class: center, middle, inverse, title-slide .title[ # Using visualizations in R to explore data ] .subtitle[ ## PSYC 259: Principles of Data Science ] .author[ ### John Franchak ] --- # Example data for today ```r glimpse(ds) ``` ``` Rows: 60 Columns: 44 $ id <dbl> 103, 103, 104, 104, 200, 200, 201, 201, 202, 2… $ condition <chr> "walk", "search", "walk", "search", "walk", "s… $ test_date <dttm> 2019-02-07, 2019-02-07, 2019-02-20, 2019-02-2… $ target <chr> "Diamond", "Diamond", "Rectangle", "Rectangle"… $ age <dbl> 21, 21, 19, 19, 19, 19, 19, 19, 19, 19, 22, 22… $ dob <dttm> 1997-02-24, 1997-02-24, 1999-02-24, 1999-02-2… $ handedness <chr> "Right", "Right", "Right", "Right", "Right", "… $ race <chr> "Other: Mexican", "Other: Mexican", "Asian", "… $ ethnicity_hispanic <chr> "Yes", "Yes", "No", "No", "No", "No", "No", "N… $ sex <chr> "Female", "Female", "Female", "Female", "Male"… $ normal_corrected_vision <chr> "Normal", "Normal", "Normal", "Normal", "Norma… $ temperature <dbl> NA, NA, 75.0, 75.0, 62.0, 62.0, 64.0, 64.0, 64… $ humidity <chr> NA, NA, "0.27", "0.27", "0.22", "0.22", "0.36"… $ len <dbl> 8023, 20152, 7092, 21344, 8606, 22818, 9448, 2… $ gazex_std <dbl> 15.5814, 21.7167, 29.3720, 30.4992, 26.3640, 3… $ gazey_std <dbl> 11.9732, 11.4963, 18.8811, 17.3218, 18.1747, 1… $ posx_mean <dbl> 8.69110, 5.59090, 9.24680, 2.84720, -1.32810, … $ posx_std <dbl> 6.1267, 10.2604, 20.4751, 23.6712, 15.9540, 18… $ posx_speed <dbl> 0.47016, 0.33660, 1.29310, 0.71103, 0.88678, 0… $ posy_mean <dbl> 4.19270, 5.10900, -1.57420, 1.04940, -4.84690,… $ posy_std <dbl> 5.2717, 6.1659, 12.2144, 10.1599, 9.6712, 7.75… $ posy_speed <dbl> 0.46020, 0.27975, 0.69328, 0.38629, 0.64011, 0… $ head_speed <dbl> 0.73548, 0.49373, 1.59650, 0.88373, 1.21290, 0… $ eyex_mean <dbl> -0.79808, -1.03740, -4.69480, -5.02410, 0.1688… $ eyex_std <dbl> 11.4808, 14.7658, 11.3377, 12.8179, 12.8603, 1… $ eyex_speed <dbl> 1.78560, 1.25150, 1.37460, 1.15100, 2.38460, 1… $ eyey_mean <dbl> -9.18720, -6.31460, -6.05730, -5.68080, -7.725… $ eyey_std <dbl> 8.5854, 8.5459, 9.9006, 11.5682, 11.3751, 11.4… $ eyey_speed <dbl> 1.17100, 0.81954, 1.07160, 0.84974, 1.43290, 0… $ eye_speed <dbl> 2.3290, 1.6107, 1.9540, 1.5808, 3.0590, 1.6620… $ eyexpos_corr <dbl> 0.520210, 0.488330, 0.637640, 0.336280, 0.5528… $ eyeypos_corr <dbl> 0.4767800, 0.1972200, 0.4724900, 0.2688200, 0.… $ bothcent <dbl> 0.79421, 0.60521, 0.47272, 0.42697, 0.65243, 0… $ eyecent <dbl> 0.038711, 0.094066, 0.323670, 0.368530, 0.1370… $ headcent <dbl> 0.115120, 0.236910, 0.090148, 0.101910, 0.0898… $ nocent_samedir <dbl> 0.04964700, 0.06257200, 0.09807100, 0.09360500… $ nocent_oppdir <dbl> 0.0023025, 0.0012493, 0.0153980, 0.0089838, 0.… $ path_total <dbl> 315.1412, 367.9581, 328.0324, 466.4539, 324.85… $ path_speed <dbl> 1.17840, 0.54777, 1.38760, 0.65562, 1.13240, 0… $ path_speed_sd <dbl> 0.15728, 0.56357, 0.24984, 0.58113, 0.23351, 0… $ straightness <dbl> 1.3662, 2.0157, 1.3359, 2.0774, 1.4265, 3.2515… $ eyexspeed_corr <dbl> -0.17286000, -0.10476000, -0.04882200, -0.1097… $ headxspeed_corr <dbl> -0.169860, 0.073603, 0.064678, 0.282630, 0.227… $ gazexspeed_corr <dbl> -0.2150700, -0.0406970, 0.0226160, 0.2039900, … ``` --- # Introducing the `ggplot2` package - Parts of a ggplot call: ```r # Parts of a ggplot call ggplot(DATASET, aes(MAPPING STATEMENT)) + geom_TYPE() + geom_TYPE() + ETC... + OTHER_FORMATTING() #axes, labels, legends, themes, etc. ``` - Note that ggplot uses `+`, not `%>%` --- # What type of aesthetic mapping depends on what type of geom .center[] --- # What type of aesthetic mapping depends on what type of geom - `?geom_point` and scroll down to find required aesthetics in bold .center[] --- count: false Single-aesthetic plots .panel1-geoms-rotate[ ```r ggplot(ds, aes(x = posy_std)) + * geom_histogram() + xlab("Average vertical head position") ``` ] .panel2-geoms-rotate[ <!-- --> ] --- count: false Single-aesthetic plots .panel1-geoms-rotate[ ```r ggplot(ds, aes(x = posy_std)) + * geom_density() + xlab("Average vertical head position") ``` ] .panel2-geoms-rotate[ <!-- --> ] --- count: false Single-aesthetic plots .panel1-geoms-rotate[ ```r ggplot(ds, aes(x = posy_std)) + * geom_boxplot() + xlab("Average vertical head position") ``` ] .panel2-geoms-rotate[ <!-- --> ] <style> .panel1-geoms-rotate { color: black; width: 49%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-geoms-rotate { color: black; width: 49%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-geoms-rotate { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- count: false Add a category .panel1-categorical-rotate[ ```r ggplot(ds) + *geom_boxplot(aes(y = posy_std)) ``` ] .panel2-categorical-rotate[ <!-- --> ] --- count: false Add a category .panel1-categorical-rotate[ ```r ggplot(ds) + *geom_boxplot(aes(x = condition, y = posy_std)) ``` ] .panel2-categorical-rotate[ <!-- --> ] --- count: false Add a category .panel1-categorical-rotate[ ```r ggplot(ds) + *geom_boxplot(aes(x = condition, y = posy_std, fill = condition)) ``` ] .panel2-categorical-rotate[ <!-- --> ] --- count: false Add a category .panel1-categorical-rotate[ ```r ggplot(ds) + *geom_boxplot(aes(x = condition, y = posy_std, fill = target)) ``` ] .panel2-categorical-rotate[ <!-- --> ] <style> .panel1-categorical-rotate { color: black; width: 65.3333333333333%; hight: 32%; float: none; padding-left: 1%; font-size: 80% } .panel2-categorical-rotate { color: black; width: 32.6666666666667%; hight: 32%; float: none; padding-left: 1%; font-size: 80% } .panel3-categorical-rotate { color: black; width: NA%; hight: 33%; float: none; padding-left: 1%; font-size: 80% } </style> --- count: false Simple scatterplot .panel1-scatterplot-auto[ ```r *ggplot(ds, aes(x = posx_mean, y = posy_mean)) ``` ] .panel2-scatterplot-auto[ <!-- --> ] --- count: false Simple scatterplot .panel1-scatterplot-auto[ ```r ggplot(ds, aes(x = posx_mean, y = posy_mean)) + * geom_point() ``` ] .panel2-scatterplot-auto[ <!-- --> ] --- count: false Simple scatterplot .panel1-scatterplot-auto[ ```r ggplot(ds, aes(x = posx_mean, y = posy_mean)) + geom_point() + * xlim(-20, 20) ``` ] .panel2-scatterplot-auto[ <!-- --> ] --- count: false Simple scatterplot .panel1-scatterplot-auto[ ```r ggplot(ds, aes(x = posx_mean, y = posy_mean)) + geom_point() + xlim(-20, 20) + * ylim(-20, 20) ``` ] .panel2-scatterplot-auto[ <!-- --> ] --- count: false Simple scatterplot .panel1-scatterplot-auto[ ```r ggplot(ds, aes(x = posx_mean, y = posy_mean)) + geom_point() + xlim(-20, 20) + ylim(-20, 20) + * xlab("Mean horizontal head position") ``` ] .panel2-scatterplot-auto[ <!-- --> ] --- count: false Simple scatterplot .panel1-scatterplot-auto[ ```r ggplot(ds, aes(x = posx_mean, y = posy_mean)) + geom_point() + xlim(-20, 20) + ylim(-20, 20) + xlab("Mean horizontal head position") + * ylab("Mean vertical head position") ``` ] .panel2-scatterplot-auto[ <!-- --> ] --- count: false Simple scatterplot .panel1-scatterplot-auto[ ```r ggplot(ds, aes(x = posx_mean, y = posy_mean)) + geom_point() + xlim(-20, 20) + ylim(-20, 20) + xlab("Mean horizontal head position") + ylab("Mean vertical head position") + * geom_vline(xintercept = 0) ``` ] .panel2-scatterplot-auto[ <!-- --> ] --- count: false Simple scatterplot .panel1-scatterplot-auto[ ```r ggplot(ds, aes(x = posx_mean, y = posy_mean)) + geom_point() + xlim(-20, 20) + ylim(-20, 20) + xlab("Mean horizontal head position") + ylab("Mean vertical head position") + geom_vline(xintercept = 0) + * geom_hline(yintercept = 0) ``` ] .panel2-scatterplot-auto[ <!-- --> ] <style> .panel1-scatterplot-auto { color: black; width: 49%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-scatterplot-auto { color: black; width: 49%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-scatterplot-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- count: false Add category to scatterplot .panel1-scatter-category-auto[ ```r *ggplot(ds, aes(x = posx_mean, y = posy_mean, color = condition)) ``` ] .panel2-scatter-category-auto[ <!-- --> ] --- count: false Add category to scatterplot .panel1-scatter-category-auto[ ```r ggplot(ds, aes(x = posx_mean, y = posy_mean, color = condition)) + * geom_point() ``` ] .panel2-scatter-category-auto[ <!-- --> ] <style> .panel1-scatter-category-auto { color: black; width: 58.8%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-scatter-category-auto { color: black; width: 39.2%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-scatter-category-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- count: false Facetting a scatterplot .panel1-facet-auto[ ```r *ggplot(ds, aes(x = posx_mean, y = posy_mean)) ``` ] .panel2-facet-auto[ <!-- --> ] --- count: false Facetting a scatterplot .panel1-facet-auto[ ```r ggplot(ds, aes(x = posx_mean, y = posy_mean)) + * geom_point() ``` ] .panel2-facet-auto[ <!-- --> ] --- count: false Facetting a scatterplot .panel1-facet-auto[ ```r ggplot(ds, aes(x = posx_mean, y = posy_mean)) + geom_point() + * facet_wrap("condition", ncol = 1) ``` ] .panel2-facet-auto[ <!-- --> ] <style> .panel1-facet-auto { color: black; width: 58.8%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-facet-auto { color: black; width: 39.2%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-facet-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- count: false Saving plots .panel1-saving-auto[ ```r *ggplot(ds, aes(x = posx_mean, y = posy_mean)) + geom_point() ``` ] .panel2-saving-auto[ <!-- --> ] --- count: false Saving plots .panel1-saving-auto[ ```r ggplot(ds, aes(x = posx_mean, y = posy_mean)) + geom_point() #ggsave will save the last plot to file *ggsave("eda/head-position-scatter.jpg") ``` ] .panel2-saving-auto[ <!-- --> ] --- count: false Saving plots .panel1-saving-auto[ ```r ggplot(ds, aes(x = posx_mean, y = posy_mean)) + geom_point() #ggsave will save the last plot to file ggsave("eda/head-position-scatter.jpg") *#You can also save plots to objects (they are lists) ou can also save plots to objects (they are lists) ``` ] .panel2-saving-auto[ <!-- --> ] --- count: false Saving plots .panel1-saving-auto[ ```r ggplot(ds, aes(x = posx_mean, y = posy_mean)) + geom_point() #ggsave will save the last plot to file ggsave("eda/head-position-scatter.jpg") #You can also save plots to objects (they are lists) ou can also save plots to objects (they are lists) *p1 <- ggplot(ds, aes(x = posx_mean, y = posy_mean)) + geom_point() ``` ] .panel2-saving-auto[ <!-- --> ] --- count: false Saving plots .panel1-saving-auto[ ```r ggplot(ds, aes(x = posx_mean, y = posy_mean)) + geom_point() #ggsave will save the last plot to file ggsave("eda/head-position-scatter.jpg") #You can also save plots to objects (they are lists) ou can also save plots to objects (they are lists) p1 <- ggplot(ds, aes(x = posx_mean, y = posy_mean)) + geom_point() *ggsave("eda/head-position-scatter.jpg", plot = p1) ``` ] .panel2-saving-auto[ <!-- --> ] <style> .panel1-saving-auto { color: black; width: 58.8%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-saving-auto { color: black; width: 39.2%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-saving-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- count: false Raw Data and Summaries .panel1-summaries-auto[ ```r *ggplot(ds, aes(x = condition, y = gazey_std)) ``` ] .panel2-summaries-auto[ <!-- --> ] --- count: false Raw Data and Summaries .panel1-summaries-auto[ ```r ggplot(ds, aes(x = condition, y = gazey_std)) + * geom_sina(scale = "width", maxwidth = .25, size = 3, alpha = .75, color = "gray") ``` ] .panel2-summaries-auto[ <!-- --> ] --- count: false Raw Data and Summaries .panel1-summaries-auto[ ```r ggplot(ds, aes(x = condition, y = gazey_std)) + geom_sina(scale = "width", maxwidth = .25, size = 3, alpha = .75, color = "gray") + * stat_summary(color = "black", geom = "errorbar", linewidth = .75, width = .2) ``` ] .panel2-summaries-auto[ <!-- --> ] --- count: false Raw Data and Summaries .panel1-summaries-auto[ ```r ggplot(ds, aes(x = condition, y = gazey_std)) + geom_sina(scale = "width", maxwidth = .25, size = 3, alpha = .75, color = "gray") + stat_summary(color = "black", geom = "errorbar", linewidth = .75, width = .2) + * coord_cartesian(ylim = c(0,30)) ``` ] .panel2-summaries-auto[ <!-- --> ] <style> .panel1-summaries-auto { color: black; width: 65.3333333333333%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-summaries-auto { color: black; width: 32.6666666666667%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-summaries-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- count: false Improving Aesthetics with Theme .panel1-theme-auto[ ```r *theme_update(text = element_text(size = 12), * axis.text.x = element_text(size = 12, color = "black"), * axis.title.x = element_text(size = 14), * axis.text.y = element_text(size = 12, color = "black"), * axis.title.y = element_text(size = 14), * panel.background = element_blank(),panel.border = element_blank(), * panel.grid.major = element_blank(), * panel.grid.minor = element_blank(), axis.line = element_blank(), * axis.ticks.length=unit(.25, "cm"), * legend.key = element_rect(fill = "white")) ``` ] .panel2-theme-auto[ ] --- count: false Improving Aesthetics with Theme .panel1-theme-auto[ ```r theme_update(text = element_text(size = 12), axis.text.x = element_text(size = 12, color = "black"), axis.title.x = element_text(size = 14), axis.text.y = element_text(size = 12, color = "black"), axis.title.y = element_text(size = 14), panel.background = element_blank(),panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_blank(), axis.ticks.length=unit(.25, "cm"), legend.key = element_rect(fill = "white")) *ggplot(ds, aes(x = condition, y = gazey_std)) ``` ] .panel2-theme-auto[ <!-- --> ] --- count: false Improving Aesthetics with Theme .panel1-theme-auto[ ```r theme_update(text = element_text(size = 12), axis.text.x = element_text(size = 12, color = "black"), axis.title.x = element_text(size = 14), axis.text.y = element_text(size = 12, color = "black"), axis.title.y = element_text(size = 14), panel.background = element_blank(),panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_blank(), axis.ticks.length=unit(.25, "cm"), legend.key = element_rect(fill = "white")) ggplot(ds, aes(x = condition, y = gazey_std)) + * geom_sina(scale = "width", maxwidth = .25, size = 3, alpha = .75, color = "gray") ``` ] .panel2-theme-auto[ <!-- --> ] --- count: false Improving Aesthetics with Theme .panel1-theme-auto[ ```r theme_update(text = element_text(size = 12), axis.text.x = element_text(size = 12, color = "black"), axis.title.x = element_text(size = 14), axis.text.y = element_text(size = 12, color = "black"), axis.title.y = element_text(size = 14), panel.background = element_blank(),panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_blank(), axis.ticks.length=unit(.25, "cm"), legend.key = element_rect(fill = "white")) ggplot(ds, aes(x = condition, y = gazey_std)) + geom_sina(scale = "width", maxwidth = .25, size = 3, alpha = .75, color = "gray") + * stat_summary(color = "black", geom = "errorbar", linewidth = .75, width = .2) ``` ] .panel2-theme-auto[ <!-- --> ] --- count: false Improving Aesthetics with Theme .panel1-theme-auto[ ```r theme_update(text = element_text(size = 12), axis.text.x = element_text(size = 12, color = "black"), axis.title.x = element_text(size = 14), axis.text.y = element_text(size = 12, color = "black"), axis.title.y = element_text(size = 14), panel.background = element_blank(),panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_blank(), axis.ticks.length=unit(.25, "cm"), legend.key = element_rect(fill = "white")) ggplot(ds, aes(x = condition, y = gazey_std)) + geom_sina(scale = "width", maxwidth = .25, size = 3, alpha = .75, color = "gray") + stat_summary(color = "black", geom = "errorbar", linewidth = .75, width = .2) + * coord_cartesian(ylim = c(0,30)) ``` ] .panel2-theme-auto[ <!-- --> ] <style> .panel1-theme-auto { color: black; 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