tests/testthat/test-output.R
In rsquaredacademy/descriptr: Generate Descriptive Statistics
context("print")
mt <- mtcars
mt$cyl <- as.factor(mt$cyl)
mt$vs <- as.factor(mt$vs)
test_that("output from print_cross matches expected output", {
k <- ds_cross_table(mt, cyl, vs)
pim <- " Cell Contents
|---------------|
| Frequency |
| Percent |
| Row Pct |
| Col Pct |
|---------------|
Total Observations: 32
-------------------------------------------------------------
| | vs |
-------------------------------------------------------------
| cyl | 0 | 1 | Row Total |
-------------------------------------------------------------
| 4 | 1 | 10 | 11 |
| | 0.031 | 0.312 | |
| | 0.09 | 0.91 | 0.34 |
| | 0.06 | 0.71 | |
-------------------------------------------------------------
| 6 | 3 | 4 | 7 |
| | 0.094 | 0.125 | |
| | 0.43 | 0.57 | 0.22 |
| | 0.17 | 0.29 | |
-------------------------------------------------------------
| 8 | 14 | 0 | 14 |
| | 0.438 | 0 | |
| | 1 | 0 | 0.44 |
| | 0.78 | 0 | |
-------------------------------------------------------------
| Column Total | 18 | 14 | 32 |
| | 0.563 | 0.437 | |
-------------------------------------------------------------"
expect_output(print_cross(k), pim)
})
test_that("output from print_screener matches expected output", {
mt <- mtcars
mt[, c(2, 8:11)] <- lapply(mt[, c(2, 8:11)], factor)
mt[sample(1:nrow(mt), 12), sample(1:ncol(mt), 6)] <- NA
k <- ds_screener(mt)
pim <- "-----------------------------------------------------------------------
| Column Name | Data Type | Levels | Missing | Missing (%) |
-----------------------------------------------------------------------
| mpg | numeric | NA | 0 | 0 |
| cyl | factor | 4 6 8 | 12 | 37.5 |
| disp | numeric | NA | 0 | 0 |
| hp | numeric | NA | 12 | 37.5 |
| drat | numeric | NA | 0 | 0 |
| wt | numeric | NA | 0 | 0 |
| qsec | numeric | NA | 12 | 37.5 |
| vs | factor | 0 1 | 12 | 37.5 |
| am | factor | 0 1 | 12 | 37.5 |
| gear | factor | 3 4 5 | 12 | 37.5 |
| carb | factor |1 2 3 4 6 8| 0 | 0 |
-----------------------------------------------------------------------
Overall Missing Values 72
Percentage of Missing Values 20.45 %
Rows with Missing Values 12
Columns With Missing Values 6 "
expect_output(print_screen(k), pim)
})
test_that("output from print_fcont matches the expected result", {
k <- ds_freq_table(mtcars, mpg)
pim <- " Variable: mpg
|-----------------------------------------------------------------------|
| Bins | Frequency | Cum Frequency | Percent | Cum Percent |
|-----------------------------------------------------------------------|
| 10.4 - 15.1 | 6 | 6 | 18.75 | 18.75 |
|-----------------------------------------------------------------------|
| 15.1 - 19.8 | 12 | 18 | 37.5 | 56.25 |
|-----------------------------------------------------------------------|
| 19.8 - 24.5 | 8 | 26 | 25 | 81.25 |
|-----------------------------------------------------------------------|
| 24.5 - 29.2 | 2 | 28 | 6.25 | 87.5 |
|-----------------------------------------------------------------------|
| 29.2 - 33.9 | 4 | 32 | 12.5 | 100 |
|-----------------------------------------------------------------------|"
expect_output(print_fcont(k), pim)
})
test_that("output from print_fcont is as expected when data has missing values", {
mt <- mtcarz
mt$mpg[c(3, 10, 14, 19)] <- NA
k <- ds_freq_table(mtcars, mpg)
pim <- " Variable: mpg
|-----------------------------------------------------------------------|
| Bins | Frequency | Cum Frequency | Percent | Cum Percent |
|-----------------------------------------------------------------------|
| 10.4 - 15.1 | 6 | 6 | 21.43 | 21.43 |
|-----------------------------------------------------------------------|
| 15.1 - 19.8 | 10 | 16 | 35.71 | 57.14 |
|-----------------------------------------------------------------------|
| 19.8 - 24.5 | 7 | 23 | 25 | 82.14 |
|-----------------------------------------------------------------------|
| 24.5 - 29.2 | 2 | 25 | 7.14 | 89.29 |
|-----------------------------------------------------------------------|
| 29.2 - 33.9 | 3 | 28 | 10.71 | 100 |
|-----------------------------------------------------------------------|
| Missing | 4 | - | 12.50 | - |
|-----------------------------------------------------------------------|
| Total | 32 | - | 100.00 | - |
|-----------------------------------------------------------------------|"
expect_output(print_fcont(k), pim)
})
test_that("output from freq_table matches the expected result", {
k <- ds_freq_table(mt, cyl)
pim <- " Variable: cyl)
|--------------------------------------------------------------------------|
| Cumulative Cumulative |
| Levels | Frequency | Frequency | Percent | Percent |
|--------------------------------------------------------------------------|
| 4 | 7 | 7 | 35 | 35 |
|--------------------------------------------------------------------------|
| 6 | 4 | 11 | 20 | 55 |
|--------------------------------------------------------------------------|
| 8 | 9 | 20 | 45 | 100 |
|--------------------------------------------------------------------------|
"
expect_output(print_ftable(k), pim)
})
test_that("output from freq_table is as expected when data has missing values", {
mt <- mtcarz
mt$cyl[c(3, 10, 14, 19)] <- NA
k <- ds_freq_table(mt, cyl)
pim <- " Variable: cyl
-----------------------------------------------------------------------
Levels Frequency Cum Frequency Percent Cum Percent
-----------------------------------------------------------------------
4 9 9 32.14 32.14
-----------------------------------------------------------------------
6 6 15 21.43 53.57
-----------------------------------------------------------------------
8 13 28 46.43 100
-----------------------------------------------------------------------
Missing 4 - -
-----------------------------------------------------------------------
Total 32 - 100.00 -
-----------------------------------------------------------------------"
expect_output(print_ftable(k), pim)
})
test_that("output from group_summary matches the expected result", {
k <- ds_group_summary(mt, cyl, mpg)
pim <- " mpg by cyl
-----------------------------------------------------------------------------------------
| Statistic/Levels| 4| 6| 8|
-----------------------------------------------------------------------------------------
| Obs| 11| 7| 14|
| Maximum| 21.4| 17.8| 10.4|
| Minimum| 33.9| 21.4| 19.2|
| Mean| 26.66| 19.74| 15.1|
| Median| 26| 19.7| 15.2|
| Mode| 22.8| 21| 10.4|
| Std. Deviation| 4.51| 1.45| 2.56|
| Variance| 20.34| 2.11| 6.55|
| Skewness| 0.35| -0.26| -0.46|
| Kurtosis| -1.43| -1.83| 0.33|
| Uncorrected SS| 8023.83| 2741.14| 3277.34|
| Corrected SS| 203.39| 12.68| 85.2|
| Coeff Variation| 16.91| 7.36| 16.95|
| Std. Error Mean| 1.36| 0.55| 0.68|
| Range| 12.5| 3.6| 8.8|
| Interquartile Range| 7.6| 2.35| 1.85|
-----------------------------------------------------------------------------------------"
expect_output(print_group(k), pim)
})
test_that("output from print_ftable2 matches the expected result", {
mt <- mtcars
mt[, c(2, 8:11)] <- lapply(mt[, c(2, 8:11)], factor)
pim <- " Variable: cyl
|--------------------------------------------------------------------------|
| Cumulative Cumulative |
| Levels | Frequency | Frequency | Percent | Percent |
|--------------------------------------------------------------------------|
| 4 | 11 | 11 | 34.38 | 34.38 |
|--------------------------------------------------------------------------|
| 6 | 7 | 18 | 21.88 | 56.25 |
|--------------------------------------------------------------------------|
| 8 | 14 | 32 | 43.75 | 100 |
|--------------------------------------------------------------------------|
Variable: vs
|--------------------------------------------------------------------------|
| Cumulative Cumulative |
| Levels | Frequency | Frequency | Percent | Percent |
|--------------------------------------------------------------------------|
| 0 | 18 | 18 | 56.25 | 56.25 |
|--------------------------------------------------------------------------|
| 1 | 14 | 32 | 43.75 | 100 |
|--------------------------------------------------------------------------|
Variable: am
|--------------------------------------------------------------------------|
| Cumulative Cumulative |
| Levels | Frequency | Frequency | Percent | Percent |
|--------------------------------------------------------------------------|
| 0 | 19 | 19 | 59.38 | 59.38 |
|--------------------------------------------------------------------------|
| 1 | 13 | 32 | 40.62 | 100 |
|--------------------------------------------------------------------------|
Variable: gear
|--------------------------------------------------------------------------|
| Cumulative Cumulative |
| Levels | Frequency | Frequency | Percent | Percent |
|--------------------------------------------------------------------------|
| 3 | 15 | 15 | 46.88 | 46.88 |
|--------------------------------------------------------------------------|
| 4 | 12 | 27 | 37.5 | 84.38 |
|--------------------------------------------------------------------------|
| 5 | 5 | 32 | 15.62 | 100 |
|--------------------------------------------------------------------------|
Variable: carb
|--------------------------------------------------------------------------|
| Cumulative Cumulative |
| Levels | Frequency | Frequency | Percent | Percent |
|--------------------------------------------------------------------------|
| 1 | 7 | 7 | 21.88 | 21.88 |
|--------------------------------------------------------------------------|
| 2 | 10 | 17 | 31.25 | 53.12 |
|--------------------------------------------------------------------------|
| 3 | 3 | 20 | 9.38 | 62.5 |
|--------------------------------------------------------------------------|
| 4 | 10 | 30 | 31.25 | 93.75 |
|--------------------------------------------------------------------------|
| 6 | 1 | 31 | 3.12 | 96.88 |
|--------------------------------------------------------------------------|
| 8 | 1 | 32 | 3.12 | 100 |
|--------------------------------------------------------------------------|
"
expect_output(ds_auto_freq_table(mt), pim)
})
test_that("output from ds_auto_freq_table() is as expected when data has missing values", {
pim <- " Variable: cyl
-----------------------------------------------------------------------
Levels Frequency Cum Frequency Percent Cum Percent
-----------------------------------------------------------------------
4 9 9 32.14 32.14
-----------------------------------------------------------------------
6 6 15 21.43 53.57
-----------------------------------------------------------------------
8 13 28 46.43 100
-----------------------------------------------------------------------
Missing 4 - 12.50 -
-----------------------------------------------------------------------
Total 32 - 100.00 -
-----------------------------------------------------------------------
Variable: gear
-----------------------------------------------------------------------
Levels Frequency Cum Frequency Percent Cum Percent
-----------------------------------------------------------------------
3 13 13 46.43 46.43
-----------------------------------------------------------------------
4 10 23 35.71 82.14
-----------------------------------------------------------------------
5 5 28 17.86 100
-----------------------------------------------------------------------
Missing 4 - 12.50 -
-----------------------------------------------------------------------
Total 32 - 100.00 -
-----------------------------------------------------------------------"
fdata <- dplyr::select(mtcarz, cyl, gear)
fdata$cyl[c(3, 10, 14, 19)] <- NA
fdata$gear[c(3, 8, 17, 24)] <- NA
expect_output(ds_auto_freq_table(fdata), pim)
})
test_that("output from print_cross2 matches the expected result", {
mt <- mtcars
mt[, c(2, 8, 9)] <- lapply(mt[, c(2, 8, 9)], factor)
pim <- " Cell Contents
|---------------|
| Frequency |
| Percent |
| Row Pct |
| Col Pct |
|---------------|
Total Observations: 32
cyl vs vs
-------------------------------------------------------------
| | vs |
-------------------------------------------------------------
| cyl | 1 | 2 | Row Total |
-------------------------------------------------------------
| 1 | 1 | 10 | 11 |
| | 0.031 | 0.312 | |
| | 0.09 | 0.91 | 0.34 |
| | 0.06 | 0.71 | |
-------------------------------------------------------------
| 2 | 3 | 4 | 7 |
| | 0.094 | 0.125 | |
| | 0.43 | 0.57 | 0.22 |
| | 0.17 | 0.29 | |
-------------------------------------------------------------
| 3 | 14 | 0 | 14 |
| | 0.438 | 0 | |
| | 1 | 0 | 0.44 |
| | 0.78 | 0 | |
-------------------------------------------------------------
| Column Total | 18 | 14 | 32 |
| | 0.563 | 0.437 | |
-------------------------------------------------------------
cyl vs am
-------------------------------------------------------------
| | am |
-------------------------------------------------------------
| cyl | 1 | 2 | Row Total |
-------------------------------------------------------------
| 1 | 3 | 8 | 11 |
| | 0.094 | 0.25 | |
| | 0.27 | 0.73 | 0.34 |
| | 0.16 | 0.62 | |
-------------------------------------------------------------
| 2 | 4 | 3 | 7 |
| | 0.125 | 0.094 | |
| | 0.57 | 0.43 | 0.22 |
| | 0.21 | 0.23 | |
-------------------------------------------------------------
| 3 | 12 | 2 | 14 |
| | 0.375 | 0.062 | |
| | 0.86 | 0.14 | 0.44 |
| | 0.63 | 0.15 | |
-------------------------------------------------------------
| Column Total | 19 | 13 | 32 |
| | 0.594 | 0.406 | |
-------------------------------------------------------------
vs vs am
-------------------------------------------------------------
| | am |
-------------------------------------------------------------
| vs | 1 | 2 | Row Total |
-------------------------------------------------------------
| 1 | 12 | 6 | 18 |
| | 0.375 | 0.188 | |
| | 0.67 | 0.33 | 0.56 |
| | 0.63 | 0.46 | |
-------------------------------------------------------------
| 2 | 7 | 7 | 14 |
| | 0.219 | 0.219 | |
| | 0.5 | 0.5 | 0.44 |
| | 0.37 | 0.54 | |
-------------------------------------------------------------
| Column Total | 19 | 13 | 32 |
| | 0.594 | 0.407 | |
-------------------------------------------------------------
"
expect_output(ds_auto_cross_table(mt), pim)
})
rsquaredacademy/descriptr documentation built on July 9, 2021, 8:13 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
context("print")
mt <- mtcars
mt$cyl <- as.factor(mt$cyl)
mt$vs <- as.factor(mt$vs)
test_that("output from print_cross matches expected output", {
k <- ds_cross_table(mt, cyl, vs)
pim <- " Cell Contents
|---------------|
| Frequency |
| Percent |
| Row Pct |
| Col Pct |
|---------------|
Total Observations: 32
-------------------------------------------------------------
| | vs |
-------------------------------------------------------------
| cyl | 0 | 1 | Row Total |
-------------------------------------------------------------
| 4 | 1 | 10 | 11 |
| | 0.031 | 0.312 | |
| | 0.09 | 0.91 | 0.34 |
| | 0.06 | 0.71 | |
-------------------------------------------------------------
| 6 | 3 | 4 | 7 |
| | 0.094 | 0.125 | |
| | 0.43 | 0.57 | 0.22 |
| | 0.17 | 0.29 | |
-------------------------------------------------------------
| 8 | 14 | 0 | 14 |
| | 0.438 | 0 | |
| | 1 | 0 | 0.44 |
| | 0.78 | 0 | |
-------------------------------------------------------------
| Column Total | 18 | 14 | 32 |
| | 0.563 | 0.437 | |
-------------------------------------------------------------"
expect_output(print_cross(k), pim)
})
test_that("output from print_screener matches expected output", {
mt <- mtcars
mt[, c(2, 8:11)] <- lapply(mt[, c(2, 8:11)], factor)
mt[sample(1:nrow(mt), 12), sample(1:ncol(mt), 6)] <- NA
k <- ds_screener(mt)
pim <- "-----------------------------------------------------------------------
| Column Name | Data Type | Levels | Missing | Missing (%) |
-----------------------------------------------------------------------
| mpg | numeric | NA | 0 | 0 |
| cyl | factor | 4 6 8 | 12 | 37.5 |
| disp | numeric | NA | 0 | 0 |
| hp | numeric | NA | 12 | 37.5 |
| drat | numeric | NA | 0 | 0 |
| wt | numeric | NA | 0 | 0 |
| qsec | numeric | NA | 12 | 37.5 |
| vs | factor | 0 1 | 12 | 37.5 |
| am | factor | 0 1 | 12 | 37.5 |
| gear | factor | 3 4 5 | 12 | 37.5 |
| carb | factor |1 2 3 4 6 8| 0 | 0 |
-----------------------------------------------------------------------
Overall Missing Values 72
Percentage of Missing Values 20.45 %
Rows with Missing Values 12
Columns With Missing Values 6 "
expect_output(print_screen(k), pim)
})
test_that("output from print_fcont matches the expected result", {
k <- ds_freq_table(mtcars, mpg)
pim <- " Variable: mpg
|-----------------------------------------------------------------------|
| Bins | Frequency | Cum Frequency | Percent | Cum Percent |
|-----------------------------------------------------------------------|
| 10.4 - 15.1 | 6 | 6 | 18.75 | 18.75 |
|-----------------------------------------------------------------------|
| 15.1 - 19.8 | 12 | 18 | 37.5 | 56.25 |
|-----------------------------------------------------------------------|
| 19.8 - 24.5 | 8 | 26 | 25 | 81.25 |
|-----------------------------------------------------------------------|
| 24.5 - 29.2 | 2 | 28 | 6.25 | 87.5 |
|-----------------------------------------------------------------------|
| 29.2 - 33.9 | 4 | 32 | 12.5 | 100 |
|-----------------------------------------------------------------------|"
expect_output(print_fcont(k), pim)
})
test_that("output from print_fcont is as expected when data has missing values", {
mt <- mtcarz
mt$mpg[c(3, 10, 14, 19)] <- NA
k <- ds_freq_table(mtcars, mpg)
pim <- " Variable: mpg
|-----------------------------------------------------------------------|
| Bins | Frequency | Cum Frequency | Percent | Cum Percent |
|-----------------------------------------------------------------------|
| 10.4 - 15.1 | 6 | 6 | 21.43 | 21.43 |
|-----------------------------------------------------------------------|
| 15.1 - 19.8 | 10 | 16 | 35.71 | 57.14 |
|-----------------------------------------------------------------------|
| 19.8 - 24.5 | 7 | 23 | 25 | 82.14 |
|-----------------------------------------------------------------------|
| 24.5 - 29.2 | 2 | 25 | 7.14 | 89.29 |
|-----------------------------------------------------------------------|
| 29.2 - 33.9 | 3 | 28 | 10.71 | 100 |
|-----------------------------------------------------------------------|
| Missing | 4 | - | 12.50 | - |
|-----------------------------------------------------------------------|
| Total | 32 | - | 100.00 | - |
|-----------------------------------------------------------------------|"
expect_output(print_fcont(k), pim)
})
test_that("output from freq_table matches the expected result", {
k <- ds_freq_table(mt, cyl)
pim <- " Variable: cyl)
|--------------------------------------------------------------------------|
| Cumulative Cumulative |
| Levels | Frequency | Frequency | Percent | Percent |
|--------------------------------------------------------------------------|
| 4 | 7 | 7 | 35 | 35 |
|--------------------------------------------------------------------------|
| 6 | 4 | 11 | 20 | 55 |
|--------------------------------------------------------------------------|
| 8 | 9 | 20 | 45 | 100 |
|--------------------------------------------------------------------------|
"
expect_output(print_ftable(k), pim)
})
test_that("output from freq_table is as expected when data has missing values", {
mt <- mtcarz
mt$cyl[c(3, 10, 14, 19)] <- NA
k <- ds_freq_table(mt, cyl)
pim <- " Variable: cyl
-----------------------------------------------------------------------
Levels Frequency Cum Frequency Percent Cum Percent
-----------------------------------------------------------------------
4 9 9 32.14 32.14
-----------------------------------------------------------------------
6 6 15 21.43 53.57
-----------------------------------------------------------------------
8 13 28 46.43 100
-----------------------------------------------------------------------
Missing 4 - -
-----------------------------------------------------------------------
Total 32 - 100.00 -
-----------------------------------------------------------------------"
expect_output(print_ftable(k), pim)
})
test_that("output from group_summary matches the expected result", {
k <- ds_group_summary(mt, cyl, mpg)
pim <- " mpg by cyl
-----------------------------------------------------------------------------------------
| Statistic/Levels| 4| 6| 8|
-----------------------------------------------------------------------------------------
| Obs| 11| 7| 14|
| Maximum| 21.4| 17.8| 10.4|
| Minimum| 33.9| 21.4| 19.2|
| Mean| 26.66| 19.74| 15.1|
| Median| 26| 19.7| 15.2|
| Mode| 22.8| 21| 10.4|
| Std. Deviation| 4.51| 1.45| 2.56|
| Variance| 20.34| 2.11| 6.55|
| Skewness| 0.35| -0.26| -0.46|
| Kurtosis| -1.43| -1.83| 0.33|
| Uncorrected SS| 8023.83| 2741.14| 3277.34|
| Corrected SS| 203.39| 12.68| 85.2|
| Coeff Variation| 16.91| 7.36| 16.95|
| Std. Error Mean| 1.36| 0.55| 0.68|
| Range| 12.5| 3.6| 8.8|
| Interquartile Range| 7.6| 2.35| 1.85|
-----------------------------------------------------------------------------------------"
expect_output(print_group(k), pim)
})
test_that("output from print_ftable2 matches the expected result", {
mt <- mtcars
mt[, c(2, 8:11)] <- lapply(mt[, c(2, 8:11)], factor)
pim <- " Variable: cyl
|--------------------------------------------------------------------------|
| Cumulative Cumulative |
| Levels | Frequency | Frequency | Percent | Percent |
|--------------------------------------------------------------------------|
| 4 | 11 | 11 | 34.38 | 34.38 |
|--------------------------------------------------------------------------|
| 6 | 7 | 18 | 21.88 | 56.25 |
|--------------------------------------------------------------------------|
| 8 | 14 | 32 | 43.75 | 100 |
|--------------------------------------------------------------------------|
Variable: vs
|--------------------------------------------------------------------------|
| Cumulative Cumulative |
| Levels | Frequency | Frequency | Percent | Percent |
|--------------------------------------------------------------------------|
| 0 | 18 | 18 | 56.25 | 56.25 |
|--------------------------------------------------------------------------|
| 1 | 14 | 32 | 43.75 | 100 |
|--------------------------------------------------------------------------|
Variable: am
|--------------------------------------------------------------------------|
| Cumulative Cumulative |
| Levels | Frequency | Frequency | Percent | Percent |
|--------------------------------------------------------------------------|
| 0 | 19 | 19 | 59.38 | 59.38 |
|--------------------------------------------------------------------------|
| 1 | 13 | 32 | 40.62 | 100 |
|--------------------------------------------------------------------------|
Variable: gear
|--------------------------------------------------------------------------|
| Cumulative Cumulative |
| Levels | Frequency | Frequency | Percent | Percent |
|--------------------------------------------------------------------------|
| 3 | 15 | 15 | 46.88 | 46.88 |
|--------------------------------------------------------------------------|
| 4 | 12 | 27 | 37.5 | 84.38 |
|--------------------------------------------------------------------------|
| 5 | 5 | 32 | 15.62 | 100 |
|--------------------------------------------------------------------------|
Variable: carb
|--------------------------------------------------------------------------|
| Cumulative Cumulative |
| Levels | Frequency | Frequency | Percent | Percent |
|--------------------------------------------------------------------------|
| 1 | 7 | 7 | 21.88 | 21.88 |
|--------------------------------------------------------------------------|
| 2 | 10 | 17 | 31.25 | 53.12 |
|--------------------------------------------------------------------------|
| 3 | 3 | 20 | 9.38 | 62.5 |
|--------------------------------------------------------------------------|
| 4 | 10 | 30 | 31.25 | 93.75 |
|--------------------------------------------------------------------------|
| 6 | 1 | 31 | 3.12 | 96.88 |
|--------------------------------------------------------------------------|
| 8 | 1 | 32 | 3.12 | 100 |
|--------------------------------------------------------------------------|
"
expect_output(ds_auto_freq_table(mt), pim)
})
test_that("output from ds_auto_freq_table() is as expected when data has missing values", {
pim <- " Variable: cyl
-----------------------------------------------------------------------
Levels Frequency Cum Frequency Percent Cum Percent
-----------------------------------------------------------------------
4 9 9 32.14 32.14
-----------------------------------------------------------------------
6 6 15 21.43 53.57
-----------------------------------------------------------------------
8 13 28 46.43 100
-----------------------------------------------------------------------
Missing 4 - 12.50 -
-----------------------------------------------------------------------
Total 32 - 100.00 -
-----------------------------------------------------------------------
Variable: gear
-----------------------------------------------------------------------
Levels Frequency Cum Frequency Percent Cum Percent
-----------------------------------------------------------------------
3 13 13 46.43 46.43
-----------------------------------------------------------------------
4 10 23 35.71 82.14
-----------------------------------------------------------------------
5 5 28 17.86 100
-----------------------------------------------------------------------
Missing 4 - 12.50 -
-----------------------------------------------------------------------
Total 32 - 100.00 -
-----------------------------------------------------------------------"
fdata <- dplyr::select(mtcarz, cyl, gear)
fdata$cyl[c(3, 10, 14, 19)] <- NA
fdata$gear[c(3, 8, 17, 24)] <- NA
expect_output(ds_auto_freq_table(fdata), pim)
})
test_that("output from print_cross2 matches the expected result", {
mt <- mtcars
mt[, c(2, 8, 9)] <- lapply(mt[, c(2, 8, 9)], factor)
pim <- " Cell Contents
|---------------|
| Frequency |
| Percent |
| Row Pct |
| Col Pct |
|---------------|
Total Observations: 32
cyl vs vs
-------------------------------------------------------------
| | vs |
-------------------------------------------------------------
| cyl | 1 | 2 | Row Total |
-------------------------------------------------------------
| 1 | 1 | 10 | 11 |
| | 0.031 | 0.312 | |
| | 0.09 | 0.91 | 0.34 |
| | 0.06 | 0.71 | |
-------------------------------------------------------------
| 2 | 3 | 4 | 7 |
| | 0.094 | 0.125 | |
| | 0.43 | 0.57 | 0.22 |
| | 0.17 | 0.29 | |
-------------------------------------------------------------
| 3 | 14 | 0 | 14 |
| | 0.438 | 0 | |
| | 1 | 0 | 0.44 |
| | 0.78 | 0 | |
-------------------------------------------------------------
| Column Total | 18 | 14 | 32 |
| | 0.563 | 0.437 | |
-------------------------------------------------------------
cyl vs am
-------------------------------------------------------------
| | am |
-------------------------------------------------------------
| cyl | 1 | 2 | Row Total |
-------------------------------------------------------------
| 1 | 3 | 8 | 11 |
| | 0.094 | 0.25 | |
| | 0.27 | 0.73 | 0.34 |
| | 0.16 | 0.62 | |
-------------------------------------------------------------
| 2 | 4 | 3 | 7 |
| | 0.125 | 0.094 | |
| | 0.57 | 0.43 | 0.22 |
| | 0.21 | 0.23 | |
-------------------------------------------------------------
| 3 | 12 | 2 | 14 |
| | 0.375 | 0.062 | |
| | 0.86 | 0.14 | 0.44 |
| | 0.63 | 0.15 | |
-------------------------------------------------------------
| Column Total | 19 | 13 | 32 |
| | 0.594 | 0.406 | |
-------------------------------------------------------------
vs vs am
-------------------------------------------------------------
| | am |
-------------------------------------------------------------
| vs | 1 | 2 | Row Total |
-------------------------------------------------------------
| 1 | 12 | 6 | 18 |
| | 0.375 | 0.188 | |
| | 0.67 | 0.33 | 0.56 |
| | 0.63 | 0.46 | |
-------------------------------------------------------------
| 2 | 7 | 7 | 14 |
| | 0.219 | 0.219 | |
| | 0.5 | 0.5 | 0.44 |
| | 0.37 | 0.54 | |
-------------------------------------------------------------
| Column Total | 19 | 13 | 32 |
| | 0.594 | 0.407 | |
-------------------------------------------------------------
"
expect_output(ds_auto_cross_table(mt), pim)
})
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