r - DF 作为矩阵的总结

Question

我已经找了很长一段时间了，但找不到一个简单的方法。我有一个仅由数值组成的 df，我想从我的 df 中创建一个汇总矩阵。

DF
V1   V2   V3   V4   V5  ...
x1   y1   z1   1    c1  
x2   NA   z2   0    c2
x3   y3   z3   1    NA
...

V4 最初是一个 TRUE/FALSE 变量，转换为通常应该起作用的数值变量。我想获得以下内容:

    N   Mean  SD  Min  1st  Median  3rd  Max
V1
V2 
V3
V4
V5
...

具有 N、平均值、SD、最小值、第一、中值、第三、最大值的相应值。我尝试过简单的as.data.frame(summary(DF))我尝试过 stargazer，但由于某种原因不起作用(我猜测是因为我有二元变量)

stargazer(DF, type= "html", nobs = TRUE, type="html", mean.sd = TRUE, median = TRUE, iqr = TRUE,
+           digits=2, align=T)

我读到了一些关于 qwraps2_summary_table 的内容。但他们似乎都给出了与我正在寻找的不同的 table “设计”。

我知道我也可以运行一个循环，例如:

for(i in (1:length(DF)){
sum$N<-(????)
sum$Mean<-mean(DF[i])
....}

但这不是最好的解决方案。有什么建议吗？谢谢!

这是我的数据集的一部分

structure(list(Year = c(2011, 2012, 2013, 2014, 2015, 2016, 2017, 
2018, 2018, 2011), Occurences = c(9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 2L, 9L), Balance = c(-1.14, 1.05, -1.06, 1.01, 1.01, 1.01, 
-1.09, -1, -1.04, -1.03), Withdrawal = c(43200, 41080, 43400, 
43183, 42600, 42100, 45900, 46000, 3892008, 48374), Verification_SA = c(NA, 
NA, NA, NA, 1, 1, NA, 1, 1, NA), Classification_num = c(NA, NA, 
NA, NA, 3, 2, NA, 4, 4, NA), Interaction_Verification_Classification = c(NA, 
NA, NA, NA, 3, 2, NA, 4, 4, NA), KnowledgeSources = c(1, 1, 1, 
0, 1, 1, 1, 1, 1, 0), KnowledgeDischarge = c(0, 0, 0, 0, 0, 1, 
1, 1, 1, 0), Scarcity_watershed = c(NA_real_, NA_real_, NA_real_, 
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_
), Scarcity_country = c(NA, NA, NA, NA, NA, NA, NA, NA, 3.35, 
NA), Knowledge_Watershed = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Knowledge_Facilities = c(0, 
0, 0, 0, 0, 0, 0, 0, 1, 1), Importance_num = c(NA, NA, NA, 3, 
3, 3, 3, 3, 5, NA), DetrimentalImpacts_num = c(0, 0, 1, 0, 0, 
0, 0, 0, 0, 0), Responsibility_num = c(1, 1, 1, 2, 2, 2, 2, 3, 
3, 1)), row.names = c(NA, -10L), class = c("tbl_df", "tbl", "data.frame"
))

Answer 1

以 Ian Campbell 的答案为基础，我们不应该害怕在需要时构建一个汇总函数。

summaryfn <- function(x){
c(min(x),
quantile(x,0.25,na.rm=TRUE),
quantile(x,0.5,na.rm=TRUE),
mean(x,na.rm=TRUE),
sd(x, na.rm=TRUE),
quantile(x,0.75,na.rm=TRUE),
max(x,na.rm=TRUE),
sum(is.na(x)))

}

res <-   do.call(rbind,lapply(df,summaryfn))
colnames(res) <- c("Min","Q1","Med","Mean","Sd","Q3","Max","NAs")



## > res
##                                              Min        Q1       Med          Mean           Sd       Q3        Max NAs
## Year                                     2011.00  2012.250  2014.500   2014.500000 2.718251e+00  2016.75    2018.00   0
## Occurences                                  2.00     9.000     9.000      8.300000 2.213594e+00     9.00       9.00   0
## Balance                                    -1.14    -1.055    -1.015     -0.228000 1.074800e+00     1.01       1.05   0
## Withdrawal                              41080.00 42745.750 43300.000 428784.500000 1.216855e+06 45975.00 3892008.00   0
## Verification_SA                               NA     1.000     1.000      1.000000 0.000000e+00     1.00       1.00   6
## Classification_num                            NA     2.750     3.500      3.250000 9.574271e-01     4.00       4.00   6
## Interaction_Verification_Classification       NA     2.750     3.500      3.250000 9.574271e-01     4.00       4.00   6
## KnowledgeSources                            0.00     1.000     1.000      0.800000 4.216370e-01     1.00       1.00   0
## KnowledgeDischarge                          0.00     0.000     0.000      0.400000 5.163978e-01     1.00       1.00   0
## Scarcity_watershed                            NA        NA        NA           NaN           NA       NA       -Inf  10
## Scarcity_country                              NA     3.350     3.350      3.350000           NA     3.35       3.35   9
## Knowledge_Watershed                         0.00     0.000     0.000      0.000000 0.000000e+00     0.00       0.00   0
## Knowledge_Facilities                        0.00     0.000     0.000      0.200000 4.216370e-01     0.00       1.00   0
## Importance_num                                NA     3.000     3.000      3.333333 8.164966e-01     3.00       5.00   4
## DetrimentalImpacts_num                      0.00     0.000     0.000      0.100000 3.162278e-01     0.00       1.00   0
## Responsibility_num                          1.00     1.000     2.000      1.800000 7.888106e-01     2.00       3.00   0

## > str(res)
##  num [1:16, 1:8] 2011 2 -1.14 41080 NA ...
##  - attr(*, "dimnames")=List of 2
##   ..$ : chr [1:16] "Year" "Occurences" "Balance" "Withdrawal" ...
##   ..$ : chr [1:8] "Min" "Q1" "Med" "Mean" ...

虽然在很多情况下 stargazer 是一个不错的选择，但我会推荐 xtable 因为它的灵活性。

print(xtable(res),type="html")