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library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## √ ggplot2 3.3.3 √ purrr 0.3.4
## √ tibble 3.0.4 √ dplyr 1.0.2
## √ tidyr 1.1.2 √ stringr 1.4.0
## √ readr 1.4.0 √ forcats 0.5.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(rmarkdown)
library(lme4)## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpacklibrary(emmeans)
library(effects)## Loading required package: carData## lattice theme set by effectsTheme()
## See ?effectsTheme for details.library(haven)
library(sjmisc)##
## Attaching package: 'sjmisc'## The following object is masked from 'package:purrr':
##
## is_empty## The following object is masked from 'package:tidyr':
##
## replace_na## The following object is masked from 'package:tibble':
##
## add_caselibrary(sjlabelled)##
## Attaching package: 'sjlabelled'## The following objects are masked from 'package:haven':
##
## as_factor, read_sas, read_spss, read_stata, write_sas, zap_labels## The following object is masked from 'package:forcats':
##
## as_factor## The following object is masked from 'package:dplyr':
##
## as_labellibrary(gtsummary)
library(table1)##
## Attaching package: 'table1'## The following objects are masked from 'package:base':
##
## units, units<-library(ggstatsplot)## Registered S3 methods overwritten by 'car':
## method from
## influence.merMod lme4
## cooks.distance.influence.merMod lme4
## dfbeta.influence.merMod lme4
## dfbetas.influence.merMod lme4## In case you would like cite this package, cite it as:
## Patil, I. (2018). ggstatsplot: "ggplot2" Based Plots with Statistical Details. CRAN.
## Retrieved from https://cran.r-project.org/web/packages/ggstatsplot/index.htmllibrary(knitr)
cgss2017 <- read_dta("C:/Users/huawei/Desktop/R/cgss/cgss2017.dta")
descr(cgss2017$a4)##
## ## Basic descriptive statistics
##
## var type label n NA.prc mean sd se md trimmed range iqr
## dd numeric 您的民族是： 12582 0 1.36 1.38 0.01 1 1.36 7 (1-8) 0
## skew
## 4.03descr(cgss2017$a10)##
## ## Basic descriptive statistics
##
## var type label n NA.prc mean sd se md trimmed range
## dd numeric 目前的政治面貌是： 12582 0 1.47 2.9 0.03 1 1.47 98 (1-99)
## iqr skew
## 0 29.89descr(cgss2017$a59g)##
## ## Basic descriptive statistics
##
## var type label n
## dd numeric 在您目前的工作中，您在多大程度上能自主决定您工作的具体方式： 4792
## NA.prc mean sd se md trimmed range iqr skew
## 61.91 2.51 6.11 0.09 2 2.51 98 (1-99) 2 15.28descr(cgss2017$a89b)##
## ## Basic descriptive statistics
##
## var type label n NA.prc mean sd se md trimmed
## dd numeric 您父亲的最高教育程度是： 12582 0 9.27 23.74 0.21 3 9.27
## range iqr skew
## 98 (1-99) 3 3.43table(cgss2017$a4)##
## 1 2 3 4 5 6 7 8
## 11636 48 94 261 10 140 2 391table(cgss2017$a58)##
## 1 2 3 4 5 6
## 4794 616 1625 1493 3370 684descr(cgss2017$a52)##
## ## Basic descriptive statistics
##
## var type label n NA.prc mean sd se md
## dd numeric 您觉得自己说英语的能力是什么水平？ 12582 0 1.55 3.43 0.03 1
## trimmed range iqr skew
## 1.55 98 (1-99) 1 26.71cgss2017[cgss2017 >= 9999997] <- NA
cgss2017[cgss2017 == 9999996] <- 999999
cgss2017[cgss2017 == 98] <- NA
cgss2017[cgss2017 == 99] <- NA
cgss2017 <- sjlabelled::drop_labels(cgss2017)
cgss2017 <- sjmisc::to_label(cgss2017)
descr(cgss2017$a8a)##
## ## Basic descriptive statistics
##
## var type label n NA.prc mean sd
## dd numeric 您个人去年(2016年)全年总收入 11900 5.42 41156.86 233509.9
## se md trimmed range iqr skew
## 2140.58 20000 41156.86 9993600 (0-9993600) 42000 37.51cgss2017$a8a <- as.numeric(cgss2017$a8a)
cgss2017$a8a2 <-
car::recode(cgss2017$a8a,"0:6000='1';6200:15000='2';15300:25000='3';25200:35000='4';35280:70000='5';72000:9993600='6' ")
table(cgss2017$a8a2)##
## 1 2 3 4 5 6
## 3821 1411 1483 1162 2714 1309cgss2017$a8a2 <-
factor(cgss2017$a8a2,
levels = c(1, 2, 3,4,5,6),
labels = c("低下", "低上", "中下","中上","上下","上上"))
table(cgss2017$a8a2)##
## 低下 低上 中下 中上 上下 上上
## 3821 1411 1483 1162 2714 1309cgssGPA4.0 <-cgss2017 %>% select(
MZ = a4,
ZZMM = a10,
JYY = a52,
F.edu = a89b,
ZZD = a59g,
income = a8a2
)
cgssGPA4.0$MZ <- as.numeric(cgssGPA4.0$MZ)
cgssGPA4.0$ZZMM <- as.numeric(cgssGPA4.0$ZZMM)
cgssGPA4.0$JYY <- as.numeric(cgssGPA4.0$JYY)
cgssGPA4.0$F.edu <- as.numeric(cgssGPA4.0$F.edu)
cgssGPA4.0$ZZD <- as.numeric(cgssGPA4.0$ZZD)
cgssGPA4.0$income <- as.numeric(cgssGPA4.0$income)
tbl_summary(cgssGPA4.0)| Characteristic | N = 12,5821 |
|---|---|
| MZ | |
| 1 | 11,636 (92%) |
| 2 | 48 (0.4%) |
| 3 | 94 (0.7%) |
| 4 | 261 (2.1%) |
| 5 | 10 (<0.1%) |
| 6 | 140 (1.1%) |
| 7 | 2 (<0.1%) |
| 8 | 391 (3.1%) |
| ZZMM | |
| 1 | 10,512 (84%) |
| 2 | 636 (5.1%) |
| 3 | 19 (0.2%) |
| 4 | 1,405 (11%) |
| Unknown | 10 |
| JYY | |
| 1 | 8,887 (71%) |
| 2 | 2,260 (18%) |
| 3 | 1,098 (8.7%) |
| 4 | 246 (2.0%) |
| 5 | 76 (0.6%) |
| Unknown | 15 |
| F.edu | 3.00 (1.00, 4.00) |
| Unknown | 832 |
| ZZD | |
| 1 | 1,246 (26%) |
| 2 | 2,101 (44%) |
| 3 | 1,002 (21%) |
| 4 | 424 (8.9%) |
| Unknown | 7,809 |
| income | |
| 1 | 3,821 (32%) |
| 2 | 1,411 (12%) |
| 3 | 1,483 (12%) |
| 4 | 1,162 (9.8%) |
| 5 | 2,714 (23%) |
| 6 | 1,309 (11%) |
| Unknown | 682 |
|
1
n (%); Median (IQR)
|
|
tbl_summary(cgssGPA4.0,statistic = list(all_continuous()~"{mean}({sd}){median}({min},{p25},{p75},{max})"),)| Characteristic | N = 12,5821 |
|---|---|
| MZ | |
| 1 | 11,636 (92%) |
| 2 | 48 (0.4%) |
| 3 | 94 (0.7%) |
| 4 | 261 (2.1%) |
| 5 | 10 (<0.1%) |
| 6 | 140 (1.1%) |
| 7 | 2 (<0.1%) |
| 8 | 391 (3.1%) |
| ZZMM | |
| 1 | 10,512 (84%) |
| 2 | 636 (5.1%) |
| 3 | 19 (0.2%) |
| 4 | 1,405 (11%) |
| Unknown | 10 |
| JYY | |
| 1 | 8,887 (71%) |
| 2 | 2,260 (18%) |
| 3 | 1,098 (8.7%) |
| 4 | 246 (2.0%) |
| 5 | 76 (0.6%) |
| Unknown | 15 |
| F.edu | 2.98(2.47)3.00(1.00,1.00,4.00,14.00) |
| Unknown | 832 |
| ZZD | |
| 1 | 1,246 (26%) |
| 2 | 2,101 (44%) |
| 3 | 1,002 (21%) |
| 4 | 424 (8.9%) |
| Unknown | 7,809 |
| income | |
| 1 | 3,821 (32%) |
| 2 | 1,411 (12%) |
| 3 | 1,483 (12%) |
| 4 | 1,162 (9.8%) |
| 5 | 2,714 (23%) |
| 6 | 1,309 (11%) |
| Unknown | 682 |
|
1
n (%); Mean(SD)Median(Minimum,25%,75%,Maximum)
|
|
cgssGPA4.0 %>%
select(MZ, ZZMM, JYY, F.edu, ZZD,income) %>% tbl_summary(
statistic = list(
all_continuous() ~ "{mean}({sd}) {median} ({min}, {p25}, {p75},{max})"
),
digits = list(c(income) ~ 1)
)| Characteristic | N = 12,5821 |
|---|---|
| MZ | |
| 1 | 11,636 (92%) |
| 2 | 48 (0.4%) |
| 3 | 94 (0.7%) |
| 4 | 261 (2.1%) |
| 5 | 10 (<0.1%) |
| 6 | 140 (1.1%) |
| 7 | 2 (<0.1%) |
| 8 | 391 (3.1%) |
| ZZMM | |
| 1 | 10,512 (84%) |
| 2 | 636 (5.1%) |
| 3 | 19 (0.2%) |
| 4 | 1,405 (11%) |
| Unknown | 10 |
| JYY | |
| 1 | 8,887 (71%) |
| 2 | 2,260 (18%) |
| 3 | 1,098 (8.7%) |
| 4 | 246 (2.0%) |
| 5 | 76 (0.6%) |
| Unknown | 15 |
| F.edu | 2.98(2.47) 3.00 (1.00, 1.00, 4.00,14.00) |
| Unknown | 832 |
| ZZD | |
| 1 | 1,246 (26%) |
| 2 | 2,101 (44%) |
| 3 | 1,002 (21%) |
| 4 | 424 (8.9%) |
| Unknown | 7,809 |
| income | |
| 1 | 3,821.0 (32.1%) |
| 2 | 1,411.0 (11.9%) |
| 3 | 1,483.0 (12.5%) |
| 4 | 1,162.0 (9.8%) |
| 5 | 2,714.0 (22.8%) |
| 6 | 1,309.0 (11.0%) |
| Unknown | 682 |
|
1
n (%); Mean(SD) Median (Minimum, IQR,Maximum)
|
|
cgssGPA4.0 %>%
select(MZ, ZZMM, JYY, F.edu,ZZD,income) %>% tbl_summary(
by = income, missing = "no",
type = all_continuous() ~ "continuous2",
statistic = list(
all_continuous() ~ c("{N_nonmiss}",
"{median} ({p25}, {p75})",
"{min}, {max}")
),
digits = list(c(income) ~ 1)
) %>%
add_n() %>%
add_p() %>%
modify_header(label = "**Variable**") %>%
bold_labels()## 682 observations missing `income` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `income` column before passing to `tbl_summary()`.## There was an error in 'add_p()/add_difference()' for variable 'MZ', p-value omitted:
## Error in stats::fisher.test(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, : FEXACT error 5.
## The hash table key cannot be computed because the largest key
## is larger than the largest representable int.
## The algorithm cannot proceed.
## Reduce the workspace, consider using 'simulate.p.value=TRUE' or another algorithm.## There was an error in 'add_p()/add_difference()' for variable 'ZZMM', p-value omitted:
## Error in stats::fisher.test(c(4, 1, 1, 1, 1, 1, 1, 1, 4, 1, 4, 1, 2, 1, : FEXACT error 501.
## The hash table key cannot be computed because the largest key
## is larger than the largest representable int.
## The algorithm cannot proceed.
## Reduce the workspace, consider using 'simulate.p.value=TRUE' or another algorithm.| Variable | N | 1, N = 3,821 | 2, N = 1,411 | 3, N = 1,483 | 4, N = 1,162 | 5, N = 2,714 | 6, N = 1,309 | p-value1 |
|---|---|---|---|---|---|---|---|---|
| MZ | 11,900 | |||||||
| 1 | 3,432 (90%) | 1,277 (91%) | 1,372 (93%) | 1,100 (95%) | 2,575 (95%) | 1,250 (95%) | ||
| 2 | 7 (0.2%) | 8 (0.6%) | 4 (0.3%) | 6 (0.5%) | 10 (0.4%) | 7 (0.5%) | ||
| 3 | 24 (0.6%) | 9 (0.6%) | 18 (1.2%) | 5 (0.4%) | 22 (0.8%) | 13 (1.0%) | ||
| 4 | 86 (2.3%) | 31 (2.2%) | 39 (2.6%) | 21 (1.8%) | 55 (2.0%) | 20 (1.5%) | ||
| 5 | 3 (<0.1%) | 1 (<0.1%) | 1 (<0.1%) | 1 (<0.1%) | 3 (0.1%) | 1 (<0.1%) | ||
| 6 | 75 (2.0%) | 20 (1.4%) | 17 (1.1%) | 6 (0.5%) | 10 (0.4%) | 4 (0.3%) | ||
| 7 | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (<0.1%) | 0 (0%) | ||
| 8 | 194 (5.1%) | 65 (4.6%) | 32 (2.2%) | 23 (2.0%) | 37 (1.4%) | 14 (1.1%) | ||
| ZZMM | 11,894 | |||||||
| 1 | 3,435 (90%) | 1,302 (92%) | 1,311 (89%) | 979 (84%) | 2,055 (76%) | 897 (69%) | ||
| 2 | 250 (6.5%) | 33 (2.3%) | 48 (3.2%) | 51 (4.4%) | 98 (3.6%) | 68 (5.2%) | ||
| 3 | 0 (0%) | 0 (0%) | 1 (<0.1%) | 1 (<0.1%) | 8 (0.3%) | 9 (0.7%) | ||
| 4 | 135 (3.5%) | 74 (5.3%) | 121 (8.2%) | 131 (11%) | 552 (20%) | 335 (26%) | ||
| JYY | 11,886 | <0.001 | ||||||
| 1 | 3,174 (83%) | 1,237 (88%) | 1,169 (79%) | 835 (72%) | 1,629 (60%) | 418 (32%) | ||
| 2 | 366 (9.6%) | 126 (8.9%) | 230 (16%) | 238 (21%) | 710 (26%) | 452 (35%) | ||
| 3 | 216 (5.7%) | 42 (3.0%) | 77 (5.2%) | 70 (6.0%) | 297 (11%) | 311 (24%) | ||
| 4 | 54 (1.4%) | 3 (0.2%) | 3 (0.2%) | 15 (1.3%) | 61 (2.3%) | 88 (6.7%) | ||
| 5 | 8 (0.2%) | 0 (0%) | 3 (0.2%) | 2 (0.2%) | 14 (0.5%) | 38 (2.9%) | ||
| F.edu | 11,148 | <0.001 | ||||||
| N | 3,564 | 1,324 | 1,380 | 1,099 | 2,541 | 1,240 | ||
| Median (IQR) | 1.00 (1.00, 3.00) | 1.00 (1.00, 3.00) | 3.00 (1.00, 3.00) | 3.00 (1.00, 4.00) | 3.00 (1.00, 4.00) | 4.00 (3.00, 6.00) | ||
| Range | 1.00, 14.00 | 1.00, 14.00 | 1.00, 14.00 | 1.00, 14.00 | 1.00, 14.00 | 1.00, 14.00 | ||
| ZZD | 4,548 | <0.001 | ||||||
| 1 | 94 (33%) | 129 (29%) | 150 (23%) | 125 (21%) | 357 (24%) | 314 (29%) | ||
| 2 | 102 (35%) | 145 (33%) | 237 (36%) | 237 (40%) | 710 (48%) | 585 (54%) | ||
| 3 | 56 (19%) | 114 (26%) | 180 (27%) | 157 (27%) | 306 (21%) | 142 (13%) | ||
| 4 | 37 (13%) | 54 (12%) | 90 (14%) | 70 (12%) | 109 (7.4%) | 48 (4.4%) | ||
|
1
Pearson's Chi-squared test; Kruskal-Wallis rank sum test
|
||||||||
table1(~ MZ+ZZMM+JYY+F.edu+ZZD+income, data=cgssGPA4.0)| Overall (N=12582) |
|
|---|---|
| MZ | |
| Mean (SD) | 1.36 (1.38) |
| Median [Min, Max] | 1.00 [1.00, 8.00] |
| ZZMM | |
| Mean (SD) | 1.39 (0.955) |
| Median [Min, Max] | 1.00 [1.00, 4.00] |
| Missing | 10 (0.1%) |
| JYY | |
| Mean (SD) | 1.44 (0.782) |
| Median [Min, Max] | 1.00 [1.00, 5.00] |
| Missing | 15 (0.1%) |
| F.edu | |
| Mean (SD) | 2.98 (2.47) |
| Median [Min, Max] | 3.00 [1.00, 14.0] |
| Missing | 832 (6.6%) |
| ZZD | |
| Mean (SD) | 2.13 (0.900) |
| Median [Min, Max] | 2.00 [1.00, 4.00] |
| Missing | 7809 (62.1%) |
| income | |
| Mean (SD) | 3.12 (1.84) |
| Median [Min, Max] | 3.00 [1.00, 6.00] |
| Missing | 682 (5.4%) |
table1(~ MZ+ZZMM+JYY+F.edu+ZZD|income, data=cgssGPA4.0)## Warning in table1.formula(~MZ + ZZMM + JYY + F.edu + ZZD | income, data =
## cgssGPA4.0): Terms to the right of '|' in formula 'x' define table columns and
## are expected to be factors with meaningful labels.| 1 (N=3821) |
2 (N=1411) |
3 (N=1483) |
4 (N=1162) |
5 (N=2714) |
6 (N=1309) |
Overall (N=12582) |
|
|---|---|---|---|---|---|---|---|
| MZ | |||||||
| Mean (SD) | 1.54 (1.71) | 1.48 (1.62) | 1.32 (1.24) | 1.24 (1.12) | 1.20 (0.991) | 1.16 (0.877) | 1.36 (1.38) |
| Median [Min, Max] | 1.00 [1.00, 8.00] | 1.00 [1.00, 8.00] | 1.00 [1.00, 8.00] | 1.00 [1.00, 8.00] | 1.00 [1.00, 8.00] | 1.00 [1.00, 8.00] | 1.00 [1.00, 8.00] |
| ZZMM | |||||||
| Mean (SD) | 1.17 (0.595) | 1.18 (0.681) | 1.28 (0.833) | 1.38 (0.957) | 1.65 (1.21) | 1.83 (1.30) | 1.39 (0.955) |
| Median [Min, Max] | 1.00 [1.00, 4.00] | 1.00 [1.00, 4.00] | 1.00 [1.00, 4.00] | 1.00 [1.00, 4.00] | 1.00 [1.00, 4.00] | 1.00 [1.00, 4.00] | 1.00 [1.00, 4.00] |
| Missing | 1 (0.0%) | 2 (0.1%) | 2 (0.1%) | 0 (0%) | 1 (0.0%) | 0 (0%) | 10 (0.1%) |
| JYY | |||||||
| Mean (SD) | 1.26 (0.645) | 1.16 (0.452) | 1.27 (0.582) | 1.37 (0.673) | 1.57 (0.813) | 2.14 (1.03) | 1.44 (0.782) |
| Median [Min, Max] | 1.00 [1.00, 5.00] | 1.00 [1.00, 4.00] | 1.00 [1.00, 5.00] | 1.00 [1.00, 5.00] | 1.00 [1.00, 5.00] | 2.00 [1.00, 5.00] | 1.00 [1.00, 5.00] |
| Missing | 3 (0.1%) | 3 (0.2%) | 1 (0.1%) | 2 (0.2%) | 3 (0.1%) | 2 (0.2%) | 15 (0.1%) |
| F.edu | |||||||
| Mean (SD) | 2.33 (2.12) | 2.23 (1.80) | 2.79 (2.30) | 3.01 (2.28) | 3.41 (2.48) | 4.80 (3.05) | 2.98 (2.47) |
| Median [Min, Max] | 1.00 [1.00, 14.0] | 1.00 [1.00, 14.0] | 3.00 [1.00, 14.0] | 3.00 [1.00, 14.0] | 3.00 [1.00, 14.0] | 4.00 [1.00, 14.0] | 3.00 [1.00, 14.0] |
| Missing | 257 (6.7%) | 87 (6.2%) | 103 (6.9%) | 63 (5.4%) | 173 (6.4%) | 69 (5.3%) | 832 (6.6%) |
| ZZD | |||||||
| Mean (SD) | 2.12 (1.01) | 2.21 (0.998) | 2.32 (0.974) | 2.29 (0.933) | 2.11 (0.854) | 1.93 (0.769) | 2.13 (0.900) |
| Median [Min, Max] | 2.00 [1.00, 4.00] | 2.00 [1.00, 4.00] | 2.00 [1.00, 4.00] | 2.00 [1.00, 4.00] | 2.00 [1.00, 4.00] | 2.00 [1.00, 4.00] | 2.00 [1.00, 4.00] |
| Missing | 3532 (92.4%) | 969 (68.7%) | 826 (55.7%) | 573 (49.3%) | 1232 (45.4%) | 220 (16.8%) | 7809 (62.1%) |
ggstatsplot::ggbetweenstats(data=cgssGPA4.0,x=income,y=MZ,title="民族与收入")
ggstatsplot::ggpiestats(
data = cgssGPA4.0,
x = ZZMM,
y = income,
title = "政治面貌与收入",
legend.title = "政治面貌",
caption = substitute(paste(italic("Source"), ": cgss2017"))
)## Warning in stats::chisq.test(x = x_arg, correct = FALSE): Chi-squared
## approximation may be incorrect
ggstatsplot::ggbarstats(
data = cgssGPA4.0,
x = JYY,
y = income,
title = "讲英文水平与收入",
xlab = "收入",
legend.title = "讲英文水平"
)
ggplot(cgssGPA4.0[is.na(cgssGPA4.0$income) == F,], aes(x=income)) +
geom_bar(stat="count",position = "dodge",na.rm = TRUE)+geom_text(stat='count',aes(label=..count..),vjust=-1,position=position_dodge(width=0.9))
ggplot(cgssGPA4.0[is.na(cgssGPA4.0$income) == F,], aes(x=ZZD,fill=income,na.rm = TRUE)) +
geom_bar(stat="count",na.rm = TRUE)+geom_text(stat='count',aes(label=..count..),position=position_stack(vjust=0.5))+
facet_wrap(~income)## Warning: Removed 7352 rows containing non-finite values (stat_count).
ggstatsplot::ggbarstats(
data = cgssGPA4.0,
x = F.edu,
y = income,
title = "父亲受教育程度与收入",
xlab = "收入",
legend.title = "父亲受教育程度"
)## Warning in stats::chisq.test(x = x_arg, correct = FALSE): Chi-squared
## approximation may be incorrect## Warning: Number of labels is greater than default palette color count.
## Try using another color `palette` (and/or `package`).## 
ggplot(cgssGPA4.0[is.na(cgssGPA4.0$income) == F,], aes(x=ZZMM,fill=income,na.rm = TRUE)) +
geom_bar(stat="count",na.rm = TRUE)+geom_text(stat='count',aes(label=..count..),position=position_stack(vjust=0.5))+
facet_wrap(~income)## Warning: Removed 6 rows containing non-finite values (stat_count).
ggplot(cgssGPA4.0[is.na(cgssGPA4.0$income) == F,], aes(x=JYY,fill=income,na.rm = TRUE)) +
geom_bar(stat="count",na.rm = TRUE)+geom_text(stat='count',aes(label=..count..),position=position_stack(vjust=0.5))+
facet_wrap(~income)## Warning: Removed 14 rows containing non-finite values (stat_count).