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skip to primary navigation skip to content skip to primary sidebar r-exercises train like you fight, fight like you train header right search this website main navigation start here to learn r! r course finder why exercise? custom exercises boost your brand contribute about team you are here: home fighting factors with cats: exercises 20 july 2018 by sindri leave a comment in this exercise set, we will practice using the forcats factor manipulation package by hadley wickham. in the last exercise set, we saw that it is entirely possible to deal with factors in base r, but also that things can get a bit involved and un-intuitive. forcats simplifies many common factor manipulation tasks. it […] filed under: exercises (beginner) , exercises (intermediate) , other basic generalized linear modeling – part 1: exercises 19 july 2018 by hanif kusuma leave a comment a generalized linear model (glm) is a flexible generalization of an ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. the glm generalizes linear regression by allowing the linear model to be related to the response variable via a link function. it also allows the magnitude […] filed under: exercises (advanced) tagged with: glm , rstat facing the facts about factors: exercises 6 july 2018 by sindri leave a comment factor variables in r can be mind-boggling. often, you can just avoid them and use characters vectors instead – just don’t forget to set stringsasfactors=false. they are, however, very useful in some circumstances, such as statistical modelling and presenting data in graphs and tables. relying on factors but misunderstanding them has been known to “eat […] filed under: exercises (advanced) , exercises (beginner) , exercises (intermediate) , other tagged with: data manipulation , factors basic generalized additive models in ecology: exercises 5 july 2018 by hanif kusuma leave a comment generalized additive models (gam) are non-parametric models that add smoother to the data. in this exercise, we will look at gams using cubic spline using the mgcv package. data-sets used can be downloaded here. the data-set is the experiment result of grassland richness over time in the yellowstone national park (skkink et al. 2007). […] filed under: exercises (intermediate) tagged with: gam , recology melt and cast the shape of your data-frame: exercises 22 june 2018 by sindri leave a comment data-sets often arrive to us in a form that is different from what we need for our modeling or visualization functions, which, in turn, don’t necessarily require the same format. reshaping data.frames is a step that all analysts need, but many struggle with. practicing this meta-skill will, in the long-run, result in more time to […] filed under: exercises , exercises (intermediate) non-linear models in r: exercises 21 june 2018 by hanif kusuma leave a comment a mechanistic model for the relationship between x and y sometimes needs parameter estimation. when model linearisation does not work,we need to use non-linear modeling. there are three main differences between non-linear and linear modeling in r: 1. specify the exact nature of the equation. 2. replace the lm() with nls(), which means non-linear least […] filed under: exercises (intermediate) tagged with: non-linear modelling , non-linear regression intro to time series analysis – part 2: exercises 20 june 2018 by biswarup ghosh leave a comment in the exercises below, we will explore more in the time series analysis. the previous exercise can be found here. please follow this in sequence. answers to these exercises are available here. exercise 1 load the airpassengers data. check its class and see the start and end of the series. exercise 2 check the cycle of […] filed under: exercises (intermediate) tagged with: time series how to create a flexdashboard: exercises 15 june 2018 by euthymios kasvikis leave a comment introduction with flexdashboard, you can easily create interactive dashboards for r. what is amazing about it is that with r markdown, you can publish a group of related data visualizations as a dashboard. additionally, it supports a wide variety of components, including htmlwidgets; base, lattice, and grid graphics; tabular data; gauges and value boxes […] filed under: exercises , exercises (beginner) sharpening the knives in the data.table toolbox: exercises 8 june 2018 by sindri leave a comment if knowledge is power, then knowledge of data.table is something of a super power, at least in the realm of data manipulation in r. in this exercise set, we will use some of the more obscure functions from the data.table package. the solutions will use set(), inrange(), chmatch(), uniquen(), tstrsplit(), rowid(), shift(), copy(), address(), setnames() […] filed under: exercises (advanced) , exercises (intermediate) how to create a flexdashboard 8 june 2018 by euthymios kasvikis leave a comment **please note** this tutorial is largely taken from the relevant package github page **please note** introduction with flexdashboard, you can easily create interactive dashboards for r. what is amazing about it is that with r markdown, you can publish a group of related data visualizations as a dashboard. additionally, it supports a wide variety of […] filed under: tutorials page 1 page 2 page 3 … page 51 next page » primary sidebar filter by topic all tags ancova (4) anova (4) arrays and matrices (3) auto-correlation (1) bayesian (1) bayesian inference (10) benchmarking (1) big data (7) bioinformatics (8) bitcoin (1) box plot (1) classification (1) coding style (1) computer vision (1) contributed packages (2) copulas (2) correlation (1) cross-correlation (1) cryptocurrency (1) data frames (1) data import (11) data importing (1) data manipulation (69) data science for doctors (10) data structure (3) data table (4) data type (1) data visualization (71) data wrangling (12) databases (2) dataframes (3) dates and times (9) descriptive analytics (10) dplyr (3) econometrics (1) entropy (1) experimental design (3) export data (2) factor (2) factors (4) fftree (2) forecast (1) forecasting (8) functions (8) gam (2) general (1) geospatial (11) ggally (1) ggplot (4) ggplot2 (4) ggvis (3) gibbs sampling (1) glm (2) googlesheets (1) googlevis (10) h2o (6) hacking stats (9) histogram (1) hydrology (3) igraph visualizations (2) image processing (1) importing data (1) information theory (2) instrumental variables (4) investing (5) iplots (2) kullback–leibler divergence (1) lattice (4) leaflet (1) linear programming (5) list (2) lists (1) lists and dataframes (8) logistic regression (1) loops (1) loops and conditional execution (4) lubridate (6) machine learning (7) markov chain monte carlo (7) mathematics (3) matrix (1) model evaluation (2) modeling (2) monte-carlo (1) multiple regression (2) mutual information (2) neural networks (3) non-linear modelling (2) non-linear regression (4) numerical modelling (9) object modes and attributes (2) openair (10) openxlsx (2) parallel computing (3) periodogram (1) plotly (2) plotly visualizations (2) polynomial regression (2) probability (2) probability distributions (2) rdstk (3) recology (10) regression (9) regular expressions (3) regular sequences (2) reshape2 (1) revoscaler (2) rhydrology (18) rmarkdown (3) rstat (4) rstats (6) seasonality and trend decomposition (1) shiny app layout (10) shiny apps (10) simulation (1) spark (3) spatial modelling (6) spreadsheets (1) stan (1) statistics (91) string (1) stringr (3) structures (2) tensorflow (4) text mining (3) tidyr (2) time series (2) tree (2) ufc (1) univariate statistics (10) vectors (4) vectors and sequences (10) volatility (4) xgboost (2) xlsx (2) like us on facebook facebook by weblizar powered by weblizar follow us subscribe to our weekly newsletter and receive a free bonus exercise set every week (available to our 2,585 subscribers only)! first name last name e-mail address receive daily updates email s

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