我正在尝试从输入中过滤数据帧。我希望所有数据显示是否选择了全部选项。这是我到目前为止的内容：
这将重现一些数据：
library(tidyverse)
lihn_service_line <- rep(c("Medical", "CVA"), 10)
dsch_date <- seq.Date(as.Date("2017/01/01"), to = as.Date("2018/08/01"), by = "month")
alos <- rnorm(20, mean = 6, sd = 0.5)
elos <- rnorm(20, mean = 5, sd = 1)
df_los <- data.frame(dsch_date, lihn_service_line, alos, elos)
df_los <- df_los %>%
tibbletime::as_tbl_time(index = dsch_date) %>%
tibbletime::collapse_by("monthly") %>%
dplyr::group_by(dsch_date, add = T) %>%
summarize(
alos = round(mean(alos), 2)
, elose = round(mean(elos), 2)
)
以下是我到目前为止的内容：
Inputs {.sidebar}
-----------------------------------------------------------------------
Pick a Service Line.
```{r}
selectInput(
"svcline"
, label = h3("Service Line")
, choices = c(
"All"
, "Medical"
, "GI Hemorrhage"
, "COPD"
, "CVA"
, "CHF"
)
, selected = "Medical"
)
```
Column {data-width=350}
-----------------------------------------------------------------------
### ALOS vs. ELOS
```{r}
svc_line <- reactive({as.character(input$svcline)})
alos <- reactive(
{
df_los %>%
filter(svc_line == "All" | lihn_service_line == svc_line) %>%
collapse_by("monthly") %>%
group_by(dsch_date, add = T) %>%
summarize(
alos = round(mean(los), 2)
, elos = round(mean(performance), 2)
)
}
)
renderPlot(
{
# alos <- df_los %>%
# collapse_by("monthly") %>%
# group_by(dsch_date, add = T) %>%
# summarize(
# alos = round(mean(los), 2)
# , elos = round(mean(performance), 2)
# ) %>%
# select(dsch_date, alos, elos)
alos() %>%
ggplot(
mapping = aes(
x = dsch_date
, y = alos
)
) +
geom_line(
color = "black"
) +
geom_point() +
geom_line(
aes(
x = dsch_date
, y = elos
)
, color = "red"
) +
geom_point(
x = alos$dsch_date
, y = alos$elos
, color = "red"
) +
labs(
x = ""
, y = "ALOS"
, caption = "Black Line is Actual and Red Line is Benchmark"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 90, hjust = 0)
) +
scale_x_date(
breaks = alos$dsch_date
, labels = date_format("%b %Y")
)
}
)
```
因此，我尝试输入并过滤data.frame并更新相应的图形。我得到的错误如下：
我收到以下警告/错误：
Warning: Error in ==: comparison (1) is possible only for atomic and list types
203: filter_impl
202: filter.tbl_df
196: function_list[[i]]
195: freduce
194: _fseq
193: eval
192: eval
190: %>%
189: <reactive> [<text>#29]
187: .func
184: contextFunc
183: env$runWith
176: ctx$run
175: self$.updateValue
173: alos
169: renderPlot [<text>#51]
167: func
127: drawPlot
113: <reactive:plotObj>
97: drawReactive
84: origRenderFunc
83: output$out7e92cd2b0c4de4e1
3: <Anonymous>
1: rmarkdown::run
答案 0 :(得分：1)
这是使用虹膜数据集的示例。下面，我用您提供的数据添加一个示例。在调用ggplot以及创建df_los数据时，存在一些错误。让我知道这种方法是否适用于您的真实数据。
---
title: "Untitled"
runtime: shiny
output: html_document
---
Inputs {.sidebar}
-----------------------------------------------------------------------
Pick a Species
```{r echo = FALSE}
selectInput(
"species",
label = h3("Species"),
choices = c("All",unique(as.character(iris$Species))),
selected = "All"
)
```
Column {data-width=350}
-----------------------------------------------------------------------
### Data & Graph
```{r echo = FALSE, message = FALSE, warning = FALSE}
library(dplyr)
library(tidyr)
library(ggplot2)
iris_reac <- reactive({
iris %>%
# this is the filter method r2evans suggested below I commented my own longer filter version out
filter(input$species == "All" | Species == input$species)
# filter(if (input$species != "All") Species == input$species else 1>0) %>%
summarise(sepal_length = mean(Sepal.Length, na.rm = T),
sepal_width = mean(Sepal.Width, na.rm = T),
petal_length = mean(Petal.Length, na.rm = T),
petal_width = mean(Petal.Width, na.rm = T)) %>%
gather(key = metric)
})
renderPlot({
print(iris_reac())
ggplot(iris_reac(), aes(x = metric, y = value)) +
geom_col(width = 0.5)
})
```
下面的方法使用您的示例数据。
---
title: "Untitled"
runtime: shiny
output: html_document
---
Inputs {.sidebar}
-----------------------------------------------------------------------
Pick a Species
```{r echo = FALSE}
selectInput(
"svc_line",
label = h3("Service Line"),
choices = c(
"All",
"Medical",
"GI Hemorrhage",
"COPD",
"CVA",
"CHF"
),
selected = "Medical"
)
```
Column {data-width=350}
-----------------------------------------------------------------------
### Data & Graph
```{r echo = FALSE, message = FALSE, warning = FALSE}
library(tidyverse)
library(tibbletime)
lihn_service_line <- rep(c("Medical", "CVA"), 10)
dsch_date <- seq.Date(as.Date("2017/01/01"), to = as.Date("2018/08/01"), by = "month")
alos <- rnorm(20, mean = 6, sd = 0.5)
elos <- rnorm(20, mean = 5, sd = 1)
df_los <- data.frame(dsch_date, lihn_service_line, alos, elos)
df_los <- df_los %>%
tibbletime::as_tbl_time(index = dsch_date)
alos_data <- reactive(
{
df_los %>%
filter(input$svc_line == "All" | lihn_service_line == input$svc_line) %>%
collapse_by("monthly") %>%
group_by(dsch_date, add = T) %>%
summarize(
alos = round(mean(alos), 2)
, elos = round(mean(elos), 2)
)
}
)
renderPlot({
print(alos_data())
alos_data() %>%
ggplot(
mapping = aes(
x = dsch_date
, y = alos
)
) +
geom_line(
color = "black"
) +
geom_point() +
geom_line(
aes(
x = dsch_date
, y = elos
)
, color = "red"
) +
geom_point(aes(
x = dsch_date
, y = elos)
, color = "red"
) +
labs(
x = ""
, y = "ALOS"
, caption = "Black Line is Actual and Red Line is Benchmark"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 90, hjust = 0)
) +
scale_x_date(
date_breaks = "1 month"
, date_labels = "%b %Y"
)
})
```