The back page shows how to import spreadsheet data from Excel files using readxl or Google Sheets using googlesheets4. The front page of this sheet shows how to import and save text files into R using readr. The basics of working with data. It works by converting R’s native data frame objects into data.tables with new and enhanced functionality. for each level of V2, that levels intercepts deviation from the global intercept) P3: A single global estimate for the effect (slope) of V3. P2: Random effect intercepts for V2 (i.e. This model will estimate: P1: A global intercept. Access with: parent. Data Transformation with data.table :: CHEAT SHEET Manipulate columns with j Functions for data.tables data.table is an extremely fast and memory efficient package for transforming data in R. Using lmer syntax, simplest model (M1) is: V1 (1V2) + V3. Library ( dplyr ) starwars %>% filter ( species = "Droid" ) #> # A tibble: 6 × 14 #> name height mass hair_color skin_color eye_color birth_year sex gender #> #> 1 C-3PO 167 75 gold yellow 112 none masculi… #> 2 R2-D2 96 32 white, blue red 33 none masculi… #> 3 R5-D4 97 32 white, red red NA none masculi… #> 4 IG-88 200 140 none metal red 15 none masculi… #> 5 R4-P17 96 NA none silver, red red, blue NA none feminine #> # ℹ 1 more row #> # ℹ 5 more variables: homeworld, species, films, #> # vehicles, starships starwars %>% select ( name, ends_with ( "color" ) ) #> # A tibble: 87 × 4 #> name hair_color skin_color eye_color #> #> 1 Luke Skywalker blond fair blue #> 2 C-3PO gold yellow #> 3 R2-D2 white, blue red #> 4 Darth Vader none white yellow #> 5 Leia Organa brown light brown #> # ℹ 82 more rows starwars %>% mutate ( name, bmi = mass / ( ( height / 100 ) ^ 2 ) ) %>% select ( name : mass, bmi ) #> # A tibble: 87 × 4 #> name height mass bmi #> #> 1 Luke Skywalker 172 77 26.0 #> 2 C-3PO 167 75 26.9 #> 3 R2-D2 96 32 34.7 #> 4 Darth Vader 202 136 33.3 #> 5 Leia Organa 150 49 21.8 #> # ℹ 82 more rows starwars %>% arrange ( desc ( mass ) ) #> # A tibble: 87 × 14 #> name height mass hair_color skin_color eye_color birth_year sex gender #> #> 1 Jabba De… 175 1358 green-tan… orange 600 herm… mascu… #> 2 Grievous 216 159 none brown, wh… green, y… NA male mascu… #> 3 IG-88 200 140 none metal red 15 none mascu… #> 4 Darth Va… 202 136 none white yellow 41.9 male mascu… #> 5 Tarfful 234 136 brown brown blue NA male mascu… #> # ℹ 82 more rows #> # ℹ 5 more variables: homeworld, species, films, #> # vehicles, starships starwars %>% group_by ( species ) %>% summarise ( n = n ( ), mass = mean ( mass, na.rm = TRUE ) ) %>% filter ( n > 1, mass > 50 ) #> # A tibble: 8 × 3 #> species n mass #> #> 1 Droid 6 69.8 #> 2 Gungan 3 74 #> 3 Human 35 82.8 #> 4 Kaminoan 2 88 #> 5 Mirialan 2 53. Machine Learning Modelling in R : : CHEAT SHEET Standard Modelling Workflow Time Series View CC BY SA Arnaud Amsellem Updated: 2018-03 Supervised & Unsupervised Learning Meta-Algorithm, Time Series & Model Validation. Data is oen stored in tabular formats, like csv files or spreadsheets. If a name is not found in an environment, then R will look in its parent (and so on).
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