我的需求:基因表达差异分析结果的表格,每行是一个基因,不同的列分别表示表达值、Fold Change、P value、Q value等信息,如果要根据基因名称筛选出特定的行,或者针对P value进行数据转化并计算出-log10(P value),可以用dplyr进行实现。 下文转载自:omicsgene https://www.omicsclass.com/article/960 转载贴过来之后...
c("DepTime","ArrTime","FlightNum")]# dplyr approachselect(flights,DepTime,ArrTime,FlightNum)# use colon to select multiple contiguous columns, and use `contains` to match columns by name# note: `starts_with`, `ends_with`, and `matches` (for regular expressions) can also be used to ma...
2.Reshaping Data -Change the layout of a data set -. Gather columns into rows tidyr::gather(cases,"year","n",2:4) -. Spread rows into columns tidyr::spread(pollution,size,amount) -. Separate one column into several. tidyr::separate(storms,date,c("y","m","d")) -. Unite seve...
3. R arrange() Descending Order By default, dplyr’s arrange() function orders in ascending order however, you can change this in R and arrange the dataframe in descending/decreasing order by using the desc() function. The desc() takes the column name as an argument and orders the values...
Breaking change: bind_rows() and combine() are more strict when coercing. Logical values are no longer coerced to integer and numeric. Date, POSIXct and other integer or double-based classes are no longer coerced to integer or double as there is chance of attributes or information being lost...
class(order_tbl) # [1] "tbl_df" "tbl" "data.frame" order_tbl # for a data.frame, is not nessary to change to tbl object # for sql object, can useful to use dplyr to manupulate data # another way to read data library(readr) ...
iris %>% group_by(Species) %>% summarise(avg = mean(Sepal.Width)) %>% arrange(avg) x %>% f(y) is the same as f(x, y) y %>% f(x, ., z) is the same as f(x, y, z )Reshaping Data - Change the layout of a data setSubset Observations (Rows)Subset Variables (Columns...
filter(): focus on a subset of rows mutate(): add new columns summarise(): reduce each group to a smaller number of summary statistics arrange(): re-order the rows They all work as similarly as possible across the range of data sources. The main difference is performance: ...
(Month, DayofMonth, DepDelay) %>% top_n(2,DepDelay) %>% arrange(UniqueCarrier, desc(DepDelay)) # for each month, calculate the number of flights and the change from the previous month flights %>% group_by(Month) %>% summarise(flight_count = n()) %>% mutate(change = flight_...
Once you start using %>% you’ll wonder to yourself why this isn’t a core part of the R language itself rather than add-on functionality provided by a package. It will fundamentally change the way you write R code, making it feel more natural and making your code more readable. There...