TSplotly (version 1.1.1)

GtoP_trans: 'GtoP_trans'

Description

a dataset transformation method, change ggplot2 dataset into dataset that can be used by plot_ly

Usage

GtoP_trans(dataframe, NAME = NULL, X = NULL, Y = NULL)

Arguments

dataframe

a data frame data that can be used on ggplot2

NAME

column that will be used on creating different lines on plot_ly

X

column that will serve as the x axis on plot_ly

Y

column that will serve as the value(y) on plot_ly

Value

a data frame with each column serve as a line in plot_ly

Details

The function GtoP_trans is used to conduct a data transformation. Usually dataset working on ggplot2 is a kind of dataset that has long rows and short columns. And we may wish to create plot based on different levels for a particular column. However, this dataset won't work for plot_ly as it requires each level as a column. So this method will separate different levels in a column to create a new dataset with multiple columns representing the original levels.

Examples

# NOT RUN {
require(ggplot2)
require(plotly)
require(zoo)
# Dataset in this example. We wish to generate plot with different lines based on variable "series".
# However, this dataset now can only be used on ggplot2
head(MCSI_Data_monthAvg_melt)

# A ggplot2 example here:
ggplot(MCSI_Data_monthAvg_melt[MCSI_Data_monthAvg_melt$series!="INCOME", ],
       aes(YYYYMM, value)) +
  geom_line(aes(linetype=series, colour = series), size=2) +
  geom_point(aes(shape=series, colour = series), size=0.3) +
  geom_smooth(aes(colour = series), se = TRUE) +
  coord_trans(y="log10") +
  xlab("Time (monthly)") + ylab("Index Values (log-scale)") +
  scale_x_date(date_breaks = "12 month", date_labels =  "%m-%Y")  +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        text = element_text(size=20))+ theme(legend.position="top")

# Change the dataset with our function
PYdf<-GtoP_trans(MCSI_Data_monthAvg_melt[MCSI_Data_monthAvg_melt$series!="INCOME", ],
NAME="series",X="YYYYMM",Y="value")
PYdf$INCOME<-NULL
head(PYdf)

#Log transformation
PYdf<-log10(PYdf)

# Generate a plot_ly result
## Note that we've done a log transformation on y-axis
plot_ly(type="scatter",mode="lines")%>%
  add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
  y=PYdf$ICS,name="ICS",line=list(color="powderblue"))%>%
  add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
  y=PYdf$ICC,name="ICC",line=list(color="red"))%>%
  add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
  y=PYdf$GOVT,name="GOVT",line=list(color="green"))%>%
  add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
  y=PYdf$DUR,name="DUR",line=list(color="orange"))%>%
  add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
  y=PYdf$HOM,name="HOM",line=list(color="purple"))%>%
  add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
  y=PYdf$CAR,name="CAR",line=list(color="pink"))%>%
  add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
  y=PYdf$AGE,name="AGE",line=list(color="brown"))%>%
  add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
  y=PYdf$EDUC,name="EDUC",line=list(color="black"))%>%
  layout(title= list(text="Time series for 8 indexes",
  font=list(family = "Times New Roman",size = 16,color = "black" )),
         paper_bgcolor='rgb(255,255,255)', plot_bgcolor='rgb(229,229,229)',
         xaxis = list(title ="Time (monthly)",
                      gridcolor = 'rgb(255,255,255)',
                      showgrid = TRUE,
                      showline = FALSE,
                      showticklabels = TRUE,
                      tickcolor = 'rgb(127,127,127)',
                      ticks = 'outside',
                      zeroline = FALSE),
         yaxis = list(title = "Index Values (log-scale)",
                      gridcolor = 'rgb(255,255,255)',
                      showgrid = TRUE,
                      showline = FALSE,
                      showticklabels = TRUE,
                      tickcolor = 'rgb(127,127,127)',
                      ticks = 'outside',
                      zeroline = FALSE))
# }