In this article, we’ll cover the top 8 packages in R we use for data pre-processing, data visualization, machine learning algorithms, etc. I use these packages on a daily basis in R for my data science projects. Now without stretching further let’s see which are those awesome libraries in R, which can be used for your data science projects!
1. Dplyr: Dplyr is mainly used for data mining and data manipulation in R. It is one of the most important libraries when you want to play with your data in R. Dplyr aims to provide a function for each basic verb of data manipulation which is mentioned below:
filter() to select cases based on their values. It allows you to select a subset of rows in a data frame.
arrange() to reorder the cases. It works similarly to filter() except that instead of filtering or selecting rows, it reorders them. It takes a data frame, and a set of column names (or more complicated expressions) to order by.
select() and rename() to select variables based on their names.
mutate() and transmute() to add new variables that are functions of existing variables. Besides selecting sets of existing columns, it’s often useful to add new columns that are functions of existing columns.
summarise() to condense multiple values to a single value.
sample_n() and sample_frac() to take random samples.
desc() to order a column in descending order.
2. Ggplot2: If you have used R in the past and have tried to visualize your data, then you must have come across this library i.e. ggplot2 for sure. It is one of the best libraries which you will use for sure. if you are planning to visualize your data in R and want to find out interesting insights from it.
Ggplot2 is the most elegant and aesthetically pleasing graphics framework available in R. It has a nicely planned structure to it. You can create all kinds of interesting graphs and visualization using this package in R.
3. Data.table: Data.table is an R package that provides an enhanced version of data.frame, which is the standard data structure for storing data in base R. It is widely used for aggregation of large datasets faster and quicker.
Data.table is a friendly file reader and parallel file writer. It has many useful features and styling options for tables.
4. Caret: The caret package (short for classification and regression training) contains functions to streamline the model training process for complex regression and classification problems.
Caret package has several functions that attempt to streamline the model building and evaluation process, as well as feature selection and other techniques. It has many powerful tools and algorithms for creating predictive models.
5. Plotly: Plotly is an R package for creating interactive web-based graphs. It is a web-based toolbox for building visualizations, it has many different features with plenty of charts available. It has capabilities to make ggplot2 graphics interactive.
6. XGBoost: XGBoost is an algorithm that has recently been dominating applied machine learning and kaggle competitions for structured or tabular data. It is short for eXtreme Gradient Boosting package.
XGBoost is used often for high speed and performance by data guys. It is an implementation of the Gradient Boosted Decision Trees algorithm.
7. GBM: GBM represents Generalized Boosted Regression Models. It is used for regression problems and it includes plenty of regression methods.
GBM also helps in variable selection for the model and final stage precision modeling.
8. Lubridate: Lubridate is an R package that makes it easier to work with dates and times. The ‘lubridate‘ package has a consistent and memorable syntax that makes working with dates easy and fun.