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To deal with missing column of row names when converting data frame in R to data.table object, we need to use keep.rownames argument while converting the data frame. For example, if we have a data frame called df that needs to be converted to a data.table object without missing row names then we can use the below command −data.table(df,keep.rownames=TRUE)Examplelibrary(data.table) head(mtcars)Output mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1Examplemtcars_data_table
To remove the plot margin in base R between the axes and the points inside the plot, we can use xaxs and yaxs argument in plot function. Depending on the choices of the arguments xaxs and yaxs, the plot region in the respective direction is 4% larger than specified by these limits or exactly matches the "i" limits.Examplex
The NA values and NaN values are very different in nature, therefore, removal of rows containing NA values is different from removal of rows containing NaN values. For example, if we have a data frame that has NaN values the rows will be removed by using the is.finite function as shown in the below examples.Consider the below data frame −Example Live Demox1
To increase the thickness of histogram lines in base R, we would need to use par function by defining the thickness size of the line. If we want to do so then line thickness must be defined first before creating the histogram. An example of line size could be line
To find the row mean for columns by ignoring missing values, we would need to use rowMeans function with na.rm. For example, if we have a data frame called df that contains five columns and some of the values are missing then the row means will be calculated by using the command: rowMeans(df,na.rm=TRUE).Consider the below data frame −Example Live Demox1
The shapiro.test has a restriction in R that it can be applied only up to a sample of size 5000 and the least sample size must be 3. Therefore, we have an alternative hypothesis test called Anderson Darling normality test. To perform this test, we need load nortest package and use the ad.test function as shown in the below examples.Consider the below data frame −Example Live Demox
By default, the positive signs are not displayed in any plot in R. It is well known that if there is no sign seen with any value then it is considered positive, therefore, we do not need the sign but to distinguish between 0 and positive values it could be done. To display positive sign for X-axis labels, we can use scale_x_continuous function.Consider the below data frame −Example Live Demox
To change the size of plots arranged using grid.arrange, we can use heights argument. The heights argument will have a vector equal to the number of plots that we want to arrange inside grid.arrange. The size of the plots will vary depending on the values in this vector.Consider the below data frame −Example Live Demox
To create a cumulative sum plot in base R, we can simply use plot function. For cumulative sums inside the plot, the cumsum function needs to be used for the variable that has to be summed up with cumulation. For example, if we have two vectors say x and y then the plot with cumulative sum plot can be created as plot(x,cumsum(y)).Examplex1
To create a plot in base R with tick marks of larger size, we can make use of axis function tck argument. The tck argument value will decide the size of the tick mark but since the ticks lie below the plot area hence the value will have a negative associated with it. Therefore, it will be like -0.05. Check out the below examples to understand how it works.Exampleplot(1:10,axes=FALSE,frame=TRUE) axis(1,1:10,tck=-0.02) axis(2,1:10,tck=-0.02)OutputExampleplot(1:10,axes=FALSE,frame=TRUE) axis(1,1:10,tck=-0.05) axis(2,1:10,tck=-0.05)Output