How to Select the First Row by Group Using dplyr in R

How to Select the First Row by Group Using dplyr in R

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

How to Count Observations by Group in R

How to Count Observations by Group in R

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

How to Create Summary Tables in R

How to Create Summary Tables in R

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

How to Impute Missing Values in R

How to Impute Missing Values in R

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

How to Calculate Five Number Summary in R

How to Calculate Five Number Summary in R

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

How to Change Legend Size in ggplot2 (With Examples)

How to Change Legend Size in ggplot2 (With Examples)

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

How to Change Legend Labels in ggplot2 (With Examples)

How to Change Legend Labels in ggplot2 (With Examples)

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

How to Remove a Legend in ggplot2 (With Examples)

How to Remove a Legend in ggplot2 (With Examples)

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