如何在 R 中找到數據框特定行的總數？

Question

我有一個更大版本的數據框 (df)：

txnID   date        product          sold   repID   lastName
1001    8/5/2020    Clobromizen      600    203     Kappoorthy
1002    6/28/2020   Alaraphosol      276    887     da Silva
1003    6/28/2020   Alaraphosol      184    887     da Silva
1004    4/16/2020   Diaprogenix       36    887     da Silva
1005    6/14/2020   Diaprogenix       40    887     da Silva
1006    5/19/2020   Xinoprozen      5640    332     McRowe
1007    8/23/2020   Diaprogenix       60    332     McRowe
1008    11/14/2020  Clobromizen     2880    332     McRowe
1009    9/26/2020   Colophrazen      738    203     Kappoorthy
1010    2/5/2020    Diaprogenix       20    332     McRowe
1011    9/23/2020   Gerantrazeophem 3740    100     Schwab
1012    12/4/2020   Clobromizen     1584    221     Sixt

我想創建一個新的數據框，它獲取顯示的每個員工的所有已售產品的總和（顯示所有員工），它看起來像這樣：

View(df1)
   lastName    totalSold
1  Kappoorthy  sum(df$sold)
2  da Silva    sum(df$sold)
3  McRowe      sum(df$sold)
4  Schwab      sum(df$sold)
5  Sixt        sum(df$sold)

Answer 1

這是一種使用dplyr的方法

library(dplyr)

df %>% 
  filter(!(product %in% c("Xinoprozen", "Diaprogenix") )%>%
  group_by(lastName) %>% 
  summarize(totalSold = sum(sold,na.rm = TRUE))

Answer 2

library(dplyr) 

 df%>%
        group_by(lastName)%>%
        summarise(Totalsold = sum(sold))

如果您想排除任何產品，例如“Xinoprozen”和“Diaprogenix”

df%>%
    filter(!(product %in% c("Xinoprozen", product!="Diaprogenix")))%>%
    group_by(lastName)%>%
    summarise(Totalsold = sum(sold))

Answer 3

在基地 R 你可以這樣做：

aggregate(sold~lastName, df, sum)

     lastName sold
1    da Silva  536
2 Kappoorthy  1338
3     McRowe  8600
4     Schwab  3740
5       Sixt  1584

將 2 子集化：

aggregate(sold~lastName, df, sum, subset = !product %in%c("Xinoprozen","Diaprogenix"))
     lastName sold
1    da Silva  460
2 Kappoorthy  1338
3     McRowe  2880
4     Schwab  3740
5       Sixt  1584

如果你有NA ：

aggregate(sold~lastName, df, sum, na.rm =TRUE)

Answer 4

使用 R 基礎aggregate

aggregate(sold ~ lastName, sum, na.rm=TRUE, data=df)