June 1st, 2022
We will work with the iris data set available in R. This data set gives the measurements in centimeters of the variables sepal length, sepal width, petal length and petal width for 50 flowers from each of 3 species of iris. The species are Iris setosa, Iris versicolor, and Iris virginica:
# view the first several rows of the iris data set
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosageom_point() and you will need to specify color = Species inside of aes().# your R code hereSpecies using facet_wrap().# your R code hereThe omp data set contains a subset of DNA microarray data measuring the differential expression of E. coli outer membrane proteins (omp) in nutrient-limited chemostatic cultures. In this particular experiment, the media is glucose-limited. The gene column denotes 8 genes that code for outer membrane proteins, time_min denotes the time point sampled in minutes, and au denotes the change in gene expression detected by the microarray chip (arbitrary units of fluorescence intensity).
# download the `omp` data set
omp <- read_csv("https://rachaelcox.github.io/classes/datasets/ecoli_omp_expression.csv")## Parsed with column specification:
## cols(
## gene = col_character(),
## time_min = col_double(),
## au = col_double()
## )head(omp)## # A tibble: 6 x 3
## gene time_min au
## <chr> <dbl> <dbl>
## 1 ompA 5 -0.32
## 2 ompA 15 0.46
## 3 ompA 30 0.18
## 4 ompA 60 -0.23
## 5 ompC 5 -0.48
## 6 ompC 15 -0.56geom_line(), coloring each line by gene. Notice anything off about this plot?# your R code heresize argument inside of geom_line().scale_color_colorblind() to convert the legend colors to a colorblind-friendly palette.xlab() and ylab to give the figure pretty labels.# your R code hereThe bacteria data set contains data from tests of the presence of the bacterium H. influenzae in children with otitis media in the Northern Territory of Australia. We are interested in two columns of this data set: presence reports the presence (y) or absence (n) of the bacterium, treatment reports the treatment, which was placebo, drug, or drug+ (drug plus high adherence).
# download the `bacteria` data set
bacteria <- read_csv("https://rachaelcox.github.io/classes/datasets/bacteria.csv")## Parsed with column specification:
## cols(
## presence = col_character(),
## ap = col_character(),
## hilo = col_character(),
## week = col_double(),
## ID = col_character(),
## treatment = col_character()
## )head(bacteria)## # A tibble: 6 x 6
## presence ap hilo week ID treatment
## <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 y p hi 0 X01 placebo
## 2 y p hi 2 X01 placebo
## 3 y p hi 4 X01 placebo
## 4 y p hi 11 X01 placebo
## 5 y a hi 0 X02 drug+
## 6 y a hi 2 X02 drug+geom_bar(), make a bar plot with the treatment column on the x-axis, assigning the presence column to the fill argument to visualize the number of patients with and without bacteria for each type of treatment.# your R code hereNotice that, by default, geom_bar() stacks the counts for each class of presence on top of each other. Now:
position='dodge' in geom_bar().scale_fill_brewer() to change the plot colors.# your R code hereposition option in geom_bar() to achieve this effect? Use ?geom_bar to find out.scale_fill_brewer() to use by specifying the type and palette arguments (see ?scale_fill_brewer for details).# your R code hereThe dandelion data set contains RNA-seq reads for a subset of genes differentially expressed in response to five conditions.
# download the dandelion differential expression data set
dandelion <- read_csv("https://rachaelcox.github.io/classes/datasets/dandelion_diffexp_tidy.csv")## Parsed with column specification:
## cols(
## transcript_dandelion = col_character(),
## baseMean = col_double(),
## z_score = col_double(),
## loci_arabidopsis = col_character(),
## protein_annotation = col_character(),
## gene_names_primary = col_character(),
## condition = col_character(),
## log2_foldchange = col_double()
## )head(dandelion)## # A tibble: 6 x 8
## transcript_dand~ baseMean z_score loci_arabidopsis protein_annotat~
## <chr> <dbl> <dbl> <chr> <chr>
## 1 DN42754_c0_g1_i1 167. 1.28 AT5G67060 Transcription f~
## 2 DN42754_c0_g1_i1 167. 1.28 AT5G67060 Transcription f~
## 3 DN42754_c0_g1_i1 167. 1.28 AT5G67060 Transcription f~
## 4 DN42754_c0_g1_i1 167. 1.28 AT5G67060 Transcription f~
## 5 DN42754_c0_g1_i1 167. 1.28 AT5G67060 Transcription f~
## 6 DN42754_c0_g1_i1 167. 1.28 AT5G67060 Transcription f~
## # ... with 3 more variables: gene_names_primary <chr>, condition <chr>,
## # log2_foldchange <dbl>geom_tile where each condition is on the x-axis and each gene (either transcript_dandelion or loci_arabidopsis) is on the y-axis.log2_foldchange column. Use scale_fill_distiller() to specify a continuous diverging color palette.(Note that I have told R Markdown to make a larger figure, by starting the code block with {r fig.height=6, fig.width=10} instead of {r}, because the default figure size is too narrow to show the resulting axes and map.)
# your R code herescale_fill_distiller() (see ?scale_fill_distiller) or you can use a different continuous color function, such as scale_fill_viridis_c() or scale_fill_gradient().# your R code herebiopsy data set, make side-by-side boxplots of clump_thickness for each outcome. The geom you need to use is geom_boxplot().# download the biopsy data set
biopsy <- read_csv("https://rachaelcox.github.io/classes/datasets/biopsy.csv")## Parsed with column specification:
## cols(
## clump_thickness = col_double(),
## uniform_cell_size = col_double(),
## uniform_cell_shape = col_double(),
## marg_adhesion = col_double(),
## epithelial_cell_size = col_double(),
## bare_nuclei = col_double(),
## bland_chromatin = col_double(),
## normal_nucleoli = col_double(),
## mitoses = col_double(),
## outcome = col_character()
## )head(biopsy)## # A tibble: 6 x 10
## clump_thickness uniform_cell_si~ uniform_cell_sh~ marg_adhesion
## <dbl> <dbl> <dbl> <dbl>
## 1 5 1 1 1
## 2 5 4 4 5
## 3 3 1 1 1
## 4 6 8 8 1
## 5 4 1 1 3
## 6 8 10 10 8
## # ... with 6 more variables: epithelial_cell_size <dbl>, bare_nuclei <dbl>,
## # bland_chromatin <dbl>, normal_nucleoli <dbl>, mitoses <dbl>, outcome <chr># your R code heregeom_violin(). How are the violin plots different from the boxplots?# your R code hereclump_thickness distribution for each value of mitoses using the facet_wrap() function.ncol = 3 argument inside of the facet_wrap() function.# your R code hereiris data set, using the default histogram settings. Use geom_histogram().binwidth = or bins =. See ?geom_histogram for more information.# your R code heregeom_density() and fill the area under the curves by species identity.# your R code herealpha argument inside of geom_density(), so the overlap of the various distributions becomes clearly visible.# your R code hereBonus challenge: For the iris data set, make a plot of the 2d distribution of petal length vs. sepal length, by making an x-y plot that shows the individual data points as well as contour lines indicating the density of points in a given spatial region.
# your R code hereNow instead of contour lines, add a fitted straight black line (not a curve, and no confidence band!) to each group of points. You'll need to check ?geom_smooth to see which arguments you'll need to specify.
# your R code hereIn this last example, because we are manually overriding the color of the lines, we need to set the group aesthetic to tell ggplot2 to draw a separate line for each species.