shinyr is developed to build dynamic shiny based dashboards to analyze the data of your choice. It provides simple yet genius dashboard design to subset the data, perform exploratory analysis and predictive analysis by means of interactive filter mechanism.

*NOTE: shinyr package does not mis-use your data, your data is not being copied or migrated to anywhere. *

Installing shinyr package

You can install shinyr package like how you install any other package from CRAN. simply fallow the folloing commands to install the package.

install.packages("shinyr")
library(shinyr)

shinyr dependencies

shinyr was developed using R programming and few R packages mentioned below along with bit of js and css.

1. R >= 3.3.1 version
2. Following R packages
   • shiny
   • shinydashboard
   • dplyr
   • tm
   • wordcloud
   • corrplot
   • RColorBrewer
   • RandomForest
   • caret
   • nnet
   • DMwR
   • plotly

Build dynamic dashboard using shinyr

Do you have your data in a csv/xlsx file and you want to get some insights of the data? Cool you can use shinyr package to build a simple dashboard for your data with filters for each of your columns.

• You can get insights such as dimention of data, column types, some stats on each column, correlation, missing values.
• You can Impute the missing values with mean, median, max, min or sum of that erticular column.
• You can aggregate/summarize values of one column by grouping values on other column.
• You can perform exploratory analysis by simple yet iinformative vsualizations such as bar plot, wordcloud, line, box, and 3D plots.
• You can also train regression, logit regression and random forest models, check model performance, predict and validate models with simple clicks.
• Run the following commands and upload your data and click on build button to create dashboard for your data. You can also choose one few example datasets from your R session.

library(shinyr) 
shinyr::shineMe()

Get list of data sets from your session.

valid_sets() will give all the data sets that are available in the data frame

library(shinyr)
dsets <- shinyr::valid_sets()
knitr::kable(dsets)

Select and load any inbuilt data

In case you want to load any data sets from the list of datasets from return of valis_sets() function you can use base::get() function to load the data sets. this will help you to choose on data sets to load dynamycally in any program.

dsets$Item <- as.character(dsets$Item)
mtcars <- get(dsets$Item[dsets$Item == "mtcars"])
knitr::kable(head(mtcars))

Find the numeric columns in the given data frame

To figure the class of each column in the given data frame use getnumericcols() it return the column names which are numeric

getnumericCols(mtcars)

##  [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"
## [11] "carb"

Split the given sentence into words

to split paragraph or sentence to induvidial words use splitAndGet(), it returns the list of induvidual words in the given input which can be later used by getFeqTable()

## [[1]]
##  [1] "**shinyr**"  "is"          "developed"   "to"          "build"      
##  [6] "dynamic"     "shiny"       "based"       "dashboards"  "to"         
## [11] "analyze"     "the"         "data"        "of"          "your"       
## [16] "choice."     ""            "It"          "provides"    "simple"     
## [21] "yet"         "genius"      "dashboard"   "design"      "to"         
## [26] "subset"      "the"         "data,"       "perform"     "exploratory"
## [31] "analysis"    "and"         "predictive"  "analysis"    "by"         
## [36] "means"       "of"

Get the frequency table for given sentence

getFeqTable will be used on the output of spliAndGet() to get the frequency of each word, which will be used by getWordCloud

Word cloud using the frequency table

Use getWordCloud() to plot word cloud.

getWordCloud(x)

Get basic insights to the data

getDataInsights() takes data frame as an input and returns the basic insights such as class, number of values missing, maximum, min, var, sd, mean, median, unique items for each column.

Correlation table

getDataInsight() also calculates the correlation table for the given data frame.

knitr::kable(res$cor_matrix)

Correlation Plot

You can use corrplot::corrplot() on correlation table to get the correlation table.

corrplot::corrplot(as.matrix(res$cor_matrix),method = "number")

Exclude few items from the given set

This function was developed to eliminate few items from the list of items for any reason.

excludeThese(mtcars$mpg, c(21.0))

##  [1] 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 10.4 14.7
## [16] 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 15.0 21.4

get most repeated item in the given set

You can find out most repeated values in the given set of values.

getMostRepeatedValue(c(1,1,1,2,2,3,4,5))

## [1] 1
## Levels: 1 2 3 4 5

Calculate total missing items in vector

missing count will calculate the total number of NA, NULL, “”, “NULL”, “NA” s in a given set of values. lets introduce some missing values to mtcars

x <- head(mtcars)
x$mpg[1:2] <- NA

missing_count(x$mpg)

## [1] 2

Impute data

You can replace the missing values in any column of given data frame with one of mean, median, max, and min, sum and mode by using ImputeMydata(). for example you can impute the missing values in the mpg column by mean of all the values in the column as shown below.

 imputeMyData(df = x, col = "mpg", FUN = "mean")

##                     mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         20.25   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     20.25   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.80   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.40   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.70   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.10   6  225 105 2.76 3.460 20.22  1  0    3    1

Group By and Summarize

You can summarize the values of one column by grouping the values in the other column using groupByandSummarize(). For example you can calculate mean of hp by am.

knitr::kable(groupByandSumarize(mtcars, grp_col = c("am"), summarise_col = "hp", FUN = "mean"))

Data partition

You can split a given data set into training set and test set by using datapartition(), you can specify the percentage to specify the size of trainset. For example you can split mtcars into 85 percent to train and 15 to test as shown below.

partition <- dataPartition(mtcars, 85)

partition is a list of length 2, which contains test and train sets.

head of train set

knitr::kable(head(partition$Train))

head of test set

knitr::kable(head(partition$Test))

Train regression model

mod <- lm(formula = wt ~ ., data = mtcars)
mod

## 
## Call:
## lm(formula = wt ~ ., data = mtcars)
## 
## Coefficients:
## (Intercept)          mpg          cyl         disp           hp         drat  
##   -0.230634    -0.041666    -0.057254     0.006685    -0.003230    -0.090083  
##        qsec           vs           am         gear         carb  
##    0.199541    -0.066368     0.018445    -0.093508     0.248688

Prediction

predictions <- predict(mod, mtcars[,-6])

Get Regression Model Metrics

get the metrics of regression model by using regressionModelmMetrics()

 actials <- mtcars[,6]
 x <- regressionModelMetrics(actuals = actials, predictions = predictions, model = mod)
 y <- as.data.frame(x)
 row.names(y) <- NULL
 knitr::kable(y)