plot two datasets on same graph r ggplot

#Plot two datasets on same graph r ggplot series#

If you use that with the column produced by pivot_longer(), the factor will be ordered by the order of the columns in the original data frame.For a one point of time, we might have multiple time series data, this could be weather for multiple cities, price variation in multiple products, demand expectancy at different locations, or anything that changes with time and measured for multiple things or locations. The fct_inorder() function allows you to reorder levels of a factor in the order of first appearance in the file. This isn’t always what you want in this situation-often the order of the variables in your data frame has some meaning to it. Ggplot(aes(x = response, colour = group)) +īy default, R will sort the levels of factors alphabetically. This is also a nice demonstration of how box plots are rarely the best way to present Likert scale data.Īny other variables are retained after you call pivot_longer(), so you can e.g. compare the responses to survey questions based on a demographic variable: survey_data %>% Labs(x = 'Question', y = 'Response (on a 1 to 5 scale)') Ggplot(aes(y = response, x = question)) + For example, you can make some box plots: survey_data %>% You can use question as a factor anywhere else you would use a categorical variable with ggplot. Labs(x = 'Response (on a 1 to 5 scale)', y = 'Number of respondents')

Pivot_longer(Q1:Q6, names_to = 'question', values_to = 'response') %>% Here I use the facet_wrap() function to plot each question in a separate panel, so we can see the distribution of all of the questions at once: survey_data %>% If you’re not going to use the data in this form for anything else, it’s simpler to pipe the data straight into ggplot2. You don’t even need to store the ‘long form’ data as a separate variable. Print(longer_data) # A tibble: 1,800 x 3 # group question response # 1 B Q1 4 # 2 B Q2 1 # 3 B Q3 4 # 4 B Q4 1 # 5 B Q5 2 # 6 B Q6 3 # 7 B Q1 5 # 8 B Q2 2 # 9 B Q3 5 # 10 B Q4 4 #. Pivot_longer(Q1:Q6, names_to = 'question', values_to = 'response') You can convert this into a longer data frame where the question number is stored in one column and the response is stored in a separate column: longer_data % In this example, I’m going to look at some mocked-up survey data, with six questions stored in variables Q1 through Q6. The best structure for your data depends on what you’re trying to do with it, and in this situation, even if your data is in the right form for analysis, it may not be right for some of the plots you want to make.įortunately, restructuring your data into the right form is straightforward using the tidyr package and the pivot_longer() function. Likewise, if you want to split a plot into panels (or facets, in ggplot2 -speak), you must plot a single response variable, with a grouping variable to indicate which panel the data should be plotted in. As a bonus, it will probably be easier to analyse your data in that form too. The usual answer in this scenario is that you should restructure your data before plotting it.

For example, in situations where you want to plot two columns on a graph as points with different colours, the two columns often really represent the same variable, and there is a hidden grouping factor which distinguishes the data points you want to colour differently. Ggplot2 doesn’t provide an easy facility to plot multiple variables at once because this is usually a sign that your data is not “tidy”.

Pivoting longer: turning your variables into rows

Plot two datasets on same graph r ggplot