Session 3 Nick Hathaway;

Transcription

1 Session 3 Nick Hathaway; nicholas.hathaway@umassmed.edu Contents Manipulating Data frames and matrices 1 Converting to long vs wide formats Manipulating data in table Piping Summarizing data Filtering data Part 1. Excercises 12 Plotting 12 ggplot2 Basics Modifying colors Changing point shape and line types Changing plot aspect not dependent on input data Switching to another layer type Controlling plotting order using factors Saving plots Part 2. Excercises 39 Manipulating Data frames and matrices The readr package reads in data as what is called a tibble which is different from the default R data.frame. The tibble class was invented to be more efficient and more user friendly than the data.frame but one major difference that trips up most people use to data.frame is that the tibble class doesn t allow rownames. While this doesn t make a big difference for most uses of the data.frame class there are instances when you need rownames for the matrix class. Below is how you would read in data that has rownames and then convert to a matrix and add the rownames. library(tidyverse) ts = read_tsv(".series.data.txt") ts_mat = as.matrix(ts[, 2:ncol(ts)]) rownames(ts_mat) = ts$x1 1

2 Converting to long vs wide formats Tidyr The tidyr package is about making your data.frames tidy. Now what is meant by tidy? There are considered two ways to organize data tables. One is referred as wide format where each cell is a different observation and you have row and column names to explain what those observations are. The other format is called long format and this format is that every column is a different variable and each row is a different observation and this long format is the format that R is the best at for organizing. tidyr is all about switching between the two formats. gather gather() will take a table in wide format and change it into long format. It takes four important arguments, 1) the data.frame to work on, 2) the name of a new column that contain the old column names, 3) the name of new column to contain the observation that were spread out in the column table, 4) the column indexes to gather together. ts = read_tsv(".series.data.txt") #rename first column colnames(ts)[1] = "gene" # or the rename function can also be used ts = read_tsv(".series.data.txt") ts = rename(ts, gene = X1) ts # A tibble: 25,87 x 2 gene Ctrl_h Lps_1h Lps_2h Lps_4h Lps_6h Lps_12h <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 A1BG e+ 3.65e+ 3.28e+ 2.78e+ 3.59e+ 2 A1BG-~ e+ 1.92e+ 1.9e+ 1.24e+ 2.12e+ 3 A1CF e e-2. 4 A2M e e e e e+2 5 A2M-A~ e+ 1.e+ 1.66e+ 9.62e e-1 6 A2ML e-2 1.e e e-2 3.8e-2 7 A2MP e e-2 1.2e-1 4.4e-2 8 A3GAL~ e A4GALT e+ 4.22e+ 9.77e+ 7.95e+ 7.7e+ 1 A4GNT e-1 3.e-2 5.e-2 3.e-2. #... with 25,797 more rows, and 13 more variables: # Lps_24h <dbl>, R848_1h <dbl>, R848_2h <dbl>, # R848_4h <dbl>, R848_6h <dbl>, R848_12h <dbl>, # R848_24h <dbl>, Ifnb_1h <dbl>, Ifnb_2h <dbl>, # Ifnb_4h <dbl>, Ifnb_6h <dbl>, Ifnb_12h <dbl>, # Ifnb_24h <dbl> ts_gat = gather(ts, Condition,, 2:ncol(ts) ) ts_gat # A tibble: 49,333 x 3 gene Condition <chr> <chr> <dbl> 2

3 Figure 1: 3

4 1 A1BG Ctrl_h A1BG-AS1 Ctrl_h A1CF Ctrl_h.54 4 A2M Ctrl_h A2M-AS1 Ctrl_h A2ML1 Ctrl_h A2MP1 Ctrl_h A3GALT2 Ctrl_h A4GALT Ctrl_h A4GNT Ctrl_h.12 #... with 49,323 more rows Figure 2: spread The opposite of the gather() function is the spread() function which can be used to undo the gather() ts_gat_sp = spread(ts_gat, Condition, ) ts_gat_sp # A tibble: 25,87 x 2 gene Ctrl_h Ifnb_12h Ifnb_1h Ifnb_24h Ifnb_2h Ifnb_4h <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 4

5 Figure 3: 5

6 1 A1BG 5.41e e e+ 6.6e+ 2 A1BG-~ 1.72e e e+ 8.65e-1 3 A1CF 5.4e-2. 5.e A2M 7.8e e e e+2 5 A2M-A~ 1.38e e e-1 7.e-2 6 A2ML1 2.75e e e-2 2.e-2 7 A2MP1 3.29e A3GAL~ 2.29e A4GALT 3.85e e e e-1 1 A4GNT 1.2e #... with 25,797 more rows, and 13 more variables: # Ifnb_6h <dbl>, Lps_12h <dbl>, Lps_1h <dbl>, # Lps_24h <dbl>, Lps_2h <dbl>, Lps_4h <dbl>, # Lps_6h <dbl>, R848_12h <dbl>, R848_1h <dbl>, # R848_24h <dbl>, R848_2h <dbl>, R848_4h <dbl>, # R848_6h <dbl> separate tidyr also has functions for manipulating columns into multiple columns, the separate function ts_gat = separate(ts_gat, Condition, c("exposure", "") ) ts_gat # A tibble: 49,333 x 4 gene exposure <chr> <chr> <chr> <dbl> 1 A1BG Ctrl h A1BG-AS1 Ctrl h A1CF Ctrl h.54 4 A2M Ctrl h A2M-AS1 Ctrl h A2ML1 Ctrl h A2MP1 Ctrl h A3GALT2 Ctrl h A4GALT Ctrl h A4GNT Ctrl h.12 #... with 49,323 more rows unite The opposite of the separate function is the the unite function ts_gat = unite(ts_gat, Condition, ts_gat exposure, ) # A tibble: 49,333 x 3 gene Condition <chr> <chr> <dbl> 1 A1BG Ctrl_h A1BG-AS1 Ctrl_h A1CF Ctrl_h.54 4 A2M Ctrl_h 78. 6

7 5 A2M-AS1 Ctrl_h A2ML1 Ctrl_h A2MP1 Ctrl_h A3GALT2 Ctrl_h A4GALT Ctrl_h A4GNT Ctrl_h.12 #... with 49,323 more rows Manipulating data in table The library dplyr can be used to manipulate the data within the table themselves while tidyr is more for reorganization ### Mutating Columns types The mutate function can be used to either create new columns or change current columns. Here I also take advantage of the gsub function, which takes three arguments, 1) a pattern to replace, 2) what to replace the pattern with, 3) what to do the replacement on library(dplyr) ts_gat = mutate(ts_gat, = as.numeric(gsub("h", "", ) ) ) Piping It is common practice with tidyverse functions to use something called piping which is using the results of function call and using that as input to the next function without saving that result in an intermediate variable, this allows for much more efficient processing of the data as not as much memory is used by the computer. This piping is accomplished by the %>% operator, which a keyboard shortcut is hitting the command+shift+m keys together (control+shirt+m for windows or ubuntu). The pipe operator takes what is given to it and places that as the first argument in the next function (e.g. mean(x) == x %>% mean() ) Below is a diagram. x = 1:1 mean(x) [1] 5.5 x %>% mean() [1] 5.5 library(tidyverse) #this will load readr, dplyr, and tidyr ts_longformat = read_tsv(".series.data.txt") ts_longformat = rename(ts_longformat, gene = X1) ts_longformat = gather(ts_longformat, Condition,, 2:ncol(ts_longFormat) ) ts_longformat = separate(ts_longformat, Condition, c("exposure", "") ) ts_longformat = mutate(ts_longformat, = as.numeric(gsub("h", "", ) ) ) ts_longformat # A tibble: 49,333 x 4 gene exposure 7

8 <chr> <chr> <dbl> <dbl> 1 A1BG Ctrl A1BG-AS1 Ctrl A1CF Ctrl A2M Ctrl A2M-AS1 Ctrl A2ML1 Ctrl A2MP1 Ctrl A3GALT2 Ctrl A4GALT Ctrl A4GNT Ctrl..12 #... with 49,323 more rows Is equivalent to library(tidyverse) #this will load readr, dplyr, and tidyr ts_longformat = read_tsv(".series.data.txt") %>% rename(gene = X1) %>% gather(condition,, 2:ncol(.) ) %>% separate(condition, c("exposure", "") ) %>% mutate( = as.numeric(gsub("h", "", ) ) ) ts_longformat # A tibble: 49,333 x 4 gene exposure <chr> <chr> <dbl> <dbl> 1 A1BG Ctrl A1BG-AS1 Ctrl A1CF Ctrl A2M Ctrl A2M-AS1 Ctrl A2ML1 Ctrl A2MP1 Ctrl A3GALT2 Ctrl A4GALT Ctrl A4GNT Ctrl..12 #... with 49,323 more rows Figure 4: Below shows the relationship between the above pipe commands and the commands executed one by one Summarizing data dplyr also offers ways to quickly group and then summarize your data using the group_by and summarize functions. 8

9 ts_longformat_exposure_summary = ts_longformat %>% group_by(exposure) %>% summarise(mean_ = mean(), median_ = median(), max_ = max(), min_ = min(), sd_ = sd()) ts_longformat_exposure_summary # A tibble: 4 x 6 exposure mean_ median_ max_ <chr> <dbl> <dbl> <dbl> 1 Ctrl Ifnb Lps R #... with 2 more variables: min_ <dbl>, # sd_ <dbl> ts_longformat_exposure summary = ts_longformat %>% group_by(exposure, ) %>% summarise(mean_ = mean(), median_ = median(), max_ = max(), min_ = min(), sd_ = sd()) ts_longformat_exposure summary # A tibble: 19 x 7 # Groups: exposure [?] exposure mean_ median_ <chr> <dbl> <dbl> <dbl> 1 Ctrl Ifnb Ifnb Ifnb Ifnb Ifnb Ifnb Lps Lps Lps Lps Lps Lps R R R R R

10 19 R #... with 3 more variables: max_ <dbl>, # min_ <dbl>, sd_ <dbl> Filtering data ts_longformat_crt = ts_longformat %>% filter(exposure == "Ctrl") ts_longformat_crt # A tibble: 25,87 x 4 gene exposure <chr> <chr> <dbl> <dbl> 1 A1BG Ctrl A1BG-AS1 Ctrl A1CF Ctrl A2M Ctrl A2M-AS1 Ctrl A2ML1 Ctrl A2MP1 Ctrl A3GALT2 Ctrl A4GALT Ctrl A4GNT Ctrl..12 #... with 25,797 more rows NAs in values Taking means, mins, maxes, etc. can be affected if NA values are present vals = c(1,3, 5, 9, 19, 23) mean(vals) [1] 11.5 min(vals) [1] 3 max(vals) [1] 23 vals = c(1,3, 5, 9, 19, 23, NA) mean(vals) [1] NA 1

11 min(vals) [1] NA max(vals) [1] NA You can handle this by setting na.rm = T vals = c(1,3, 5, 9, 19, 23, NA) mean(vals, na.rm = T) [1] 11.5 min(vals, na.rm = T) [1] 3 max(vals, na.rm = T) [1] 23 You can also just get rid of the NA values as well and if within in a data.frame you can use filter() you can do. # use na.rm =T ts_longformat_exposure summary = ts_longformat %>% group_by(exposure, ) %>% summarise(mean_ = mean(, na.rm =T), median_ = median(, na.rm =T), max_ = max(, na.rm =T), min_ = min(, na.rm =T), sd_ = sd(, na.rm =T)) # use filter to keep only values of that aren't (!) NA (is.na) ts_longformat_exposure summary = ts_longformat %>% filter(!is.na()) %>% group_by(exposure, ) %>% summarise(mean_ = mean(), median_ = median(), max_ = max(), min_ = min(), sd_ = sd()) dplyr offers a large array of available functions for manipulating data frames. Here is a list of resources for more options: 11

12 1. cheatsheet a few basics tutorial a webinar - Part 1. Excercises Download Average Temperatures USA Average Temperatures USA 1. convert to long format by using gather() on the temperate columns 2. separate columns so you have a column for year and month 3. create a table of mean temperatures for month, city, and months for each city (various group_by calls) 4. filter table to just one city or just one month Plotting Base R offers several basic plotting functions but in this course we will be focusing on using ggplot2 for plotting. A basic introduction can be found here, ggplot2 Basics A basic ggplot2 call # filter data to gene of interest ts_longformat_sod2 = ts_longformat %>% filter("sod2" == gene) ggplot(ts_longformat_sod2) + geom_point(aes(x =, y = ) ) 12

13 ggplot2 is based off of what is called Grammar of Graphics (hence gg plot), which is a book by Leland Wilkinson The philosophy of the book is that you should have a plotting system that allows you to simply describe what the plot should be based on and the computer will take care of it. Of note, ggplot2 is the name of the library but the function call itself is ggplot() and not ggplot2(). ggplot2 works best by working on a long-format data frame, you then describe all layers on the plot, you add each layer with another geom_[type] functions. There are many layers available, see http: //ggplot2.tidyverse.org/reference/index.html#section-layer-geoms for a list and examples of each. ggplot(ts_longformat_sod2) + geom_point(aes(x =, y = ) ) + geom_line(aes(x =, y = ) ) 13

14 Below is a diagram of how a generic ggplot2 is structured. The aspects of the plot that you want to map to specific column in the data frame are given in the aes() call within the layer calls, (aes is short for aesthetic). If the mapping aesthetics are shared between layers you can give them in the top ggplot() call and they will be applied to each layer. ggplot(ts_longformat_sod2, aes(x =, y = )) + geom_point() + geom_line() 14

15 Figure 5: Figure 6: 15

16 Now clearly this plot is not what we actually want to display, we are ignoring the exposure variable and this is causing the plot to look funny, so let s tell ggplot that we have a grouping variable, exposure. ggplot(ts_longformat_sod2, aes(x =, y =, group = exposure)) + geom_point() + geom_line() 16

17 Now let s add some coloring to make this plot a little more exciting. ggplot(ts_longformat_sod2, aes(x =, y =, group = exposure, color = exposure)) + geom_point() + geom_line() 17

18 1 exposure Ctrl Ifnb Lps R Also when you add plotting aspects like coloring, ggplot2 assumes that this is also a grouping variable so you no longer have to supply grouping if you are giving a coloring variable. plotsod2 = ggplot(ts_longformat_sod2, aes(x =, y =, color = exposure)) + geom_point() + geom_line() Modifying colors Now the default color variables kind of leave a lot to be desired and we can set these colors to something else by using ggplot2 s scale_color_[func_name] to change the color plotting aspects. Here we are using the colors supplied by RColorBrewer, which you use by using scale_color_brewer(). 18

19 ggplot(ts_longformat_sod2, aes(x =, y =, color = exposure)) + geom_point() + geom_line() + scale_color_brewer(palette = "Dark2") 1 exposure Ctrl Ifnb Lps R The color brewer palettes were developed by Martin Krzywinski on which he wrote a Nature paper on the subject, certain palettes were developed to be color blind safe, more information about his work can be found here and a website for helping choosing colors can be found here For more information on how you can change the color other than scale_color_brewer() see here http: //ggplot2.tidyverse.org/reference/index.html#section-scales. 19

20 Setting colors manually As most PIs are extremely picky about colors, there are also easy ways of setting specific colors for specific grouping using scale_color_manual(). exposurecolors = c("#5ac8", "#AAA3C", "#AB45A", "#14D2DC") ggplot(ts_longformat_sod2, aes(x =, y =, color = exposure)) + geom_point() + geom_line() + scale_color_manual(values = exposurecolors) 1 exposure Ctrl Ifnb Lps R The above will assign the colors in the order they appear in the data frame and you can make it sure it doesn t matter the order or if certain levels are missing (which could then mess up the ordering) you can 2

21 name the color vector so that coloring is consistent. exposurecolors = c("#5ac8", "#AAA3C", "#AB45A", "#14D2DC", "#8214A") names(exposurecolors) = c("ctrl", "R848", "Ifnb", "Lps", "other") ggplot(ts_longformat_sod2, aes(x =, y =, color = exposure)) + geom_point() + geom_line() + scale_color_manual(values = exposurecolors) 1 exposure Ctrl Ifnb Lps R If you don t name one of the layer it will not get a color, so be careful of case etc. 21

22 exposurecolors = c("#5ac8", "#AAA3C", "#AB45A", "#14D2DC", "#8214A") names(exposurecolors) = c("ctrl", "R848", "Ifnb", "LPS", "other") ggplot(ts_longformat_sod2, aes(x =, y =, color = exposure)) + geom_point() + geom_line() + scale_color_manual(values = exposurecolors) 1 exposure Ctrl Ifnb Lps R

23 Changing point shape and line types ts_longformat_sod2_cd74 = ts_longformat %>% filter("sod2" == gene "CD74" == gene) # taking advantage of the or operator to do a check for either # also you can also the %in% operator that R offers ts_longformat_sod2_cd74 = ts_longformat %>% filter(gene %in% c("sod2", "CD74") ) # create a grouping variable to make plotting easier ts_longformat_sod2_cd74 = ts_longformat_sod2_cd74 %>% mutate(grouping = paste(gene, "-", exposure)) # using group = grouping to separate out the different genes and the exposure but still color by exposur ggplot(ts_longformat_sod2_cd74, aes(x =, y =, color = exposure, group = grouping)) + geom_point() + geom_line() + scale_color_brewer(palette = "Dark2") 23

24 15 1 exposure Ctrl Ifnb Lps R But just coloring by exposure we can t tell which lines and points are from which genes so lets change the shape and line types so we distinguish ggplot(ts_longformat_sod2_cd74, aes(x =, y =, color = exposure, group = grouping)) + geom_point(aes(shape = gene)) + geom_line(aes(linetype = gene)) + scale_color_brewer(palette = "Dark2") 24

25 15 1 gene CD74 SOD2 exposure Ctrl Ifnb Lps R Changing plot aspect not dependent on input data If you want to change certain aspect about the plot that doesn t depend on mapping data from the input data frame you put these setting on the output of the aes() call. Figure 7: 25

26 # make the points larger, the value given to size is a relative number ggplot(ts_longformat_sod2_cd74, aes(x =, y =, color = exposure, group = grouping)) + geom_point(aes(shape = gene), size = 3) + geom_line(aes(linetype = gene)) + scale_color_brewer(palette = "Dark2") 15 1 gene CD74 SOD2 exposure Ctrl Ifnb Lps R You can also then change the linetypes and shapes with scale_ functions genelinetypes =c("dotted", "solid") names(genelinetypes) = c("cd74", "SOD2") # make the points larger, the value given to size is a relative number ggplot(ts_longformat_sod2_cd74, aes(x =, y =, color = exposure, group = grouping)) + geom_point(aes(shape = gene), size = 3) + 26

27 geom_line(aes(linetype = gene)) + scale_color_brewer(palette = "Dark2") + scale_shape_manual(values = c(1, 3)) + scale_linetype_manual(values = genelinetypes) 15 1 gene CD74 SOD2 exposure Ctrl Ifnb Lps R Switching to another layer type Say you decide to take your plot and switch to a different layer type, you can reuse a lot of what you have already done. For example lets switch from a dot/line plot to a bar plot by using geom_bar(). By default geom_bar() does plotting by counting up all values that fall into a group, but if you want a specific values instead you have to give geom_bar() stat = "identity" 27

28 ggplot(ts_longformat_sod2, aes(x =, y =, color = exposure)) + geom_bar(stat = "identity") + scale_color_brewer(palette = "Dark2") exposure Ctrl Ifnb Lps R Notice now that for geom_bar() color controls the border of the bars but if we want the bars themselves to be the given color we have to use fill instead. ggplot(ts_longformat_sod2, aes(x =, y =, fill = exposure)) + geom_bar(stat = "identity") + scale_color_brewer(palette = "Dark2") 28

29 exposure Ctrl Ifnb Lps R But we lost the colors we were trying to set and that s because we are using the scale_color_brewer but we are now using fill instead so we need the scale_fill_brewer function instead. ggplot(ts_longformat_sod2, aes(x =, y =, group = exposure, fill = exposure)) + geom_bar(stat = "identity") + scale_fill_brewer(palette = "Dark2") 29

30 exposure Ctrl Ifnb Lps R By default geom_bar() stacks all the bars belonging to the same x-axis grouping on top of each other but if we wanted them next to each other instead we give geom_bar() position = "dodge". ggplot(ts_longformat_sod2, aes(x =, y =, group = exposure, fill = exposure)) + geom_bar(stat = "identity", position = "dodge") + scale_fill_brewer(palette = "Dark2") 3

31 1 exposure Ctrl Ifnb Lps R We can make the bars stand out more by giving change the border color for all bars to be black ggplot(ts_longformat_sod2, aes(x =, y =, group = exposure, fill = exposure)) + geom_bar(stat = "identity", position = "dodge", color = "black") + scale_fill_brewer(palette = "Dark2") 31

32 1 exposure Ctrl Ifnb Lps R Now lets dress up the plot a little bit, we can do this by using ggplot2 s theme() function which allows the tweaking of many different aspects of how the plot itself looks in general, let s change the legend position so it s on the bottom instead. ggplot(ts_longformat_sod2, aes(x =, y =, group = exposure, fill = exposure)) + geom_bar(stat = "identity", position = "dodge", color = "black") + scale_fill_brewer(palette = "Dark2") + theme(legend.position = "bottom") 32

33 exposure Ctrl Ifnb Lps R848 To see all the things that theme() can do use the help function help(theme) We can also take advantage of preset themes supplied by ggplot2 ggplot(ts_longformat_sod2, aes(x =, y =, group = exposure, fill = exposure)) + geom_bar(stat = "identity", position = "dodge", color = "black") + scale_fill_brewer(palette = "Dark2") + theme_bw() + theme(legend.position = "bottom") 33

34 exposure Ctrl Ifnb Lps R848 We can also change the title and labels of axis with labs() function and lets get rid of the panel around the plot (panel.border = element_blank()). Also center the title (plot.title = element_text(hjust =.5)). ggplot(ts_longformat_sod2, aes(x =, y =, group = exposure, fill = exposure)) + geom_bar(stat = "identity", position = "dodge", color = "black") + scale_fill_brewer(palette = "Dark2") + labs(title = "Expression of SOD2 gene", y = "Gene Expression", x = "Time (hrs)") + theme_bw() + theme(legend.position = "bottom", panel.border = element_blank(), plot.title = element_text(hjust =.5)) 34

35 Expression of SOD2 gene 1 Gene Expression Time (hrs) exposure Ctrl Ifnb Lps R848 Controlling plotting order using factors Let s say we didn t change the column into a numeric column. Notice how the order isn t what we would want, this is because R will determine the order automatically by sorting the input values. ts_longformat_sod2 = read_tsv(".series.data.txt") %>% rename(gene = X1) %>% gather(condition,, 2:ncol(.) ) %>% separate(condition, c("exposure", "") ) %>% filter("sod2" == gene) ggplot(ts_longformat_sod2, aes(x =, y =, group = exposure, fill = exposure)) + geom_bar(stat = "identity", position = "dodge", color = "black") + 35

36 scale_fill_brewer(palette = "Dark2") + labs(title = "Expression of SOD2 gene", y = "Gene Expression", x = "Time (hrs)") + theme_bw() + theme(legend.position = "bottom", panel.border = element_blank(), plot.title = element_text(hjust =.5)) Expression of SOD2 gene 1 Gene Expression 5 h 12h 1h 24h 2h 4h 6h Time (hrs) exposure Ctrl Ifnb Lps R848 This can be fixed by changing the column into a factor from a character and set the order of levels ts_longformat_sod2 = ts_longformat_sod2 %>% mutate( = factor(, levels = c("h", "1h", "2h", "4h", "6h", "12h", "24h"))) ggplot(ts_longformat_sod2, aes(x =, y =, group = exposure, fill = exposure)) + geom_bar(stat = "identity", position = "dodge", color = "black") + 36

37 scale_fill_brewer(palette = "Dark2") + labs(title = "Expression of SOD2 gene", y = "Gene Expression", x = "Time (hrs)") + theme_bw() + theme(legend.position = "bottom", panel.border = element_blank(), plot.title = element_text(hjust =.5)) Expression of SOD2 gene 1 Gene Expression 5 h 1h 2h 4h 6h 12h 24h Time (hrs) exposure Ctrl Ifnb Lps R848 Saving plots Plots can be saved to a variety of image types but the most useful is likely pdf, this will allow you to be able to manipulate the plot in programs like Illustrator or Inkscape or save as any other image type after. To save as pdf we will use the function pdf(). How this functions works is that it opens up a pdf graphic 37

38 device which will catch all plot calls (rather than going to the plot window in RStudio) until the function dev.off() is called pdf("example_plot.pdf", width = 11, height = 8.5, usedingbats = F) ggplot(ts_longformat_sod2, aes(x =, y =, group = exposure, fill = exposure)) + geom_bar(stat = "identity", position = "dodge", color = "black") + scale_fill_brewer(palette = "Dark2") + labs(title = "Expression of SOD2 gene", y = "Gene Expression", x = "Time (hrs)") + theme_bw() + theme(legend.position = "bottom", panel.border = element_blank(), plot.title = element_text(hjust =.5)) dev.off() pdf 2 example_plot.pdf - The first argument is the name of a file you want to save the plots to, this will erase any file with this name if it already exists so be careful. width - This is the width of the plot in inches height - This is the height of the plot in inches usedingbats=f - This turns off the graphic library Dingbats, you always want to set this to FALSE, it causes R to create a larger file but if you don t turn off Dingbats it causes problems when editing the pdf latter in something like Illustrator. Multiple pages R will keeping adding pages to the opened pdf until dev.off() is called. pdf("example_plot_2_pages.pdf", width = 11, height = 8.5, usedingbats = F) ggplot(ts_longformat_sod2, aes(x =, y =, group = exposure, fill = exposure)) + geom_bar(stat = "identity", position = "dodge", color = "black") + scale_fill_brewer(palette = "Dark2") + labs(title = "Expression of SOD2 gene", y = "Gene Expression", x = "Time (hrs)") + theme_bw() + theme(legend.position = "bottom", panel.border = element_blank(), plot.title = element_text(hjust =.5)) ggplot(ts_longformat_sod2_cd74, aes(x =, y =, color = exposure, group = grouping)) + geom_point(aes(shape = gene), size = 3) + geom_line(aes(linetype = gene)) + scale_color_brewer(palette = "Dark2") + scale_shape_manual(values = c(1, 3)) + scale_linetype_manual(values = genelinetypes) dev.off() pdf 2 38

39 Part 2. Excercises Using the Temperature data frame read in earlier Average Temperatures USA 1. Filter the long format data frame created in Part 1 to just one Station_name 2. Modify the month column into a factor so that the months are organized in chronological order. (hint use this vector c("january","february","march","april","may","june","july","august","september","october", 3. Create a line and dot plot of temperature for the Station_name you picked in 1 with months on x-axis and temperatures on y-axis, color the lines by years (see what happens to the colors when you change years into a factor rather than a numeric data type) 4. Now create a barplot 5. Now filter the long format data frame again to be from 3 different stations and to just the year Take the new data frame from 5 and create a barplot x = months and y = temperature and color the bars by station name (try setting the station names to new custom colors of your choosing, you can use to pick colors) 39