library(dplyr) dat %>% group_by(grp = consecutive_id(value <= -50)) %>% filter(any(value <= -50), row_number() == n()) %>% ungroup() %>% filter(row_number() == 1L | difftime(time, time[1], units="day") >= (2*365+2)) # # A tibble: 2 × 3 # time value g...
My question to you is: how to fill the '@' in the where statement in load_data.R based on the ui.R selectInput() to remerge and replot the SpatialPolygonsDataFrame(SalesMap) when the user applies a filter? load_data.R library(RSQLite)library(rgdal)library(dplyr)#...
The post How to Filter Rows In R? appeared first on Data Science Tutorials How to Filter Rows In R, it’s common to want to subset a data frame based on particular conditions. Fortunately, using the filter() function from the dplyr package makes this simple. library(dplyr) This tutorial...
2. Introduction to cProfile cProfile is a built-in python module that can perform profiling. It is the most commonly used profiler currently. But, why cProfile is preferred? It gives you the total run time taken by the entire code. It also shows the time taken by each individual step....
To get a bug fix or to use a feature from the development version, you can install the development version of dplyr from GitHub.# install.packages("devtools") devtools::install_github("tidyverse/dplyr")Cheat SheetUsagelibrary(dplyr) starwars %>% filter(species == "Droid") #> # A tibble...
# dplyr crime.ny.2005 <- filter(crime.by.state, State=="New York", Year==2005) There is a lot going on with my base R solution. It uses logical subsetting to extract choice rows from crime.by.state. Specifically, it creates a two boolean vectors: one that is true only when the ...
Another solution to remove duplicate rows by column values is to group the data frame with the column variable and then filter elements usingfilterandduplicatedfunctions. The first step is done with thegroup_byfunction that is part of thedplyrpackage. Next, the output of the previous operation ...
> install.packages("dplyr") > library(dplyr) Attaching package: ‘dplyr’ The following objects are masked from ‘package:stats’: filter, lag The following objects are masked from ‘package:base’: intersect, setdiff, setequal, union Step 8: Make a connection to database for dplyr > monet...
You can also use other dplyr functions like mutate, filter, select, and more on grouped data. While diving deep into all of these functions could take up a whole article by itself, the dplyr grouped data vignette is a helpful guide to how these functions behave with grouped data....
…which is expected as the predictions only exist where there were no NA's in the spatial points. How can I accomplish this? Do I need an NA assignment first followed by a where clause to filter out only the points where there are no NA's? Thanks! Reply Cansu (Statistics Globe) July...