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_tutorials/data-scaling.md
@@ -83,7 +83,7 @@ library(ggeffects) # model predictions
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library(broom) # extracting model summaries
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# Import Data
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-LPI_species <- read.csv('data/LPI_species.csv', stringsAsFactors = FALSE) # remember to change the filepath appropriately
+LPI_species <- read.csv('LPI_species.csv', stringsAsFactors = FALSE) # remember to change the filepath appropriately
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```
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Now we can look at the basic structure of the dataframe to get some idea of the different variables it contains.
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