Colors in R

 http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/

Function to calculate CI of linear regression coefficients

Amateur code

```

 jad<-function(x, y) {

  model<-lm(y~x)

  std.err<-coef(summary(model))[, 2]

  coef.model1<-coef(summary(model))[, 1]

  upper.ci<-coef.model1+1.96*std.err

  lower.ci<-coef.model1-1.96*std.err

  print(upper.ci)

  print(lower.ci)

}

```

Pro code

jad <- function(x, y) {

  `colnames<-`(

    coef(summary(lm(y ~ x)))[,1:2] %*% matrix(c(1, 1.96, 1, -1.96), nrow = 2), c("upper.ci", "lower.ci")

  )

}

Some data management Libraries for R

Library (magrittr)

Library (dplyr)

https://uc-r.github.io/pipe#:~:text=The%20compound%20assignment%20%25%25,and%20then%20assigning%20the%20result.&text=The%20exposition%20(%20%25%24%25%20)%20operator%20is,to%20some%20of%20the%20columns.

wrs%>%

extract(1:20,c("age", "edu")) %>%

head

or

wrs%>%

extract(1:20,1:2) %>%

head

Practical way to label factors

```

library(memisc)

labels(anemia$anem)<-c("not anemic"=0, "anemic"=1) #library memisc

```

How to generate a simulated data in R

Package {simstudy}

The below example uses the library(simstudy) to generate a data of 10 observation for 3 variables: nr, x1 and y1. The function defData allows us to define the name, the formula and the distribution of each variable.
Check CRAN webiste for additional documentation on the simstudy package.

```
def <- defData(varname = "nr", dist = "nonrandom", formula = 7, id = "idnum")
def <- defData(def, varname = "x1", dist = "uniform", formula = "10;20")
def <- defData(def, varname = "y1", formula = "nr + x1 * 2", variance = 8)

dt <- genData(10, def)

dt

```

How to convert all characters of a data frame to Numeric

```
char_columns<-sapply(merged_data, is.character) #identify character columns
data_char_num<-merged_data #replicate data
data_char_num[,char_columns]<-as.data.frame(apply(data_char_num[,char_columns],2, as.numeric))#recode character as numeric
sapply(data_char_num, class) #print classes of all columns
```

How to make predictions from a linear model

Note: for numerical values you enter numbers without " ", for factor variables you enter the "label"


```
df<-data.frame(res=c("ModHigh"), age=c(21), sex=("female"), train=c("clinical"), bdi=c(4))
pred<-predict(adj.model6,df, data=resilience)
print(pred)
```