如何使用ggplot2将x轴从几年更改为几个月
我在时间图表上进行了网络访问，该图表显示了从2014年到现在的每日流量，看起来像这样：
ggplot(subset(APRA, Post_Day > "2013-12-31"), aes(x = Post_Day, y = Page_Views))+
geom_line()+
scale_y_continuous(labels = comma)+
ylim(0,50000)
正如你所看到的那样，它不是一个很好的图表，更有意义的是按月分解而不是白天。但是，当我尝试这段代码时：
ggplot(subset(APRA, Post_Day > "2013-12-31"), aes(x = Post_Day, y = Page_Views))+
geom_line()+
scale_y_continuous(labels = comma)+
ylim(0,50000)+
scale_x_date(date_breaks = "1 month", minor_breaks = "1 week", labels = date_format("%B"))
我收到此错误：
错误：输入无效：date_trans仅适用于类Date的对象
日期字段Post_Day为POSIXct。 Page_Views是数字。数据如下：
Post_Title Post_Day Page_Views
Title 1 2016-05-15 139
Title 2 2016-05-15 61
Title 3 2016-05-15 79
Title 4 2016-05-16 125
Title 5 2016-05-17 374
Title 6 2016-05-17 39
Title 7 2016-05-17 464
Title 8 2016-05-17 319
Title 9 2016-05-18 84
Title 10 2016-05-18 64
Title 11 2016-05-19 433
Title 12 2016-05-19 418
Title 13 2016-05-19 124
Title 14 2016-05-19 422
我希望将X轴从每日粒度更改为每月。
2 个答案:
答案 0 :(得分：2)
问题中显示的样本数据集每天有多个数据点。因此，无论如何，它需要在日常汇总。对于按天或月的汇总，使用---
output: html_document
runtime: shiny
---
```{r setup, include=FALSE, echo=FALSE}
knitr::opts_chunk$set(warning=FALSE, message=FALSE, echo=FALSE)
```
```{r}
# non reactive stuff
library(leaflet)
library(rbokeh)
library(tidyverse)
locs <- structure(list(loc = c("S-US-611: BAD RIVER", "H-US-216: TROUT RIVER",
"M-US-67: GIERKE CREEK", "H-US-71: TROUT CREEK", "S-US-13: PENDILLS CREEK",
"O-US-67: RICE CR.", "M-US-271: EPHRAIM CREEK", "M-US-674: GIBSON CREEK (HALFWAY CREEK)",
"S-US-64: SUCKER RIVER", "M-US-339: EAST TWIN RIVER"), lon = c(-90.652399,
-83.826602, -86.336641, -84.103548, -84.819236, -76.56845, -87.179319,
-86.206658, -85.942378, -87.563722), lat = c(46.637999, 45.428862,
45.849507, 45.979098, 46.443969, 43.443795, 45.148478, 42.719827,
46.674155, 44.151644), le = c(1.10611, 3.10216, 2.10067, 3.10071,
1.10013, 5.10067, 2.10271, 2.10674, 1.10064, 2.10339)), .Names = c("loc",
"lon", "lat", "le"), row.names = c(NA, -10L), class = "data.frame")
row.names(locs) <- locs$loc
chem <- structure(list(le = c(1.00093, 1.00093, 1.00093, 1.00093, 1.00093,
1.00093, 1.00093, 1.00093, 1.00093, 1.00116, 1.00116, 1.00116,
1.00116, 1.00116, 1.00301, 1.00301, 1.00301, 1.00301, 1.00301,
1.00301, 1.00301, 1.00374, 1.00374, 1.00374, 1.00374, 1.00374,
1.00374, 1.00374, 1.00374, 1.00374, 1.00374, 1.00374, 1.00374,
1.00374, 1.10013, 1.10013, 1.10013, 1.10013, 1.10013, 1.10013,
1.10015, 1.10064, 1.10064, 1.10064, 1.10064, 1.10064, 1.10064,
1.10064, 1.10064, 1.10064, 1.10064, 1.10064, 1.10064, 1.10064,
1.10064, 1.10064, 1.10064, 1.10064, 1.10064, 1.10064, 1.10064,
1.10064, 1.10064, 1.10064, 1.10064, 1.10064, 1.10064, 1.10064,
1.10064, 1.10064, 1.10064, 1.10064, 1.10611, 1.10611, 1.10611,
1.10611, 1.10611, 1.10611, 1.10611, 1.10611, 1.10611, 1.10611,
1.10611, 1.10611, 1.10611, 1.10611, 1.10611, 1.10611, 1.10611,
1.10611, 1.10611, 1.10611, 1.10611, 2.10271, 2.10339, 2.10339,
2.10339, 2.10339, 2.10339, 2.10339, 2.10339, 2.10339, 2.10523,
2.10523, 2.10523, 2.10523, 2.10523, 2.10523, 2.10523, 2.10523,
2.10523, 2.10523, 2.10523, 2.10523, 2.10523, 2.10674, 2.10674,
3.10071, 3.10071, 3.10071, 3.10071, 3.10071, 3.10071, 3.10071,
3.10071, 3.10071, 3.10071, 3.10071, 3.10071, 3.10202, 3.10202,
3.10202, 3.10202, 3.10202, 3.10202, 3.10202, 3.10202, 3.10202,
3.10202, 3.10202, 3.10202, 3.10202, 3.10202, 3.10202, 3.10202,
3.10202, 3.10202, 3.10202, 3.10202, 3.10202, 3.10216, 3.10216,
3.10216, 3.10216, 3.10216, 3.10216, 3.10216, 3.10216, 3.10216,
3.10216, 3.10216, 3.10216, 3.10216, 3.10216, 3.10216, 3.10296,
3.10296, 3.10296, 3.10296, 3.10296, 3.10296, 3.10296, 3.10296,
3.10296, 3.10296, 3.10296, 3.10296, 3.10296, 3.10296, 3.10296,
5.10067, 5.10071, 5.10071, 5.10071, 5.10071, 5.10071, 5.10071,
5.10071, 5.10071, 5.10071, 5.10071), year = c(1962L, 1966L, 1971L,
1975L, 1984L, 1997L, 2001L, 2008L, 2012L, 1991L, 1995L, 1999L,
2004L, 2009L, 1963L, 1966L, 1971L, 1978L, 1988L, 2005L, 2012L,
1963L, 1967L, 1971L, 1975L, 1978L, 1982L, 1986L, 1990L, 1994L,
1999L, 2003L, 2007L, 2009L, 1959L, 1963L, 1973L, 1982L, 1988L,
2012L, 2012L, 1958L, 1959L, 1961L, 1963L, 1965L, 1967L, 1969L,
1971L, 1972L, 1973L, 1974L, 1975L, 1977L, 1979L, 1980L, 1981L,
1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1989L, 1990L, 1992L,
1994L, 1996L, 1998L, 2002L, 2006L, 2010L, 1960L, 1963L, 1964L,
1968L, 1969L, 1971L, 1973L, 1977L, 1978L, 1980L, 1984L, 1988L,
1991L, 1995L, 1998L, 2001L, 2003L, 2005L, 2007L, 2008L, 2011L,
1963L, 1975L, 1979L, 1982L, 1987L, 1995L, 2000L, 2004L, 2008L,
1963L, 1967L, 1971L, 1974L, 1978L, 1983L, 1987L, 1991L, 1995L,
1999L, 2002L, 2006L, 2010L, 1965L, 1984L, 1966L, 1970L, 1972L,
1973L, 1975L, 1979L, 1984L, 1989L, 1994L, 2001L, 2005L, 2009L,
1968L, 1972L, 1974L, 1976L, 1977L, 1979L, 1980L, 1982L, 1984L,
1985L, 1986L, 1988L, 1991L, 1993L, 1994L, 1997L, 1998L, 2002L,
2008L, 2009L, 2012L, 1967L, 1970L, 1974L, 1978L, 1982L, 1985L,
1989L, 1993L, 1997L, 2000L, 2004L, 2005L, 2006L, 2007L, 2011L,
1969L, 1972L, 1975L, 1979L, 1980L, 1983L, 1985L, 1989L, 1993L,
1997L, 2000L, 2002L, 2006L, 2008L, 2011L, 1972L, 1978L, 1982L,
1985L, 1988L, 1991L, 1995L, 1998L, 2002L, 2005L, 2011L), alk.mgl = c(33,
27, 20, 26, 14, 27, 51, 28, 26, 19, 20, 22, 27, 20, 78, 78, 68,
73, 71, 83, 73, 27, 19, 27, 18, 15, 12, 13, 15, 12, 30, 17, 12,
37, 38, 34, 34, 30, 36, 40, 62, 60, 68, 48, 66, 65, 56, 68, 48,
46, 50, 60, 70, 54, 56, 54, 76, 50, 24, 68, 62, 70, 80, 67, 71,
70, 62, 60, 61, 70, 77, 45, 46, 20, 56, 91, 50, 52, 46, 82, 54,
58, 82, 96, 86, 86, 99, 84, 86, 96, 67, 86, 99, 200, 175, 266,
256, 288, 280, 250, 202, 264, 142, 158, 150, 165, 182, 162, 148,
160, 158, 155, 150, 170, 160, 84, 68, 95, 58, 80, 116, 55, 55,
58, 36, 62, 60, 93, 80, 149, 159, 165, 164, 176, 150, 168, 154,
154, 166, 140, 148, 170, 160, 160, 155, 155, 163, 175, 155, 165,
145, 170, 190, 200, 164, 188, 188, 170, 130, 170, 160, 140, 150,
200, 170, 174, 182, 180, 197, 144, 154, 175, 180, 180, 178, 180,
180, 185, 185, 180, 70, 104, 137, 113, 133, 123, 147, 117, 101,
146, 125)), .Names = c("le", "year", "alk.mgl"), row.names = c(NA,
-191L), class = "data.frame")
```
```{r}
# reactive stuff
theworks <- reactive({
i <- input$location
pick <- locs$loc == i
j <- locs$le[pick]
# map data
infosub <- locs[pick, ]
# chemistry data
CHEMsub <- chem[chem$le==j, ]
list(infosub=infosub, CHEMsub=CHEMsub)
})
```
```{r}
# server
acm_defaults <- function(map, x, y) {
addCircleMarkers(map, x, y, radius=6, color="black",
fillColor="orange", fillOpacity=1, opacity=1, weight=2, stroke=TRUE,
layerId="Selected")
}
# map
output$Map <- renderLeaflet({
leaflet() %>%
# Great Lakes centered
setView(lng=-84, lat=45, zoom=6) %>%
addTiles() %>%
addCircleMarkers(data=locs, radius=6, color="black", label=~loc,
stroke=FALSE, fillOpacity=0.5, group="locations", layerId=~loc)
})
# update the map markers and view on map clicks
observeEvent(input$Map_marker_click, {
p <- input$Map_marker_click
proxy <- leafletProxy("Map")
if(p$id=="Selected"){
proxy %>%
removeMarker(layerId="Selected")
} else {
proxy %>%
setView(lng=p$lng, lat=p$lat, input$Map_zoom) %>%
acm_defaults(p$lng, p$lat)
}
})
# update the location selectInput on map clicks
observeEvent(input$Map_marker_click, {
p <- input$Map_marker_click
if(!is.null(p$id)) {
if(is.null(input$location) || input$location!=p$id) {
updateSelectInput(session, "location", selected=p$id)
}
}
})
# update the map markers and view on location selectInput changes
observeEvent(input$location, {
p <- input$Map_marker_click
p2 <- subset(locs, loc==input$location)
proxy <- leafletProxy("Map")
if(nrow(p2)==0) {
proxy %>%
removeMarker(layerId="Selected")
} else {
if(length(p$id) && input$location!=p$id) {
proxy %>%
setView(lng=p2$lon, lat=p2$lat, input$Map_zoom) %>%
acm_defaults(p2$lon, p2$lat)
} else {
if(!length(p$id)) {
proxy %>%
setView(lng=p2$lon, lat=p2$lat, input$Map_zoom) %>%
acm_defaults(p2$lon, p2$lat)
}
}
}
})
output$alk <- renderRbokeh({
df <- theworks()$CHEMsub
if(dim(df)[1] > 0) {
figure() %>%
ly_points(df$year, df$alk.mgl)
} else {
return()
}
})
```
```{r}
# ui
fluidPage(
fluidRow(
column(4,
h4(strong("Select stream from list or map")),
selectInput("location", "", c("", locs$loc), selected=""),
br(),
h4("Alkalinity"),
rbokehOutput("alk")
),
column(7,
p("(Hover to see identities of other streams.)"),
leafletOutput("Map", width="510px", height="510px")
)
)
)
```
和data.table。
创建样本数据
由于没有提供可重现的示例，因此创建了一个示例数据集：
lubridatelibrary(data.table) n_rows <- 5000L n_days <- 365L*3L set.seed(123L) DT <- data.table(Post_Title = paste("Title", 1:n_rows), Post_Day = as.Date("2014-01-01") + sample(0:n_days, n_rows, replace = TRUE), Page_Views = round(abs(rnorm(n_rows, 500, 200))))[order(Post_Day)] DT
绘制原始数据
如果没有聚合，可以通过
绘制数据 Post_Title Post_Day Page_Views
1: Title 74 2014-01-01 536
2: Title 478 2014-01-01 465
3: Title 3934 2014-01-01 289
4: Title 4136 2014-01-01 555
5: Title 740 2014-01-02 442
---
4996: Title 1478 2016-12-31 586
4997: Title 2251 2016-12-31 467
4998: Title 2647 2016-12-31 468
4999: Title 3243 2016-12-31 498
5000: Title 4302 2016-12-31 309
按日汇总
library(ggplot2)
ggplot(DT) + aes(Post_Day, Page_Views) + geom_line()
要按日聚合，请使用ggplot(DT[, .(Page_Views = sum(Page_Views)), by = Post_Day]) +
aes(Post_Day, Page_Views) + geom_line()
的分组参数by，并使用data.table作为聚合函数。聚合将数据点的数量从5000减少到1087.因此，情节看起来不那么复杂。
按月汇总
sum()为了按月汇总，使用了分组参数ggplot(DT[, .(Page_Views = sum(Page_Views)),
by = .(Post_Month = lubridate::floor_date(Post_Day, "month"))]) +
aes(Post_Month, Page_Views) + geom_line()
，但此次by已映射到相应月份的第一天。因此，Post_Day成为2014-03-26的{{1}}，仍然属于Post_Month类。这样，x轴保持连续，具有日期刻度。这样可以避免在使用2014-03-01将POSIXct转换为因子（例如Post_Day时出现问题，其中x轴将变为离散。
答案 1 :(得分：0)
APRA$month <- as.factor(stftime(APRA$Post_Day, "%m")
APRA <- APRA[order(as.numeric(APRA$month)),]
这会为您的数据创建一个月份列
z <- apply(split(APRA, APRA$month), function(x) {sum(as.numeric(APRA$Page_Views))})
z <- do.call(rbind, z)
z$month <- unique(APRA$month)
colnames(Z) <- c("Page_Views", "month")
这会创建一个z dataframe，每月有月份和页面浏览次数
现在绘制它
ggplot(z, aes(x = month, y = Page_Views)) + geom_line()
如果您正在寻找，请告诉我。我还没有编译它，请告诉它是否会引发一些错误。
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