Transforming variables for multiple regression in R I am trying to perform a multiple regression in R. However, my dependent variable has the following plot:

Here is a scatterplot matrix with all my variables (WAR is the dependent variable):

I know that I need to perform a transformation on this variable (and possibly the independent variables?) but I am not sure of the exact transformation required. Can someone point me in the right direction? I am happy to provide any additional information about the relationship between the independent and dependent variables.
The diagnostic graphics from my regression look as follows:

EDIT
After transforming the dependent and independent variables using Yeo-Johnson transformations, the diagnostic plots look like this:

If I use a GLM with a log-link, the diagnostic graphics are:

 A: You should tell us more about the nature of your response (outcome, dependent) variable. From your first plot it is strongly positively skewed with many values near zero and some negative. From that it is possible, but not inevitable, that transformation would help you, but the most important question is whether transformation would make your data closer to a linear relationship. 
Note that negative values for the response rule out straight logarithmic transformation, but not log(response + constant), and not a generalised linear model with logarithmic link.  
There are many answers on this site discussing log(response + constant), which divides statistical people: some people dislike it as being ad hoc and difficult to work with, while others regard it as a legitimate device. 
A GLM with log link is still possible. 
Alternatively, it may be that your model reflects some kind of mixed process, in which case a customised model reflecting the data generation process more closely would be a good idea. 
(LATER) 
The OP has a dependent variable WAR with values ranging roughly from about 100 to -2. To get over problems with taking logarithms of zero or negative values, OP proposes a fudge of zeros and negatives to 0.000001. Now on a logarithmic scale (base 10) those values range from about 2 (100 or so) through to -6 (0.000001). The minority of fudged points on a logarithmic scale are now a minority of massive outliers. Plot log_10(fudged WAR) against anything else to see this. 
A: John Fox's book An R companion to applied regression is an excellent ressource on applied regression modelling with R. The package car which I use throughout in this answer is the accompanying package. The book also has as website with additional chapters.

Transforming the response (aka dependent variable, outcome)
Box-Cox transformations offer a possible way for choosing a transformation of the response. After fitting your regression model containing untransformed variables with the R function lm, you can use the function boxCox from the car package to estimate $\lambda$ (i.e. the power parameter) by maximum likelihood. Because your dependent variable isn't strictly positive, Box-Cox transformations will not work and you have to specify the option family="yjPower" to use the Yeo-Johnson transformations (see the original paper here and this related post):
boxCox(my.regression.model, family="yjPower", plotit = TRUE)

This produces a plot like the following one:

The best estimate of $\lambda$ is the value that maximizes the profile likelhod which in this example is about 0.2. Usually, the estimate of $\lambda$ is rounded to a familiar value that is still within the 95%-confidence interval, such as -1, -1/2, 0, 1/3, 1/2, 1 or 2.
To transform your dependent variable now, use the function yjPower from the car package: 
depvar.transformed <- yjPower(my.dependent.variable, lambda)

In the function, the lambda should be the rounded $\lambda$ you have found before using boxCox. Then fit the regression again with the transformed dependent variable.
Important: Rather than just log-transform the dependent variable, you should consider to fit a GLM with a log-link. Here are some references that provide further information: first, second, third. To do this in R, use glm: 
glm.mod <- glm(y~x1+x2, family=gaussian(link="log"))

where y is your dependent variable and x1, x2 etc. are your independent variables.

Transformations of predictors
Transformations of strictly positive predictors can be estimated by maximum likelihood after the transformation of the dependent variable. To do so, use the function boxTidwell from the car package (for the original paper see here). Use it like that: boxTidwell(y~x1+x2, other.x=~x3+x4). The important thing here is that option other.x indicates the terms of the regression that are not to be transformed. This would be all your categorical variables. The function produces an output of the following form:
boxTidwell(prestige ~ income + education, other.x=~ type + poly(women, 2), data=Prestige)

          Score Statistic   p-value MLE of lambda
income          -4.482406 0.0000074    -0.3476283
education        0.216991 0.8282154     1.2538274

In that case, the score test suggests that the variable income should be transformed. The maximum likelihood estimates of $\lambda$ for income is -0.348. This could be rounded to -0.5 which is analogous to the transformation $\text{income}_{new}=1/\sqrt{\text{income}_{old}}$.
Another very interesting post on the site about the transformation of the independent variables is this one.

Disadvantages of transformations
While log-transformed dependent and/or independent variables can be interpreted relatively easy, the interpretation of other, more complicated transformations is less intuitive (for me at least). How would you, for example, interpret the regression coefficients after the dependent variables has been transformed by $1/\sqrt{y}$? There are quite a few posts on this site that deal exactly with that question: first, second, third, fourth. If you use the $\lambda$ from Box-Cox directly, without rounding (e.g. $\lambda$=-0.382), it is even more difficult to interpret the regression coefficients.

Modelling nonlinear relationships
Two quite flexible methods to fit nonlinear relationships are fractional polynomials and splines. These three papers offer a very good introduction to both methods: First, second and third. There is also a whole book about fractional polynomials and R. The R package mfp implements multivariable fractional polynomials. This presentation might be informative regarding fractional polynomials. To fit splines, you can use the function gam (generalized additive models, see here for an excellent introduction with R) from the package mgcv or the functions ns (natural cubic splines) and bs (cubic B-splines) from the package splines (see here for an example of the usage of these functions). Using gam you can specify which predictors you want to fit using splines using the s() function:
my.gam <- gam(y~s(x1) + x2, family=gaussian())

here, x1 would be fitted using a spline and x2 linearly as in a normal linear regression. Inside gam you can specify the distribution family and the link function as in glm. So to fit a model with a log-link function, you can specify the option family=gaussian(link="log") in gam as in glm.
Have a look at this post from the site.
