Time Series Interactive Plots with R dygraphs
Plots can be zoomed, highlighted, and many more can be done.
These interactive plots provide users to play with plots and understand in a better way.
I see this plots are equally dazzling as Tableau and Qlikview does.
I choose walmart sales data which you can download from:
Data Processing
#### load the needed libraries####
library("plyr")
library("reshape")##
## Attaching package: 'reshape'
##
## The following objects are masked from 'package:plyr':
##
## rename, round_anylibrary("dygraphs")
library("xts")## Loading required package: zoo
##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericsetwd("D:\\Personal\\Blogs\\Jan_05_2015")
#### load the datasets####
walmartSales <- read.csv("WalmartSales.csv")
features <- read.csv("features.csv")
#### look of the data#####
head(walmartSales)## Store Dept Date Weekly_Sales IsHoliday
## 1 1 1 2010-02-05 24924.50 FALSE
## 2 1 1 2010-02-12 46039.49 TRUE
## 3 1 1 2010-02-19 41595.55 FALSE
## 4 1 1 2010-02-26 19403.54 FALSE
## 5 1 1 2010-03-05 21827.90 FALSE
## 6 1 1 2010-03-12 21043.39 FALSE#### number of stores and departments#####
unique(walmartSales$Store)## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45unique(walmartSales$Dept)## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20 21 22 23 24
## [24] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 40 41 42 44 45 46 47 48 49
## [47] 51 52 54 55 56 58 59 60 67 71 72 74 77 78 79 80 81 82 83 85 87 90 91
## [70] 92 93 94 95 96 97 98 99 39 50 43 65#### Average sales in each store across all departments ###
AvgSalesStore<-ddply(walmartSales,.(Store,Date),summarise,sum(Weekly_Sales))
##Change the colname
names(AvgSalesStore)[3]<-"sales"
## Merge the data sets
mergedSales <- merge(AvgSalesStore,features,by = c("Store","Date"))
## glance of AvgSalesStore
head(mergedSales)## Store Date sales Temperature Fuel_Price MarkDown1 MarkDown2
## 1 1 2010-02-05 1643691 42.31 2.572 NA NA
## 2 1 2010-02-12 1641957 38.51 2.548 NA NA
## 3 1 2010-02-19 1611968 39.93 2.514 NA NA
## 4 1 2010-02-26 1409728 46.63 2.561 NA NA
## 5 1 2010-03-05 1554807 46.50 2.625 NA NA
## 6 1 2010-03-12 1439542 57.79 2.667 NA NA
## MarkDown3 MarkDown4 MarkDown5 CPI Unemployment IsHoliday
## 1 NA NA NA 211.0964 8.106 FALSE
## 2 NA NA NA 211.2422 8.106 TRUE
## 3 NA NA NA 211.2891 8.106 FALSE
## 4 NA NA NA 211.3196 8.106 FALSE
## 5 NA NA NA 211.3501 8.106 FALSE
## 6 NA NA NA 211.3806 8.106 FALSEStore 3 time series plot
## Select store 3
store3 <- mergedSales[mergedSales$Store==3,]
names(store3)[3] <-"store3"
head(store3[100:106,])## Store Date store3 Temperature Fuel_Price MarkDown1 MarkDown2
## 3246 3 2011-12-30 410553.9 48.29 3.129 1449.16 14356.07
## 3247 3 2012-01-06 398178.2 52.42 3.157 3824.22 9389.98
## 3248 3 2012-01-13 367438.6 51.86 3.261 3927.76 3323.64
## 3249 3 2012-01-20 365818.6 56.20 3.268 720.82 1629.26
## 3250 3 2012-01-27 349518.1 58.06 3.290 774.55 349.99
## 3251 3 2012-02-03 424960.7 59.33 3.360 9667.50 268.29
## MarkDown3 MarkDown4 MarkDown5 CPI Unemployment IsHoliday
## 3246 66.66 81.43 777.35 223.0091 7.197 TRUE
## 3247 15.00 415.36 1476.85 223.1923 6.833 FALSE
## 3248 NA 226.97 1725.19 223.3755 6.833 FALSE
## 3249 1.46 16.05 799.50 223.4701 6.833 FALSE
## 3250 NA 4.00 328.72 223.5646 6.833 FALSE
## 3251 0.60 8368.15 804.24 223.6591 6.833 FALSE ## Create xts object
store3_xts<-xts(store3[,-2],order.by=as.POSIXct(store3$Date))
class(store3_xts)## [1] "xts" "zoo"## Time Series plot
dygraph(store3_xts[,2],col)## Warning in FUN(X[[4L]], ...): converting an R function to JSON as null. To
## change this, define a method for toJSON() for a 'function' object.Store 16 time series plot
## Select store 16
store16 <- mergedSales[mergedSales$Store==16,]
names(store16)[3] <-"store16"
head(store16[100:106,])## Store Date store16 Temperature Fuel_Price MarkDown1 MarkDown2
## 1101 16 2011-12-30 665861.1 23.91 3.119 921.67 11778.68
## 1102 16 2012-01-06 564538.1 26.49 3.095 443.59 5524.49
## 1103 16 2012-01-13 508520.1 19.55 3.077 1353.58 2551.73
## 1104 16 2012-01-20 474389.8 29.30 3.055 2087.19 1399.47
## 1105 16 2012-01-27 453979.2 28.17 3.038 612.01 1011.32
## 1106 16 2012-02-03 475905.1 25.53 3.031 32702.46 747.82
## MarkDown3 MarkDown4 MarkDown5 CPI Unemployment IsHoliday
## 1101 101.87 272.51 1673.25 196.3586 6.232 TRUE
## 1102 15.70 35.25 3296.96 196.5458 6.162 FALSE
## 1103 0.24 289.67 1899.49 196.7331 6.162 FALSE
## 1104 5.58 75.83 4742.74 196.7797 6.162 FALSE
## 1105 NA 8.41 2482.91 196.8263 6.162 FALSE
## 1106 2.39 8063.07 2940.68 196.8729 6.162 FALSE ## Create xts object
store16_xts<-xts(store16[,-2],order.by=as.POSIXct(store16$Date))
class(store16_xts)## [1] "xts" "zoo"## Time Series plot
dygraph(store16_xts[,2])Store 20 time series plot
## Select store 20
store20 <- mergedSales[mergedSales$Store==20,]
names(store20)[3] <-"store20"
head(store20[100:106,])## Store Date store20 Temperature Fuel_Price MarkDown1 MarkDown2
## 1816 20 2011-12-30 2043245 36.35 3.389 6918.65 97740.99
## 1817 20 2012-01-06 1964702 33.42 3.422 9273.46 45971.43
## 1818 20 2012-01-13 1911511 37.79 3.513 3627.81 18632.14
## 1819 20 2012-01-20 1892776 27.65 3.533 2465.98 5030.40
## 1820 20 2012-01-27 1761017 37.19 3.567 1023.60 3044.15
## 1821 20 2012-02-03 2203523 39.93 3.617 58928.52 8766.77
## MarkDown3 MarkDown4 MarkDown5 CPI Unemployment IsHoliday
## 1816 270.38 1648.03 2852.19 212.4036 7.082 TRUE
## 1817 207.60 1365.38 5460.86 212.5711 6.961 FALSE
## 1818 108.48 847.70 3614.04 212.7386 6.961 FALSE
## 1819 106.54 999.54 3380.92 212.8336 6.961 FALSE
## 1820 106.08 52.87 2447.73 212.9286 6.961 FALSE
## 1821 226.27 53603.99 8301.25 213.0236 6.961 FALSE ## Create xts object
store20_xts<-xts(store20[,-2],order.by=as.POSIXct(store20$Date))
class(store20_xts)## [1] "xts" "zoo"## Time Series plot
dygraph(store20_xts[,2])Store 8 time series plot
## Select store 8
store8 <- mergedSales[mergedSales$Store==8,]
names(store8)[3] <-"store8"
head(store8[100:106,])## Store Date store8 Temperature Fuel_Price MarkDown1 MarkDown2
## 6249 8 2011-12-30 858572.2 36.33 3.129 3701.08 36381.83
## 6250 8 2012-01-06 872113.2 43.47 3.157 3093.39 26001.46
## 6251 8 2012-01-13 817661.8 36.46 3.261 6725.29 12764.99
## 6252 8 2012-01-20 813954.8 46.81 3.268 1643.08 1055.47
## 6253 8 2012-01-27 778178.5 45.52 3.290 6065.01 730.16
## 6254 8 2012-02-03 927610.7 45.56 3.360 41524.03 1412.41
## MarkDown3 MarkDown4 MarkDown5 CPI Unemployment IsHoliday
## 6249 164.34 178.40 1508.56 223.0577 6.123 TRUE
## 6250 96.35 500.91 4468.63 223.2410 5.825 FALSE
## 6251 15.98 299.73 3851.69 223.4243 5.825 FALSE
## 6252 9.02 411.88 3161.92 223.5188 5.825 FALSE
## 6253 15.18 30.28 24241.12 223.6134 5.825 FALSE
## 6254 83.15 18723.56 3886.31 223.7079 5.825 FALSE ## Create xts object
store8_xts<-xts(store8[,-2],order.by=as.POSIXct(store8$Date))
class(store8_xts)## [1] "xts" "zoo"## Time Series plot
dygraph(store8_xts[,2])Store 6 time series plot
## Select store 6
store6 <- mergedSales[mergedSales$Store==6,]
names(store6)[3] <-"store6"
head(store6[100:106,])## Store Date store6 Temperature Fuel_Price MarkDown1 MarkDown2
## 5963 6 2011-12-30 1598081 46.80 3.129 6098.54 82881.16
## 5964 6 2012-01-06 1395340 50.82 3.157 3220.17 32056.43
## 5965 6 2012-01-13 1344243 48.33 3.261 4779.48 11967.81
## 5966 6 2012-01-20 1326256 55.37 3.268 3413.91 1540.99
## 5967 6 2012-01-27 1315611 53.95 3.290 1510.59 630.92
## 5968 6 2012-02-03 1496306 57.45 3.360 36848.38 1772.68
## MarkDown3 MarkDown4 MarkDown5 CPI Unemployment IsHoliday
## 5963 326.68 814.58 2728.99 221.1283 6.551 TRUE
## 5964 108.24 985.76 6650.63 221.3088 6.132 FALSE
## 5965 65.72 575.13 4512.18 221.4893 6.132 FALSE
## 5966 33.02 449.06 6587.63 221.5831 6.132 FALSE
## 5967 37.10 17.00 2641.90 221.6769 6.132 FALSE
## 5968 161.55 28199.34 7151.33 221.7707 6.132 FALSE ## Create xts object
store6_xts<-xts(store6[,-2],order.by=as.POSIXct(store6$Date))
class(store6_xts)## [1] "xts" "zoo"## Time Series plot
dygraph(store6_xts[,2])Comparision of 5 stores sales
## let us compare sales of 5 walmart stores
AllStores <- cbind(store3_xts[,2],store16_xts[,2],store20_xts[,2],store8_xts[,2],store6_xts[,2])
dygraph(AllStores) %>%
dyLegend(width = 200)%>%
dyHighlight(highlightCircleSize = 3,
highlightSeriesBackgroundAlpha = 0.2,
hideOnMouseOut = FALSE)
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