R 如何在列表中保存多个图表？

我正在尝试做一个函数。在我给它一个带有等级的数据框之后，函数必须为我提供一个图表列表

我看不出哪里出了问题，如果你能给我一点动力，让我继续写我的报告：）

---

function\u pie.chart数据框中的第五列和第六列属于
字符类。要执行诸如
length
之类的数值操作，需要传递一个
numeric
值。此外，第五列中的某些行具有“NA”值。我们还需要处理这个问题

在数据帧上执行以下操作

# Convert NA values to 0
data[data=="NA"] <- 0

# Convert columns from character to numeric
data[[5]] <- as.numeric(data[[5]])
data[[6]] <- as.numeric(data[[6]])

希望这是清楚的
 你能在你的问题中添加样本数据吗？你能解释一下我如何上传样本数据文档吗？它是一个具有5列和132个等级（比例为0.0到10.0）的矩阵。使用
dput
。在控制台上，执行
dput（）
。如果重新运行，您得到的输出将再次生成矩阵。编辑到问题的输出。。。这可以吗？请使用代码块而不仅仅是复制粘贴来更新R代码段。我收到以下错误：“错误在
$Try using
data[[5]]
而不是实际的列名。感谢您迄今为止的帮助，但是列表饼图似乎是空的…？@J.O.P.P.您必须在函数中返回列表
饼图
。否则您的函数将不会返回任何值。编辑了我的答案。请查看一下。感谢您更新函数！但您能告诉我如何更新吗我要查一下名单里有什么？
data <- data.frame(
`BSTAT-THI Beschrijvende Statistiek` = c(7.3, 6.3, 5.8, 6.9, 5.7, 8.9, 6.1, 7, 8.1, 7.1, 6.2, 6.5, 7, 4.9, 9, 6.2, 5.6, 7.3, 6.6, 7.7, 8.3, 7.3, 7.1, 5.3, 9.3, 7.4, 6.6, 6.3, 7, 5.7, 6.7, 6.9, 6.9, 6.6, 5.3, 6.5, 5.7, 7.5, 6.3, 6.8, 7.3, 5.9, 7.1, 6.9, 7, 7.6, 7, 7.1, 6.5, 5.2, 5.8, 6.2, 6.4, 6.7, 5.3, 7.3, 7, 7.3, 8.6, 6.2, 5.8, 5.8, 6.6, 7.1, 6, 9.5, 7.3, 8, 7.3, 6.5, 9, 8.2, 7.3, 6.5, 7.8, 7.4, 6.9, 7.3, 6.2, 7.5, 7.6, 7.1, 6.3, 6.9, 7.5, 7.7, 8.3, 7.4, 5.2, 7, 6.6, 6.5, 7.5, 6.3, 8.1, 6.9, 6.8, 8.1, 7.4, 7.2, 9.2, 7.8, 6.6, 6.1, 8.3, 6.2, 9.2, 7.4, 9, 8.6, 7.1, 5.8, 6.5, 6.4, 5.7, 7.1, 7.5, 8.4, 5.9, 7.2, 8.8, 8.2, 7.6, 8.8, 8.9, 5.7, 7.1, 6, 7.7, 6.3, 5.6, 7.5),
`GRAAF-PRI Grafentheorie opdracht` = c(8.4, 7.5, 8.7, 9.3, 7.5, 8.8, 7.8, 6.8, 6.3, 7.2, 7.2, 7.6, 7.1, 7.1, 8.5, 9.1, 1.4, 8.6, 6.8, 9, 7.6, 8.5, 6.2, 8.7, 7.6, 6.4, 6.7, 7.3, 8.2, 7.6, 7.6, 7.5, 6.7, 8.6, 6.5, 8.2, 7.4, 8.9, 9.2, 9, 7.4, 8.1, 6.9, 7.2, 5.5, 6, 6.3, 5.9, 8.3, 8.8, 8.7, 8.3, 7.8, 6.3, 7.6, 8.2, 8.2, 7.5, 8.8, 7.5, 7.7, 7.7, 7.8, 7.2, 8.9, 6.9, 8, 9.7, 7.4, 7.9, 9.8, 7.9, 8.9, 5.9, 7.8, 7.8, 7.4, 7.8, 8.1, 8.6, 8.7, 8.4, 8.8, 8.4, 8.5, 6.3, 6.9, 8.6, 8.4, 7.5, 1, 8.4, 7.6, 6.9, 6.8, 6.4, 6.1, 8.9, 8.2, 7.9, 9.4, 9, 7.5, 6.4, 8.8, 6.8, 9.7, 9.7, 9.7, 6.3, 7.9, 7.2, 6.4, 7.6, 8.9, 7.4, 8.8, 5.9, 7.2, 8.6, 7.5, 6.4, 8.5, 5.3, 7.5, 6.3, 6.4, 7.9, 6.8, 7.8, 8.6, 9.2),
`GRAAF-THI Grafentheorie theorie` = c(6.6, 7, 7.3, 8.3, 6.3, 9.7, 5.9, 5.2, 4.5, 7.7, 5.9, 7.2, 6.7, 6.4, 10, 6.5, 7.3, 8.7, 7.4, 5.8, 9.3, 7, 9.4, 7.4, 8.2, 9.4, 7.4, 5.6, 7.1, 6.1, 5.5, 8.4, 10, 6.9, 6.3, 5.5, 5.3, 6.8, 6.2, 6.7, 8, 8.8, 7.4, 6.5, 7.4, 4.3, 6.1, 9.4, 5.7, 7.5, 6.5, 6.3, 9.3, 8.9, 6, 9.5, 5.6, 7, 10, 9.1, 7.4, 7.5, 6.1, 7.4, 3.5, 8.3, 9.7, 7.2, 7.9, 5.7, 7.8, 8, 6.5, 5.5, 7.2, 8.4, 6.5, 7, 6.6, 6.4, 8.6, 5.8, 3.2, 9.6, 6.5, 9.8, 7.2, 6.7, 6.7, 6.4, 6.1, 5.4, 8.1, 6.6, 7, 5.7, 6.7, 7.9, 5.3, 7.7, 7.7, 7.3, 7.8, 9.3, 9.8, 7.7, 8.2, 7.4, 7.6, 4.2, 5.5, 9, 7.1, 6, 6.1, 6.3, 6.7, 4.9, 5.4, 8.9, 9.8, 4.3, 8.4, 6, 8.6, 8.4, 7.3, 7.1, 7.5, 9, 7.1, 8.6),
`BWISK-TH|Basiswiskunde` = c(8.5, 9.3, 6.7, 5.5, 8.7, 8.6, 6.8, 6.1, 7.7, 5.2, 5.4, 8.9, 6.3, 6.1, 7.2, 6.3, 5.8, 9.4, 5.5, 3.8, 9.3, 9.4, 6, 7.3, 5.4, 6.3, 5.3, 5.7, 8.7, 6.9, 4.7, 6.6, 6.2, 6.9, 5.8, 4.8, 5.8, 9, 6.3, 6, 6.1, 8.1, 7.1, 6.2, 6.3, 8, 6.1, 6.3, 4.7, 7.7, 5.9, 6.2, 4.9, 6.6, 6.5, 8.4, 3.4, 7, 7.4, 8, 9.8, 8.5, 5.5, 7.1, 6.1, 5.3, 8.2, 4.2, 7.6, 5.3, 5.2, 9.5, 6, 5.9, 8.5, 5.9, 5.4, 6.2, 6.2, 9.2, 5.7, 3.3, 6.2, 8.3, 9.4, 6.5, 8.3, 9.4, 5.3, 6.3, 7.6, 6.2, 6.5, 6.6, 8.3, 6.1, 5.8, 7.6, 4, 8.5, 5.2, 8.5, 7.4, 8.5, 8.6, 6.4, 5.8, 3.8, 5.1, 7.7, 5.8, 7.8, 5.5, 7.1, 6, 6.1, 9.4, 4.2, 5.5, 5.4, 8.9, 7.7, 9, 4.3, 5.4, 4.7, 6.4, 8.5, 7.1, 8.2, 6.5, 7.2),
`CALEID-TH|Caleidoscoop theorie` = c("7.4000000000000004", "6.2999999999999998", "5.7000000000000002", "7.0999999999999996", "6.0999999999999996", "7.5", "5.5", "6", "5.0999999999999996", "6.5999999999999996", "6.2999999999999998", "6.5", "4.9000000000000004", "NA", "8.5", "6.9000000000000004", "3.2000000000000002", "7.2999999999999998", "4.2999999999999998", "4.7000000000000002", "7.9000000000000004", "8.6999999999999993", "5.2999999999999998", "6", "6.4000000000000004", "5.7999999999999998", "3.8999999999999999", "5.7999999999999998", "7.7000000000000002", "4.7999999999999998", "5.7000000000000002", "7.2000000000000002", "5.2999999999999998", "5.7999999999999998", "5.7999999999999998", "5.5", "5.7000000000000002", "8.9000000000000004", "6.4000000000000004", "5.7999999999999998", "4.7000000000000002", "5", "5.7000000000000002", "4.9000000000000004", "5.0999999999999996", "6", "5.7999999999999998", "5.5999999999999996", "6", "6.2999999999999998", "6.9000000000000004", "6.2000000000000002", "5", "5.5999999999999996", "NA", "8.5999999999999996", "4.2999999999999998", "7", "8.9000000000000004", "5.2000000000000002", "5.2000000000000002", "6", "5.5", "6.7999999999999998", "3.6000000000000001", "5.9000000000000004", "8.9000000000000004", "4.4000000000000004", "6.2000000000000002", "6.2000000000000002", "6.2000000000000002", "5.2999999999999998", "5.5999999999999996", "6", "7.5999999999999996", "6.5", "7.5", "4.2999999999999998", "6.2000000000000002", "8.5999999999999996", "6", "4.7000000000000002", "3.6000000000000001", "8.4000000000000004", "8.9000000000000004", "6.2999999999999998", "8.1999999999999993", "8.8000000000000007", "6.5999999999999996", "4.4000000000000004", "8", "6.5999999999999996", "6.0999999999999996", "6.0999999999999996", "8", "6.2000000000000002", "5.2999999999999998", "6.2000000000000002", "5.7999999999999998", "7.4000000000000004", "6.5", "8", "6.9000000000000004", "4.5", "7.5999999999999996", "6.0999999999999996", "6.0999999999999996", "4.7999999999999998", "6", "5.5999999999999996", "5", "5.4000000000000004", "4.4000000000000004", "5.4000000000000004", "6.2999999999999998", "7.4000000000000004", "9", "6.2000000000000002", "5.5", "6.2000000000000002", "7.7999999999999998", "6", "7", "5.9000000000000004", "6.2000000000000002", "4.2999999999999998", "7.0999999999999996", "5.4000000000000004", "7.5999999999999996", "5", "5.7999999999999998", "6.9000000000000004"),
`COVA1-PRI Communicatieve vaardigheden 1` = c("7", "6", "6", "7.5", "5.5", "7.5", "6", "8", "7.5", "7.5", "6", "6", "7", "6.5", "8", "6.5", "NA", "6.5", "6", "7.5", "7", "7.5", "5.5", "6.5", "6.5", "5", "6", "6", "7", "6", "7.5", "6", "5", "7.5", "6.5", "7.5", "NA", "7.5", "7", "8.5", "8", "6", "7", "8", "6.5", "7.5", "7", "5", "7.5", "6.5", "7.5", "8", "7.7000000000000002", "5.5", "6", "7.5", "7.5", "6.5", "7.5", "6.5", "6", "5.5", "6", "6", "5.5", "6", "8", "6.5", "7.5", "5.5", "6", "7", "8", "8.5", "7.5", "8", "7", "7.5", "8.5", "7.5", "8.5", "7.5", "5.5", "7.5", "7.5", "6", "6.5", "7", "7", "8", "6.5", "7", "8", "NA", "6", "8", "6.5", "8", "7.5", "6.5", "6", "7.5", "7.5", "6.5", "7.5", "6.5", "6.5", "6.5", "6", "8", "6.5", "6", "6", "6", "7", "6", "7.5", "6.5", "NA", "8", "6.5", "7.5", "7", "6", "6", "6.5", "6", "6", "6.5", "6.5", "6", "8"),
stringsAsFactors = FALSE)


# Convert NA values to 0
data[data=="NA"] <- 0

# Convert columns from character to numeric
data[[5]] <- as.numeric(data[[5]])
data[[6]] <- as.numeric(data[[6]])

# Updated function using plotly

library(plotly)

plotfunc <- function(data) {
    pie_charts <- list()
    for(i in 1:ncol(data)) {

        slices <- c(count_if(lt(5.5), data[,i]), count_if(gt(5.4),data[,i]))
        lbls <- c("Passed", "Failed")
        pct <- round(slices/sum(slices)*100)
        lbls <- paste(lbls, pct)
        lbls <- paste(lbls,"%",sep="")
        pie_charts[[i]] <- plot_ly(labels = lbls, values = slices, type = 'pie')
    }
    pie_charts
}

# Pass the dataframe to the function

plots <- plotfunc(data)

# The 'plots' array will have individual plots
# This will print the first plot
plots[[1]]

# The second...and so on
plots[[2]]