To select the first row in each group using the dplyr package in R, you can “combine group_by(), arrange(), and filter()” functions. Syntax df %>% group_by(group_variable) %>% arrange(values_variable) %>% filter(row_number()==1) Example 1: Select the First Row by Group in R library(dplyr) df <- data.frame( Age = c(20, 21, 19, 22, 23, 20, 21), Gender = c(“Male”, “Female”, “Male”, … Read more
To find the maximum value of a particular variable by group in R, you can “use the group_by() and summarise() functions from the dplyr package.” Example 1: Find Max Value by Group Here’s a step-by-step example using the dataframe to find the maximum score within each gender group: Group the data by the desired variable, … Read more
To Count Observations by Group in R, you can “use the count() function from the dplyr library.” The count() function in the dplyr library is a convenient way to count the number of observations per group. It’s essentially a faster and more concise version of group_by() + summarize() for this specific task. Syntax library(dplyr) df … Read more
To create summary tables in R, you can “use the describe() and describeBy() functions from the psych package.” The summary table provides the following information: vars: It represents the column number. n: It represents the number of valid cases. mean: It represents the mean value. median: It represents the median value. min: It represents the … Read more
Missing data can be a common problem in data analysis. In R, there are several methods you can use to impute or replace missing values. Here are the 3 ways to impute missing values in R. Mean/Median/Mode Imputation Using Hmisc Library and imputing with Median value Impute the entire dataset Method 1: Mean/Median/Mode Imputation This … Read more
Here are the three ways to count the number of rows within each group in the R DataFrame: Using the dplyr package Using data.table package Using aggregate() method Method 1: Using the dplyr package The “dplyr” package in R is used to perform data enhancements and manipulations. We can use certain functions from this method … Read more
The Five Number Summary is a “set of descriptive statistics that provides information about a dataset.” It consists of the following five values: Minimum: The smallest value in the dataset. First Quartile (Q1): The value that separates the lowest 25% of the dataset. Median (Q2): The middle value of the dataset. If there is an … Read more
To change the legend size of ggplot2 in R, you can use the “theme()” function, where you can control the text size, key size, and other aspects of the legend’s appearance. Syntax ggplot(data, aes(x=x, y=y)) + theme(legend.key.size = unit(1, ‘cm’), #change legend key size legend.key.height = unit(1, ‘cm’), #change legend key height legend.key.width = unit(1, … Read more
The scale_fill_discrete() function is “used to change the legend labels for discrete fill aesthetics in ggplot2.” You can use the labels argument to specify the new labels. Syntax p + scale_fill_discrete(labels=c(‘label1’, ‘label2’, ‘label3’, …)) Example 1: Using scale_fill_discrete() function # Load the ggplot2 library library(ggplot2) # Create a sample data frame df <- data.frame( category … Read more
To remove a legend from ggplot2 in R, use the “theme()” function along with the legend.position argument set to “none” to completely remove the legend from the plot. Example 1: Set legend.position = none Here’s an example that demonstrates how to create a scatter plot without a legend: library(ggplot2) # Create a sample data frame df … Read more