当你需要一组简单的示例数据用以模拟分析时，除了使用R种自带的数据集，也可以自己通过随机数产生。
下列文件展示了一组基于已有数据额外拓展生成两组额外sd的随机数据，以便模拟两组sd变化时对第三组数据的影响。过程是生成对应数且指定均值的两组sd。随后通过公式计算第三组结果，以模拟三组数据在指定公式下的相互影响。

# 文件预览
> head(row_data)
  sample number trait_A trait_B trait_C
1     Dhi      1 62.40 80.72 143.12
2     Dhi      1 68.01 88.09 156.10
3     Dhi      2 59.30 62.24 121.54
4     Dhi      2 61.27 65.52 126.79
5     Dhi      3 47.01 64.12 111.13
6     Dhi      3 47.01 75.06 122.07

# 统计均值，分型
sum_tmp <- row_data%>%
  group_by(sample, number)%>%
  summarise(trait_A = sum(sum_A),
            trait_B= sum(sum_B),
            trait_C = sum(sum_C))%>%
  data.frame()%>%
  mutate(AR = trait_B/trait_A,
         CI = trait_A/(trait_A + trait_B)*100)%>%
  mutate(check = if_else(trait_A <= trait_B, "TRUE","FALSE"))%>%
  mutate(type = if_else(AR >= 1.00 & AR <= 1.70 ,"typeA",
                            if_else(AR > 1.70 & AR <= 3.00,"typeB",
                                    if_else(AR > 3.00 & AR <= 7.00,"typeC","typeD"))))%>%
  rownames_to_column(var = "label")
### 两个数据的sd
sd_1 <- rnorm(sum_tmp$sample%>%
                #length确定数据数量，mean和sd指定方差的比例和浮动情况
                length(),mean = 0.05,sd = 0.008)%>%
  data.frame()%>%
  dplyr::rename(tmp_AR = ".")%>%
                # label 用以多表关联
  rownames_to_column(var = "label")
sd_2 <- rnorm(sum_tmp$sample%>%
                length(),mean = 0.02,sd = 0.001)%>%
  data.frame()%>%
  dplyr::rename(tmp_CI = ".")%>%
  rownames_to_column(var = "label")
# 根据label合并
sum_data <- sum_tmp%>%
  left_join(sd_1,by = "label")%>%
  left_join(sd_2,by = "label")%>%
  mutate(AR_sd = AR*tmp_AR,
         CI_sd = CI*tmp_CI)%>%
  dplyr::select(sample,number,AR,AR_sd,CI,CI_sd,type)

将上述分步脚本合并，同时增加对小数点位数的统计等。

# 整合脚本
sum_tmp <- row_data%>%
  group_by(sample, number)%>%
  summarise(trait_A = sum(sum_A),
            trait_B = sum(sum_B),
            trait_C = sum(sum_C))%>%
  data.frame()%>%
  #直接在计算后控制小数点位置
  mutate(AR = round(trait_B/trait_A,2),
         CI = round(trait_A/(trait_A + trait_B)*100,2))%>%
  left_join(row_data%>%
              group_by(sample, number)%>%
              summarise(trait_C = sum(sum_C))%>%
              data.frame()%>%
              group_by(sample)%>%
              summarise(sum_C = sum(trait_C))%>%
              data.frame(),by = "sample")%>%
  mutate(R_sum_C = round(trait_C*100/sum_C,2))%>%
  mutate(check = if_else(trait_A <= trait_B, "TRUE","FALSE"))%>%
  mutate(chr_type = if_else(AR >= 1.00 & AR <= 1.70 ,"TypeA",
                            if_else(AR > 1.70 & AR <= 3.00,"TypeB",
                                    if_else(AR > 3.00 & AR <= 7.00,"TypeC","TypeD"))))%>%
  rownames_to_column(var = "label")%>%
  #通过length控制生成的数目
  left_join(round(rnorm(row_data$sample%>%
                          # 由于数目过小，先保留4位小数
                          length(),mean = 0.05,sd = 0.008),4)%>%
              #转换格式
              data.frame()%>%
              #修改名称，否则后面重复
              dplyr::rename(tmp_AR = ".")%>%
              rownames_to_column(var = "label"),by = "label")%>%
  left_join(round(rnorm(row_data$sample%>%
                          length(),mean = 0.02,sd = 0.001),4)%>%
              data.frame()%>%
              dplyr::rename(tmp_CI = ".")%>%
              rownames_to_column(var = "label"),by = "label")%>%
  left_join(round(rnorm(row_data$sample%>%
                          length(),mean = 0.04,sd = 0.002),4)%>%
              data.frame()%>%
              dplyr::rename(tmp_RL = ".")%>%
              rownames_to_column(var = "label"),by = "label")%>%
  #将生成的sd与原数据相乘，此时回到两位小数
  mutate(AR_sd = paste(AR," ± ",round(tmp_AR*AR,2)),
         CI_sd = paste(CI," ± ",round(tmp_CI*CI,2)),
         RL_sd = paste(R_sum_C," ± ",round(tmp_RL*R_sum_C,2)))%>%
  dplyr::select(sample,number,AR_sd,CI_sd,RL_sd,chr_type)%>%
  mutate(AR = if_else(chr_type == "T","--",AR_sd),
         CI = if_else(chr_type == "T","--",CI_sd),
         RL = if_else(chr_type == "T","--",RL_sd))%>%
  dplyr::select(sample,number,AR,CI,RL,chr_type)