mean and SD in parenthese

[Klaris]

Please how can i have mean and SD in the same column? with SD in the parenthese.

how can plot cencentratin Curve for many Countries in one Graphic?

How to compute Concentration Index for many countries in one time?

[andresrcs]

Hi, welcome!

We don't really have enough info to help you out. Could you ask this with a minimal REPRoducible EXample (reprex)? A reprex makes it much easier for others to understand your issue and figure out how to help.

If you've never heard of a reprex before, you might want to start by reading this FAQ:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[bayesian]

For your first question, you can use the glue package to embed expressions within curly brackets, which then gets converted into a string

library(dplyr)
library(glue)

mydf <- data.frame(means = round(runif(n = 10, min = 10, max = 15), 2), 
              sd = round(runif(n = 10, min = 2, max = 5), 2))
   means   sd
1  10.90 2.79
2  12.06 4.83
3  14.87 3.51
4  12.35 2.67
5  11.08 3.13
6  10.76 3.44
7  14.04 4.05
8  10.78 3.96
9  13.31 2.86
10 13.35 2.82

# create a new column called `together` with the means and SD in parentheses
mydf %>% mutate(together = glue("{means} ({sd})"))

   means   sd     together
1  10.90 2.79  10.9 (2.79)
2  12.06 4.83 12.06 (4.83)
3  14.87 3.51 14.87 (3.51)
4  12.35 2.67 12.35 (2.67)
5  11.08 3.13 11.08 (3.13)
6  10.76 3.44 10.76 (3.44)
7  14.04 4.05 14.04 (4.05)
8  10.78 3.96 10.78 (3.96)
9  13.31 2.86 13.31 (2.86)
10 13.35 2.82 13.35 (2.82)