In this blog I have made an attempt to create a shiny dashboard using connections data downloaded from LinkedIn.
Requirements- Knowledge of shiny, shinydashboard and data cleaning and transformation using dplyr.
The data can be downloaded from LinkedIn using the following steps:
In this blog, I will discuss a trick using dplyr package that makes life easier. I will create a data frame in R and use it for analysis.
dplyr functions group_by and summarise are used to get summarised values for the variable used in group_by. Many summarise functions are available that help us get the desired result.
The problem discussed in the blog uses a combination of dplyr functions to get the answer.
The data used can be created using the code below:
# Creating an array of customercustomer = 1001:1010# Creating an array of order amountorderamount…
HR function is an integral part of any organization. HR need to track employee count every month to project the hiring requirements. The data may be collected in an excel or csv file and analyzed to get the required numbers.
In this blog I have made an attempt to create a simple HR dashboard in R Studio using shinydashboard to get summary numbers for an imaginary organization.
The dataset used is core_dataset.csv file from Kaggle
To follow this blog understanding of tidyverse, shiny and shinydashboard packages are required.
The final dashboard will look like this.
The dashboard will be prepared…
I have written three posts with some tricks to use R Studio while querying a database.
But why should we use R Studio to query? Are there any specific benefits to use R for querying a database. I can think of a few reasons as I use it regularly for my work.
Pros of R Studio for querying a database
In the previous posts R for Querying Database — SQL not required — Part I and R for Querying Database -Tricks — SQL not required — Part II, I have discussed how R can be used to query a MySQL database and some tricks to write a complex query.
In this blog I will discuss some more tips that will help in writing queries. I will cover the following sub topics in this blog.
4. Adding columns from date column
5. Filtering rows using %like%
6. Adding columns using %like%
Database: The database is financial and is available for download…
In the previous post R for Querying Database — SQL not required — Part I, I have discussed how R can be used to query a MySQL database. You can use the skills acquired in querying data frame in R to query a database.
In this blog, I will discuss some tips that will help in writing queries. I will cover the following sub topics in the blog.
Database: The database is financial and is available for download using MySQL Workbench
Requirements — Familiarity with dplyr
Trick 1: Selecting…
Querying is the most used skill for any data scientist/analyst job. Is learning SQL mandatory for querying? May be not if you are able to connect your database with R Studio and you are comfortable with dplyr package in R.
Learning SQL is important but what if you have started you data science journey using R with no prior experience of querying a database. You have learned data science by loading csv files in R Studio. You have used dplyr/ data.table/ base r packages for querying the data frame obtained by loading the csv files. The skills acquired in querying…
I have experience in Predictive Modelling and Dashboards. I have rich working experience on various tools and software like R and Python