iGEM_measurement <- read_csv("~/Desktop/iGEM /iGEM_measurement.csv")
## Parsed with column specification:
## cols(
## Team = col_character(),
## OT2 = col_logical(),
## F_uM = col_double(),
## a.u. = col_double()
## )
transform(iGEM_measurement, iGEM_measurement$F_uM <- as.numeric(iGEM_measurement$F_uM))
## # A tibble: 624 x 4
## Team OT2 F_uM a.u.
## <chr> <lgl> <dbl> <dbl>
## 1 LND TRUE 10 47466
## 2 LND TRUE 10 47277
## 3 LND TRUE 10 48460
## 4 LND TRUE 10 49357
## 5 LND TRUE 5 30369
## 6 LND TRUE 5 30109
## 7 LND TRUE 5 30363
## 8 LND TRUE 5 28209
## 9 LND TRUE 2.5 16490
## 10 LND TRUE 2.5 15915
## # … with 614 more rows
transform(iGEM_measurement, iGEM_measurement$a.u. <- as.numeric(iGEM_measurement$a.u.))
## # A tibble: 624 x 4
## Team OT2 F_uM a.u.
## <chr> <lgl> <dbl> <dbl>
## 1 LND TRUE 10 47466
## 2 LND TRUE 10 47277
## 3 LND TRUE 10 48460
## 4 LND TRUE 10 49357
## 5 LND TRUE 5 30369
## 6 LND TRUE 5 30109
## 7 LND TRUE 5 30363
## 8 LND TRUE 5 28209
## 9 LND TRUE 2.5 16490
## 10 LND TRUE 2.5 15915
## # … with 614 more rows
head(iGEM_measurement)
## # A tibble: 6 x 4
## Team OT2 F_uM a.u.
## <chr> <lgl> <dbl> <dbl>
## 1 LND TRUE 10 47466
## 2 LND TRUE 10 47277
## 3 LND TRUE 10 48460
## 4 LND TRUE 10 49357
## 5 LND TRUE 5 30369
## 6 LND TRUE 5 30109
OT2 <- iGEM_measurement %>% filter(., iGEM_measurement$OT2 == T)
head(OT2)
## # A tibble: 6 x 4
## Team OT2 F_uM a.u.
## <chr> <lgl> <dbl> <dbl>
## 1 LND TRUE 10 47466
## 2 LND TRUE 10 47277
## 3 LND TRUE 10 48460
## 4 LND TRUE 10 49357
## 5 LND TRUE 5 30369
## 6 LND TRUE 5 30109
Human <- iGEM_measurement %>% filter(., iGEM_measurement$OT2 == F)
head(Human)
## # A tibble: 6 x 4
## Team OT2 F_uM a.u.
## <chr> <lgl> <dbl> <dbl>
## 1 LND FALSE 10 37492
## 2 LND FALSE 10 36369
## 3 LND FALSE 10 36378
## 4 LND FALSE 10 35796
## 5 LND FALSE 5 20719
## 6 LND FALSE 5 21004
LDN <- iGEM_measurement %>% filter(., iGEM_measurement$Team =="LND" )
head(LDN)
## # A tibble: 6 x 4
## Team OT2 F_uM a.u.
## <chr> <lgl> <dbl> <dbl>
## 1 LND TRUE 10 47466
## 2 LND TRUE 10 47277
## 3 LND TRUE 10 48460
## 4 LND TRUE 10 49357
## 5 LND TRUE 5 30369
## 6 LND TRUE 5 30109
UCD<- iGEM_measurement %>% filter(., iGEM_measurement$Team =="UCD" )
head(UCD)
## # A tibble: 6 x 4
## Team OT2 F_uM a.u.
## <chr> <lgl> <dbl> <dbl>
## 1 UCD TRUE 10 41521
## 2 UCD TRUE 10 42351
## 3 UCD TRUE 10 42437
## 4 UCD TRUE 10 42216
## 5 UCD TRUE 10 43352
## 6 UCD TRUE 10 44204
STB <- iGEM_measurement %>% filter(., iGEM_measurement$Team =="STB" )
head(STB)
## # A tibble: 6 x 4
## Team OT2 F_uM a.u.
## <chr> <lgl> <dbl> <dbl>
## 1 STB TRUE 10 97000000
## 2 STB TRUE 10 95000000
## 3 STB TRUE 10 90000000
## 4 STB TRUE 10 92000000
## 5 STB TRUE 5 54000000
## 6 STB TRUE 5 54000000
MEFL <- (6.0221409*10^23)*(0.0001)/(10^6)
UCD <- iGEM_measurement %>% filter(., .$Team == "UCD")
mean_UCD <- mean(UCD$a.u.)
UCD_au <- 10/mean_UCD
UCD_au_scale <- MEFL*UCD_au
UCD_au_scale
## [1] 73818612961
LND <- iGEM_measurement %>% filter(., .$Team == "LND")
mean_LND <- mean(LND$a.u.)
LND_au <- 10/mean_LND
LND_au_scale <- MEFL*LND_au
LND_au_scale
## [1] 66854771974
STB <- iGEM_measurement %>% filter(., .$Team == "STB")
mean_STB <- mean(STB$a.u.)
STB_au <- 10/mean_STB
STB_au_scale <- MEFL*STB_au
STB_au_scale
## [1] 32556788
## [1] 1.291667
## [1] 40.6
## [1] 31875
adj <- 1
i <- 1
iGEM_measurement$adj_au <- iGEM_measurement$a.u.
for( i in seq(1,624)){
if(iGEM_measurement$Team[i] == "LND"){
adj = LND_au_scale
iGEM_measurement$adj_au[i] <- iGEM_measurement$a.u.[i]*adj
next
}
if(iGEM_measurement$Team[i] == "UCD"){
adj = UCD_au_scale
#measurement <- iGEM_measurement$a.u.[i]/adj
#mutate(iGEM_measurement, iGEM_measurement$adj_au.[i], measurement)
iGEM_measurement$adj_au[i] <- iGEM_measurement$a.u.[i]*adj
next
}
if(iGEM_measurement$Team[i] == "STB"){
adj = STB_au_scale
#measurement <- iGEM_measurement$a.u.[i]/adj
#mutate(iGEM_measurement, iGEM_measurement$adj_au[i], measurement)
iGEM_measurement$adj_au[i] <- iGEM_measurement$a.u.[i]*adj
next
}
}
UCD_ot2 <- lm(F_uM ~ adj_au, data = iGEM_measurement, subset = ( OT2 == T & Team == "UCD" ))
#summary(UCD_ot2)
"UCD OT2:"
## [1] "UCD OT2:"
summary(UCD_ot2)$r.squared
## [1] 0.9405259
UCD_hs <- lm(F_uM ~ adj_au, data = iGEM_measurement, subset = ( OT2 == F & Team == "UCD" ))
#summary(UCD_hs)
"UCD Team Member:"
## [1] "UCD Team Member:"
summary(UCD_hs)$r.squared
## [1] 0.9938576
LDN_ot2 <- lm(F_uM ~ adj_au, data = iGEM_measurement, subset = ( OT2 == T & Team == "LND" ))
#summary(LDN_ot2)
"Liden OT2:"
## [1] "Liden OT2:"
summary(LDN_ot2)$r.squared
## [1] 0.9818086
LDN_hs <- lm(F_uM ~ adj_au, data = iGEM_measurement, subset = ( OT2 == F & Team == "LND" ))
#summary(LDN_hs)
"Liden Team Member:"
## [1] "Liden Team Member:"
summary(LDN_hs)$r.squared
## [1] 0.9526302
STB_ot2 <- lm(F_uM ~ adj_au, data = iGEM_measurement, subset = ( OT2 == T & Team == "STB" ))
#summary(STB_ot2)
"Stony Brook OT2:"
## [1] "Stony Brook OT2:"
summary(STB_ot2)$r.squared
## [1] 0.9943175
STB_hs <- lm(F_uM ~ adj_au, data = iGEM_measurement, subset = ( OT2 == F & Team == "STB" ))
#summary(STB_hs)
"Stony Brook Team Member:"
## [1] "Stony Brook Team Member:"
summary(STB_hs)$r.squared
## [1] 0.9981491
Human <- lm(F_uM ~ adj_au, data = iGEM_measurement, subset = ( OT2 == F ))
#summary(Human)
summary(Human)$r.squared
## [1] 0.9656537
OT2 <- lm(F_uM ~ adj_au, data = iGEM_measurement, subset = ( OT2 == T ))
#summary(OT2)
summary(OT2)$r.squared
## [1] 0.963528
iGEM_measurement %>%
filter(., Team == "UCD") %>%
ggplot(aes(x= F_uM, y = adj_au)) +
#geom_boxplot(aes(color = Team)) +
geom_point(aes(color = Team, alpha = .5)) +
geom_smooth(method = lm, color = "black") +
xlab("uM Fluorescein") +
ylab("Absorbance") +
#labs(color = "Population") +
facet_wrap(~ OT2) #+
#scale_y_continuous(trans='log10')
iGEM_measurement %>%
filter(., Team == "LND") %>%
ggplot(aes(x= F_uM, y = adj_au)) +
#geom_boxplot(aes(color = Team)) +
geom_point(aes(color = Team, alpha = .5)) +
geom_smooth(method = lm, color = "black") +
xlab("uM Fluorescein") +
ylab("Absorbance") +
#labs(color = "Population") +
facet_wrap(~ OT2) #+
#scale_y_continuous(trans='log10')
iGEM_measurement %>%
filter(., Team == "STB") %>%
ggplot(aes(x= F_uM, y = adj_au)) +
#geom_boxplot(aes(color = Team)) +
geom_point(aes(color = Team, alpha = .5)) +
geom_smooth(method = lm, color = "black") +
xlab("uM Fluorescein") +
ylab("Absorbance") +
#labs(color = "Population") +
facet_wrap(~ OT2) #+
# scale_y_continuous(trans='log10')
# ALL in one graph
iGEM_measurement %>%
#filter(., Team == "UCD") %>%
ggplot(aes(x= F_uM, y = adj_au)) +
#geom_boxplot(aes(color = Team)) +
geom_point(aes(color = Team, alpha = .5 )) +
geom_smooth(method = lm, color = "black") +
xlab("uM Fluorescein") +
ylab("Absorbance") +
#labs(color = "Population") +
facet_grid(~ OT2 + Team, labeller = label_context)
#Humans vs Robots
iGEM_measurement %>%
ggplot(aes(x= F_uM, y = adj_au)) +
geom_point(aes(color = Team, alpha = .5 )) +
geom_smooth(method = lm, color = "black") +
xlab("uM Fluorescein") +
ylab("Absorbance") +
#labs(color = "Population") +
facet_grid(~ OT2, labeller = label_context)
#Humans vs Robots log
iGEM_measurement %>%
ggplot(aes(x= F_uM, y = adj_au)) +
geom_point(aes(color = Team, alpha = .5 )) +
geom_smooth(method = lm, color = "black") +
xlab("uM Fluorescein") +
ylab("Absorbance") +
#labs(color = "Population") +
facet_grid(~ OT2, labeller = label_context) +
scale_y_continuous(trans='log10') +
scale_x_continuous(trans='log10')
## Warning: Transformation introduced infinite values in continuous x-axis
## Warning: Transformation introduced infinite values in continuous x-axis
## Warning: Removed 52 rows containing non-finite values (stat_smooth).
