Pca columns center in dataframe r3/6/2024 ![]() Pca_res <- prcomp(gapminder_life, scale=TRUE) This standardize the input data so that it has zero mean and variance one before doing PCA. The prcomp function takes in the data as input, and it is highly recommended to set the argument scale=TRUE. Now the dataframe only contains data and we are ready to do principal component analysis. Unite("continent_country", c(continent,country)) %>% Now, the dataframe contains life expectancy over time for all countries in the two continents. Let us combine the continent and country variables in the data into rownames using unite and column_to_rownames functions. life expectancy for each country in from years 1952 to 2007. Now our data mainly contains the data we need, i.e. ![]() Since we have life expectancy data from over the years from all the countries, our PCA analysis can capture the variation in life expectancy between the two continents. On an average African countries have lower life expectancy than European countries. Select(continent,country,starts_with('lifeExp')) We first use filter statement to filter for two continents and then select continent, country and all columns starting with “lifeExp”.įilter(continent %in% c("Africa","Europe")) %>%
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