In [1]:

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library("ggplot2")
library("pander")

In [2]:

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ggAggHist <- getFromNamespace("ggAggHist", "dataMaid")
ggAggBarplot <- getFromNamespace("ggAggBarplot", "dataMaid")

Data report overview

The dataset examined has the following dimensions:

Feature	Result
Number of observations	122
Number of variables	41

Codebook summary table

Variable	Class	# unique values	Missing
[AGE]	integer	43	0.82 %
[EDUCATION]	integer	5	0.00 %
[SOCIALSTATUS]	integer	9	0.00 %
[INCOME]	integer	7	0.00 %
[RELIGIOUS_IDENTITY]	character	9	0.00 %
[RACE]	character	14	0.00 %
[STREETRACE]	integer	8	1.64 %
[GENDER]	integer	3	0.00 %
[SEXUAL_IDENTITY]	integer	5	0.00 %
[POLITICALBELIEFS]	integer	7	1.64 %
[POLITICALAFFIL]	integer	6	15.57 %
[VOTE2024]	integer	6	2.46 %
[SERIOUS]	integer	2	0.82 %
[POLITICALPARTY]	character	3	0.00 %
[SEX]	character	2	0.00 %
[ETHNICITY]	character	4	0.00 %
[Nationality]	character	1	0.00 %
[Student.status]	character	3	0.00 %
[Employment.status]	character	7	0.00 %
[ZEROSUM_1]	integer	7	0.00 %
[ZEROSUM_2]	integer	8	0.82 %
[ZEROSUM_3]	integer	8	0.82 %
[ZEROSUM_4]	integer	8	0.82 %
[ZEROSUM_5]	integer	7	0.00 %
[ZEROSUM_6]	integer	8	0.82 %
[ZEROSUM_7]	integer	8	1.64 %
[ZEROSUM_8]	integer	8	0.82 %
[ZEROSUM_9]	integer	8	0.82 %
[ZEROSUM_10]	integer	7	0.00 %
[ZEROSUM_11]	integer	8	0.82 %
[RACIALIDENTITY.6]	character	6	0.00 %
[RACIALIDENTITY.4]	character	4	0.00 %
[RACIALIDENTITY.2]	character	2	0.00 %
[RI_White]	integer	2	0.00 %
[RI_Else]	integer	2	0.00 %
[GENDER_MALE]	integer	2	0.00 %
[RACE_BLACK]	integer	2	0.00 %
[RACE_ASIAN]	integer	2	0.00 %
[RACE_OTHER]	integer	2	0.00 %
[RELIGIOUS_YES]	integer	2	0.00 %
[EDUCATION_HIGH]	integer	2	0.00 %

Variable list

AGE

Feature	Result
Variable type	integer
Number of missing obs.	1 (0.82 %)
Number of unique values	42
Median	33
1st and 3rd quartiles	27; 43
Min. and max.	19; 73

In [3]:

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ggAggHist(data = structure(list(xmin = c(15L, 20L, 25L, 30L, 
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L), xmax = c(20L, 25L, 30L, 
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L), ymin = c(0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ymax = c(6L, 21L, 25L, 17L, 16L, 
9L, 6L, 9L, 6L, 2L, 2L, 2L)), class = "data.frame", row.names = c(NA, 
-12L)), vnam = "AGE")

EDUCATION

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	5
Median	5
1st and 3rd quartiles	4; 6
Min. and max.	2; 6

In [4]:

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ggAggHist(data = structure(list(xmin = c(2, 2.5, 3, 3.5, 4, 4.5, 
5, 5.5), xmax = c(2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6), ymin = c(0, 
0, 0, 0, 0, 0, 0, 0), ymax = c(8L, 21L, 0L, 7L, 0L, 46L, 0L, 
40L)), class = "data.frame", row.names = c(NA, -8L)), vnam = "EDUCATION")

SOCIALSTATUS

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	9
Median	5.5
1st and 3rd quartiles	4; 6
Min. and max.	1; 9

In [5]:

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ggAggHist(data = structure(list(xmin = 1:8, xmax = 2:9, ymin = c(0, 
0, 0, 0, 0, 0, 0, 0), ymax = c(7L, 15L, 16L, 23L, 31L, 18L, 9L, 
3L)), class = "data.frame", row.names = c(NA, -8L)), vnam = "SOCIALSTATUS")

INCOME

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	7
Median	4
1st and 3rd quartiles	2; 5
Min. and max.	1; 7

In [6]:

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ggAggHist(data = structure(list(xmin = 1:6, xmax = 2:7, ymin = c(0, 
0, 0, 0, 0, 0), ymax = c(37L, 22L, 14L, 36L, 11L, 2L)), class = "data.frame", row.names = c(NA, 
-6L)), vnam = "INCOME")

RELIGIOUS_IDENTITY

Feature	Result
Variable type	character
Number of missing obs.	0 (0 %)
Number of unique values	9
Mode	“1”

In [7]:

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ggAggBarplot(data = structure(list(x = structure(1:9, levels = c("-99", 
"1", "2", "4", "5,9", "6", "7", "8", "9"), class = "factor"), 
    y = c(3L, 65L, 2L, 2L, 1L, 1L, 1L, 4L, 43L)), class = "data.frame", row.names = c(NA, 
-9L)), vnam = "RELIGIOUS_IDENTITY")

Observed factor levels: “-99”, “1”, “2”, “4”, “5,9”, “6”, “7”, “8”, “9”.

RACE

Feature	Result
Variable type	character
Number of missing obs.	0 (0 %)
Number of unique values	14
Mode	“7”

In [8]:

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ggAggBarplot(data = structure(list(x = structure(1:14, levels = c("1,7", 
"2", "2,3", "2,7", "3", "3,4,7", "3,6", "3,7", "4", "4,7", "5", 
"6", "7", "8"), class = "factor"), y = c(2L, 23L, 1L, 2L, 28L, 
1L, 1L, 2L, 9L, 1L, 1L, 1L, 49L, 1L)), class = "data.frame", row.names = c(NA, 
-14L)), vnam = "RACE")

Observed factor levels: “1,7”, “2”, “2,3”, “2,7”, “3”, “3,4,7”, “3,6”, “3,7”, “4”, “4,7”, “5”, “6”, “7”, “8”.

STREETRACE

Feature	Result
Variable type	integer
Number of missing obs.	2 (1.64 %)
Number of unique values	7
Median	3
1st and 3rd quartiles	3; 5
Min. and max.	1; 8

In [9]:

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ggAggHist(data = structure(list(xmin = 1:7, xmax = 2:8, ymin = c(0, 
0, 0, 0, 0, 0, 0), ymax = c(24L, 54L, 7L, 30L, 0L, 4L, 1L)), class = "data.frame", row.names = c(NA, 
-7L)), vnam = "STREETRACE")

GENDER

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	3
Median	2
1st and 3rd quartiles	1; 2
Min. and max.	1; 3

In [10]:

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ggAggHist(data = structure(list(xmin = c(1, 1.2, 1.4, 1.6, 1.8, 
2, 2.2, 2.4, 2.6, 2.8), xmax = c(1.2, 1.4, 1.6, 1.8, 2, 2.2, 
2.4, 2.6, 2.8, 3), ymin = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ymax = c(58L, 
0L, 0L, 0L, 60L, 0L, 0L, 0L, 0L, 4L)), class = "data.frame", row.names = c(NA, 
-10L)), vnam = "GENDER")

SEXUAL_IDENTITY

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	5
Median	1
1st and 3rd quartiles	1; 2
Min. and max.	1; 5

In [11]:

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ggAggHist(data = structure(list(xmin = c(1, 1.5, 2, 2.5, 3, 3.5, 
4, 4.5), xmax = c(1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5), ymin = c(0, 
0, 0, 0, 0, 0, 0, 0), ymax = c(85L, 10L, 0L, 22L, 0L, 4L, 0L, 
1L)), class = "data.frame", row.names = c(NA, -8L)), vnam = "SEXUAL_IDENTITY")

POLITICALBELIEFS

Feature	Result
Variable type	integer
Number of missing obs.	2 (1.64 %)
Number of unique values	6
Median	4
1st and 3rd quartiles	3; 5
Min. and max.	1; 6

In [12]:

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ggAggHist(data = structure(list(xmin = c(1, 1.5, 2, 2.5, 3, 3.5, 
4, 4.5, 5, 5.5), xmax = c(1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 
6), ymin = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ymax = c(7L, 17L, 
0L, 32L, 0L, 27L, 0L, 27L, 0L, 10L)), class = "data.frame", row.names = c(NA, 
-10L)), vnam = "POLITICALBELIEFS")

POLITICALAFFIL

Feature	Result
Variable type	integer
Number of missing obs.	19 (15.57 %)
Number of unique values	5
Median	2
1st and 3rd quartiles	2; 4
Min. and max.	1; 5

In [13]:

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ggAggHist(data = structure(list(xmin = c(1, 1.5, 2, 2.5, 3, 3.5, 
4, 4.5), xmax = c(1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5), ymin = c(0, 
0, 0, 0, 0, 0, 0, 0), ymax = c(9L, 52L, 0L, 8L, 0L, 33L, 0L, 
1L)), class = "data.frame", row.names = c(NA, -8L)), vnam = "POLITICALAFFIL")

VOTE2024

Feature	Result
Variable type	integer
Number of missing obs.	3 (2.46 %)
Number of unique values	5
Median	2
1st and 3rd quartiles	1; 2
Min. and max.	1; 8

In [14]:

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ggAggHist(data = structure(list(xmin = 1:7, xmax = 2:8, ymin = c(0, 
0, 0, 0, 0, 0, 0), ymax = c(101L, 0L, 0L, 1L, 0L, 1L, 16L)), class = "data.frame", row.names = c(NA, 
-7L)), vnam = "VOTE2024")

SERIOUS

The variable only takes one (non-missing) value: “1”. The variable contains 0.82 % missing observations.

POLITICALPARTY

Feature	Result
Variable type	character
Number of missing obs.	0 (0 %)
Number of unique values	3
Mode	“Democrat”

In [15]:

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ggAggBarplot(data = structure(list(x = structure(1:3, levels = c("Democrat", 
"Independent", "Republican"), class = "factor"), y = c(43L, 40L, 
39L)), class = "data.frame", row.names = c(NA, -3L)), vnam = "POLITICALPARTY")

Observed factor levels: “Democrat”, “Independent”, “Republican”.

SEX

Feature	Result
Variable type	character
Number of missing obs.	0 (0 %)
Number of unique values	2
Mode	“Male”

In [16]:

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ggAggBarplot(data = structure(list(x = structure(1:2, levels = c("Female", 
"Male"), class = "factor"), y = c(59L, 63L)), class = "data.frame", row.names = c(NA, 
-2L)), vnam = "SEX")

Observed factor levels: “Female”, “Male”.

ETHNICITY

Feature	Result
Variable type	character
Number of missing obs.	0 (0 %)
Number of unique values	4
Mode	“White”

In [17]:

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ggAggBarplot(data = structure(list(x = structure(1:4, levels = c("Asian", 
"Black", "Mixed/Other", "White"), class = "factor"), y = c(24L, 
25L, 25L, 48L)), class = "data.frame", row.names = c(NA, -4L)), 
    vnam = "ETHNICITY")

Observed factor levels: “Asian”, “Black”, “Mixed/Other”, “White”.

Nationality

The variable only takes one (non-missing) value: “United States”. The variable contains 0 % missing observations.

Student.status

Feature	Result
Variable type	character
Number of missing obs.	0 (0 %)
Number of unique values	3
Mode	“No”

In [18]:

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ggAggBarplot(data = structure(list(x = structure(1:3, levels = c("DATA_EXPIRED", 
"No", "Yes"), class = "factor"), y = c(18L, 74L, 30L)), class = "data.frame", row.names = c(NA, 
-3L)), vnam = "Student.status")

Observed factor levels: “DATA_EXPIRED”, “No”, “Yes”.

Employment.status

Feature	Result
Variable type	character
Number of missing obs.	0 (0 %)
Number of unique values	7
Mode	“Full-Time”

In [19]:

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ggAggBarplot(data = structure(list(x = structure(1:7, levels = c("DATA_EXPIRED", 
"Due to start a new job within the next month", "Full-Time", 
"Not in paid work (e.g. homemaker', 'retired or disabled)", "Other", 
"Part-Time", "Unemployed (and job seeking)"), class = "factor"), 
    y = c(17L, 1L, 50L, 10L, 3L, 29L, 12L)), class = "data.frame", row.names = c(NA, 
-7L)), vnam = "Employment.status")

Observed factor levels: “DATA_EXPIRED”, “Due to start a new job within the next month”, “Full-Time”, “Not in paid work (e.g. homemaker’, ’retired or disabled)”, “Other”, “Part-Time”, “Unemployed (and job seeking)”.

ZEROSUM_1

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	7
Median	4
1st and 3rd quartiles	2.25; 5
Min. and max.	1; 7

In [20]:

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ggAggHist(data = structure(list(xmin = 1:6, xmax = 2:7, ymin = c(0, 
0, 0, 0, 0, 0), ymax = c(31L, 15L, 21L, 31L, 18L, 6L)), class = "data.frame", row.names = c(NA, 
-6L)), vnam = "ZEROSUM_1")

ZEROSUM_2

Feature	Result
Variable type	integer
Number of missing obs.	1 (0.82 %)
Number of unique values	7
Median	5
1st and 3rd quartiles	4; 6
Min. and max.	1; 7

In [21]:

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ggAggHist(data = structure(list(xmin = 1:6, xmax = 2:7, ymin = c(0, 
0, 0, 0, 0, 0), ymax = c(12L, 11L, 25L, 35L, 18L, 20L)), class = "data.frame", row.names = c(NA, 
-6L)), vnam = "ZEROSUM_2")

ZEROSUM_3

Feature	Result
Variable type	integer
Number of missing obs.	1 (0.82 %)
Number of unique values	7
Median	5
1st and 3rd quartiles	4; 6
Min. and max.	1; 7

In [22]:

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ggAggHist(data = structure(list(xmin = 1:6, xmax = 2:7, ymin = c(0, 
0, 0, 0, 0, 0), ymax = c(10L, 7L, 26L, 35L, 23L, 20L)), class = "data.frame", row.names = c(NA, 
-6L)), vnam = "ZEROSUM_3")

ZEROSUM_4

Feature	Result
Variable type	integer
Number of missing obs.	1 (0.82 %)
Number of unique values	7
Median	3
1st and 3rd quartiles	1; 4
Min. and max.	1; 7

In [23]:

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ggAggHist(data = structure(list(xmin = 1:6, xmax = 2:7, ymin = c(0, 
0, 0, 0, 0, 0), ymax = c(47L, 23L, 24L, 17L, 8L, 2L)), class = "data.frame", row.names = c(NA, 
-6L)), vnam = "ZEROSUM_4")

ZEROSUM_5

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	7
Median	3
1st and 3rd quartiles	1; 4
Min. and max.	1; 7

In [24]:

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ggAggHist(data = structure(list(xmin = 1:6, xmax = 2:7, ymin = c(0, 
0, 0, 0, 0, 0), ymax = c(55L, 25L, 20L, 9L, 11L, 2L)), class = "data.frame", row.names = c(NA, 
-6L)), vnam = "ZEROSUM_5")

ZEROSUM_6

Feature	Result
Variable type	integer
Number of missing obs.	1 (0.82 %)
Number of unique values	7
Median	3
1st and 3rd quartiles	1; 5
Min. and max.	1; 7

In [25]:

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ggAggHist(data = structure(list(xmin = 1:6, xmax = 2:7, ymin = c(0, 
0, 0, 0, 0, 0), ymax = c(47L, 15L, 17L, 16L, 17L, 9L)), class = "data.frame", row.names = c(NA, 
-6L)), vnam = "ZEROSUM_6")

ZEROSUM_7

Feature	Result
Variable type	integer
Number of missing obs.	2 (1.64 %)
Number of unique values	7
Median	4
1st and 3rd quartiles	2; 5
Min. and max.	1; 7

In [26]:

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ggAggHist(data = structure(list(xmin = 1:6, xmax = 2:7, ymin = c(0, 
0, 0, 0, 0, 0), ymax = c(40L, 13L, 27L, 26L, 7L, 7L)), class = "data.frame", row.names = c(NA, 
-6L)), vnam = "ZEROSUM_7")

ZEROSUM_8

Feature	Result
Variable type	integer
Number of missing obs.	1 (0.82 %)
Number of unique values	7
Median	3
1st and 3rd quartiles	1; 4
Min. and max.	1; 7

In [27]:

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ggAggHist(data = structure(list(xmin = 1:6, xmax = 2:7, ymin = c(0, 
0, 0, 0, 0, 0), ymax = c(48L, 26L, 25L, 16L, 4L, 2L)), class = "data.frame", row.names = c(NA, 
-6L)), vnam = "ZEROSUM_8")

ZEROSUM_9

Feature	Result
Variable type	integer
Number of missing obs.	1 (0.82 %)
Number of unique values	7
Median	2
1st and 3rd quartiles	1; 4
Min. and max.	1; 7

In [28]:

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ggAggHist(data = structure(list(xmin = 1:6, xmax = 2:7, ymin = c(0, 
0, 0, 0, 0, 0), ymax = c(67L, 17L, 16L, 13L, 5L, 3L)), class = "data.frame", row.names = c(NA, 
-6L)), vnam = "ZEROSUM_9")

ZEROSUM_10

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	7
Median	3
1st and 3rd quartiles	1; 5
Min. and max.	1; 7

In [29]:

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ggAggHist(data = structure(list(xmin = 1:6, xmax = 2:7, ymin = c(0, 
0, 0, 0, 0, 0), ymax = c(45L, 23L, 21L, 19L, 13L, 1L)), class = "data.frame", row.names = c(NA, 
-6L)), vnam = "ZEROSUM_10")

ZEROSUM_11

Feature	Result
Variable type	integer
Number of missing obs.	1 (0.82 %)
Number of unique values	7
Median	3
1st and 3rd quartiles	2; 4
Min. and max.	1; 7

In [30]:

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ggAggHist(data = structure(list(xmin = 1:6, xmax = 2:7, ymin = c(0, 
0, 0, 0, 0, 0), ymax = c(49L, 18L, 26L, 13L, 13L, 2L)), class = "data.frame", row.names = c(NA, 
-6L)), vnam = "ZEROSUM_11")

RACIALIDENTITY.6

Feature	Result
Variable type	character
Number of missing obs.	0 (0 %)
Number of unique values	6
Mode	“White”

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ggAggBarplot(data = structure(list(x = structure(1:6, levels = c("Asian", 
"Black", "Latine", "Mixed", "Other", "White"), class = "factor"), 
    y = c(23L, 28L, 9L, 10L, 3L, 49L)), class = "data.frame", row.names = c(NA, 
-6L)), vnam = "RACIALIDENTITY.6")

Observed factor levels: “Asian”, “Black”, “Latine”, “Mixed”, “Other”, “White”.

RACIALIDENTITY.4

Feature	Result
Variable type	character
Number of missing obs.	0 (0 %)
Number of unique values	4
Mode	“White”

In [32]:

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ggAggBarplot(data = structure(list(x = structure(1:4, levels = c("Asian", 
"Black", "Mixed/Other", "White"), class = "factor"), y = c(23L, 
28L, 22L, 49L)), class = "data.frame", row.names = c(NA, -4L)), 
    vnam = "RACIALIDENTITY.4")

Observed factor levels: “Asian”, “Black”, “Mixed/Other”, “White”.

RACIALIDENTITY.2

Feature	Result
Variable type	character
Number of missing obs.	0 (0 %)
Number of unique values	2
Mode	“Else”

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ggAggBarplot(data = structure(list(x = structure(1:2, levels = c("Else", 
"White"), class = "factor"), y = c(73L, 49L)), class = "data.frame", row.names = c(NA, 
-2L)), vnam = "RACIALIDENTITY.2")

Observed factor levels: “Else”, “White”.

RI_White

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	2
Median	0
1st and 3rd quartiles	0; 1
Min. and max.	0; 1

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ggAggHist(data = structure(list(xmin = c(0, 0.1, 0.2, 0.3, 0.4, 
0.5, 0.6, 0.7, 0.8, 0.9), xmax = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 
0.7, 0.8, 0.9, 1), ymin = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ymax = c(73L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 49L)), class = "data.frame", row.names = c(NA, 
-10L)), vnam = "RI_White")

RI_Else

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	2
Median	1
1st and 3rd quartiles	0; 1
Min. and max.	0; 1

In [35]:

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ggAggHist(data = structure(list(xmin = c(0, 0.1, 0.2, 0.3, 0.4, 
0.5, 0.6, 0.7, 0.8, 0.9), xmax = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 
0.7, 0.8, 0.9, 1), ymin = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ymax = c(49L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 73L)), class = "data.frame", row.names = c(NA, 
-10L)), vnam = "RI_Else")

GENDER_MALE

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	2
Median	1
1st and 3rd quartiles	0; 1
Min. and max.	0; 1

In [36]:

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ggAggHist(data = structure(list(xmin = c(0, 0.1, 0.2, 0.3, 0.4, 
0.5, 0.6, 0.7, 0.8, 0.9), xmax = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 
0.7, 0.8, 0.9, 1), ymin = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ymax = c(59L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 63L)), class = "data.frame", row.names = c(NA, 
-10L)), vnam = "GENDER_MALE")

RACE_BLACK

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	2
Median	0
1st and 3rd quartiles	0; 0
Min. and max.	0; 1

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ggAggHist(data = structure(list(xmin = c(0, 0.1, 0.2, 0.3, 0.4, 
0.5, 0.6, 0.7, 0.8, 0.9), xmax = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 
0.7, 0.8, 0.9, 1), ymin = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ymax = c(94L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 28L)), class = "data.frame", row.names = c(NA, 
-10L)), vnam = "RACE_BLACK")

RACE_ASIAN

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	2
Median	0
1st and 3rd quartiles	0; 0
Min. and max.	0; 1

In [38]:

Show the code

ggAggHist(data = structure(list(xmin = c(0, 0.1, 0.2, 0.3, 0.4, 
0.5, 0.6, 0.7, 0.8, 0.9), xmax = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 
0.7, 0.8, 0.9, 1), ymin = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ymax = c(99L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 23L)), class = "data.frame", row.names = c(NA, 
-10L)), vnam = "RACE_ASIAN")

RACE_OTHER

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	2
Median	0
1st and 3rd quartiles	0; 0
Min. and max.	0; 1

In [39]:

Show the code

ggAggHist(data = structure(list(xmin = c(0, 0.1, 0.2, 0.3, 0.4, 
0.5, 0.6, 0.7, 0.8, 0.9), xmax = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 
0.7, 0.8, 0.9, 1), ymin = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ymax = c(100L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 22L)), class = "data.frame", row.names = c(NA, 
-10L)), vnam = "RACE_OTHER")

RELIGIOUS_YES

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	2
Median	1
1st and 3rd quartiles	0; 1
Min. and max.	0; 1

In [40]:

Show the code

ggAggHist(data = structure(list(xmin = c(0, 0.1, 0.2, 0.3, 0.4, 
0.5, 0.6, 0.7, 0.8, 0.9), xmax = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 
0.7, 0.8, 0.9, 1), ymin = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ymax = c(47L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 75L)), class = "data.frame", row.names = c(NA, 
-10L)), vnam = "RELIGIOUS_YES")

EDUCATION_HIGH

Feature	Result
Variable type	integer
Number of missing obs.	0 (0 %)
Number of unique values	2
Median	1
1st and 3rd quartiles	0; 1
Min. and max.	0; 1

In [41]:

Show the code

ggAggHist(data = structure(list(xmin = c(0, 0.1, 0.2, 0.3, 0.4, 
0.5, 0.6, 0.7, 0.8, 0.9), xmax = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 
0.7, 0.8, 0.9, 1), ymin = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ymax = c(36L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 86L)), class = "data.frame", row.names = c(NA, 
-10L)), vnam = "EDUCATION_HIGH")

Report generation information:

Created by: Could not determine from system (username: r2672606).
Report creation time: Sat Jul 05 2025 20:23:53
Report was run from directory: /cloud/project
dataMaid v1.4.2 [Pkg: 2025-04-13 from RSPM (R 4.3.0)]
R version 4.3.3 (2024-02-29).
Platform: x86_64-pc-linux-gnu (64-bit)(UTC).
Function call: dataMaid::makeDataReport(data = select_data, render = TRUE, mode = c("summarize", "visualize", "check"), smartNum = FALSE, file = "select_data_codebook", replace = TRUE, checks = list(character = "showAllFactorLevels", factor = "showAllFactorLevels", labelled = "showAllFactorLevels", haven_labelled = "showAllFactorLevels", numeric = NULL, integer = NULL, logical = NULL, Date = NULL), listChecks = FALSE, maxProbVals = Inf, codebook = TRUE, reportTitle = "Codebook for select_data")

Codebook for select_data

Data report overview

Codebook summary table

Variable list

AGE

EDUCATION

SOCIALSTATUS

INCOME

RELIGIOUS_IDENTITY

RACE

STREETRACE

GENDER

SEXUAL_IDENTITY

POLITICALBELIEFS

POLITICALAFFIL

VOTE2024

SERIOUS

POLITICALPARTY

SEX

ETHNICITY

Nationality

Student.status

Employment.status

ZEROSUM_1

ZEROSUM_2

ZEROSUM_3

ZEROSUM_4

ZEROSUM_5

ZEROSUM_6

ZEROSUM_7

ZEROSUM_8

ZEROSUM_9

ZEROSUM_10

ZEROSUM_11

RACIALIDENTITY.6

RACIALIDENTITY.4

RACIALIDENTITY.2

RI_White

RI_Else

GENDER_MALE

RACE_BLACK

RACE_ASIAN

RACE_OTHER

RELIGIOUS_YES

EDUCATION_HIGH