Make it COVID-19 Trends, not COVID Trends

Author: capnrefsmmat

docs/api/covidcast-signals/fb-survey.md

@@ -1,10 +1,10 @@
 ---
-title: COVID Trends and Impact Survey
+title: COVID-19 Trends and Impact Survey
 parent: Data Sources and Signals
 grand_parent: COVIDcast Epidata API
 ---
 
-# COVID Trends and Impact Survey
+# COVID-19 Trends and Impact Survey
 {: .no_toc}
 
 * **Source name:** `fb-survey`
@@ -17,7 +17,7 @@ grand_parent: COVIDcast Epidata API
 
 ## Overview
 
-This data source is based on the [COVID Trends and Impact Survey
+This data source is based on the [COVID-19 Trends and Impact Survey
 (CTIS)](../../symptom-survey/) run by the Delphi group at Carnegie Mellon.
 Facebook directs a random sample of its users to these surveys, which are
 voluntary. Users age 18 or older are eligible to complete the surveys, and

docs/symptom-survey/coding.md

@@ -1,6 +1,6 @@
 ---
 title: Questions and Coding
-parent: COVID Trends and Impact Survey
+parent: COVID-19 Trends and Impact Survey
 nav_order: 6
 ---
 

docs/symptom-survey/collaboration-revision.md

@@ -1,24 +1,24 @@
 ---
 title: Collaboration and Survey Revision
-parent: COVID Trends and Impact Survey
+parent: COVID-19 Trends and Impact Survey
 nav_order: 1
 ---
 
 # Collaboration and Survey Revision
 
-Delphi continues to revise the COVID Trends and Impact Survey (CTIS)
-instruments in order to prioritize items that have the greatest utility for
-the response to the COVID-19 pandemic. We conduct revisions in collaboration
-with data users, fellow researchers, and public health officials, to ensure
-the survey data best serves public health and research goals.
+Delphi continues to revise the COVID-19 Trends and Impact Survey (CTIS)
+instruments in order to prioritize items that have the greatest utility for the
+response to the COVID-19 pandemic. We conduct revisions in collaboration with
+data users, fellow researchers, and public health officials, to ensure the
+survey data best serves public health and research goals.
 
 ## Proposing Revisions
 
 If there is a revision or question you would like us to consider, please fill
 out [this form requesting details about your
 proposal](https://forms.gle/q6NS8fPJJofKQ9mM8). This request can be submitted by
 researchers regardless of whether they have a signed Data Use Agreement for the
-individual responses to the COVID Trends and Impact Survey.
+individual responses to the COVID-19 Trends and Impact Survey.
 
 ## Collaboration Meetings
 

docs/symptom-survey/contingency-tables.md

@@ -1,14 +1,14 @@
 ---
 title: Contingency Tables
-parent: COVID Trends and Impact Survey
+parent: COVID-19 Trends and Impact Survey
 nav_order: 4
 ---
 
 # Contingency Tables
 {: .no_toc}
 
 This documentation describes the fine-resolution contingency tables produced by
-grouping [COVID Trends and Impact Survey (CTIS)](./index.md) individual responses by various
+grouping [COVID-19 Trends and Impact Survey (CTIS)](./index.md) individual responses by various
 self-reported demographic features.
 
 * [Weekly files](https://www.cmu.edu/delphi-web/surveys/weekly-rollup/)

docs/symptom-survey/data-access.md

@@ -1,13 +1,13 @@
 ---
 title: Getting Data Access
-parent: COVID Trends and Impact Survey
+parent: COVID-19 Trends and Impact Survey
 nav_order: 0
 ---
 
 # Getting Data Access
 
 The Delphi Research Group at Carnegie Mellon University (CMU), in partnership
-with Facebook, has conducted the COVID Trends and Impact Survey (CTIS) to
+with Facebook, has conducted the COVID-19 Trends and Impact Survey (CTIS) to
 better understand the spread of COVID-19 and its effects on public health and
 well-being. This may help improve our local and national responses to the
 pandemic and our understanding of how it has affected society.

docs/symptom-survey/index.md

@@ -1,10 +1,10 @@
 ---
-title: COVID Trends and Impact Survey
+title: COVID-19 Trends and Impact Survey
 has_children: true
 nav_order: 2
 ---
 
-# COVID Trends and Impact Survey
+# COVID-19 Trends and Impact Survey
 
 Since April 2020, Delphi has conducted a voluntary COVID-19 symptom survey,
 distributed daily to users in the United States via a partnership with Facebook.
@@ -29,7 +29,7 @@ If you have questions about the survey or getting access to data, contact us at
 
 ## Credits
 
-The COVID Trends and Impact Survey (CTIS) is a project of the [Delphi
+The COVID-19 Trends and Impact Survey (CTIS) is a project of the [Delphi
 Group](https://delphi.cmu.edu/) at Carnegie Mellon University. The Principal
 Investigator is [Alex Reinhart](https://www.refsmmat.com/); Wichada La
 Motte-Kerr is Survey Coordinator. The survey protocol is reviewed by the
@@ -59,7 +59,7 @@ the survey in publications based on the data. Specifically, we ask that you:
 2. Cite this web page for details about the survey. For example, you may cite it
    as
 
-    > Delphi Group (2021). COVID Trends and Impact Survey.
+    > Delphi Group (2021). COVID-19 Trends and Impact Survey.
     > <https://cmu-delphi.github.io/delphi-epidata/symptom-survey/>
 
     A journal article describing the survey and its methods is currently in

docs/symptom-survey/modules.md

@@ -1,14 +1,14 @@
 ---
 title: Survey Modules & Randomization
-parent: COVID Trends and Impact Survey
+parent: COVID-19 Trends and Impact Survey
 nav_order: 7
 ---
 
 # Questions and Coding
 {: .no_toc}
 
 To reduce the overall length of the instrument and minimize response burden,
-the COVID Trends and Impact Survey (CTIS) will consist of a block of daily
+the COVID-19 Trends and Impact Survey (CTIS) will consist of a block of daily
 core questions and will use a randomized module approach for the other topics.
 Implementation of this approach started in [Wave 11](coding.md#wave-11), which
 launched on May 20, 2021.

docs/symptom-survey/problems.md

@@ -1,13 +1,13 @@
 ---
 title: Problems and Data Errors
-parent: COVID Trends and Impact Survey
+parent: COVID-19 Trends and Impact Survey
 nav_order: 8
 ---
 
 # Problems and Data Errors
 {: .no_toc}
 
-Given the scale of the COVID Trends and Impact Survey (CTIS), we occasionally
+Given the scale of the COVID-19 Trends and Impact Survey (CTIS), we occasionally
 encounter data errors or survey implementation problems that affect the
 interpretation of results. All problems will be logged here.
 

docs/symptom-survey/server-access.md

@@ -1,12 +1,12 @@
 ---
 title: SFTP Server Access
-parent: COVID Trends and Impact Survey
+parent: COVID-19 Trends and Impact Survey
 nav_order: 2
 ---
 
 # SFTP Server Access
 
-Researchers with data use agreements to access the raw data from the COVID
+Researchers with data use agreements to access the raw data from the COVID-19
 Trends and Impact Survey (CTIS) can access the data over SFTP. (If you do not
 have a data use agreement, see the [main survey page](index.md) for
 information about getting access and about aggregate data that is available

docs/symptom-survey/survey-files.md

@@ -1,13 +1,13 @@
 ---
 title: Response Files
-parent: COVID Trends and Impact Survey (CTIS)
+parent: COVID-19 Trends and Impact Survey (CTIS)
 nav_order: 3
 ---
 
 # Response Files
 {: .no_toc}
 
-Users with access to the [COVID Trends and Impact Survey (CTIS)](./index.md)
+Users with access to the [COVID-19 Trends and Impact Survey (CTIS)](./index.md)
 individual response data should have received SFTP credentials for a private
 server where the data are stored. To connect to the server, see the [server
 access documentation](server-access.md). This documentation describes the

docs/symptom-survey/survey-utils.R

@@ -12,7 +12,7 @@ library(dplyr)
 #' This function extracts the date from each file, determines which files
 #' contain reissued data, and produces a single data frame representing the most
 #' recent data available for each day. It can read gzip-compressed CSV files,
-#' such as those on the SFTP site, using `readr::read_csv`.
+#' such as those on the SFTP site, using `readr::read_csv()`.
 #'
 #' This function handles column types correctly for surveys up to Wave 4.
 #'
@@ -38,57 +38,83 @@ get_survey_df <- function(directory, pattern = "*.csv.gz$") {
   big_df <- map_dfr(
     latest_files,
     function(f) {
-      # stop readr from thinking commas = thousand separators,
-      # and from inferring column types incorrectly
-      read_csv(file.path(directory, f), locale = locale(grouping_mark = ""),
+      # stop readr from thinking commas = thousand separators, and from
+      # inferring column types incorrectly
+      read_csv(file.path(directory, f),
+               locale = locale(grouping_mark = ""),
                col_types = cols(
+                 UserLanguage = col_character(),
+                 StartDatetime = col_datetime(),
+                 EndDatetime = col_datetime(),
+                 weight = col_number(),
+                 wave = col_integer(),
+                 fips = col_character(),
+                 A2 = col_number(),
+                 A5_1 = col_number(),
+                 A5_2 = col_number(),
+                 A5_3 = col_number(),
                  A2b = col_number(),
                  A3 = col_character(),
                  A4 = col_number(),
-                 B2 = col_character(),
-                 B2_14_TEXT = col_character(),
-                 B2c = col_character(),
-                 B2c_14_TEXT = col_character(),
-                 B4 = col_number(),
-                 B5 = col_number(),
-                 B7 = col_character(),
-                 B10b = col_character(),
-                 B12a = col_character(),
-                 C1 = col_character(),
-                 C3 = col_number(),
-                 C4 = col_number(),
-                 C5 = col_number(),
-                 C7 = col_number(),
-                 C13 = col_character(),
-                 C13a = col_character(),
-                 D1_4_TEXT = col_character(),
-                 E3 = col_character(),
-                 fips = col_character(),
-                 UserLanguage = col_character(),
-                 StartDatetime = col_character(),
-                 EndDatetime = col_character(),
-                 Q65 = col_integer(),
-                 Q66 = col_integer(),
-                 Q67 = col_integer(),
-                 Q68 = col_integer(),
-                 Q69 = col_integer(),
-                 Q70 = col_integer(),
-                 Q71 = col_integer(),
-                 Q72 = col_integer(),
-                 Q73 = col_integer(),
-                 Q74 = col_integer(),
-                 Q75 = col_integer(),
-                 Q76 = col_integer(),
-                 Q77 = col_integer(),
-                 Q78 = col_integer(),
-                 Q79 = col_integer(),
-                 Q80 = col_integer(),
-                 .default = col_number()))
+                 B2b = col_number(),
+                 .default = col_character()))
     }
   )
   return(big_df)
 }
 
+#' Split multiselect options into codable form
+#'
+#' Multiselect options are coded by Qualtrics as a comma-separated string of
+#' selected options, like "1,14", or the empty string if no options are
+#' selected. Split these into vectors of selected options, which can be queried
+#' using `is_selected()`.
+#'
+#' @param column vector of selections, like c("1,4", "5", ...)
+#' @return list of same length, each entry of which is a vector of selected
+#'   options
+split_options <- function(column) {
+  return(strsplit(column, ",", fixed = TRUE))
+}
+
+#' Test if a specific choice is selected in a multiselect item
+#'
+#' This is used for items that allow respondents to select multiple options from
+#' a list, such as the symptoms items. Checking whether a specific selection is
+#' selected in either "" (empty string) or `NA` responses will produce `NA`s, so
+#' that empty responses are treated as missing, rather than as the item not
+#' being selected.
+#'
+#' @param vec A list whose entries are character vectors, such as c("14", "15"),
+#'   as produced by `split_options()`.
+#' @param selection one string, such as "14", representing the answer choice of
+#'   interest
+#' @return a logical vector; for each entry in `vec`, the boolean indicates
+#'   whether `selection` is contained in the character vector.
+#' @examples
+#' \dontrun{
+#' symptoms <- split_options(data$B2)
+#'
+#' # vector of T/F/NA for each respondent's fever status
+#' fever <- is_selected(symptoms, "1")
+#' }
+is_selected <- function(vec, selection) {
+  selections <- unlist(lapply(
+    vec,
+    function(resp) {
+      if (length(resp) == 0 || all(is.na(resp))) {
+        # Qualtrics files code no selection as "" (empty string), which is
+        # parsed by `read_csv()` as `NA` (missing) by default. Since all our
+        # selection items include "None of the above" or similar, treat both no
+        # selection ("") or missing (NA) as missing, for generality.
+        NA
+      } else {
+        selection %in% resp
+      }
+    }))
+
+  return(selections)
+}
 ## Helper function to extract dates from each file's filename.
 get_file_properties <- function(filename) {
   short <- strsplit(filename, ".", fixed = TRUE)[[1]][1]

docs/symptom-survey/weights.md

@@ -1,6 +1,6 @@
 ---
 title: Survey Weights
-parent: COVID Trends and Impact Survey
+parent: COVID-19 Trends and Impact Survey
 nav_order: 5
 ---