgtp55 (int), pwgtp56 (int), pwgtp57
+## (int), pwgtp58 (int), pwgtp59 (int), pwgtp60 (int), pwgtp61 (int), pwgtp62
+## (int), pwgtp63 (int), pwgtp64 (int), pwgtp65 (int), pwgtp66 (int), pwgtp67
+## (int), pwgtp68 (int), pwgtp69 (int), pwgtp70 (int), pwgtp71 (int), pwgtp72
+## (int), pwgtp73 (int), pwgtp74 (int), pwgtp75 (int), pwgtp76 (int), pwgtp77
+## (int), pwgtp78 (int), pwgtp79 (int), pwgtp80 (int), agep (int), hicov (int),
+## sex (int), st (chr), rt (chr)Because srvyr stores the survey variables locally, the srvyr object takes up much more memory than the dplyr one. However, this object would not grow in size if you added more data variables to your survey, so if your survey is very wide, it will save a lot space.
- +object.size(acs_m_db_svy)## 8391408 bytesAnalysis commands from srvyr are also similar to ones that work on local data.frames. The main differences come from the issues discussed above about explicitly creating variables difficulties in translating R commands, and variable types.
The following analysis is based on the asdfree analysis and shows some basic analysis on the total population, insurance coverage, age and sex.
-# You can calculate the population of the united states #
-# by state
-acs_m_db_svy %>%
- mutate(one = 1L) %>% # Note that because of weird behavior of MonetDB, need to use 1L not just 1
- group_by(st) %>%
- summarize(count = survey_total(one))# You can calculate the population of the united states #
+# by state
+acs_m_db_svy %>%
+ mutate(one = 1L) %>% # Note that because of weird behavior of MonetDB, need to use 1L not just 1
+ group_by(st) %>%
+ summarize(count = survey_total(one))## # A tibble: 2 x 3
## st count count_se
## <chr> <dbl> <dbl>
## 1 02 722718 0
## 2 15 1374810 0# Or the average age of downloaded states
-acs_m_db_svy %>%
- summarize(agep = survey_mean(agep, na.rm = TRUE))# Or the average age of downloaded states
+acs_m_db_svy %>%
+ summarize(agep = survey_mean(agep, na.rm = TRUE))## # A tibble: 1 x 2
## agep agep_se
## <dbl> <dbl>
## 1 37.4 0.0532# Average age by state
-acs_m_db_svy %>%
- group_by(st) %>%
- summarize(agep = survey_mean(agep, na.rm = TRUE))# Average age by state
+acs_m_db_svy %>%
+ group_by(st) %>%
+ summarize(agep = survey_mean(agep, na.rm = TRUE))## # A tibble: 2 x 3
## st agep agep_se
## <chr> <dbl> <dbl>
## 1 02 34.6 0.133
## 2 15 38.8 0.0404# percent uninsured - nationwide (of downloaded states)
-acs_m_db_svy %>%
- mutate(hicov = as.character(hicov)) %>%
- group_by(hicov) %>%
- summarize(pct = survey_mean(na.rm = TRUE))# percent uninsured - nationwide (of downloaded states)
+acs_m_db_svy %>%
+ mutate(hicov = as.character(hicov)) %>%
+ group_by(hicov) %>%
+ summarize(pct = survey_mean(na.rm = TRUE))## # A tibble: 2 x 3
## hicov pct pct_se
## <chr> <dbl> <dbl>
## 1 1 0.885 0.00339
## 2 2 0.115 0.00339# by state
-acs_m_db_svy %>%
- mutate(hicov = as.character(hicov)) %>%
- group_by(st, hicov) %>%
- summarize(pct = survey_mean(na.rm = TRUE))# by state
+acs_m_db_svy %>%
+ mutate(hicov = as.character(hicov)) %>%
+ group_by(st, hicov) %>%
+ summarize(pct = survey_mean(na.rm = TRUE))## # A tibble: 4 x 4
## st hicov pct pct_se
## <chr> <chr> <dbl> <dbl>
@@ -336,48 +334,48 @@
## 2 02 2 0.198 0.00793
## 3 15 1 0.928 0.00383
## 4 15 2 0.0719 0.00383
# 25th, median, and 75th percentile of age of residents of the united states (downloaded states)
-acs_m_db_svy %>%
- summarize(agep = survey_quantile(agep, c(0.25, 0.5, 0.75), na.rm = TRUE))# 25th, median, and 75th percentile of age of residents of the united states (downloaded states)
+acs_m_db_svy %>%
+ summarize(agep = survey_quantile(agep, c(0.25, 0.5, 0.75), na.rm = TRUE))## # A tibble: 1 x 6
## agep_q25 agep_q50 agep_q75 agep_q25_se agep_q50_se agep_q75_se
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-## 1 19 36 55 0.25 0.25 0.25# Filter works, so we can restrict the acs_m object to females only
-acs_m_db_svy_female <- acs_m_db_svy %>%
- filter(sex == 2)
-
-# Now any of the above commands can be re-run using the acs_m_female object
-# instead of the acs_m object in order to analyze females only
-
-# This is equivalent to using acs_m, and applying the filter every time.
-
-# average age - nationwide (of downloaded states), restricted to females
-acs_m_db_svy_female %>%
- summarize(agep = survey_mean(agep, na.rm = TRUE))# Filter works, so we can restrict the acs_m object to females only
+acs_m_db_svy_female <- acs_m_db_svy %>%
+ filter(sex == 2)
+
+# Now any of the above commands can be re-run using the acs_m_female object
+# instead of the acs_m object in order to analyze females only
+
+# This is equivalent to using acs_m, and applying the filter every time.
+
+# average age - nationwide (of downloaded states), restricted to females
+acs_m_db_svy_female %>%
+ summarize(agep = survey_mean(agep, na.rm = TRUE))## # A tibble: 1 x 2
## agep agep_se
## <dbl> <dbl>
## 1 38.2 0.103# median age - nationwide (of downloaded states), restricted to females
-acs_m_db_svy_female %>%
- summarize(agep = survey_median(agep, na.rm = TRUE))# median age - nationwide (of downloaded states), restricted to females
+acs_m_db_svy_female %>%
+ summarize(agep = survey_median(agep, na.rm = TRUE))## # A tibble: 1 x 2
## agep agep_se
## <dbl> <dbl>
-## 1 37 0.25# Note that though some R functions are translated by dplyr into SQL, not
-# all of them are. For example, when constructing a new age category
-# variable in the dataset neither findIntervals nor cut work on databases,
-# so we have to spell out groups with ifelse()
-acs_m_db_svy %>%
- mutate(agecat = ifelse(agep < 19, "0-18",
- ifelse(agep >= 19 & agep < 35, "19-34",
- ifelse(agep >= 35 & agep < 50, "35-49",
- ifelse(agep >= 50 & agep < 65, "50-64",
- ifelse(agep >= 65, "65+", NA)))))) %>%
- group_by(agecat) %>%
- summarize(pct = survey_mean(na.rm = TRUE))# Note that though some R functions are translated by dplyr into SQL, not
+# all of them are. For example, when constructing a new age category
+# variable in the dataset neither findIntervals nor cut work on databases,
+# so we have to spell out groups with ifelse()
+acs_m_db_svy %>%
+ mutate(agecat = ifelse(agep < 19, "0-18",
+ ifelse(agep >= 19 & agep < 35, "19-34",
+ ifelse(agep >= 35 & agep < 50, "35-49",
+ ifelse(agep >= 50 & agep < 65, "50-64",
+ ifelse(agep >= 65, "65+", NA)))))) %>%
+ group_by(agecat) %>%
+ summarize(pct = survey_mean(na.rm = TRUE))## # A tibble: 5 x 3
## agecat pct pct_se
## <chr> <dbl> <dbl>
@@ -393,11 +391,11 @@
Running survey commands with collect
If you’d like to run a command from the survey package, you’ll need to collect the data locally first. You can select only the variables you’ll need for the analysis so that you don’t have to store the whole dataset in memory.
-acs_m_db_svy %>%
- select(agep, hicov, sex) %>%
- collect() %>%
- {survey::svyglm(hicov ~ sex + agep, .)} %>%
- summary()
+acs_m_db_svy %>%
+ select(agep, hicov, sex) %>%
+ collect() %>%
+ {survey::svyglm(hicov ~ sex + agep, .)} %>%
+ summary()
## Warning in log(wt): NaNs produced
## Warning in log(wt): NaNs produced
@@ -486,7 +484,8 @@
Database Setup
Srvyr Setup
-Analysis
diff --git a/docs/articles/srvyr-database_files/elevate-section-attrs-2.0/elevate-section-attrs.js b/docs/articles/srvyr-database_files/elevate-section-attrs-2.0/elevate-section-attrs.js
new file mode 100644
index 0000000..c1547d4
--- /dev/null
+++ b/docs/articles/srvyr-database_files/elevate-section-attrs-2.0/elevate-section-attrs.js
@@ -0,0 +1,20 @@
+$(function() {
+ $("div.section[class*='level'], section[class*='level']").each(function(i, el) {
+ var $section = $(el);
+ var $header = $section.children().filter(":header").first();
+ if ($header.length === 0) return;
+ var attrs = $header[0].attributes;
+ for (var a = 0; a < attrs.length; a++) {
+ var nm = attrs[a].name;
+ var val = attrs[a].value;
+ if (nm === "class") {
+ $section.addClass(val);
+ $header.removeClass(val);
+ continue;
+ }
+ $section.attr(nm, val);
+ $header.attr(nm, null);
+ }
+ });
+});
+
diff --git a/docs/articles/srvyr-database_files/jquery-1.11.3/jquery.min.js b/docs/articles/srvyr-database_files/jquery-1.11.3/jquery.min.js
new file mode 100644
index 0000000..0f60b7b
--- /dev/null
+++ b/docs/articles/srvyr-database_files/jquery-1.11.3/jquery.min.js
@@ -0,0 +1,5 @@
+/*! jQuery v1.11.3 | (c) 2005, 2015 jQuery Foundation, Inc. | jquery.org/license */
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diff --git a/docs/articles/srvyr-vs-survey.html b/docs/articles/srvyr-vs-survey.html
index b8b10c6..2b9ccd0 100644
--- a/docs/articles/srvyr-vs-survey.html
+++ b/docs/articles/srvyr-vs-survey.html
@@ -32,7 +32,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -89,14 +89,14 @@
-
+
srvyr compared to the survey package
Greg Freedman
- 2019-10-12
+ 2020-01-16
Source: vignettes/srvyr-vs-survey.Rmd
srvyr-vs-survey.Rmd
@@ -111,22 +111,22 @@ 2019-10-12
Motivating example
The dplyr package has made it fast and easy to write code to summarize data. For example, if we wanted to check how the year-to-year change in academic progress indicator score varied by school level and percent of parents were high school graduates, we can do this:
-library(survey)
-library(ggplot2)
-library(dplyr)
-
-data(api)
-
-out <- apistrat %>%
- mutate(hs_grad_pct = cut(hsg, c(0, 20, 100), include.lowest = TRUE,
- labels = c("<20%", "20+%"))) %>%
- group_by(stype, hs_grad_pct) %>%
- summarize(api_diff = weighted.mean(api00 - api99, pw),
- n = n())
-
-ggplot(data = out, aes(x = stype, y = api_diff, group = hs_grad_pct, fill = hs_grad_pct)) +
- geom_bar(stat = "identity", position = "dodge") +
- geom_text(aes(y = 0, label = n), position = position_dodge(width = 0.9), vjust = -1)
+library(survey)
+library(ggplot2)
+library(dplyr)
+
+data(api)
+
+out <- apistrat %>%
+ mutate(hs_grad_pct = cut(hsg, c(0, 20, 100), include.lowest = TRUE,
+ labels = c("<20%", "20+%"))) %>%
+ group_by(stype, hs_grad_pct) %>%
+ summarize(api_diff = weighted.mean(api00 - api99, pw),
+ n = n())
+
+ggplot(data = out, aes(x = stype, y = api_diff, group = hs_grad_pct, fill = hs_grad_pct)) +
+ geom_bar(stat = "identity", position = "dodge") +
+ geom_text(aes(y = 0, label = n), position = position_dodge(width = 0.9), vjust = -1)
However, if we wanted to add error bars to the graph to capture the uncertainty due to sampling variation, we have to completely rewrite the dplyr code for the survey package. srvyr allows a more direct translation.
@@ -134,36 +134,36 @@
Preparing a survey dataset
as_survey_design(), as_survey_rep() and as_survey_twophase() are analogous to survey::svydesign(), survey::svrepdesign() and survey::twophase() respectively. Because they are designed to match dplyr’s style of non-standard evaluation, they accept bare column names instead of formulas (~). They also move the data argument first, so that it is easier to use magrittr pipes (%>%).
-library(srvyr)
-
-# simple random sample
-srs_design_srvyr <- apisrs %>% as_survey_design(ids = 1, fpc = fpc)
-
-srs_design_survey <- svydesign(ids = ~1, fpc = ~fpc, data = apisrs)
+library(srvyr)
+
+# simple random sample
+srs_design_srvyr <- apisrs %>% as_survey_design(ids = 1, fpc = fpc)
+
+srs_design_survey <- svydesign(ids = ~1, fpc = ~fpc, data = apisrs)
The srvyr functions also accept dplyr::select()’s special selection functions (such as starts_with(), one_of(), etc.), so these functions are analogous:
-# selecting variables to keep in the survey object (stratified example)
-strat_design_srvyr <- apistrat %>%
- as_survey_design(1, strata = stype, fpc = fpc, weight = pw,
- variables = c(stype, starts_with("api")))
-
-strat_design_survey <- svydesign(~1, strata = ~stype, fpc = ~fpc,
- variables = ~stype + api99 + api00 + api.stu,
- weight = ~pw, data = apistrat)
+# selecting variables to keep in the survey object (stratified example)
+strat_design_srvyr <- apistrat %>%
+ as_survey_design(1, strata = stype, fpc = fpc, weight = pw,
+ variables = c(stype, starts_with("api")))
+
+strat_design_survey <- svydesign(~1, strata = ~stype, fpc = ~fpc,
+ variables = ~stype + api99 + api00 + api.stu,
+ weight = ~pw, data = apistrat)
The function as_survey() will automatically choose between the three as_survey_* functions based on the arguments, so you can save a few keystrokes.
-
+# simple random sample (again)
+srs_design_srvyr2 <- apisrs %>% as_survey(ids = 1, fpc = fpc)
Data manipulation
Once you’ve set up your survey data, you can use dplyr verbs such as mutate(), select(), filter() and rename().
-strat_design_srvyr <- strat_design_srvyr %>%
- mutate(api_diff = api00 - api99) %>%
- rename(api_students = api.stu)
-
-strat_design_survey$variables$api_diff <- strat_design_survey$variables$api00 -
- strat_design_survey$variables$api99
-names(strat_design_survey$variables)[names(strat_design_survey$variables) == "api.stu"] <- "api_students"
+strat_design_srvyr <- strat_design_srvyr %>%
+ mutate(api_diff = api00 - api99) %>%
+ rename(api_students = api.stu)
+
+strat_design_survey$variables$api_diff <- strat_design_survey$variables$api00 -
+ strat_design_survey$variables$api99
+names(strat_design_survey$variables)[names(strat_design_survey$variables) == "api.stu"] <- "api_students"
Note that arrange() is not available, because the srvyr object expects to stay in the same order. Nor are two-table verbs such as full_join(), bind_rows(), etc. available to srvyr objects either because they may have implications on the survey design. If you need to use these functions, you should use them earlier in your analysis pipeline, when the objects are still stored as data.frames.
@@ -173,22 +173,22 @@
Of the entire population
srvyr also provides summarize() and several survey-specific functions that calculate summary statistics on numeric variables: survey_mean(), survey_total(), survey_quantile() and survey_ratio(). These functions differ from their counterparts in survey because they always return a data.frame in a consistent format. As such, they do not return the variance-covariance matrix, and so are not as flexible.
-# Using srvyr
-out <- strat_design_srvyr %>%
- summarize(api_diff = survey_mean(api_diff, vartype = "ci"))
-
-out
+# Using srvyr
+out <- strat_design_srvyr %>%
+ summarize(api_diff = survey_mean(api_diff, vartype = "ci"))
+
+out
## # A tibble: 1 x 3
## api_diff api_diff_low api_diff_upp
## <dbl> <dbl> <dbl>
## 1 32.9 28.8 36.9
-
+# Using survey
+out <- svymean(~api_diff, strat_design_survey)
+
+out
## mean SE
## api_diff 32.893 2.0511
-
+confint(out)
## 2.5 % 97.5 %
## api_diff 28.87241 36.91262
@@ -196,19 +196,19 @@
By group
srvyr also allows you to calculate statistics on numeric variables by group, using group_by().
-# Using srvyr
-strat_design_srvyr %>%
- group_by(stype) %>%
- summarize(api_increase = survey_total(api_diff >= 0),
- api_decrease = survey_total(api_diff < 0))
+# Using srvyr
+strat_design_srvyr %>%
+ group_by(stype) %>%
+ summarize(api_increase = survey_total(api_diff >= 0),
+ api_decrease = survey_total(api_diff < 0))
## # A tibble: 3 x 5
## stype api_increase api_increase_se api_decrease api_decrease_se
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 E 4067. 119. 354. 119.
## 2 H 498. 49.4 257. 49.4
## 3 M 998. 19.9 20.4 19.9
-
+# Using survey
+svyby(~api_diff >= 0, ~stype, strat_design_survey, svytotal)
## stype api_diff >= 0FALSE api_diff >= 0TRUE se.api_diff >= 0FALSE
## E E 353.68 4067.32 119.17185
## H H 256.70 498.30 49.37208
@@ -222,22 +222,22 @@
Proportions by group
You can also calculate the proportion or count in each group of a factor or character variable by leaving x empty in survey_mean() or survey_total().
-# Using srvyr
-srs_design_srvyr %>%
- group_by(awards) %>%
- summarize(proportion = survey_mean(),
- total = survey_total())
+# Using srvyr
+srs_design_srvyr %>%
+ group_by(awards) %>%
+ summarize(proportion = survey_mean(),
+ total = survey_total())
## # A tibble: 2 x 5
## awards proportion proportion_se total total_se
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 No 0.38 0.0338 2354. 210.
## 2 Yes 0.62 0.0338 3840. 210.
-
+# Using survey
+svymean(~awards, srs_design_survey)
## mean SE
## awardsNo 0.38 0.0338
## awardsYes 0.62 0.0338
-
+svytotal(~awards, srs_design_survey)
## total SE
## awardsNo 2353.7 209.65
## awardsYes 3840.3 209.65
@@ -246,18 +246,18 @@
Unweighted calculations
Finally, the unweighted() function can act as an escape hatch to calculate unweighted calculations on the dataset.
-
+# Using srvyr
+strat_design_srvyr %>%
+ group_by(stype) %>%
+ summarize(n = unweighted(n()))
## # A tibble: 3 x 2
## stype n
## <fct> <int>
## 1 E 100
## 2 H 50
## 3 M 50
-
+# Using survey
+svyby(~api99, ~stype, strat_design_survey, unwtd.count)
## stype counts se
## E E 100 0
## H H 50 0
@@ -268,21 +268,21 @@
Back to the example
So now, we have all the tools needed to create the first graph and add error bounds. Notice that the data manipulation code is nearly identical to the dplyr code, with a little extra set up, and replacing weighted.mean() with survey_mean.
-strat_design <- apistrat %>%
- as_survey_design(strata = stype, fpc = fpc, weight = pw)
-
-out <- strat_design %>%
- mutate(hs_grad_pct = cut(hsg, c(0, 20, 100), include.lowest = TRUE,
- labels = c("<20%", "20+%"))) %>%
- group_by(stype, hs_grad_pct) %>%
- summarize(api_diff = survey_mean(api00 - api99, vartype = "ci"),
- n = unweighted(n()))
-
-ggplot(data = out, aes(x = stype, y = api_diff, group = hs_grad_pct, fill = hs_grad_pct,
- ymax = api_diff_upp, ymin = api_diff_low)) +
- geom_bar(stat = "identity", position = "dodge") +
- geom_errorbar(position = position_dodge(width = 0.9), width = 0.1) +
- geom_text(aes(y = 0, label = n), position = position_dodge(width = 0.9), vjust = -1)
+strat_design <- apistrat %>%
+ as_survey_design(strata = stype, fpc = fpc, weight = pw)
+
+out <- strat_design %>%
+ mutate(hs_grad_pct = cut(hsg, c(0, 20, 100), include.lowest = TRUE,
+ labels = c("<20%", "20+%"))) %>%
+ group_by(stype, hs_grad_pct) %>%
+ summarize(api_diff = survey_mean(api00 - api99, vartype = "ci"),
+ n = unweighted(n()))
+
+ggplot(data = out, aes(x = stype, y = api_diff, group = hs_grad_pct, fill = hs_grad_pct,
+ ymax = api_diff_upp, ymin = api_diff_low)) +
+ geom_bar(stat = "identity", position = "dodge") +
+ geom_errorbar(position = position_dodge(width = 0.9), width = 0.1) +
+ geom_text(aes(y = 0, label = n), position = position_dodge(width = 0.9), vjust = -1)
@@ -292,8 +292,8 @@
Using survey functions on srvyr objects
Because srvyr objects are just survey objects with some extra structure, all of the functions from survey will still work with them. If you need to calculate something beyond simple summary statistics, you can use survey functions.
-
+
##
## Call:
## svyglm(formula = api00 ~ ell + meals + mobility, design = strat_design)
@@ -317,20 +317,20 @@
Standard evaluation
-Srvyr now supports the standard evaluation conventions introduced in dplyr version 0.7 and rlang. If you’d like to use a function programmatically, you can use the functions from rlang like rlang::quo() or rlang::sym() to capture the expression and rlang::!! to unquote it. All of these functions are re-exported by srvyr, so you don’t need to load the rlang library to use them.
+Srvyr now supports the standard evaluation conventions introduced in dplyr version 0.7 and rlang. If you’d like to use a function programmatically, you can use the functions from rlang like rlang::quo() or rlang::sym() to capture the expression and rlang::!! to unquote it. All of these functions are re-exported by srvyr, so you don’t need to load the rlang library to use them.
Here’s a quick example, but please see the dplyr vignette vignette("programming", package = "dplyr") for more details.
-fpc_var <- sym("fpc")
-srs_design_srvyr <- apisrs %>% as_survey_design(fpc = !!fpc_var)
-
-grouping_var <- sym("stype")
-api_diff <- quo(api00 - api99)
-
-srs_design_srvyr %>%
- group_by(!!grouping_var) %>%
- summarize(
- api_increase = survey_total((!!api_diff) >= 0),
- api_decrease = survey_total((!!api_diff) < 0)
- )
+fpc_var <- sym("fpc")
+srs_design_srvyr <- apisrs %>% as_survey_design(fpc = !!fpc_var)
+
+grouping_var <- sym("stype")
+api_diff <- quo(api00 - api99)
+
+srs_design_srvyr %>%
+ group_by(!!grouping_var) %>%
+ summarize(
+ api_increase = survey_total((!!api_diff) >= 0),
+ api_decrease = survey_total((!!api_diff) < 0)
+ )
## # A tibble: 3 x 5
## stype api_increase api_increase_se api_decrease api_decrease_se
## <fct> <dbl> <dbl> <dbl> <dbl>
@@ -343,9 +343,9 @@
Scoped functions
Srvyr has also been able to take advantage of the new-ish dplyr “scoped” variants of the main manipulation functions like summarize_at(). These functions allow you to quickly calculate summary statistics for multiple variables. For example:
-# Calculate survey mean for all variables that have names starting with "api"
-strat_design %>%
- summarize_at(vars(starts_with("api")), survey_mean)
+# Calculate survey mean for all variables that have names starting with "api"
+strat_design %>%
+ summarize_at(vars(starts_with("api")), survey_mean)
## # A tibble: 1 x 6
## api00 api00_se api99 api99_se api.stu api.stu_se
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
@@ -365,7 +365,8 @@
Preparing a survey dataset
Data manipulation
-Summary statistics
Back to the example
-Grab Bag
+Grab Bag
+
- Using
survey functions on srvyr objects
- Standard evaluation
- Scoped functions
diff --git a/docs/articles/srvyr-vs-survey_files/elevate-section-attrs-2.0/elevate-section-attrs.js b/docs/articles/srvyr-vs-survey_files/elevate-section-attrs-2.0/elevate-section-attrs.js
new file mode 100644
index 0000000..c1547d4
--- /dev/null
+++ b/docs/articles/srvyr-vs-survey_files/elevate-section-attrs-2.0/elevate-section-attrs.js
@@ -0,0 +1,20 @@
+$(function() {
+ $("div.section[class*='level'], section[class*='level']").each(function(i, el) {
+ var $section = $(el);
+ var $header = $section.children().filter(":header").first();
+ if ($header.length === 0) return;
+ var attrs = $header[0].attributes;
+ for (var a = 0; a < attrs.length; a++) {
+ var nm = attrs[a].name;
+ var val = attrs[a].value;
+ if (nm === "class") {
+ $section.addClass(val);
+ $header.removeClass(val);
+ continue;
+ }
+ $section.attr(nm, val);
+ $header.attr(nm, null);
+ }
+ });
+});
+
diff --git a/docs/articles/srvyr-vs-survey_files/figure-html/unnamed-chunk-1-1.png b/docs/articles/srvyr-vs-survey_files/figure-html/unnamed-chunk-1-1.png
index 9bfaf8f..9f1f3bd 100644
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diff --git a/docs/articles/srvyr-vs-survey_files/figure-html/unnamed-chunk-10-1.png b/docs/articles/srvyr-vs-survey_files/figure-html/unnamed-chunk-10-1.png
index c24b44b..eecbf78 100644
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diff --git a/docs/articles/srvyr-vs-survey_files/jquery-1.11.3/jquery.min.js b/docs/articles/srvyr-vs-survey_files/jquery-1.11.3/jquery.min.js
new file mode 100644
index 0000000..0f60b7b
--- /dev/null
+++ b/docs/articles/srvyr-vs-survey_files/jquery-1.11.3/jquery.min.js
@@ -0,0 +1,5 @@
+/*! jQuery v1.11.3 | (c) 2005, 2015 jQuery Foundation, Inc. | jquery.org/license */
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diff --git a/docs/authors.html b/docs/authors.html
index bbd69b3..9207351 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -68,7 +68,7 @@
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/index.html b/docs/index.html
index f579102..5f52c1f 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -34,7 +34,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -93,79 +93,79 @@
-
+
srvyr brings parts of dplyr’s syntax to survey analysis, using the survey package.
srvyr focuses on calculating summary statistics from survey data, such as the mean, total or quantile. It allows for the use of many dplyr verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions, rlang’s style of non-standard evaluation and more consistent return types than the survey package.
You can try it out:
-install.packages("srvyr")
-# or for development version
-# devtools::install_github("gergness/srvyr")
+install.packages("srvyr")
+# or for development version
+# devtools::install_github("gergness/srvyr")
Example usage
First, describe the variables that define the survey’s structure with the function as_survey()with the bare column names of the names that you would use in functions from the survey package like survey::svydesign(), survey::svrepdesign() or survey::twophase().
-library(srvyr, warn.conflicts = FALSE)
-data(api, package = "survey")
-
-dstrata <- apistrat %>%
- as_survey_design(strata = stype, weights = pw)
+library(srvyr, warn.conflicts = FALSE)
+data(api, package = "survey")
+
+dstrata <- apistrat %>%
+ as_survey_design(strata = stype, weights = pw)
Now many of the dplyr verbs are available.
-
mutate() adds or modifies a variable.
-
+dstrata <- dstrata %>%
+ mutate(api_diff = api00 - api99)
-
summarise() calculates summary statistics such as mean, total, quantile or ratio.
-dstrata %>%
- summarise(api_diff = survey_mean(api_diff, vartype = "ci"))
-#> # A tibble: 1 x 3
-#> api_diff api_diff_low api_diff_upp
-#> <dbl> <dbl> <dbl>
-#> 1 32.9 28.8 37.0
+dstrata %>%
+ summarise(api_diff = survey_mean(api_diff, vartype = "ci"))
+#> # A tibble: 1 x 3
+#> api_diff api_diff_low api_diff_upp
+#> <dbl> <dbl> <dbl>
+#> 1 32.9 28.8 37.0
-
group_by() and then summarise() creates summaries by groups.
-dstrata %>%
- group_by(stype) %>%
- summarise(api_diff = survey_mean(api_diff, vartype = "ci"))
-#> # A tibble: 3 x 4
-#> stype api_diff api_diff_low api_diff_upp
-#> <fct> <dbl> <dbl> <dbl>
-#> 1 E 38.6 33.1 44.0
-#> 2 H 8.46 1.74 15.2
-#> 3 M 26.4 20.4 32.4
+dstrata %>%
+ group_by(stype) %>%
+ summarise(api_diff = survey_mean(api_diff, vartype = "ci"))
+#> # A tibble: 3 x 4
+#> stype api_diff api_diff_low api_diff_upp
+#> <fct> <dbl> <dbl> <dbl>
+#> 1 E 38.6 33.1 44.0
+#> 2 H 8.46 1.74 15.2
+#> 3 M 26.4 20.4 32.4
- Functions from the survey package are still available:
-my_model <- survey::svyglm(api99 ~ stype, dstrata)
-summary(my_model)
-#>
-#> Call:
-#> svyglm(formula = api99 ~ stype, design = dstrata)
-#>
-#> Survey design:
-#> Called via srvyr
-#>
-#> Coefficients:
-#> Estimate Std. Error t value Pr(>|t|)
-#> (Intercept) 635.87 13.34 47.669 <2e-16 ***
-#> stypeH -18.51 20.68 -0.895 0.372
-#> stypeM -25.67 21.42 -1.198 0.232
-#> ---
-#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#>
-#> (Dispersion parameter for gaussian family taken to be 16409.56)
-#>
-#> Number of Fisher Scoring iterations: 2
+my_model <- survey::svyglm(api99 ~ stype, dstrata)
+summary(my_model)
+#>
+#> Call:
+#> svyglm(formula = api99 ~ stype, design = dstrata)
+#>
+#> Survey design:
+#> Called via srvyr
+#>
+#> Coefficients:
+#> Estimate Std. Error t value Pr(>|t|)
+#> (Intercept) 635.87 13.34 47.669 <2e-16 ***
+#> stypeH -18.51 20.68 -0.895 0.372
+#> stypeM -25.67 21.42 -1.198 0.232
+#> ---
+#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+#>
+#> (Dispersion parameter for gaussian family taken to be 16409.56)
+#>
+#> Number of Fisher Scoring iterations: 2
diff --git a/docs/news/index.html b/docs/news/index.html
index 0f527c2..d6e1937 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -68,7 +68,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -132,18 +132,29 @@ Changelog
Source: NEWS.md
-
+
-srvyr 0.3.6 2019-10-05
+srvyr 0.3.7 Unreleased
+
+
+
+
+
+srvyr 0.3.6 2019-10-05
Small update to quasiquotation syntax inside unweighted to improve consistency with recent rlang updates (#54).
Added functions survey_tally() and survey_count() (#53)
-
+
-srvyr 0.3.5 2019-07-09
+srvyr 0.3.5 2019-07-09
New functions survey_var and survey_sd to calculate population variance and standard deviaton.
Small updates to replace soft-deprecated dplyr functions with their tibble and tidyselect equivalents (#52, thanks @bschneidr).
-
+
-
+
-srvyr 0.3.3 2018-05-22
+srvyr 0.3.3 2018-05-22
Add warning to explain that design effects cannot be calculated on proportions. (#39, thanks @mlaviolet)
Remove dependency on stringr in tests and add DBI to suggests so that test dependencies are correctly specified (#40, thanks CRAN!)
-
+
-srvyr 0.3.2 2018-05-04
+srvyr 0.3.2 2018-05-04
- Bug fix for calculating multiple quantiles on grouped data (#38, thanks @iantperry)
-
+
-srvyr 0.3.1 2018-03-10
+srvyr 0.3.1 2018-03-10
When converting from a survey db-backed survey to a srvyr one srvyr now tries to capture the updates you’ve already sent. If dbplyr can convert the function, then it will bring the update. If it can’t it will warn you (#35).
Small bug fixes, mostly having to do with CRAN checks, running on CI services, or for upstream rev dep checks.
-
+
-srvyr 0.3.0 2018-01-24
+srvyr 0.3.0 2018-01-24
srvyr now uses tidy evaluation from rlang. The “underscore” functions have been soft deprecated in favor of quosure splicing. See dplyr’s vignette “programming” for more details. In almost all cases, the old syntax will still work, with one exception: the standard evaluation function as_survey_twophase_() had to be changed slightly so that the entire list is inside quotation.
srvyr now has a pkgdown site, check it out at http://gdfe.co/srvyr
-
+
-
+
-srvyr 0.2.1 2017-04-26
+srvyr 0.2.1 2017-04-26
Added support for dplyr mutate_at/_if/_all and summarize_at/_if/_all for srvyr surveys.
Fixed a few bugs introduced with dplyr 0.6. This version of srvyr will work with both old versions of dplyr and 0.6, but may be full of warnings if you update dplyr. Full support for the new dplyr is coming soon.
-
+
-srvyr 0.2.0 2016-09-26
+srvyr 0.2.0 2016-09-26
- Added support for database backed surveys, using dplyr’s handling of DBI. Because of problems interacting with the survey package twophase designs do not work.
-
+
-srvyr 0.1.2 2016-06-28
+srvyr 0.1.2 2016-06-28
Fixed a problem with confidence levels not being passed into quantiles
summarize and mutate match dplyr’s behavior when arguments aren’t named (uses dplyr::auto_name())
-
+
-srvyr 0.1.1 2016-04-03
+srvyr 0.1.1 2016-04-03
New function cascade summarizes groups, and cascades to create summary statistics of groups of groups.
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 894e810..adcb57a 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,4 +1,4 @@
-pandoc: 2.3.1
+pandoc: 2.9.1.1
pkgdown: 1.4.1
pkgdown_sha: ~
articles:
diff --git a/docs/reference/as_survey.html b/docs/reference/as_survey.html
index d1786ed..ceb6557 100644
--- a/docs/reference/as_survey.html
+++ b/docs/reference/as_survey.html
@@ -73,7 +73,7 @@
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/reference/as_survey_design.html b/docs/reference/as_survey_design.html
index 800c5a1..9e433b9 100644
--- a/docs/reference/as_survey_design.html
+++ b/docs/reference/as_survey_design.html
@@ -70,7 +70,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -142,19 +142,39 @@ Create a tbl_svy survey object using sampling design
as_survey_design(.data, ...)
# S3 method for data.frame
-as_survey_design(.data, ids = NULL, probs = NULL,
- strata = NULL, variables = NULL, fpc = NULL, nest = FALSE,
- check_strata = !nest, weights = NULL, pps = FALSE,
- variance = c("HT", "YG"), ...)
+as_survey_design(
+ .data,
+ ids = NULL,
+ probs = NULL,
+ strata = NULL,
+ variables = NULL,
+ fpc = NULL,
+ nest = FALSE,
+ check_strata = !nest,
+ weights = NULL,
+ pps = FALSE,
+ variance = c("HT", "YG"),
+ ...
+)
# S3 method for survey.design2
as_survey_design(.data, ...)
# S3 method for tbl_lazy
-as_survey_design(.data, ids = NULL, probs = NULL,
- strata = NULL, variables = NULL, fpc = NULL, nest = FALSE,
- check_strata = !nest, weights = NULL, pps = FALSE,
- variance = c("HT", "YG"), ...)
+as_survey_design(
+ .data,
+ ids = NULL,
+ probs = NULL,
+ strata = NULL,
+ variables = NULL,
+ fpc = NULL,
+ nest = FALSE,
+ check_strata = !nest,
+ weights = NULL,
+ pps = FALSE,
+ variance = c("HT", "YG"),
+ ...
+)
Arguments
diff --git a/docs/reference/as_survey_rep.html b/docs/reference/as_survey_rep.html
index 692a1a9..be68df7 100644
--- a/docs/reference/as_survey_rep.html
+++ b/docs/reference/as_survey_rep.html
@@ -70,7 +70,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -142,35 +142,69 @@ Create a tbl_svy survey object using replicate weights
as_survey_rep(.data, ...)
# S3 method for data.frame
-as_survey_rep(.data, variables = NULL,
- repweights = NULL, weights = NULL, type = c("BRR", "Fay", "JK1",
- "JKn", "bootstrap", "other"), combined_weights = TRUE, rho = NULL,
- bootstrap_average = NULL, scale = NULL, rscales = NULL,
- fpc = NULL, fpctype = c("fraction", "correction"),
- mse = getOption("survey.replicates.mse"), ...)
+as_survey_rep(
+ .data,
+ variables = NULL,
+ repweights = NULL,
+ weights = NULL,
+ type = c("BRR", "Fay", "JK1", "JKn", "bootstrap", "other"),
+ combined_weights = TRUE,
+ rho = NULL,
+ bootstrap_average = NULL,
+ scale = NULL,
+ rscales = NULL,
+ fpc = NULL,
+ fpctype = c("fraction", "correction"),
+ mse = getOption("survey.replicates.mse"),
+ ...
+)
# S3 method for tbl_lazy
-as_survey_rep(.data, variables = NULL,
- repweights = NULL, weights = NULL, type = c("BRR", "Fay", "JK1",
- "JKn", "bootstrap", "other"), combined_weights = TRUE, rho = NULL,
- bootstrap_average = NULL, scale = NULL, rscales = NULL,
- fpc = NULL, fpctype = c("fraction", "correction"),
- mse = getOption("survey.replicates.mse"), ...)
+as_survey_rep(
+ .data,
+ variables = NULL,
+ repweights = NULL,
+ weights = NULL,
+ type = c("BRR", "Fay", "JK1", "JKn", "bootstrap", "other"),
+ combined_weights = TRUE,
+ rho = NULL,
+ bootstrap_average = NULL,
+ scale = NULL,
+ rscales = NULL,
+ fpc = NULL,
+ fpctype = c("fraction", "correction"),
+ mse = getOption("survey.replicates.mse"),
+ ...
+)
# S3 method for svyrep.design
as_survey_rep(.data, ...)
# S3 method for survey.design2
-as_survey_rep(.data, type = c("auto", "JK1",
- "JKn", "BRR", "bootstrap", "subbootstrap", "mrbbootstrap", "Fay"),
- rho = 0, fpc = NULL, fpctype = NULL, ..., compress = TRUE,
- mse = getOption("survey.replicates.mse"))
+as_survey_rep(
+ .data,
+ type = c("auto", "JK1", "JKn", "BRR", "bootstrap", "subbootstrap", "mrbbootstrap",
+ "Fay"),
+ rho = 0,
+ fpc = NULL,
+ fpctype = NULL,
+ ...,
+ compress = TRUE,
+ mse = getOption("survey.replicates.mse")
+)
# S3 method for tbl_svy
-as_survey_rep(.data, type = c("auto", "JK1", "JKn",
- "BRR", "bootstrap", "subbootstrap", "mrbbootstrap", "Fay"), rho = 0,
- fpc = NULL, fpctype = NULL, ..., compress = TRUE,
- mse = getOption("survey.replicates.mse"))
+as_survey_rep(
+ .data,
+ type = c("auto", "JK1", "JKn", "BRR", "bootstrap", "subbootstrap", "mrbbootstrap",
+ "Fay"),
+ rho = 0,
+ fpc = NULL,
+ fpctype = NULL,
+ ...,
+ compress = TRUE,
+ mse = getOption("survey.replicates.mse")
+)
Arguments
diff --git a/docs/reference/as_survey_twophase.html b/docs/reference/as_survey_twophase.html
index 057599d..358aa2e 100644
--- a/docs/reference/as_survey_twophase.html
+++ b/docs/reference/as_survey_twophase.html
@@ -74,7 +74,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -150,9 +150,17 @@ Create a tbl_svy survey object using two phase design
as_survey_twophase(.data, ...)
# S3 method for data.frame
-as_survey_twophase(.data, id, strata = NULL,
- probs = NULL, weights = NULL, fpc = NULL, subset,
- method = c("full", "approx", "simple"), ...)
+as_survey_twophase(
+ .data,
+ id,
+ strata = NULL,
+ probs = NULL,
+ weights = NULL,
+ fpc = NULL,
+ subset,
+ method = c("full", "approx", "simple"),
+ ...
+)
# S3 method for twophase2
as_survey_twophase(.data, ...)
diff --git a/docs/reference/as_tibble.html b/docs/reference/as_tibble.html
index 57d416b..bab6811 100644
--- a/docs/reference/as_tibble.html
+++ b/docs/reference/as_tibble.html
@@ -70,7 +70,7 @@
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/reference/cascade.html b/docs/reference/cascade.html
index ac175e0..4e22e0d 100644
--- a/docs/reference/cascade.html
+++ b/docs/reference/cascade.html
@@ -71,7 +71,7 @@
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/reference/collect.html b/docs/reference/collect.html
index 7df2054..c2ab29c 100644
--- a/docs/reference/collect.html
+++ b/docs/reference/collect.html
@@ -74,7 +74,7 @@
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/reference/current_svy.html b/docs/reference/current_svy.html
index 281023f..bb204d1 100644
--- a/docs/reference/current_svy.html
+++ b/docs/reference/current_svy.html
@@ -38,7 +38,7 @@
@@ -74,7 +74,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -142,7 +142,7 @@ Get the survey data for the current context
This is a helper to allow srvyr's syntactic style. In particular, it tells
functions inside of a summarize call what survey to use. In general, users
-will not have to worry about getting (or setting) the current context's survey,
+will not have to worry about getting (or setting) the current conext's survey,
unless they are trying to extend srvyr. See vignette("extending-srvyr")
for more details.
diff --git a/docs/reference/dplyr_single.html b/docs/reference/dplyr_single.html
index c7eadb1..5c1c7a0 100644
--- a/docs/reference/dplyr_single.html
+++ b/docs/reference/dplyr_single.html
@@ -70,7 +70,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -147,6 +147,8 @@ Details
mutate for more details.
select and rename keep or rename variables. See
select for more details.
+pull extracts a variable as a vector (whereas select returns a tbl_svy).
+See pull for more details.
filter keeps certain observations. See filter
for more details.
arrange is not implemented for tbl_svy objects. Nor are any
diff --git a/docs/reference/get_var_est.html b/docs/reference/get_var_est.html
index d65250f..40cdbce 100644
--- a/docs/reference/get_var_est.html
+++ b/docs/reference/get_var_est.html
@@ -75,7 +75,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -149,8 +149,15 @@
Get the variance estimates for a survey estimate
See vignette("extending-srvyr") for more details.
- get_var_est(stat, vartype, grps = "", level = 0.95, df = Inf,
- pre_calc_ci = FALSE, deff = FALSE)
+ get_var_est(
+ stat,
+ vartype,
+ grps = "",
+ level = 0.95,
+ df = Inf,
+ pre_calc_ci = FALSE,
+ deff = FALSE
+)
Arguments
diff --git a/docs/reference/group_by.html b/docs/reference/group_by.html
index b9cc4bd..5fc26a3 100644
--- a/docs/reference/group_by.html
+++ b/docs/reference/group_by.html
@@ -73,7 +73,7 @@
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/reference/groups.html b/docs/reference/groups.html
index 864c795..872ae92 100644
--- a/docs/reference/groups.html
+++ b/docs/reference/groups.html
@@ -73,7 +73,7 @@
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/reference/index.html b/docs/reference/index.html
index a14dcc2..f7ef2d0 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -68,7 +68,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -198,6 +198,12 @@ <
Summarise multiple values to a single value.
+
+
+
+ Manipulate multiple columns.
+
@@ -222,6 +228,18 @@ <
Calculate the ratio and its variation using survey methods
+
+
+
+ Count/tally survey weighted observations by group
+
+
+
+
+ Calculate the population variance and its variation using survey methods
+
diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html
index 2e923b9..300f8e5 100644
--- a/docs/reference/pipe.html
+++ b/docs/reference/pipe.html
@@ -70,7 +70,7 @@
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/reference/rlang-tidyeval.html b/docs/reference/rlang-tidyeval.html
index bdc9142..fda2de6 100644
--- a/docs/reference/rlang-tidyeval.html
+++ b/docs/reference/rlang-tidyeval.html
@@ -73,7 +73,7 @@
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/reference/set_survey_vars.html b/docs/reference/set_survey_vars.html
index 8d63288..df72ed3 100644
--- a/docs/reference/set_survey_vars.html
+++ b/docs/reference/set_survey_vars.html
@@ -75,7 +75,7 @@
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/reference/srvyr-se-deprecated.html b/docs/reference/srvyr-se-deprecated.html
index 51fdc5f..65fd954 100644
--- a/docs/reference/srvyr-se-deprecated.html
+++ b/docs/reference/srvyr-se-deprecated.html
@@ -38,7 +38,7 @@
@@ -74,7 +74,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -142,27 +142,53 @@ Deprecated SE versions of main srvyr verbs
srvyr has updated it's standard evaluation semantics to match dplyr 0.7, so
these underscore functions are no longer required (but are still supported
-for backward compatability reasons). See se-deprecated or the
+for backward compatibility reasons). See se-deprecated or the
dplyr vignette on programming (vignette("programming", package =
"dplyr")) for more details.
as_survey_(.data, ...)
-as_survey_design_(.data, ids = NULL, probs = NULL, strata = NULL,
- variables = NULL, fpc = NULL, nest = FALSE, check_strata = !nest,
- weights = NULL, pps = FALSE, variance = c("HT", "YG"))
-
-as_survey_rep_(.data, variables = NULL, repweights = NULL,
- weights = NULL, type = c("BRR", "Fay", "JK1", "JKn", "bootstrap",
- "other"), combined_weights = TRUE, rho = NULL,
- bootstrap_average = NULL, scale = NULL, rscales = NULL,
- fpc = NULL, fpctype = c("fraction", "correction"),
- mse = getOption("survey.replicates.mse"))
-
-as_survey_twophase_(.data, id, strata = NULL, probs = NULL,
- weights = NULL, fpc = NULL, subset, method = c("full", "approx",
- "simple"))
+as_survey_design_(
+ .data,
+ ids = NULL,
+ probs = NULL,
+ strata = NULL,
+ variables = NULL,
+ fpc = NULL,
+ nest = FALSE,
+ check_strata = !nest,
+ weights = NULL,
+ pps = FALSE,
+ variance = c("HT", "YG")
+)
+
+as_survey_rep_(
+ .data,
+ variables = NULL,
+ repweights = NULL,
+ weights = NULL,
+ type = c("BRR", "Fay", "JK1", "JKn", "bootstrap", "other"),
+ combined_weights = TRUE,
+ rho = NULL,
+ bootstrap_average = NULL,
+ scale = NULL,
+ rscales = NULL,
+ fpc = NULL,
+ fpctype = c("fraction", "correction"),
+ mse = getOption("survey.replicates.mse")
+)
+
+as_survey_twophase_(
+ .data,
+ id,
+ strata = NULL,
+ probs = NULL,
+ weights = NULL,
+ fpc = NULL,
+ subset,
+ method = c("full", "approx", "simple")
+)
cascade_(.data, ..., .dots, .fill = NA)
diff --git a/docs/reference/srvyr.html b/docs/reference/srvyr.html
index 8daea18..8f72ead 100644
--- a/docs/reference/srvyr.html
+++ b/docs/reference/srvyr.html
@@ -73,7 +73,7 @@
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/reference/summarise.html b/docs/reference/summarise.html
index 22fdcd9..96c6c05 100644
--- a/docs/reference/summarise.html
+++ b/docs/reference/summarise.html
@@ -70,7 +70,7 @@
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/reference/summarise_all.html b/docs/reference/summarise_all.html
index 41b711a..c9f3eae 100644
--- a/docs/reference/summarise_all.html
+++ b/docs/reference/summarise_all.html
@@ -71,7 +71,7 @@
srvyr
- 0.3.6
+ 0.3.7
diff --git a/docs/reference/survey_mean.html b/docs/reference/survey_mean.html
index 9b495ab..0af167d 100644
--- a/docs/reference/survey_mean.html
+++ b/docs/reference/survey_mean.html
@@ -73,7 +73,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -145,10 +145,18 @@ Calculate the mean and its variation using survey methods
called from summarise.
- survey_mean(x, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"),
- level = 0.95, proportion = FALSE, prop_method = c("logit",
- "likelihood", "asin", "beta", "mean"), deff = FALSE, df = NULL,
- .svy = current_svy(), ...)
+ survey_mean(
+ x,
+ na.rm = FALSE,
+ vartype = c("se", "ci", "var", "cv"),
+ level = 0.95,
+ proportion = FALSE,
+ prop_method = c("logit", "likelihood", "asin", "beta", "mean"),
+ deff = FALSE,
+ df = NULL,
+ .svy = current_svy(),
+ ...
+)
Arguments
diff --git a/docs/reference/survey_quantile.html b/docs/reference/survey_quantile.html
index 725ae7d..86beff7 100644
--- a/docs/reference/survey_quantile.html
+++ b/docs/reference/survey_quantile.html
@@ -72,7 +72,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -143,17 +143,34 @@ Calculate the quantile and its variation using survey methods
survey_median should always be called from summarise.
- survey_quantile(x, quantiles, na.rm = FALSE, vartype = NULL,
- level = 0.95, q_method = "linear", f = 1,
- interval_type = c("Wald", "score", "betaWald", "probability",
- "quantile"), ties = c("discrete", "rounded"), df = Inf,
- .svy = current_svy(), ...)
-
-survey_median(x, na.rm = FALSE, vartype = c("se", "ci"),
- level = 0.95, q_method = "linear", f = 1,
- interval_type = c("Wald", "score", "betaWald", "probability",
- "quantile"), ties = c("discrete", "rounded"), df = Inf,
- .svy = current_svy(), ...)
+ survey_quantile(
+ x,
+ quantiles,
+ na.rm = FALSE,
+ vartype = NULL,
+ level = 0.95,
+ q_method = "linear",
+ f = 1,
+ interval_type = c("Wald", "score", "betaWald", "probability", "quantile"),
+ ties = c("discrete", "rounded"),
+ df = NULL,
+ .svy = current_svy(),
+ ...
+)
+
+survey_median(
+ x,
+ na.rm = FALSE,
+ vartype = c("se", "ci"),
+ level = 0.95,
+ q_method = "linear",
+ f = 1,
+ interval_type = c("Wald", "score", "betaWald", "probability", "quantile"),
+ ties = c("discrete", "rounded"),
+ df = NULL,
+ .svy = current_svy(),
+ ...
+)
Arguments
@@ -226,15 +243,15 @@ Examp
api00 = survey_median(api00, vartype = c("ci")))#> # A tibble: 1 x 9
#> api99_q25 api99_q50 api99_q75 api99_q25_se api99_q50_se api99_q75_se api00
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 525. 631 727. 12.3 18.2 17.9 667.
+#> 1 525. 631 727. 12.5 18.4 17.9 667.
#> # … with 2 more variables: api00_low <dbl>, api00_upp <dbl>#> Warning: Only diagonal of vcov() available#> Warning: Only diagonal of vcov() available#> # A tibble: 2 x 3
#> awards api00 api00_se
#> <fct> <dbl> <dbl>
-#> 1 No 641. 23.5
-#> 2 Yes 671. 18.9
+#> 1 No 641. 23.4
+#> 2 Yes 671. 18.8
@@ -143,9 +143,17 @@ Calculate the ratio and its variation using survey methods
should always be called from summarise.
- survey_ratio(numerator, denominator, na.rm = FALSE, vartype = c("se",
- "ci", "var", "cv"), level = 0.95, deff = FALSE, df = NULL,
- .svy = current_svy(), ...)
+ survey_ratio(
+ numerator,
+ denominator,
+ na.rm = FALSE,
+ vartype = c("se", "ci", "var", "cv"),
+ level = 0.95,
+ deff = FALSE,
+ df = NULL,
+ .svy = current_svy(),
+ ...
+)
Arguments
diff --git a/docs/reference/survey_tally.html b/docs/reference/survey_tally.html
index 355382e..ef8bfda 100644
--- a/docs/reference/survey_tally.html
+++ b/docs/reference/survey_tally.html
@@ -74,7 +74,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -147,12 +147,23 @@ Count/tally survey weighted observations by group
before calculating the count and then returns the data to the original groupings.
- survey_tally(x, wt, sort = FALSE, name = "n", vartype = c("se", "ci",
- "var", "cv"))
-
-survey_count(x, ..., wt = NULL, sort = FALSE, name = "n",
- .drop = dplyr::group_by_drop_default(x), vartype = c("se", "ci",
- "var", "cv"))
+ survey_tally(
+ x,
+ wt,
+ sort = FALSE,
+ name = "n",
+ vartype = c("se", "ci", "var", "cv")
+)
+
+survey_count(
+ x,
+ ...,
+ wt = NULL,
+ sort = FALSE,
+ name = "n",
+ .drop = dplyr::group_by_drop_default(x),
+ vartype = c("se", "ci", "var", "cv")
+)
Arguments
diff --git a/docs/reference/survey_total.html b/docs/reference/survey_total.html
index 2eb28fe..49be546 100644
--- a/docs/reference/survey_total.html
+++ b/docs/reference/survey_total.html
@@ -72,7 +72,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -143,8 +143,16 @@ Calculate the total and its variation using survey methods
called from summarise.
- survey_total(x, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"),
- level = 0.95, deff = FALSE, df = NULL, .svy = current_svy(), ...)
+ survey_total(
+ x,
+ na.rm = FALSE,
+ vartype = c("se", "ci", "var", "cv"),
+ level = 0.95,
+ deff = FALSE,
+ df = NULL,
+ .svy = current_svy(),
+ ...
+)
Arguments
diff --git a/docs/reference/survey_var.html b/docs/reference/survey_var.html
index e53fb2e..4158348 100644
--- a/docs/reference/survey_var.html
+++ b/docs/reference/survey_var.html
@@ -72,7 +72,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -143,8 +143,15 @@ Calculate the population variance and its variation using survey methods
summarise.
- survey_var(x, na.rm = FALSE, vartype = c("se", "ci", "var"),
- level = 0.95, df = Inf, .svy = current_svy(), ...)
+ survey_var(
+ x,
+ na.rm = FALSE,
+ vartype = c("se", "ci", "var"),
+ level = 0.95,
+ df = NULL,
+ .svy = current_svy(),
+ ...
+)
survey_sd(x, na.rm = FALSE, .svy = current_svy(), ...)
diff --git a/docs/reference/svychisq.html b/docs/reference/svychisq.html
index e7eb32f..5f11e3d 100644
--- a/docs/reference/svychisq.html
+++ b/docs/reference/svychisq.html
@@ -70,7 +70,7 @@
srvyr
- 0.3.6
+ 0.3.7
@@ -143,14 +143,6 @@ Chisquared tests of association for survey data.
Arguments
- formula
- Model formula specifying margins for the table (using + only)
-
- design
- survey object
formula
See details in svychisq