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What is happening in the first line of code and why does the result differ from the two next results?
library(tidyverse)
library(magrittr)
data.frame(A=c(2,2),B=c(1,1)) %>%
summarise(A = sum(A),B = sum(B), D=sum(A)-sum(B))
yields D=0
data.frame(A=c(2,2),B=c(1,1)) %>%
summarise(A = sum(A),B = sum(B), D=sum(A-B) )
yields in D=2
data.frame(A=c(2,2),B=c(1,1)) %>%
summarise(sum_A = sum(A),sum_B = sum(B), D=sum(A)-sum(B))
yields in D=2.
I can't seem to come up with an explanation to how the D=0 can be a result of such an operation. How can D=0 be a sensible result?
It is a bug, see https://github.com/tidyverse/dplyr/issues/3233. It is fixed in 0.7.4.9001.
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I have assigned a value for a variable but still am getting an error message. Kindly help me rectify
Code:
n <- 1000
x <- seq(1,n)
sum(x)
Error: object 'x' not found
You either need to use a new line for each command or end each command with semicolon (";")
n <- 1000
x <- seq(1,n)
sum(x)
Or:
n <- 1000; x <- seq(1,n); sum(x)
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> df <- read.csv("DATA ONLY.csv", header = TRUE, sep = ";")
> dim(df)
[1] 439 1
This is the code I use and this is the CSV
https://docs.google.com/spreadsheets/d/1SOqDKXZ7BAMW5LdqBcBIvQE9_PnFcNIHDfYDty3cTto/edit?usp=sharing
I am 99% sure that you have defined the field separator wrong. data.table::fread is really good at sniffing the correct format of csv's, and I quite often use fread even if I just convert the resulting data.table back to vanilla data frame, i.e.
library(data.table)
df <- fread("DATA ONLY.csv")
as.data.frame(df) -> df
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I have a dataset with 3 factor columns and 4 numeric columns. I want to use group_by() to summarize it. But no matter how I try it doesn't work, there is no group.
freetick <- read.csv("FreeTickAll.csv", stringsAsFactors=FALSE)
library(dplyr)
group1 <- freetick %>% group_by(Habitat, Month) %>% summarize(
meanAd = mean(Adult),
meanNy = mean(Nymph),
meanLa = mean(Larva)
)
group1
The result:
> group1
meanAd meanNy meanLa
1 0.6129032 4.258065 20.1129
And my group1 data.frame also show:
mean Ad mean Ny mean La
1 0.6129032 4.258065 20.1129
If a function is common in multiple packages and those packages are loaded into the working env, then there is a possibility of masking the function from the last loaded package. In such cases, either restart the R session with only the package of interest loaded (dplyr in this case) or specify the function to be loaded explicitly from the package of interest (dplyr::summarise)
freetick %>%
dplyr::group_by(Habitat, Month) %>%
dplyr::summarise(meanAd = mean(Adult),
meanNy = mean(Nymph),
meanLa = mean(Larva))
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I wonder how I can write this if statement correct. I have tried a lot of things, but none of them worked.
b <- matrix(NA,10,10)
> for (row in 1:10)
>> for (column in 1:10)
>>> if(!is.na(a[row,column] && a==(1 || 2 || 3))
b[row,column]==1
>>> else
b[row,column]==0
The problem is here:
if(!is.na(a[row,column] && a==(1 || 2 || 3))
Assuming that 'a' is a matrix with the same dimension as 'b', we can do this without any if/else
+((a %in% 1:3) & !is.na(a))
data
set.seed(24)
a <- matrix(sample(c(1:9, NA), 10*10, replace = TRUE), 10, 10)
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I try to use gather function of tidyr package to convert wide data into a long one with following demo.
library(tidyr)
library(dplyr)
messy <- data.frame(
name = c("Wilbur", "Petunia", "Gregory"),
a = c(67, 80, 64),
b = c(56, 90, 50)
)
gather(messy,drug, heartrate, a:b)
Although I have used drug and heartrate to indicate condition and value, however, it still names as the default one variable and value. Why?
It must be something related with masking of functions. It should work when you rerun the code on a fresh R sessioin with only tidyr and dplyr loaded.