Question

我有一个df，看起来或多或少是这样的：

await member.send(`You have been kicked from \`${message.guild.name}\`. Reason: \`${reason}\``)
  .catch(err => message.channel.send(`⚠ Unable to alert ${member} of reason.`));

await member.kick(reason);
await message.channel.send(` ${member} has been kicked!`);

console.log(`${message.author.tag} kicked ${member.user.tag} from '${message.guild.name}'.`);

我正在尝试绘制这样的图形：

表示每个组（A，B，C）与上一年的变化（％）。问题是，有时先例年份是-1，有时是-2或-3等...

要做这样的图，我需要计算滞后值，然后计算百分比差如下：

year <- c(2000,2000,2000,2002,2002,2002,2003,2003,2003,2006,2006,2006)
type <- c("A","B","C","A","B","C","A","B","C","A","B","C")
value <- c(71,80,99,79,61,62,56,88,50,85,56,85)
df <- data.frame(year, type, value)
rm(year, type, value)

我的问题分为2个：

您如何正确计算滞后值？
有没有一种快速的方法来计算与上一年相比的百分比变化？

Answer 1

我们可以按“滞后”分组来完成此操作，然后进行区别

decryptTest(input: string) {
    let key = "secret";
    return crypto.AES.decrypt(input, key, {
      iv: 'aTestIvValueKey',
      mode: crypto.mode.CBC,
      padding: crypto.pad.Pkcs7
    }).toString();
  }

数据

library(dplyr)
df %>%
   group_by(type) %>%
   mutate(lag = value - lag(value),
          perc = lag/lag(value))
# A tibble: 12 x 5
# Groups:   type [3]
#    year type  value   lag    perc
#   <dbl> <fct> <dbl> <dbl>   <dbl>
# 1  2000 A        71    NA  NA    
# 2  2000 B        80    NA  NA    
# 3  2000 C        99    NA  NA    
# 4  2002 A        79     8   0.113
# 5  2002 B        61   -19  -0.238
# 6  2002 C        62   -37  -0.374
# 7  2003 A        56   -23  -0.291
# 8  2003 B        88    27   0.443
# 9  2003 C        50   -12  -0.194
#10  2006 A        85    29   0.518
#11  2006 B        56   -32  -0.364
#12  2006 C        85    35   0.7

Answer 2

考虑以R为基础的ave，用diff计算 lag 并计算百分比变化：

df <- cbind.data.frame(year, type, value)

df <- within(df, {
          lag <- ave(value, type, FUN=function(x) c(NA, diff(x)))
          prev <- ave(value, type, FUN=function(x) c(NA, x[1:length(x)-1]))

          pct <- ifelse(is.na(prev), NA, lag / prev)
          rm(prev)
    })

df

#    year type value        pct lag
# 1  2000    A    71         NA  NA
# 2  2000    B    80         NA  NA
# 3  2000    C    99         NA  NA
# 4  2002    A    79  0.1126761   8
# 5  2002    B    61 -0.2375000 -19
# 6  2002    C    62 -0.3737374 -37
# 7  2003    A    56 -0.2911392 -23
# 8  2003    B    88  0.4426230  27
# 9  2003    C    50 -0.1935484 -12
# 10 2006    A    85  0.5178571  29
# 11 2006    B    56 -0.3636364 -32
# 12 2006    C    85  0.7000000  35

时间变量不完整和不规则的组的滞后值

2 个答案:

数据