1.R
rstudio-user
2021-05-22
# 主題:社群平台議題分析
# 任務：RQ.1社群平台種類使用分析
# 組別：應用藝術學系 第一組### 資料框處理
# 請至台灣傳播資料庫下載「2019年調查」的sav檔
# 網址:https://www.crctaiwan.nctu.edu.tw/AnnualSurvey.asp
## 1. 將輸入的sav檔案命名為tcs2019
library(sjlabelled)
tcs2019 <- read_spss("tcs2019.sav")
library(sjlabelled)
tcs2019 <- read_spss("tcs2019.sav")
# (2)變數整理
# 年齡「變數重新分類」為4類：18-35,36-49,50-64,65UP
# 備註：break的值(x,y,z)是指: group1 >x & <=y; group2 >y & <=z
tcs2019$agegroup <- cut(tcs2019$ra2, breaks = c(17,35,49,64,Inf),
labels = c("18至35歲", "36至49歲","50至64歲","65歲以上"))
# 製作次數分配表
# install.packages("sjmisc")
library(sjmisc)
frq(tcs2019$agegroup, encoding = "big-5", out="v")| val | label | frq | raw.prc | valid.prc | cum.prc | |
|---|---|---|---|---|---|---|
| 18至35歲 |
|
343 | 17.15 | 17.15 | 17.15 | |
| 36至49歲 |
|
534 | 26.70 | 26.70 | 43.85 | |
| 50至64歲 |
|
583 | 29.15 | 29.15 | 73.00 | |
| 65歲以上 |
|
540 | 27.00 | 27.00 | 100.00 | |
| NA | NA | 0 | 0.00 | NA | NA | |
| total N=2000 · valid N=2000 · x̄=2.66 · σ=1.05 | ||||||
# b.確認各社群平台種類數據? k3.1,k3.3,k3.6,k3.7
### 複選題處理方式：
# 1. 先篩選出年齡層、確認各社群平台種類
names(tcs2019)## [1] "id" "type" "a1" "a2" "a3" "a3.a"
## [7] "a4" "a5.1" "a6" "a6.a" "a7" "a7.a"
## [13] "a8" "a8.a" "a9" "a9.a" "b1" "b2a"
## [19] "b3a" "b3b" "b3c" "b3d" "b3e" "b3f"
## [25] "b4.1.a.1" "b4.1.a.2" "b4.1.b.1" "b4.1.b.2" "b4.2.a.1" "b4.2.a.2"
## [31] "b4.2.b.1" "b4.2.b.2" "b4.3.a.1" "b4.3.a.2" "b4.3.b.1" "b4.3.b.2"
## [37] "c1a" "c1b.1" "c1b.2" "c1c.1" "c1c.2" "c1c.3"
## [43] "c1c.4" "c1c.5" "c1c.6" "c1c.7" "c1c.13" "c1c.12"
## [49] "c1c.36" "c1c.10" "c1c.14" "c1c.17" "c1c.11" "c1c.15"
## [55] "c1c.19" "c1c.41" "c1c.42" "c1c.43" "c1c.28" "c1c.35"
## [61] "c1c.16" "c1c.20" "c1c.21" "c1c.22" "c1c.26" "c1c.30"
## [67] "c1c.31" "c1c.32" "c1c.33" "c1c.34" "c1c.37" "c1c.38"
## [73] "c1c.40" "c1c.44" "c1c.88" "c1c.a" "c3a" "c3b.1"
## [79] "c3b.2" "c3c.24" "c3c.28" "c3c.21" "c3c.26" "c3c.6"
## [85] "c3c.1" "c3c.2" "c3c.4" "c3c.5" "c3c.18" "c3c.8"
## [91] "c3c.17" "c3c.19" "c3c.9" "c3c.23" "c3c.16" "c3c.13"
## [97] "c3c.22" "c3c.11" "c3c.15" "c3c.14" "c3c.20" "c3c.12"
## [103] "c3c.10" "c3c.25" "c3c.27" "c3c.7" "c3c.29" "c3c.30"
## [109] "c3c.32" "c3c.33" "c3c.34" "c3c.35" "c3c.36" "c3c.37"
## [115] "c3c.38" "c3c.39" "c3c.40" "c3c.41" "c3c.42" "c3c.43"
## [121] "c3c.44" "c3c.45" "c3c.46" "c3c.47" "c3c.48" "c3c.49"
## [127] "c3c.50" "c3c.51" "c3c.52" "c3c.53" "c3c.54" "c3c.55"
## [133] "c3c.56" "c3c.57" "c3c.58" "c3c.59" "c3c.60" "c3c.61"
## [139] "c3c.88" "c3c.a" "d1a" "d1b.1" "d1b.2" "d2a"
## [145] "d2b.1" "d2b.2" "e1" "e2.1" "e2.2" "f1"
## [151] "f2.1" "f2.2" "f3" "f4.1" "f4.2" "f5"
## [157] "f6.1" "f6.2" "f7" "f7.a" "g1" "g2.1"
## [163] "g2.2" "g3.1" "g3.2" "g3.3" "g3.4" "g3.5"
## [169] "g3.6" "g3.7" "g3.8" "g3.9" "g3.10" "g3.11"
## [175] "g3.12" "g3.13" "g3.14" "g3.15" "g3.16" "g3.17"
## [181] "g3.18" "g3.19" "g3.20" "g3.21" "g3.22" "g3.23"
## [187] "g3.24" "g3.25" "g3.26" "g3.27" "g3.28" "g3.29"
## [193] "g3.30" "g3.31" "g3.32" "g3.33" "g3.34" "g3.35"
## [199] "g3.36" "g3.88" "g3.a" "g4.1" "g4.1.a" "g4.2"
## [205] "g4.2.a" "g5.0.1" "g5.0.2" "g5.0.3" "g5.0.4" "g5.0.5"
## [211] "g5.0.6" "g5.0.7" "g5.0.8" "g5.0.9" "g5.0.10" "g5.0.11"
## [217] "g5.0.12" "g5.0.13" "g5.0.14" "g5.0.15" "g5.0.88" "g5.0.a"
## [223] "g5.5" "g5.6" "g5.7" "g5.8" "g5.9" "g5.10"
## [229] "g5.11" "g5.12" "g5.14" "h1" "h2" "h3"
## [235] "h4.1.a.1" "h4.1.a.2" "h4.1.b.1" "h4.1.b.2" "h4.2.a.1" "h4.2.a.2"
## [241] "h4.2.b.1" "h4.2.b.2" "h4.3.a.1" "h4.3.a.2" "h4.3.b.1" "h4.3.b.2"
## [247] "h5.1" "h5.2" "h5.3" "h5.4" "h5.5" "h5.6"
## [253] "h5.7" "h5.8" "ha1" "ha2" "ha3a.1" "ha3a.2"
## [259] "ha3a.3" "ha3a.4" "ha3a.5" "ha3a.6" "ha3a.7" "ha3a.99"
## [265] "ha3b.1" "ha3b.2" "ha3b.3" "ha3b.4" "ha3b.5" "ha3b.6"
## [271] "ha3b.7" "ha3b.99" "ha4" "ha5a.1" "ha5a.2" "ha5a.3"
## [277] "ha5a.4" "ha5a.5" "ha5a.6" "ha5a.99" "ha5b.1" "ha5b.2"
## [283] "ha5b.3" "ha5b.4" "ha5b.5" "ha5b.6" "ha5b.99" "ha5c.1"
## [289] "ha5c.2" "ha5c.3" "ha5c.4" "ha5c.5" "ha5c.6" "ha5c.99"
## [295] "ha6.1" "ha6.2" "ha6.3" "ha6.4" "ha6.5" "ha6.6"
## [301] "ha6.7" "ha6.99" "ha7.1" "ha7.2" "ha7.3" "ha7.4"
## [307] "ha7.5" "ha7.6" "ha7.7" "ha7.99" "i1" "i1.a"
## [313] "i2" "i2.a" "i3" "i3.a" "i4.1" "i4.2"
## [319] "i4.3" "i4.4" "i4.5" "i5a.1" "i5a.2" "i5a.3"
## [325] "i5a.4" "i5a.5" "i5a.6" "i5a.7" "i5a.8" "i5a.9"
## [331] "i5a.10" "i5a.88" "i5a.a" "i5a.90" "i5b.1" "i5b.2"
## [337] "i5b.3" "i5b.4" "i5b.5" "i5b.6" "i5b.88" "i5b.a"
## [343] "i5b.90" "i6.1" "i6.2" "i6.3" "i6.4" "i6.5"
## [349] "i6.90" "i7a" "i7b" "i7c" "i8.1" "i8.2"
## [355] "i8.3" "i8.4" "i8.5" "i8.6" "i8.7" "i8.8"
## [361] "i8.88" "i8.a" "i9.1.1" "i9.1.2" "i9.1.3" "i9.1.4"
## [367] "i9.1.5" "i9.1.6" "i9.1.7" "i9.1.8" "i9.1.9" "i9.1.10"
## [373] "i9.1.88" "i9.1.a" "i9.2.1" "i9.2.2" "i9.2.3" "i9.2.4"
## [379] "i9.2.5" "i9.2.6" "i9.2.7" "i9.2.8" "i9.2.88" "i9.2.a"
## [385] "i10.1" "i10.2" "i10.3" "i10.4" "i10.5" "i10.6"
## [391] "i10.7" "i10.8" "i10.88" "i10.a" "i11.1" "i11.2"
## [397] "i11.3" "i11.4" "i11.5" "i11.6" "i11.7" "i11.8"
## [403] "i11.88" "i11.a" "i12.1" "i12.2.1" "i12.2.2" "i12.2.3"
## [409] "i12.2.4" "i12.2.5" "i12.2.6" "i12.2.7" "i12.2.8" "i12.2.88"
## [415] "i12.2.a" "n1.1" "n1.2" "n1.3" "n1.4" "n1.5"
## [421] "n1.6" "n1.7" "n1.8" "n1.9" "n1.10" "n2.1"
## [427] "n2.2" "n2.3" "n2.4" "n2.5" "n2.6" "n2.7"
## [433] "n2.8" "n2.9" "n2.10" "n2.11" "n2.12" "n2.13"
## [439] "n2.14" "j1a" "j1b.1" "j1b.2" "j1c.1" "j1c.2"
## [445] "j1c.3" "j1c.4" "j1c.5" "j1c.6" "j1c.8" "j1c.9"
## [451] "j1c.10" "j1c.11" "j1c.12" "j1c.13" "j1c.88" "j1c.a"
## [457] "j2.1" "j2.2" "j2.3" "j2.4" "j2.5" "j2.6"
## [463] "j2.7" "j2.8" "j2.9" "k1" "k2.1" "k2.2"
## [469] "k3.1" "k3.3" "k3.4" "k3.6" "k3.7" "k3.8"
## [475] "k3.88" "k3.a" "k4.1" "k4.2" "k4.3" "k4.4"
## [481] "k4.5" "k4.6" "k4.7" "k4.8" "k4.9" "k4.10"
## [487] "k4.11" "k4.12" "k4.13" "k4.14" "k4.15" "k4.88"
## [493] "k4.a" "k5.1" "k5.2" "k5.3" "k5.4" "k5.5"
## [499] "k5.6" "k6.1" "k6.2" "k6.3" "k6.4" "k7"
## [505] "k9.1" "k9.2" "k9.3" "k9.4" "k9.5" "k10"
## [511] "k11.1" "k11.2" "k11.3" "k11.4" "k11.5" "k12"
## [517] "k13" "k14" "k15.1" "k15.2" "k15.3" "k15.4"
## [523] "k15.5" "k15.6" "k15.7" "k15.88" "k15.a" "k16.1"
## [529] "k16.2" "l1a" "l1b" "l3a" "l3b" "l5.1"
## [535] "l5.2" "l6" "l7" "l8" "l9a" "l9b.1"
## [541] "l9b.2" "l9b.3" "l9b.4" "l10a" "l10b.1" "l10b.2"
## [547] "l10b.3" "l10b.4" "l11a" "l11b.1" "l11b.2" "l11b.3"
## [553] "l11b.4" "l12" "l13" "l14" "l14.a" "l15"
## [559] "m1.1" "m1.2" "m1.3" "m1.4" "m1.5" "m1.6"
## [565] "m1.7" "m1.8" "m1.9" "m2a.1" "m2a.2" "m2a.3"
## [571] "m2a.4" "m2a.5" "m2a.6" "m2a.7" "m2a.8" "m2a.9"
## [577] "n3a.1" "n3a.2" "n3a.3" "n3a.4" "n3a.5" "n3b"
## [583] "n4" "n5" "n6" "n7" "n8a" "n8b"
## [589] "n9.1" "n9.2" "n9.3" "n9.4" "n10a" "n10b"
## [595] "n11a" "n11b" "o1" "o1.a" "o2" "o2.a"
## [601] "o3a" "o3a.a" "o3b" "o3b.a" "o4" "ra2"
## [607] "rra2" "rcity" "ra9" "rb3a" "rb3b" "rb3c"
## [613] "rb3e" "rb4.1.a" "rrb4.1.a" "rb4.1.b" "rrb4.1.b" "rb4.2.a"
## [619] "rrb4.2.a" "rb4.2.b" "rrb4.2.b" "rb4.3.a" "rrb4.3.a" "rb4.3.b"
## [625] "rrb4.3.b" "rc1b" "rrc1b" "rc3b" "rrc3b" "rd1b"
## [631] "rrd1b" "rd2b" "rrd2b" "re2" "rre2" "rf2"
## [637] "rrf2" "rf4" "rrf4" "rf6" "rrf6" "rg2"
## [643] "rrg2" "rh4.1.a" "rrh4.1.a" "rh4.1.b" "rrh4.1.b" "rh4.2.a"
## [649] "rrh4.2.a" "rh4.2.b" "rrh4.2.b" "rh4.3.a" "rrh4.3.a" "rh4.3.b"
## [655] "rrh4.3.b" "rh5.8" "ri4.1" "ri4.2" "ri4.3" "ri4.4"
## [661] "ri4.5" "rj1b" "rrj1b" "rk2" "rrk2" "rl6"
## [667] "rl7" "ro3b.a" "weight" "agegroup"DF <- data.frame(tcs2019[,c(670, 469:473)])
# 2. 需填補遺漏值為1
DF[is.na(DF)] <- 0
# 3. 透過tidyr套件中的gather將寬格式轉為長格式
install.packages("tidyr")## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)library(tidyr)##
## Attaching package: 'tidyr'## The following object is masked from 'package:sjmisc':
##
## replace_naDF1 <- gather(DF, key = "identify", value = "count", k3.1,k3.3,k3.6,k3.7)## Warning: attributes are not identical across measure variables;
## they will be dropped# 可以不必理會警告訊息
# 4. 篩選出count==1的資料框
DF2 <- subset(DF1, count==1)
### (3)回答RQ
#RQ.1各年齡層使用社群平台上人數的差異
## 製表
install.packages("sjPlot")## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)library(sjPlot)
sjt.xtab(DF2$agegroup, DF2$identify,encoding = "big-5",
show.cell.prc = T,show.row.prc = T,show.col.prc = T) | agegroup | identify | Total | |||
|---|---|---|---|---|---|
| k3.1 | k3.3 | k3.6 | k3.7 | ||
| 18至35歲 |
319 55 % 26.1 % 18.4 % |
48 8.3 % 51.1 % 2.8 % |
184 31.7 % 54.1 % 10.6 % |
29 5 % 36.7 % 1.7 % |
580 100 % 33.4 % 33.5 % |
| 36至49歲 |
474 72.3 % 38.8 % 27.3 % |
33 5 % 35.1 % 1.9 % |
121 18.4 % 35.6 % 7 % |
28 4.3 % 35.4 % 1.6 % |
656 100 % 37.8 % 37.8 % |
| 50至64歲 |
322 83.4 % 26.4 % 18.6 % |
12 3.1 % 12.8 % 0.7 % |
32 8.3 % 9.4 % 1.8 % |
20 5.2 % 25.3 % 1.2 % |
386 100 % 22.2 % 22.3 % |
| 65歲以上 |
107 94.7 % 8.8 % 6.2 % |
1 0.9 % 1.1 % 0.1 % |
3 2.7 % 0.9 % 0.2 % |
2 1.8 % 2.5 % 0.1 % |
113 100 % 6.5 % 6.6 % |
| Total |
1222 70.4 % 100 % 70.4 % |
94 5.4 % 100 % 5.4 % |
340 19.6 % 100 % 19.6 % |
79 4.6 % 100 % 4.6 % |
1735 100 % 100 % 100 % |
χ2=143.950 · df=9 · Cramer’s V=0.166 · p=0.000 |
## 製圖
# 1. 變數處理
# (1) 將要繪製的變數變成類別變數或先進行排序
class(DF2$agegroup) ## [1] "factor"class(DF2$identify)## [1] "character"DF2$identify <- as.factor(DF2$identify)
# 2. 安裝並載入 ggplot2
#install.packages("ggplot2")
# 載入 ggplot2
library(ggplot2)
# 解決Rstudio cloud圖形中文顯示問題
#install.packages("showtext")
library(showtext)## Loading required package: sysfonts## Loading required package: showtextdbshowtext_auto()
ggplot(DF2,
aes(identify, fill=agegroup))+
geom_bar()+
labs(title = "台灣民眾各年齡層社群平台使用比較",x="社群平台種類",y="人數",
subtitle="年齡使用社群平台的差異?",
caption="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))+
scale_x_discrete("社群平台種類",labels = c("k3.1" = "Facebook","k3.3" = "Twitter", "k3.6" = "Instagram",
"k3.7" = "微信"))+
scale_fill_manual("年齡層",values=c("lightskyblue1", "rosybrown2", "steelblue1","tomato1"),
labels=c("18至35歲", "36至49歲", "50至64歲","65歲以上"))