小编Nis*_*ant的帖子

我有以下数据：

head(df2,20)
     LAF_Date variable  value   pos
1  2001-04-03      Net  -1470 FALSE
2  2001-04-04      Net -17675 FALSE
3  2001-04-09      Net -13820 FALSE
4  2001-04-10      Net -16885 FALSE
5  2001-04-11      Net -18160 FALSE
6  2001-04-12      Net -19170 FALSE
7  2001-04-16      Net -13715 FALSE
8  2001-04-17      Net -17265 FALSE
9  2001-04-18      Net -11115 FALSE
10 2001-04-19      Net   -600 FALSE
11 2001-04-20      Net -11375 FALSE
12 2001-04-23      Net  -8200 FALSE
13 2001-04-24      Net  -5600 FALSE
14 2001-04-25      Net  -5300 FALSE
15 2001-04-26      Net      0 …

我创建了一个自定义函数来绘制回归诊断,就像这些版本的ggplot2和gridextra一样:

ggplot2        * 1.0.1      2015-03-17 CRAN (R 3.2.1)                
gridExtra      * 2.0.0      2015-07-14 CRAN (R 3.2.1)

head(dadHospital)
      SL. BODY.WEIGHT TOTAL.COST.TO.HOSPITAL
## 1   1          49                 660293
## 2   2          41                 809130
## 3   3          47                 362231
## 4   4          80                 629990
## 5   5          58                 444876
## 6   6          45                 372357

fit1<-lm(TOTAL.COST.TO.HOSPITAL~BODY.WEIGHT,data=dadHospital)   

#custom function of plotting model diagnostics using ggplot2

library(ggplot2)
diagPlot<-function(model){
p1<-ggplot(model, aes(.fitted, .resid))+geom_point()
p1<-p1+stat_smooth(method="loess")+geom_hline(yintercept=0, col="red", linetype="dashed")
p1<-p1+xlab("Fitted values")+ylab("Residuals")
p1<-p1+ggtitle("Residual vs Fitted Plot")+theme_bw()

p2<-ggplot(model, aes(qqnorm(.stdresid)[[1]], .stdresid))+geom_point(na.rm = TRUE)
p2<-p2+geom_abline(aes(qqline(.stdresid)))+xlab("Theoretical Quantiles")+ylab("Standardized Residuals") …

我有以下数据：

df
    rowname   repo
1   revrepo  0.888
2  bankrate  0.402
3       CRR  0.250
4  Callrate  0.723
5       WPI  0.049
6       GDP -0.318
7       FED  0.110
8     width  0.209
9       nse  0.059
10      usd  0.185

我正在绘制小节，如下所示：

df %>% mutate(rowname = factor(rowname, levels = rowname[order(repo)])) %>%
ggplot(aes(x = rowname, y = repo)) +
geom_bar(stat = "identity") +
ylab("Correlation with repo") +
xlab("Independent Variable")

我得到以下情节：

我想将负极条涂成红色，将所有正极条涂成灰色。

我正在尝试一个小巧的应用程序,其中我从本地目录加载CSV文件,然后从数据框中选择特定列,并使用此子集化数据框进行进一步的数据分析.

library(shiny)

# Define UI for application that draws a histogram
ui <- fluidPage(

   # Application title
   titlePanel("Old Faithful Geyser Data"),

   # Sidebar with a slider input for number of bins 
   sidebarLayout(
      sidebarPanel(
        fileInput("dataset", "Choose CSV File",
                  multiple = TRUE,
                  accept = c("text/csv",
                             "text/comma-separated-values,text/plain",
                             ".csv")),
        # Include clarifying text ----
        #helpText(em("Note: This app requires file in csv format only!!")),
        helpText(em("Note:Select all the inputs and click on button as given below to exectute the app")),
        # Input: Checkbox if file has …

我有以下 df：

head(vardata)
       Month repo Callrate  WPI GDP  FED    nse   usd
1 2001-04-01 9.00     7.49 5.41 4.6 4.50 1125.2 46.79
2 2001-05-01 8.75     8.03 5.60 4.6 4.00 1167.9 46.92
3 2001-06-01 8.50     7.24 5.30 4.6 3.75 1107.9 47.00
4 2001-07-01 8.50     7.19 5.23 5.3 3.75 1072.8 47.14
5 2001-08-01 8.50     6.94 5.41 5.3 3.50 1053.8 47.13
6 2001-09-01 8.50     7.30 4.52 5.3 3.00  913.9 47.65

我想使用以下规则集对所有 7 个变量进行 Box.test、adf.test 和 kpss.test：

假设我将显着性水平设置为 5%。那么规则是：

1) 对于 Box.test，如果 p 值 < 0.05 …

我有一个数据框：

df = pd.DataFrame(
    {'title':['a1','a2','a3','a4','a5'],
     'genre_name':[
         ['family', 'animation'],
         ['action', 'family', 'comedy'],
         ['family', 'comedy'],
         ['horror','action'],
         ['family', 'animation','comedy']]}
)

df
      title    genre_name
0      a1     ['family', 'animation']
1      a2     ['action', 'family', 'comedy']
2      a3     ['family', 'comedy']
3      a4     ['horror','action]
4      a5     ['family', 'animation','comedy']

我有字典：

dict={'1':'family','2':'animation','3':'action','4':'comedy','5':'horror'}

我想创建一个名为“genre_ids”的新列，它将所有的流派名称映射到字典“dict”中的键。

所需的 df 是：

df
          title    genre_name                       genre_ids
    0      a1     ['family', 'animation']           [1,2]
    1      a2     ['action', 'family', 'comedy']    [3,1,4]
    2      a3     ['family', 'comedy']              [1,4]
    3      a4     ['horror','action]                [5,3]
    4      a5     ['family', 'animation','comedy']  [1,2,4]

我怎样才能做到这一点？

我有以下 df：

df<-data.frame(geo_num=c(11,12,22,41,42,43,77,71),
               cust_id=c("A","A","B","C","C","C","D","D"),
               sales=c(2,3,2,1,2,4,6,3))
> df
  geo_num cust_id sales
1      11       A     2
2      12       A     3
3      22       B     2
4      41       C     1
5      42       C     2
6      43       C     4
7      77       D     6
8      71       D     3

需要创建一个新列“geo_num_new”，其中“cust_id”中的每个组都具有“geo_num”中的第一个值，如下所示：

> df_new
  geo_num cust_id sales geo_num_new
1      11       A     2          11
2      12       A     3          11
3      22       B     2          22
4      41       C     1          41
5      42       C     2          41
6      43       C     4          41
7 …