英文:
Plot in R with the percentage change beetwen 2022 and 2023
问题
I'd like a little legend above each column that shows the change % using this formula: '((monthvalue2023-monthvalue2022)/monthvalue2022)*100'. So, for October, it would be '((319.4 - 235.7)/235.7)*100', which is 61.7%.
英文:
I have a dataset with the column Date and a column Price, I would like a graph that shows the percentage change beetwen the months.
Date Price30/10/2022 129,60 €31/10/2022 106,10 €01/11/2022 87,40 €02/11/2022 103,80 €01/12/2022 60,00 €02/12/2022 380,50 €01/10/2023 243,40 €02/10/2023 76,00 €08/11/2023 318,30 €09/12/2023 156,30 €10/12/2023 49,00 €this is what I have done:# Estrazione del mese e dell'annodf$Month<- format(df$Data, "%m")df$Year <- format(df$Data, "%Y")# Calcolo del totale del prezzo per ciascun mesetotal<- aggregate(Price ~ Month + Year, df, sum)# Creazione del grafico a istogrammaplot <- plot_ly(totali_mese, x = ~Month, y = ~Price, color = ~Year, type = "bar") %>%layout(barmode = "group",xaxis = list(title = "Month"),yaxis = list(title = "Price"),legend = list(title = "Year"),x = totali_mese$Mese[2:nrow(totale)],y = totali_mese$Prezzo[2:nrow(totale)] + 20)#plot
these codes show of course the graph that I wanted, but now I'd like a little legend above each colmun that show the change %
(I'd used this formula: '((monthvalue2023-monthvalue2022)/montvalue2022)*100').
so for october I'd have '((319,4 - 235,7)/235,7)*100' which is 61,7%
this is the graph (the values are different 'cause I used a different dataset) 
答案1
得分: 1
以下是您要翻译的内容：
library(plotly)library(dplyr)library(tidyr)library(lubridate)df_wide <- df_ %>%group_by(Year = year(Date), Month = month(Date)) %>%summarise(Total = sum(Price), .groups = "drop") %>%pivot_wider(names_from = Year, values_from = Total) %>%mutate(Change_pct = ((`2023` - `2022`) / `2022`) %>% scales::label_percent()())df_wide#> # A tibble: 3 × 4#> Month `2022` `2023` Change_pct#> <dbl> <dbl> <dbl> <chr>#> 1 10 236. 319. 36%#> 2 11 191. 318. 66%#> 3 12 440. 205. -53%plot_ly(df_wide, x = ~ Month) %>%add_bars(y = ~ `2022`, name = "2022") %>%add_bars(y = ~ `2023`, name = "2023", text = ~ Change_pct , textposition = "outside") %>%layout(yaxis = list(title = "Price"))
示例数据：
df_ <- structure(list(Date = structure(c(19295, 19296, 19297, 19298,19327, 19328, 19631, 19632, 19669, 19700, 19701), class = "Date"),Price = c(129.6, 106.1, 87.4, 103.8, 60, 380.5, 243.4, 76,318.3, 156.3, 49)), row.names = c(NA, -11L), class = c("tbl_df","tbl", "data.frame"))
Created on 2023-05-25 with reprex v2.0.2
<details><summary>英文:</summary>With just 2 years I'd go with wide format, column per year. Makes change calculation easier to follow and when adding 2 traces to Plotly, we can just add text labels to one of those:``` rlibrary(plotly)library(dplyr)library(tidyr)library(lubridate)df_wide <- df_ %>%group_by(Year = year(Date), Month = month(Date)) %>%summarise(Total = sum(Price), .groups = "drop") %>%pivot_wider(names_from = Year, values_from = Total) %>%mutate(Change_pct = ((`2023` - `2022`) / `2022`) %>% scales::label_percent()())df_wide#> # A tibble: 3 × 4#> Month `2022` `2023` Change_pct#> <dbl> <dbl> <dbl> <chr>#> 1 10 236. 319. 36%#> 2 11 191. 318. 66%#> 3 12 440. 205. -53%plot_ly(df_wide, x = ~ Month) %>%add_bars(y = ~ `2022`, name = "2022") %>%add_bars(y = ~ `2023`, name = "2023", text = ~ Change_pct , textposition = "outside") %>%layout(yaxis = list(title = "Price"))
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Sample data:
df_ <- structure(list(Date = structure(c(19295, 19296, 19297, 19298,19327, 19328, 19631, 19632, 19669, 19700, 19701), class = "Date"),Price = c(129.6, 106.1, 87.4, 103.8, 60, 380.5, 243.4, 76,318.3, 156.3, 49)), row.names = c(NA, -11L), class = c("tbl_df","tbl", "data.frame"))
<sup>Created on 2023-05-25 with reprex v2.0.2</sup>
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