Vignettes

vignettes/Introduction.R

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+## ----setup, include=FALSE------------------------------------------------
+knitr::opts_chunk$set(echo = TRUE)
+
+## ---- eval=FALSE---------------------------------------------------------
+#       mtcars %>%
+#          let(mpg_hp = mpg/hp) %>%
+#          take(mean(mpg_hp), by = am)
+
+## ---- eval=FALSE---------------------------------------------------------
+#        mtcars %>%
+#           let(new_var = 42,
+#               new_var2 = new_var*hp) %>%
+#           head()
+
+## ---- eval=FALSE---------------------------------------------------------
+#      new_var = "my_var"
+#      old_var = "mpg"
+#      mtcars %>%
+#          let((new_var) := get(old_var)*2) %>%
+#          head()
+#  
+#      # or,
+#      expr = quote(mean(cyl))
+#      mtcars %>%
+#          let((new_var) := eval(expr)) %>%
+#          head()
+#  
+#      # the same with `take`
+#      by_var = "vs,am"
+#      take(mtcars, (new_var) := eval(expr), by = by_var)
+
+## ------------------------------------------------------------------------
+library(maditr)
+
+data(mtcars)
+
+# Newly created variables are available immediately
+mtcars %>%
+    let(
+        cyl2 = cyl * 2,
+        cyl4 = cyl2 * 2
+    ) %>% head()
+
+# You can also use let() to remove variables and
+# modify existing variables
+mtcars %>%
+    let(
+        mpg = NULL,
+        disp = disp * 0.0163871 # convert to litres
+    ) %>% head()
+
+
+# window functions are useful for grouped computations
+mtcars %>%
+    let(rank = rank(-mpg, ties.method = "min"),
+        by = cyl) %>%
+    head()
+
+# You can drop variables by setting them to NULL
+mtcars %>%
+    let(cyl = NULL) %>%
+    head()
+
+# keeps all existing variables
+mtcars %>%
+    let(displ_l = disp / 61.0237) %>%
+    head()
+
+# keeps only the variables you create
+mtcars %>%
+    take(displ_l = disp / 61.0237) %>% 
+    head()
+
+
+# can refer to both contextual variables and variable names:
+var = 100
+mtcars %>%
+    let(cyl = cyl * var) %>%
+    head()
+
+# filter by condition
+mtcars %>%
+    take_if(am==0) %>% 
+    head()
+
+# filter by compound condition
+mtcars %>%
+    take_if(am==0 & mpg>mean(mpg))
+
+
+# A 'take' with summary functions applied without 'by' argument returns an aggregated data
+mtcars %>%
+    take(mean = mean(disp), n = .N)
+
+# Usually, you'll want to group first
+mtcars %>%
+    take(mean = mean(disp), n = .N, by = am)
+
+# grouping by multiple variables
+mtcars %>%
+    take(mean = mean(disp), n = .N, by = list(am, vs))
+
+# You can group by expressions:
+mtcars %>%
+    take(
+        fun = mean,
+        by = list(vsam = vs + am)
+    )
+
+# parametric evaluation:
+var = quote(mean(cyl))
+mtcars %>% 
+    let(mean_cyl = eval(var)) %>% 
+    head()
+take(mtcars, eval(var))
+
+# all together
+new_var = "mean_cyl"
+mtcars %>% 
+    let((new_var) := eval(var)) %>% 
+    head()
+take(mtcars, (new_var) := eval(var))
+
+
+
+## ------------------------------------------------------------------------
+workers = fread("
+    name company
+    Nick Acme
+    John Ajax
+    Daniela Ajax
+")
+
+positions = fread("
+    name position
+    John designer
+    Daniela engineer
+    Cathie manager
+")
+
+workers
+positions
+
+## ------------------------------------------------------------------------
+workers %>% dt_inner_join(positions)
+workers %>% dt_left_join(positions)
+workers %>% dt_right_join(positions)
+workers %>% dt_full_join(positions)
+
+# filtering joins
+workers %>% dt_anti_join(positions)
+workers %>% dt_semi_join(positions)
+
+## ---- eval=FALSE---------------------------------------------------------
+#  workers %>% dt_left_join(positions, by = "name")
+
+## ---- eval=FALSE---------------------------------------------------------
+#  positions2 = setNames(positions, c("worker", "position")) # rename first column in 'positions'
+#  workers %>% dt_inner_join(positions2, by = c("name" = "worker"))
+
+## ------------------------------------------------------------------------
+# examples from 'dplyr'
+# newly created variables are available immediately
+mtcars  %>%
+    dt_mutate(
+        cyl2 = cyl * 2,
+        cyl4 = cyl2 * 2
+    ) %>%
+    head()
+
+
+# you can also use dt_mutate() to remove variables and
+# modify existing variables
+mtcars %>%
+    dt_mutate(
+        mpg = NULL,
+        disp = disp * 0.0163871 # convert to litres
+    ) %>%
+    head()
+
+
+# window functions are useful for grouped mutates
+mtcars %>%
+    dt_mutate(
+        rank = rank(-mpg, ties.method = "min"),
+        keyby = cyl) %>%
+    print()
+
+
+# You can drop variables by setting them to NULL
+mtcars %>% dt_mutate(cyl = NULL) %>% head()
+
+# A summary applied without by returns a single row
+mtcars %>%
+    dt_summarise(mean = mean(disp), n = .N)
+
+# Usually, you'll want to group first
+mtcars %>%
+    dt_summarise(mean = mean(disp), n = .N, by = cyl)
+
+
+# Multiple 'by' - variables
+mtcars %>%
+    dt_summarise(cyl_n = .N, by = list(cyl, vs))
+
+# Newly created summaries immediately
+# doesn't overwrite existing variables
+mtcars %>%
+    dt_summarise(disp = mean(disp),
+                  sd = sd(disp),
+                  by = cyl)
+
+# You can group by expressions:
+mtcars %>%
+    dt_summarise_all(mean, by = list(vsam = vs + am))
+
+# filter by condition
+mtcars %>%
+    dt_filter(am==0)
+
+# filter by compound condition
+mtcars %>%
+    dt_filter(am==0,  mpg>mean(mpg))
+
+
+# select
+mtcars %>% dt_select(vs:carb, cyl)
+mtcars %>% dt_select(-am, -cyl)
+
+# sorting
+dt_arrange(mtcars, cyl, disp)
+dt_arrange(mtcars, -disp)
+