将 2015 年和 2016 年的销售额与 R 中以下数据框中的 Tier 和 Region 进行比较

Question

Dataframe

Output to be displayed

我写了这段代码，但没有得到上面想要的输出：

dataframe%>%
group_by(Region,Tier)%>%
summarise(TotalSales2015=sum(Sales2015),TotalSales2016=sum(Sales2016))%>%
ggplot(aes(x=Tier, y=Sales, fill=Year)) +
geom_bar(stat="identity", position=position_dodge())+
geom_text(aes(label=Sales))

dput(数据帧)

    structure(list(AccountId = c(1116L, 1116L, 2391L, 2391L, 2397L, 
    2400L, 2400L, 2404L, 2406L, 2408L), AccountName = c("Account1", 
    "Account1", "Account2", "Account2", "Account3", "Account4", "Account4", 
    "Account5", "Account6", "Account7"), Region = c("West", "West", 
    "East", "East", "East", "East", "East", "East", "East", "East"
    ), Division = c("DIAMONDBACK", "DIAMONDBACK", "MINUTEMEN", "MINUTEMEN", 
    "MINUTEMEN", "MINUTEMEN", "MINUTEMEN", "EMPIRE", "BIG APPLE", 
    "BIG APPLE"), City = c("PHOENIX W", "PHOENIX W", "HARTFORD", 
    "HARTFORD", "WORCESTER", "PORTLAND", "PORTLAND", "BRIDGEPORT", 
    "JERSEY CITY", "JERSEY CITY"), State = c("AZ", "AZ", "CT", "CT", 
    "MA", "ME", "ME", "CT", "NJ", "NJ"), Tier = c("Low", "Low", "Med", 
    "Med", "Med", "High", "High", "Low", "Med", "High"), Month = c("Aug", 
    "Oct", "Jun", "Mar", "Sep", "Jul", "Feb", "Mar", "Mar", "Aug"
    ), Sales2015 = c(0, 10500.78, 0, 19881, 3684.48, 0, 2631.31, 
    4153.89, 0, 0), Sales2016 = c(13208.52, 23114.91, 6627, 13254, 
    0, 10525.24, 42812.62, 3918.77, 6951.86, 10994.54), Units2015 = c(0, 
    3, 0, 9, 1, 0, 1, 1, 0, 0), Units2016 = c(4, 7, 3, 6, 0, 4, 17, 
    1, 2, 4), TargetAchevied2015 = c(0.7, 0.84, 1.15, 1.33, 1.02, 
    1.03, 1.08, 0.79, 1.12, 1.11), TargetAchevied2016 = c(1.53, 1.31, 
    1.29, 1.17, 1.53, 1.45, 0.99, 1.46, 1.02, 1.54)), row.names = c(NA, 
    10L), class = "data.frame")

Answer 1

和 tidyverse 朋友 dpyr、tidyr、scales 和 ggplot 你可以试试这个：

library(dplyr)
library(tidyr)
library(ggplot2)

df <- 
  dataframe %>%
  pivot_longer(cols = contains("20"), names_pattern = "(\D*)(\d{4})", names_to = c("metric", "Year")) %>% 
  pivot_wider(names_from = metric, values_from = value) %>% 
  group_by(Region, Tier, Year) %>%
  summarise(Sales = sum(Sales))


  ggplot(df, aes(x = Tier, y = Sales, fill = Year)) +
  geom_col(position = position_dodge(width = 0.9))+
  geom_text(aes(label = scales::comma(Sales)),
            position = position_dodge(width = 0.9),
            angle = 90,
            hjust = 0)+
    scale_y_continuous(expand = expansion(mult = c(0.06, 0.2)))+
    labs(fill = "Total sales")+
    facet_wrap(~Region, nrow = 1)

^{由 reprex package (v2.0.0)}

于 2021-08-29 创建

数据

dataframe <- structure(list(AccountId = c(1116L, 1116L, 2391L, 2391L, 2397L, 
2400L, 2400L, 2404L, 2406L, 2408L), AccountName = c("Account1", 
"Account1", "Account2", "Account2", "Account3", "Account4", "Account4", 
"Account5", "Account6", "Account7"), Region = c("West", "West", 
"East", "East", "East", "East", "East", "East", "East", "East"
), Division = c("DIAMONDBACK", "DIAMONDBACK", "MINUTEMEN", "MINUTEMEN", 
"MINUTEMEN", "MINUTEMEN", "MINUTEMEN", "EMPIRE", "BIG APPLE", 
"BIG APPLE"), City = c("PHOENIX W", "PHOENIX W", "HARTFORD", 
"HARTFORD", "WORCESTER", "PORTLAND", "PORTLAND", "BRIDGEPORT", 
"JERSEY CITY", "JERSEY CITY"), State = c("AZ", "AZ", "CT", "CT", 
"MA", "ME", "ME", "CT", "NJ", "NJ"), Tier = c("Low", "Low", "Med", 
"Med", "Med", "High", "High", "Low", "Med", "High"), Month = c("Aug", 
"Oct", "Jun", "Mar", "Sep", "Jul", "Feb", "Mar", "Mar", "Aug"
), Sales2015 = c(0, 10500.78, 0, 19881, 3684.48, 0, 2631.31, 
4153.89, 0, 0), Sales2016 = c(13208.52, 23114.91, 6627, 13254, 
0, 10525.24, 42812.62, 3918.77, 6951.86, 10994.54), Units2015 = c(0, 
3, 0, 9, 1, 0, 1, 1, 0, 0), Units2016 = c(4, 7, 3, 6, 0, 4, 17, 
1, 2, 4), TargetAchevied2015 = c(0.7, 0.84, 1.15, 1.33, 1.02, 
1.03, 1.08, 0.79, 1.12, 1.11), TargetAchevied2016 = c(1.53, 1.31, 
1.29, 1.17, 1.53, 1.45, 0.99, 1.46, 1.02, 1.54)), row.names = c(NA, 
10L), class = "data.frame")

将 2015 年和 2016 年的销售额与 R 中以下数据框中的 Tier 和 Region 进行比较

Compare the sales of 2015 and 2016 with Tier and Region from the following dataframe in R

r

bar-chart

ggplot2

geom-bar