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Numeric calculations using dplyr piping commands


Numeric calculations using dplyr piping commands

By : Greg New
Date : November 19 2020, 03:01 PM
help you fix your problem You have an operator precedence problem. It's trying to start by doing
code :
0.15 %>% 3.8416
0.15 %>% {3.8416 * (.*(1-.))/(0.03^2)}


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Piping histograms in dplyr (R)

Piping histograms in dplyr (R)


By : Dove
Date : March 29 2020, 07:55 AM
it helps some times lapply
To put some of my comments from above into an answer, the simplest way make a histogram of each variable is
code :
# let's put them in a single plot
par(mfrow = c(1, 3))

lapply(birdsss, hist, breaks = 50)    # or chain into it: birdsss %>% lapply(hist, breaks = 50)

# set back to normal
par(mfrow = c(1, 1))
par(mfrow = c(1, 3))

Map(function(x, y){hist(x, breaks = 50, main = y, xlab = y)}, 
    birdsss, 
    names(birdsss))

par(mfrow = c(1, 1))
birdsss %>% 
    Map(function(x, y){hist(x, breaks = 50, main = y, xlab = y)}, 
        ., 
        names(.))
library(purrr)

walk2(birdsss, names(birdsss), ~hist(.x, breaks = 50, main = .y, xlab = .y))
birdsss %>% walk2(names(.), ~hist(.x, breaks = 50, main = .y, xlab = .y))
library(ggplot2)

# gather to long form, so there is a variable of variables to split facets by
birdsss %>% 
    tidyr::gather(variable, value) %>% 
    ggplot(aes(value)) + 
        # it sets bins intead of breaks, so add 1
        geom_histogram(bins = 51) + 
        # make a new "facet" for each value of `variable` (formerly column names), and 
        # use a convenient x-scale instead of the same for all 3
        facet_wrap(~variable, scales = 'free_x')
Aggregating while concatenating in R using dplyr piping

Aggregating while concatenating in R using dplyr piping


By : Belfastboy95
Date : March 29 2020, 07:55 AM
I wish this help you Instead of count, use summarise to paste Owner as string per nWins-Type, then spread:
code :
DF1 %>% 
    group_by(nWins, Type) %>% 
    summarise(Owner = paste(Owner, collapse=";")) %>% 
    spread(Type, Owner, fill="") %>% 
    as.data.frame()

#  nWins               A               B       C
#1     1 Owner A;Owner D                        
#2     2                                 Owner B
#3     4                 Owner B;Owner C        
dplyr piping data - difference between `.` and `.x`

dplyr piping data - difference between `.` and `.x`


By : user2861526
Date : March 29 2020, 07:55 AM
I wish did fix the issue. The . is the basic unit of transfer for the magrittr pipelines (which dplyr imports). It contains the value coming from the pipe.
The .x value is something that the tidyverse world added. It's used then you have anonymous functions created with the ~ (tilde) syntax. This calls rlang::as_function to turn that formula into a function. It's basically a short cut so rather than having to type out function(x) x+5, you can just write ~.x+5. Since functions can have more than one parameter, it can be helpful to use names for that parameter so .x refers to the first parameter (and .y the second). The as_function also allows you to use . as an alias for the first parameter. It can do this because the ~ creates a formula and magrittr doesn't generally replaces . in formulas so the mapper is free to re-interpret the .. You can see the function signature here
code :
f <- rlang::as_function(~.x+5)
f
# <lambda>
# function (..., .x = ..1, .y = ..2, . = ..1) 
# .x + 5
# attr(,"class")
# [1] "rlang_lambda_function"
Normalizing columns in mixed numeric/non-numeric DataFrame with tidyverse (dplyr)?

Normalizing columns in mixed numeric/non-numeric DataFrame with tidyverse (dplyr)?


By : user3343239
Date : March 29 2020, 07:55 AM
wish help you to fix your issue I frequently need to normalize columns of DataFrames that have a mixture of numeric and non-numeric columns. Sometimes I know the names of the numeric columns, other times not. , First problem
code :
test = df %>% mutate_if(is.numeric, ~./sum(.))
test %>% select_if(is.numeric) %>% colSums( ,na.rm = T)

test = df %>% mutate_if(is.numeric, function(x) x/sum(x))
test %>% select_if(is.numeric) %>% colSums()
test = df %>% mutate_if(is.numeric, function(x){ifelse(x > 0, x/sum(x), rep(0, length(x)))})
test %>%  select_if(is.numeric) %>% colSums()

test = df %>% mutate_if(is.numeric, function(x) ifelse(sum(x)>0, x/sum(x), 0))
test %>% select_if(is.numeric) %>% colSums()
test = df %>% mutate_if(is.numeric, ~apply(., 2, function(x) x/sum(x)))
test = df %>% mutate_if(is.numeric, function(x) if(sum(x)>0) x/sum(x))
test %>% select_if(is.numeric) %>% colSums()
test = df %>% mutate_if(is.numeric, function(x){ifelse(x > 0, x/sum(x), rep(0, length(x)))})
test %>%  select_if(is.numeric) %>% colSums()
test = df %>% mutate_if(is.numeric, function(x) if(sum(x)>0){x/sum(x)}else{0})
get lhs object name when piping with dplyr

get lhs object name when piping with dplyr


By : Christal
Date : March 29 2020, 07:55 AM
hop of those help? The SO answer that JBGruber links to in the comments mostly solves the problem. It works by moving upwards through execution environments until a certain variable is found, then returns the lhs from that environment. The only thing missing is the requirement that the function outputs both the name of the original data frame and the manipulated data – I gleaned the latter requirement from one of the OP's comments. For that we just need to output a list containing these things, which we can do by modifying MrFlick's answer:
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