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When we create a plot and put a title above it, the alignment of the title is left-adjusted by default and that lies on the edge of the plot area. But sometimes, we want to display the title on the above side of the Y-axis labels, therefore, we can use theme function and set the hjust argument accordingly.ExampleConsider the below data frame − Live Demox
If we have summary data for a group variable then we might want to look at the errors or say differences between mean and standard deviations visually, therefore, we can create a bar plot with error bars of standard deviations. This can be done by using geom_errorbar function of ggplot2 package.ExampleConsider the below data frame − Live DemoGroup
When we draw a scatterplot, there might be some crucial points that we would want to display, hence we create a vertical or horizontal based on our objective. These vertical or horizontal lines can be drawn by using geom_vline or geom_hline function of ggplot2 but to add some value with them we can use geom_text function.ExampleConsider the below data frame − Live Demo> x y df dfOutput x y 1 1.2474363 -0.15892165 2 1.7511870 -1.18938250 3 -1.3001612 -0.32313571 4 -1.4220049 1.52915756 5 0.4355646 0.18282983 6 0.3128323 0.16467130 7 1.5099580 1.15199751 8 -0.4907705 -1.98635182 9 -1.4249190 ... Read More
Mostly, the bar plot is created with frequency or count on the Y-axis in any way, whether it is manual or by using any software or programming language but sometimes we want to use percentages. It can be done by using scales package in R, that gives us the option labels=percent_format() to change the labels to percentage.ExampleConsider the below data frame − Live Demo> x df dfOutput x 1 2 2 3 3 3 4 1 5 2 6 4 7 4 8 4 9 2 10 3 11 3 12 4 13 3 14 4 15 4 16 1 17 3 ... Read More
To replace a value in an R vector, we can use replace function. It is better to save the replacement with a new object, even if you name that new object same as the original, otherwise the replacements will not work with further analysis. As you can see in the object x5(in examples), when we replaced 5 with 3, the previous replacement of -1 with 0 returned as in original vector. Therefore, we should save it in a new object.Examples Live Demo> x1 x1Output[1] 1 2 3 4 5 6 7 8 9 10 > replace(x1, x1==5, 10)Output[1] 1 2 3 ... Read More
While doing the data analysis, often we have to deal with factor data and we might want to find the frequency or count of a level of factor and the other variable combination. This helps us to make comparison within and between factor levels. Therefore, we can add a new column as count to find the required frequency and it can be done by using group_by and mutate function of dplyr package.ExampleConsider the below data frame − Live Demo> Group Rating df head(df, 20)Output Group Rating 1 A 1 2 B 6 3 C 2 ... Read More
Cumulative sums are often used to display the running totals of values and these sums also help us to identify the overall total. In this way, we can analyze the variation in the running totals over time. To create the cumulative sum chart with count on Y-axis we can use stat_bin function of ggplot2 package.ExampleConsider the below data frame − Live Demo> x df head(df, 20)Output x 1 1.755900133 2 1.185746239 3 0.821489888 4 1.358420721 5 2.719636441 6 2.885153151 7 1.131452570 8 0.302981998 9 0.433865254 10 2.373338327 11 0.428436149 12 1.835789725 13 2.600838211 14 2.108302471 15 1.164818373 16 1.547473189 ... Read More
Often the data frames and matrices in R, we get have missing values and if we want to find the correlation matrix for those data frames and matrices, we stuck. It happens with almost everyone in Data Analysis but we can solve that problem by using na.omit while using the cor function to calculate the correlation matrix. Check out the examples below for that.ExampleConsider the below data frame − Live Demo> x1 x2 x3 x4 df head(df, 20)Output x1 x2 x3 x4 1 2 2.6347839 4 2.577690 2 3 0.3082031 1 6.250998 3 1 0.3082031 3 7.786711 4 ... Read More
When we create a boxplot using ggplot2, the default width of the lines in the boxplot is very thin and we might want to increase that width to make the visibility of the edges of the boxplot clearer. This will help viewers to understand the edges of the boxplot in just a single shot. We can do this by using lwd argument of geom_boxplot function of ggplto2 package.ExampleConsider the below data frame − Live Demo> ID Count df head(df, 20)Output ID Count 1 S1 20 2 S2 14 3 S3 17 4 S4 30 5 S1 17 6 S2 23 7 S3 ... Read More
Here’s an example with two loops, outer and inner −Examplelet demoForLoop = ()=>{ for(var outer=1;outer