1. Getting your data into R
Yesterday you did:
* Start R Studio and set up a project
* Prepare and run R script files
* what an R package is and how to install and load them
* Load a simple data file
* Understand how (& why) R uses dataframes & vectors
* Prepare simple tables
* Produce good quality graphs, and save these
* Locate and perform some statistical tests on your data
* Prepare a simple function

2. Getting your data into R
Download -> CSV -> data.frame -> data cleaning
Later: EDA
At the end of this session, you will be able to:
Upload, download, create or import data into R
Manipulate large datasets as tables in R
Explore datasets using multiple approaches
Test for missing, partial or inconsistent data
Summarise and compare datasets with R

3. CSV data First line = names of variables, separated by commas
Variables = proper numbers or plain text - no spaces,
funny characters or punctuation
Data = mix of numbers and grouping variables
= time sequence is ok
= dates and times are not
Missing data = represented by the two letters NA and
nothing else - no dashes, no 999, 77, 88 or anything else

4. CSV data

5. Data input and output R comes with several pre-packaged datasets
You can access these datasets with the data function
eg 1990 Davis PMID
"Body image and weight preoccupation: A comparison between exercising and non-exercising women"
View(Davis)
head(Davis)
str(Davis)
glimpse(Davis)
summary(Davis)

7. Data input and output Davis table(Davis$weight) table(Davis$height)
data.frame(Davis$height,Davis$weight)
# create a dataframe from heights and weights only
data.frame(Davis$height,Davis$weight)[1:5,]
look at dataframe for heights and weights - samples 1-5

8. Dplyr -> Data cleaning + EDA
=> manipulating data:
Davis %>% filter(height<quantile(height,0.5))
# subset rows of smallest 50%
%>% arrange(desc(height))
# sort rows of smallest 50%
%>% select(sex,weight,repwt)
# select columns we want
%>% mutate(weight_diff=(weight-repwt))
# create new variables, eg "weight_diff"
%>% group_by(sex)
%>% summarise(mean=mean(weight))
# summarise details of interest

9. Dplyr -> Data cleaning + EDA
=> manipulating data:
# assign Davis heights vs weights using ggplot2
x <- ggplot(Davis,aes(x=weight, y=height, colour=sex)) + geom_jitter() + geom_line() + stat_smooth(span=0.5) + ggtitle('heights v weights') + xlab('height') + ylab('weight')
# Higher spans = smoother
# plot it (this way avoids errors)
ggsave(filename='heights-weights-plot1.png', plot=x, dpi=1200)

11. Dplyr -> Data cleaning + EDA
=> manipulating data:
# assign Davis heights vs weights using ggplot2
x2 <- ggplot(Davis,aes(x=weight, y=height, colour=sex)) + geom_boxplot() + coord_flip() + facet_wrap(~sex) + ggtitle('heights v weights')
# plot it (NB not all plot types are sensible)
ggsave(filename = 'heights-weights-plot2.png', plot=x2, dpi=1200)

12. Dplyr -> Data cleaning + EDA
=> manipulating data:
scale_h <- function(height) {
return(height/185200) }
# create function to scale heights as nautical miles
Davis$height_nm <-scale_h(Davis$height)
# assign a new variable with nautical mile heights
Davis$height_nm[1:5]
# always check the output