Here are two ways to import Excel files in R: Using read_excel() Using the built-in menu Options of Rstudio Let’s say we have an Excel file like this, and we will import this file in R. Method 1: Using read_excel() The easiest method for reading Excel files in R is using the readxl package’s “read_excel()” … Read more
The error: discrete value supplied to continuous scale typically occurs in R when you use “erroneous use of the scale function along with ggplot().“ library(ggplot2) df <- data.frame(x = 1:10, y = c(“bmw”, “audi”, “mercedez”, “ferrari”, “jaguar”)) ggplot(df, aes(x, y)) + geom_point() + scale_y_continuous(limits = c(0, 10)) Output Error: Discrete value supplied to continuous scale … Read more
The n_distinct() function in R is “used to count the number of unique/distinct combinations in a set of one or more vectors.” It’s a faster and more concise equivalent to the nrow(unique(data.frame(…))) function. Syntax n_distinct(…, na.rm = FALSE) Parameters …: Unnamed vectors. If multiple vectors are supplied, then they should have the same length. na.rm: … Read more
To Rank Variables by Group using dplyr in R, you can “combine arrange(), group_by(), or mutate() functions.” Syntax df %>% arrange(group_var, numeric_var) %>% group_by(group_var) %>% mutate(rank = rank(numeric_var)) Example 1: Rank in Ascending Order library(dplyr) df <- data.frame( Age = c(20, 21, 19, 22, 23, 20, 21), Gender = c(“Male”, “Female”, “Male”, “Female”, “Male”, “Female”, … Read more
To recode values using dplyr in R, you can “use the recode() function from the dplyr package.” The recode() function from the dplyr package is a handy tool to change or replace values in a column. Syntax df %>% mutate(column_name = recode(column_name, old_value1 = new_value1, old_value2 = new_value2, …)) Example 1: Recode a Single Column … Read more
To calculate relative frequencies using the dplyr package in R, you typically follow these steps: Count the number of occurrences of each value or group. Calculate the sum of all counts to get the total. Divide each count by the total to get the relative frequency. Example 1: Calculating the Relative Frequency of One Variable … Read more
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