pacman::p_load(pacman, ggplot2, dplyr, readr)
# Read the data
data <- read_csv("Productivity_Gradient_Germany.csv")
## Rows: 420 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Species
## dbl (3): Indicator_Value_Nitrogen, Individual, FA_Index
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Check the structure of the data
str(data)
## spc_tbl_ [420 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Species : chr [1:420] "Centaurea jacea" "Centaurea jacea" "Centaurea jacea" "Centaurea jacea" ...
## $ Indicator_Value_Nitrogen: num [1:420] 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 ...
## $ Individual : num [1:420] 1 2 3 4 5 6 7 8 9 10 ...
## $ FA_Index : num [1:420] 0.011 0.1 0.003 0.007 0.014 0.063 0.036 0.013 0.046 0.038 ...
## - attr(*, "spec")=
## .. cols(
## .. Species = col_character(),
## .. Indicator_Value_Nitrogen = col_double(),
## .. Individual = col_double(),
## .. FA_Index = col_double()
## .. )
## - attr(*, "problems")=<externalptr>
# Summary statistics
summary_stats <- data %>%
group_by(Species) %>%
summarize(
mean_FA = mean(FA_Index, na.rm = TRUE),
sd_FA = sd(FA_Index, na.rm = TRUE),
mean_IVN = mean(Indicator_Value_Nitrogen, na.rm = TRUE),
sd_IVN = sd(Indicator_Value_Nitrogen, na.rm = TRUE)
)
# Display the complete summary statistics
options(tibble.print_max = Inf, tibble.width = Inf)
print(summary_stats)
## # A tibble: 9 × 5
## Species mean_FA sd_FA mean_IVN sd_IVN
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Centaurea jacea 0.0304 0.0247 3.72 0.688
## 2 Lathyrus pratensis 0.0300 0.0402 3.77 0.583
## 3 Lotus corniculatus 0.0399 0.0363 3.63 0.593
## 4 Plantago lanceolata 0.0300 0.0284 3.63 0.593
## 5 Potentilla erecta 0.0297 0.0173 3.6 0.586
## 6 Prunella vulgaris 0.0353 0.0308 3.76 0.570
## 7 Ranunculus acris 0.0455 0.0343 3.74 0.595
## 8 Rumex acetosa 0.0253 0.0193 3.8 0.545
## 9 Trifolium pratense 0.0410 0.0346 3.63 0.593
# Boxplot of FA_Index by Species
ggplot(data, aes(x = Species, y = FA_Index)) +
geom_boxplot() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Boxplot of FA_Index by Species", x = "Species", y = "FA Index")
# Scatter plot of FA_Index vs Indicator_Value_Nitrogen
ggplot(data, aes(x = Indicator_Value_Nitrogen, y = FA_Index, color = Species)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Scatter plot of FA_Index vs Indicator_Value_Nitrogen", x = "Indicator Value Nitrogen", y = "FA Index")
## `geom_smooth()` using formula = 'y ~ x'
# Faceted scatter plots for each species
ggplot(data, aes(x = Indicator_Value_Nitrogen, y = FA_Index)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", se = FALSE) +
facet_wrap(~ Species, scales = "free") +
labs(title = "Scatter plot of FA_Index vs Indicator_Value_Nitrogen by Species", x = "Indicator Value Nitrogen", y = "FA Index")
## `geom_smooth()` using formula = 'y ~ x'
# Summary statistics for each combination of Species and Indicator_Value_Nitrogen
summary_by_species_ivn <- data %>%
group_by(Species, Indicator_Value_Nitrogen) %>%
summarize(
mean_FA = mean(FA_Index, na.rm = TRUE),
sd_FA = sd(FA_Index, na.rm = TRUE),
count = n()
) %>%
arrange(Species, Indicator_Value_Nitrogen)
## `summarise()` has grouped output by 'Species'. You can override using the
## `.groups` argument.
# Display the complete summary statistics
options(tibble.print_max = Inf, tibble.width = Inf)
print(summary_by_species_ivn)
## # A tibble: 42 × 5
## # Groups: Species [9]
## Species Indicator_Value_Nitrogen mean_FA sd_FA count
## <chr> <dbl> <dbl> <dbl> <int>
## 1 Centaurea jacea 3 0.0352 0.0249 10
## 2 Centaurea jacea 3.1 0.0159 0.00866 10
## 3 Centaurea jacea 4.3 0.0373 0.0262 10
## 4 Centaurea jacea 4.5 0.0331 0.0306 10
## 5 Lathyrus pratensis 3.1 0.0214 0.0174 10
## 6 Lathyrus pratensis 3.7 0.0437 0.0639 10
## 7 Lathyrus pratensis 4.5 0.0248 0.0221 10
## 8 Lotus corniculatus 3 0.0279 0.0276 10
## 9 Lotus corniculatus 3.1 0.0384 0.0301 10
## 10 Lotus corniculatus 3.2 0.0468 0.0528 10
## 11 Lotus corniculatus 3.7 0.0411 0.0258 10
## 12 Lotus corniculatus 4.3 0.036 0.0217 10
## 13 Lotus corniculatus 4.5 0.0491 0.0521 10
## 14 Plantago lanceolata 3 0.0484 0.0417 10
## 15 Plantago lanceolata 3.1 0.0271 0.0178 10
## 16 Plantago lanceolata 3.2 0.0144 0.0141 10
## 17 Plantago lanceolata 3.7 0.0327 0.0306 10
## 18 Plantago lanceolata 4.3 0.0342 0.0240 10
## 19 Plantago lanceolata 4.5 0.023 0.0278 10
## 20 Potentilla erecta 3 0.0301 0.0190 10
## 21 Potentilla erecta 3.2 0.0343 0.0196 10
## 22 Potentilla erecta 3.7 0.0302 0.0145 10
## 23 Potentilla erecta 4.5 0.0242 0.0170 10
## 24 Prunella vulgaris 3.1 0.0235 0.0266 10
## 25 Prunella vulgaris 3.2 0.0396 0.0392 10
## 26 Prunella vulgaris 3.7 0.0192 0.0163 10
## 27 Prunella vulgaris 4.3 0.0567 0.0308 10
## 28 Prunella vulgaris 4.5 0.0377 0.0270 10
## 29 Ranunculus acris 3 0.0505 0.0456 10
## 30 Ranunculus acris 3.2 0.0362 0.0217 10
## 31 Ranunculus acris 3.7 0.0529 0.0418 10
## 32 Ranunculus acris 4.3 0.0417 0.0370 10
## 33 Ranunculus acris 4.5 0.0462 0.0229 10
## 34 Rumex acetosa 3.2 0.0258 0.0254 10
## 35 Rumex acetosa 3.7 0.0224 0.0189 10
## 36 Rumex acetosa 4.5 0.0277 0.0134 10
## 37 Trifolium pratense 3 0.0528 0.0447 10
## 38 Trifolium pratense 3.1 0.0288 0.0383 10
## 39 Trifolium pratense 3.2 0.0483 0.0326 10
## 40 Trifolium pratense 3.7 0.0406 0.0427 10
## 41 Trifolium pratense 4.3 0.0356 0.0215 10
## 42 Trifolium pratense 4.5 0.0398 0.0250 10
# Boxplot of FA_Index by Indicator_Value_Nitrogen
ggplot(data, aes(x = as.factor(Indicator_Value_Nitrogen), y = FA_Index)) +
geom_boxplot() +
facet_wrap(~ Species, scales = "free") +
labs(title = "Boxplot of FA_Index by Indicator_Value_Nitrogen and Species", x = "Indicator Value Nitrogen", y = "FA Index")
