ethiopia_small$G_TOWNSIZE2_recode <- fct_collapse(ethiopia_small$G_TOWNSIZE2,
"Under 5,000" = c("Under 5,000"),
"5,000-20,000" = c("5000-20000"),
"20,000-100,000" = c("20000-100000"),
"100,000 and more" = c("100000-500000", "500000 and more")
)10 Tables for 2 or More Variables
This chapter is a companion to Chapter 9, which explains how to graph two and three variables.
If you create bar graphs, you will want to create tables. While graphs are excellent for providing the overview of relationships, tables allow a reader to look at details.
To create tables you will use the sjtab command from the sjPlot package. We will look at an example of a two variable table and a three variable table.
10.1 A two variable table using categorical variables
We will use the data from graph examples in sections 9.1.3 and 9.1.4. As before we will recode the variable to collapse 4 categories for the population of the town into four categories.
Next we will create the code for the table. We add a row percentages using “show.row.prc”.
ethiopia_small |>
select(G_TOWNSIZE2_recode, E1_LITERACY) |>
sjtab(fun = "xtab",
show.col.prc = TRUE)| Settlement size_5 groups |
Respondent´s literacy |
Total | |
|---|---|---|---|
| Literate | Illiterate | ||
| Under 5,000 | 105 12.7 % |
91 22.5 % |
196 15.9 % |
| 5,000-20,000 | 411 49.8 % |
234 57.9 % |
645 52.4 % |
| 20,000-100,000 | 280 33.9 % |
77 19.1 % |
357 29 % |
| 100,000 and more | 30 3.6 % |
2 0.5 % |
32 2.6 % |
| Total | 826 100 % |
404 100 % |
1230 100 % |
| χ2=50.686 · df=3 · Cramer's V=0.203 · p=0.000 | |||
10.2 Adding a third variable to a table using categorical variables
We will use the data from graph examples in sections 9.1.4. We will use “group_by” to add a third variable. In this instance we will use Q260, the sex of the respondent.
ethiopia_small |>
group_by(Q260) |>
select(G_TOWNSIZE2_recode, E1_LITERACY) |>
sjtab(fun = "xtab",
show.col.prc = TRUE)Adding missing grouping variables: `Q260`| Settlement size_5 groups |
Respondent´s literacy |
Total | |
|---|---|---|---|
| Literate | Illiterate | ||
| Under 5,000 | 58 12.6 % |
43 26.4 % |
101 16.2 % |
| 5,000-20,000 | 240 52.3 % |
91 55.8 % |
331 53.2 % |
| 20,000-100,000 | 144 31.4 % |
28 17.2 % |
172 27.7 % |
| 100,000 and more | 17 3.7 % |
1 0.6 % |
18 2.9 % |
| Total | 459 100 % |
163 100 % |
622 100 % |
| χ2=27.010 · df=3 · Cramer's V=0.208 · p=0.000 | |||
| Settlement size_5 groups |
Respondent´s literacy |
Total | |
|---|---|---|---|
| Literate | Illiterate | ||
| Under 5,000 | 47 12.8 % |
48 19.9 % |
95 15.6 % |
| 5,000-20,000 | 171 46.6 % |
143 59.3 % |
314 51.6 % |
| 20,000-100,000 | 136 37.1 % |
49 20.3 % |
185 30.4 % |
| 100,000 and more | 13 3.5 % |
1 0.4 % |
14 2.3 % |
| Total | 367 100 % |
241 100 % |
608 100 % |
| χ2=28.833 · df=3 · Cramer's V=0.218 · p=0.000 | |||