#Load necessary packages here
#install.packages('janitor')#Load necessary libraries here
library(tidyverse)
library(readxl)
library(janitor)
library(readr)
library(sjPlot)#Load data from local repository
# Step 1: Load raw data
tnbs_raw <- read_excel("Label_TNBS_July_all_modified.xlsx") %>% select(-`...112`)
colnames(tnbs_raw)## [1] "StartDate" "EndDate" "IPAddress"
## [4] "Duration (in seconds)" "RecordedDate" "ResponseId"
## [7] "LocationLatitude" "LocationLongitude" "Q3_1"
## [10] "Q3_2" "Q3_3" "Q3_4"
## [13] "Q3_5" "Q3_6" "Q3_7"
## [16] "Q4_1" "Q4_2" "Q4_3"
## [19] "Q4_4" "Q4_5" "Q4_6"
## [22] "Q4_7" "Q5_1" "Q5_2"
## [25] "Q5_3" "Q5_4" "Q5_5"
## [28] "Q5_6" "Q5_7" "Q6"
## [31] "Q7" "Q7_7_TEXT" "Q9"
## [34] "Q9_7_TEXT" "Q10" "Q11"
## [37] "Q12" "Q13" "Q14"
## [40] "Q14_10_TEXT" "Q15_1" "Q16_1"
## [43] "Q18_1" "Q18_2" "Q18_3"
## [46] "Q18_4" "Q19_1" "Q19_2"
## [49] "Q19_3" "Q19_4" "Q19_5"
## [52] "Q19_6" "Q19_7" "Q19_8"
## [55] "Q20_1" "Q21" "Q23"
## [58] "Q24" "Q25" "Q26_1"
## [61] "Q27" "Q28" "Q28_7_TEXT"
## [64] "Q29" "Q29_8_TEXT" "Q30"
## [67] "Q31" "Q32" "Q33"
## [70] "Q34" "Q35" "Q36"
## [73] "Q37" "Q38" "Q38_4_TEXT"
## [76] "Q39" "Q41" "Q42_1"
## [79] "Q42_3" "Q42_4" "Q43_1"
## [82] "Q44" "Q45" "Q46"
## [85] "Q47" "Q47_12_TEXT" "Q48"
## [88] "Q49" "Q50" "Q51"
## [91] "Q52_1" "Q52_2" "Q52_3"
## [94] "Q52_4" "Q52_12" "Q52_5"
## [97] "Q52_11" "Q52_6" "Q52_10"
## [100] "Q52_9" "Q52_7" "Q52_8"
## [103] "Q52_8_TEXT" "Q52_13" "Q53"
## [106] "Q54_1" "_os_api_gender" "_os_api_age"
## [109] "_os_api_ethnicity" "_os_api_region" "_os_api_hispanic"na_count <- sapply(tnbs_raw, function(x) sum(is.na(x)))
na_df <- data.frame(Variable = names(na_count), NA_Count = na_count)
# View the top rows of the result
head(na_df)## Variable NA_Count
## StartDate StartDate 0
## EndDate EndDate 0
## IPAddress IPAddress 0
## Duration (in seconds) Duration (in seconds) 0
## RecordedDate RecordedDate 0
## ResponseId ResponseId 0sapply(tnbs_raw, class) # returns class of each column## $StartDate
## [1] "POSIXct" "POSIXt"
##
## $EndDate
## [1] "POSIXct" "POSIXt"
##
## $IPAddress
## [1] "character"
##
## $`Duration (in seconds)`
## [1] "numeric"
##
## $RecordedDate
## [1] "POSIXct" "POSIXt"
##
## $ResponseId
## [1] "character"
##
## $LocationLatitude
## [1] "numeric"
##
## $LocationLongitude
## [1] "numeric"
##
## $Q3_1
## [1] "character"
##
## $Q3_2
## [1] "character"
##
## $Q3_3
## [1] "character"
##
## $Q3_4
## [1] "character"
##
## $Q3_5
## [1] "character"
##
## $Q3_6
## [1] "character"
##
## $Q3_7
## [1] "character"
##
## $Q4_1
## [1] "character"
##
## $Q4_2
## [1] "character"
##
## $Q4_3
## [1] "character"
##
## $Q4_4
## [1] "character"
##
## $Q4_5
## [1] "character"
##
## $Q4_6
## [1] "character"
##
## $Q4_7
## [1] "character"
##
## $Q5_1
## [1] "character"
##
## $Q5_2
## [1] "character"
##
## $Q5_3
## [1] "character"
##
## $Q5_4
## [1] "character"
##
## $Q5_5
## [1] "character"
##
## $Q5_6
## [1] "character"
##
## $Q5_7
## [1] "character"
##
## $Q6
## [1] "character"
##
## $Q7
## [1] "character"
##
## $Q7_7_TEXT
## [1] "character"
##
## $Q9
## [1] "character"
##
## $Q9_7_TEXT
## [1] "character"
##
## $Q10
## [1] "character"
##
## $Q11
## [1] "character"
##
## $Q12
## [1] "character"
##
## $Q13
## [1] "character"
##
## $Q14
## [1] "character"
##
## $Q14_10_TEXT
## [1] "character"
##
## $Q15_1
## [1] "numeric"
##
## $Q16_1
## [1] "numeric"
##
## $Q18_1
## [1] "character"
##
## $Q18_2
## [1] "character"
##
## $Q18_3
## [1] "character"
##
## $Q18_4
## [1] "character"
##
## $Q19_1
## [1] "character"
##
## $Q19_2
## [1] "character"
##
## $Q19_3
## [1] "character"
##
## $Q19_4
## [1] "character"
##
## $Q19_5
## [1] "character"
##
## $Q19_6
## [1] "character"
##
## $Q19_7
## [1] "character"
##
## $Q19_8
## [1] "character"
##
## $Q20_1
## [1] "numeric"
##
## $Q21
## [1] "character"
##
## $Q23
## [1] "character"
##
## $Q24
## [1] "character"
##
## $Q25
## [1] "character"
##
## $Q26_1
## [1] "character"
##
## $Q27
## [1] "character"
##
## $Q28
## [1] "character"
##
## $Q28_7_TEXT
## [1] "logical"
##
## $Q29
## [1] "character"
##
## $Q29_8_TEXT
## [1] "character"
##
## $Q30
## [1] "character"
##
## $Q31
## [1] "character"
##
## $Q32
## [1] "character"
##
## $Q33
## [1] "character"
##
## $Q34
## [1] "character"
##
## $Q35
## [1] "character"
##
## $Q36
## [1] "character"
##
## $Q37
## [1] "character"
##
## $Q38
## [1] "character"
##
## $Q38_4_TEXT
## [1] "character"
##
## $Q39
## [1] "character"
##
## $Q41
## [1] "character"
##
## $Q42_1
## [1] "character"
##
## $Q42_3
## [1] "character"
##
## $Q42_4
## [1] "character"
##
## $Q43_1
## [1] "numeric"
##
## $Q44
## [1] "character"
##
## $Q45
## [1] "character"
##
## $Q46
## [1] "character"
##
## $Q47
## [1] "character"
##
## $Q47_12_TEXT
## [1] "character"
##
## $Q48
## [1] "character"
##
## $Q49
## [1] "character"
##
## $Q50
## [1] "character"
##
## $Q51
## [1] "character"
##
## $Q52_1
## [1] "numeric"
##
## $Q52_2
## [1] "numeric"
##
## $Q52_3
## [1] "numeric"
##
## $Q52_4
## [1] "numeric"
##
## $Q52_12
## [1] "numeric"
##
## $Q52_5
## [1] "numeric"
##
## $Q52_11
## [1] "numeric"
##
## $Q52_6
## [1] "numeric"
##
## $Q52_10
## [1] "numeric"
##
## $Q52_9
## [1] "numeric"
##
## $Q52_7
## [1] "numeric"
##
## $Q52_8
## [1] "numeric"
##
## $Q52_8_TEXT
## [1] "character"
##
## $Q52_13
## [1] "numeric"
##
## $Q53
## [1] "character"
##
## $Q54_1
## [1] "character"
##
## $`_os_api_gender`
## [1] "character"
##
## $`_os_api_age`
## [1] "numeric"
##
## $`_os_api_ethnicity`
## [1] "character"
##
## $`_os_api_region`
## [1] "character"
##
## $`_os_api_hispanic`
## [1] "character"sjmisc::frq(x = tnbs_raw[c(
"Q28", "Q29", "Q30", "Q32", "Q35", "Q36", "Q38", "Q23", "Q24", "Q50", "Q51",
"Q25", "_os_api_gender", "_os_api_age", "_os_api_ethnicity", "_os_api_region"
)], out = "viewer")| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| Female |
|
312 | 50.65 | 50.90 | 50.90 | |
| Male |
|
293 | 47.56 | 47.80 | 98.69 | |
| Prefer not to say |
|
7 | 1.14 | 1.14 | 99.84 | |
| Prefer to self-describe |
|
1 | 0.16 | 0.16 | 100.00 | |
| NA | NA | 3 | 0.49 | NA | NA | |
| total N=616 · valid N=613 · x̄=1.51 · σ=0.53 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| American Indian or Alaska Native |
|
10 | 1.62 | 1.62 | 1.62 | |
| American Indian or Alaska Native,Black or African American |
|
1 | 0.16 | 0.16 | 1.79 | |
| American Indian or Alaska Native,Black or African American,White |
|
1 | 0.16 | 0.16 | 1.95 | |
| American Indian or Alaska Native,Hispanic or Latino |
|
1 | 0.16 | 0.16 | 2.11 | |
| American Indian or Alaska Native,White |
|
3 | 0.49 | 0.49 | 2.60 | |
| Another category, please provide: |
|
1 | 0.16 | 0.16 | 2.76 | |
| Asian |
|
47 | 7.63 | 7.63 | 10.39 | |
| Asian,American Indian or Alaska Native,Black or African American,White |
|
1 | 0.16 | 0.16 | 10.55 | |
| Asian,Black or African American |
|
2 | 0.32 | 0.32 | 10.88 | |
| Asian,Black or African American,Hispanic or Latino |
|
1 | 0.16 | 0.16 | 11.04 | |
| Asian,Hispanic or Latino |
|
4 | 0.65 | 0.65 | 11.69 | |
| Asian,Hispanic or Latino,White |
|
1 | 0.16 | 0.16 | 11.85 | |
| Asian,White |
|
3 | 0.49 | 0.49 | 12.34 | |
| Black or African American |
|
169 | 27.44 | 27.44 | 39.77 | |
| Black or African American,Hispanic or Latino |
|
7 | 1.14 | 1.14 | 40.91 | |
| Black or African American,Hispanic or Latino,White |
|
1 | 0.16 | 0.16 | 41.07 | |
| Black or African American,Middle Eastern or North Arfican |
|
2 | 0.32 | 0.32 | 41.40 | |
| Black or African American,Native Hawaiian or other Pacific Islander |
|
1 | 0.16 | 0.16 | 41.56 | |
| Black or African American,White |
|
6 | 0.97 | 0.97 | 42.53 | |
| Hispanic or Latino |
|
37 | 6.01 | 6.01 | 48.54 | |
| Hispanic or Latino,Middle Eastern or North Arfican,Native Hawaiian or other Pacific Islander |
|
1 | 0.16 | 0.16 | 48.70 | |
| Hispanic or Latino,Native Hawaiian or other Pacific Islander |
|
1 | 0.16 | 0.16 | 48.86 | |
| Hispanic or Latino,White |
|
13 | 2.11 | 2.11 | 50.97 | |
| Middle Eastern or North Arfican |
|
4 | 0.65 | 0.65 | 51.62 | |
| Native Hawaiian or other Pacific Islander |
|
3 | 0.49 | 0.49 | 52.11 | |
| White |
|
295 | 47.89 | 47.89 | 100.00 | |
| NA | NA | 0 | 0.00 | NA | NA | |
| total N=616 · valid N=616 · x̄=19.60 · σ=7.15 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| Bachelor’s degree or higher |
|
143 | 23.21 | 23.33 | 23.33 | |
| Graduate or professional degree |
|
67 | 10.88 | 10.93 | 34.26 | |
| High school graduate (includes equivalency) |
|
181 | 29.38 | 29.53 | 63.78 | |
| Less than high school graduate |
|
39 | 6.33 | 6.36 | 70.15 | |
| Some college or associate’s degree |
|
183 | 29.71 | 29.85 | 100.00 | |
| NA | NA | 3 | 0.49 | NA | NA | |
| total N=616 · valid N=613 · x̄=3.08 · σ=1.52 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| No |
|
79 | 12.82 | 12.85 | 12.85 | |
| Prefer not to answer |
|
4 | 0.65 | 0.65 | 13.50 | |
| Yes |
|
532 | 86.36 | 86.50 | 100.00 | |
| NA | NA | 1 | 0.16 | NA | NA | |
| total N=616 · valid N=615 · x̄=2.74 · σ=0.67 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| I don’t know |
|
15 | 2.44 | 2.64 | 2.64 | |
| I prefer not to answer |
|
11 | 1.79 | 1.94 | 4.58 | |
| Own |
|
273 | 44.32 | 48.06 | 52.64 | |
| Rent |
|
269 | 43.67 | 47.36 | 100.00 | |
| NA | NA | 48 | 7.79 | NA | NA | |
| total N=616 · valid N=568 · x̄=3.40 · σ=0.66 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| Excellent |
|
130 | 21.10 | 21.17 | 21.17 | |
| Fair |
|
93 | 15.10 | 15.15 | 36.32 | |
| Good |
|
200 | 32.47 | 32.57 | 68.89 | |
| Poor |
|
16 | 2.60 | 2.61 | 71.50 | |
| Very good |
|
175 | 28.41 | 28.50 | 100.00 | |
| NA | NA | 2 | 0.32 | NA | NA | |
| total N=616 · valid N=614 · x̄=3.02 · σ=1.47 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| English |
|
543 | 88.15 | 89.90 | 89.90 | |
| English,Others (specify) |
|
9 | 1.46 | 1.49 | 91.39 | |
| English,Spanish |
|
30 | 4.87 | 4.97 | 96.36 | |
| English,Spanish,Others (specify) |
|
1 | 0.16 | 0.17 | 96.52 | |
| I prefer not to answer |
|
2 | 0.32 | 0.33 | 96.85 | |
| Others (specify) |
|
2 | 0.32 | 0.33 | 97.19 | |
| Spanish |
|
15 | 2.44 | 2.48 | 99.67 | |
| Spanish,I prefer not to answer |
|
2 | 0.32 | 0.33 | 100.00 | |
| NA | NA | 12 | 1.95 | NA | NA | |
| total N=616 · valid N=604 · x̄=1.32 · σ=1.15 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| Rural |
|
98 | 15.91 | 15.96 | 15.96 | |
| Suburban |
|
255 | 41.40 | 41.53 | 57.49 | |
| Urban |
|
261 | 42.37 | 42.51 | 100.00 | |
| NA | NA | 2 | 0.32 | NA | NA | |
| total N=616 · valid N=614 · x̄=2.27 · σ=0.72 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| No |
|
125 | 20.29 | 20.29 | 20.29 | |
| Yes |
|
491 | 79.71 | 79.71 | 100.00 | |
| NA | NA | 0 | 0.00 | NA | NA | |
| total N=616 · valid N=616 · x̄=1.80 · σ=0.40 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| Neutral |
|
118 | 19.16 | 22.26 | 22.26 | |
| Somewhat affordable |
|
181 | 29.38 | 34.15 | 56.42 | |
| Somewhat unaffordable |
|
52 | 8.44 | 9.81 | 66.23 | |
| Very affordable |
|
158 | 25.65 | 29.81 | 96.04 | |
| Very unaffordable |
|
21 | 3.41 | 3.96 | 100.00 | |
| NA | NA | 86 | 13.96 | NA | NA | |
| total N=616 · valid N=530 · x̄=2.59 · σ=1.23 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| Neither satisfied nor unsatsified |
|
97 | 15.75 | 16.67 | 16.67 | |
| Somewhat satisfied |
|
178 | 28.90 | 30.58 | 47.25 | |
| Somewhat unsatisfied |
|
77 | 12.50 | 13.23 | 60.48 | |
| Very satisfied |
|
166 | 26.95 | 28.52 | 89.00 | |
| Very unsatisfied |
|
64 | 10.39 | 11.00 | 100.00 | |
| NA | NA | 34 | 5.52 | NA | NA | |
| total N=616 · valid N=582 · x̄=2.87 · σ=1.30 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| Employed full time |
|
261 | 42.37 | 42.79 | 42.79 | |
| Employed full time,Employed part time |
|
1 | 0.16 | 0.16 | 42.95 | |
| Employed full time,Not working (retired, disabled, stay-at-home parent, etc) |
|
1 | 0.16 | 0.16 | 43.11 | |
| Employed full time,Student |
|
4 | 0.65 | 0.66 | 43.77 | |
| Employed part time |
|
84 | 13.64 | 13.77 | 57.54 | |
| Employed part time,Not working (retired, disabled, stay-at-home parent, etc) |
|
2 | 0.32 | 0.33 | 57.87 | |
| Employed part time,Student |
|
10 | 1.62 | 1.64 | 59.51 | |
| Not working (retired, disabled, stay-at-home parent, etc) |
|
218 | 35.39 | 35.74 | 95.25 | |
| Student |
|
27 | 4.38 | 4.43 | 99.67 | |
| Student,Not working (retired, disabled, stay-at-home parent, etc) |
|
2 | 0.32 | 0.33 | 100.00 | |
| NA | NA | 6 | 0.97 | NA | NA | |
| total N=616 · valid N=610 · x̄=4.58 · σ=3.28 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| Female |
|
310 | 50.32 | 50.32 | 50.32 | |
| Male |
|
306 | 49.68 | 49.68 | 100.00 | |
| NA | NA | 0 | 0.00 | NA | NA | |
| total N=616 · valid N=616 · x̄=1.50 · σ=0.50 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| 18 |
|
8 | 1.30 | 1.30 | 1.30 | |
| 19 |
|
10 | 1.62 | 1.62 | 2.92 | |
| 20 |
|
10 | 1.62 | 1.62 | 4.55 | |
| 21 |
|
12 | 1.95 | 1.95 | 6.49 | |
| 22 |
|
17 | 2.76 | 2.76 | 9.25 | |
| 23 |
|
25 | 4.06 | 4.06 | 13.31 | |
| 24 |
|
15 | 2.44 | 2.44 | 15.75 | |
| 25 |
|
19 | 3.08 | 3.08 | 18.83 | |
| 26 |
|
12 | 1.95 | 1.95 | 20.78 | |
| 27 |
|
16 | 2.60 | 2.60 | 23.38 | |
| 28 |
|
12 | 1.95 | 1.95 | 25.32 | |
| 29 |
|
14 | 2.27 | 2.27 | 27.60 | |
| 30 |
|
13 | 2.11 | 2.11 | 29.71 | |
| 31 |
|
3 | 0.49 | 0.49 | 30.19 | |
| 32 |
|
12 | 1.95 | 1.95 | 32.14 | |
| 33 |
|
13 | 2.11 | 2.11 | 34.25 | |
| 34 |
|
18 | 2.92 | 2.92 | 37.18 | |
| 35 |
|
11 | 1.79 | 1.79 | 38.96 | |
| 36 |
|
12 | 1.95 | 1.95 | 40.91 | |
| 37 |
|
9 | 1.46 | 1.46 | 42.37 | |
| 38 |
|
5 | 0.81 | 0.81 | 43.18 | |
| 39 |
|
16 | 2.60 | 2.60 | 45.78 | |
| 40 |
|
13 | 2.11 | 2.11 | 47.89 | |
| 41 |
|
8 | 1.30 | 1.30 | 49.19 | |
| 42 |
|
9 | 1.46 | 1.46 | 50.65 | |
| 43 |
|
5 | 0.81 | 0.81 | 51.46 | |
| 44 |
|
10 | 1.62 | 1.62 | 53.08 | |
| 45 |
|
12 | 1.95 | 1.95 | 55.03 | |
| 46 |
|
10 | 1.62 | 1.62 | 56.66 | |
| 47 |
|
7 | 1.14 | 1.14 | 57.79 | |
| 48 |
|
7 | 1.14 | 1.14 | 58.93 | |
| 49 |
|
10 | 1.62 | 1.62 | 60.55 | |
| 50 |
|
15 | 2.44 | 2.44 | 62.99 | |
| 51 |
|
5 | 0.81 | 0.81 | 63.80 | |
| 52 |
|
11 | 1.79 | 1.79 | 65.58 | |
| 53 |
|
13 | 2.11 | 2.11 | 67.69 | |
| 54 |
|
18 | 2.92 | 2.92 | 70.62 | |
| 55 |
|
10 | 1.62 | 1.62 | 72.24 | |
| 56 |
|
10 | 1.62 | 1.62 | 73.86 | |
| 57 |
|
8 | 1.30 | 1.30 | 75.16 | |
| 58 |
|
9 | 1.46 | 1.46 | 76.62 | |
| 59 |
|
6 | 0.97 | 0.97 | 77.60 | |
| 60 |
|
13 | 2.11 | 2.11 | 79.71 | |
| 61 |
|
5 | 0.81 | 0.81 | 80.52 | |
| 62 |
|
10 | 1.62 | 1.62 | 82.14 | |
| 63 |
|
8 | 1.30 | 1.30 | 83.44 | |
| 64 |
|
8 | 1.30 | 1.30 | 84.74 | |
| 65 |
|
8 | 1.30 | 1.30 | 86.04 | |
| 66 |
|
12 | 1.95 | 1.95 | 87.99 | |
| 67 |
|
10 | 1.62 | 1.62 | 89.61 | |
| 68 |
|
6 | 0.97 | 0.97 | 90.58 | |
| 69 |
|
3 | 0.49 | 0.49 | 91.07 | |
| 70 |
|
5 | 0.81 | 0.81 | 91.88 | |
| 71 |
|
4 | 0.65 | 0.65 | 92.53 | |
| 72 |
|
3 | 0.49 | 0.49 | 93.02 | |
| 73 |
|
4 | 0.65 | 0.65 | 93.67 | |
| 74 |
|
9 | 1.46 | 1.46 | 95.13 | |
| 75 |
|
5 | 0.81 | 0.81 | 95.94 | |
| 76 |
|
6 | 0.97 | 0.97 | 96.92 | |
| 77 |
|
4 | 0.65 | 0.65 | 97.56 | |
| 78 |
|
2 | 0.32 | 0.32 | 97.89 | |
| 79 |
|
1 | 0.16 | 0.16 | 98.05 | |
| 80 |
|
4 | 0.65 | 0.65 | 98.70 | |
| 81 |
|
1 | 0.16 | 0.16 | 98.86 | |
| 82 |
|
1 | 0.16 | 0.16 | 99.03 | |
| 83 |
|
2 | 0.32 | 0.32 | 99.35 | |
| 84 |
|
1 | 0.16 | 0.16 | 99.51 | |
| 90 |
|
2 | 0.32 | 0.32 | 99.84 | |
| 91 |
|
1 | 0.16 | 0.16 | 100.00 | |
| NA | NA | 0 | 0.00 | NA | NA | |
| total N=616 · valid N=616 · x̄=44.07 · σ=17.38 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| American Indian |
|
6 | 0.97 | 0.97 | 0.97 | |
| Asian |
|
67 | 10.88 | 10.88 | 11.85 | |
| Black |
|
194 | 31.49 | 31.49 | 43.34 | |
| Other |
|
25 | 4.06 | 4.06 | 47.40 | |
| Pacific Islander |
|
1 | 0.16 | 0.16 | 47.56 | |
| White |
|
323 | 52.44 | 52.44 | 100.00 | |
| NA | NA | 0 | 0.00 | NA | NA | |
| total N=616 · valid N=616 · x̄=4.49 · σ=1.64 | ||||||
| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| Midwest |
|
136 | 22.08 | 22.08 | 22.08 | |
| Northeast |
|
109 | 17.69 | 17.69 | 39.77 | |
| South |
|
231 | 37.50 | 37.50 | 77.27 | |
| West |
|
140 | 22.73 | 22.73 | 100.00 | |
| NA | NA | 0 | 0.00 | NA | NA | |
| total N=616 · valid N=616 · x̄=2.61 · σ=1.07 | ||||||
tnbs_raw$employment<- dplyr::case_when(
grepl("Employed full time", tnbs_raw$Q25) ~ "Employed full time",
grepl("Employed part time", tnbs_raw$Q25) ~ "Employed part time",
grepl("Student", tnbs_raw$Q25) ~ "Student",
grepl("Not working", tnbs_raw$Q25) ~ "Not working",
is.na(tnbs_raw$Q25) ~ NA_character_,
TRUE ~ "Other"
)
sjmisc::frq(tnbs_raw$employment)## x <character>
## # total N=616 valid N=610 mean=2.01 sd=0.99
##
## Value | N | Raw % | Valid % | Cum. %
## ---------------------------------------------------
## Employed full time | 267 | 43.34 | 43.77 | 43.77
## Employed part time | 96 | 15.58 | 15.74 | 59.51
## Not working | 218 | 35.39 | 35.74 | 95.25
## Student | 29 | 4.71 | 4.75 | 100.00
## <NA> | 6 | 0.97 | <NA> | <NA>tnbs_raw$age<- dplyr::case_when(
tnbs_raw$`_os_api_age` >= 18 & tnbs_raw$`_os_api_age` <= 24 ~ "18-24",
tnbs_raw$`_os_api_age` >= 25 & tnbs_raw$`_os_api_age` <= 34 ~ "25-34",
tnbs_raw$`_os_api_age` >= 35 & tnbs_raw$`_os_api_age` <= 44 ~ "35-44",
tnbs_raw$`_os_api_age` >= 45 & tnbs_raw$`_os_api_age` <= 54 ~ "45-54",
tnbs_raw$`_os_api_age` >= 55 & tnbs_raw$`_os_api_age` <= 64 ~ "55-64",
tnbs_raw$`_os_api_age` >= 65 ~ "65+",
TRUE ~ "Not provided"
)
sjmisc::frq(tnbs_raw$age)## x <character>
## # total N=616 valid N=616 mean=3.39 sd=1.68
##
## Value | N | Raw % | Valid % | Cum. %
## --------------------------------------
## 18-24 | 97 | 15.75 | 15.75 | 15.75
## 25-34 | 132 | 21.43 | 21.43 | 37.18
## 35-44 | 98 | 15.91 | 15.91 | 53.08
## 45-54 | 108 | 17.53 | 17.53 | 70.62
## 55-64 | 87 | 14.12 | 14.12 | 84.74
## 65+ | 94 | 15.26 | 15.26 | 100.00
## <NA> | 0 | 0.00 | <NA> | <NA>tnbs_raw$language <- dplyr::case_when(
grepl("I prefer not to answer", tnbs_raw$Q38, ignore.case = TRUE) ~ "Prefer not to answer",
grepl("Spanish", tnbs_raw$Q38, ignore.case = TRUE) ~ "Spanish",
grepl("English", tnbs_raw$Q38, ignore.case = TRUE) ~ "English",
grepl("Others", tnbs_raw$Q38, ignore.case = TRUE) ~ "Other languages",
is.na(tnbs_raw$Q38) ~ "NA"
)
sjmisc::frq(tnbs_raw$language)## x <character>
## # total N=616 valid N=616 mean=1.34 sd=1.08
##
## Value | N | Raw % | Valid % | Cum. %
## -----------------------------------------------------
## English | 552 | 89.61 | 89.61 | 89.61
## NA | 12 | 1.95 | 1.95 | 91.56
## Other languages | 2 | 0.32 | 0.32 | 91.88
## Prefer not to answer | 4 | 0.65 | 0.65 | 92.53
## Spanish | 46 | 7.47 | 7.47 | 100.00
## <NA> | 0 | 0.00 | <NA> | <NA>sjmisc::frq(tnbs_raw$Q12)## x <character>
## # total N=616 valid N=570 mean=2.10 sd=0.66
##
## Value
## ------------------------------------------------------------------------------------------------------------------------------
## Car-less Traveler: I rarely or never use a car and mostly rely on walking, biking, or public transportation.
## Daily Driver: I use a car for nearly every trip.
## Multimodal Traveler: I use a mix of transportation modes—car for some trips, and walk, bike, rideshare, or transit for others.
## <NA>
##
## N | Raw % | Valid % | Cum. %
## ------------------------------
## 97 | 15.75 | 17.02 | 17.02
## 318 | 51.62 | 55.79 | 72.81
## 155 | 25.16 | 27.19 | 100.00
## 46 | 7.47 | <NA> | <NA>tnbs_raw$traveler_type <- dplyr::case_when(
grepl("Car-less Traveler", tnbs_raw$Q12, ignore.case = TRUE) ~ "Car-less",
grepl("Daily Driver", tnbs_raw$Q12, ignore.case = TRUE) ~ "Daily driver",
grepl("Multimodal Traveler", tnbs_raw$Q12, ignore.case = TRUE) ~ "Multimodal travelr",
is.na(tnbs_raw$Q12) ~ NA_character_
)
sjmisc::frq(tnbs_raw$traveler_type)## x <character>
## # total N=616 valid N=570 mean=2.10 sd=0.66
##
## Value | N | Raw % | Valid % | Cum. %
## ---------------------------------------------------
## Car-less | 97 | 15.75 | 17.02 | 17.02
## Daily driver | 318 | 51.62 | 55.79 | 72.81
## Multimodal travelr | 155 | 25.16 | 27.19 | 100.00
## <NA> | 46 | 7.47 | <NA> | <NA>sjmisc::frq(tnbs_raw$Q24)## x <character>
## # total N=616 valid N=616 mean=1.80 sd=0.40
##
## Value | N | Raw % | Valid % | Cum. %
## --------------------------------------
## No | 125 | 20.29 | 20.29 | 20.29
## Yes | 491 | 79.71 | 79.71 | 100.00
## <NA> | 0 | 0.00 | <NA> | <NA>sjmisc::frq(tnbs_raw$Q32)## x <character>
## # total N=616 valid N=615 mean=2.74 sd=0.67
##
## Value | N | Raw % | Valid % | Cum. %
## -----------------------------------------------------
## No | 79 | 12.82 | 12.85 | 12.85
## Prefer not to answer | 4 | 0.65 | 0.65 | 13.50
## Yes | 532 | 86.36 | 86.50 | 100.00
## <NA> | 1 | 0.16 | <NA> | <NA>sjmisc::frq(tnbs_raw$Q41)## x <character>
## # total N=616 valid N=581 mean=11.24 sd=5.25
##
## Value | N | Raw % | Valid % | Cum. %
## --------------------------------------------------------------------
## ,3 | 1 | 0.16 | 0.17 | 0.17
## .. | 1 | 0.16 | 0.17 | 0.34
## .2 | 1 | 0.16 | 0.17 | 0.52
## 0 | 8 | 1.30 | 1.38 | 1.89
## 1 | 129 | 20.94 | 22.20 | 24.10
## 1@ | 1 | 0.16 | 0.17 | 24.27
## 100000 | 1 | 0.16 | 0.17 | 24.44
## 12 | 1 | 0.16 | 0.17 | 24.61
## 2 | 163 | 26.46 | 28.06 | 52.67
## 200 | 1 | 0.16 | 0.17 | 52.84
## 2000 | 1 | 0.16 | 0.17 | 53.01
## 2100 | 1 | 0.16 | 0.17 | 53.18
## 3 | 104 | 16.88 | 17.90 | 71.08
## 3 of my children and that it is all | 1 | 0.16 | 0.17 | 71.26
## 3500 | 1 | 0.16 | 0.17 | 71.43
## 4 | 87 | 14.12 | 14.97 | 86.40
## 5 | 38 | 6.17 | 6.54 | 92.94
## 6 | 18 | 2.92 | 3.10 | 96.04
## 7 | 2 | 0.32 | 0.34 | 96.39
## 8 | 4 | 0.65 | 0.69 | 97.07
## 9 | 1 | 0.16 | 0.17 | 97.25
## E | 1 | 0.16 | 0.17 | 97.42
## Housing | 1 | 0.16 | 0.17 | 97.59
## Husband | 1 | 0.16 | 0.17 | 97.76
## I don’t know | 1 | 0.16 | 0.17 | 97.93
## None | 4 | 0.65 | 0.69 | 98.62
## Not for sure | 1 | 0.16 | 0.17 | 98.80
## Roomate | 1 | 0.16 | 0.17 | 98.97
## T | 1 | 0.16 | 0.17 | 99.14
## Thank you | 1 | 0.16 | 0.17 | 99.31
## Timing | 1 | 0.16 | 0.17 | 99.48
## Unknown | 1 | 0.16 | 0.17 | 99.66
## w | 1 | 0.16 | 0.17 | 99.83
## Wert | 1 | 0.16 | 0.17 | 100.00
## <NA> | 35 | 5.68 | <NA> | <NA># Convert Q41 to numeric, non-numeric entries will become NA automatically
tnbs_raw$household_size <- suppressWarnings(as.numeric(tnbs_raw$Q41))
unique(tnbs_raw$Q41[is.na(tnbs_raw$household_size)])## [1] NA ",3"
## [3] ".." "Unknown"
## [5] "Not for sure" "w"
## [7] "None" "T"
## [9] "Roomate" "Housing"
## [11] "Thank you" "I don’t know"
## [13] "Wert" "1@"
## [15] "Husband" "3 of my children and that it is all"
## [17] "Timing" "E"sjmisc::frq(tnbs_raw$household_size)## x <numeric>
## # total N=616 valid N=561 mean=194.80 sd=4225.65
##
## Value | N | Raw % | Valid % | Cum. %
## -----------------------------------------
## 0.00 | 8 | 1.30 | 1.43 | 1.43
## 0.20 | 1 | 0.16 | 0.18 | 1.60
## 1.00 | 129 | 20.94 | 22.99 | 24.60
## 2.00 | 163 | 26.46 | 29.06 | 53.65
## 3.00 | 104 | 16.88 | 18.54 | 72.19
## 4.00 | 87 | 14.12 | 15.51 | 87.70
## 5.00 | 38 | 6.17 | 6.77 | 94.47
## 6.00 | 18 | 2.92 | 3.21 | 97.68
## 7.00 | 2 | 0.32 | 0.36 | 98.04
## 8.00 | 4 | 0.65 | 0.71 | 98.75
## 9.00 | 1 | 0.16 | 0.18 | 98.93
## 12.00 | 1 | 0.16 | 0.18 | 99.11
## 200.00 | 1 | 0.16 | 0.18 | 99.29
## 2000.00 | 1 | 0.16 | 0.18 | 99.47
## 2100.00 | 1 | 0.16 | 0.18 | 99.64
## 3500.00 | 1 | 0.16 | 0.18 | 99.82
## 1.00e+05 | 1 | 0.16 | 0.18 | 100.00
## <NA> | 55 | 8.93 | <NA> | <NA># Recode: Treat 0 and 0.2 as 1
tnbs_raw$household_size[tnbs_raw$household_size %in% c(0, 0.2)] <- 1
# Recode: Set entries > 12 as NA
tnbs_raw$household_size[tnbs_raw$household_size > 12] <- NA
sjmisc::frq(tnbs_raw$household_size)## x <numeric>
## # total N=616 valid N=556 mean=2.68 sd=1.53
##
## Value | N | Raw % | Valid % | Cum. %
## --------------------------------------
## 1 | 138 | 22.40 | 24.82 | 24.82
## 2 | 163 | 26.46 | 29.32 | 54.14
## 3 | 104 | 16.88 | 18.71 | 72.84
## 4 | 87 | 14.12 | 15.65 | 88.49
## 5 | 38 | 6.17 | 6.83 | 95.32
## 6 | 18 | 2.92 | 3.24 | 98.56
## 7 | 2 | 0.32 | 0.36 | 98.92
## 8 | 4 | 0.65 | 0.72 | 99.64
## 9 | 1 | 0.16 | 0.18 | 99.82
## 12 | 1 | 0.16 | 0.18 | 100.00
## <NA> | 60 | 9.74 | <NA> | <NA># Create household size categories
tnbs_raw$hh_size_cat <- dplyr::case_when(
tnbs_raw$household_size == 1 ~ "1",
tnbs_raw$household_size == 2 ~ "2",
tnbs_raw$household_size == 3 ~ "3",
tnbs_raw$household_size == 4 ~ "4",
tnbs_raw$household_size == 5 ~ "5",
tnbs_raw$household_size >= 6 ~ "6+",
is.na(tnbs_raw$household_size) ~ NA_character_
)
# View frequency table
sjmisc::frq(tnbs_raw$hh_size_cat)## x <character>
## # total N=616 valid N=556 mean=2.64 sd=1.42
##
## Value | N | Raw % | Valid % | Cum. %
## --------------------------------------
## 1 | 138 | 22.40 | 24.82 | 24.82
## 2 | 163 | 26.46 | 29.32 | 54.14
## 3 | 104 | 16.88 | 18.71 | 72.84
## 4 | 87 | 14.12 | 15.65 | 88.49
## 5 | 38 | 6.17 | 6.83 | 95.32
## 6+ | 26 | 4.22 | 4.68 | 100.00
## <NA> | 60 | 9.74 | <NA> | <NA>sjmisc::frq(tnbs_raw$Q42_1)## x <character>
## # total N=616 valid N=556 mean=2.68 sd=0.81
##
## Value | N | Raw % | Valid % | Cum. %
## ---------------------------------------------
## ... | 1 | 0.16 | 0.18 | 0.18
## 0 | 240 | 38.96 | 43.17 | 43.35
## 1 | 273 | 44.32 | 49.10 | 92.45
## 2 | 36 | 5.84 | 6.47 | 98.92
## 4 | 1 | 0.16 | 0.18 | 99.10
## 48 | 1 | 0.16 | 0.18 | 99.28
## Not for sure | 1 | 0.16 | 0.18 | 99.46
## Q | 1 | 0.16 | 0.18 | 99.64
## T | 1 | 0.16 | 0.18 | 99.82
## Unknown | 1 | 0.16 | 0.18 | 100.00
## <NA> | 60 | 9.74 | <NA> | <NA>sjmisc::frq(tnbs_raw$Q42_3)## x <character>
## # total N=616 valid N=539 mean=3.16 sd=1.63
##
## Value | N | Raw % | Valid % | Cum. %
## --------------------------------------
## .. | 1 | 0.16 | 0.19 | 0.19
## 0 | 319 | 51.79 | 59.18 | 59.37
## 0p | 1 | 0.16 | 0.19 | 59.55
## 1 | 108 | 17.53 | 20.04 | 79.59
## 2 | 69 | 11.20 | 12.80 | 92.39
## 3 | 26 | 4.22 | 4.82 | 97.22
## 4 | 8 | 1.30 | 1.48 | 98.70
## 5 | 2 | 0.32 | 0.37 | 99.07
## 6 | 1 | 0.16 | 0.19 | 99.26
## I | 1 | 0.16 | 0.19 | 99.44
## No | 1 | 0.16 | 0.19 | 99.63
## T | 1 | 0.16 | 0.19 | 99.81
## Yes | 1 | 0.16 | 0.19 | 100.00
## <NA> | 77 | 12.50 | <NA> | <NA>sjmisc::frq(tnbs_raw$Q42_4)## x <character>
## # total N=616 valid N=529 mean=3.66 sd=2.19
##
## Value | N | Raw % | Valid % | Cum. %
## --------------------------------------
## ... | 1 | 0.16 | 0.19 | 0.19
## 0 | 238 | 38.64 | 44.99 | 45.18
## 1 | 138 | 22.40 | 26.09 | 71.27
## 18 | 1 | 0.16 | 0.19 | 71.46
## 19 | 1 | 0.16 | 0.19 | 71.64
## 2 | 68 | 11.04 | 12.85 | 84.50
## 3 | 58 | 9.42 | 10.96 | 95.46
## 36 | 1 | 0.16 | 0.19 | 95.65
## 4 | 17 | 2.76 | 3.21 | 98.87
## 5 | 4 | 0.65 | 0.76 | 99.62
## No | 1 | 0.16 | 0.19 | 99.81
## T | 1 | 0.16 | 0.19 | 100.00
## <NA> | 87 | 14.12 | <NA> | <NA># Coerce to numeric and remove invalid entries
clean_numeric <- function(x) {
suppressWarnings(as.numeric(trimws(x)))
}
tnbs_raw$spouse <- clean_numeric(tnbs_raw$Q42_1)
tnbs_raw$children <- clean_numeric(tnbs_raw$Q42_3)
tnbs_raw$adults <- clean_numeric(tnbs_raw$Q42_4)#Replace Extreme Values and NAs
tnbs_raw <- tnbs_raw |>
dplyr::mutate(
spouse = ifelse(spouse > 2 | spouse < 0, NA, spouse),
children = ifelse(children > 8 | children < 0, NA, children),
adults = ifelse(adults > 12 | adults < 0, NA, adults)
)sjmisc::frq(tnbs_raw$children)## x <numeric>
## # total N=616 valid N=533 mean=0.70 sd=1.04
##
## Value | N | Raw % | Valid % | Cum. %
## --------------------------------------
## 0 | 319 | 51.79 | 59.85 | 59.85
## 1 | 108 | 17.53 | 20.26 | 80.11
## 2 | 69 | 11.20 | 12.95 | 93.06
## 3 | 26 | 4.22 | 4.88 | 97.94
## 4 | 8 | 1.30 | 1.50 | 99.44
## 5 | 2 | 0.32 | 0.38 | 99.81
## 6 | 1 | 0.16 | 0.19 | 100.00
## <NA> | 83 | 13.47 | <NA> | <NA># Filter rows where children is NA
tnbs_raw %>%
filter(is.na(children)) %>%
count(hh_size_cat)## # A tibble: 6 × 2
## hh_size_cat n
## <chr> <int>
## 1 1 13
## 2 2 28
## 3 3 14
## 4 4 5
## 5 5 2
## 6 <NA> 21tnbs_raw <- tnbs_raw %>%
mutate(child_cat = case_when(
is.na(children) ~ "NA",
children == 0 ~ "0",
children == 1 ~ "1",
children >= 2 ~ "2+"
))
sjmisc::frq(tnbs_raw$child_cat)## x <character>
## # total N=616 valid N=616 mean=1.92 sd=1.11
##
## Value | N | Raw % | Valid % | Cum. %
## --------------------------------------
## 0 | 319 | 51.79 | 51.79 | 51.79
## 1 | 108 | 17.53 | 17.53 | 69.32
## 2+ | 106 | 17.21 | 17.21 | 86.53
## NA | 83 | 13.47 | 13.47 | 100.00
## <NA> | 0 | 0.00 | <NA> | <NA>#Derive Household Type
tnbs_raw <- tnbs_raw |>
dplyr::mutate(
HH_type = dplyr::case_when(
children > 0 & is.na(spouse) ~ "Single parent household",
children > 0 & spouse > 0 ~ "HH with children",
children == 0 ~ "HH without children",
TRUE ~ "HH without children and spouse"
)
)
# View frequency table
sjmisc::frq(tnbs_raw$HH_type)## x <character>
## # total N=616 valid N=616 mean=1.99 sd=0.74
##
## Value | N | Raw % | Valid % | Cum. %
## ---------------------------------------------------------------
## HH with children | 160 | 25.97 | 25.97 | 25.97
## HH without children | 319 | 51.79 | 51.79 | 77.76
## HH without children and spouse | 123 | 19.97 | 19.97 | 97.73
## Single parent household | 14 | 2.27 | 2.27 | 100.00
## <NA> | 0 | 0.00 | <NA> | <NA>#sjmisc::frq(tnbs_raw$Q43_1)
# Clean and convert Q43_1 to numeric (invalid strings become NA)
tnbs_raw$income_raw <- suppressWarnings(as.numeric(tnbs_raw$Q43_1))
# Step 2: Create income category
tnbs_raw <- tnbs_raw %>%
mutate(income_cat = case_when(
is.na(income_raw) ~ "NA",
income_raw < 1000 ~ "<$1,000",
income_raw >= 1000 & income_raw < 3000 ~ "$1,000–2,999",
income_raw >= 3000 & income_raw < 5000 ~ "$3,000–4,999",
income_raw >= 5000 & income_raw < 8000 ~ "$5,000–7,999",
income_raw >= 8000 & income_raw < 12000 ~ "$8,000–11,999",
income_raw >= 12000 ~ "$12,000+"
))
sjmisc::frq(tnbs_raw$income_cat)## x <character>
## # total N=616 valid N=616 mean=3.63 sd=1.56
##
## Value | N | Raw % | Valid % | Cum. %
## ----------------------------------------------
## <$1,000 | 38 | 6.17 | 6.17 | 6.17
## $1,000–2,999 | 155 | 25.16 | 25.16 | 31.33
## $12,000+ | 110 | 17.86 | 17.86 | 49.19
## $3,000–4,999 | 106 | 17.21 | 17.21 | 66.40
## $5,000–7,999 | 106 | 17.21 | 17.21 | 83.60
## $8,000–11,999 | 100 | 16.23 | 16.23 | 99.84
## NA | 1 | 0.16 | 0.16 | 100.00
## <NA> | 0 | 0.00 | <NA> | <NA># Step 2: Convert monthly income to annual income
tnbs_raw$annual_income <- tnbs_raw$income_raw * 12
tnbs_raw <- tnbs_raw %>%
mutate(annual_income_cat = case_when(
is.na(annual_income) ~ "$150,000+",
annual_income < 15000 ~ "<$15,000",
annual_income >= 15000 & annual_income < 30000 ~ "$15,000–29,999",
annual_income >= 30000 & annual_income < 50000 ~ "$30,000–49,999",
annual_income >= 50000 & annual_income < 75000 ~ "$50,000–74,999",
annual_income >= 75000 & annual_income < 100000 ~ "$75,000–99,999",
annual_income >= 100000 & annual_income < 150000 ~ "$100,000–149,999",
annual_income >= 150000 ~ "$150,000+"
))
sjmisc::frq(tnbs_raw$annual_income_cat)## x <character>
## # total N=616 valid N=616 mean=4.01 sd=1.86
##
## Value | N | Raw % | Valid % | Cum. %
## -------------------------------------------------
## <$15,000 | 65 | 10.55 | 10.55 | 10.55
## $100,000–149,999 | 94 | 15.26 | 15.26 | 25.81
## $15,000–29,999 | 95 | 15.42 | 15.42 | 41.23
## $150,000+ | 107 | 17.37 | 17.37 | 58.60
## $30,000–49,999 | 92 | 14.94 | 14.94 | 73.54
## $50,000–74,999 | 98 | 15.91 | 15.91 | 89.45
## $75,000–99,999 | 65 | 10.55 | 10.55 | 100.00
## <NA> | 0 | 0.00 | <NA> | <NA>sjmisc::frq(tnbs_raw$Q44)## x <character>
## # total N=616 valid N=615 mean=3.05 sd=1.54
##
## Value | N | Raw % | Valid % | Cum. %
## ---------------------------------------------------
## 1 to 3 years | 137 | 22.24 | 22.28 | 22.28
## 3-5 years | 127 | 20.62 | 20.65 | 42.93
## 5-10 years | 102 | 16.56 | 16.59 | 59.51
## Less than 1 year | 69 | 11.20 | 11.22 | 70.73
## More than 10 years | 180 | 29.22 | 29.27 | 100.00
## <NA> | 1 | 0.16 | <NA> | <NA>sjmisc::frq(tnbs_raw$Q49)## x <character>
## # total N=616 valid N=613 mean=2.32 sd=0.93
##
## Value | N | Raw % | Valid % | Cum. %
## ------------------------------------------
## 0 | 104 | 16.88 | 16.97 | 16.97
## 1 | 285 | 46.27 | 46.49 | 63.46
## 2 | 161 | 26.14 | 26.26 | 89.72
## 3 | 47 | 7.63 | 7.67 | 97.39
## 4 or more | 16 | 2.60 | 2.61 | 100.00
## <NA> | 3 | 0.49 | <NA> | <NA>