TAS Descriptive Statistics
Sher May
2024-06-14
TAS Descriptive statistics
We will be going through
Step 1: Loading Packages
library(tidyverse)
library(readxl)Step 2: Import the data
TAS_original_data <- read_excel("C:/ZZ Sher May/TAS_original_data.xlsx")Step 3: Preview the data
view(TAS_original_data)head(TAS_original_data)## # A tibble: 6 × 75
## TAS TAS05 TAS15 ER30001 ER30002 ER32000 ER32006 ER33801 ER33802 ER33803
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 NA 1 4 39 2 2 289 3 60
## 2 1 NA 1 4 41 2 2 1157 3 30
## 3 1 1 NA 4 180 2 3 771 2 22
## 4 1 1 NA 5 32 2 2 624 3 30
## 5 1 NA 1 5 33 1 2 1504 3 30
## 6 1 1 NA 6 34 1 2 1202 51 30
## # ℹ 65 more variables: TA050001 <dbl>, TA050002 <dbl>, TA050003 <dbl>,
## # TA050004 <dbl>, TA050044 <dbl>, TA050047 <dbl>, TA050050 <dbl>,
## # TA050065 <dbl>, TA050066 <dbl>, TA050067 <dbl>, TA050070 <dbl>,
## # TA050071 <dbl>, TA050127 <dbl>, TA050128 <dbl>, TA050129 <dbl>,
## # TA050130 <dbl>, TA050573 <dbl>, TA050574 <dbl>, TA050575 <dbl>,
## # TA050594 <dbl>, TA050595 <dbl>, TA050639 <dbl>, TA050663 <dbl>,
## # TA050664 <dbl>, TA050665 <dbl>, TA050670 <dbl>, TA050675 <dbl>, …Step 4: Descriptive statistics
2005
TA050044: B5A Responsibility for own
Count
TAS_original_data %>% count(TA050044)## # A tibble: 6 × 2
## TA050044 n
## <dbl> <int>
## 1 1 31
## 2 2 124
## 3 3 155
## 4 4 238
## 5 5 197
## 6 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050044))
Mean, sd, range
TAS_original_data %>% select(TA050044) %>% drop_na() %>% summarize(mean_B5A = mean(TA050044), sd_B5A = sd(TA050044), range_B5A = range(TA050044))## # A tibble: 2 × 3
## mean_B5A sd_B5A range_B5A
## <dbl> <dbl> <dbl>
## 1 3.60 1.16 1
## 2 3.60 1.16 5TA050047: B5D Responsibility for own money
Count
TAS_original_data %>% count(TA050047)## # A tibble: 6 × 2
## TA050047 n
## <dbl> <int>
## 1 1 18
## 2 2 19
## 3 3 70
## 4 4 184
## 5 5 454
## 6 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050047))
Mean, sd, range
TAS_original_data %>% select(TA050047) %>% drop_na() %>% summarize(mean_B5D = mean(TA050047), sd_B5D = sd(TA050047), range_B5D = range(TA050047))## # A tibble: 2 × 3
## mean_B5D sd_B5D range_B5D
## <dbl> <dbl> <dbl>
## 1 4.39 0.933 1
## 2 4.39 0.933 5TA050050: B6C Scale on managing money
Count
TAS_original_data %>% count(TA050050)## # A tibble: 8 × 2
## TA050050 n
## <dbl> <int>
## 1 1 15
## 2 2 26
## 3 3 39
## 4 4 92
## 5 5 204
## 6 6 170
## 7 7 199
## 8 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050050))
Mean, sd, range
TAS_original_data %>% select(TA050050) %>% drop_na() %>% summarize(mean_B6C = mean(TA050050), sd_B6C = sd(TA050050), range_B6C = range(TA050050))## # A tibble: 2 × 3
## mean_B6C sd_B6C range_B6C
## <dbl> <dbl> <dbl>
## 1 5.35 1.47 1
## 2 5.35 1.47 7TA050065: C2D Scale on worry about money
Count
TAS_original_data %>% count(TA050065)## # A tibble: 8 × 2
## TA050065 n
## <dbl> <int>
## 1 1 114
## 2 2 115
## 3 3 122
## 4 4 130
## 5 5 113
## 6 6 70
## 7 7 81
## 8 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050065))
Mean, sd, range
TAS_original_data %>% select(TA050065) %>% drop_na() %>% summarize(mean_C2D = mean(TA050065), sd_C2D = sd(TA050065), range_C2D = range(TA050065))## # A tibble: 2 × 3
## mean_C2D sd_C2D range_C2D
## <dbl> <dbl> <dbl>
## 1 3.73 1.90 1
## 2 3.73 1.90 7TA050066: C2E Scale on worry about future job
Count
TAS_original_data %>% count(TA050066)## # A tibble: 8 × 2
## TA050066 n
## <dbl> <int>
## 1 1 144
## 2 2 126
## 3 3 115
## 4 4 87
## 5 5 109
## 6 6 85
## 7 7 79
## 8 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050066))
Mean, sd, range
TAS_original_data %>% select(TA050066) %>% drop_na() %>% summarize(mean_C2E = mean(TA050066), sd_C2E = sd(TA050066), range_C2E = range(TA050066))## # A tibble: 2 × 3
## mean_C2E sd_C2E range_C2E
## <dbl> <dbl> <dbl>
## 1 3.62 2.00 1
## 2 3.62 2.00 7TA050067: C2F Scale on discouragement
Count
TAS_original_data %>% count(TA050067)## # A tibble: 9 × 2
## TA050067 n
## <dbl> <int>
## 1 1 191
## 2 2 153
## 3 3 124
## 4 4 100
## 5 5 80
## 6 6 50
## 7 7 45
## 8 8 2
## 9 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050067))
Mean, sd, range
TAS_original_data %>% select(TA050067) %>% drop_na() %>% filter(TA050067 <= 7) %>% summarize(mean_C2F = mean(TA050067), sd_C2F = sd(TA050067), range_C2F = range(TA050067))## # A tibble: 2 × 3
## mean_C2F sd_C2F range_C2F
## <dbl> <dbl> <dbl>
## 1 3.07 1.84 1
## 2 3.07 1.84 7TA050071: D2D3 Year Divorced
Count
TAS_original_data %>% count(TA050071)## # A tibble: 3 × 2
## TA050071 n
## <dbl> <int>
## 1 0 744
## 2 2005 1
## 3 NA 1641Mean, sd, range
TAS_original_data %>% select(TA050071) %>% drop_na() %>% filter(TA050071 > 0) %>% summarize(mean_D2D3 = mean(TA050071))## # A tibble: 1 × 1
## mean_D2D3
## <dbl>
## 1 2005TA050127: E1 Employment status 1st mention
Count
TAS_original_data %>% count(TA050127)## # A tibble: 8 × 2
## TA050127 n
## <dbl> <int>
## 1 1 364
## 2 2 4
## 3 3 98
## 4 5 2
## 5 6 23
## 6 7 246
## 7 8 8
## 8 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050127))
Mean, sd, range
TAS_original_data %>% select(TA050127) %>% drop_na() %>% filter(TA050127 <= 7) %>% summarize(mean_E1 = mean(TA050127), sd_E1 = sd(TA050127), range_E1 = range(TA050127))## # A tibble: 2 × 3
## mean_E1 sd_E1 range_E1
## <dbl> <dbl> <dbl>
## 1 3.44 2.73 1
## 2 3.44 2.73 7TA050128: E1 Employment status 2nd mention
Count
TAS_original_data %>% count(TA050128)## # A tibble: 7 × 2
## TA050128 n
## <dbl> <int>
## 1 0 560
## 2 1 32
## 3 3 13
## 4 5 1
## 5 6 8
## 6 7 131
## 7 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050128))
Mean, sd, range
TAS_original_data %>% select(TA050128) %>% drop_na() %>% filter(TA050128 > 0) %>% summarize(mean_E1 = mean(TA050128), sd_E1 = sd(TA050128), range_E1 = range(TA050128))## # A tibble: 2 × 3
## mean_E1 sd_E1 range_E1
## <dbl> <dbl> <dbl>
## 1 5.63 2.36 1
## 2 5.63 2.36 7TA050129: E1 Employment status 3rd mention
Count
TAS_original_data %>% count(TA050129)## # A tibble: 6 × 2
## TA050129 n
## <dbl> <int>
## 1 0 740
## 2 1 1
## 3 3 1
## 4 6 1
## 5 7 2
## 6 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050129))
Mean, sd, range
TAS_original_data %>% select(TA050129) %>% drop_na() %>% filter(TA050129 > 0) %>% summarize(mean_E1 = mean(TA050129), sd_E1 = sd(TA050129), range_E1 = range(TA050129))## # A tibble: 2 × 3
## mean_E1 sd_E1 range_E1
## <dbl> <dbl> <dbl>
## 1 4.8 2.68 1
## 2 4.8 2.68 7TA050130: E3 Working
Count
TAS_original_data %>% count(TA050130)## # A tibble: 5 × 2
## TA050130 n
## <dbl> <int>
## 1 0 400
## 2 1 86
## 3 5 258
## 4 9 1
## 5 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050130))
Mean, sd, range
TAS_original_data %>% select(TA050130) %>% drop_na() %>% filter(TA050130 > 0) %>% filter(TA050130 < 9) %>% summarize(mean_E3 = mean(TA050130), sd_E3 = sd(TA050130), range_E3 = range(TA050130))## # A tibble: 2 × 3
## mean_E3 sd_E3 range_E3
## <dbl> <dbl> <dbl>
## 1 4 1.73 1
## 2 4 1.73 5TA050573: G1 Graduated high school
Count
TAS_original_data %>% count(TA050573)## # A tibble: 5 × 2
## TA050573 n
## <dbl> <int>
## 1 1 601
## 2 2 46
## 3 3 96
## 4 9 2
## 5 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050573))
Mean, sd, range
TAS_original_data %>% select(TA050573) %>% drop_na() %>% filter(TA050573 < 3) %>% summarize(mean_G1 = mean(TA050573), sd_G1 = sd(TA050573), range_G1 = range(TA050573))## # A tibble: 2 × 3
## mean_G1 sd_G1 range_G1
## <dbl> <dbl> <dbl>
## 1 1.07 0.257 1
## 2 1.07 0.257 2TA050575: G2 Year Graduated high school
Count
TAS_original_data %>% count(TA050575)## # A tibble: 8 × 2
## TA050575 n
## <dbl> <int>
## 1 0 146
## 2 2000 2
## 3 2001 2
## 4 2002 83
## 5 2003 164
## 6 2004 169
## 7 2005 179
## 8 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050575))
Mean, sd, range
TAS_original_data %>% select(TA050575) %>% drop_na() %>% filter(TA050575 > 0) %>% summarize(mean_G2 = mean(TA050575), sd_G2 = sd(TA050575), range_G2 = range(TA050575))## # A tibble: 2 × 3
## mean_G2 sd_G2 range_G2
## <dbl> <dbl> <dbl>
## 1 2004. 1.07 2000
## 2 2004. 1.07 2005TA050594: G10 Attended college
Count
TAS_original_data %>% count(TA050594)## # A tibble: 5 × 2
## TA050594 n
## <dbl> <int>
## 1 0 97
## 2 1 487
## 3 5 160
## 4 9 1
## 5 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050594))
Mean, sd, range
TAS_original_data %>% select(TA050594) %>% drop_na() %>% filter(TA050594 > 0) %>% filter(TA050594 < 9) %>% summarize(mean_G10 = mean(TA050594), sd_G10 = sd(TA050594), range_G10 = range(TA050594))## # A tibble: 2 × 3
## mean_G10 sd_G10 range_G10
## <dbl> <dbl> <dbl>
## 1 1.99 1.73 1
## 2 1.99 1.73 5TA050595: G11 Attending college
Count
TAS_original_data %>% count(TA050595)## # A tibble: 4 × 2
## TA050595 n
## <dbl> <int>
## 1 0 258
## 2 1 397
## 3 5 90
## 4 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050595))
Mean, sd, range
TAS_original_data %>% select(TA050595) %>% drop_na() %>% filter(TA050595 > 0) %>% summarize(mean_G11 = mean(TA050595), sd_G11 = sd(TA050595), range_G11 = range(TA050595))## # A tibble: 2 × 3
## mean_G11 sd_G11 range_G11
## <dbl> <dbl> <dbl>
## 1 1.74 1.55 1
## 2 1.74 1.55 5TA050639: G30A Importance on job that pays well
Count
TAS_original_data %>% count(TA050639)## # A tibble: 9 × 2
## TA050639 n
## <dbl> <int>
## 1 0 79
## 2 1 3
## 3 2 1
## 4 3 7
## 5 4 39
## 6 5 132
## 7 6 235
## 8 7 249
## 9 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050639))
Mean, sd, range
TAS_original_data %>% select(TA050639) %>% drop_na() %>% filter(TA050639 > 0) %>% summarize(mean_G30A = mean(TA050639), sd_G30A = sd(TA050639), range_G11 = range(TA050639))## # A tibble: 2 × 3
## mean_G30A sd_G30A range_G11
## <dbl> <dbl> <dbl>
## 1 6.00 1.02 1
## 2 6.00 1.02 7TA050663: G41A Importance of job with status and prestige
Count
TAS_original_data %>% count(TA050663)## # A tibble: 8 × 2
## TA050663 n
## <dbl> <int>
## 1 1 26
## 2 2 29
## 3 3 47
## 4 4 78
## 5 5 168
## 6 6 160
## 7 7 237
## 8 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050663))
Mean, sd, range
TAS_original_data %>% select(TA050663) %>% drop_na() %>% summarize(mean_G41A = mean(TA050663), sd_G41A = sd(TA050663), range_G41A = range(TA050663))## # A tibble: 2 × 3
## mean_G41A sd_G41A range_G41A
## <dbl> <dbl> <dbl>
## 1 5.36 1.62 1
## 2 5.36 1.62 7TA050664: G41B Importance on job with decision making
Count
TAS_original_data %>% count(TA050664)## # A tibble: 8 × 2
## TA050664 n
## <dbl> <int>
## 1 1 3
## 2 2 8
## 3 3 21
## 4 4 52
## 5 5 181
## 6 6 250
## 7 7 230
## 8 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050664))
Mean, sd, range
TAS_original_data %>% select(TA050664) %>% drop_na() %>% summarize(mean_G41B = mean(TA050664), sd_G41B = sd(TA050664), range_G41B = range(TA050664))## # A tibble: 2 × 3
## mean_G41B sd_G41B range_G41B
## <dbl> <dbl> <dbl>
## 1 5.78 1.14 1
## 2 5.78 1.14 7TA050665: G41C Importance of job with challenging problems
Count
TAS_original_data %>% count(TA050665)## # A tibble: 8 × 2
## TA050665 n
## <dbl> <int>
## 1 1 2
## 2 2 9
## 3 3 29
## 4 4 94
## 5 5 212
## 6 6 223
## 7 7 176
## 8 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050665))
Mean, sd, range
TAS_original_data %>% select(TA050665) %>% drop_na() %>% summarize(mean_G41C = mean(TA050665), sd_G41C = sd(TA050665), range_G41C = range(TA050665))## # A tibble: 2 × 3
## mean_G41C sd_G41C range_G41C
## <dbl> <dbl> <dbl>
## 1 5.52 1.19 1
## 2 5.52 1.19 7TA050670: G41H Importance of job health care benefits
Count
TAS_original_data %>% count(TA050670)## # A tibble: 8 × 2
## TA050670 n
## <dbl> <int>
## 1 1 3
## 2 2 4
## 3 3 8
## 4 4 28
## 5 5 74
## 6 6 176
## 7 7 452
## 8 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050670))
Mean, sd, range
TAS_original_data %>% select(TA050670) %>% drop_na() %>% summarize(mean_G41H = mean(TA050670), sd_G41H = sd(TA050670), range_G41H = range(TA050670))## # A tibble: 2 × 3
## mean_G41H sd_G41H range_G41H
## <dbl> <dbl> <dbl>
## 1 6.36 1.01 1
## 2 6.36 1.01 7TA050675: G41P Importance of job central to identity
Count
TAS_original_data %>% count(TA050675)## # A tibble: 10 × 2
## TA050675 n
## <dbl> <int>
## 1 1 22
## 2 2 29
## 3 3 63
## 4 4 121
## 5 5 187
## 6 6 166
## 7 7 154
## 8 8 2
## 9 9 1
## 10 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050675))
Mean, sd, range
TAS_original_data %>% select(TA050675) %>% drop_na() %>% filter(TA050675 < 8) %>% summarize(mean_G41P = mean(TA050675), sd_G41P = sd(TA050675), range_G41P = range(TA050675))## # A tibble: 2 × 3
## mean_G41P sd_G41P range_G41P
## <dbl> <dbl> <dbl>
## 1 5.07 1.54 1
## 2 5.07 1.54 7TA050676: H1 Health level
Count
TAS_original_data %>% count(TA050676)## # A tibble: 7 × 2
## TA050676 n
## <dbl> <int>
## 1 1 188
## 2 2 288
## 3 3 207
## 4 4 53
## 5 5 7
## 6 9 2
## 7 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050676))
Mean, sd, range
TAS_original_data %>% select(TA050676) %>% drop_na() %>% filter(TA050676 < 8) %>% summarize(mean_H1 = mean(TA050676), sd_H1 = sd(TA050676), range_H1 = range(TA050676))## # A tibble: 2 × 3
## mean_H1 sd_H1 range_H1
## <dbl> <dbl> <dbl>
## 1 2.20 0.930 1
## 2 2.20 0.930 5TA050884: L7 Race mention 1
Count
TAS_original_data %>% count(TA050884)## # A tibble: 9 × 2
## TA050884 n
## <dbl> <int>
## 1 1 378
## 2 2 312
## 3 3 6
## 4 4 8
## 5 5 3
## 6 7 8
## 7 8 2
## 8 9 28
## 9 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050884))
Mean, sd, range
TAS_original_data %>% select(TA050884) %>% drop_na() %>% filter(TA050884 < 8) %>% summarize(mean_L7 = mean(TA050884), sd_L7 = sd(TA050884), range_L7 = range(TA050884))## # A tibble: 2 × 3
## mean_L7 sd_L7 range_L7
## <dbl> <dbl> <dbl>
## 1 1.57 0.846 1
## 2 1.57 0.846 7TA050885: L7 Race mention 2
Count
TAS_original_data %>% count(TA050885)## # A tibble: 7 × 2
## TA050885 n
## <dbl> <int>
## 1 0 728
## 2 1 1
## 3 2 4
## 4 3 7
## 5 5 2
## 6 7 3
## 7 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050885))
Mean, sd, range
TAS_original_data %>% select(TA050885) %>% drop_na() %>% filter(TA050885 > 0) %>% summarize(mean_L7 = mean(TA050885), sd_L7 = sd(TA050885), range_L7 = range(TA050885))## # A tibble: 2 × 3
## mean_L7 sd_L7 range_L7
## <dbl> <dbl> <dbl>
## 1 3.59 1.91 1
## 2 3.59 1.91 7TA050886: L7 Race mention 3
Count
TAS_original_data %>% count(TA050886)## # A tibble: 4 × 2
## TA050886 n
## <dbl> <int>
## 1 0 743
## 2 3 1
## 3 5 1
## 4 NA 1641ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA050886))
Mean, sd, range
TAS_original_data %>% select(TA050886) %>% drop_na() %>% filter(TA050886 > 0) %>% summarize(mean_L7 = mean(TA050886), sd_L7 = sd(TA050886), range_L7 = range(TA050886))## # A tibble: 2 × 3
## mean_L7 sd_L7 range_L7
## <dbl> <dbl> <dbl>
## 1 4 1.41 3
## 2 4 1.41 52015
TA150045: B5A Responsibility for own
Count
TAS_original_data %>% count(TA150045)## # A tibble: 7 × 2
## TA150045 n
## <dbl> <int>
## 1 1 52
## 2 2 147
## 3 3 217
## 4 4 422
## 5 5 800
## 6 8 3
## 7 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150045))
Mean, sd, range
TAS_original_data %>% select(TA150045) %>% drop_na() %>% filter(TA150045 < 8) %>% summarize(mean_B5A = mean(TA150045), sd_B5A = sd(TA150045), range_B5A = range(TA150045))## # A tibble: 2 × 3
## mean_B5A sd_B5A range_B5A
## <dbl> <dbl> <dbl>
## 1 4.08 1.12 1
## 2 4.08 1.12 5TA150048: B5D Responsibility for own money
Count
TAS_original_data %>% count(TA150048)## # A tibble: 7 × 2
## TA150048 n
## <dbl> <int>
## 1 1 28
## 2 2 39
## 3 3 92
## 4 4 243
## 5 5 1237
## 6 9 2
## 7 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150048))
Mean, sd, range
TAS_original_data %>% select(TA150048) %>% drop_na() %>% filter(TA150048 < 8) %>% summarize(mean_B5D = mean(TA150048), sd_B5D = sd(TA150048), range_B5D = range(TA150048))## # A tibble: 2 × 3
## mean_B5D sd_B5D range_B5D
## <dbl> <dbl> <dbl>
## 1 4.60 0.837 1
## 2 4.60 0.837 5TA150051: B6C Scale on managing money
Count
TAS_original_data %>% count(TA150051)## # A tibble: 8 × 2
## TA150051 n
## <dbl> <int>
## 1 1 13
## 2 2 29
## 3 3 76
## 4 4 229
## 5 5 470
## 6 6 428
## 7 7 396
## 8 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150051))
Mean, sd, range
TAS_original_data %>% select(TA150051) %>% drop_na() %>% summarize(mean_B6C = mean(TA150051), sd_B6C = sd(TA150051), range_B6C = range(TA150051))## # A tibble: 2 × 3
## mean_B6C sd_B6C range_B6C
## <dbl> <dbl> <dbl>
## 1 5.43 1.29 1
## 2 5.43 1.29 7TA150066: C2D Scale on worry about money
Count
TAS_original_data %>% count(TA150066)## # A tibble: 8 × 2
## TA150066 n
## <dbl> <int>
## 1 1 268
## 2 2 285
## 3 3 283
## 4 4 257
## 5 5 245
## 6 6 141
## 7 7 162
## 8 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150066))
Mean, sd, range
TAS_original_data %>% select(TA150066) %>% drop_na() %>% summarize(mean_C2D = mean(TA150066), sd_C2D = sd(TA150066), range_C2D = range(TA150066))## # A tibble: 2 × 3
## mean_C2D sd_C2D range_C2D
## <dbl> <dbl> <dbl>
## 1 3.61 1.89 1
## 2 3.61 1.89 7TA150067: C2E Scale on worry about future job
Count
TAS_original_data %>% count(TA150067)## # A tibble: 8 × 2
## TA150067 n
## <dbl> <int>
## 1 1 321
## 2 2 308
## 3 3 249
## 4 4 243
## 5 5 215
## 6 6 130
## 7 7 175
## 8 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150067))
Mean, sd, range
TAS_original_data %>% select(TA150067) %>% drop_na() %>% summarize(mean_C2E = mean(TA150067), sd_C2E = sd(TA150067), range_C2E = range(TA150067))## # A tibble: 2 × 3
## mean_C2E sd_C2E range_C2E
## <dbl> <dbl> <dbl>
## 1 3.50 1.95 1
## 2 3.50 1.95 7TA150068: C2F Scale on discouragement
Count
TAS_original_data %>% count(TA150068)## # A tibble: 8 × 2
## TA150068 n
## <dbl> <int>
## 1 1 378
## 2 2 375
## 3 3 272
## 4 4 239
## 5 5 194
## 6 6 86
## 7 7 97
## 8 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150068))
Mean, sd, range
TAS_original_data %>% select(TA150068) %>% drop_na() %>% filter(TA150068 <= 7) %>% summarize(mean_C2F = mean(TA150068), sd_C2F = sd(TA150068), range_C2F = range(TA150068))## # A tibble: 2 × 3
## mean_C2F sd_C2F range_C2F
## <dbl> <dbl> <dbl>
## 1 3.09 1.78 1
## 2 3.09 1.78 7TA150072: D2D3 Year Divorced
Count
TAS_original_data %>% count(TA150072)## # A tibble: 7 × 2
## TA150072 n
## <dbl> <int>
## 1 0 1620
## 2 2009 2
## 3 2010 2
## 4 2011 1
## 5 2012 4
## 6 2014 12
## 7 NA 745Mean, sd, range
TAS_original_data %>% select(TA150072) %>% drop_na() %>% filter(TA150072 > 0) %>% summarize(mean_D2D3 = mean(TA150072))## # A tibble: 1 × 1
## mean_D2D3
## <dbl>
## 1 2013.TA150128: E1 Employment status 1st mention
Count
TAS_original_data %>% count(TA150128)## # A tibble: 7 × 2
## TA150128 n
## <dbl> <int>
## 1 1 1103
## 2 2 6
## 3 3 246
## 4 5 9
## 5 6 60
## 6 7 217
## 7 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150128))
Mean, sd, range
TAS_original_data %>% select(TA150128) %>% drop_na() %>% filter(TA150128 <= 7) %>% summarize(mean_E1 = mean(TA150128), sd_E1 = sd(TA150128), range_E1 = range(TA150128))## # A tibble: 2 × 3
## mean_E1 sd_E1 range_E1
## <dbl> <dbl> <dbl>
## 1 2.30 2.16 1
## 2 2.30 2.16 7TA150129: E1 Employment status 2nd mention
Count
TAS_original_data %>% count(TA150129)## # A tibble: 7 × 2
## TA150129 n
## <dbl> <int>
## 1 0 1248
## 2 1 55
## 3 2 1
## 4 3 34
## 5 6 36
## 6 7 267
## 7 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150129))
Mean, sd, range
TAS_original_data %>% select(TA150129) %>% drop_na() %>% filter(TA150129 > 0) %>% summarize(mean_E1 = mean(TA150129), sd_E1 = sd(TA150129), range_E1 = range(TA150129))## # A tibble: 2 × 3
## mean_E1 sd_E1 range_E1
## <dbl> <dbl> <dbl>
## 1 5.71 2.22 1
## 2 5.71 2.22 7TA150130: E1 Employment status 3rd mention
Count
TAS_original_data %>% count(TA150130)## # A tibble: 6 × 2
## TA150130 n
## <dbl> <int>
## 1 0 1637
## 2 1 1
## 3 3 1
## 4 6 1
## 5 7 1
## 6 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150130))
Mean, sd, range
TAS_original_data %>% select(TA150130) %>% drop_na() %>% filter(TA150130 > 0) %>% summarize(mean_E1 = mean(TA150130), sd_E1 = sd(TA150130), range_E1 = range(TA150130))## # A tibble: 2 × 3
## mean_E1 sd_E1 range_E1
## <dbl> <dbl> <dbl>
## 1 4.25 2.75 1
## 2 4.25 2.75 7TA150131: E3 Working
Count
TAS_original_data %>% count(TA150131)## # A tibble: 5 × 2
## TA150131 n
## <dbl> <int>
## 1 0 1165
## 2 1 51
## 3 5 424
## 4 9 1
## 5 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150131))
Mean, sd, range
TAS_original_data %>% select(TA150131) %>% drop_na() %>% filter(TA150131 > 0) %>% filter(TA150131 < 9) %>% summarize(mean_E3 = mean(TA150131), sd_E3 = sd(TA150131), range_E3 = range(TA150131))## # A tibble: 2 × 3
## mean_E3 sd_E3 range_E3
## <dbl> <dbl> <dbl>
## 1 4.57 1.24 1
## 2 4.57 1.24 5TA150701: G1 Graduated high school
Count
TAS_original_data %>% count(TA150701)## # A tibble: 6 × 2
## TA150701 n
## <dbl> <int>
## 1 0 710
## 2 1 799
## 3 2 52
## 4 3 79
## 5 9 1
## 6 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150701))
Mean, sd, range
TAS_original_data %>% select(TA150701) %>% drop_na() %>% filter(TA150701 > 0) %>% filter(TA150701 < 3) %>% summarize(mean_G1 = mean(TA150701), sd_G1 = sd(TA150701), range_G1 = range(TA150701))## # A tibble: 2 × 3
## mean_G1 sd_G1 range_G1
## <dbl> <dbl> <dbl>
## 1 1.06 0.240 1
## 2 1.06 0.240 2TA150703: G2 Year Graduated high school
Count
TAS_original_data %>% count(TA150703)## # A tibble: 14 × 2
## TA150703 n
## <dbl> <int>
## 1 0 940
## 2 2001 1
## 3 2004 1
## 4 2006 15
## 5 2007 31
## 6 2008 37
## 7 2009 55
## 8 2010 93
## 9 2011 146
## 10 2012 32
## 11 2013 36
## 12 2014 163
## 13 2015 91
## 14 NA 745Mean, sd, range
TAS_original_data %>% select(TA150703) %>% drop_na() %>% filter(TA150703 > 0) %>% summarize(mean_G2 = mean(TA150703), sd_G2 = sd(TA150703), range_G2 = range(TA150703))## # A tibble: 2 × 3
## mean_G2 sd_G2 range_G2
## <dbl> <dbl> <dbl>
## 1 2012. 2.54 2001
## 2 2012. 2.54 2015TA150730: G10 Attended college
Count
TAS_original_data %>% count(TA150730)## # A tibble: 4 × 2
## TA150730 n
## <dbl> <int>
## 1 0 762
## 2 1 531
## 3 5 348
## 4 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150730))
Mean, sd, range
TAS_original_data %>% select(TA150730) %>% drop_na() %>% filter(TA150730 > 0) %>% filter(TA150730 < 9) %>% summarize(mean_G10 = mean(TA150730), sd_G10 = sd(TA150730), range_G10 = range(TA150730))## # A tibble: 2 × 3
## mean_G10 sd_G10 range_G10
## <dbl> <dbl> <dbl>
## 1 2.58 1.96 1
## 2 2.58 1.96 5TA150731: G11 Attending college
Count
TAS_original_data %>% count(TA150731)## # A tibble: 4 × 2
## TA150731 n
## <dbl> <int>
## 1 0 455
## 2 1 482
## 3 5 704
## 4 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150731))
Mean, sd, range
TAS_original_data %>% select(TA150731) %>% drop_na() %>% filter(TA150731 > 0) %>% summarize(mean_G11 = mean(TA150731), sd_G11 = sd(TA150731), range_G11 = range(TA150731))## # A tibble: 2 × 3
## mean_G11 sd_G11 range_G11
## <dbl> <dbl> <dbl>
## 1 3.37 1.97 1
## 2 3.37 1.97 5TA150784: G30A Importance on job that pays well
Count
TAS_original_data %>% count(TA150784)## # A tibble: 9 × 2
## TA150784 n
## <dbl> <int>
## 1 1 8
## 2 2 7
## 3 3 19
## 4 4 99
## 5 5 332
## 6 6 453
## 7 7 721
## 8 9 2
## 9 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150784))
Mean, sd, range
TAS_original_data %>% select(TA150784) %>% drop_na() %>% filter(TA150784 > 0) %>% filter(TA150784 < 9) %>% summarize(mean_G30A = mean(TA150784), sd_G30A = sd(TA150784), range_G11 = range(TA150784))## # A tibble: 2 × 3
## mean_G30A sd_G30A range_G11
## <dbl> <dbl> <dbl>
## 1 6.04 1.09 1
## 2 6.04 1.09 7TA150808: G41A Importance of job with status and prestige
Count
TAS_original_data %>% count(TA150808)## # A tibble: 9 × 2
## TA150808 n
## <dbl> <int>
## 1 1 140
## 2 2 108
## 3 3 122
## 4 4 231
## 5 5 372
## 6 6 286
## 7 7 380
## 8 9 2
## 9 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150808))
Mean, sd, range
TAS_original_data %>% select(TA150808) %>% drop_na() %>% filter(TA150808 < 8) %>% summarize(mean_G41A = mean(TA150808), sd_G41A = sd(TA150808), range_G41A = range(TA150808))## # A tibble: 2 × 3
## mean_G41A sd_G41A range_G41A
## <dbl> <dbl> <dbl>
## 1 4.81 1.86 1
## 2 4.81 1.86 7TA150809: G41B Importance on job with decision making
Count
TAS_original_data %>% count(TA150809)## # A tibble: 9 × 2
## TA150809 n
## <dbl> <int>
## 1 1 38
## 2 2 21
## 3 3 55
## 4 4 146
## 5 5 429
## 6 6 451
## 7 7 499
## 8 9 2
## 9 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150809))
Mean, sd, range
TAS_original_data %>% select(TA150809) %>% drop_na() %>% filter(TA150809 < 8) %>% summarize(mean_G41B = mean(TA150809), sd_G41B = sd(TA150809), range_G41B = range(TA150809))## # A tibble: 2 × 3
## mean_G41B sd_G41B range_G41B
## <dbl> <dbl> <dbl>
## 1 5.60 1.36 1
## 2 5.60 1.36 7TA150810: G41C Importance of job with challenging problems
Count
TAS_original_data %>% count(TA150810)## # A tibble: 10 × 2
## TA150810 n
## <dbl> <int>
## 1 0 500
## 2 1 16
## 3 2 12
## 4 3 51
## 5 4 132
## 6 5 319
## 7 6 326
## 8 7 283
## 9 9 2
## 10 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150810))
Mean, sd, range
TAS_original_data %>% select(TA150810) %>% drop_na() %>% filter(TA150810 > 0) %>% filter(TA150810 < 8) %>% summarize(mean_G41C = mean(TA150810), sd_G41C = sd(TA150810), range_G41C = range(TA150810))## # A tibble: 2 × 3
## mean_G41C sd_G41C range_G41C
## <dbl> <dbl> <dbl>
## 1 5.49 1.29 1
## 2 5.49 1.29 7TA150815: G41H Importance of job health care benefits
Count
TAS_original_data %>% count(TA150815)## # A tibble: 9 × 2
## TA150815 n
## <dbl> <int>
## 1 1 22
## 2 2 22
## 3 3 25
## 4 4 67
## 5 5 213
## 6 6 377
## 7 7 913
## 8 9 2
## 9 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150815))
Mean, sd, range
TAS_original_data %>% select(TA150815) %>% drop_na() %>% filter(TA150815 < 8) %>% summarize(mean_G41H = mean(TA150815), sd_G41H = sd(TA150815), range_G41H = range(TA150815))## # A tibble: 2 × 3
## mean_G41H sd_G41H range_G41H
## <dbl> <dbl> <dbl>
## 1 6.18 1.23 1
## 2 6.18 1.23 7TA150820: G41P Importance of job central to identity
Count
TAS_original_data %>% count(TA150820)## # A tibble: 10 × 2
## TA150820 n
## <dbl> <int>
## 1 1 79
## 2 2 83
## 3 3 126
## 4 4 303
## 5 5 387
## 6 6 300
## 7 7 358
## 8 8 2
## 9 9 3
## 10 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150820))
Mean, sd, range
TAS_original_data %>% select(TA150820) %>% drop_na() %>% filter(TA150820 < 8) %>% summarize(mean_G41P = mean(TA150820), sd_G41P = sd(TA150820), range_G41P = range(TA150820))## # A tibble: 2 × 3
## mean_G41P sd_G41P range_G41P
## <dbl> <dbl> <dbl>
## 1 4.94 1.67 1
## 2 4.94 1.67 7TA150821: H1 Health level
Count
TAS_original_data %>% count(TA150821)## # A tibble: 7 × 2
## TA150821 n
## <dbl> <int>
## 1 1 333
## 2 2 694
## 3 3 423
## 4 4 170
## 5 5 19
## 6 9 2
## 7 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA150821))
Mean, sd, range
TAS_original_data %>% select(TA150821) %>% drop_na() %>% filter(TA150821 < 8) %>% summarize(mean_H1 = mean(TA150821), sd_H1 = sd(TA150821), range_H1 = range(TA150821))## # A tibble: 2 × 3
## mean_H1 sd_H1 range_H1
## <dbl> <dbl> <dbl>
## 1 2.30 0.945 1
## 2 2.30 0.945 5TA151132: L7 Race mention 1
Count
TAS_original_data %>% count(TA151132)## # A tibble: 9 × 2
## TA151132 n
## <dbl> <int>
## 1 1 818
## 2 2 694
## 3 3 15
## 4 4 32
## 5 5 3
## 6 7 70
## 7 8 1
## 8 9 8
## 9 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA151132))
Mean, sd, range
TAS_original_data %>% select(TA151132) %>% drop_na() %>% filter(TA151132 < 8) %>% summarize(mean_L7 = mean(TA151132), sd_L7 = sd(TA151132), range_L7 = range(TA151132))## # A tibble: 2 × 3
## mean_L7 sd_L7 range_L7
## <dbl> <dbl> <dbl>
## 1 1.77 1.27 1
## 2 1.77 1.27 7TA151133: L7 Race mention 2
Count
TAS_original_data %>% count(TA151133)## # A tibble: 9 × 2
## TA151133 n
## <dbl> <int>
## 1 0 1523
## 2 1 15
## 3 2 17
## 4 3 56
## 5 4 7
## 6 5 8
## 7 7 14
## 8 9 1
## 9 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA151133))
Mean, sd, range
TAS_original_data %>% select(TA151133) %>% drop_na() %>% filter(TA151133 > 0) %>% filter(TA151133 < 8) %>% summarize(mean_L7 = mean(TA151133), sd_L7 = sd(TA151133), range_L7 = range(TA151133))## # A tibble: 2 × 3
## mean_L7 sd_L7 range_L7
## <dbl> <dbl> <dbl>
## 1 3.27 1.69 1
## 2 3.27 1.69 7TA151134: L7 Race mention 3
Count
TAS_original_data %>% count(TA151134)## # A tibble: 6 × 2
## TA151134 n
## <dbl> <int>
## 1 0 1625
## 2 1 9
## 3 3 4
## 4 5 1
## 5 7 2
## 6 NA 745ggplot(data=TAS_original_data) + geom_bar(mapping = aes(x=TA151134))
Mean, sd, range
TAS_original_data %>% select(TA151134) %>% drop_na() %>% filter(TA151134 > 0) %>% summarize(mean_L7 = mean(TA151134), sd_L7 = sd(TA151134), range_L7 = range(TA151134))## # A tibble: 2 × 3
## mean_L7 sd_L7 range_L7
## <dbl> <dbl> <dbl>
## 1 2.5 2.13 1
## 2 2.5 2.13 7