library(tidyverse)set.seed(98735)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_dataggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -1.64 -0.530 2 -0.107 -0.744 3 0.185 0.476 4 1.36 1.32 5 0.210 -0.0852 6 -0.658 -1.36 7 1.53 -1.18 8 1.40 -0.867 9 1.54 0.115 10 0.131 0.960 # … with 40 more rows
library(tidyverse)set.seed(98735)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_dataggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8)cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -1.64 -0.530 2 -0.107 -0.744 3 0.185 0.476 4 1.36 1.32 5 0.210 -0.0852 6 -0.658 -1.36 7 1.53 -1.18 8 1.40 -0.867 9 1.54 0.115 10 0.131 0.960 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = -0.14569, df = 48, p-value = 0.8848alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.2976302 0.2588382sample estimates: cor -0.02102428library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 1.14 0.271 2 0.252 0.359 3 1.27 0.704 4 -0.332 -3.60 5 -0.555 0.0577 6 0.632 -0.812 7 0.202 -0.206 8 -0.819 -0.866 9 1.08 0.319 10 0.403 -1.62 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = 0.15018, df = 48, p-value = 0.8812alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.2582334 0.2982208sample estimates: cor 0.02167209library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 0.555 0.689 2 1.37 0.408 3 -1.20 0.0598 4 -0.824 0.0650 5 0.398 0.109 6 -0.713 -0.918 7 0.267 0.293 8 0.0750 0.308 9 0.231 -0.183 10 -0.837 0.868 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = -0.53715, df = 48, p-value = 0.5936alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.3481556 0.2054699sample estimates: cor -0.07729874library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -1.93 0.591 2 1.37 1.60 3 -0.728 0.625 4 -0.0645 -0.812 5 0.297 -0.337 6 -0.912 -1.63 7 -1.13 -1.09 8 -2.04 -0.756 9 -1.32 1.28 10 0.409 2.17 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = 1.073, df = 48, p-value = 0.2886alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.1308697 0.4137734sample estimates: cor 0.1530533library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -0.231 0.291 2 -0.575 0.113 3 0.251 -1.28 4 -1.38 -3.90 5 0.129 0.405 6 0.677 -1.29 7 -0.532 -1.13 8 -0.699 -0.00250 9 -1.51 -0.845 10 0.992 -0.560 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = 2.5832, df = 48, p-value = 0.01289alternative hypothesis: true correlation is not equal to 095 percent confidence interval: 0.07865544 0.57207274sample estimates: cor 0.3493545library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -1.25 0.429 2 0.754 0.143 3 -0.0323 -0.173 4 0.353 -0.931 5 0.872 0.226 6 -0.963 0.293 7 0.178 0.890 8 0.694 -2.32 9 -0.717 0.47010 0.866 -1.29 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = 0.3823, df = 48, p-value = 0.7039alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.2267286 0.3284075sample estimates: cor 0.05509622library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -0.390 0.383 2 -0.577 2.63 3 0.639 0.0420 4 -0.921 0.0238 5 -0.195 1.01 6 0.934 0.209 7 -1.55 0.261 8 -1.05 1.04 9 1.32 0.478 10 -1.74 -1.21 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = -0.83665, df = 48, p-value = 0.4069alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.3853765 0.1639290sample estimates: cor -0.1198892library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -1.29 1.83 2 -0.538 -0.658 3 -0.439 0.410 4 0.884 0.383 5 -0.826 0.593 6 -0.645 -0.643 7 -0.277 0.526 8 1.33 -0.0301 9 -1.26 2.05 10 -0.329 -1.18 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = -0.43527, df = 48, p-value = 0.6653alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.3351996 0.2194761sample estimates: cor -0.06270212library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 1.73 -0.403 2 0.769 0.460 3 0.625 -1.42 4 0.251 0.940 5 -0.139 -0.347 6 1.85 -1.04 7 -0.538 0.448 8 1.14 0.727 9 -0.00658 -2.39 10 0.226 0.783# … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = -0.95076, df = 48, p-value = 0.3465alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.3991969 0.1479922sample estimates: cor -0.135956library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -1.36 0.237 2 -0.674 -1.29 3 0.820 0.439 4 0.126 -1.36 5 0.531 -0.706 6 -0.255 -0.168 7 0.620 -0.152 8 0.0415 -0.0826 9 0.977 -1.27 10 1.88 1.38 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = -0.038026, df = 48, p-value = 0.9698alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.2834033 0.2732767sample estimates: cor -0.005488532library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -0.785 0.111 2 0.674 1.56 3 0.0216 0.976 4 0.223 1.04 5 -0.575 -0.0359 6 0.796 -1.27 7 -2.24 -0.921 8 0.817 1.73 9 -2.11 2.84 10 0.453 -1.24 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = 0.53597, df = 48, p-value = 0.5945alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.2056319 0.3480069sample estimates: cor 0.07713057library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 0.631 -0.544 2 0.236 -1.47 3 -0.583 1.90 4 -0.148 -0.00957 5 -0.616 1.11 6 1.08 -1.39 7 0.325 0.557 8 -1.24 0.323 9 -0.0400 -0.637 10 -0.726 1.64 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = -1.9265, df = 48, p-value = 0.05998alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.50834031 0.01129057sample estimates: cor -0.2678991library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -0.470 -0.470 2 -1.46 -0.600 3 -0.134 -0.0555 4 -1.39 0.183 5 0.874 -1.34 6 -1.12 -1.46 7 -0.208 1.53 8 -1.01 -0.175 9 -0.209 0.402 10 -0.222 -0.242 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = 1.3943, df = 48, p-value = 0.1697alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.08576768 0.45087766sample estimates: cor 0.19729library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -1.55 1.33 2 0.320 0.882 3 -0.0949 0.483 4 0.756 -1.69 5 0.804 -0.390 6 -0.251 1.00 7 -1.86 0.358 8 1.06 -0.00587 9 0.912 -0.265 10 -2.25 -0.117 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = 0.52319, df = 48, p-value = 0.6032alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.2073927 0.3463893sample estimates: cor 0.07530195library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -0.491 0.554 2 -0.351 -0.724 3 0.995 -0.559 4 -1.80 0.949 5 -0.798 0.337 6 0.582 -0.0307 7 -0.415 -0.574 8 -1.07 1.24 9 1.13 -0.163 10 -0.788 0.201 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = 1.5728, df = 48, p-value = 0.1223alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.06071377 0.47071754sample estimates: cor 0.2213751library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -0.745 0.0542 2 -0.625 -0.667 3 0.343 -0.405 4 -0.672 1.08 5 0.631 -0.909 6 -0.312 -0.718 7 0.796 -0.437 8 0.712 0.141 9 -1.28 0.238 10 0.181 0.0345# … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = -0.43976, df = 48, p-value = 0.6621alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.3357731 0.2188609sample estimates: cor -0.06334581library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -1.24 0.123 2 -1.61 0.382 3 0.511 0.507 4 -0.593 0.637 5 -1.15 -0.474 6 0.228 1.22 7 -0.232 -0.509 8 -0.102 -0.244 9 0.367 -1.80 10 -0.300 0.101# … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = 0.25132, df = 48, p-value = 0.8026alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.2445639 0.3114566sample estimates: cor 0.03625162library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 1.33 1.17 2 1.09 0.724 3 -0.651 -1.40 4 -1.05 1.61 5 -0.334 -1.50 6 -0.216 -2.62 7 0.839 -0.204 8 -1.34 2.19 9 -0.578 0.49010 -1.42 -0.466# … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = -0.22808, df = 48, p-value = 0.8206alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.3084259 0.2477135sample estimates: cor -0.03290287library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -2.18 0.751 2 -0.254 0.185 3 -0.484 0.471 4 0.742 -0.597 5 1.45 0.672 6 1.56 1.18 7 1.34 2.17 8 2.67 -0.410 9 0.877 0.26610 0.0153 1.09 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = -0.50468, df = 48, p-value = 0.6161alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.3440425 0.2099411sample estimates: cor -0.07265216library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -0.145 -0.873 2 2.29 0.622 3 -1.47 -0.162 4 -0.285 -1.73 5 -0.598 -0.736 6 0.782 0.662 7 0.102 -0.312 8 0.970 -1.42 9 0.422 0.37010 -1.24 2.63 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = -0.78761, df = 48, p-value = 0.4348alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.3793744 0.1707621sample estimates: cor -0.1129545library(tidyverse)tibble(tail_length = rnorm(n = 50), brain_weight = rnorm(n = 50)) ->my_data; my_data#############ggplot(data = my_data) + aes(x = tail_length) + aes(y = brain_weight) + geom_point(color = "steelblue", alpha = .8) + theme(panel.background = element_rect(color = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, "red", "black"), size = ifelse(cor.test(my_data$tail_length, my_data$brain_weight)[[3]]<.05, 8, 3)))cor.test(x = my_data$tail_length, y = my_data$brain_weight)# A tibble: 50 x 2 tail_length brain_weight <dbl> <dbl> 1 -0.0717 -0.736 2 0.724 0.141 3 -0.562 -1.57 4 -0.189 0.808 5 0.957 -0.553 6 -0.489 -0.244 7 1.09 0.141 8 0.437 -0.872 9 -0.0162 0.042210 -2.04 -0.420 # … with 40 more rows
Pearson's product-moment correlationdata: my_data$tail_length and my_data$brain_weightt = -1.4716, df = 48, p-value = 0.1477alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.45954213 0.07490899sample estimates: cor -0.2077709tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 female 6.05 2 female 5.76 3 female 5.18 4 female 4.40 5 female 4.66 6 male 6.07 7 female 6.66 8 female 5.78 9 female 6.3910 male 5.47# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = 0.17273, df = 46.923, p-value = 0.8636alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.277471 0.329592sample estimates:mean in group female mean in group male 5.569354 5.543294tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 female 4.80 2 male 5.75 3 male 5.35 4 female 5.43 5 male 5.07 6 male 5.88 7 male 5.00 8 female 4.07 9 male 5.8310 female 6.18# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -0.92503, df = 47.468, p-value = 0.3596alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.4019456 0.1486889sample estimates:mean in group female mean in group male 5.414265 5.540893tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 female 5.00 2 female 5.09 3 female 5.05 4 female 5.11 5 male 4.95 6 male 5.83 7 female 5.28 8 female 5.16 9 male 5.4210 male 5.52# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -0.69666, df = 46.762, p-value = 0.4895alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.3692719 0.1793219sample estimates:mean in group female mean in group male 5.399995 5.494970tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 female 6.40 2 male 5.95 3 male 6.93 4 female 5.58 5 female 5.68 6 female 5.46 7 female 5.37 8 male 5.43 9 male 5.8610 female 5.05# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -1.4178, df = 35.013, p-value = 0.1651alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.48788008 0.08663595sample estimates:mean in group female mean in group male 5.428950 5.629572tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 female 5.04 2 female 5.91 3 male 5.05 4 female 6.51 5 male 5.42 6 male 5.58 7 female 5.67 8 female 5.10 9 male 5.6810 female 5.52# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = 2.6639, df = 40.441, p-value = 0.01104alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: 0.08008769 0.58298641sample estimates:mean in group female mean in group male 5.671535 5.339998tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 male 5.84 2 male 6.31 3 female 5.65 4 male 5.29 5 male 5.80 6 male 5.84 7 female 5.72 8 male 5.46 9 female 5.5010 male 5.72# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -1.0138, df = 47.933, p-value = 0.3158alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.4228784 0.1393819sample estimates:mean in group female mean in group male 5.469518 5.611266tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 male 5.01 2 female 5.68 3 female 5.44 4 female 5.10 5 female 5.81 6 male 5.19 7 male 5.12 8 male 6.21 9 male 5.5610 male 5.26# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -1.0251, df = 47.828, p-value = 0.3105alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.4501601 0.1461517sample estimates:mean in group female mean in group male 5.567211 5.719215tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 female 5.66 2 female 5.72 3 female 5.63 4 female 5.48 5 male 5.47 6 male 5.89 7 male 6.00 8 female 5.57 9 male 5.4110 male 4.77# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -1.58, df = 47.842, p-value = 0.1207alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.55526540 0.06662354sample estimates:mean in group female mean in group male 5.347316 5.591637tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 female 4.23 2 male 5.99 3 male 5.43 4 female 5.40 5 male 6.16 6 male 5.97 7 female 5.78 8 female 6.03 9 female 5.5210 female 5.15# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -0.80309, df = 41.183, p-value = 0.4265alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.4026129 0.1734895sample estimates:mean in group female mean in group male 5.387418 5.501980tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 female 6.20 2 male 5.04 3 female 5.22 4 female 5.69 5 female 5.97 6 female 5.64 7 male 5.48 8 female 5.64 9 male 5.3310 female 5.83# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -0.19366, df = 47.782, p-value = 0.8473alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.3081422 0.2540043sample estimates:mean in group female mean in group male 5.500872 5.527941tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 female 5.02 2 male 6.63 3 male 5.35 4 male 5.46 5 male 5.53 6 female 5.55 7 male 6.33 8 female 5.02 9 male 5.4910 male 6.09# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -2.0574, df = 47.703, p-value = 0.04513alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.569594465 -0.006505921sample estimates:mean in group female mean in group male 5.491601 5.779651tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 female 5.47 2 female 5.71 3 male 6.34 4 male 5.81 5 male 4.99 6 female 4.89 7 female 6.02 8 male 5.77 9 male 5.4710 male 5.42# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -0.025281, df = 48, p-value = 0.9799alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.2517547 0.2455024sample estimates:mean in group female mean in group male 5.495759 5.498885tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 female 5.76 2 female 4.89 3 female 5.64 4 male 5.47 5 female 6.03 6 male 6.47 7 female 5.97 8 female 5.50 9 male 6.2010 male 5.62# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -0.81926, df = 35.107, p-value = 0.4182alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.3814890 0.1620987sample estimates:mean in group female mean in group male 5.550585 5.660281tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 male 5.74 2 female 4.58 3 male 5.47 4 male 5.26 5 male 5.01 6 male 4.95 7 female 5.86 8 male 5.98 9 female 5.5110 male 6.12# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -0.39696, df = 47.977, p-value = 0.6932alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.3193852 0.2140676sample estimates:mean in group female mean in group male 5.374303 5.426962tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 male 4.97 2 male 5.02 3 female 4.54 4 female 5.16 5 female 4.75 6 female 5.71 7 male 5.69 8 male 6.76 9 male 5.1010 male 5.73# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -0.28826, df = 39.644, p-value = 0.7747alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.3477257 0.2609390sample estimates:mean in group female mean in group male 5.460529 5.503923tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 female 5.11 2 male 6.06 3 female 5.21 4 female 4.66 5 male 5.61 6 male 5.05 7 male 5.36 8 male 5.47 9 male 5.4310 male 5.42# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = 0.36447, df = 47.974, p-value = 0.7171alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.2070206 0.2986901sample estimates:mean in group female mean in group male 5.447395 5.401561tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 male 6.16 2 female 5.33 3 male 6.05 4 female 5.52 5 female 5.36 6 male 4.47 7 male 5.40 8 female 5.78 9 female 5.3810 male 4.96# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = 0.43687, df = 37.401, p-value = 0.6647alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.2420049 0.3751106sample estimates:mean in group female mean in group male 5.467101 5.400549tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 male 5.36 2 male 4.54 3 male 5.56 4 male 5.19 5 female 5.13 6 male 5.08 7 male 6.45 8 male 4.87 9 male 4.9310 female 5.59# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = 0.54448, df = 36.519, p-value = 0.5894alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.2020496 0.3504542sample estimates:mean in group female mean in group male 5.442526 5.368323tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 male 5.44 2 male 5.01 3 female 5.36 4 female 5.22 5 female 5.10 6 female 5.04 7 female 6.61 8 male 6.52 9 female 5.3610 female 5.35# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -1.5886, df = 43.93, p-value = 0.1193alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.51175262 0.06060527sample estimates:mean in group female mean in group male 5.504991 5.730564tibble(sex = sample(x = c("male","female"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50, sd = .5) + 5.5) ->height_data; height_data################## visualizationggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)# statistical testt.test(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 female 5.18 2 female 5.06 3 male 5.97 4 male 4.80 5 female 5.91 6 male 5.65 7 male 4.61 8 female 5.30 9 female 5.1710 female 5.36# … with 40 more rows
Welch Two Sample t-testdata: height_data$height by height_data$sext = -1.6498, df = 45.988, p-value = 0.1058alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -0.45846363 0.04544978sample estimates:mean in group female mean in group male 5.348123 5.554630tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 2nd 0.0646 2 3rd -0.280 3 3rd 1.01 4 2nd 1.21 5 3rd 0.358 6 1st 0.483 7 2nd 0.420 8 Crew 0.739 9 3rd -1.01 10 3rd 0.688 # … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st -0.1690278 -1.6428611 1.3048055 0.98995353rd-1st 0.1520384 -1.3724568 1.6765336 0.9933337Crew-1st -0.2781325 -1.7785234 1.2222585 0.96001033rd-2nd 0.3210662 -0.6493905 1.2915229 0.8142269Crew-2nd -0.1091047 -1.0412387 0.8230293 0.9893361Crew-3rd -0.4301709 -1.4405049 0.5801631 0.6701215tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 Crew 0.184 2 2nd -0.549 3 Crew 1.10 4 1st -1.08 5 Crew -0.236 6 3rd 0.260 7 2nd 0.336 8 Crew 1.44 9 2nd 0.86010 1st -1.20 # … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st 0.4340208 -0.6991114 1.5671529 0.73809643rd-1st 0.9033191 -0.1924056 1.9990438 0.1391734Crew-1st 0.6144885 -0.4812362 1.7102132 0.44883953rd-2nd 0.4692983 -0.5718003 1.5103970 0.6290138Crew-2nd 0.1804677 -0.8606310 1.2215664 0.9669081Crew-3rd -0.2888306 -1.2890859 0.7114246 0.8676859tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 Crew -1.79 2 Crew -0.276 3 2nd 0.341 4 3rd 1.48 5 3rd -0.942 6 3rd -2.00 7 2nd -0.0154 8 2nd 0.243 9 Crew 0.127 10 Crew 0.291 # … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st 0.02417633 -1.087864 1.1362170 0.99992913rd-1st -0.21625886 -1.373707 0.9411898 0.9591147Crew-1st -0.61673597 -1.834915 0.6014435 0.53689093rd-2nd -0.24043519 -1.397884 0.9170134 0.9450743Crew-2nd -0.64091230 -1.859092 0.5772672 0.5043507Crew-3rd -0.40047711 -1.660245 0.8592904 0.8315205tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 3rd 1.00 2 3rd 0.506 3 2nd 0.321 4 3rd -1.49 5 Crew 0.676 6 Crew -0.890 7 3rd -0.247 8 2nd 0.278 9 3rd 0.29910 2nd 0.243# … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st 0.31775146 -1.0543358 1.689839 0.92604813rd-1st 0.24293466 -0.9923450 1.478214 0.9528147Crew-1st 0.51033616 -0.8617511 1.882423 0.75501743rd-2nd -0.07481679 -1.2313865 1.081753 0.9981509Crew-2nd 0.19258470 -1.1090916 1.494261 0.9789518Crew-3rd 0.26740149 -0.8891682 1.423971 0.9263765tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 1st -0.508 2 3rd -0.794 3 2nd 0.469 4 2nd -1.14 5 Crew 0.975 6 2nd 0.762 7 3rd -1.69 8 Crew 0.0958 9 Crew -1.93 10 3rd 0.190 # … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st 0.06671792 -1.0922126 1.2256484 0.99869353rd-1st -0.08218738 -1.2549426 1.0905678 0.9976538Crew-1st 0.46171866 -0.8162617 1.7396990 0.77093043rd-2nd -0.14890529 -1.1485458 0.8507352 0.9785367Crew-2nd 0.39500074 -0.7262308 1.5162323 0.7840762Crew-3rd 0.54390603 -0.5916093 1.6794214 0.5821262tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 2nd -0.569 2 2nd 0.361 3 1st -0.292 4 Crew -0.483 5 2nd -0.160 6 3rd 1.58 7 2nd 0.301 8 3rd 0.224 9 2nd -0.42710 2nd -0.445# … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st -0.4976309 -1.4783810 0.4831193 0.53504143rd-1st -0.3340012 -1.3719295 0.7039272 0.8263871Crew-1st -0.1880390 -1.2087153 0.8326373 0.96069993rd-2nd 0.1636297 -0.7352426 1.0625020 0.9620011Crew-2nd 0.3095919 -0.5693030 1.1884868 0.7841348Crew-3rd 0.1459622 -0.7963121 1.0882364 0.9759842tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 Crew -0.0726 2 3rd -1.14 3 1st 0.540 4 3rd 0.0692 5 1st -0.481 6 3rd 1.32 7 2nd 0.153 8 3rd 0.197 9 2nd 0.120 10 2nd -0.0825# … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st -0.73135897 -1.5974410 0.1347231 0.12493953rd-1st -0.28882958 -1.2317003 0.6540411 0.8463537Crew-1st -0.03505996 -0.8660633 0.7959434 0.99948353rd-2nd 0.44252939 -0.5435484 1.4286072 0.6323153Crew-2nd 0.69629901 -0.1834230 1.5760210 0.1652531Crew-3rd 0.25376962 -0.7016454 1.2091846 0.8934204tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 Crew -0.264 2 2nd -0.280 3 1st -0.191 4 3rd -0.154 5 3rd -0.902 6 3rd -1.61 7 3rd -1.01 8 2nd 0.315 9 3rd 0.76210 1st 0.612# … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st 0.16275305 -0.9710578 1.2965639 0.98070773rd-1st -0.65568092 -1.7682972 0.4569353 0.4050145Crew-1st -0.02476586 -1.2824543 1.2329226 0.99994733rd-2nd -0.81843397 -1.9522448 0.3153769 0.2323237Crew-2nd -0.18751891 -1.4639954 1.0889576 0.9793780Crew-3rd 0.63091506 -0.6267734 1.8886035 0.5445379tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 Crew -0.178 2 1st 0.332 3 3rd -0.350 4 1st -0.0232 5 1st -0.731 6 Crew -1.50 7 2nd -1.15 8 3rd 0.671 9 1st -1.18 10 3rd 0.281 # … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st 0.08690935 -0.9197195 1.0935382 0.99564443rd-1st 0.11918501 -0.8874438 1.1258139 0.9889722Crew-1st -0.02914881 -1.1112423 1.0529447 0.99986523rd-2nd 0.03227565 -0.9928250 1.0573763 0.9997849Crew-2nd -0.11605816 -1.2153561 0.9832398 0.9921183Crew-3rd -0.14833382 -1.2476318 0.9509642 0.9838674tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 Crew -0.709 2 3rd 1.27 3 Crew -1.57 4 2nd -0.763 5 1st -0.0208 6 1st -0.784 7 3rd 0.644 8 3rd 1.23 9 2nd 0.224 10 Crew 0.623 # … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st -0.72308614 -1.792492 0.3463200 0.28538793rd-1st -0.64836299 -1.780116 0.4833901 0.4300447Crew-1st -0.88161058 -1.951017 0.1877955 0.13918133rd-2nd 0.07472315 -0.994683 1.1441293 0.9976743Crew-2nd -0.15852444 -1.161716 0.8446674 0.9745747Crew-3rd -0.23324759 -1.302654 0.8361585 0.9371878tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 Crew -1.17 2 1st 2.08 3 3rd -0.628 4 1st -0.872 5 3rd 0.537 6 1st -2.35 7 3rd 1.02 8 2nd -1.97 9 Crew 1.31 10 1st 0.931# … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st -0.08727205 -1.1507634 0.9762194 0.99625233rd-1st -0.16521339 -1.2287048 0.8982780 0.9757855Crew-1st 0.17966865 -0.8179747 1.1773120 0.96313473rd-2nd -0.07794134 -1.2034348 1.0475522 0.9977354Crew-2nd 0.26694070 -0.7965507 1.3304321 0.9082050Crew-3rd 0.34488204 -0.7186094 1.4083734 0.8230653tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 Crew 0.433 2 2nd -0.0234 3 2nd -1.69 4 1st 0.0612 5 1st 0.266 6 1st -0.403 7 3rd -0.762 8 Crew -1.09 9 3rd -0.079910 1st -0.412 # … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st -0.49469057 -1.2611645 0.2717834 0.32507373rd-1st -0.03844713 -0.9414338 0.8645395 0.9994692Crew-1st -0.15392662 -0.9355000 0.6276467 0.95262673rd-2nd 0.45624344 -0.4578916 1.3703785 0.5487530Crew-2nd 0.34076396 -0.4536635 1.1351914 0.6649455Crew-3rd -0.11547948 -1.0423115 0.8113525 0.9872030tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 2nd -0.793 2 3rd 0.574 3 1st 0.479 4 3rd -1.03 5 Crew 0.484 6 2nd 0.562 7 1st -0.0160 8 3rd 2.51 9 1st 0.071510 3rd 1.17 # … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st 0.06107997 -1.0764645 1.1986244 0.99893863rd-1st -0.06861325 -1.1088647 0.9716382 0.9980408Crew-1st 0.49735172 -0.6027324 1.5974358 0.62680923rd-2nd -0.12969321 -1.0666063 0.8072199 0.9826290Crew-2nd 0.43627175 -0.5666580 1.4392015 0.6550294Crew-3rd 0.56596497 -0.3250927 1.4570227 0.3389832tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 Crew 0.689 2 Crew 1.05 3 2nd 0.769 4 1st 0.302 5 3rd 1.17 6 Crew 0.616 7 3rd 0.435 8 2nd -0.143 9 1st 1.10 10 3rd 1.72 # … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st -0.2637570 -1.326638 0.7991240 0.91095533rd-1st 0.1261369 -1.010699 1.2629724 0.9908810Crew-1st -0.4605313 -1.488838 0.5677751 0.63384223rd-2nd 0.3898939 -0.726890 1.5066778 0.7886407Crew-2nd -0.1967743 -1.202868 0.8093198 0.9535344Crew-3rd -0.5866682 -1.670598 0.4972619 0.4799025tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 2nd 1.03 2 1st -0.306 3 3rd 0.939 4 3rd 1.04 5 2nd -0.974 6 1st 0.0136 7 Crew 0.117 8 1st 1.13 9 Crew -0.444 10 1st 0.0134# … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st 0.5442886 -0.4866955 1.5752728 0.50140933rd-1st 0.1250536 -0.7715174 1.0216246 0.9822438Crew-1st 0.3688955 -0.8731682 1.6109593 0.85786283rd-2nd -0.4192350 -1.3639132 0.5254432 0.6405627Crew-2nd -0.1753931 -1.4526166 1.1018303 0.9830263Crew-3rd 0.2438419 -0.9275722 1.4152560 0.9447585tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 2nd -0.868 2 1st -1.10 3 Crew -0.887 4 3rd -0.274 5 2nd -2.02 6 1st -1.20 7 3rd -0.0952 8 Crew 1.77 9 2nd 0.518 10 2nd 0.373 # … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st -0.71301214 -1.6919907 0.2659665 0.22538283rd-1st -0.39014505 -1.3290118 0.5487217 0.6867008Crew-1st -0.01296422 -0.9355957 0.9096673 0.99998083rd-2nd 0.32286709 -0.6379348 1.2836690 0.8070837Crew-2nd 0.70004792 -0.2448957 1.6449915 0.2123895Crew-3rd 0.37718083 -0.5261409 1.2805025 0.6834704tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 1st 1.24 2 3rd -2.13 3 2nd 1.14 4 Crew -0.408 5 3rd -0.528 6 3rd 0.415 7 1st -0.142 8 Crew -0.286 9 1st 0.11410 1st -1.34 # … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st -0.0832625535 -1.0657594 0.8992343 0.99587763rd-1st -0.4965960818 -1.4790929 0.4859007 0.5382667Crew-1st 0.0006832311 -0.9460744 0.9474408 1.00000003rd-2nd -0.4133335283 -1.3958303 0.5691633 0.6783714Crew-2nd 0.0839457846 -0.8628118 1.0307034 0.9952876Crew-3rd 0.4972793128 -0.4494783 1.4440369 0.5057813tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 Crew 0.610 2 Crew 0.110 3 1st -1.97 4 1st 0.0303 5 3rd -0.793 6 2nd -1.08 7 Crew -0.413 8 1st -1.92 9 Crew -0.307 10 3rd -0.426 # … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st 0.1580195 -0.7723181 1.0883571 0.96876423rd-1st -0.3578741 -1.3885239 0.6727757 0.7913457Crew-1st 0.3603245 -0.4811975 1.2018465 0.66618843rd-2nd -0.5158935 -1.5076368 0.4758497 0.5140344Crew-2nd 0.2023051 -0.5910895 0.9956996 0.9042889Crew-3rd 0.7181986 -0.1907491 1.6271463 0.1664027tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 2nd -0.768 2 1st -0.901 3 Crew -0.449 4 3rd -0.306 5 3rd -1.12 6 2nd -0.625 7 3rd 0.0779 8 Crew 0.0111 9 2nd -0.747 10 2nd -0.825 # … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st -0.47334284 -1.3710309 0.4243452 0.50245023rd-1st -0.29575403 -1.1934421 0.6019340 0.8161022Crew-1st -0.26589576 -1.1454468 0.6136553 0.85136623rd-2nd 0.17758881 -0.7378769 1.0930546 0.9545842Crew-2nd 0.20744708 -0.6902410 1.1051351 0.9264729Crew-3rd 0.02985827 -0.8678298 0.9275463 0.9997464tibble(sex = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(height = rnorm(n = 50)) ->height_data; height_data##################ggplot(height_data) + aes(x = sex) + aes(y = height) + aes(group = sex) + geom_boxplot() + geom_jitter(height = 0, width = .02) + stat_summary(fun.y = mean, geom = "point", col = "goldenrod3", size = 5)############# statistical testTukeyHSD(aov(height_data$height ~ # the continuous variable by (~) # the continuous variable by (~) height_data$sex)) # the discrete variable# A tibble: 50 x 2 sex height <chr> <dbl> 1 2nd 0.233 2 2nd -0.927 3 1st 0.840 4 1st 0.101 5 1st -0.175 6 3rd -0.687 7 1st -2.54 8 3rd -2.06 9 3rd 0.85810 2nd 0.351# … with 40 more rows
Tukey multiple comparisons of means 95% family-wise confidence levelFit: aov(formula = height_data$height ~ height_data$sex)$`height_data$sex` diff lwr upr p adj2nd-1st 0.27839893 -0.9749367 1.531735 0.93397333rd-1st 0.15182387 -1.0293735 1.333021 0.9859901Crew-1st 0.19796508 -1.0040174 1.399948 0.97138493rd-2nd -0.12657506 -1.3363374 1.083187 0.9923242Crew-2nd -0.08043385 -1.3104988 1.149631 0.9980906Crew-3rd 0.04614121 -1.1103348 1.202617 0.9995630library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 female right 2 female left 3 male right 4 male right 5 female right 6 female right 7 male right 8 male right 9 female right 10 female left # … with 90 more rows
yx left right female 16 35 male 13 36 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 0.09797, df = 1, p-value = 0.7543library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 male right 2 male right 3 female right 4 male right 5 male right 6 male right 7 male right 8 male left 9 male left 10 female right # … with 90 more rows
yx left right female 12 31 male 24 33 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 1.5726, df = 1, p-value = 0.2098library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 female right 2 female left 3 male right 4 male right 5 male right 6 male right 7 male right 8 male left 9 male right 10 male left # … with 90 more rows
yx left right female 16 39 male 21 24 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 2.5692, df = 1, p-value = 0.109library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 male left 2 male left 3 female right 4 female right 5 female right 6 male left 7 male right 8 male right 9 male left 10 female left # … with 90 more rows
yx left right female 13 40 male 15 32 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 0.35756, df = 1, p-value = 0.5499library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 female right 2 female right 3 male right 4 female right 5 male right 6 male right 7 male right 8 female left 9 male right 10 male right # … with 90 more rows
yx left right female 8 43 male 13 36 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 1.1781, df = 1, p-value = 0.2777library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 female left 2 female left 3 female right 4 male left 5 female right 6 female right 7 male right 8 male left 9 male right 10 female left # … with 90 more rows
yx left right female 18 33 male 15 34 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 0.081245, df = 1, p-value = 0.7756library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 male right 2 female right 3 male right 4 female right 5 female left 6 male right 7 male left 8 female left 9 male right 10 female right # … with 90 more rows
yx left right female 10 34 male 16 40 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 0.18638, df = 1, p-value = 0.6659library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 female left 2 female right 3 female left 4 female right 5 female left 6 male left 7 female right 8 female right 9 female left 10 female left # … with 90 more rows
yx left right female 21 33 male 13 33 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 0.82159, df = 1, p-value = 0.3647library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 male left 2 male right 3 female left 4 male left 5 male right 6 female right 7 female right 8 female left 9 male right 10 male right # … with 90 more rows
yx left right female 22 33 male 14 31 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 0.5068, df = 1, p-value = 0.4765library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 female right 2 female right 3 female right 4 male right 5 male left 6 female left 7 male right 8 female right 9 female left 10 female right # … with 90 more rows
yx left right female 16 35 male 20 29 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 0.60087, df = 1, p-value = 0.4382library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 male right 2 male left 3 male left 4 female left 5 male right 6 female left 7 male right 8 female right 9 female left 10 male right # … with 90 more rows
yx left right female 19 35 male 18 28 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 0.039791, df = 1, p-value = 0.8419library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 female right 2 female right 3 female right 4 female left 5 female right 6 female right 7 female right 8 female right 9 female right 10 female left # … with 90 more rows
yx left right female 14 46 male 13 27 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 0.61094, df = 1, p-value = 0.4344library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 male right 2 male right 3 male right 4 male right 5 female left 6 male left 7 male right 8 male right 9 female right 10 female left # … with 90 more rows
yx left right female 15 34 male 15 36 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 1.6836e-30, df = 1, p-value = 1library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 male right 2 female right 3 female right 4 female right 5 female right 6 female left 7 male left 8 male right 9 male right 10 female right # … with 90 more rows
yx left right female 19 35 male 14 32 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 0.084193, df = 1, p-value = 0.7717library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 male right 2 female left 3 male right 4 male right 5 male left 6 female right 7 male right 8 female right 9 female left 10 male right # … with 90 more rows
yx left right female 11 35 male 16 38 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 0.17288, df = 1, p-value = 0.6776library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 female right 2 female right 3 female right 4 female left 5 male left 6 male right 7 female right 8 male right 9 male left 10 female right # … with 90 more rows
yx left right female 13 44 male 14 29 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 0.73942, df = 1, p-value = 0.3898library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 male left 2 female right 3 male left 4 female right 5 male right 6 male left 7 female right 8 male right 9 female right 10 female left # … with 90 more rows
yx left right female 9 38 male 22 31 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 4.8243, df = 1, p-value = 0.02806library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 male right 2 female right 3 male left 4 male right 5 male right 6 male right 7 female right 8 female right 9 male right 10 female right # … with 90 more rows
yx left right female 8 33 male 17 42 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 0.67521, df = 1, p-value = 0.4112library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 female left 2 female left 3 male left 4 male right 5 male right 6 male right 7 female left 8 male right 9 male left 10 male left # … with 90 more rows
yx left right female 20 29 male 11 40 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 3.4752, df = 1, p-value = 0.0623library(tidyverse)tibble(sex = sample(x = c("male","female"), size = 100, replace = T), handed = sample(x = c("left", "right"), size = 100, replace = T, prob = c(.3,.7))) ->my_data ; my_data##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = my_data) + geom_mosaic(aes(x = product(sex), fill = handed))########## contingency table or two by two tabletable(x = my_data$sex, y = my_data$handed)#####chisq.test(x = my_data$sex, y = my_data$handed)# A tibble: 100 x 2 sex handed <chr> <chr> 1 female right 2 male right 3 female right 4 male right 5 male right 6 female right 7 female right 8 male right 9 female left 10 female right # … with 90 more rows
yx left right female 13 41 male 9 37 Pearson's Chi-squared test with Yates' continuity correctiondata: my_data$sex and my_data$handedX-squared = 0.090181, df = 1, p-value = 0.7639library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 milk milk 2 milk milk 3 milk tea 4 tea tea 5 milk tea 6 tea tea 7 tea milk 8 tea milk
Pearson's Chi-squared testdata: tea_data$truth and tea_data$guessX-squared = 0, df = 1, p-value = 1library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 tea milk 2 tea tea 3 milk milk 4 milk milk 5 milk tea 6 milk milk 7 tea tea 8 tea tea
Pearson's Chi-squared test with Yates' continuity correctiondata: tea_data$truth and tea_data$guessX-squared = 0.5, df = 1, p-value = 0.4795library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 tea tea 2 tea milk 3 tea milk 4 milk milk 5 tea tea 6 milk tea 7 milk tea 8 milk milk
Pearson's Chi-squared testdata: tea_data$truth and tea_data$guessX-squared = 0, df = 1, p-value = 1library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 milk tea 2 tea tea 3 milk milk 4 tea milk 5 milk tea 6 tea milk 7 tea tea 8 milk milk
Pearson's Chi-squared testdata: tea_data$truth and tea_data$guessX-squared = 0, df = 1, p-value = 1library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 milk milk 2 milk tea 3 milk milk 4 tea milk 5 tea tea 6 tea milk 7 tea tea 8 milk tea
Pearson's Chi-squared testdata: tea_data$truth and tea_data$guessX-squared = 0, df = 1, p-value = 1library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 milk milk 2 milk tea 3 tea milk 4 milk tea 5 tea tea 6 tea milk 7 milk milk 8 tea tea
Pearson's Chi-squared testdata: tea_data$truth and tea_data$guessX-squared = 0, df = 1, p-value = 1library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 tea tea 2 milk tea 3 tea tea 4 tea milk 5 milk milk 6 milk milk 7 tea tea 8 milk milk
Pearson's Chi-squared test with Yates' continuity correctiondata: tea_data$truth and tea_data$guessX-squared = 0.5, df = 1, p-value = 0.4795library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 tea tea 2 milk tea 3 milk tea 4 tea milk 5 milk milk 6 tea milk 7 milk tea 8 tea milk
Pearson's Chi-squared test with Yates' continuity correctiondata: tea_data$truth and tea_data$guessX-squared = 0.5, df = 1, p-value = 0.4795library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 tea tea 2 milk milk 3 milk tea 4 tea milk 5 tea tea 6 tea tea 7 milk milk 8 milk milk
Pearson's Chi-squared test with Yates' continuity correctiondata: tea_data$truth and tea_data$guessX-squared = 0.5, df = 1, p-value = 0.4795library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 milk milk 2 milk tea 3 tea tea 4 milk milk 5 milk milk 6 tea tea 7 tea tea 8 tea milk
Pearson's Chi-squared test with Yates' continuity correctiondata: tea_data$truth and tea_data$guessX-squared = 0.5, df = 1, p-value = 0.4795library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 tea milk 2 milk tea 3 tea tea 4 milk milk 5 milk tea 6 milk milk 7 tea milk 8 tea tea
Pearson's Chi-squared testdata: tea_data$truth and tea_data$guessX-squared = 0, df = 1, p-value = 1library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 tea tea 2 tea milk 3 milk milk 4 milk tea 5 milk milk 6 milk milk 7 tea tea 8 tea tea
Pearson's Chi-squared test with Yates' continuity correctiondata: tea_data$truth and tea_data$guessX-squared = 0.5, df = 1, p-value = 0.4795library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 milk tea 2 tea tea 3 tea milk 4 tea tea 5 milk milk 6 milk milk 7 tea tea 8 milk milk
Pearson's Chi-squared test with Yates' continuity correctiondata: tea_data$truth and tea_data$guessX-squared = 0.5, df = 1, p-value = 0.4795library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 milk tea 2 tea milk 3 milk tea 4 tea tea 5 milk milk 6 milk tea 7 tea milk 8 tea milk
Pearson's Chi-squared test with Yates' continuity correctiondata: tea_data$truth and tea_data$guessX-squared = 0.5, df = 1, p-value = 0.4795library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 milk tea 2 milk milk 3 milk tea 4 tea milk 5 tea milk 6 milk milk 7 tea tea 8 tea tea
Pearson's Chi-squared testdata: tea_data$truth and tea_data$guessX-squared = 0, df = 1, p-value = 1library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 milk milk 2 tea milk 3 milk milk 4 tea milk 5 tea tea 6 milk tea 7 milk tea 8 tea tea
Pearson's Chi-squared testdata: tea_data$truth and tea_data$guessX-squared = 0, df = 1, p-value = 1library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 milk tea 2 tea tea 3 tea milk 4 tea tea 5 tea milk 6 milk milk 7 milk tea 8 milk milk
Pearson's Chi-squared testdata: tea_data$truth and tea_data$guessX-squared = 0, df = 1, p-value = 1library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 tea milk 2 milk tea 3 tea tea 4 milk milk 5 tea milk 6 milk milk 7 milk tea 8 tea tea
Pearson's Chi-squared testdata: tea_data$truth and tea_data$guessX-squared = 0, df = 1, p-value = 1library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 milk tea 2 tea milk 3 tea milk 4 milk tea 5 tea tea 6 tea milk 7 milk tea 8 milk milk
Pearson's Chi-squared test with Yates' continuity correctiondata: tea_data$truth and tea_data$guessX-squared = 0.5, df = 1, p-value = 0.4795library(tidyverse)tibble(truth = sample(rep(c("tea", "milk"), 4))) %>% mutate(guess = sample(rep(c("tea", "milk"), 4))) ->tea_data ; tea_data##############library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = tea_data) + geom_mosaic(aes(x = product(truth), fill = guess))######################chisq.test(x = tea_data$truth, y = tea_data$guess)# A tibble: 8 x 2 truth guess <chr> <chr>1 tea milk 2 milk tea 3 milk milk 4 tea tea 5 milk milk 6 tea tea 7 milk tea 8 tea milk
Pearson's Chi-squared testdata: tea_data$truth and tea_data$guessX-squared = 0, df = 1, p-value = 1library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 2nd Survive 2 1st Survive 3 2nd Perish 4 Crew Perish 5 1st Perish 6 2nd Survive 7 1st Perish 8 3rd Survive 9 2nd Perish 10 Crew Survive# … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 2.3518, df = 3, p-value = 0.5027library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 3rd Survive 2 Crew Perish 3 Crew Perish 4 3rd Perish 5 2nd Perish 6 Crew Survive 7 3rd Perish 8 3rd Perish 9 2nd Perish 10 3rd Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 12.751, df = 3, p-value = 0.005207library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 2nd Perish 2 2nd Perish 3 2nd Survive 4 3rd Survive 5 Crew Survive 6 Crew Perish 7 3rd Survive 8 Crew Survive 9 Crew Perish 10 3rd Survive# … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 8.9383, df = 3, p-value = 0.03012library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 2nd Perish 2 3rd Perish 3 1st Perish 4 2nd Survive 5 Crew Perish 6 3rd Perish 7 2nd Perish 8 Crew Perish 9 2nd Perish 10 1st Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 3.1174, df = 3, p-value = 0.3739library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 2nd Survive 2 Crew Survive 3 2nd Perish 4 Crew Perish 5 2nd Perish 6 1st Perish 7 2nd Perish 8 3rd Perish 9 3rd Survive10 3rd Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 2.4046, df = 3, p-value = 0.4928library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 2nd Perish 2 Crew Perish 3 3rd Perish 4 3rd Perish 5 1st Survive 6 2nd Perish 7 3rd Perish 8 Crew Perish 9 3rd Survive10 Crew Survive# … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 0.42067, df = 3, p-value = 0.9359library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 3rd Perish 2 Crew Perish 3 Crew Perish 4 2nd Perish 5 2nd Perish 6 3rd Perish 7 Crew Perish 8 1st Perish 9 3rd Perish 10 3rd Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 0.47864, df = 3, p-value = 0.9236library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 3rd Perish 2 Crew Perish 3 1st Perish 4 3rd Perish 5 2nd Perish 6 2nd Survive 7 3rd Perish 8 3rd Survive 9 3rd Perish 10 3rd Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 0.50755, df = 3, p-value = 0.9172library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 Crew Perish 2 1st Survive 3 2nd Survive 4 Crew Perish 5 3rd Perish 6 3rd Perish 7 3rd Survive 8 Crew Perish 9 3rd Perish 10 2nd Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 1.7228, df = 3, p-value = 0.6319library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 2nd Survive 2 Crew Perish 3 2nd Perish 4 3rd Perish 5 2nd Perish 6 2nd Survive 7 3rd Perish 8 Crew Perish 9 3rd Perish 10 3rd Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 0.94684, df = 3, p-value = 0.8141library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 Crew Survive 2 3rd Perish 3 Crew Perish 4 3rd Survive 5 1st Survive 6 2nd Survive 7 Crew Perish 8 1st Perish 9 3rd Perish 10 1st Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 1.976, df = 3, p-value = 0.5774library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 1st Perish 2 2nd Perish 3 3rd Survive 4 3rd Survive 5 1st Survive 6 2nd Survive 7 Crew Survive 8 3rd Perish 9 1st Perish 10 Crew Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 3.8352, df = 3, p-value = 0.2798library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 Crew Perish 2 3rd Perish 3 3rd Survive 4 2nd Perish 5 1st Survive 6 2nd Perish 7 Crew Perish 8 2nd Perish 9 Crew Survive10 1st Survive# … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 1.295, df = 3, p-value = 0.7303library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 3rd Perish 2 1st Perish 3 1st Perish 4 1st Perish 5 3rd Perish 6 1st Perish 7 3rd Survive 8 1st Perish 9 3rd Survive10 Crew Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 1.6422, df = 3, p-value = 0.6498library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 3rd Survive 2 1st Perish 3 1st Perish 4 2nd Perish 5 Crew Perish 6 3rd Perish 7 2nd Survive 8 3rd Perish 9 Crew Perish 10 2nd Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 2.4201, df = 3, p-value = 0.4899library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 Crew Perish 2 3rd Survive 3 Crew Perish 4 2nd Perish 5 1st Survive 6 Crew Perish 7 2nd Perish 8 1st Survive 9 Crew Perish 10 3rd Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 5.7481, df = 3, p-value = 0.1245library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 Crew Perish 2 2nd Survive 3 Crew Perish 4 Crew Perish 5 Crew Perish 6 1st Survive 7 2nd Perish 8 Crew Survive 9 2nd Perish 10 3rd Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 2.5353, df = 3, p-value = 0.4689library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 3rd Perish 2 1st Survive 3 3rd Perish 4 1st Perish 5 2nd Perish 6 Crew Perish 7 1st Perish 8 3rd Survive 9 1st Perish 10 1st Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 2.4223, df = 3, p-value = 0.4895library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 2nd Perish 2 1st Survive 3 2nd Perish 4 3rd Perish 5 1st Perish 6 Crew Perish 7 1st Perish 8 3rd Perish 9 3rd Survive10 2nd Perish # … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 0.4343, df = 3, p-value = 0.9331library(tidyverse)tibble(class = sample(x = c("1st","2nd","3rd","Crew"), size = 50, replace = T)) %>% mutate(survive = sample(x = c("Survive", "Perish"), size = 50, replace = T, prob = c(.3, .7))) ->fake_titanic ; fake_titanic##########library(ggmosaic) # geom_mosaic() and product() # geom_mosaic() and product()ggplot(data = fake_titanic) + geom_mosaic(aes(x = product(class), fill = survive)) -> g1ggplot(data = fake_titanic) + aes(x = class) + aes(y = survive) + geom_count(alpha = .5, color = "blue") + geom_jitter(width = .2, height = .2) -> g2library(patchwork)g1 + g2#############chisq.test(x = fake_titanic$class, y = fake_titanic$survive)# A tibble: 50 x 2 class survive <chr> <chr> 1 3rd Perish 2 2nd Perish 3 Crew Perish 4 1st Perish 5 3rd Perish 6 3rd Survive 7 1st Survive 8 Crew Perish 9 3rd Survive10 Crew Survive# … with 40 more rows
Pearson's Chi-squared testdata: fake_titanic$class and fake_titanic$surviveX-squared = 2.4098, df = 3, p-value = 0.4918