luca2
7/7/2017 - 1:27 PM
any trick, or trivial way to do thing that I easily tend to forget, about most used R packages
any trick, or trivial way to do thing that I easily tend to forget, about most used R packages
...
glimpse(dt)
dt %>%
filter(condition) %>%
select() %>%
mutate() %>%
delete multiple columns at once
cols2del <- c('v1', 'v2', 'v3')
dt[, (cols2del) := NULL]
summarizing multiple columns
- sum all columns
dt[, lapply(.SD, sum, na.rm = TRUE), .SDcols = names(dt)] - sum all columns except the id:
dt[, lapply(.SD, sum, na.rm = TRUE), .SDcols = setdiff(names(dt), 'id')]
count number of NA in dt (or whatever other condition)
- only one column:
dt[, sum(is.na(var))] - some columns:
dt[, sum(is.na(var))] - all table:
dt[, sum(is.na(var))]
eliminate duplicates keeping only the first (last) given the order
dt[order(V1, V2)][, .SD[1], id]dt[order(V1, V2)][, .SD[.N], id]
select first n rows within group
There are different methods:
dt[, head(.SD, n), grp]dt[, .SD[1:n], grp]dt[dt[, .I[1:n], grp]$V1]this is the fastest, despite being a bit weird to write down
View all 35 palettes, grouped by their main use:
display.brewer.all()
Uses of palettes
Sequential
Suited to ordered data that progress from low to high. Lightness steps dominate the look of these schemes, with light colors for low data values to dark colors for high data values.Qualitative
Qualitative schemes are best suited to representing nominal or categorical data.do not imply magnitude differences between legend classes, and hues are used to create the primary visual differences between classes.Diverging
Put equal emphasis on mid-range critical values and extremes at both ends of the data range. The critical class or break in the middle of the legend is emphasized with light colors and low and high extremes are emphasized with dark colors that have contrasting hues.