Added code from workshop

practiceCode.R

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+##'
+##' Outline and concepts for workshop
+##' =================================
+##' 
+##' This `<-` command basically takes anything on the right hand side
+##' and puts it into the left hand side.  Like an equation.  This is
+##' called variable assignment (?).
+##' 
+df <- read.csv('train.csv')
+
+##' `head` checks only the first few rows of the dataset.
+head(df)
+
+##' `dim` checks the dimensions of the dataset (number of rows and
+##' number of columns, in that order).
+dim(df)
+
+##' `str` checks the structure of the dataset, showing what the `df`
+##' object is, what each item (or column) in the dataset is, such as
+##' numeric, factor, etc.  This is pretty useful as it can give you a
+##' quick overview of what the variables have been classified as and
+##' if there are any problems.
+str(df)
+
+##' `class` checks only the type of object you are asking, not the
+##' contents (unlike `str`).
+class(df)
+
+##' `summary` is an extremely useful command to check the basic
+##' descriptive statistics (mean, median, range, count for factors).
+##' I usually use this anytime I want to quickly look at my dataset,
+##' to get a sense of it.
+summary(df)
+
+##' If you want to see specific columns or rows, you can use the `[`
+##' command.  The first number is the row `[row, ]`, and the second
+##' number is the column `[, column]`.  So together: `[row, column]`.
+##'
+df[1:2, 1:5]
+
+##' Also, numbers can be put together like so:
+1:10
+-1:10
+1:-10
+-10:1
+
+##' In dataframes, a negative number means remove:
+df[1:2, ]
+df[1:2, -2:-4]
+
+##' You can use strings (real words) to select a specific column.
+df[1:2, 'Age']
+
+##' You can use the combine command `c()` to put two strings or
+##' numbers together.
+df[c(1, 4), c('Age', 'Sex')]
+
+##' You can also subset the data using these commands:
+head(df[df$Sex == 'male', ])
+head(df[c(df$Sex == 'male', df$Age < 40), ])
+
+##'
+##' `dplyr` and `tidyr` approach
+##' ----------------------------
+##' 
+##' However, these are a bit complicated, and hard to read!  There is
+##' a better way.  Install and/or load these packages:
+##+ dplyrInstall, eval = FALSE
+install.packages('dplyr')
+install.packages('tidyr')
+##+ loadDplyr, eval = TRUE
+library(dplyr)
+library(tidyr)
+
+##' To do the same as the above, use:
+df %>%
+  filter(Sex == 'male', Age < 40) %>%
+  ## You can keep chaining
+  tbl_df() %>%
+  ## ... and chaining
+  select(Sex, Age, Pclass, Parch) %>%
+  ## ... and chaining
+  summary()
+
+##' The extremely useful `%>%` chain, or pipe command, is just like in
+##' the shell/terminal. It takes the output of the previous command
+##' and inputs it into the next command.  Otherwise, without the `%>%`
+##' pipe, it looks like:
+summary(select(tbl_df(filter(df, Sex == 'male', Age < 40)), Sex, Age, Pclass, Parch))
+
+##' The pipe does this by basically making the output be named `.`, so
+##' really, the pipe is doing this:
+df %>% select(., Fare, Sex) %>% filter(., Sex == 'male') %>%
+  select(., Fare) %>% round(., 3)
+
+##' `tbl_df` makes the dataframe also a tbl object, so that the outout
+##' can be printed easily.  The verbs for dplyr are:
+##' 
+##' * select
+##' * filter
+##' * mutate
+##' * summarise
+##' * arrange
+##' * group_by
+##'
+##' For more explanation of dplyr, check the documentation:
+##' https://github.com/hadley/dplyr or run this command
+##' `vignette('introduction', package = 'dplyr')`
+##' 
+df <- tbl_df(df)
+df %>% summary
+
+df %>%
+  ## subset the data by SibSp
+  filter(SibSp >= 2) %>%
+  ## select only the relevant columns
+  select(Age, Sex, Survived, Cabin, Fare) %>%
+  ## order the data (in descending) by Age
+  arrange(Age) %>%
+  ## create a new column
+  mutate(d.Fare = cut(Fare, 3, labels = c('Low', 'Middle', 'High')))
+
+##' To do even more interesting things, we can combine the dplyr
+##' package with the tidyr package.  The tidyr has basically two main
+##' verbs:
+##'
+##' * gather
+##' * spread
+##'
+df %>%
+  filter(SibSp >= 2) %>%
+  select(Age, Sex, Survived, Cabin, Fare) %>%
+  ## convert the data into a very long format
+  gather(Measure, Value, -Sex) %>%
+  ## make each summarise command run on the groups Sex and Measure
+  group_by(Sex, Measure) %>%
+  ## create summary statistics, in this cause the sample in each group
+  ## (Sex and Measure)
+  summarise(n = n()) %>%
+  ## convert the data into a wide format
+  spread(Sex, n)
+
+##' Check the content again:
+df %>% summary
+
+##' Compare the means of continuous variables of those who survived
+##' and those who didn't.
+prep.table <- df %>%
+  select(Survived, Age, Pclass, SibSp, Parch, Fare) %>%
+  gather(Measure, Value, -Survived) %>%
+  group_by(Survived, Measure) %>%
+  ## remove missing values
+  na.omit() %>%
+  ## create a summary statistic (means)
+  summarise(mean = mean(Value) %>% round(2)) %>%
+  spread(Survived, mean)
+
+##' This can be created into a markdown table, so that it can be
+##' easily put into a manuscript or report.  A very useful package is
+##' called `pander` which allows you to create markdown tables.
+##+ panderInstall, eval = FALSE
+install.packages('pander')
+##+ table, results = 'asis'
+library(pander)
+prep.table %>% pander()
+
+##' There is also join commands from dplyr:
+##'
+##' * left_join
+##' * outer_join
+##' * inner_join
+##' * anti_join
+##'
+##' Code used in workshop
+##' =====================
+##' 
+##' Exact code used in the workshop
+ds %>% select(., Sex, Cabin, Fare) %>%
+    filter(., Sex == 'female', Fare > 10)
+
+filter(select(ds, Sex, Cabin, Fare),
+       Sex == 'female')
+
+ds %>%
+    select(Sex, Cabin, Fare) %>%
+    mutate(newcol = 10 * Fare) %>%
+    arrange(Fare)
+
+ds %>%
+    select(Sex, Age, Fare) %>%
+    group_by(Sex) %>%
+    na.omit() %>%
+    summarise(n = n(),
+              mean = mean(Age))
+
+##' Remove a column using the `-` sign.
+ds %>% select(-Age)
+
+prep.table2 <- ds %>%
+    select(Sex, Fare, Age, Pclass, SibSp) %>%
+    mutate(AgeDouble = Age * 2) %>%
+    gather(Measure, Value, -Sex) %>%
+    na.omit() %>%
+    group_by(Sex, Measure) %>%
+    ## create a column with the mean and standard deviation
+    summarise(meanSD = paste0(mean(Value) %>% round(2),
+                             ' (', sd(Value) %>% round(2),
+                             ')')) %>%
+    spread(Sex, meanSD)
+
+##' Create a table, with a caption!
+##+ tableTesting, results = 'asis'
+pander(prep.table2, style = 'rmarkdown', caption = 'Testing')
+
+##' To use the `grid.table` function instead of `pander`, load the
+##' `gridExtra` package.  You may need to install first
+##' (`install.packages('gridExtra')`).
+library(gridExtra)
+grid.table(prep.table2, rows = NULL)
+
+##' A package that does a tutorial *within* R!  I've heard good things
+##' from it.
+install.packages('swirl')
+
+
+##' To see help files, you can run the `vignette` command.
+##+ vignHelp, eval = FALSE
+vignette('introduction', package = 'dplyr')
+
+##+ knit, eval = FALSE, echo = FALSE
+library(knitr)
+spin('practice.R')