7.4 tidyr::pivot_wider() to convert from longer-to-wider format.7.3 tidyr::pivot_longer() to reshape from wider-to-longer format.7.2.2 read_excel() to read in data from an Excel worksheet.7.2.1 Create a new R Markdown and attach packages.6.5.1 Knit, push, & show differences on GitHub.3.8 Assigning objects with % summarize().3.4.3 Writing code in a file vs. Console.2.2 Guiding principles / recurring themes.This pattern can be particularly useful when # creating more complex graphics with many layers using data from multiple # data frames. Both those arguments are now required in # each `geom_point()` layer. ggplot (data = sample_df ) + geom_point (mapping = aes (x = group, y = value ) ) + geom_point ( mapping = aes (x = group, y = group_mean ), data = group_means_df, colour = 'red', size = 3 ) # Pattern 3 # Same plot as above, passing neither the `data` or `mapping` arguments # into the `ggplot()` call. The `mapping` arguments are now required in each `geom_point()` # layer because there is no `mapping` argument passed along from the # `ggplot()` call. ggplot (data = sample_df, mapping = aes (x = group, y = value ) ) + geom_point ( ) + geom_point ( mapping = aes (y = group_mean ), data = group_means_df, colour = 'red', size = 3 ) # Pattern 2 # Same plot as above, passing only the `data` argument into the `ggplot()` # call. Note that the # second `geom_point()` layer re-uses the `x = group` aesthetic through # that mechanism but overrides the y-position aesthetic. Those arguments are omitted in the first `geom_point()` layer # because they get passed along from the `ggplot()` call. # Pattern 1 # Both the `data` and `mapping` arguments are passed into the `ggplot()` # call. In each graphic, the sample data # are plotted in the first layer and the group means data frame is used to # plot larger red points on top of the sample data in the second layer. set.seed ( 1 ) sample_df <- ame ( group = factor ( rep ( letters, each = 10 ) ), value = rnorm ( 30 ) ) group_means_df <- setNames ( aggregate ( value ~ group, sample_df, mean ), c ( "group", "group_mean" ) ) # The following three code blocks create the same graphic, each using one # of the three patterns specified above. # Create a data frame with some sample data, then create a data frame # containing the mean value for each group in the sample data. In the examplesīelow, however, they are left in place for clarity. Values are passed into the function in the right order. (and are often omitted in practice), so long as the data and the mapping The data = and mapping = specifications in the arguments are optional Multiple data frames are used to produce different layers, as The third pattern initializes a skeleton ggplot object, which Plot, but the aesthetics vary from one layer to another. Is useful when one data frame is used predominantly for the The second pattern specifies the default data frame to useįor the plot, but no aesthetics are defined up front. The first pattern is recommended if all layers use the sameĭata and the same set of aesthetics, although this methodĬan also be used when adding a layer using data from another Ggplot(data = df, mapping = aes(x, y, other aesthetics)) There are three common patterns used to invoke ggplot(): Ggplot() is used to construct the initial plot object,Īnd is almost always followed by a plus sign ( +) to add
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