我有一些调查数据用来衡量信任度，每个国家/地区的每个受访者回答“倾向于信任”，“倾向于不信任”或“不知道”。
数据是时间序列的，跨国家的，我想对其进行转换，因此每年每个变量都有一个数值。
我已经下载了SPSS格式的数据，并使用read.spss函数将其放入R中，但是现在我对如何更改它感到困惑。
我在下面有一个用于网络信任的公式，但不知道在R中需要什么命令或软件包。
“净信任=信任/（信任+不信任+不知道）-不信任/（信任+不信任+不知道）”
很抱歉，如果以前已经发布过此问题，但我真的很感谢您的一些建议。
干杯！
structure(list(qb1_2 = structure(c(2L, 3L, 1L, 2L, 2L, 2L, 3L,
2L, 1L, 2L), .Label = c("Totally agree", "Tend to agree", "Tend to disagree"
), class = "factor"), qb1_3 = structure(c(2L, 4L, 1L, 2L, 2L,
2L, 3L, 3L, 1L, 1L), .Label = c("Totally agree", "Tend to agree",
"Tend to disagree", "Totally disagree"), class = "factor"), qb1_4 = structure(c(2L,
3L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L), .Label = c("Totally agree",
"Tend to agree", "Tend to disagree"), class = "factor"), qb2_1 = structure(c(2L,
2L, 1L, 1L, 2L, 2L, 2L, 3L, 1L, 1L), .Label = c("Very important",
"Fairly important", "Not very important"), class = "factor"),
qb2_2 = structure(c(2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L
), .Label = c("Very important", "Fairly important"), class = "factor"),
qb2_3 = structure(c(1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L
), .Label = c("Very important", "Fairly important"), class = "factor"),
qb2_4 = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 1L, 3L, 1L, 1L
), .Label = c("Very important", "Fairly important", "Not very important"
), class = "factor"), qb2_5 = structure(c(2L, 2L, 1L, 3L,
2L, 2L, 2L, 1L, 1L, 2L), .Label = c("Very important", "Fairly important",
"Not very important"), class = "factor"), qb3_1 = structure(c(2L,
4L, 1L, 3L, 3L, 2L, 3L, 2L, 3L, 3L), .Label = c("Totally agree",
"Tend to agree", "Tend to disagree", "Totally disagree"), class = "factor"),
qb3_2 = structure(c(2L, 3L, 1L, 2L, 3L, 2L, 2L, 2L, 3L, 3L
), .Label = c("Totally agree", "Tend to agree", "Tend to disagree"
), class = "factor"), qb3_3 = structure(c(2L, 3L, 1L, 2L,
2L, 2L, 3L, 2L, 2L, 1L), .Label = c("Totally agree", "Tend to agree",
"Tend to disagree"), class = "factor"), qb3_4 = structure(c(2L,
2L, 1L, 4L, 2L, 2L, 3L, 1L, 3L, 2L), .Label = c("Totally agree",
"Tend to agree", "Tend to disagree", "Totally disagree"), class = "factor"),
qb3_5 = structure(c(2L, 4L, 1L, 3L, 2L, 2L, 2L, 2L, 3L, 2L
), .Label = c("Totally agree", "Tend to agree", "Tend to disagree",
"Totally disagree"), class = "factor"), qb3_6 = structure(c(2L,
4L, 1L, 5L, 3L, 2L, 3L, 1L, 3L, 3L), .Label = c("Totally agree",
"Tend to agree", "Tend to disagree", "Totally disagree",
"DK"), class = "factor"), qb3_7 = structure(c(2L, 4L, 1L,
3L, 3L, 2L, 2L, 5L, 3L, 2L), .Label = c("Totally agree",
"Tend to agree", "Tend to disagree", "Totally disagree",
"DK"), class = "factor"), qb4_1 = structure(c(2L, 3L, 1L,
3L, 2L, 2L, 2L, 2L, 3L, 2L), .Label = c("Totally agree",
"Tend to agree", "Tend to disagree"), class = "factor"),
qb4_2 = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L
), .Label = c("Totally agree", "Tend to agree"), class = "factor"),
qb5 = structure(c(2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("On a case by case basis",
"Always, in every case"), class = "factor"), qb6.1 = structure(c(2L,
2L, NA, 2L, 2L, NA, 2L, 1L, 2L, 2L), .Label = c("Not mentioned",
"Mentioned"), class = "factor"), qb6.2 = structure(c(2L,
2L, NA, 1L, 2L, NA, 1L, 1L, 2L, 1L), .Label = c("Not mentioned",
"Mentioned"), class = "factor"), qb6.3 = structure(c(2L,
1L, NA, 2L, 2L, NA, 1L, 2L, 2L, 1L), .Label = c("Not mentioned",
"Mentioned"), class = "factor")), .Names = c("qb1_2", "qb1_3",
"qb1_4", "qb2_1", "qb2_2", "qb2_3", "qb2_4", "qb2_5", "qb3_1",
"qb3_2", "qb3_3", "qb3_4", "qb3_5", "qb3_6", "qb3_7", "qb4_1",
"qb4_2", "qb5", "qb6.1", "qb6.2", "qb6.3"), row.names = c(NA,
10L), class = "data.frame")