我有這樣的資料：
| 約會時間 | aws.pr_sum | aws.ta_2m | 時間戳 |
|---|---|---|---|
| 2020-06-14 12:00:00 | 0.00 | 19.20000 | 1635171717 |
| 2020-06-14 13:00:00 | 0.85 | 19.73833 | 1635171717 |
| 2020-06-14 14:00:00 | 0.00 | 21.95667 | 1635171717 |
| 2020-06-14 15:00:00 | 0.00 | 21.71333 | 1635171717 |
| 2020-06-14 16:00:00 | 0.00 | 21.04667 | 1635171717 |
| 2020-06-14 17:00:00 | 0.00 | 20.66500 | 1635171717 |
正如標題所說，我想在同一張圖中繪制每日累積和每日每小時降水量。我所做的：
df <- df %>% group_by(timestamp) %>%
mutate(aws_pcp_cumulative = cumsum(aws.pr_sum))
c3 <- c("inca" = "#6aa6fa", "AWS" = "#2ECBE9")
p3 = ggplot(df, aes(x=datetime))
geom_bar(stat = "identity", aes(x = datetime shift/2, y = aws.pr_sum, color="AWS"), size=0.7, fill=NA, width=wd )
geom_line(data = df, aes(x = datetime, y = aws_pcp_cumulative, color = "AWS"), size=1.2,alpha=0.2)
geom_bar(stat = "identity", aes(x = datetime - shift/2, y = inca.RR.max, color="inca"), size=0.7, fill=NA, width=wd)
geom_line(data = df, aes(x = datetime, y = inca_pcp_cumulative, color = "inca"), size=1.2,alpha=0.2)
geom_text(aes(label=laws, x = datetime, y = aws.pr_sum, color="AWS"), vjust = -0.5, size=3)
geom_text(aes(label=linca, x = datetime, y = inca.RR.max, color="inca"), vjust = -0.5, size=3)
scale_x_datetime(breaks=brks, labels = lbls, date_labels="%d.%m %H:%M")
labs(x="",
y= "",
color="zrá?ky [mm/h]")
scale_color_manual(values = c3)
theme(
panel.grid.major.x = element_blank(),
legend.position = c(0.01,0.95),
legend.justification = c("left", "top"),
legend.box.just = "left",
legend.margin = margin(6, 30, 6,6),
legend.title = element_text(size = 12))
結果如下：https : //imgur.com/a/MYmJhy7
正如我們所見，對整個期間進行了累積總和，但我每天都想要。有任何想法嗎 ？
uj5u.com熱心網友回復：
不幸的是，您所有的時間點都來自同一日期。您可以使用dplyr::group_by和獲取累積值sum：
library(tidyverse)
data <-
tribble(
~datetime, ~aws.pr_sum, ~aws.ta_2m, ~timestamp,
"2020-06-14 12:00:00", 0, 19.2, 1635171717L,
"2020-06-14 13:00:00", 0.85, 19.73833, 1635171717L,
"2020-06-14 14:00:00", 0, 21.95667, 1635171717L,
"2020-06-14 15:00:00", 0, 21.71333, 1635171717L,
"2020-06-14 16:00:00", 0, 21.04667, 1635171717L,
"2020-06-14 17:00:00", 0, 20.665, 1635171717L
)
data
#> # A tibble: 6 x 4
#> datetime aws.pr_sum aws.ta_2m timestamp
#> <chr> <dbl> <dbl> <int>
#> 1 2020-06-14 12:00:00 0 19.2 1635171717
#> 2 2020-06-14 13:00:00 0.85 19.7 1635171717
#> 3 2020-06-14 14:00:00 0 22.0 1635171717
#> 4 2020-06-14 15:00:00 0 21.7 1635171717
#> 5 2020-06-14 16:00:00 0 21.0 1635171717
#> 6 2020-06-14 17:00:00 0 20.7 1635171717
data %>%
mutate(
datetime = datetime %>% as.POSIXct(),
day = datetime %>% as.Date()
) %>%
group_by(day) %>%
mutate(
aws.pr_su.day = sum(aws.pr_sum)
) %>%
pivot_longer(c(aws.pr_sum, aws.pr_su.day)) %>%
qplot(datetime, value, color = name, data = .)
由reprex 包(v2.0.1)于 2021 年 10 月 27 日創建
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