Lab5A - Intro to GGPLOT2 Z.Sang Sept 24, 2018

Transcription

1 LabA - Intro to GGPLOT2 Z.Sang Sept 24, 218 In this lab you will learn to visualize raw data by plotting exploratory graphics with ggplot2 package. Unlike final graphs for publication or thesis, exploratory graphics are usually made very quickly in the process of checking for errors, outliers, distribution, and correlations of variables. The goal of making graphs is usually developing a personal understanding of the data and to prioritize tasks for follow up analysis. Grammar of ggplot2 ggplot2, one other important package of tidyverse, is designed for data visualization of data frames. gg of the name represents grammar of graphic, and ggplot2 has been recognized as one of three main graphic system of R. The most important thing to get used to with ggplot2 is the logical structure of plots. The code you write specifies the connections between the variables in your data, and the x and y location, colors, size, shapes etc that you can see on the screen. In ggplot2, these logical connections between your data and the plot elements are called aesthetic mappings or just aesthetics. You begin every plot by telling the ggplot() function what your data is, and then how the variables in this data logically map onto the plot s aesthetic mapping. Then you take the result and say what general sort of plot you want, such as a scatterplot, a boxplot, or a bar chart. In ggplot2, the overall type of plot is called a geom. Each geom has a function that creates it and the function s name follows the pattern of geom_... (). For example, geom_point() makes scatterplots, geom_bar() makes bar plots, geom_boxplot() makes boxplots, and so on. You combine these two pieces, the ggplot(data, mapping) object and the geom_... (), by literally adding them together in an expression, using the + symbol. Data, mapping (or aesthetics), and geometry (geom) are three mandatory components for ggplot2. As other functions, the output of ggplot2 can be assigned to an object for further editing. Other optional ggplot2 grammar components will be introduced in Lab for figure customization. 1

2 A little too complex? Don t worry; you will get familiar with the grammar system very soon. In this lab, we will use this ggplot2 syntax to plot the following exploratory graphics: histogram (density plot), boxplot, scatterplot, and scatterplot matrix. Data preparation For this exercise, use a weather station dataset AB_Stations.csv that you can download from the course website. The first three columns specify the weather station ID, as well as the ecosystems and the biome of Alberta in which the weather station is located. This is followed by a number of climate variables that you can use for exploration (=mean annual temp, MWMT= mean warmest month temp, MCMT=mean coldest month temp, MAP=mean annual precipitation, MSP=mean summer precipitation, =an index). Load required packages. #install.package('tidyverse') library(tidyverse) # if no tidyverse package installed Import the dataset with the code below, and use head(), tail(), str()or View() functions to check the imported data table. dat1 <- read.csv("e:\\lab3\\ab_stations.csv") head(dat1, 1) ## STATION ECOSYS MWMT MCMT MAP MSP ## G-NF ## G-DMG ## G-MG ## G-DMG ## G-NF ## B-AP ## B-UBH ## M-M ## B-CP ## B-KU Hisograms One useful plot type for exploration of raw data is histograms. They are commonly used to visually check the distribution of continuous variables. The geom of histogram is geom_histogram(). For histograms the y axis is ing the number of observations in each bin (default of ggplot2), but y can also be set as density. According to the ggplot2 syntax, we can execute the following command to get a histogram for a variable, in this case the variable : hist_a <- ggplot(dat1, aes(x = )) + geom_histogram(color = 'gray9') hist_a ## `stat_bin()` using `bins = 3`. Pick better value with `binwidth`. 2

3 1 1 1 ggplot2 chooses the bin width by default when generating histograms, but chances are that bin width is not the most appropriate one for any histogram you may want to make. It is therefore critical to change bins to verify whether the resulting histogram reflects the data accurately. Too many bins makes histograms overly peaky and losses the whole picture of distribution, while too few bins cover limited details of the distribution. The following two methods to change bins: 1. First method: set the number of bins you want for the histogram; ggplot(dat1, aes(x = )) + geom_histogram(bins =, color = 'gray9') # bins ggplot(dat1, aes(x = )) + geom_histogram(bins = 2, color = 'gray9')#2 bin ggplot(dat1, aes(x = )) + geom_histogram(bins =, color = 'gray9')# bin Second method: set the width of bins: ggplot(dat1, aes(x = )) + geom_histogram(binwidth = 1, color = 'gray9') ggplot(dat1, aes(x = )) + geom_histogram(binwidth =, color = 'gray9') ggplot(dat1, aes(x = )) + geom_histogram(binwidth = 1, color = 'gray9') Great to visually check the effectiveness of data transformations. In this case, the square-root transformation achieves approximately a normal distribution. 3

4 hist_b <- ggplot(dat1, aes(x = sqrt())) + geom_histogram(color = 'gray9') hist_b ## `stat_bin()` using `bins = 3`. Pick better value with `binwidth` sqrt() You can also fill colors of the bins by group/class. In many scenarios we have multiple distributions we would like to visualize simultaneously. For example, were the biomes having similar dryness situation? One commonly employed visualization strategy is stacking bars on top of each other and filling histogram in different colors for groups; hist_c <- ggplot(dat1, aes(x =, fill = )) + geom_histogram() Although ing numbers is used as y axis by default, you can change y axis as density. Given uneven sample size for each group/class, density histograms may show inconsistent pattern with the frequency ones. hist_d <- ggplot(dat1, aes(x =, fill = )) + geom_histogram(aes(y =..density..))#specify y as density ## `stat_bin()` using `bins = 3`. Pick better value with `binwidth`. ## `stat_bin()` using `bins = 3`. Pick better value with `binwidth`. hist_c hist_d density One biggest disadvantage of the stacked histogram is hard to quantify each group; for example, how many samples of have values around 4? About 1 or 8? It s not so clear to compare distributions among groups. To solve this, one way is to change the positions of bins. One common way is dodging which preserves the vertical position of a geom while adjusting the horizontal position. hist_e <- ggplot(dat1, aes(x =, fill = )) + geom_histogram(position = 'dodge') #change bin positions hist_e ## `stat_bin()` using `bins = 3`. Pick better value with `binwidth`. 4

5 Histograms have been a popular visualization option since at least the 18th century, in part because they are easily generated by hand. More recently, as extensive computing power has become popularized in everyday devices such as laptops and cell phones, we see them increasingly being replaced by density plots. In a density plot, we attempt to visualize the underlying probability distribution of the data by drawing an appropriate continuous curve; hist_f <- ggplot(dat1, aes(x =, fill = )) + geom_density(alpha =.4) #introduce transparency hist_f density Similarly, we fill density curve with different colors. The alpha argument is used to introduce transparency of the color, and alpha value in the range of (totally transparent) to 1 (no transparent). Also, try to add multiple geom: hist_f + geom_histogram(aes(y =..density..), alpha =.6, position = 'dodge') ## `stat_bin()` using `bins = 3`. Pick better value with `binwidth`.

6 6 density Scatter plots With scatter plots you can visually check the relationships among variables. Are they linear or curvilinear? Outliers are also easily visible. Now, try to use a scatter plot to visually check the relationships among variables and to identify outliers. To check the relationships between Mean Summer Precipitation (MSP) and Mean Annual Precipitation (MAP), we can use them as x, y in the plot respectively (normally, y axis is for dependent variable, and x is for independent variable, but in this case it s ok to exchange axes). The geom function for scatter plot is geom_point() ggplot(dat1, aes(x = MAP, y = MSP)) + geom_point() MSP MAP Cool! It seems there is a positive relationship between these two variables. However, several overlapped points could influence the interpretability of the plot. One easy way is to introduce the transparency of points. plt <- ggplot(dat1, aes(x = MAP, y = MSP)) + geom_point(alpha =.3) plt 6

7 MSP MAP Besides changing the transparency, changing the point position by erintuitively adding random noise could be helpful to see each point. plt_jittered <- ggplot(dat1, aes(x = MAP, y = MSP)) + geom_point(position = "jitter") plt_jittered MSP MAP You can also add labels to your plot with the geom function geom_text().in this case, we want to label STATION name of points. hjust and vjust is used to control the placement of labels. plt_label <- ggplot(dat1, aes(x = MAP, y = MSP, label = STATION)) + geom_point() + geom_text(hjust =, vjust =, size = 2.2, color = 'gray4') plt_label MSP MAP 7

8 Could you tell the STATION ID of the two outliers around the lower right corner of the plot? However, do all types follow the same relationship between MAP and MSP? To figure it out, we need add some visual aid to separate these types (e.g., color, shape) plt_biome <- ggplot(dat1, aes(x = MAP, y = MSP, color =, shape = )) + geom_point() plt_biome 37 MSP MAP How about density plot for 2-D plot? Try: plt + geom_density2d() MSP MAP Box plots Just like scatter plots, boxplots is a good way to visually check the relationships among two variables. If one variable is continuous (as y) and the other is categorical (as x), then boxplot is a good option. For instance, to understand the general distribution of mean annual temperature () of each biome types (). The geom for boxplot is geom_boxplot() ggplot(dat1, aes(x =, y = )) + geom_boxplot() 8

9 . If you still have time, you can add the following arguments within the parentheses of geom_boxplot()and see what their functions could be: varwidth = T, notch = T ggplot(dat1, aes(x =, y = )) + geom_boxplot(varwidth = T) # the width of box reflecting the ggplot(dat1, aes(x =, y = )) + geom_boxplot(notch = T) # add notch to box(es) ## notch went outside hinges. Try setting notch=false... Similarly, we can make boxplot for mean annual temperature () of ecosystems (ECOSYS). ggplot(dat1, aes(x = ECOSYS, y = )) + geom_boxplot(). B APB BSA B CMB CPB DMB KUB LBHB NMB Peac B PRP B UBH G DMGG FFG FPG MG NFM AM LFM MM SAM UF ECOSYS 9

10 Since the names of ecosystems takes spaces and can easily overlap, we prefer to use ECOSYS as y axis and have horizontal boxplot: ggplot(dat1, aes(x = ECOSYS, y = )) + geom_boxplot() + coord_flip() # horizontal: flip the x, y axes # Great! Now you can color boxplots based their types; ggplot(dat1, aes(x = ECOSYS, y =, fill = )) + geom_boxplot(varwidth = T) + coord_flip() # colored by groups a M UF b M UF M SA M SA M M M M M LF M LF M A M A G NF G NF G MG G MG G FP G FP ECOSYS G FF G DMG B UBH B PRP B Peac ECOSYS G FF G DMG B UBH B PRP B Peac B NM B NM B LBH B LBH B KU B KU B DM B DM B CP B CP B CM B CM B BSA B BSA B AP B AP.. Boxplots are generally useful, but it does only focus on five numbers of the samples (min, max, 2th, th, and 7th). To add more details about distribution, we can add points (jittered) or violin plot as alternatives. ggplot(dat1, aes(x =, y = )) + geom_violin()+ geom_boxplot(width =.1) ggplot(dat1, aes(x =, y = )) + geom_boxplot() + geom_point(position='jitter', alpha=.2, size=2) 1

11 7.... The first commend narrowed the width of boxplots and added them into violin plot, and the second one added scatter plots into boxplots. Well done! So far we just analyze one continuous variable once a time. Can we visual multiple variables in one plot? Hope you still remember in lab2b we applied gather() function to transform a data frame from wide to long. In ggplot2, x or y must only be determined by one single variable. Therefore, first we need to gather multiple interested variables into one, and then use the new data table for ggplot2 plotting. For example, let make a boxplot to check the distribution of three types of mean annual temperature (), mean warmest month temperature (MWMT) and mean coldest month temperature (MCMT). dat2 <- gather(dat1, key = 'temp', value = 'value',, MCMT, MWMT) head(dat2) #quick check the new data table ## STATION ECOSYS MAP MSP temp value ## G-NF ## G-DMG ## G-MG ## G-DMG ## G-NF ## B-AP ggplot(dat2, aes(x = temp, y = value, fill = )) + geom_boxplot() #using different colors for types 2 value 2 MCMT MWMT temp 11

12 Looks nice! If you change the temperature variables as treatments, then different performance among and within groups is a strong clue of interaction..4. Multi-panel scatter plots in R So far, in this lab we learnt 1-dimensional (histogram, density plot), 2-dimensional (scatter plot, boxplot) exploratory graphics, they normally can only analyze one or a pair of variables a time. If you have a data table with 1 potential independent variables, plotting them one by one is not effective. To get the general idea of the relationships among variables in very short time: #need use ggpairs() function of GGally package #install.packages('ggally') library(ggally) ggpairs(dat1[, c('', 'MAP', 'MSP', '', '')], aes(color = )) MAP MSP Cor :.177 :.24 ssland:.26 :.91 Cor : 431 :.311 :.374 :.171 Cor :.711 :.47 :.4 : 371 Cor :.73 :.727 :.1 :.98 Cor :.667 :.117 :.74 :.892 Cor :.492 :.123 :.818 : MAP MSP Voilà. Now you can see the plot matrix among, MAP, MSP, with, and also use different colors distinguish types. 12