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This is a public tank you for @heatherturner’s contribution. Now the SciencesPo’s crosstable can work in a chain (%>%) fashion; useful for using along with other packages that have integrated the magrittr operator.
> candidatos %>%
+ filter(desc_cargo == 'DEPUTADO ESTADUAL'|
desc_cargo =='DEPUTADO DISTRITAL' | desc_cargo =='DEPUTADO FEDERAL' |
desc_cargo =='VEREADOR' | desc_cargo =='SENADOR') %>%
tab(desc_cargo,desc_sexo)
====================================================
desc_sexo
-------------------------
desc_cargo NA FEMININO MASCULINO Total
----------------------------------------------------
DEPUTADO DISTRITAL 1 826 2457 3284
0.03% 25% 75% 100%
DEPUTADO ESTADUAL 122 12595 48325 61042
0.20% 21% 79% 100%
DEPUTADO FEDERAL 40 5006 20176 25222
0.16% 20% 80% 100%
SENADOR 4 161 1002 1167
0.34% 14% 86% 100%
VEREADOR 9682 376576 1162973 1549231
0.62% 24% 75% 100%
----------------------------------------------------
Total 9849 395164 1234933 1639946
0.60% 24% 75% 100%
====================================================
Chi-Square Test for Independence
Number of cases in table: 1639946
Number of factors: 2
Test for independence of all factors:
Chisq = 1077.4, df = 8, p-value = 2.956e-227
X^2 df P(> X^2)
Likelihood Ratio 1216.0 8 0
Pearson 1077.4 8 0
Phi-Coefficient : 0.026
Contingency Coeff.: 0.026
Cramer's V : 0.018
# Reproducible example:
library(SciencesPo)
gender = rep(c("female","male"),c(1835,2691))
admitted = rep(c("yes","no","yes","no"),c(557,1278,1198,1493))
dept = rep(c("A","B","C","D","E","F","A","B","C","D","E","F"),
c(89,17,202,131,94,24,19,8,391,244,299,317))
dept2 = rep(c("A","B","C","D","E","F","A","B","C","D","E","F"),
c(512,353,120,138,53,22,313,207,205,279,138,351))
department = c(dept,dept2)
ucb = data.frame(gender,admitted,department)
> ucb %>% tab(admitted, gender, department)
================================================================
department
-----------------------------------------
admitted gender A B C D E F Total
----------------------------------------------------------------
no female 19 8 391 244 299 317 1278
1.5% 0.63% 31% 19% 23.4% 25% 100%
male 313 207 205 279 138 351 1493
21.0% 13.86% 14% 19% 9.2% 24% 100%
-------------------------------------------------------
Total 332 215 596 523 437 668 2771
12.0% 7.76% 22% 19% 15.8% 24% 100%
----------------------------------------------------------------
yes female 89 17 202 131 94 24 557
16% 3.1% 36% 24% 16.9% 4.3% 100%
male 512 353 120 138 53 22 1198
43% 29.5% 10% 12% 4.4% 1.8% 100%
-------------------------------------------------------
Total 601 370 322 269 147 46 1755
34% 21.1% 18% 15% 8.4% 2.6% 100%
----------------------------------------------------------------
Total female 108 25 593 375 393 341 1835
5.9% 1.4% 32% 20% 21.4% 19% 100%
male 825 560 325 417 191 373 2691
30.7% 20.8% 12% 15% 7.1% 14% 100%
-------------------------------------------------------
Total 933 585 918 792 584 714 4526
20.6% 12.9% 20% 17% 12.9% 16% 100%
================================================================
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