Programming Articles - Page 910 of 3366

To color scatterplot points based on a threshold using ggplot2, we first need to define a column with the threshold value and then we can use that column inside aes for coloring. The column with threshold can be created by using cut function.Check out the example given below to understand how it can be done.ExampleFollowing snippet creates a sample data frame −x

To find the number of non-missing values in each column by group in an R data frame, we can use summarise_each function of dplyr package with negation of is.na function.For Example, if we have a data frame called df that contains a grouping column say G and some other columns having few NAs then we can find the number of non-missing values in each column by grouping column G with the help of below command −df%%group_by(G)%%summarise_each(funs(sum(!is.na(.))))Example 1Following snippet creates a sample data frame −Group

To create multiple regression lines using ggplot2, we can use grouping inside aes.For example, if we have a data frame called that contains two numerical columns say x and y and a categorical column say C then the regression lines between x and y for all the categories in C can be created by using the below given command −ggplot(df, aes(x, y, group=C))+geom_point()+stat_smooth(method="lm")ExampleFollowing snippet creates a sample data frame −data(mtcars) head(mtcars, 20)OutputThe following dataframe is created −                   mpg  cyl disp   hp drat  wt    qsec   vs am gear carb Mazda ... Read More

To find the minimum for each row based on few columns in an R data frame, we can use pmin function inside with function.For example, if we have a data frame called df that contains five columns say x, y, z, a, and b then minimum for each row based on columns x, y, and b can be found by using the command given below −with(df,pmin(x,y,b))Example 1Following snippet creates a sample data frame −x1

To remove duplicate columns from a matrix in R, we can use unique function.For Example, if we have a matrix called M that contains some duplicate columns then we can use the below command to remove those duplicate columns −unique(M,MARGIN=2)Example 1Following snippet creates a sample matrix −M1

The display of text in base R plot with outline is not possible, for this purpose we would need to use shadowtext function of TeachingDemos package. The shadowtext function will be applied after creating the plot in base R.We will have to provide the location of the text inside the plot and some other arguments such as text that needs to be displayed, color of outline, and size for better display.Example 1Use the following code to display text in base R plot with outline −plot(1) shadowtext(1.2, 1.2, "Point at 1", col="white", cex=2)OutputIf you execute the above given code, it generates ... Read More

For categorical variable, each level is considered as an independent variable and is recognized by factor function. On the other hand, the numerical independent variable is either continuous or discrete in nature.Check out the Example given below for linear regression model summary to understand the difference between categorical and numerical independent variables.ExampleFollowing snippet creates a sample data frame −x

To create an upper triangular matrix using vector elements, we can first create the matrix with appropriate number of columns and rows then take the transpose of that matrix. After that we will assign the lower triangular matrix elements to 0.The selection of number of rows and columns plays an important role here so we need to be careful while choosing them.Check out the examples given below to understand how it can be done.Example 1Following snippet creates a vector −x1

To set values in categorical column to numeric values in R data frame, we can use combine function c.For Example, if we have a data frame called df that contains a categorical column say C which has two categories as Low and High and if we want to represent these categories with 1 and 10 then we can use the below command −df$C

If we create histogram for multiple categories using ggplot2 then the legend is generated automatically based on the categories. And if we want to change that legend or create a histogram with different legend values having different colors for histograms then scale_fill_manual function can be used as shown in the below example.ExampleFollowing snippet creates a sample data frame −Height