toplines

In pollster: Calculate Crosstab and Topline Tables of Weighted Survey Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(pollster)
library(dplyr)
library(knitr)
library(ggplot2)

The default topline table comes with columns for response category, frequency count, percent, valid percent, and cumulative percent.

topline(df = illinois, variable = voter, weight = weight) %>%
  kable()

Because the output is a tibble, it's simple to manipulate it in any way you want after creating it. Use dplyr::select to remove columns or dplyr::filter to remove rows. For convenience, the topline function also provides ways to do this within the function call. For example, the remove argument accepts a character vector of response values to be removed from the table after all statistics are calculated. This is especially useful for survey data with a "refused" category.

topline(df = illinois, variable = voter, weight = weight, 
        remove = c("(Missing)"), pct = FALSE) %>%
  mutate(Frequency = prettyNum(Frequency, big.mark = ",")) %>%
  kable(digits = 0)

Refer to the kableExtra package for lots of examples on how to format the appearance of these tables in either HTML or PDF latex formats. I recommend the vignettes "Create Awesome HTML Table with knitr::kable and kableExtra" and "Create Awesome PDF Table with knitr::kable and kableExtra.

Graphs

topline(df = illinois, variable = voter, weight = weight) %>%
  ggplot(aes(Response, Percent, fill = Response)) +
  geom_bar(stat = "identity")

Margin of error

Get at topline table with the margin of error in a separate column using the moe_topline function. By default, a z-score of 1.96 (95% confidence interval is used). Supply your own desired z-score using the zscore argument.

moe_topline(df = illinois, variable = educ6, weight = weight)

The margin of error is calculated including the design effect of the sample weights, using the following formula:

sqrt(design effect)*zscore*sqrt((pct*(1-pct))/(n-1))*100

The design effect is calculated using the formula length(weights)*sum(weights^2)/(sum(weights)^2).

pollster documentation built on May 31, 2023, 7:39 p.m.