Code
prepared_data
Output
# A tibble: 4 x 2
var1 var2
<dbl> <dbl>
1 1 -2
2 2 -3
3 -1 -3
4 5 -9
Code
prepared_data
Output
# A tibble: 1 x 2
var1 var2
<dbl> <dbl>
1 2 -3
Code
data_clean(tibble::tibble(var1 = c(1, 2, -1, -9, -50, -999)))
Output
# A tibble: 6 x 1
var1
<dbl>
1 1
2 2
3 -1
4 -9
5 -50
6 -999
Code
data_clean(spss_data, remove.na.numbers = c(-999, -555))
Output
# A tibble: 4 x 1
var1
<dbl>
1 1
2 2
3 -50
4 NA
Code
data_rm_negatives(tibble::tibble(var1 = c(1, 2, -1, -9)), var1)
Output
# A tibble: 2 x 1
var1
<dbl>
1 1
2 2
Code
data_clean(tibble::tibble(var1 = c(1, 2, -9)))
Output
# A tibble: 3 x 1
var1
<dbl>
1 1
2 2
3 -9
Code
data_rm_na_levels(tibble::tibble(value1 = factor(c("A", "B", "[no answer]"))))
Output
# A tibble: 3 x 1
value1
<fct>
1 A
2 B
3 [no answer]
Code
get_baseline(result)
Output
[1] "Frequencies based on values: agree, strongly agree. 4 missing case(s) omitted."
Code
get_baseline(result)
Output
[1] "3 zero case(s) omitted."
Code
get_baseline(result)
Output
[1] "2 negative case(s) omitted."
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