111.R
jinbo
2021-05-27
# install.packages("sjlabelled")
library(sjlabelled)
TY1<- read_spss("tcs2018.sav")
# View(TY1)
names(TY1)## [1] "ID" "A1" "A2" "A3" "A3.a" "A4a.1"
## [7] "A4b" "A5" "A5.a" "A6" "A6.a" "A7"
## [13] "A7.a" "A8" "A8.a" "B1" "B2.1" "B2.2"
## [19] "B3" "B4.1" "B4.2" "B5" "B6.1" "B6.2"
## [25] "B7" "B8.1" "B8.2" "B8.3" "B8.4" "B8.5"
## [31] "B8.6" "B8.7" "B8.8" "B8.9" "B8.10" "B8.11"
## [37] "B8.12" "B8.13" "B8.14" "B8.15" "B8.16" "B8.17"
## [43] "B8.18" "B8.19" "B8.20" "B8.21" "B8.22" "B8.23"
## [49] "B8.24" "B8.25" "B8.26" "B8.27" "B8.28" "B8.29"
## [55] "B8.30" "B8.31" "B8.32" "B8.33" "B8.34" "B8.35"
## [61] "B8.36" "B8.37" "B8.38" "B8.88" "B7.a" "B8.a"
## [67] "C1" "C2.1" "C2.2" "C3.1" "C3.2" "C3.3"
## [73] "C3.4" "C3.5" "C3.6" "C3.7" "C3.8" "C3.9"
## [79] "C3.10" "C3.11" "C3.12" "C3.13" "C3.14" "C3.15"
## [85] "C3.16" "C3.17" "C3.18" "C3.19" "C3.20" "C3.21"
## [91] "C3.22" "C3.23" "C3.24" "C3.25" "C3.26" "C3.27"
## [97] "C3.28" "C3.88" "C3.a" "D1" "D2" "D3"
## [103] "D4" "D5.1.a" "D5.1.b" "D5.2.a" "D5.2.b" "D5.3.a"
## [109] "D5.3.b" "D6.1" "D6.2" "D6.3" "D6.4.a" "D6.4.b"
## [115] "D6.4.c" "D6.5" "D6.6" "D7.1" "D7.2" "D7.3"
## [121] "D7.4" "D7.5" "D7.6" "D7.7" "D7.8" "D7.9"
## [127] "D7.10" "D7.11" "D7.12" "D7.13" "D7.14" "D7.15"
## [133] "D7.16" "D7.17" "D7.18" "D7.19" "D7.20" "D7.21"
## [139] "D7.22" "D7.23" "D7.24" "D7.25" "D7.26" "D7.27"
## [145] "D7.28" "D7.29" "D7.30" "D7.31" "D7.32" "D7.33"
## [151] "D7.34" "D7.35" "D7.36" "D7.37" "D7.38" "D7.39"
## [157] "D7.40" "D7.41" "D7.42" "D7.43" "D7.44" "D7.45"
## [163] "D7.46" "D7.47" "D7.48" "D7.49" "D7.88" "D7.a"
## [169] "E1a" "E1b.1" "E1b.2" "E2a" "E2b.1" "E2b.2"
## [175] "E3.1" "E3.2" "E3.3" "E3.4" "E3.5" "E3.6"
## [181] "E3.7" "E3.8" "E3.9" "E3.10" "E3.11" "E3.12"
## [187] "E3.13" "E3.14" "E3.15" "E3.16" "E3.17" "E3.18"
## [193] "E3.19" "E3.20" "E3.21" "E3.22" "E3.23" "E3.24"
## [199] "E3.25" "E3.26" "E3.27" "E3.28" "E3.29" "E3.30"
## [205] "E3.31" "E3.88" "E3.a" "F1a" "F1b.1" "F1b.2"
## [211] "F1c.1" "F1c.2" "F1c.3" "F1c.4" "F1c.5" "F1c.6"
## [217] "F1c.7" "F1c.8" "F1c.9" "F1c.10" "F1c.11" "F1c.12"
## [223] "F1c.13" "F1c.14" "F1c.15" "F1c.16" "F1c.17" "F1c.18"
## [229] "F1c.19" "F1c.20" "F1c.21" "F1c.22" "F1c.23" "F1c.24"
## [235] "F1c.25" "F1c.26" "F1c.27" "F1c.28" "F1c.29" "F1c.30"
## [241] "F1c.31" "F1c.32" "F1c.33" "F1c.34" "F1c.35" "F1c.88"
## [247] "F1c.a" "F2a" "F2b.1" "F2b.2" "F2c.1" "F2c.2"
## [253] "F2c.3" "F2c.4" "F2c.5" "F2c.6" "F2c.7" "F2c.8"
## [259] "F2c.9" "F2c.10" "F2c.11" "F2c.12" "F2c.13" "F2c.14"
## [265] "F2c.15" "F2c.16" "F2c.17" "F2c.18" "F2c.19" "F2c.20"
## [271] "F2c.21" "F2c.22" "F2c.23" "F2c.24" "F2c.25" "F2c.26"
## [277] "F2c.27" "F2c.28" "F2c.29" "F2c.30" "F2c.31" "F2c.32"
## [283] "F2c.33" "F2c.34" "F2c.35" "F2c.36" "F2c.37" "F2c.38"
## [289] "F2c.39" "F2c.40" "F2c.41" "F2c.42" "F2c.43" "F2c.88"
## [295] "F2c.a" "F3.1" "F3.2" "F3.3" "F3.4" "F3.5"
## [301] "F3.6" "F3.7" "F3.8" "F3.9" "F3.10" "F3.11"
## [307] "F3.12" "F3.13" "F3.14" "F3.15" "F3.16" "F3.17"
## [313] "F3.18" "F3.19" "F3.20" "F3.21" "F3.22" "F3.23"
## [319] "F3.24" "F3.25" "F3.26" "F3.88" "F3.a" "G1a"
## [325] "G1b" "G1c" "G1d" "G1e" "G1f" "G2.1.a.1"
## [331] "G2.1.a.2" "G2.1.b.1" "G2.1.b.2" "G2.1.c.1" "G2.1.c.2" "G2.2.a.1"
## [337] "G2.2.a.2" "G2.2.b.1" "G2.2.b.2" "G2.2.c.1" "G2.2.c.2" "H1"
## [343] "H2.1" "H2.2" "H3.1" "H3.2" "H3.3" "H3.4"
## [349] "H3.5" "H3.6" "H3.7" "H3.8" "H3.9" "H3.10"
## [355] "H3.11" "H3.12" "H3.13" "H3.14" "H3.15" "H3.16"
## [361] "H3.17" "H3.18" "H3.19" "H3.20" "H3.21" "H3.22"
## [367] "H3.23" "H3.24" "H3.25" "H3.26" "H3.27" "H3.28"
## [373] "H3.29" "H3.30" "H3.31" "H3.32" "H3.33" "H3.34"
## [379] "H3.35" "H3.36" "H3.37" "H3.38" "H3.39" "H3.40"
## [385] "H3.41" "H3.42" "H3.43" "H3.44" "H3.45" "H3.46"
## [391] "H3.47" "H3.48" "H3.49" "H3.50" "H3.51" "H3.52"
## [397] "H3.53" "H3.54" "H3.55" "H3.56" "H3.57" "H3.88"
## [403] "H4.1" "H4.2" "H4.3" "H3.a" "I1.1.1" "I1.1.2"
## [409] "I1.1.3" "I1.1.4" "I1.1.5" "I1.1.6" "I1.1.7" "I1.1.88"
## [415] "I1.1.90" "I1.2.1" "I1.2.2" "I1.2.3" "I1.2.4" "I1.2.5"
## [421] "I1.2.6" "I1.2.7" "I1.2.88" "I1.2.90" "I2a" "I2b.1"
## [427] "I2b.2" "I2c.1" "I2c.2" "I2c.3" "I2c.4" "I2c.5"
## [433] "I2c.6" "I2c.7" "I2c.8" "I2c.9" "I2c.10" "I2c.11"
## [439] "I2c.12" "I2c.13" "I2c.14" "I2c.15" "I2c.16" "I2c.88"
## [445] "I2d.1" "I2d.2" "I2d.3" "I2d.4" "I2d.5" "I2d.6"
## [451] "I2d.7" "I2d.8" "I2d.9" "I2d.10" "I2d.11" "I2d.12"
## [457] "I2d.88" "I2e" "I2e.a" "I2f.1" "I2f.2" "I2f.3"
## [463] "I2f.4" "I2f.5" "I2f.6" "I2f.7" "I2f.8" "I2f.9"
## [469] "I2f.10" "I3a" "I3b.1" "I3b.2" "I3c" "I3d.1"
## [475] "I3d.2" "I3d.3" "I3d.4" "I3d.5" "I3d.6" "I3d.7"
## [481] "I3d.8" "I3d.9" "I3d.10" "I3d.11" "I3d.12" "I3d.13"
## [487] "I3d.14" "I3d.15" "I3d.16" "I3d.17" "I3d.88" "I3e.1"
## [493] "I3e.2" "I3e.3" "I3e.4" "I3f" "I1.2.a" "I1.1.a"
## [499] "I2c.a" "I2d.a" "I3c.a" "I3d.a" "J1" "J1.a"
## [505] "J2" "J2.a" "J3" "J3.a" "J4.1" "J4.2"
## [511] "J4.3" "J4.4" "J4.5" "J5a.1" "J5a.2" "J5a.3"
## [517] "J5a.4" "J5a.5" "J5a.6" "J5a.7" "J5a.8" "J5a.9"
## [523] "J5a.10" "J5a.88" "J5a.90" "J5a.a" "J5b.1" "J5b.2"
## [529] "J5b.3" "J5b.4" "J5b.5" "J5b.6" "J5b.88" "J5b.90"
## [535] "J5b.101" "J5b.a" "K1.1" "K1.2" "K1.3" "K1.4"
## [541] "K1.5" "K1.6" "K1.7" "L1a" "L1b" "L1c"
## [547] "L2.1" "L2.2" "L2.3" "L2.4" "L2.5" "L2.6"
## [553] "L3" "L4" "L5" "L6" "L7a" "L7b"
## [559] "L7c" "L8" "M1" "M2" "M3a" "M3b"
## [565] "M4" "M5.1" "M5.2" "N1a" "N1b" "N1c"
## [571] "N2.1" "N2.2" "N2.3" "N2.4" "N2.5" "N2.6"
## [577] "N2.7" "N2.8" "O1a" "O1a.a" "O1b" "O2"
## [583] "O3a" "O3b.1" "O3b.2" "O3b.3" "O3b.4" "O3b.5"
## [589] "O3b.6" "O3b.7" "O3b.8" "O3b.9" "O3b.10" "O3b.90"
## [595] "O3c.1" "O3c.2" "O3c.3" "O3c.4" "O3c.5" "O3c.6"
## [601] "O3c.7" "O3c.8" "O3c.9" "O3c.10" "O3c.90" "O4a.1"
## [607] "O4a.2" "O4a.3" "O4a.4" "O4a.5" "O4a.6" "O4a.7"
## [613] "O4a.8" "O4a.9" "O4a.10" "O4a.90" "O4b.1" "O4b.2"
## [619] "O4b.3" "O4b.4" "O4b.5" "O4b.6" "O4b.7" "O4b.8"
## [625] "O4b.9" "O4b.10" "O4b.90" "O5" "O6" "O7a"
## [631] "O8" "O9" "O9.a" "O10" "P1.1" "P1.2"
## [637] "P1.3" "P1.4" "P1.5" "P1.6" "P1.7" "P1.8"
## [643] "P1.9" "P1.10" "P2.1" "P2.2" "P2.3" "P2.4"
## [649] "P2.5" "P2.6" "P2.7" "P2.8" "P2.9" "P2.10"
## [655] "P3.1" "P3.2" "P3.3" "P3.4" "P3.5" "P4"
## [661] "P5" "Q1" "Q1.a" "Q2" "Q3a" "Q3b"
## [667] "Q2.a" "Q3a.a" "Q3b.a" "Q4" "RA2" "RRA2"
## [673] "Rcity" "ORcity1" "RA8" "RB2" "RRB2" "RB4"
## [679] "RRB4" "RB6" "RRB6" "RC2" "RRC2" "RD5.1.a"
## [685] "RD5.1.b" "RD5.2.a" "RD5.2.b" "RD5.3.a" "RD5.3.b" "RD6.6"
## [691] "RE1b" "RRE1b" "RE2b" "RRE2b" "RF1b" "RRF1b"
## [697] "RF2b" "RRF2b" "RG1a" "RG1b" "RG1c" "RG1e"
## [703] "RG2.1.a" "RRG2.1.a" "RG2.1.b" "RRG2.1.b" "RG2.1.c" "RRG2.1.c"
## [709] "RG2.2.a" "RRG2.2.a" "RG2.2.b" "RRG2.2.b" "RG2.2.c" "RRG2.2.c"
## [715] "RH2" "RRH2" "RI2b" "RRI2b" "RI2e" "RI3b"
## [721] "RRI3b" "RI3c" "RJ4.1" "RJ4.2" "RJ4.3" "RJ4.4"
## [727] "RJ4.5" "RQ3b.a" "RB13" "Weight"#
# library(sjPlot)
# view_df(TY1,
# file="tcs2018.html", # 結果直接另存新檔
# show.na = T, # 顯示未重新編碼前的無效值個數
# show.frq = T, # 顯示次數
# show.prc = T, # 顯示百分比
# encoding = "big5"
# )
#install.packages("tidyr")
library(tidyr)
#install.packages("showtext")
library(showtext)## Warning: package 'showtext' was built under R version 4.0.5## Loading required package: sysfonts## Warning: package 'sysfonts' was built under R version 4.0.5## Loading required package: showtextdbshowtext_auto()
#install.packages("sjmisc")
library(sjmisc)##
## Attaching package: 'sjmisc'## The following object is masked from 'package:tidyr':
##
## replace_na#install.packages("ggplot2")
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.0.5#install.packages("sjPlot")
library(sjPlot)## Warning: package 'sjPlot' was built under R version 4.0.5TY1$agegroup <- cut(TY1$A2, breaks=c(0,47,62, Inf),
labels=c("老年","中年","青年"))
names(TY1)## [1] "ID" "A1" "A2" "A3" "A3.a" "A4a.1"
## [7] "A4b" "A5" "A5.a" "A6" "A6.a" "A7"
## [13] "A7.a" "A8" "A8.a" "B1" "B2.1" "B2.2"
## [19] "B3" "B4.1" "B4.2" "B5" "B6.1" "B6.2"
## [25] "B7" "B8.1" "B8.2" "B8.3" "B8.4" "B8.5"
## [31] "B8.6" "B8.7" "B8.8" "B8.9" "B8.10" "B8.11"
## [37] "B8.12" "B8.13" "B8.14" "B8.15" "B8.16" "B8.17"
## [43] "B8.18" "B8.19" "B8.20" "B8.21" "B8.22" "B8.23"
## [49] "B8.24" "B8.25" "B8.26" "B8.27" "B8.28" "B8.29"
## [55] "B8.30" "B8.31" "B8.32" "B8.33" "B8.34" "B8.35"
## [61] "B8.36" "B8.37" "B8.38" "B8.88" "B7.a" "B8.a"
## [67] "C1" "C2.1" "C2.2" "C3.1" "C3.2" "C3.3"
## [73] "C3.4" "C3.5" "C3.6" "C3.7" "C3.8" "C3.9"
## [79] "C3.10" "C3.11" "C3.12" "C3.13" "C3.14" "C3.15"
## [85] "C3.16" "C3.17" "C3.18" "C3.19" "C3.20" "C3.21"
## [91] "C3.22" "C3.23" "C3.24" "C3.25" "C3.26" "C3.27"
## [97] "C3.28" "C3.88" "C3.a" "D1" "D2" "D3"
## [103] "D4" "D5.1.a" "D5.1.b" "D5.2.a" "D5.2.b" "D5.3.a"
## [109] "D5.3.b" "D6.1" "D6.2" "D6.3" "D6.4.a" "D6.4.b"
## [115] "D6.4.c" "D6.5" "D6.6" "D7.1" "D7.2" "D7.3"
## [121] "D7.4" "D7.5" "D7.6" "D7.7" "D7.8" "D7.9"
## [127] "D7.10" "D7.11" "D7.12" "D7.13" "D7.14" "D7.15"
## [133] "D7.16" "D7.17" "D7.18" "D7.19" "D7.20" "D7.21"
## [139] "D7.22" "D7.23" "D7.24" "D7.25" "D7.26" "D7.27"
## [145] "D7.28" "D7.29" "D7.30" "D7.31" "D7.32" "D7.33"
## [151] "D7.34" "D7.35" "D7.36" "D7.37" "D7.38" "D7.39"
## [157] "D7.40" "D7.41" "D7.42" "D7.43" "D7.44" "D7.45"
## [163] "D7.46" "D7.47" "D7.48" "D7.49" "D7.88" "D7.a"
## [169] "E1a" "E1b.1" "E1b.2" "E2a" "E2b.1" "E2b.2"
## [175] "E3.1" "E3.2" "E3.3" "E3.4" "E3.5" "E3.6"
## [181] "E3.7" "E3.8" "E3.9" "E3.10" "E3.11" "E3.12"
## [187] "E3.13" "E3.14" "E3.15" "E3.16" "E3.17" "E3.18"
## [193] "E3.19" "E3.20" "E3.21" "E3.22" "E3.23" "E3.24"
## [199] "E3.25" "E3.26" "E3.27" "E3.28" "E3.29" "E3.30"
## [205] "E3.31" "E3.88" "E3.a" "F1a" "F1b.1" "F1b.2"
## [211] "F1c.1" "F1c.2" "F1c.3" "F1c.4" "F1c.5" "F1c.6"
## [217] "F1c.7" "F1c.8" "F1c.9" "F1c.10" "F1c.11" "F1c.12"
## [223] "F1c.13" "F1c.14" "F1c.15" "F1c.16" "F1c.17" "F1c.18"
## [229] "F1c.19" "F1c.20" "F1c.21" "F1c.22" "F1c.23" "F1c.24"
## [235] "F1c.25" "F1c.26" "F1c.27" "F1c.28" "F1c.29" "F1c.30"
## [241] "F1c.31" "F1c.32" "F1c.33" "F1c.34" "F1c.35" "F1c.88"
## [247] "F1c.a" "F2a" "F2b.1" "F2b.2" "F2c.1" "F2c.2"
## [253] "F2c.3" "F2c.4" "F2c.5" "F2c.6" "F2c.7" "F2c.8"
## [259] "F2c.9" "F2c.10" "F2c.11" "F2c.12" "F2c.13" "F2c.14"
## [265] "F2c.15" "F2c.16" "F2c.17" "F2c.18" "F2c.19" "F2c.20"
## [271] "F2c.21" "F2c.22" "F2c.23" "F2c.24" "F2c.25" "F2c.26"
## [277] "F2c.27" "F2c.28" "F2c.29" "F2c.30" "F2c.31" "F2c.32"
## [283] "F2c.33" "F2c.34" "F2c.35" "F2c.36" "F2c.37" "F2c.38"
## [289] "F2c.39" "F2c.40" "F2c.41" "F2c.42" "F2c.43" "F2c.88"
## [295] "F2c.a" "F3.1" "F3.2" "F3.3" "F3.4" "F3.5"
## [301] "F3.6" "F3.7" "F3.8" "F3.9" "F3.10" "F3.11"
## [307] "F3.12" "F3.13" "F3.14" "F3.15" "F3.16" "F3.17"
## [313] "F3.18" "F3.19" "F3.20" "F3.21" "F3.22" "F3.23"
## [319] "F3.24" "F3.25" "F3.26" "F3.88" "F3.a" "G1a"
## [325] "G1b" "G1c" "G1d" "G1e" "G1f" "G2.1.a.1"
## [331] "G2.1.a.2" "G2.1.b.1" "G2.1.b.2" "G2.1.c.1" "G2.1.c.2" "G2.2.a.1"
## [337] "G2.2.a.2" "G2.2.b.1" "G2.2.b.2" "G2.2.c.1" "G2.2.c.2" "H1"
## [343] "H2.1" "H2.2" "H3.1" "H3.2" "H3.3" "H3.4"
## [349] "H3.5" "H3.6" "H3.7" "H3.8" "H3.9" "H3.10"
## [355] "H3.11" "H3.12" "H3.13" "H3.14" "H3.15" "H3.16"
## [361] "H3.17" "H3.18" "H3.19" "H3.20" "H3.21" "H3.22"
## [367] "H3.23" "H3.24" "H3.25" "H3.26" "H3.27" "H3.28"
## [373] "H3.29" "H3.30" "H3.31" "H3.32" "H3.33" "H3.34"
## [379] "H3.35" "H3.36" "H3.37" "H3.38" "H3.39" "H3.40"
## [385] "H3.41" "H3.42" "H3.43" "H3.44" "H3.45" "H3.46"
## [391] "H3.47" "H3.48" "H3.49" "H3.50" "H3.51" "H3.52"
## [397] "H3.53" "H3.54" "H3.55" "H3.56" "H3.57" "H3.88"
## [403] "H4.1" "H4.2" "H4.3" "H3.a" "I1.1.1" "I1.1.2"
## [409] "I1.1.3" "I1.1.4" "I1.1.5" "I1.1.6" "I1.1.7" "I1.1.88"
## [415] "I1.1.90" "I1.2.1" "I1.2.2" "I1.2.3" "I1.2.4" "I1.2.5"
## [421] "I1.2.6" "I1.2.7" "I1.2.88" "I1.2.90" "I2a" "I2b.1"
## [427] "I2b.2" "I2c.1" "I2c.2" "I2c.3" "I2c.4" "I2c.5"
## [433] "I2c.6" "I2c.7" "I2c.8" "I2c.9" "I2c.10" "I2c.11"
## [439] "I2c.12" "I2c.13" "I2c.14" "I2c.15" "I2c.16" "I2c.88"
## [445] "I2d.1" "I2d.2" "I2d.3" "I2d.4" "I2d.5" "I2d.6"
## [451] "I2d.7" "I2d.8" "I2d.9" "I2d.10" "I2d.11" "I2d.12"
## [457] "I2d.88" "I2e" "I2e.a" "I2f.1" "I2f.2" "I2f.3"
## [463] "I2f.4" "I2f.5" "I2f.6" "I2f.7" "I2f.8" "I2f.9"
## [469] "I2f.10" "I3a" "I3b.1" "I3b.2" "I3c" "I3d.1"
## [475] "I3d.2" "I3d.3" "I3d.4" "I3d.5" "I3d.6" "I3d.7"
## [481] "I3d.8" "I3d.9" "I3d.10" "I3d.11" "I3d.12" "I3d.13"
## [487] "I3d.14" "I3d.15" "I3d.16" "I3d.17" "I3d.88" "I3e.1"
## [493] "I3e.2" "I3e.3" "I3e.4" "I3f" "I1.2.a" "I1.1.a"
## [499] "I2c.a" "I2d.a" "I3c.a" "I3d.a" "J1" "J1.a"
## [505] "J2" "J2.a" "J3" "J3.a" "J4.1" "J4.2"
## [511] "J4.3" "J4.4" "J4.5" "J5a.1" "J5a.2" "J5a.3"
## [517] "J5a.4" "J5a.5" "J5a.6" "J5a.7" "J5a.8" "J5a.9"
## [523] "J5a.10" "J5a.88" "J5a.90" "J5a.a" "J5b.1" "J5b.2"
## [529] "J5b.3" "J5b.4" "J5b.5" "J5b.6" "J5b.88" "J5b.90"
## [535] "J5b.101" "J5b.a" "K1.1" "K1.2" "K1.3" "K1.4"
## [541] "K1.5" "K1.6" "K1.7" "L1a" "L1b" "L1c"
## [547] "L2.1" "L2.2" "L2.3" "L2.4" "L2.5" "L2.6"
## [553] "L3" "L4" "L5" "L6" "L7a" "L7b"
## [559] "L7c" "L8" "M1" "M2" "M3a" "M3b"
## [565] "M4" "M5.1" "M5.2" "N1a" "N1b" "N1c"
## [571] "N2.1" "N2.2" "N2.3" "N2.4" "N2.5" "N2.6"
## [577] "N2.7" "N2.8" "O1a" "O1a.a" "O1b" "O2"
## [583] "O3a" "O3b.1" "O3b.2" "O3b.3" "O3b.4" "O3b.5"
## [589] "O3b.6" "O3b.7" "O3b.8" "O3b.9" "O3b.10" "O3b.90"
## [595] "O3c.1" "O3c.2" "O3c.3" "O3c.4" "O3c.5" "O3c.6"
## [601] "O3c.7" "O3c.8" "O3c.9" "O3c.10" "O3c.90" "O4a.1"
## [607] "O4a.2" "O4a.3" "O4a.4" "O4a.5" "O4a.6" "O4a.7"
## [613] "O4a.8" "O4a.9" "O4a.10" "O4a.90" "O4b.1" "O4b.2"
## [619] "O4b.3" "O4b.4" "O4b.5" "O4b.6" "O4b.7" "O4b.8"
## [625] "O4b.9" "O4b.10" "O4b.90" "O5" "O6" "O7a"
## [631] "O8" "O9" "O9.a" "O10" "P1.1" "P1.2"
## [637] "P1.3" "P1.4" "P1.5" "P1.6" "P1.7" "P1.8"
## [643] "P1.9" "P1.10" "P2.1" "P2.2" "P2.3" "P2.4"
## [649] "P2.5" "P2.6" "P2.7" "P2.8" "P2.9" "P2.10"
## [655] "P3.1" "P3.2" "P3.3" "P3.4" "P3.5" "P4"
## [661] "P5" "Q1" "Q1.a" "Q2" "Q3a" "Q3b"
## [667] "Q2.a" "Q3a.a" "Q3b.a" "Q4" "RA2" "RRA2"
## [673] "Rcity" "ORcity1" "RA8" "RB2" "RRB2" "RB4"
## [679] "RRB4" "RB6" "RRB6" "RC2" "RRC2" "RD5.1.a"
## [685] "RD5.1.b" "RD5.2.a" "RD5.2.b" "RD5.3.a" "RD5.3.b" "RD6.6"
## [691] "RE1b" "RRE1b" "RE2b" "RRE2b" "RF1b" "RRF1b"
## [697] "RF2b" "RRF2b" "RG1a" "RG1b" "RG1c" "RG1e"
## [703] "RG2.1.a" "RRG2.1.a" "RG2.1.b" "RRG2.1.b" "RG2.1.c" "RRG2.1.c"
## [709] "RG2.2.a" "RRG2.2.a" "RG2.2.b" "RRG2.2.b" "RG2.2.c" "RRG2.2.c"
## [715] "RH2" "RRH2" "RI2b" "RRI2b" "RI2e" "RI3b"
## [721] "RRI3b" "RI3c" "RJ4.1" "RJ4.2" "RJ4.3" "RJ4.4"
## [727] "RJ4.5" "RQ3b.a" "RB13" "Weight" "agegroup"#LINE
DF <- TY1[,c(731,428:443)]
DF[is.na(DF)] <- 0
DF1 <- gather(DF, key = "cope", value = "count", I2c.1,I2c.2,I2c.3,I2c.4,I2c.5,I2c.6,I2c.7,I2c.8,I2c.9,
I2c.10,I2c.11,I2c.12,I2c.13,I2c.14,I2c.15,I2c.16)## Warning: attributes are not identical across measure variables;
## they will be droppedDF2 <- subset(DF1, count==1)
sjt.xtab(DF2$agegroup,DF2$cope,encoding = "big-5",show.cell.prc = T,
show.row.prc = T,
show.col.prc = T)| agegroup | cope | Total | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| I2c.1 | I2c.10 | I2c.11 | I2c.12 | I2c.13 | I2c.14 | I2c.15 | I2c.16 | I2c.2 | I2c.3 | I2c.4 | I2c.5 | I2c.6 | I2c.7 | I2c.8 | I2c.9 | ||
| 老年 |
222 19.2 % 15.5 % 3 % |
69 6 % 15.9 % 0.9 % |
64 5.5 % 15.8 % 0.9 % |
41 3.6 % 20.4 % 0.5 % |
1 0.1 % 4.2 % 0 % |
90 7.8 % 16.2 % 1.2 % |
50 4.3 % 14.4 % 0.7 % |
61 5.3 % 15.5 % 0.8 % |
216 18.7 % 17.9 % 2.9 % |
28 2.4 % 14.9 % 0.4 % |
132 11.4 % 16.5 % 1.8 % |
32 2.8 % 16.2 % 0.4 % |
14 1.2 % 22.6 % 0.2 % |
66 5.7 % 19.2 % 0.9 % |
27 2.3 % 9.9 % 0.4 % |
41 3.6 % 6.3 % 0.5 % |
1154 100 % 15.3 % 15.5 % |
| 中年 |
489 20 % 34.1 % 6.5 % |
129 5.3 % 29.8 % 1.7 % |
159 6.5 % 39.2 % 2.1 % |
77 3.1 % 38.3 % 1 % |
9 0.4 % 37.5 % 0.1 % |
183 7.5 % 32.9 % 2.4 % |
112 4.6 % 32.3 % 1.5 % |
124 5.1 % 31.6 % 1.6 % |
398 16.3 % 33 % 5.3 % |
42 1.7 % 22.3 % 0.6 % |
262 10.7 % 32.7 % 3.5 % |
66 2.7 % 33.5 % 0.9 % |
16 0.7 % 25.8 % 0.2 % |
111 4.5 % 32.3 % 1.5 % |
63 2.6 % 23.2 % 0.8 % |
208 8.5 % 31.8 % 2.8 % |
2448 100 % 32.6 % 32.5 % |
| 青年 |
723 18.5 % 50.4 % 9.6 % |
235 6 % 54.3 % 3.1 % |
183 4.7 % 45.1 % 2.4 % |
83 2.1 % 41.3 % 1.1 % |
14 0.4 % 58.3 % 0.2 % |
283 7.2 % 50.9 % 3.8 % |
185 4.7 % 53.3 % 2.5 % |
208 5.3 % 52.9 % 2.8 % |
591 15.1 % 49 % 7.9 % |
118 3 % 62.8 % 1.6 % |
408 10.4 % 50.9 % 5.4 % |
99 2.5 % 50.3 % 1.3 % |
32 0.8 % 51.6 % 0.4 % |
167 4.3 % 48.5 % 2.2 % |
182 4.6 % 66.9 % 2.4 % |
406 10.4 % 62 % 5.4 % |
3917 100 % 52.1 % 52.1 % |
| Total |
1434 19.1 % 100 % 19.1 % |
433 5.8 % 100 % 5.8 % |
406 5.4 % 100 % 5.4 % |
201 2.7 % 100 % 2.7 % |
24 0.3 % 100 % 0.3 % |
556 7.4 % 100 % 7.4 % |
347 4.6 % 100 % 4.6 % |
393 5.2 % 100 % 5.2 % |
1205 16 % 100 % 16 % |
188 2.5 % 100 % 2.5 % |
802 10.7 % 100 % 10.7 % |
197 2.6 % 100 % 2.6 % |
62 0.8 % 100 % 0.8 % |
344 4.6 % 100 % 4.6 % |
272 3.6 % 100 % 3.6 % |
655 8.7 % 100 % 8.7 % |
7519 100 % 100 % 100 % |
χ2=123.416 · df=30 · Cramer’s V=0.091 · Fisher’s p=0.000 |
class(DF2$agegroup)## [1] "factor"class(DF2$cope)## [1] "character"DF2$cope <- as.factor(DF2$cope)
# #使用Line的動機(未採用)
# ggplot(DF2,
# aes(cope, fill=agegroup))+
# geom_bar(position = "dodge")+
# labs(title = "使用Line的動機",
# x="使用原因和動機",y="人數",
# caption="藝馨製 資料來源：台灣傳播調查資料庫")+
# theme(panel.background = element_blank())+
# theme(plot.title = element_text(hjust = 0.5))+
# theme(axis.title.y = element_text(vjust = 0.5, hjust = 0.5, angle = 0))+
# geom_text(stat="count",aes(label=..count..),size=3,
# position = position_dodge(width = 1))+
# scale_x_discrete("使用Line的主要原因和動機",labels = c("I2c.1" = "聯絡事情","I2c.2" = "維持與親友之間的關係",
# "I2c.3" = "交新朋友", "I2c.4" = "與朋友分享心情",
# "I2c.5" = "分享時事或發表個人評論","I2c.6" = "展現你個人特色",
# "I2c.7" = "怕漏掉親友間發生的事情或話題", "I2c.8" = "怕漏掉同儕間發生的事情或話題",
# "I2c.9"="工作或課業所需","I2c.10"="安排活動或行程",
# "I2c.11"="獲得新聞訊息","I2c.12"="學習新事物",
# "I2c.13"="逃避學校或工作的事情","I2c.14"="打發時間","I2c.15"="娛樂","I12C.16"="習慣"))+
# scale_fill_manual("各年齡層",values=c("lightskyblue1", "rosybrown2", "steelblue1","tomato1"))+
# theme(axis.text.x = element_text(vjust = 0.9,hjust = 1, angle=30))
#
#使用Line的動機(表六)
ggplot(DF2,aes(x=cope, fill=agegroup))+geom_bar()+
facet_grid(.~agegroup)+
labs(title = "使用LINE的動機",
x="使用動機",y="人數",
caption="藝馨製 資料來源：台灣傳播調查資料庫")+
theme(panel.background = element_blank())+
theme(plot.title = element_text(hjust = 0.5))+
theme(axis.title.y = element_text(vjust = 0.5, hjust = 0.5, angle = 0))+
geom_text(stat="count",aes(label=..count..),size=3,
position = position_dodge(width = 1))+
scale_x_discrete("使用原因和動機",labels = c("I2c.1" = "聯絡事情","I2c.2" = "維持與親友之間的關係",
"I2c.3" = "交新朋友", "I2c.4" = "與朋友分享心情",
"I2c.5" = "分享時事或發表個人評論","I2c.6" = "展現你個人特色",
"I2c.7" = "怕漏掉親友間發生的事情或話題", "I2c.8" = "怕漏掉同儕間發生的事情或話題",
"I2c.9"="工作或課業所需","I2c.10"="安排活動或行程",
"I2c.11"="獲得新聞訊息","I2c.12"="學習新事物",
"I2c.13"="逃避學校或工作的事情","I2c.14"="打發時間","I2c.15"="娛樂","I2c.16"="習慣"))+
theme(axis.text.x = element_text(vjust = 0.9,hjust = 1, angle=30))
#FACEBOOK
names(TY1)## [1] "ID" "A1" "A2" "A3" "A3.a" "A4a.1"
## [7] "A4b" "A5" "A5.a" "A6" "A6.a" "A7"
## [13] "A7.a" "A8" "A8.a" "B1" "B2.1" "B2.2"
## [19] "B3" "B4.1" "B4.2" "B5" "B6.1" "B6.2"
## [25] "B7" "B8.1" "B8.2" "B8.3" "B8.4" "B8.5"
## [31] "B8.6" "B8.7" "B8.8" "B8.9" "B8.10" "B8.11"
## [37] "B8.12" "B8.13" "B8.14" "B8.15" "B8.16" "B8.17"
## [43] "B8.18" "B8.19" "B8.20" "B8.21" "B8.22" "B8.23"
## [49] "B8.24" "B8.25" "B8.26" "B8.27" "B8.28" "B8.29"
## [55] "B8.30" "B8.31" "B8.32" "B8.33" "B8.34" "B8.35"
## [61] "B8.36" "B8.37" "B8.38" "B8.88" "B7.a" "B8.a"
## [67] "C1" "C2.1" "C2.2" "C3.1" "C3.2" "C3.3"
## [73] "C3.4" "C3.5" "C3.6" "C3.7" "C3.8" "C3.9"
## [79] "C3.10" "C3.11" "C3.12" "C3.13" "C3.14" "C3.15"
## [85] "C3.16" "C3.17" "C3.18" "C3.19" "C3.20" "C3.21"
## [91] "C3.22" "C3.23" "C3.24" "C3.25" "C3.26" "C3.27"
## [97] "C3.28" "C3.88" "C3.a" "D1" "D2" "D3"
## [103] "D4" "D5.1.a" "D5.1.b" "D5.2.a" "D5.2.b" "D5.3.a"
## [109] "D5.3.b" "D6.1" "D6.2" "D6.3" "D6.4.a" "D6.4.b"
## [115] "D6.4.c" "D6.5" "D6.6" "D7.1" "D7.2" "D7.3"
## [121] "D7.4" "D7.5" "D7.6" "D7.7" "D7.8" "D7.9"
## [127] "D7.10" "D7.11" "D7.12" "D7.13" "D7.14" "D7.15"
## [133] "D7.16" "D7.17" "D7.18" "D7.19" "D7.20" "D7.21"
## [139] "D7.22" "D7.23" "D7.24" "D7.25" "D7.26" "D7.27"
## [145] "D7.28" "D7.29" "D7.30" "D7.31" "D7.32" "D7.33"
## [151] "D7.34" "D7.35" "D7.36" "D7.37" "D7.38" "D7.39"
## [157] "D7.40" "D7.41" "D7.42" "D7.43" "D7.44" "D7.45"
## [163] "D7.46" "D7.47" "D7.48" "D7.49" "D7.88" "D7.a"
## [169] "E1a" "E1b.1" "E1b.2" "E2a" "E2b.1" "E2b.2"
## [175] "E3.1" "E3.2" "E3.3" "E3.4" "E3.5" "E3.6"
## [181] "E3.7" "E3.8" "E3.9" "E3.10" "E3.11" "E3.12"
## [187] "E3.13" "E3.14" "E3.15" "E3.16" "E3.17" "E3.18"
## [193] "E3.19" "E3.20" "E3.21" "E3.22" "E3.23" "E3.24"
## [199] "E3.25" "E3.26" "E3.27" "E3.28" "E3.29" "E3.30"
## [205] "E3.31" "E3.88" "E3.a" "F1a" "F1b.1" "F1b.2"
## [211] "F1c.1" "F1c.2" "F1c.3" "F1c.4" "F1c.5" "F1c.6"
## [217] "F1c.7" "F1c.8" "F1c.9" "F1c.10" "F1c.11" "F1c.12"
## [223] "F1c.13" "F1c.14" "F1c.15" "F1c.16" "F1c.17" "F1c.18"
## [229] "F1c.19" "F1c.20" "F1c.21" "F1c.22" "F1c.23" "F1c.24"
## [235] "F1c.25" "F1c.26" "F1c.27" "F1c.28" "F1c.29" "F1c.30"
## [241] "F1c.31" "F1c.32" "F1c.33" "F1c.34" "F1c.35" "F1c.88"
## [247] "F1c.a" "F2a" "F2b.1" "F2b.2" "F2c.1" "F2c.2"
## [253] "F2c.3" "F2c.4" "F2c.5" "F2c.6" "F2c.7" "F2c.8"
## [259] "F2c.9" "F2c.10" "F2c.11" "F2c.12" "F2c.13" "F2c.14"
## [265] "F2c.15" "F2c.16" "F2c.17" "F2c.18" "F2c.19" "F2c.20"
## [271] "F2c.21" "F2c.22" "F2c.23" "F2c.24" "F2c.25" "F2c.26"
## [277] "F2c.27" "F2c.28" "F2c.29" "F2c.30" "F2c.31" "F2c.32"
## [283] "F2c.33" "F2c.34" "F2c.35" "F2c.36" "F2c.37" "F2c.38"
## [289] "F2c.39" "F2c.40" "F2c.41" "F2c.42" "F2c.43" "F2c.88"
## [295] "F2c.a" "F3.1" "F3.2" "F3.3" "F3.4" "F3.5"
## [301] "F3.6" "F3.7" "F3.8" "F3.9" "F3.10" "F3.11"
## [307] "F3.12" "F3.13" "F3.14" "F3.15" "F3.16" "F3.17"
## [313] "F3.18" "F3.19" "F3.20" "F3.21" "F3.22" "F3.23"
## [319] "F3.24" "F3.25" "F3.26" "F3.88" "F3.a" "G1a"
## [325] "G1b" "G1c" "G1d" "G1e" "G1f" "G2.1.a.1"
## [331] "G2.1.a.2" "G2.1.b.1" "G2.1.b.2" "G2.1.c.1" "G2.1.c.2" "G2.2.a.1"
## [337] "G2.2.a.2" "G2.2.b.1" "G2.2.b.2" "G2.2.c.1" "G2.2.c.2" "H1"
## [343] "H2.1" "H2.2" "H3.1" "H3.2" "H3.3" "H3.4"
## [349] "H3.5" "H3.6" "H3.7" "H3.8" "H3.9" "H3.10"
## [355] "H3.11" "H3.12" "H3.13" "H3.14" "H3.15" "H3.16"
## [361] "H3.17" "H3.18" "H3.19" "H3.20" "H3.21" "H3.22"
## [367] "H3.23" "H3.24" "H3.25" "H3.26" "H3.27" "H3.28"
## [373] "H3.29" "H3.30" "H3.31" "H3.32" "H3.33" "H3.34"
## [379] "H3.35" "H3.36" "H3.37" "H3.38" "H3.39" "H3.40"
## [385] "H3.41" "H3.42" "H3.43" "H3.44" "H3.45" "H3.46"
## [391] "H3.47" "H3.48" "H3.49" "H3.50" "H3.51" "H3.52"
## [397] "H3.53" "H3.54" "H3.55" "H3.56" "H3.57" "H3.88"
## [403] "H4.1" "H4.2" "H4.3" "H3.a" "I1.1.1" "I1.1.2"
## [409] "I1.1.3" "I1.1.4" "I1.1.5" "I1.1.6" "I1.1.7" "I1.1.88"
## [415] "I1.1.90" "I1.2.1" "I1.2.2" "I1.2.3" "I1.2.4" "I1.2.5"
## [421] "I1.2.6" "I1.2.7" "I1.2.88" "I1.2.90" "I2a" "I2b.1"
## [427] "I2b.2" "I2c.1" "I2c.2" "I2c.3" "I2c.4" "I2c.5"
## [433] "I2c.6" "I2c.7" "I2c.8" "I2c.9" "I2c.10" "I2c.11"
## [439] "I2c.12" "I2c.13" "I2c.14" "I2c.15" "I2c.16" "I2c.88"
## [445] "I2d.1" "I2d.2" "I2d.3" "I2d.4" "I2d.5" "I2d.6"
## [451] "I2d.7" "I2d.8" "I2d.9" "I2d.10" "I2d.11" "I2d.12"
## [457] "I2d.88" "I2e" "I2e.a" "I2f.1" "I2f.2" "I2f.3"
## [463] "I2f.4" "I2f.5" "I2f.6" "I2f.7" "I2f.8" "I2f.9"
## [469] "I2f.10" "I3a" "I3b.1" "I3b.2" "I3c" "I3d.1"
## [475] "I3d.2" "I3d.3" "I3d.4" "I3d.5" "I3d.6" "I3d.7"
## [481] "I3d.8" "I3d.9" "I3d.10" "I3d.11" "I3d.12" "I3d.13"
## [487] "I3d.14" "I3d.15" "I3d.16" "I3d.17" "I3d.88" "I3e.1"
## [493] "I3e.2" "I3e.3" "I3e.4" "I3f" "I1.2.a" "I1.1.a"
## [499] "I2c.a" "I2d.a" "I3c.a" "I3d.a" "J1" "J1.a"
## [505] "J2" "J2.a" "J3" "J3.a" "J4.1" "J4.2"
## [511] "J4.3" "J4.4" "J4.5" "J5a.1" "J5a.2" "J5a.3"
## [517] "J5a.4" "J5a.5" "J5a.6" "J5a.7" "J5a.8" "J5a.9"
## [523] "J5a.10" "J5a.88" "J5a.90" "J5a.a" "J5b.1" "J5b.2"
## [529] "J5b.3" "J5b.4" "J5b.5" "J5b.6" "J5b.88" "J5b.90"
## [535] "J5b.101" "J5b.a" "K1.1" "K1.2" "K1.3" "K1.4"
## [541] "K1.5" "K1.6" "K1.7" "L1a" "L1b" "L1c"
## [547] "L2.1" "L2.2" "L2.3" "L2.4" "L2.5" "L2.6"
## [553] "L3" "L4" "L5" "L6" "L7a" "L7b"
## [559] "L7c" "L8" "M1" "M2" "M3a" "M3b"
## [565] "M4" "M5.1" "M5.2" "N1a" "N1b" "N1c"
## [571] "N2.1" "N2.2" "N2.3" "N2.4" "N2.5" "N2.6"
## [577] "N2.7" "N2.8" "O1a" "O1a.a" "O1b" "O2"
## [583] "O3a" "O3b.1" "O3b.2" "O3b.3" "O3b.4" "O3b.5"
## [589] "O3b.6" "O3b.7" "O3b.8" "O3b.9" "O3b.10" "O3b.90"
## [595] "O3c.1" "O3c.2" "O3c.3" "O3c.4" "O3c.5" "O3c.6"
## [601] "O3c.7" "O3c.8" "O3c.9" "O3c.10" "O3c.90" "O4a.1"
## [607] "O4a.2" "O4a.3" "O4a.4" "O4a.5" "O4a.6" "O4a.7"
## [613] "O4a.8" "O4a.9" "O4a.10" "O4a.90" "O4b.1" "O4b.2"
## [619] "O4b.3" "O4b.4" "O4b.5" "O4b.6" "O4b.7" "O4b.8"
## [625] "O4b.9" "O4b.10" "O4b.90" "O5" "O6" "O7a"
## [631] "O8" "O9" "O9.a" "O10" "P1.1" "P1.2"
## [637] "P1.3" "P1.4" "P1.5" "P1.6" "P1.7" "P1.8"
## [643] "P1.9" "P1.10" "P2.1" "P2.2" "P2.3" "P2.4"
## [649] "P2.5" "P2.6" "P2.7" "P2.8" "P2.9" "P2.10"
## [655] "P3.1" "P3.2" "P3.3" "P3.4" "P3.5" "P4"
## [661] "P5" "Q1" "Q1.a" "Q2" "Q3a" "Q3b"
## [667] "Q2.a" "Q3a.a" "Q3b.a" "Q4" "RA2" "RRA2"
## [673] "Rcity" "ORcity1" "RA8" "RB2" "RRB2" "RB4"
## [679] "RRB4" "RB6" "RRB6" "RC2" "RRC2" "RD5.1.a"
## [685] "RD5.1.b" "RD5.2.a" "RD5.2.b" "RD5.3.a" "RD5.3.b" "RD6.6"
## [691] "RE1b" "RRE1b" "RE2b" "RRE2b" "RF1b" "RRF1b"
## [697] "RF2b" "RRF2b" "RG1a" "RG1b" "RG1c" "RG1e"
## [703] "RG2.1.a" "RRG2.1.a" "RG2.1.b" "RRG2.1.b" "RG2.1.c" "RRG2.1.c"
## [709] "RG2.2.a" "RRG2.2.a" "RG2.2.b" "RRG2.2.b" "RG2.2.c" "RRG2.2.c"
## [715] "RH2" "RRH2" "RI2b" "RRI2b" "RI2e" "RI3b"
## [721] "RRI3b" "RI3c" "RJ4.1" "RJ4.2" "RJ4.3" "RJ4.4"
## [727] "RJ4.5" "RQ3b.a" "RB13" "Weight" "agegroup"DF3 <- TY1[,c(731,474:479,481:489)]
DF3[is.na(DF3)] <- 0
DF4 <- gather(DF3, key = "cope", value = "count", I3d.1,I3d.2,I3d.3,I3d.4,I3d.5,I3d.6,I3d.8,I3d.9,
I3d.10,I3d.11,I3d.12,I3d.13,I3d.14,I3d.15,I3d.16)## Warning: attributes are not identical across measure variables;
## they will be droppedDF5 <- subset(DF4, count==1)
#install.packages("sjPlot")
library(sjPlot)
sjt.xtab(DF5$agegroup,DF5$cope,encoding = "big-5",show.cell.prc = T,
show.row.prc = T,
show.col.prc = T)| agegroup | cope | Total | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| I3d.1 | I3d.10 | I3d.11 | I3d.12 | I3d.13 | I3d.14 | I3d.15 | I3d.16 | I3d.2 | I3d.3 | I3d.4 | I3d.5 | I3d.6 | I3d.8 | I3d.9 | ||
| 老年 |
55 11.4 % 10.1 % 1 % |
11 2.3 % 6.1 % 0.2 % |
17 3.5 % 11 % 0.3 % |
40 8.3 % 9.2 % 0.7 % |
21 4.3 % 7.5 % 0.4 % |
0 0 % 0 % 0 % |
55 11.4 % 9.1 % 1 % |
31 6.4 % 7.3 % 0.6 % |
94 19.4 % 11.3 % 1.7 % |
15 3.1 % 5.2 % 0.3 % |
67 13.8 % 9.5 % 1.2 % |
28 5.8 % 7.1 % 0.5 % |
10 2.1 % 6.5 % 0.2 % |
32 6.6 % 11.7 % 0.6 % |
8 1.7 % 4 % 0.1 % |
484 100 % 8.8 % 8.8 % |
| 中年 |
160 10.3 % 29.3 % 2.9 % |
50 3.2 % 27.8 % 0.9 % |
36 2.3 % 23.2 % 0.7 % |
127 8.2 % 29.1 % 2.3 % |
89 5.7 % 31.9 % 1.6 % |
1 0.1 % 5 % 0 % |
181 11.6 % 30.1 % 3.3 % |
111 7.1 % 26.1 % 2 % |
260 16.7 % 31.2 % 4.7 % |
69 4.4 % 23.8 % 1.3 % |
225 14.4 % 31.9 % 4.1 % |
95 6.1 % 24.1 % 1.7 % |
33 2.1 % 21.6 % 0.6 % |
75 4.8 % 27.5 % 1.4 % |
46 3 % 23.2 % 0.8 % |
1558 100 % 28.4 % 28.3 % |
| 青年 |
332 9.6 % 60.7 % 6 % |
119 3.4 % 66.1 % 2.2 % |
102 3 % 65.8 % 1.9 % |
270 7.8 % 61.8 % 4.9 % |
169 4.9 % 60.6 % 3.1 % |
19 0.6 % 95 % 0.3 % |
366 10.6 % 60.8 % 6.7 % |
284 8.2 % 66.7 % 5.2 % |
480 13.9 % 57.6 % 8.7 % |
206 6 % 71 % 3.7 % |
414 12 % 58.6 % 7.5 % |
271 7.9 % 68.8 % 4.9 % |
110 3.2 % 71.9 % 2 % |
166 4.8 % 60.8 % 3 % |
144 4.2 % 72.7 % 2.6 % |
3452 100 % 62.8 % 62.7 % |
| Total |
547 10 % 100 % 10 % |
180 3.3 % 100 % 3.3 % |
155 2.8 % 100 % 2.8 % |
437 8 % 100 % 8 % |
279 5.1 % 100 % 5.1 % |
20 0.4 % 100 % 0.4 % |
602 11 % 100 % 11 % |
426 7.8 % 100 % 7.8 % |
834 15.2 % 100 % 15.2 % |
290 5.3 % 100 % 5.3 % |
706 12.9 % 100 % 12.9 % |
394 7.2 % 100 % 7.2 % |
153 2.8 % 100 % 2.8 % |
273 5 % 100 % 5 % |
198 3.6 % 100 % 3.6 % |
5494 100 % 100 % 100 % |
χ2=72.018 · df=28 · Cramer’s V=0.081 · Fisher’s p=0.000 |
class(DF5$agegroup)## [1] "factor"class(DF5$cope)## [1] "character"DF5$cope <- as.factor(DF5$cope)
library(plyr)## Warning: package 'plyr' was built under R version 4.0.5ess3 = ddply(DF5,.(cope),function(.){
res = prop.table(table(factor(.$agegroup)))
res3 = table(factor(.$agegroup))
data.frame(lab=names(res), y=c(res),yy =c(res3))
})
# #使用Facebook的動機(未採用)
# ggplot(DF5,
# aes(cope, fill=agegroup))+
# geom_bar(position = "dodge")+
# labs(title = "使用Facebook的動機",
# x="各年齡層",y="人數",
# caption="藝馨製 資料來源：台灣傳播調查資料庫")+
# theme(panel.background = element_blank())+
# theme(plot.title = element_text(hjust = 0.5))+
# theme(axis.title.y = element_text(vjust = 0.5, hjust = 0.5, angle = 0))+
# geom_text(stat="count",aes(label=..count..),size=3,
# position = position_dodge(width = 1))+
# scale_x_discrete("使用動機",labels = c("I3d.1" = "聯絡事情","I3d.2" = "維持與親友之間的關係",
# "I3d.3" = "交新朋友", "I3d.4" = "與朋友分享心情",
# "I3d.5" = "分享時事或發表個人評論","I3d.6" = "展現你個人特色",
# "I3d.8" = "怕漏掉親友間發生的事情或話題", "I3d.9" = "怕漏掉同儕間發生的事情或話題",
# "I3d.10"="工作或課業所需","I3d.11"="安排活動或行程",
# "I3d.12"="獲得新聞訊息","I3d.13"="學習新事物",
# "I3d.14"="逃避學校或工作的事情","I3d.15"="打發時間","I3d.16"="娛樂","I3d.17"="習慣"))+
# scale_fill_manual("年齡層",values=c("lightskyblue1", "rosybrown2", "steelblue1","tomato1"))+
# theme(axis.text.x = element_text(vjust = 0.9,hjust = 1, angle=30))
#使用Facebook的動機(表五)
ggplot(DF5,aes(x=cope, fill=agegroup))+geom_bar()+ #+geom_bar()預設為+geom_bar(position="stack")
facet_grid(.~agegroup)+
labs(title = "使用Facebook的動機",
x="使用動機",y="人數",
caption="藝馨製 資料來源：台灣傳播調查資料庫")+
theme(panel.background = element_blank())+
theme(plot.title = element_text(hjust = 0.5))+
theme(axis.title.y = element_text(vjust = 0.5, hjust = 0.5, angle = 0))+
geom_text(stat="count",aes(label=..count..),size=3,
position = position_dodge(width = 1))+
scale_x_discrete("使用動機",labels = c("I3d.1" = "聯絡事情","I3d.2" = "維持與親友之間的關係",
"I3d.3" = "交新朋友", "I3d.4" = "與朋友分享心情",
"I3d.5" = "分享時事或發表個人評論","I3d.6" = "展現你個人特色",
"I3d.8" = "怕漏掉親友間發生的事情或話題", "I3d.9" = "怕漏掉同儕間發生的事情或話題",
"I3d.10"="工作或課業所需","I3d.11"="安排活動或行程",
"I3d.12"="獲得新聞訊息","I3d.13"="學習新事物",
"I3d.14"="逃避學校或工作的事情","I3d.15"="打發時間","I3d.16"="娛樂"))+
theme(axis.text.x = element_text(vjust = 0.9,hjust = 1, angle=30))
#偏好使用之即時通訊軟體(表二)
names(TY1)## [1] "ID" "A1" "A2" "A3" "A3.a" "A4a.1"
## [7] "A4b" "A5" "A5.a" "A6" "A6.a" "A7"
## [13] "A7.a" "A8" "A8.a" "B1" "B2.1" "B2.2"
## [19] "B3" "B4.1" "B4.2" "B5" "B6.1" "B6.2"
## [25] "B7" "B8.1" "B8.2" "B8.3" "B8.4" "B8.5"
## [31] "B8.6" "B8.7" "B8.8" "B8.9" "B8.10" "B8.11"
## [37] "B8.12" "B8.13" "B8.14" "B8.15" "B8.16" "B8.17"
## [43] "B8.18" "B8.19" "B8.20" "B8.21" "B8.22" "B8.23"
## [49] "B8.24" "B8.25" "B8.26" "B8.27" "B8.28" "B8.29"
## [55] "B8.30" "B8.31" "B8.32" "B8.33" "B8.34" "B8.35"
## [61] "B8.36" "B8.37" "B8.38" "B8.88" "B7.a" "B8.a"
## [67] "C1" "C2.1" "C2.2" "C3.1" "C3.2" "C3.3"
## [73] "C3.4" "C3.5" "C3.6" "C3.7" "C3.8" "C3.9"
## [79] "C3.10" "C3.11" "C3.12" "C3.13" "C3.14" "C3.15"
## [85] "C3.16" "C3.17" "C3.18" "C3.19" "C3.20" "C3.21"
## [91] "C3.22" "C3.23" "C3.24" "C3.25" "C3.26" "C3.27"
## [97] "C3.28" "C3.88" "C3.a" "D1" "D2" "D3"
## [103] "D4" "D5.1.a" "D5.1.b" "D5.2.a" "D5.2.b" "D5.3.a"
## [109] "D5.3.b" "D6.1" "D6.2" "D6.3" "D6.4.a" "D6.4.b"
## [115] "D6.4.c" "D6.5" "D6.6" "D7.1" "D7.2" "D7.3"
## [121] "D7.4" "D7.5" "D7.6" "D7.7" "D7.8" "D7.9"
## [127] "D7.10" "D7.11" "D7.12" "D7.13" "D7.14" "D7.15"
## [133] "D7.16" "D7.17" "D7.18" "D7.19" "D7.20" "D7.21"
## [139] "D7.22" "D7.23" "D7.24" "D7.25" "D7.26" "D7.27"
## [145] "D7.28" "D7.29" "D7.30" "D7.31" "D7.32" "D7.33"
## [151] "D7.34" "D7.35" "D7.36" "D7.37" "D7.38" "D7.39"
## [157] "D7.40" "D7.41" "D7.42" "D7.43" "D7.44" "D7.45"
## [163] "D7.46" "D7.47" "D7.48" "D7.49" "D7.88" "D7.a"
## [169] "E1a" "E1b.1" "E1b.2" "E2a" "E2b.1" "E2b.2"
## [175] "E3.1" "E3.2" "E3.3" "E3.4" "E3.5" "E3.6"
## [181] "E3.7" "E3.8" "E3.9" "E3.10" "E3.11" "E3.12"
## [187] "E3.13" "E3.14" "E3.15" "E3.16" "E3.17" "E3.18"
## [193] "E3.19" "E3.20" "E3.21" "E3.22" "E3.23" "E3.24"
## [199] "E3.25" "E3.26" "E3.27" "E3.28" "E3.29" "E3.30"
## [205] "E3.31" "E3.88" "E3.a" "F1a" "F1b.1" "F1b.2"
## [211] "F1c.1" "F1c.2" "F1c.3" "F1c.4" "F1c.5" "F1c.6"
## [217] "F1c.7" "F1c.8" "F1c.9" "F1c.10" "F1c.11" "F1c.12"
## [223] "F1c.13" "F1c.14" "F1c.15" "F1c.16" "F1c.17" "F1c.18"
## [229] "F1c.19" "F1c.20" "F1c.21" "F1c.22" "F1c.23" "F1c.24"
## [235] "F1c.25" "F1c.26" "F1c.27" "F1c.28" "F1c.29" "F1c.30"
## [241] "F1c.31" "F1c.32" "F1c.33" "F1c.34" "F1c.35" "F1c.88"
## [247] "F1c.a" "F2a" "F2b.1" "F2b.2" "F2c.1" "F2c.2"
## [253] "F2c.3" "F2c.4" "F2c.5" "F2c.6" "F2c.7" "F2c.8"
## [259] "F2c.9" "F2c.10" "F2c.11" "F2c.12" "F2c.13" "F2c.14"
## [265] "F2c.15" "F2c.16" "F2c.17" "F2c.18" "F2c.19" "F2c.20"
## [271] "F2c.21" "F2c.22" "F2c.23" "F2c.24" "F2c.25" "F2c.26"
## [277] "F2c.27" "F2c.28" "F2c.29" "F2c.30" "F2c.31" "F2c.32"
## [283] "F2c.33" "F2c.34" "F2c.35" "F2c.36" "F2c.37" "F2c.38"
## [289] "F2c.39" "F2c.40" "F2c.41" "F2c.42" "F2c.43" "F2c.88"
## [295] "F2c.a" "F3.1" "F3.2" "F3.3" "F3.4" "F3.5"
## [301] "F3.6" "F3.7" "F3.8" "F3.9" "F3.10" "F3.11"
## [307] "F3.12" "F3.13" "F3.14" "F3.15" "F3.16" "F3.17"
## [313] "F3.18" "F3.19" "F3.20" "F3.21" "F3.22" "F3.23"
## [319] "F3.24" "F3.25" "F3.26" "F3.88" "F3.a" "G1a"
## [325] "G1b" "G1c" "G1d" "G1e" "G1f" "G2.1.a.1"
## [331] "G2.1.a.2" "G2.1.b.1" "G2.1.b.2" "G2.1.c.1" "G2.1.c.2" "G2.2.a.1"
## [337] "G2.2.a.2" "G2.2.b.1" "G2.2.b.2" "G2.2.c.1" "G2.2.c.2" "H1"
## [343] "H2.1" "H2.2" "H3.1" "H3.2" "H3.3" "H3.4"
## [349] "H3.5" "H3.6" "H3.7" "H3.8" "H3.9" "H3.10"
## [355] "H3.11" "H3.12" "H3.13" "H3.14" "H3.15" "H3.16"
## [361] "H3.17" "H3.18" "H3.19" "H3.20" "H3.21" "H3.22"
## [367] "H3.23" "H3.24" "H3.25" "H3.26" "H3.27" "H3.28"
## [373] "H3.29" "H3.30" "H3.31" "H3.32" "H3.33" "H3.34"
## [379] "H3.35" "H3.36" "H3.37" "H3.38" "H3.39" "H3.40"
## [385] "H3.41" "H3.42" "H3.43" "H3.44" "H3.45" "H3.46"
## [391] "H3.47" "H3.48" "H3.49" "H3.50" "H3.51" "H3.52"
## [397] "H3.53" "H3.54" "H3.55" "H3.56" "H3.57" "H3.88"
## [403] "H4.1" "H4.2" "H4.3" "H3.a" "I1.1.1" "I1.1.2"
## [409] "I1.1.3" "I1.1.4" "I1.1.5" "I1.1.6" "I1.1.7" "I1.1.88"
## [415] "I1.1.90" "I1.2.1" "I1.2.2" "I1.2.3" "I1.2.4" "I1.2.5"
## [421] "I1.2.6" "I1.2.7" "I1.2.88" "I1.2.90" "I2a" "I2b.1"
## [427] "I2b.2" "I2c.1" "I2c.2" "I2c.3" "I2c.4" "I2c.5"
## [433] "I2c.6" "I2c.7" "I2c.8" "I2c.9" "I2c.10" "I2c.11"
## [439] "I2c.12" "I2c.13" "I2c.14" "I2c.15" "I2c.16" "I2c.88"
## [445] "I2d.1" "I2d.2" "I2d.3" "I2d.4" "I2d.5" "I2d.6"
## [451] "I2d.7" "I2d.8" "I2d.9" "I2d.10" "I2d.11" "I2d.12"
## [457] "I2d.88" "I2e" "I2e.a" "I2f.1" "I2f.2" "I2f.3"
## [463] "I2f.4" "I2f.5" "I2f.6" "I2f.7" "I2f.8" "I2f.9"
## [469] "I2f.10" "I3a" "I3b.1" "I3b.2" "I3c" "I3d.1"
## [475] "I3d.2" "I3d.3" "I3d.4" "I3d.5" "I3d.6" "I3d.7"
## [481] "I3d.8" "I3d.9" "I3d.10" "I3d.11" "I3d.12" "I3d.13"
## [487] "I3d.14" "I3d.15" "I3d.16" "I3d.17" "I3d.88" "I3e.1"
## [493] "I3e.2" "I3e.3" "I3e.4" "I3f" "I1.2.a" "I1.1.a"
## [499] "I2c.a" "I2d.a" "I3c.a" "I3d.a" "J1" "J1.a"
## [505] "J2" "J2.a" "J3" "J3.a" "J4.1" "J4.2"
## [511] "J4.3" "J4.4" "J4.5" "J5a.1" "J5a.2" "J5a.3"
## [517] "J5a.4" "J5a.5" "J5a.6" "J5a.7" "J5a.8" "J5a.9"
## [523] "J5a.10" "J5a.88" "J5a.90" "J5a.a" "J5b.1" "J5b.2"
## [529] "J5b.3" "J5b.4" "J5b.5" "J5b.6" "J5b.88" "J5b.90"
## [535] "J5b.101" "J5b.a" "K1.1" "K1.2" "K1.3" "K1.4"
## [541] "K1.5" "K1.6" "K1.7" "L1a" "L1b" "L1c"
## [547] "L2.1" "L2.2" "L2.3" "L2.4" "L2.5" "L2.6"
## [553] "L3" "L4" "L5" "L6" "L7a" "L7b"
## [559] "L7c" "L8" "M1" "M2" "M3a" "M3b"
## [565] "M4" "M5.1" "M5.2" "N1a" "N1b" "N1c"
## [571] "N2.1" "N2.2" "N2.3" "N2.4" "N2.5" "N2.6"
## [577] "N2.7" "N2.8" "O1a" "O1a.a" "O1b" "O2"
## [583] "O3a" "O3b.1" "O3b.2" "O3b.3" "O3b.4" "O3b.5"
## [589] "O3b.6" "O3b.7" "O3b.8" "O3b.9" "O3b.10" "O3b.90"
## [595] "O3c.1" "O3c.2" "O3c.3" "O3c.4" "O3c.5" "O3c.6"
## [601] "O3c.7" "O3c.8" "O3c.9" "O3c.10" "O3c.90" "O4a.1"
## [607] "O4a.2" "O4a.3" "O4a.4" "O4a.5" "O4a.6" "O4a.7"
## [613] "O4a.8" "O4a.9" "O4a.10" "O4a.90" "O4b.1" "O4b.2"
## [619] "O4b.3" "O4b.4" "O4b.5" "O4b.6" "O4b.7" "O4b.8"
## [625] "O4b.9" "O4b.10" "O4b.90" "O5" "O6" "O7a"
## [631] "O8" "O9" "O9.a" "O10" "P1.1" "P1.2"
## [637] "P1.3" "P1.4" "P1.5" "P1.6" "P1.7" "P1.8"
## [643] "P1.9" "P1.10" "P2.1" "P2.2" "P2.3" "P2.4"
## [649] "P2.5" "P2.6" "P2.7" "P2.8" "P2.9" "P2.10"
## [655] "P3.1" "P3.2" "P3.3" "P3.4" "P3.5" "P4"
## [661] "P5" "Q1" "Q1.a" "Q2" "Q3a" "Q3b"
## [667] "Q2.a" "Q3a.a" "Q3b.a" "Q4" "RA2" "RRA2"
## [673] "Rcity" "ORcity1" "RA8" "RB2" "RRB2" "RB4"
## [679] "RRB4" "RB6" "RRB6" "RC2" "RRC2" "RD5.1.a"
## [685] "RD5.1.b" "RD5.2.a" "RD5.2.b" "RD5.3.a" "RD5.3.b" "RD6.6"
## [691] "RE1b" "RRE1b" "RE2b" "RRE2b" "RF1b" "RRF1b"
## [697] "RF2b" "RRF2b" "RG1a" "RG1b" "RG1c" "RG1e"
## [703] "RG2.1.a" "RRG2.1.a" "RG2.1.b" "RRG2.1.b" "RG2.1.c" "RRG2.1.c"
## [709] "RG2.2.a" "RRG2.2.a" "RG2.2.b" "RRG2.2.b" "RG2.2.c" "RRG2.2.c"
## [715] "RH2" "RRH2" "RI2b" "RRI2b" "RI2e" "RI3b"
## [721] "RRI3b" "RI3c" "RJ4.1" "RJ4.2" "RJ4.3" "RJ4.4"
## [727] "RJ4.5" "RQ3b.a" "RB13" "Weight" "agegroup"DF6 <- TY1[,c(731,407:413)]
DF6[is.na(DF6)] <- 0
DF7 <- gather(DF6, key = "cope", value = "count", I1.1.1,I1.1.2,I1.1.3,I1.1.4,I1.1.5,I1.1.6,I1.1.7)## Warning: attributes are not identical across measure variables;
## they will be droppedDF8 <- subset(DF7, count==1)
sjt.xtab(DF8$agegroup,DF8$cope,encoding = "big-5",show.cell.prc = T,
show.row.prc = T,
show.col.prc = T)| agegroup | cope | Total | ||||||
|---|---|---|---|---|---|---|---|---|
| I1.1.1 | I1.1.2 | I1.1.3 | I1.1.4 | I1.1.5 | I1.1.6 | I1.1.7 | ||
| 老年 |
254 77.7 % 16.1 % 9.3 % |
39 11.9 % 5.3 % 1.4 % |
21 6.4 % 9.6 % 0.8 % |
4 1.2 % 7 % 0.1 % |
1 0.3 % 11.1 % 0 % |
6 1.8 % 9.7 % 0.2 % |
2 0.6 % 3.2 % 0.1 % |
327 100 % 12 % 11.9 % |
| 中年 |
537 63.5 % 34.1 % 19.7 % |
205 24.2 % 27.9 % 7.5 % |
71 8.4 % 32.4 % 2.6 % |
10 1.2 % 17.5 % 0.4 % |
3 0.4 % 33.3 % 0.1 % |
14 1.7 % 22.6 % 0.5 % |
6 0.7 % 9.5 % 0.2 % |
846 100 % 31.1 % 31 % |
| 青年 |
785 50.7 % 49.8 % 28.8 % |
491 31.7 % 66.8 % 18 % |
127 8.2 % 58 % 4.7 % |
43 2.8 % 75.4 % 1.6 % |
5 0.3 % 55.6 % 0.2 % |
42 2.7 % 67.7 % 1.5 % |
55 3.6 % 87.3 % 2 % |
1548 100 % 56.9 % 56.8 % |
| Total |
1576 57.9 % 100 % 57.9 % |
735 27 % 100 % 27 % |
219 8 % 100 % 8 % |
57 2.1 % 100 % 2.1 % |
9 0.3 % 100 % 0.3 % |
62 2.3 % 100 % 2.3 % |
63 2.3 % 100 % 2.3 % |
2721 100 % 100 % 100 % |
χ2=119.140 · df=12 · Cramer’s V=0.148 · Fisher’s p=0.000 |
class(DF8$agegroup)## [1] "factor"class(DF8$cope)## [1] "character"DF8$cope <- as.factor(DF8$cope)
library(plyr)
ess2 = ddply(DF8,.(cope),function(.){
res = prop.table(table(factor(.$agegroup)))
res2 = table(factor(.$agegroup))
data.frame(lab=names(res), y=c(res),yy =c(res2))
})
ggplot(DF8,
aes(cope, fill=agegroup))+
geom_bar(position = "dodge")+
labs(title = "偏好使用之即時通訊軟體",
x="各年齡層",y="人數",
caption="藝馨製 資料來源：台灣傳播調查資料庫")+
theme(panel.background = element_blank())+
theme(plot.title = element_text(hjust = 0.5))+
theme(axis.title.y = element_text(vjust = 0.5, hjust = 0.5, angle = 0))+
geom_text(stat="count",aes(label=..count..),size=3,
position = position_dodge(width = 1))+
scale_x_discrete("即時通訊軟體",labels = c("I1.1.1"="Line","I1.1.2"="Facebook Messenger","I1.1.3"="Wechat",
"I1.1.4"="WhatsApp","I1.1.5"="Hangouts","I1.1.6"="Skype","I1.1.7"="Facetime"))+
scale_fill_manual("各年齡層",values=c("lightskyblue1", "rosybrown2", "steelblue1","tomato1"))+
theme(axis.text.x = element_text(vjust = 0.9,hjust = 1, angle=30))
#偏好使用之社群媒體(表一)
names(TY1)## [1] "ID" "A1" "A2" "A3" "A3.a" "A4a.1"
## [7] "A4b" "A5" "A5.a" "A6" "A6.a" "A7"
## [13] "A7.a" "A8" "A8.a" "B1" "B2.1" "B2.2"
## [19] "B3" "B4.1" "B4.2" "B5" "B6.1" "B6.2"
## [25] "B7" "B8.1" "B8.2" "B8.3" "B8.4" "B8.5"
## [31] "B8.6" "B8.7" "B8.8" "B8.9" "B8.10" "B8.11"
## [37] "B8.12" "B8.13" "B8.14" "B8.15" "B8.16" "B8.17"
## [43] "B8.18" "B8.19" "B8.20" "B8.21" "B8.22" "B8.23"
## [49] "B8.24" "B8.25" "B8.26" "B8.27" "B8.28" "B8.29"
## [55] "B8.30" "B8.31" "B8.32" "B8.33" "B8.34" "B8.35"
## [61] "B8.36" "B8.37" "B8.38" "B8.88" "B7.a" "B8.a"
## [67] "C1" "C2.1" "C2.2" "C3.1" "C3.2" "C3.3"
## [73] "C3.4" "C3.5" "C3.6" "C3.7" "C3.8" "C3.9"
## [79] "C3.10" "C3.11" "C3.12" "C3.13" "C3.14" "C3.15"
## [85] "C3.16" "C3.17" "C3.18" "C3.19" "C3.20" "C3.21"
## [91] "C3.22" "C3.23" "C3.24" "C3.25" "C3.26" "C3.27"
## [97] "C3.28" "C3.88" "C3.a" "D1" "D2" "D3"
## [103] "D4" "D5.1.a" "D5.1.b" "D5.2.a" "D5.2.b" "D5.3.a"
## [109] "D5.3.b" "D6.1" "D6.2" "D6.3" "D6.4.a" "D6.4.b"
## [115] "D6.4.c" "D6.5" "D6.6" "D7.1" "D7.2" "D7.3"
## [121] "D7.4" "D7.5" "D7.6" "D7.7" "D7.8" "D7.9"
## [127] "D7.10" "D7.11" "D7.12" "D7.13" "D7.14" "D7.15"
## [133] "D7.16" "D7.17" "D7.18" "D7.19" "D7.20" "D7.21"
## [139] "D7.22" "D7.23" "D7.24" "D7.25" "D7.26" "D7.27"
## [145] "D7.28" "D7.29" "D7.30" "D7.31" "D7.32" "D7.33"
## [151] "D7.34" "D7.35" "D7.36" "D7.37" "D7.38" "D7.39"
## [157] "D7.40" "D7.41" "D7.42" "D7.43" "D7.44" "D7.45"
## [163] "D7.46" "D7.47" "D7.48" "D7.49" "D7.88" "D7.a"
## [169] "E1a" "E1b.1" "E1b.2" "E2a" "E2b.1" "E2b.2"
## [175] "E3.1" "E3.2" "E3.3" "E3.4" "E3.5" "E3.6"
## [181] "E3.7" "E3.8" "E3.9" "E3.10" "E3.11" "E3.12"
## [187] "E3.13" "E3.14" "E3.15" "E3.16" "E3.17" "E3.18"
## [193] "E3.19" "E3.20" "E3.21" "E3.22" "E3.23" "E3.24"
## [199] "E3.25" "E3.26" "E3.27" "E3.28" "E3.29" "E3.30"
## [205] "E3.31" "E3.88" "E3.a" "F1a" "F1b.1" "F1b.2"
## [211] "F1c.1" "F1c.2" "F1c.3" "F1c.4" "F1c.5" "F1c.6"
## [217] "F1c.7" "F1c.8" "F1c.9" "F1c.10" "F1c.11" "F1c.12"
## [223] "F1c.13" "F1c.14" "F1c.15" "F1c.16" "F1c.17" "F1c.18"
## [229] "F1c.19" "F1c.20" "F1c.21" "F1c.22" "F1c.23" "F1c.24"
## [235] "F1c.25" "F1c.26" "F1c.27" "F1c.28" "F1c.29" "F1c.30"
## [241] "F1c.31" "F1c.32" "F1c.33" "F1c.34" "F1c.35" "F1c.88"
## [247] "F1c.a" "F2a" "F2b.1" "F2b.2" "F2c.1" "F2c.2"
## [253] "F2c.3" "F2c.4" "F2c.5" "F2c.6" "F2c.7" "F2c.8"
## [259] "F2c.9" "F2c.10" "F2c.11" "F2c.12" "F2c.13" "F2c.14"
## [265] "F2c.15" "F2c.16" "F2c.17" "F2c.18" "F2c.19" "F2c.20"
## [271] "F2c.21" "F2c.22" "F2c.23" "F2c.24" "F2c.25" "F2c.26"
## [277] "F2c.27" "F2c.28" "F2c.29" "F2c.30" "F2c.31" "F2c.32"
## [283] "F2c.33" "F2c.34" "F2c.35" "F2c.36" "F2c.37" "F2c.38"
## [289] "F2c.39" "F2c.40" "F2c.41" "F2c.42" "F2c.43" "F2c.88"
## [295] "F2c.a" "F3.1" "F3.2" "F3.3" "F3.4" "F3.5"
## [301] "F3.6" "F3.7" "F3.8" "F3.9" "F3.10" "F3.11"
## [307] "F3.12" "F3.13" "F3.14" "F3.15" "F3.16" "F3.17"
## [313] "F3.18" "F3.19" "F3.20" "F3.21" "F3.22" "F3.23"
## [319] "F3.24" "F3.25" "F3.26" "F3.88" "F3.a" "G1a"
## [325] "G1b" "G1c" "G1d" "G1e" "G1f" "G2.1.a.1"
## [331] "G2.1.a.2" "G2.1.b.1" "G2.1.b.2" "G2.1.c.1" "G2.1.c.2" "G2.2.a.1"
## [337] "G2.2.a.2" "G2.2.b.1" "G2.2.b.2" "G2.2.c.1" "G2.2.c.2" "H1"
## [343] "H2.1" "H2.2" "H3.1" "H3.2" "H3.3" "H3.4"
## [349] "H3.5" "H3.6" "H3.7" "H3.8" "H3.9" "H3.10"
## [355] "H3.11" "H3.12" "H3.13" "H3.14" "H3.15" "H3.16"
## [361] "H3.17" "H3.18" "H3.19" "H3.20" "H3.21" "H3.22"
## [367] "H3.23" "H3.24" "H3.25" "H3.26" "H3.27" "H3.28"
## [373] "H3.29" "H3.30" "H3.31" "H3.32" "H3.33" "H3.34"
## [379] "H3.35" "H3.36" "H3.37" "H3.38" "H3.39" "H3.40"
## [385] "H3.41" "H3.42" "H3.43" "H3.44" "H3.45" "H3.46"
## [391] "H3.47" "H3.48" "H3.49" "H3.50" "H3.51" "H3.52"
## [397] "H3.53" "H3.54" "H3.55" "H3.56" "H3.57" "H3.88"
## [403] "H4.1" "H4.2" "H4.3" "H3.a" "I1.1.1" "I1.1.2"
## [409] "I1.1.3" "I1.1.4" "I1.1.5" "I1.1.6" "I1.1.7" "I1.1.88"
## [415] "I1.1.90" "I1.2.1" "I1.2.2" "I1.2.3" "I1.2.4" "I1.2.5"
## [421] "I1.2.6" "I1.2.7" "I1.2.88" "I1.2.90" "I2a" "I2b.1"
## [427] "I2b.2" "I2c.1" "I2c.2" "I2c.3" "I2c.4" "I2c.5"
## [433] "I2c.6" "I2c.7" "I2c.8" "I2c.9" "I2c.10" "I2c.11"
## [439] "I2c.12" "I2c.13" "I2c.14" "I2c.15" "I2c.16" "I2c.88"
## [445] "I2d.1" "I2d.2" "I2d.3" "I2d.4" "I2d.5" "I2d.6"
## [451] "I2d.7" "I2d.8" "I2d.9" "I2d.10" "I2d.11" "I2d.12"
## [457] "I2d.88" "I2e" "I2e.a" "I2f.1" "I2f.2" "I2f.3"
## [463] "I2f.4" "I2f.5" "I2f.6" "I2f.7" "I2f.8" "I2f.9"
## [469] "I2f.10" "I3a" "I3b.1" "I3b.2" "I3c" "I3d.1"
## [475] "I3d.2" "I3d.3" "I3d.4" "I3d.5" "I3d.6" "I3d.7"
## [481] "I3d.8" "I3d.9" "I3d.10" "I3d.11" "I3d.12" "I3d.13"
## [487] "I3d.14" "I3d.15" "I3d.16" "I3d.17" "I3d.88" "I3e.1"
## [493] "I3e.2" "I3e.3" "I3e.4" "I3f" "I1.2.a" "I1.1.a"
## [499] "I2c.a" "I2d.a" "I3c.a" "I3d.a" "J1" "J1.a"
## [505] "J2" "J2.a" "J3" "J3.a" "J4.1" "J4.2"
## [511] "J4.3" "J4.4" "J4.5" "J5a.1" "J5a.2" "J5a.3"
## [517] "J5a.4" "J5a.5" "J5a.6" "J5a.7" "J5a.8" "J5a.9"
## [523] "J5a.10" "J5a.88" "J5a.90" "J5a.a" "J5b.1" "J5b.2"
## [529] "J5b.3" "J5b.4" "J5b.5" "J5b.6" "J5b.88" "J5b.90"
## [535] "J5b.101" "J5b.a" "K1.1" "K1.2" "K1.3" "K1.4"
## [541] "K1.5" "K1.6" "K1.7" "L1a" "L1b" "L1c"
## [547] "L2.1" "L2.2" "L2.3" "L2.4" "L2.5" "L2.6"
## [553] "L3" "L4" "L5" "L6" "L7a" "L7b"
## [559] "L7c" "L8" "M1" "M2" "M3a" "M3b"
## [565] "M4" "M5.1" "M5.2" "N1a" "N1b" "N1c"
## [571] "N2.1" "N2.2" "N2.3" "N2.4" "N2.5" "N2.6"
## [577] "N2.7" "N2.8" "O1a" "O1a.a" "O1b" "O2"
## [583] "O3a" "O3b.1" "O3b.2" "O3b.3" "O3b.4" "O3b.5"
## [589] "O3b.6" "O3b.7" "O3b.8" "O3b.9" "O3b.10" "O3b.90"
## [595] "O3c.1" "O3c.2" "O3c.3" "O3c.4" "O3c.5" "O3c.6"
## [601] "O3c.7" "O3c.8" "O3c.9" "O3c.10" "O3c.90" "O4a.1"
## [607] "O4a.2" "O4a.3" "O4a.4" "O4a.5" "O4a.6" "O4a.7"
## [613] "O4a.8" "O4a.9" "O4a.10" "O4a.90" "O4b.1" "O4b.2"
## [619] "O4b.3" "O4b.4" "O4b.5" "O4b.6" "O4b.7" "O4b.8"
## [625] "O4b.9" "O4b.10" "O4b.90" "O5" "O6" "O7a"
## [631] "O8" "O9" "O9.a" "O10" "P1.1" "P1.2"
## [637] "P1.3" "P1.4" "P1.5" "P1.6" "P1.7" "P1.8"
## [643] "P1.9" "P1.10" "P2.1" "P2.2" "P2.3" "P2.4"
## [649] "P2.5" "P2.6" "P2.7" "P2.8" "P2.9" "P2.10"
## [655] "P3.1" "P3.2" "P3.3" "P3.4" "P3.5" "P4"
## [661] "P5" "Q1" "Q1.a" "Q2" "Q3a" "Q3b"
## [667] "Q2.a" "Q3a.a" "Q3b.a" "Q4" "RA2" "RRA2"
## [673] "Rcity" "ORcity1" "RA8" "RB2" "RRB2" "RB4"
## [679] "RRB4" "RB6" "RRB6" "RC2" "RRC2" "RD5.1.a"
## [685] "RD5.1.b" "RD5.2.a" "RD5.2.b" "RD5.3.a" "RD5.3.b" "RD6.6"
## [691] "RE1b" "RRE1b" "RE2b" "RRE2b" "RF1b" "RRF1b"
## [697] "RF2b" "RRF2b" "RG1a" "RG1b" "RG1c" "RG1e"
## [703] "RG2.1.a" "RRG2.1.a" "RG2.1.b" "RRG2.1.b" "RG2.1.c" "RRG2.1.c"
## [709] "RG2.2.a" "RRG2.2.a" "RG2.2.b" "RRG2.2.b" "RG2.2.c" "RRG2.2.c"
## [715] "RH2" "RRH2" "RI2b" "RRI2b" "RI2e" "RI3b"
## [721] "RRI3b" "RI3c" "RJ4.1" "RJ4.2" "RJ4.3" "RJ4.4"
## [727] "RJ4.5" "RQ3b.a" "RB13" "Weight" "agegroup"DF9 <- data.frame(TY1[,c(731, 416:422)])
DF9[is.na(DF9)] <- 0
DF10 <- gather(DF9, key = "cope", value = "count",I1.2.1,I1.2.2,I1.2.3,I1.2.4,I1.2.5,I1.2.6,I1.2.7)## Warning: attributes are not identical across measure variables;
## they will be droppedDF11 <- subset(DF10, count==1)
#install.packages("sjPlot")
library(sjPlot)
sjt.xtab(DF11$agegroup,DF11$cope,encoding = "big-5",show.cell.prc = T,
show.row.prc = T,
show.col.prc = T)| agegroup | cope | Total | ||||||
|---|---|---|---|---|---|---|---|---|
| I1.2.1 | I1.2.2 | I1.2.3 | I1.2.4 | I1.2.5 | I1.2.6 | I1.2.7 | ||
| 老年 |
135 46.9 % 10.8 % 4.6 % |
141 49 % 12.3 % 4.8 % |
2 0.7 % 3.9 % 0.1 % |
3 1 % 17.6 % 0.1 % |
4 1.4 % 1 % 0.1 % |
2 0.7 % 4.7 % 0.1 % |
1 0.3 % 3.8 % 0 % |
288 100 % 9.8 % 9.8 % |
| 中年 |
400 47.3 % 31.9 % 13.6 % |
359 42.4 % 31.3 % 12.2 % |
5 0.6 % 9.8 % 0.2 % |
5 0.6 % 29.4 % 0.2 % |
60 7.1 % 14.7 % 2 % |
14 1.7 % 32.6 % 0.5 % |
3 0.4 % 11.5 % 0.1 % |
846 100 % 28.7 % 28.8 % |
| 青年 |
717 39.6 % 57.3 % 24.4 % |
646 35.7 % 56.4 % 21.9 % |
44 2.4 % 86.3 % 1.5 % |
9 0.5 % 52.9 % 0.3 % |
345 19.1 % 84.4 % 11.7 % |
27 1.5 % 62.8 % 0.9 % |
22 1.2 % 84.6 % 0.7 % |
1810 100 % 61.5 % 61.4 % |
| Total |
1252 42.5 % 100 % 42.5 % |
1146 38.9 % 100 % 38.9 % |
51 1.7 % 100 % 1.7 % |
17 0.6 % 100 % 0.6 % |
409 13.9 % 100 % 13.9 % |
43 1.5 % 100 % 1.5 % |
26 0.9 % 100 % 0.9 % |
2944 100 % 100 % 100 % |
χ2=141.546 · df=12 · Cramer’s V=0.155 · Fisher’s p=0.000 |
class(DF11$agegroup)## [1] "factor"class(DF11$cope)## [1] "character"DF11$cope <- as.factor(DF11$cope)
library(plyr)
ess2 = ddply(DF11,.(cope),function(.){
res = prop.table(table(factor(.$agegroup)))
res2 = table(factor(.$agegroup))
data.frame(lab=names(res), y=c(res),yy =c(res2))
})
ggplot(DF11,
aes(cope, fill=agegroup))+
geom_bar(position = "dodge")+
labs(title = "使用社群媒體",
x="各年齡層",y="人數",
subtitle="60+熟齡族與其他年齡有差異?",
caption="第三組製 資料來源：台灣傳播調查資料庫")+
theme(panel.background = element_blank())+
theme(plot.title = element_text(hjust = 0.5))+
theme(axis.title.y = element_text(vjust = 0.5, hjust = 0.5, angle = 0))+
geom_text(stat="count",aes(label=..count..),size=3,
position = position_dodge(width = 1))+
scale_x_discrete("使用社群媒體",labels = c("I1.2.1"="Facebook","I1.2.2"="YouTube","I1.2.3"="Twitter",
"I1.2.4"="LinkedIn","I1.2.5"="Instagram","I1.2.6"="微博","I1.2.7"="Plurk"))+
scale_fill_manual("各年齡層",values=c("lightskyblue1", "rosybrown2", "steelblue1","tomato1"))+
theme(axis.text.x = element_text(vjust = 0.9,hjust = 1, angle=30))