library(pollster)
library(dplyr)
library(knitr)
library(ggplot2)Crosstabs can come in wide or long format. Each is useful, depending on your purpose. Wide data is best for display tables. Long data is usually better for making plots, for instance..
Here is a wide table.
crosstab(df = illinois, x = sex, y = educ6, weight = weight) %>%
kable()| sex | LT HS | HS | Some Col | AA | BA | Post-BA | n |
|---|---|---|---|---|---|---|---|
| Male | 10.61114 | 31.08939 | 20.66614 | 7.498318 | 19.58311 | 10.55190 | 49108796 |
| Female | 10.37899 | 30.13231 | 21.64697 | 8.526414 | 19.26636 | 10.04895 | 53569718 |
And here is long format.
crosstab(df = illinois, x = sex, y = educ6, weight = weight, format = "long")
#> # A tibble: 12 × 4
#> sex educ6 pct n
#> <fct> <fct> <dbl> <dbl>
#> 1 Male LT HS 10.6 49108796.
#> 2 Male HS 31.1 49108796.
#> 3 Male Some Col 20.7 49108796.
#> 4 Male AA 7.50 49108796.
#> 5 Male BA 19.6 49108796.
#> 6 Male Post-BA 10.6 49108796.
#> 7 Female LT HS 10.4 53569718.
#> 8 Female HS 30.1 53569718.
#> 9 Female Some Col 21.6 53569718.
#> 10 Female AA 8.53 53569718.
#> 11 Female BA 19.3 53569718.
#> 12 Female Post-BA 10.0 53569718.By default, row percentages are used. You can also explicitly choose
cell or column percentages using the pct_type argument. I
discourage the use of column percentages–it’s better to just flip the x
and y variables and make row percents–but the option is included to
match functionality provided by other standard statistical software.
# cell percentages
crosstab(df = illinois, x = sex, y = educ6, weight = weight, pct_type = "cell")
#> # A tibble: 2 × 8
#> sex `LT HS` HS `Some Col` AA BA `Post-BA` n
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Male 5.08 14.9 9.88 3.59 9.37 5.05 102678514.
#> 2 Female 5.41 15.7 11.3 4.45 10.1 5.24 102678514.
# column percentages
crosstab(df = illinois, x = sex, y = educ6, weight = weight, pct_type = "column")
#> # A tibble: 3 × 7
#> sex `LT HS` HS `Some Col` AA BA `Post-BA`
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Male 48.4 48.6 46.7 44.6 48.2 49.0
#> 2 Female 51.6 51.4 53.3 55.4 51.8 51.0
#> 3 n 10770999. 31409418. 21745113. 8249909. 19937965. 10565110.To make a graph, just feed your tibble output to a
ggplot2 function.
crosstab(df = illinois, x = sex, y = educ6, weight = weight, format = "long") %>%
ggplot(aes(x = educ6, y = pct, fill = sex)) +
geom_bar(stat = "identity", position = position_dodge()) +
labs(title = "Educational attainment of the Illinois adult population by gender")
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).
Get at topline table with the margin of error in a separate column
using the moe_crosstab function. By default, a z-score of
1.96 (95% confidence interval is used). Supply your own desired z-score
using the zscore argument. Only row and cell percents are
supported. By default, the table format is long because I anticipate
making visualizations will be the most common use-case for this
graphic.
moe_crosstab(illinois, educ6, voter, weight)
#> Your data includes weights equal to zero. These are removed before calculating the design effect.
#> # A tibble: 12 × 5
#> educ6 voter pct moe n
#> <fct> <fct> <dbl> <dbl> <dbl>
#> 1 LT HS Voted 42.5 1.75 8999310.
#> 2 LT HS Not voted 57.5 1.75 8999310.
#> 3 HS Voted 56.5 1.03 26638087.
#> 4 HS Not voted 43.5 1.03 26638087.
#> 5 Some Col Voted 63.6 1.20 18697544.
#> 6 Some Col Not voted 36.4 1.20 18697544.
#> 7 AA Voted 67.6 1.89 7196039.
#> 8 AA Not voted 32.4 1.89 7196039.
#> 9 BA Voted 73.6 1.13 17447907.
#> 10 BA Not voted 26.4 1.13 17447907.
#> 11 Post-BA Voted 83.1 1.32 9322214.
#> 12 Post-BA Not voted 16.9 1.32 9322214.A wide format table looks like this.
moe_crosstab(illinois, educ6, voter, weight, format = "wide")
#> Your data includes weights equal to zero. These are removed before calculating the design effect.
#> # A tibble: 6 × 6
#> educ6 pct_Voted `pct_Not voted` moe_Voted `moe_Not voted` n
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 LT HS 42.5 57.5 1.75 1.75 8999310.
#> 2 HS 56.5 43.5 1.03 1.03 26638087.
#> 3 Some Col 63.6 36.4 1.20 1.20 18697544.
#> 4 AA 67.6 32.4 1.89 1.89 7196039.
#> 5 BA 73.6 26.4 1.13 1.13 17447907.
#> 6 Post-BA 83.1 16.9 1.32 1.32 9322214.ggplot2 offers multiple
ways to visualize the margin of error. Here is one good option.
(Please note, if you don’t have ggplot2 >= 3.3.0
you’ll get an error message.)
illinois %>%
filter(year == 2016) %>%
moe_crosstab(educ6, voter, weight) %>%
ggplot(aes(x = pct, y = educ6, xmin = (pct - moe), xmax = (pct + moe),
color = voter)) +
geom_pointrange(position = position_dodge(width = 0.2))
If the x-variable in your crosstab uniquely identifies survey waves
for which the weights were independently generated, it is best practice
to calculate the design effect independently for each wave.
moe_wave_crosstab does just that. All of the arguments
remain the same as in moe_crosstab.
moe_wave_crosstab(df = illinois, x = year, y = rv, weight = weight)
#> Your data includes weights equal to zero. These are removed before calculating the design effect.
#> Your data includes weights equal to zero. These are removed before calculating the design effect.
#> Your data includes weights equal to zero. These are removed before calculating the design effect.
#> Your data includes weights equal to zero. These are removed before calculating the design effect.
#> Your data includes weights equal to zero. These are removed before calculating the design effect.
#> Your data includes weights equal to zero. These are removed before calculating the design effect.
#> Your data includes weights equal to zero. These are removed before calculating the design effect.
#> Your data includes weights equal to zero. These are removed before calculating the design effect.
#> Your data includes weights equal to zero. These are removed before calculating the design effect.
#> Your data includes weights equal to zero. These are removed before calculating the design effect.
#> Your data includes weights equal to zero. These are removed before calculating the design effect.
#> Joining with `by = join_by(year)`
#> # A tibble: 24 × 5
#> year rv pct moe n
#> <dbl> <fct> <dbl> <dbl> <dbl>
#> 1 1996 Registered 77.7 1.49 7485319.
#> 2 1996 Not Registered 22.3 1.49 7485319.
#> 3 1998 Registered 75.1 1.57 7364191.
#> 4 1998 Not Registered 24.9 1.57 7364191.
#> 5 2000 Registered 81.2 1.44 7276876.
#> 6 2000 Not Registered 18.8 1.44 7276876.
#> 7 2002 Registered 77.7 1.56 7185545.
#> 8 2002 Not Registered 22.3 1.56 7185545.
#> 9 2004 Registered 83.4 1.38 7719084.
#> 10 2004 Not Registered 16.6 1.38 7719084.
#> # … with 14 more rows