Hadley Wickham’s tidy tools
In this video at 8 mins 50 seconds he says “these four tools do 90% of the job”
- subset,
- transform,
- summarise, and
- arrange
- TODO I noticed at the website for an Rstudio course transform has been replaced by mutate as one of the “four basic verbs of data manipulation”.
Tidy Data from Drew Conway on Vimeo.
So I thought what’s the other 10? Here’s a few contenders for my work:
- merge
- reshape::cast and reshape::melt
- unlist
- t() transpose
- sprintf or paste
R-subset
# Filter rows by criteria
subset(airquality, Temp > 90, select = c(Ozone, Temp))
## NB This is a convenience function intended for use interactively. For
## programming it is better to use the standard subsetting functions like
## ‘[’, and in particular the non-standard evaluation of argument
## ‘subset’ can have unanticipated consequences.
with(airquality,
airquality[Temp > 90, c("Ozone", "Temp")]
)
# OR
airquality[airquality$Temp > 90, c("Ozone", "Temp")] #### R-transform
# New columns that are functions of other columns
df <- transform(airquality,
new = -Ozone,
Temp2 = (Temp-32)/1.8
)
head(df) #### R-mutate
require(plyr)
# same thing as transform
df <- mutate(airquality, new = -Ozone, Temp = (Temp - 32) / 1.8)
# Things transform can't do
df <- mutate(airquality, Temp = (Temp - 32) / 1.8, OzT = Ozone / Temp)
# mutate is rather faster than transform
system.time(transform(baseball, avg_ab = ab / g))
system.time(mutate(baseball, avg_ab = ab / g)) #### R-summarise
# New data.frame where columns are functions of existing columns
require(plyr)
df <- ddply(.data = airquality,
.variables = "Month",
.fun = summarise,
tmax = max(Temp),
tav = mean(Temp),
ndays = length(unique(Day))
)
head(df)
Passing variables to ddply for summary
# Notice how the name of the variable Temp doesn't need quotes?
# this means that you need to hard code the names
# But if you want to pass variables to this inside a function we need a
# different approach.
summarise_df <- function(x, by, var1, var2, var3)
{
data_out <- ddply(x,
by,
function(df) return(
c(
tmax = max(df[,var1]),
tav = mean(df[,var2]),
ndays = length(unique(df[,var3]))
)
)
)
return(data_out)
}
df2 <- summarise_df(x = airquality, by = "Month",
var1 = "Temp", var2 = "Temp", var3 = "Day"
)
head(df2)
all.equal(df,df2)
# TRUE
Another alternative, if we want to pass the dataset as string too
summarise_df2 <- function(x, by, var1, var2, var3)
{
data_out <- eval(
parse(
text =
sprintf(
"ddply(.data = %s,
.variables = '%s',
.fun = summarise,
tmax = max(%s),
tav = mean(%s),
ndays = length(unique(%s))
)", x, by, var1, var2, var3
)
)
)
return(data_out)
}
df3 <- summarise_df2(x = "airquality", by = "Month",
var1 = "Temp", var2 = "Temp", var3 = "Day"
)
head(df3)
all.equal(df, df3)
# TRUE #### R-arrange
# Re-order the rows of a data.frame
df <- arrange(airquality, Temp, Ozone)
head(df)
