library(tidyverse)
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag(): dplyr, stats
library(RCurl)
## Loading required package: bitops
##
## Attaching package: 'RCurl'
## The following object is masked from 'package:tidyr':
##
## complete
ward<-read_csv(getURL("https://raw.githubusercontent.com/chrischizinski/SNR_R_Group/master/data/ExperimentalDesignData/chpt3/ward.csv"))
head(ward)
## # A tibble: 6 × 2
## ZONE EGGS
## <chr> <int>
## 1 Mussel 11
## 2 Mussel 8
## 3 Mussel 18
## 4 Mussel 10
## 5 Mussel 9
## 6 Mussel 13
ward_summ<-ward %>%
group_by(ZONE) %>%
summarize(N = n(),
Meaneggs = mean(EGGS),
Medianeggs = median(EGGS),
SDeggs = sd(EGGS)) %>%
mutate(SEeggs = SDeggs/sqrt(N),
CI_hi = Meaneggs + 1.96* SEeggs,
CI_lo = Meaneggs - 1.96* SEeggs)
ggplot(data = ward) +
geom_boxplot(aes(x = ZONE, y = EGGS)) +
theme_bw()
ggplot(data = ward) +
geom_violin(aes(x = ZONE, y = EGGS, fill = ZONE)) +
theme_bw()
data.frame(ZONE = ward_summ$ZONE, CV = ward_summ$SDeggs/ward_summ$Meaneggs)
## ZONE CV
## 1 Littor 0.2327708
## 2 Mussel 0.2037969
t.test(EGGS ~ ZONE, data = ward, var.equal = TRUE)
##
## Two Sample t-test
##
## data: EGGS by ZONE
## t = -5.3899, df = 77, p-value = 0.0000007457
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.63511 -1.67377
## sample estimates:
## mean in group Littor mean in group Mussel
## 8.702703 11.357143
t.test(EGGS ~ ZONE, data = ward, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: EGGS by ZONE
## t = -5.4358, df = 76.998, p-value = 0.0000006192
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.626816 -1.682065
## sample estimates:
## mean in group Littor mean in group Mussel
## 8.702703 11.357143
furness<-read_csv(getURL("https://raw.githubusercontent.com/chrischizinski/SNR_R_Group/master/data/ExperimentalDesignData/chpt3/furness.csv"))
head(furness)
## # A tibble: 6 × 2
## SEX METRATE
## <chr> <dbl>
## 1 Male 2950.0
## 2 Female 1956.1
## 3 Male 2308.7
## 4 Male 2135.6
## 5 Male 1945.6
## 6 Female 1490.5
furn_summ<-furness %>%
group_by(SEX) %>%
summarize(N = n(),
Meanmetrate = mean(METRATE),
Medianmetrate = median(METRATE),
SDmetrate = sd(METRATE)) %>%
mutate(SEmetrate = SDmetrate/sqrt(N),
CI_hi = Meanmetrate + 1.96* SEmetrate,
CI_lo = Meanmetrate - 1.96* SEmetrate)
ggplot(data = furness) +
geom_boxplot(aes(x = SEX, y = METRATE)) +
theme_bw()
ggplot(data = furness) +
geom_violin(aes(x = SEX, y = METRATE, fill = METRATE)) +
theme_bw()
data.frame(SEX = furn_summ$SEX, CV = furn_summ$SDmetrate/furn_summ$Meanmetrate)
## SEX CV
## 1 Female 0.3274656
## 2 Male 0.5719318
t.test(METRATE ~ SEX, data = furness, var.equal = TRUE)
##
## Two Sample t-test
##
## data: METRATE by SEX
## t = -0.70086, df = 12, p-value = 0.4968
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1143.3057 586.7891
## sample estimates:
## mean in group Female mean in group Male
## 1285.517 1563.775
t.test(METRATE ~ SEX, data = furness, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: METRATE by SEX
## t = -0.77317, df = 10.468, p-value = 0.4565
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1075.3208 518.8042
## sample estimates:
## mean in group Female mean in group Male
## 1285.517 1563.775
elgar<-read_csv(getURL("https://raw.githubusercontent.com/chrischizinski/SNR_R_Group/master/data/ExperimentalDesignData/chpt3/elgar.csv"))
head(elgar)
## # A tibble: 6 × 5
## PAIR VERTDIM HORIZDIM VERTLIGH HORIZLIG
## <chr> <int> <int> <int> <int>
## 1 K 300 295 80 60
## 2 M 240 260 120 140
## 3 N 250 280 170 160
## 4 O 220 250 90 120
## 5 P 160 160 150 180
## 6 R 170 150 110 90
names(elgar) <-c("PAIR", "VERT_DIM", "HORIZ_DIM", "VERT_LIGH","HORIZ_LIGH")
elgar %>%
gather(type, value, -PAIR) %>%
separate(type, c("DIMEN","L_COND"), sep = "_") %>%
group_by(DIMEN,L_COND) %>%
summarize(N = n(),
Meanradius = mean(value),
Medianradius = median(value),
SDradius = sd(value)) %>%
mutate(SEradius = SDradius/sqrt(N),
CI_hi = Meanradius + 1.96* SEradius,
CI_lo = Meanradius - 1.96* SEradius)
## Source: local data frame [4 x 9]
## Groups: DIMEN [2]
##
## DIMEN L_COND N Meanradius Medianradius SDradius SEradius CI_hi
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 HORIZ DIM 17 207.3529 210 60.21103 14.60332 235.9754
## 2 HORIZ LIGH 17 161.1765 160 65.46777 15.87827 192.2979
## 3 VERT DIM 17 198.2353 190 60.54289 14.68381 227.0156
## 4 VERT LIGH 17 177.6471 160 73.86892 17.91585 212.7621
## # ... with 1 more variables: CI_lo <dbl>
elgar %>%
gather(type, value, -PAIR) %>%
separate(type, c("DIMEN","L_COND"), sep = "_") %>%
ggplot() +
geom_violin(aes(x = L_COND, y = value, fill = L_COND)) +
facet_wrap(~DIMEN, ncol = 1) +
theme_bw()
elgar %>%
gather(type, value, -PAIR) %>%
separate(type, c("DIMEN","L_COND"), sep = "_") %>%
mutate(DIMEN2 = factor(DIMEN, labels = c("beta", "sqrt(x,y)"))) %>%
ggplot() +
geom_violin(aes(x = L_COND, y = value, fill = L_COND)) +
facet_wrap(~DIMEN, ncol = 2, labeller = label_bquote(cols = alpha^.(DIMEN))) +
theme_bw()
head(elgar)
## # A tibble: 6 × 5
## PAIR VERT_DIM HORIZ_DIM VERT_LIGH HORIZ_LIGH
## <chr> <int> <int> <int> <int>
## 1 K 300 295 80 60
## 2 M 240 260 120 140
## 3 N 250 280 170 160
## 4 O 220 250 90 120
## 5 P 160 160 150 180
## 6 R 170 150 110 90
t.test(elgar$VERT_DIM, elgar$VERT_LIGH, paired = TRUE)
##
## Paired t-test
##
## data: elgar$VERT_DIM and elgar$VERT_LIGH
## t = 0.96545, df = 16, p-value = 0.3487
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -24.61885 65.79532
## sample estimates:
## mean of the differences
## 20.58824
t.test(elgar$HORIZ_DIM, elgar$HORIZ_LIGH, paired = TRUE)
##
## Paired t-test
##
## data: elgar$HORIZ_DIM and elgar$HORIZ_LIGH
## t = 2.1482, df = 16, p-value = 0.04735
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.6085725 91.7443687
## sample estimates:
## mean of the differences
## 46.17647