i have a dataframe of variables for 11 species of plants recorded in 2 locations. for each specie, I am attempting to compare the mean of variables between two different locations using a t.test(or wilcoxon test).
Here is the first few rows of my data
SPECIES LOCATION X.COLONIZATION SPORE_DENSITY pH NO3 NH4 P Organic_C K Cu Mn Zn BD X.Sand
1 C. comosa Gauteng 90 387 5.40 8.24 1.35 1.10 0.95 94.40 3.36 84.40 4.72 1.45 68.0
2 C. comosa Gauteng 84 270 5.25 8.36 1.37 1.20 0.99 94.87 3.39 84.87 4.77 1.36 76.0
3 C. comosa Gauteng 96 404 5.55 8.19 1.32 1.11 0.94 94.01 3.35 84.01 4.68 1.54 78.0
4 C. comosa Mpumalanga 79 382 5.84 4.05 3.46 3.04 1.55 130.40 0.28 25.43 2.00 1.66 73.6
5 C. comosa Mpumalanga 82 383 5.49 4.45 3.48 3.09 1.53 131.36 0.27 25.35 2.12 1.45 76.5
6 C. comosa Mpumalanga 86 371 6.19 4.43 3.44 3.04 1.58 129.95 0.29 25.45 2.14 1.87 74.9
7 C. distans Gauteng 80 334 5.48 8.88 1.96 3.33 0.99 130.24 0.99 40.01 3.94 1.55 70.0
8 C. distans Gauteng 75 409 5.29 8.54 1.99 3.28 0.99 130.28 0.95 40.25 3.89 1.48 79.0
9 C. distans Gauteng 85 259 5.67 8.63 1.93 3.39 1.02 130.30 0.98 40.12 3.97 1.62 79.0
10 C. distans Mpumalanga 65 326 5.61 6.02 2.65 4.45 2.58 163.25 1.79 53.11 6.11 1.68 72.0
11 C. distans Mpumalanga 79 351 5.43 6.58 2.55 4.49 2.59 163.55 1.78 52.89 6.04 1.63 78.0
12 C. distans Mpumalanga 71 251 5.79 6.24 2.59 4.41 2.59 163.27 1.75 53.03 6.19 1.73 75.0
X.Silt X.Clay
1 12 9
2 16 13
3 14 14
4 9 10
5 11 16
6 13 16
7 8 11
8 12 15
9 10 16
10 8 10
11 15 14
12 16 12
for instance, for each specie, i want to compare (test for significane difference) the mean value of spore density in Gauteng and Mpumalanga. Any help please?
We group by 'SPECIES' and then use summarise with across on the numeric columns, subset the column values were 'LOCATION' is 'Gauteng' or the other one, apply the t.test and extract the pvalue
library(dplyr) #1.0.0
df1 %>%
group_by(SPECIES) %>%
summarise(across(where(is.numeric), ~
t.test(.[LOCATION == 'Gauteng'], .[LOCATION == 'Mpumalanga'])$p.value))
# A tibble: 2 x 16
# SPECIES X.COLONIZATION SPORE_DENSITY pH NO3 NH4 P Organic_C K Cu Mn Zn BD X.Sand X.Silt X.Clay
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 C. comosa 0.146 0.614 0.149 0.000269 7.27e-8 1.35e-5 0.00000970 2.15e-6 3.12e-7 1.35e-5 7.23e-6 0.219 0.779 0.140 0.474
#2 C. dista… 0.177 0.667 0.438 0.000624 1.94e-4 2.04e-5 0.00000670 4.48e-6 1.22e-6 1.90e-8 2.07e-5 0.0653 0.791 0.363 0.359
df1 <- structure(list(SPECIES = c("C. comosa", "C. comosa", "C. comosa",
"C. comosa", "C. comosa", "C. comosa", "C. distans", "C. distans",
"C. distans", "C. distans", "C. distans", "C. distans"), LOCATION = c("Gauteng",
"Gauteng", "Gauteng", "Mpumalanga", "Mpumalanga", "Mpumalanga",
"Gauteng", "Gauteng", "Gauteng", "Mpumalanga", "Mpumalanga",
"Mpumalanga"), X.COLONIZATION = c(90L, 84L, 96L, 79L, 82L, 86L,
80L, 75L, 85L, 65L, 79L, 71L), SPORE_DENSITY = c(387L, 270L,
404L, 382L, 383L, 371L, 334L, 409L, 259L, 326L, 351L, 251L),
pH = c(5.4, 5.25, 5.55, 5.84, 5.49, 6.19, 5.48, 5.29, 5.67,
5.61, 5.43, 5.79), NO3 = c(8.24, 8.36, 8.19, 4.05, 4.45,
4.43, 8.88, 8.54, 8.63, 6.02, 6.58, 6.24), NH4 = c(1.35,
1.37, 1.32, 3.46, 3.48, 3.44, 1.96, 1.99, 1.93, 2.65, 2.55,
2.59), P = c(1.1, 1.2, 1.11, 3.04, 3.09, 3.04, 3.33, 3.28,
3.39, 4.45, 4.49, 4.41), Organic_C = c(0.95, 0.99, 0.94,
1.55, 1.53, 1.58, 0.99, 0.99, 1.02, 2.58, 2.59, 2.59), K = c(94.4,
94.87, 94.01, 130.4, 131.36, 129.95, 130.24, 130.28, 130.3,
163.25, 163.55, 163.27), Cu = c(3.36, 3.39, 3.35, 0.28, 0.27,
0.29, 0.99, 0.95, 0.98, 1.79, 1.78, 1.75), Mn = c(84.4, 84.87,
84.01, 25.43, 25.35, 25.45, 40.01, 40.25, 40.12, 53.11, 52.89,
53.03), Zn = c(4.72, 4.77, 4.68, 2, 2.12, 2.14, 3.94, 3.89,
3.97, 6.11, 6.04, 6.19), BD = c(1.45, 1.36, 1.54, 1.66, 1.45,
1.87, 1.55, 1.48, 1.62, 1.68, 1.63, 1.73), X.Sand = c(68,
76, 78, 73.6, 76.5, 74.9, 70, 79, 79, 72, 78, 75), X.Silt = c(12L,
16L, 14L, 9L, 11L, 13L, 8L, 12L, 10L, 8L, 15L, 16L), X.Clay = c(9L,
13L, 14L, 10L, 16L, 16L, 11L, 15L, 16L, 10L, 14L, 12L)), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"))