Publications by John Akwei
Reshape 2 Exercises
The Reshape 2 package is based on differentiating between identification variables, and measurement variables. The functions of the Reshape 2 package then “melt” datasets from wide to long format, and “cast” datasets from long to wide format. Required package: library(reshape2) Answers to the exercises are available here. Exercise 1 Set a...
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Efficient Processing With Apply() Exercises
The apply() function is an alternative to writing loops, via applying a function to columns, rows, or individual values of an array or matrix. The structure of the apply() function is: apply(X, MARGIN, FUN, ...) The matrix variable used for the exercises is: dataset1 <- cbind(observationA = 16:8, observationB = c(20:19, 6:12)) Answers to the exer...
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Applying Functions To Lists Exercises
The lapply() function applies a function to individual values of a list, and is a faster alternative to writing loops. Structure of the lapply() function: lapply(LIST, FUNCTION, ...) The list variable used for these exercises: list1 <- list(observationA = c(1:5, 7:3), observationB=matrix(1:6, nrow=2)) Answers to the exercises are available here. ...
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Optimize Data Exploration With Sapply() – Exercises
The apply() functions in R are a utilization of the Split-Apply-Combine strategy for Data Analysis, and are a faster alternative to writing loops. The sapply() function applies a function to individual values of a dataframe, and simplifies the output. Structure of the sapply() function: sapply(data, function, ...) The dataframe used for these exe...
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Multivariate Apply Exercises
mapply() works with multivariate arrays, and applys a function to a set of vector or list arguments. mapply() also simplifies the output. Structure of the mapply() function: mapply(FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE, USE.NAMES = TRUE) Answers to the exercises are available here. Exercise 1 Beginning level Required dataframe: PersonnelData...
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