Filtering for Unique Values in R, Using the dplyr package in R, you may filter for unique values in a data frame using the following methods. Method 1: In one column, filter for unique values. df %>% distinct(var1) Method 2: Filtering for Unique Values in Multiple Columns df %>% distinct(var1, var2) Method 3: In all…
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Error in rbind(deparse.level …) numbers of columns of arguments do not match, This issue happens when you try to row-bind two or more data frames together in R using the rbind() function, but the data frames don’t all have the same amount of columns. R Percentage by Group Calculation – Data Science Tutorials This guide…
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R Percentage by Group Calculation, The usage of this syntax in practice is demonstrated by the example that follows. R Percentage by Group Calculation droplevels in R with examples – Data Science Tutorials Consider the following data frame, which displays the number of points different basketball players on different teams have scored: Let’s create a…
How to Label Outliers in Boxplots in ggplot2, This article offers a detailed illustration of how to name outliers in ggplot2 boxplots. Step 1: Construct the data frame. Create the following data frame first, which will include details on the 60 distinct basketball players who played for three separate teams and scored points. How to…
How to Scale Only Numeric Columns in R, To scale only the numeric columns in a data frame in R, use the dplyr package’s following syntax. Best Books to learn Tensorflow – Data Science Tutorials library(dplyr) df %>% mutate(across(where(is.numeric), scale)) How to actually use this function is demonstrated in the example that follows. Use dplyr…
How to Standardize Data in R?, A dataset must be scaled so that the mean value is 0 and the standard deviation is 1, which is known as standardization. The z-score standardization, which scales numbers using the following formula, is the most used method for doing this. Two-Way ANOVA Example in R-Quick Guide – Data…
Separate a data frame column into multiple columns, To divide a data frame column into numerous columns, use the separate() function from the tidyr package. How to Use Gather Function in R?-tidyr Part2 The basic syntax used by this function is as follows. separate(data, col, into, sep) where: data: Name of the data frame col:…
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How to Use Gather Function in R?, To “collect” a key-value pair across many columns, use the gather() function from the tidyr package. How to Use Spread Function in R?-tidyr Part1 The basic syntax used by this function is as follows. gather(data, key value, …) where: data: Name of the data frame key: Name of…
How to Use Spread Function in R, To “spread” a key-value pair across multiple columns, use the spread() method from the tidyr package. The basic syntax used by this function is as follows. spread(data, key value) where: data: Name of the data frame key: column whose values will serve as the names of variables value:…
Add new calculated variables to a data frame and drop all existing variables, I hope you enjoyed reading about the dplyr package magics in earlier posts, here is the last update while using dplyr package. With R’s transmute() function, you can drop all of the existing variables and add new calculated variables to a data…
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