Plot Differences in Measurements, the discrepancies in measurements between two different instruments or measurement methodologies are visualized using a Bland-Altman plot. It can be used to compare the accuracy of two instruments or approaches...
Log Rank Test in R, the most frequent technique to compare survival curves between two groups is to use a log-rank test. Test hypotheses: Ho: In terms of survivability, there is no difference between...
Plot Categorical Data in R, Categorical variables are data types that can be separated into categories. Race, sex, age group, and educational level are examples of categorical variables. This tutorial describes three approaches to...
Boxplots in R, A boxplot is a plot that displays the five-digit summary of a dataset. The five-digit summary is the lowest value, the first quartile, the median, the third quartile, and the maximum value. We can use a boxplot to easily visualize a set of data. Principal component analysis (PCA)...
In this article, we are going to compare pairs and ggpairs functions in R. 1. pairs() in R pairs() function mainly used to plot a scatter diagram corresponding to each data frame. Syntax: pairs(data)...
Data visualization plays a crucial role in exploratory data analysis and business intelligence. While scatter plots are excellent for displaying relationships between two numeric variables, they become even more powerful when enhanced with additional...
QQ-plots in R, first need to understand the Q-Q plot. The Q-Q plot is a graphical tool to help us examine if a set of data plausibly came from some theoretical distribution such as...
There are six pre-described line types available in base R. You can use those for any type of graphics, like plotting for line charts or developing simple shapes. In R base plot functions, two...
Error bar Plot, Error bars are visual representations of the variability of data and used on graphs to suggest the error in a reported measurement. They give a general idea of how precise a...
when we have a dataset and to get clear idea about each parameter the summary of a variable is important.
Summarized data will provide the clear idea about the data set.
In this tutorial we are going to talk about summarize () function from dplyr package.